Data_Science/Diabetes Analysis/Model/Logistic+Decision+Random_Forest.ipynb

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    "<div style=\"\n",
    "    background: linear-gradient(135deg, #4cc9f0, #feb47b, #fca311);border-radius: 10px;padding: 20px;color: #fff;font-family: 'Arial', sans-serif;text-align: justify;\n",
    "    animation: fadeIn 1.5s ease-out;box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);margin: 20px 0;overflow: hidden;max-width:80%;\">\n",
    "    <h2 style=\"color: #f77f00;\">Understanding Diabetes</h2>\n",
    "    <p>\n",
    "        Diabetes is a condition where blood glucose, or blood sugar, is elevated. According to the National Institute of Health (NIH), it occurs when the body's insulin production or utilization is insufficient.\n",
    "    </p>\n",
    "    <p>\n",
    "        Glucose, derived from the food we eat, is normally broken down with the help of insulin—a crucial hormone facilitating glucose entry into our cells.\n",
    "    </p>\n",
    "    <p>\n",
    "        Unfortunately, diabetes arises when the body fails to produce enough insulin or cannot use it effectively. This leads to an accumulation of excess glucose in the blood, contributing to various health complications. Although there is no cure for diabetes, proactive steps can be taken to safeguard one's health.\n",
    "    </p>\n",
    "</div>\n",
    "\n",
    "<style>\n",
    "    @keyframes fadeIn {\n",
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    "</style>\n"
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   "cell_type": "markdown",
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   "source": [
    "<div style=\"\n",
    "    background: linear-gradient(135deg, #6B5B95, #feb47b, #6B5B95);border-radius: 10px;padding: 20px;color: #fff;font-family: 'Arial', sans-serif;text-align: justify;\n",
    "    animation: fadeIn 1.5s ease-out;box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);margin: 20px 0;overflow: hidden;max-width:80%\">\n",
    "    <h2 style=\"color: #F8F1E9;\">Types of Diabetes</h2>\n",
    "    <div style=\"margin-left: 20px;\">\n",
    "        <div style=\"margin-bottom: 15px;\">\n",
    "            <h3 style=\"color: #F8F1E9;\">Type 1 Diabetes</h3>\n",
    "            <p>\n",
    "                Your body does not produce insulin if you have type 1 diabetes. The immune system targets and kills the insulin-producing cells in your pancreas. It is most commonly diagnosed in children and young adults. If you have type 1 diabetes, you need to take insulin every day.\n",
    "            </p>\n",
    "        </div>\n",
    "        <div style=\"margin-bottom: 15px;\">\n",
    "            <h3 style=\"color: #F8F1E9;\">Type 2 Diabetes</h3>\n",
    "            <p>\n",
    "                Your body does not generate or utilize insulin well if you have type 2 diabetes. This is the most common type of diabetes and can be developed at any age, even in childhood. However, it is more prevalent in middle-aged or older people. It is critical to get tested if at risk because no symptoms may appear. Type 2 diabetes can be delayed or prevented by leading a healthy lifestyle.\n",
    "            </p>\n",
    "        </div>\n",
    "        <div>\n",
    "            <h3 style=\"color: #F8F1E9;\">Gestational Diabetes</h3>\n",
    "            <p>\n",
    "                This happens to certain women during pregnancy. This form of diabetes usually goes away once the baby is born. If you’ve experienced gestational diabetes, you’re more likely to acquire type 2 diabetes later. If you have gestational diabetes, your baby is more likely to suffer health issues, including type 2 diabetes.\n",
    "            </p>\n",
    "        </div>\n",
    "    </div>\n",
    "</div>\n",
    "<style>\n",
    "    @keyframes fadeIn {\n",
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   "cell_type": "markdown",
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   "source": [
    "<div style=\"\n",
    "    background: linear-gradient(135deg, #7F7FD5, #86A8E7, #91EAE4);\n",
    "    border-radius: 10px;\n",
    "    padding: 20px;\n",
    "    color: #fff;\n",
    "    font-family: 'Arial', sans-serif;\n",
    "    text-align: justify;\n",
    "    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);\n",
    "    margin: 20px 0;\n",
    "    overflow: hidden;\n",
    "\">\n",
    "    <h2 style=\"color: #1A212C;\">Diabetes Dataset Overview</h2>\n",
    "    <p>\n",
    "        This dataset, originally from the National Institute of Diabetes and Digestive and Kidney Diseases and downloaded from Kaggle, aims to diagnose diabetes in Pima Indians living in America. It comprises 768 instances with eight attributes, and the target variable is 'Outcome,' where 0 represents no diabetes, and 1 indicates diabetes.\n",
    "    </p>\n",
    "    <ul style=\"list-style-type:upper-roman; padding: 0; margin: 0;\">\n",
    "        <li>\n",
    "            <strong>Pregnancies:</strong> Number of times pregnant\n",
    "        </li>\n",
    "        <li>\n",
    "            <strong>Glucose:</strong> Glucose concentration in blood (plasma). Normal range: 4.0 to 5.4 mmol/L (72 to 99 mg/dL) and up to 7.8 mmol/L (140 mg/dL) 2 hours after eating.\n",
    "        </li>\n",
    "        <li>\n",
    "            <strong>Blood Pressure:</strong> Diastolic blood pressure (mm Hg). Normal range: less than or equal to 120/80 mmHg.\n",
    "        </li>\n",
    "        <li>\n",
    "            <strong>SkinThickness:</strong> Triceps skinfold thickness (mm). Standard values: 2.5mm (men) or about 20% fat; 18.0mm (women) or about 30% fat.\n",
    "        </li>\n",
    "        <li>\n",
    "            <strong>Insulin:</strong> 2-Hour serum insulin (mu U/ml). Normal range: 16–166 mIU/L 2 hours after glucose administration.\n",
    "        </li>\n",
    "        <li>\n",
    "            <strong>BMI:</strong> Body mass index (weight in kg / (height in m²)). BMI categories: underweight (BMI ≤ 18.5), normal weight (18.5–24.9), overweight (25–29.9), obese (BMI ≥ 30).\n",
    "        </li>\n",
    "        <li>\n",
    "            <strong>DiabetesPedigreeFunction:</strong> A function scoring diabetes likelihood based on family history.\n",
    "        </li>\n",
    "        <li>\n",
    "            <strong>Outcome (target variable):</strong> 0 — no diabetes, 1 — has diabetes.\n",
    "        </li>\n",
    "    </ul>\n",
    "</div>\n",
    "\n",
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    "        margin-bottom: 15px;\n",
    "    }\n",
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing all libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "# ML Libraries\n",
    "from sklearn import metrics\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import make_scorer\n",
    "from mlxtend.plotting import plot_confusion_matrix\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "\n",
    "#ignore warning messages \n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "# Setting figure size\n",
    "fig_size = plt.rcParams[\"figure.figsize\"]\n",
    "fig_size[0] = 8\n",
    "fig_size[1] = 6\n",
    "plt.rcParams[\"figure.figsize\"] = fig_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"../data/diabetes.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Descriptive Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Pregnancies               768 non-null    int64  \n",
      " 1   Glucose                   768 non-null    int64  \n",
      " 2   BloodPressure             768 non-null    int64  \n",
      " 3   SkinThickness             768 non-null    int64  \n",
      " 4   Insulin                   768 non-null    int64  \n",
      " 5   BMI                       768 non-null    float64\n",
      " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
      " 7   Age                       768 non-null    int64  \n",
      " 8   Outcome                   768 non-null    int64  \n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\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>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭──────────────────────────────────────────────── skimpy summary ─────────────────────────────────────────────────╮\n",
       "│ <span style=\"font-style: italic\">         Data Summary         </span> <span style=\"font-style: italic\">      Data Types       </span>                                                          │\n",
       "│ ┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓ ┏━━━━━━━━━━━━━┳━━━━━━━┓                                                          │\n",
       "│ ┃<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\"> dataframe         </span>┃<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\"> Values </span>┃ ┃<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\"> Column Type </span>┃<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\"> Count </span>┃                                                          │\n",
       "│ ┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩ ┡━━━━━━━━━━━━━╇━━━━━━━┩                                                          │\n",
       "│ │ Number of rows    │ 768    │ │ int64       │ 7     │                                                          │\n",
       "│ │ Number of columns │ 9      │ │ float64     │ 2     │                                                          │\n",
       "│ └───────────────────┴────────┘ └─────────────┴───────┘                                                          │\n",
       "│ <span style=\"font-style: italic\">                                                    number                                                    </span>  │\n",
       "│ ┏━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━┳━━━━━━━┳━━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━┳━━━━━━━━┓  │\n",
       "│ ┃<span style=\"font-weight: bold\"> column_name            </span>┃<span style=\"font-weight: bold\"> NA  </span>┃<span style=\"font-weight: bold\"> NA %  </span>┃<span style=\"font-weight: bold\"> mean    </span>┃<span style=\"font-weight: bold\"> sd     </span>┃<span style=\"font-weight: bold\"> p0    </span>┃<span style=\"font-weight: bold\"> p25    </span>┃<span style=\"font-weight: bold\"> p50    </span>┃<span style=\"font-weight: bold\"> p75    </span>┃<span style=\"font-weight: bold\"> p100 </span>┃<span style=\"font-weight: bold\"> hist   </span>┃  │\n",
       "│ ┡━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━╇━━━━━━━╇━━━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━╇━━━━━━━━┩  │\n",
       "│ │ <span style=\"color: #af87ff; text-decoration-color: #af87ff\">Pregnancies           </span> │ <span style=\"color: #008080; text-decoration-color: #008080\">  0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">  3.845</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">  3.37</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">     1</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">     3</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">     6</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">  17</span> │ <span style=\"color: #008000; text-decoration-color: #008000\"> ▇▅▃▁ </span> │  │\n",
       "│ │ <span style=\"color: #af87ff; text-decoration-color: #af87ff\">Glucose               </span> │ <span style=\"color: #008080; text-decoration-color: #008080\">  0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">  120.9</span> │ <span style=\"color: #008080; text-decoration-color: #008080\"> 31.97</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    99</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">   117</span> │ <span style=\"color: #008080; text-decoration-color: #008080\"> 140.2</span> │ <span style=\"color: #008080; text-decoration-color: #008080\"> 199</span> │ <span style=\"color: #008000; text-decoration-color: #008000\">  ▅▇▃▂</span> │  │\n",
       "│ │ <span style=\"color: #af87ff; text-decoration-color: #af87ff\">BloodPressure         </span> │ <span style=\"color: #008080; text-decoration-color: #008080\">  0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">  69.11</span> │ <span style=\"color: #008080; text-decoration-color: #008080\"> 19.36</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    62</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    72</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    80</span> │ <span style=\"color: #008080; text-decoration-color: #008080\"> 122</span> │ <span style=\"color: #008000; text-decoration-color: #008000\">▁ ▂▇▃ </span> │  │\n",
       "│ │ <span style=\"color: #af87ff; text-decoration-color: #af87ff\">SkinThickness         </span> │ <span style=\"color: #008080; text-decoration-color: #008080\">  0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">  20.54</span> │ <span style=\"color: #008080; text-decoration-color: #008080\"> 15.95</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">     0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    23</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    32</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">  99</span> │ <span style=\"color: #008000; text-decoration-color: #008000\"> ▇▇▅  </span> │  │\n",
       "│ │ <span style=\"color: #af87ff; text-decoration-color: #af87ff\">Insulin               </span> │ <span style=\"color: #008080; text-decoration-color: #008080\">  0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">   79.8</span> │ <span style=\"color: #008080; text-decoration-color: #008080\"> 115.2</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">     0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">  30.5</span> │ <span style=\"color: #008080; text-decoration-color: #008080\"> 127.2</span> │ <span style=\"color: #008080; text-decoration-color: #008080\"> 846</span> │ <span style=\"color: #008000; text-decoration-color: #008000\">  ▇▂  </span> │  │\n",
       "│ │ <span style=\"color: #af87ff; text-decoration-color: #af87ff\">BMI                   </span> │ <span style=\"color: #008080; text-decoration-color: #008080\">  0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">  31.99</span> │ <span style=\"color: #008080; text-decoration-color: #008080\"> 7.884</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">  27.3</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    32</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">  36.6</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">67.1</span> │ <span style=\"color: #008000; text-decoration-color: #008000\"> ▁▇▆▁ </span> │  │\n",
       "│ │ <span style=\"color: #af87ff; text-decoration-color: #af87ff\">DiabetesPedigreeFuncti</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">  0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\"> 0.4719</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">0.3313</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">0.078</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">0.2437</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">0.3725</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">0.6262</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">2.42</span> │ <span style=\"color: #008000; text-decoration-color: #008000\"> ▇▃▁  </span> │  │\n",
       "│ │ <span style=\"color: #af87ff; text-decoration-color: #af87ff\">on                    </span> │     │       │         │        │       │        │        │        │      │        │  │\n",
       "│ │ <span style=\"color: #af87ff; text-decoration-color: #af87ff\">Age                   </span> │ <span style=\"color: #008080; text-decoration-color: #008080\">  0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">  33.24</span> │ <span style=\"color: #008080; text-decoration-color: #008080\"> 11.76</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">   21</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    24</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    29</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    41</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">  81</span> │ <span style=\"color: #008000; text-decoration-color: #008000\"> ▇▃▂▁ </span> │  │\n",
       "│ │ <span style=\"color: #af87ff; text-decoration-color: #af87ff\">Outcome               </span> │ <span style=\"color: #008080; text-decoration-color: #008080\">  0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">  0.349</span> │ <span style=\"color: #008080; text-decoration-color: #008080\"> 0.477</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">     0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">     0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">     1</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">   1</span> │ <span style=\"color: #008000; text-decoration-color: #008000\">▇    ▅</span> │  │\n",
       "│ └────────────────────────┴─────┴───────┴─────────┴────────┴───────┴────────┴────────┴────────┴──────┴────────┘  │\n",
       "╰────────────────────────────────────────────────────── End ──────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭──────────────────────────────────────────────── skimpy summary ─────────────────────────────────────────────────╮\n",
       "│ \u001b[3m         Data Summary         \u001b[0m \u001b[3m      Data Types       \u001b[0m                                                          │\n",
       "│ ┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓ ┏━━━━━━━━━━━━━┳━━━━━━━┓                                                          │\n",
       "│ ┃\u001b[1;36m \u001b[0m\u001b[1;36mdataframe        \u001b[0m\u001b[1;36m \u001b[0m┃\u001b[1;36m \u001b[0m\u001b[1;36mValues\u001b[0m\u001b[1;36m \u001b[0m┃ ┃\u001b[1;36m \u001b[0m\u001b[1;36mColumn Type\u001b[0m\u001b[1;36m \u001b[0m┃\u001b[1;36m \u001b[0m\u001b[1;36mCount\u001b[0m\u001b[1;36m \u001b[0m┃                                                          │\n",
       "│ ┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩ ┡━━━━━━━━━━━━━╇━━━━━━━┩                                                          │\n",
       "│ │ Number of rows    │ 768    │ │ int64       │ 7     │                                                          │\n",
       "│ │ Number of columns │ 9      │ │ float64     │ 2     │                                                          │\n",
       "│ └───────────────────┴────────┘ └─────────────┴───────┘                                                          │\n",
       "│ \u001b[3m                                                    number                                                    \u001b[0m  │\n",
       "│ ┏━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━┳━━━━━━━┳━━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━┳━━━━━━━━┓  │\n",
       "│ ┃\u001b[1m \u001b[0m\u001b[1mcolumn_name           \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mNA \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mNA % \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mmean   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1msd    \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mp0   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mp25   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mp50   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mp75   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mp100\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mhist  \u001b[0m\u001b[1m \u001b[0m┃  │\n",
       "│ ┡━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━╇━━━━━━━╇━━━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━╇━━━━━━━━┩  │\n",
       "│ │ \u001b[38;5;141mPregnancies           \u001b[0m │ \u001b[36m  0\u001b[0m │ \u001b[36m    0\u001b[0m │ \u001b[36m  3.845\u001b[0m │ \u001b[36m  3.37\u001b[0m │ \u001b[36m    0\u001b[0m │ \u001b[36m     1\u001b[0m │ \u001b[36m     3\u001b[0m │ \u001b[36m     6\u001b[0m │ \u001b[36m  17\u001b[0m │ \u001b[32m ▇▅▃▁ \u001b[0m │  │\n",
       "│ │ \u001b[38;5;141mGlucose               \u001b[0m │ \u001b[36m  0\u001b[0m │ \u001b[36m    0\u001b[0m │ \u001b[36m  120.9\u001b[0m │ \u001b[36m 31.97\u001b[0m │ \u001b[36m    0\u001b[0m │ \u001b[36m    99\u001b[0m │ \u001b[36m   117\u001b[0m │ \u001b[36m 140.2\u001b[0m │ \u001b[36m 199\u001b[0m │ \u001b[32m  ▅▇▃▂\u001b[0m │  │\n",
       "│ │ \u001b[38;5;141mBloodPressure         \u001b[0m │ \u001b[36m  0\u001b[0m │ \u001b[36m    0\u001b[0m │ \u001b[36m  69.11\u001b[0m │ \u001b[36m 19.36\u001b[0m │ \u001b[36m    0\u001b[0m │ \u001b[36m    62\u001b[0m │ \u001b[36m    72\u001b[0m │ \u001b[36m    80\u001b[0m │ \u001b[36m 122\u001b[0m │ \u001b[32m▁ ▂▇▃ \u001b[0m │  │\n",
       "│ │ \u001b[38;5;141mSkinThickness         \u001b[0m │ \u001b[36m  0\u001b[0m │ \u001b[36m    0\u001b[0m │ \u001b[36m  20.54\u001b[0m │ \u001b[36m 15.95\u001b[0m │ \u001b[36m    0\u001b[0m │ \u001b[36m     0\u001b[0m │ \u001b[36m    23\u001b[0m │ \u001b[36m    32\u001b[0m │ \u001b[36m  99\u001b[0m │ \u001b[32m ▇▇▅  \u001b[0m │  │\n",
       "│ │ \u001b[38;5;141mInsulin               \u001b[0m │ \u001b[36m  0\u001b[0m │ \u001b[36m    0\u001b[0m │ \u001b[36m   79.8\u001b[0m │ \u001b[36m 115.2\u001b[0m │ \u001b[36m    0\u001b[0m │ \u001b[36m     0\u001b[0m │ \u001b[36m  30.5\u001b[0m │ \u001b[36m 127.2\u001b[0m │ \u001b[36m 846\u001b[0m │ \u001b[32m  ▇▂  \u001b[0m │  │\n",
       "│ │ \u001b[38;5;141mBMI                   \u001b[0m │ \u001b[36m  0\u001b[0m │ \u001b[36m    0\u001b[0m │ \u001b[36m  31.99\u001b[0m │ \u001b[36m 7.884\u001b[0m │ \u001b[36m    0\u001b[0m │ \u001b[36m  27.3\u001b[0m │ \u001b[36m    32\u001b[0m │ \u001b[36m  36.6\u001b[0m │ \u001b[36m67.1\u001b[0m │ \u001b[32m ▁▇▆▁ \u001b[0m │  │\n",
       "│ │ \u001b[38;5;141mDiabetesPedigreeFuncti\u001b[0m │ \u001b[36m  0\u001b[0m │ \u001b[36m    0\u001b[0m │ \u001b[36m 0.4719\u001b[0m │ \u001b[36m0.3313\u001b[0m │ \u001b[36m0.078\u001b[0m │ \u001b[36m0.2437\u001b[0m │ \u001b[36m0.3725\u001b[0m │ \u001b[36m0.6262\u001b[0m │ \u001b[36m2.42\u001b[0m │ \u001b[32m ▇▃▁  \u001b[0m │  │\n",
       "│ │ \u001b[38;5;141mon                    \u001b[0m │     │       │         │        │       │        │        │        │      │        │  │\n",
       "│ │ \u001b[38;5;141mAge                   \u001b[0m │ \u001b[36m  0\u001b[0m │ \u001b[36m    0\u001b[0m │ \u001b[36m  33.24\u001b[0m │ \u001b[36m 11.76\u001b[0m │ \u001b[36m   21\u001b[0m │ \u001b[36m    24\u001b[0m │ \u001b[36m    29\u001b[0m │ \u001b[36m    41\u001b[0m │ \u001b[36m  81\u001b[0m │ \u001b[32m ▇▃▂▁ \u001b[0m │  │\n",
       "│ │ \u001b[38;5;141mOutcome               \u001b[0m │ \u001b[36m  0\u001b[0m │ \u001b[36m    0\u001b[0m │ \u001b[36m  0.349\u001b[0m │ \u001b[36m 0.477\u001b[0m │ \u001b[36m    0\u001b[0m │ \u001b[36m     0\u001b[0m │ \u001b[36m     0\u001b[0m │ \u001b[36m     1\u001b[0m │ \u001b[36m   1\u001b[0m │ \u001b[32m▇    ▅\u001b[0m │  │\n",
       "│ └────────────────────────┴─────┴───────┴─────────┴────────┴───────┴────────┴────────┴────────┴──────┴────────┘  │\n",
       "╰────────────────────────────────────────────────────── End ──────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from skimpy import skim\n",
    "skim(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Replacing 0 with ‘NaN’\n",
    "- Some features in the dataset have a value of 0, which denotes missing data. Hence, we’d replace 0 for NaN.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replacting 0 with nan\n",
    "df[['Glucose','BloodPressure','SkinThickness','Insulin','BMI']] = \\\n",
    "df[['Glucose','BloodPressure','SkinThickness','Insulin','BMI']].replace(0,np.nan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\")\n",
    "fig, ax = plt.subplots(figsize=(15, 10))\n",
    "ax.set(xlim=(-5, 205))\n",
    "plt.xlabel('Variables')\n",
    "plt.ylabel('Distribution')\n",
    "plt.title(\"Overview of Data Set - Boxplot\")\n",
    "sns.boxplot(data=df, orient='h', palette='Set2', ax=ax)\n",
    "sns.despine(left=True, bottom=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking NULL Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                   0\n",
       "Glucose                       5\n",
       "BloodPressure                35\n",
       "SkinThickness               227\n",
       "Insulin                     374\n",
       "BMI                          11\n",
       "DiabetesPedigreeFunction      0\n",
       "Age                           0\n",
       "Outcome                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function to find the mean \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def median(var):\n",
    "    temp = df[df[var].notnull()]\n",
    "    temp = round(temp[[var, 'Outcome']].groupby(['Outcome'])[[var]].mean().reset_index(), 1)\n",
    "    return temp\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean for Pregnancies:\n",
      "   Outcome  Pregnancies\n",
      "0        0          3.3\n",
      "1        1          4.9\n",
      "------------------------------\n",
      "Mean for Glucose:\n",
      "   Outcome  Glucose\n",
      "0        0    110.6\n",
      "1        1    142.3\n",
      "------------------------------\n",
      "Mean for BloodPressure:\n",
      "   Outcome  BloodPressure\n",
      "0        0           70.9\n",
      "1        1           75.3\n",
      "------------------------------\n",
      "Mean for SkinThickness:\n",
      "   Outcome  SkinThickness\n",
      "0        0           27.2\n",
      "1        1           33.0\n",
      "------------------------------\n",
      "Mean for Insulin:\n",
      "   Outcome  Insulin\n",
      "0        0    130.3\n",
      "1        1    206.8\n",
      "------------------------------\n",
      "Mean for BMI:\n",
      "   Outcome   BMI\n",
      "0        0  30.9\n",
      "1        1  35.4\n",
      "------------------------------\n",
      "Mean for DiabetesPedigreeFunction:\n",
      "   Outcome  DiabetesPedigreeFunction\n",
      "0        0                       0.4\n",
      "1        1                       0.6\n",
      "------------------------------\n",
      "Mean for Age:\n",
      "   Outcome   Age\n",
      "0        0  31.2\n",
      "1        1  37.1\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "#  List to store results\n",
    "means_by_column = []\n",
    "\n",
    "# Iterate through columns in the DataFrame\n",
    "for column in df.columns:\n",
    "    if column != 'Outcome':  # Exclude the target variable\n",
    "        result = median(column)\n",
    "        means_by_column.append(result)\n",
    "\n",
    "# Display or use the results as needed\n",
    "for i, column in enumerate(df.columns):\n",
    "    if column != 'Outcome':\n",
    "        print(f\"Mean for {column}:\")\n",
    "        print(means_by_column[i])\n",
    "        print('-' * 30)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[(df['Outcome'] == 0 ) & (df['Glucose'].isnull()), 'Glucose'] = 110.6\n",
    "df.loc[(df['Outcome'] == 1 ) & (df['Glucose'].isnull()), 'Glucose'] = 142.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[(df['Outcome'] == 0 ) & (df['BloodPressure'].isnull()), 'BloodPressure'] = 70.9\n",
    "df.loc[(df['Outcome'] == 1 ) & (df['BloodPressure'].isnull()), 'BloodPressure'] = 75.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[(df['Outcome'] == 0 ) & (df['SkinThickness'].isnull()), 'SkinThickness'] = 27.2\n",
    "df.loc[(df['Outcome'] == 1 ) & (df['SkinThickness'].isnull()), 'SkinThickness'] = 33.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[(df['Outcome'] == 0 ) & (df['Insulin'].isnull()), 'Insulin'] = 130.3\n",
    "df.loc[(df['Outcome'] == 1 ) & (df['Insulin'].isnull()), 'Insulin'] = 206.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[(df['Outcome'] == 0 ) & (df['BMI'].isnull()), 'BMI'] = 30.9\n",
    "df.loc[(df['Outcome'] == 1 ) & (df['BMI'].isnull()), 'BMI'] = 35.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\")\n",
    "plt.figure(figsize=(8, 6))\n",
    "g = sns.countplot(x='Outcome', data=df, palette='pastel')\n",
    "plt.title('Distribution of Outcome Variable', fontsize=16)\n",
    "plt.xlabel('Outcome', fontsize=14)\n",
    "plt.ylabel('Count', fontsize=14)\n",
    "\n",
    "total = len(df['Outcome'])\n",
    "for p in g.patches:\n",
    "    percentage = f\"{100 * p.get_height() / total:.1f}%\"\n",
    "    g.annotate(percentage, (p.get_x() + p.get_width() / 2., p.get_height()),\n",
    "               ha='center', va='center', xytext=(0, 7), textcoords='offset points', fontsize=12, color='red')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SNHz4cNuXOAXtasabo3///hoyZIiaNm2q0NBQHThwQAcOHJCLi4teeOEFp9/CAAC4MgR/AMB1Lzg4WMuXL9eWLVv06aefateuXfr++++Vnp6ugIAANWrUSB06dFCfPn3sHsxmMpk0c+ZMtW/fXu+//7727dun5ORklS5dWrfddpvuu+++QgthUvZy+U8//VQrVqzQ5s2btXfvXqWlpSk4OFjlypXTLbfcom7dul1RX926ddOKFSs0d+5c7d+/X8eOHVOlSpX08MMPa9iwYRo+fHiu7WrXrq3IyEgtWrRIu3fv1vfffy+r1aqyZcteNvgHBgZq5cqVWr58uT777DNt27ZNWVlZqlixou68804NGzZM/v7+Ts9LccnPz+Opp55SeHi43n33Xf32229ycXFRWFiYZs+erV69euUZpAcOHKgLFy7ok08+0ebNm21P37/lllsK9ZzL73gvbl+tWjW9//772rt3r+21jA899NA1sbIDAK5VJiuPTwUAAEAhCg8PlyT9+eefxTwSALg+cY8/AAAAAAAGRvAHAAAAAMDACP4AAAAAABgY9/gDAAAAAGBgXPEHAAAAAMDACP4AAAAAABgYwR8AAAAAAAMj+AMAAAAAYGAEfwAAAAAADIzgDwAAAACAgRH8AQAAAAAwMII/AAAAAAAGRvAHAAAAAMDACP4AAAAAABgYwR8AAAAAAAMj+AMAAAAAYGAEfwAAAAAADIzgDwAAAACAgRH8AQAAAAAwMII/AAAAAAAGRvAHAAAAAMDACP4AAAAAABgYwR8AAAAAAAMj+AMAAAAAYGAEfwAAAAAADIzgDwAAAACAgRH8AQAAAAAwMII/AAAAAAAGRvAHAAAAAMDACP4AAAAAABgYwR8AAAAAAAMj+AMAAAAAYGAEfwAAAAAADIzgDwAAAACAgRH8AQAAAAAwMII/AAAAAAAGRvAHAAAAAMDACP4AAAAAABgYwd+gIiMjFR4ebvvTsmVLDR48WD/99FNxD61Y7dixQ+Hh4dq7d29xDwUAAAAAioRrcQ8AhcfT01NLly6VJEVHR2v+/Pm69957tWbNGoWFhRXz6IpH3bp19f7776tGjRrFPRQAAAAAKBJc8Tcws9mshg0bqmHDhurRo4cWLFigjIwMrVy50qGu1WpVWlpaMYyyaPn4+Khhw4by8vIq7qEAAAAAQJEg+F9Hypcvr6CgIB0/flwTJkzQTTfdpM2bN+uWW25RRESEvv76a0nSrl27NHjwYDVs2FBNmjTRY489ptjYWLu+oqOjNXLkSDVo0EAdOnTQkiVL9Pzzz6tTp062OmvWrFF4eLh+//13jRgxQg0bNlS3bt20du1au76+/fZbDR06VK1atVLjxo11xx13aMuWLXZ1rrSvnP4GDBigBg0aqFmzZrrnnnv0+++/S8p9qb/VatWiRYvUvXt31atXT507d9aSJUscjveRRx5R69atFRERoU6dOumFF15w9kcAAAAAAEWOpf7XkfPnzys+Pl4hISHKyMjQ6dOn9dxzz+nBBx9UuXLlVL58ee3atUv33HOPOnTooNmzZys5OVmvvvqqHnroIb3//vuSsoPyQw89pLNnz2rq1Kny9fXVokWLdPLkSZnNjt8ljR8/XnfeeaeGDh2qDz74QBMmTFBERIRtuf3x48d14403atiwYTKbzdqyZYvuv/9+LV26VC1atHCqr88++0zjxo1T586dNWvWLLm5uemXX35RTEyM6tSpk+u8PP/881q1apUeeOABNWjQQL/88otefvlleXh4aODAgZKkJ554QqdPn9bkyZMVHBysU6dO6bfffiuwnw0AAAAAFBaCv8FlZGRIyr5i/eKLLyozM1Pdu3fXp59+qoSEBL355ptq0KCBrf6kSZNUr149zZ07VyaTSZIUFhZmWx3QoUMHbdmyRfv27dM777yjpk2bSpJatmypDh06yM/Pz2EMgwYN0qBBgyRJjRo10ubNm/XFF1/ooYcekiTdfffdtrpZWVlq0aKF/v77b33wwQcOwf9SfVmtVr344otq06aN5s2bZ2vToUOHPOfn6NGjWrFihaZOnar+/ftLklq3bq2UlBTNmzdP/fv3l9ls1t69ezVu3Dj16tXL1rZv376XmX0AAAAAKH4s9TewpKQk1a1bV3Xr1lXnzp21Y8cOTZkyRe3atZMkBQQE2IX+5ORk/fLLL+rRo4cyMzOVkZGhjIwMVa1aVeXKlbMtj9+7d6/8/PxsoV+SvL291apVq1zH0bZtW9v/9/LyUvny5RUdHW0ri46O1pNPPql27dqpTp06qlu3rr777jsdOnTIqb6ioqIUHR2t22+//YrnaPv27ZKkbt262Y43IyNDrVu31pkzZ3Tq1ClJUp06dbR48WK9++67OnLkyBX3DwAAAADFjSv+Bubp6akVK1bIZDIpMDBQ5cqVs1uKX7p0abv6iYmJyszM1PTp0zV9+nSH/nJC8OnTpxUUFOSwPbcySfL19bX77ObmZnuQYFZWlh588EFZLBaNGTNGVapUUalSpTRnzhzb/q60r/j4eElSSEhIruPIzblz52S1WtWyZctct586dUoVKlTQ7NmzNXv2bL366quaOnWqqlWrpnHjxqlbt25XvC8AAAAAKA4EfwMzm82KiIjIc3vOUv4cvr6+MplMGjlypLp06eJQPzAwUFJ2sI6Li3PYnlvZ5Rw5ckS///675s2bZ7fPlJQUp/sKCAiQlP3FxJXy9/eXyWTSu+++Kzc3N4ft1apVk5R9zNOnT1dWVpZ+++03vf766xo7dqw2bNigSpUqOT1WAAAAACgqBH/YeHl5qWHDhoqKirrkFwYRERFKTEzUzp071axZM0nShQsX9P333+d6j/+lpKamSpJd6D5x4oR27dqlqlWrOtVX9erVVbZsWa1Zs8buXvxLybk9IT4+3u6NBHkxm82qX7++Hn30UX399dc6cuQIwR8AAABAiUbwh50nnnhCQ4YM0aOPPqrevXvLz89P0dHR2r59u2677Ta1aNFC7du3V926dfXYY49p3Lhx8vPz01tvvSVvb2+HVQSXkxPWZ82apaysLCUlJWnOnDlOLdfPYTKZ9OSTT2rcuHEaPXq0+vTpI3d3d/3666+KiIjQjTfe6NCmWrVqGjRokJ544gkNHz5cDRo0UHp6ug4fPqwdO3Zo/vz5slgsGj58uPr06aNq1aopPT1dy5cvl5+fX55vCgAAAACAkoLgDzuNGzfWu+++q8jISE2cOFHp6ekqW7asWrZsqSpVqkjKDtjz58/XlClTNGXKFPn5+Wnw4ME6dOiQ/vjjD6f25+7ursjISE2bNk2PPPKIypUrpwcffFA//PBDvl6X16tXL3l6emrBggUaN26cPDw8VKdOHXXt2jXPNpMnT1a1atX0/vvva968efL29la1atXUo0cPSZKHh4fCwsK0fPlynTp1Sp6enqpXr54WLVqU53MNAAAAAKCkMFmtVmtxDwLXvrS0NPXu3VtNmzbN9cGAAAAAAIDiwRV/5Mv777+vrKwsVatWTYmJiXrvvfd04sQJvfLKK8U9NAAAAADARQj+yBcPDw+98cYbOnHihCSpdu3aWrhw4SUfCggAAAAAKHos9QcAAAAAwMDMxT0AAAAAAABQeAj+AAAAAAAYGMEfAAAAAAADI/gDAAAAAGBgBH8AAAAAAAyM4A8AAAAAgIER/AEAAAAAMDCCPwAAAAAABkbwBwAAAADAwAj+AAAAAAAYGMEfAAAAAAADI/gDAAAAAGBgBH8AAAAAAAyM4A8AAAAAgIER/AEAAAAAMDCCPwAAAAAABkbwBwAAAADAwAj+AAAAAAAYGMEfAAAAAAADI/gDAAAAAGBgBH8AAAAAAAyM4A8AAAAAgIER/AEAAAAAMDCCPwAAAAAABkbwBwAAAADAwAj+AAAAAAAYGMEfAAAAAAADI/gDAAAAAGBgBH8AAAAAAAyM4A8AAAAAgIG5FvcAgOtZl2HninsI14yvFgcW9xAAAACAaxJX/AEAAAAAMDCCPwAAAAAABkbwBwAAAADAwAj+AAAAAAAYGMEfAAAAAAADI/gDAAAAAGBgBH8AAAAAAAyM4A8AAAAAgIER/AEAAAAAMDCCPwAAAAAABkbwBwAAAADAwAj+AAAAAAAYGMEfAAAAAAADI/gDAAAAAGBgBH8AAAAAAAyM4A8AAAAAgIER/AEAAAAAMDCCPwAAAAAABkbwBwAAAADAwAj+AAAAAAAYGMEfAAAAAAADI/gDAAAAAGBgBH8AAAAAAAyM4A8AAAAAgIER/AEAAAAAMDCCPwAAAAAABkbwBwAAAADAwAj+AAAAAAAYmGtxDwDGt3nzZq1YsUJ79+6VxWKRv7+/6tWrp1tuuUW9evWS2WzWPffcIy8vLy1cuLC4h3vNc3OVhvQtpS6t3eXrZVLU8Uy9vSZZv/yeccl2FcuadXNHD9Wu7qpaVVzk7mbSoMcTFBOb5VD3wQGlVD/cVaHBZrm7mRQTm6XNO9P0wYYUpaQW1pEBAAAAyA+CPwrVK6+8ooULF6pr166aMmWKypQpo7Nnz+qrr77S448/Ln9/f7Vr1664h2kojw/3VvsmblqzMVUnTmeqWxsPvfCoj8a/ZNFvf2Xm2a5ODVf17eKhoyezdPRkpmpWyfufh/Bqrtp7IENfnM5SWrpVNSu7aEAvTzWu46axMyyyWgvjyAAAAADkB8Efhebbb7/VwoUL9fDDD2v06NF223r27KkhQ4bI1ZVTsCCFV3NRpxbuWvh+klZ9kX3p/cttaXrrWT/dd4eXHnnBkmfb739NV9+H45WcIt3R3eOSwf/R6Y79nDyTpQf6e6l2NRf9EZX3FwwAAAAAihb3+KPQvP322ypTpowefPDBXLfXr19fderUyXXbhAkTdNNNN9mVJSYmKjw8XGvWrLErX7t2rfr27auIiAi1aNFC9913n06cOGHb/ueff2r48OFq2LChmjRpojFjxujkyZN2fXz44Yfq3bu36tevrxYtWmjgwIHas2ePbbvVatWiRYvUvXt31atXT507d9aSJUucmY4i0b6puzIzrfp087/r7dMzpA1b01S3pqvKBJrybGu5YFVySv73HXM2+5YAH6+89wEAAACg6HG5FYUiIyNDv/zyi7p3716oV/XfeustvfTSS+rXr5/Gjh2r9PR0/fDDD4qLi1OFChV06tQp3X333apUqZJeeuklpaamavbs2br77rv1ySefyMfHRzt37tSkSZM0bNgwdejQQSkpKdqzZ48sln+vaj///PNatWqVHnjgATVo0EC//PKLXn75ZXl4eGjgwIGFdnzOqlnZRcdjspT0nwC//1D2/f01KrvqzLn0AtmX2Zwd8t1cpaoVXHTvraV0Idmq/Ye42g8AAACUJAR/FIr4+HilpaWpXLlyduVWq1WZmf8GQ7PZLLM5fwtPLBaL5s6dq/79+2vatGm28i5dutj+/5IlS5SRkaHFixcrICBAknTDDTeod+/e+uijj3TPPfdoz549CggI0JNPPmlr17FjR9v/P3r0qFasWKGpU6eqf//+kqTWrVsrJSVF8+bNU//+/fN9DAUtyN+s2HjHh/HFJmSXBQcU3NX48KouipzsZ/t89FSmpsw5L8sFbvAHAAAASpKSkVZgWCaTfdD84osvVLduXduf5557Lt9979q1S8nJyerXr1+edX766Se1aNHCFvolqUaNGqpdu7Z+/vlnSVKdOnUUHx+vCRMmaNu2bUpOTrbrY/v27ZKkbt26KSMjw/andevWOnPmjE6dOpXvYyhoHu7ZS/v/Kz09Z3vBBf8jJzP1xMsWTZlzXis/S1FKqlWenizzBwAAAEoarvijUAQEBMjd3V3R0dF25a1atdKHH34oSXne+3+l4uPjJUkhISF51klMTNQNN9zgUB4cHKyEhATbmGbOnKlly5Zp+PDh8vDwUPfu3fXUU08pICBA586dk9VqVcuWLXPdx6lTp1ShQoWrOpaCkpqW/Tq//3Jzy9lecFfjk1Jke0Xg9l/T1amFm6aN9taDUy2KOsZyfwAAAKCkIPijULi6uqpx48b6/vvvlZmZKRcXF0mSv7+/IiIiJEnu7u55tnd3d1d6uv296DlBPUfOVfzTp0+rbNmyufbj7++v2NhYh/LY2FhVrVrV9rlPnz7q06eP4uLitGnTJk2fPl2urq564YUX5O/vL5PJpHfffVduOQn6ItWqVcvzOIpaXEKWSgc6LuQJ9s8ui40vvGX4W39O15P3STc2dyP4AwAAACUIS/1RaIYOHarTp09rwYIFTrctW7asoqOjdeHCBVvZtm3b7Oo0atRIpUqV0urVq/Psp0mTJvrhhx/svjSIiorSn3/+qSZNmjjUDwoK0h133KE2bdooKipKUvaKACl7hUFERITDHx8fH6ePr7AcPJqpiqFmeXnal9eu7vrP9lzuAygg7m6Si9kk71Is9wcAAABKEq74o9B07NhR999/v+bMmaP9+/erZ8+eCgkJkcVi0U8//aQzZ87I29s717bdunXTnDlz9NRTT+nOO+/UX3/9ZbtFIIevr69GjRqll19+WVarVZ07d1ZWVpZ27Nih3r17KyIiQvfee6/WrFmjYcOG6cEHH1RqaqpeffVVlStXTrfeeqskac6cOYqPj1fz5s0VHBysAwcOaOvWrbr33nslZV/RHzRokJ544gkNHz5cDRo0UHp6ug4fPqwdO3Zo/vz5hTqPztjyc5ru7Omp3h08tOqL7Ff6ublK3du664+DGTpzLvuKf0iQSR7uJh2LdnwQ4OV4lzIpJc2qzP9c1O/Z3kOSdOAwV/sBAACAkoTgj0L12GOPqUmTJnrnnXc0depUnT9/Xv7+/qpbt65eeOEF9e7dO9d2NWvW1IwZMzR//nw99NBDatKkiV5++WX16dPHrt59992noKAgLVmyRGvWrJG3t7caNWqk4OBgSVK5cuW0fPlyzZw5U+PHj5fZbFabNm00YcIE25X6iIgILV26VJ9//rnOnz+vsmXLavjw4XbPIJg8ebKqVaum999/X/PmzZO3t7eqVaumHj16FNLM5c/+qExt3pmm4beXUoCfWSdPZ6praw+VDTZr1tvnbfWeHOGtBrXd1GXYOVuZdympb+fspQJ1a2X/09Cns4cuJFl1Psmqj7/O/iKhQW1XPXyXl7b8lKYTMZlydTUpIsxVbRu76c9DGfrq+7QiPGIAAAAAl2OyWq28ewsoJhcH74Li5ioNvbWUOrdyl6+3SVHHMrXko2T9tO/fZf6znvBxCP6hwWa985J/rn1Gn83U3U8kSpLKlTHrnls8Va+Wq4L8zTKZpJOns7T15zR98HmKUgop93+1OLBwOgYAAAAMjuAPFKPCCP5GRfAHAAAA8oeH+wEAAAAAYGAEfwAAAAAADIzgDwAAAACAgRH8AQAAAAAwMII/AAAAAAAGRvAHAAAAAMDACP4AAAAAABgYwR8AAAAAAAMj+AMAAAAAYGAEfwAAAAAADIzgDwAAAACAgRH8AQAAAAAwMII/AAAAAAAGRvAHAAAAAMDACP4AAAAAABgYwR8AAAAAAAMj+AMAAAAAYGAEfwAAAAAADIzgDwAAAACAgRH8AQAAAAAwMII/AAAAAAAGRvAHAAAAAMDACP4AAAAAABgYwR8AAAAAAAMj+AMAAAAAYGAEfwAAAAAADIzgDwAAAACAgRH8AQAAAAAwMII/AAAAAAAGRvAHAAAAAMDACP4AAAAAABiYyWq1Wot7EAAAAAAAoHC4FvcAgOvZzT8mFPcQrhnrmvszX05Y19y/uIcAAACAEoKl/gAAAAAAGBjBHwAAAAAAAyP4AwAAAABgYAR/AAAAAAAMjOAPAAAAAICBEfwBAAAAADAwgj8AAAAAAAZG8AcAAAAAwMAI/gAAAAAAGBjBHwAAAACAfDh48KCGDh2qhg0bqk2bNpo5c6bS0tIu2278+PHq1q2bGjZsqGbNmmnQoEH67rvvHOpZLBY99dRTat68uRo1aqQxY8bo9OnTTo/T1ekWAAAAAABc5xISEjRkyBBVrVpVkZGRiomJ0YwZM5SSkqIpU6Zcsm16erruvfdeVa1aVampqfrwww91//33a9myZWratKmt3qOPPqq///5bzzzzjDw8PPTqq6/qvvvu0+rVq+XqeuVxnuAPAAAAAICTVq5cqQsXLmju3LkKCAiQJGVmZmrq1KkaOXKkQkND82z72muv2X1u3769OnfurI8//tgW/Hft2qXvvvtOixYtUtu2bSVJ1apVU69evfTll1+qV69eVzxWlvoDAAAAAOCkLVu2qFWrVrbQL0k9e/ZUVlaWtm3b5lRfLi4u8vX1VXp6ul3/fn5+atOmja2sevXquuGGG7Rlyxan+if4AwAAAADgpKioKFWvXt2uzM/PT2XKlFFUVNRl21utVmVkZOjcuXNatGiRjhw5ov79+9v1X61aNZlMJrt21atXv6L+L8ZSfwAAAADAdatz586X3L5p06ZcyxMTE+Xn5+dQ7u/vr4SEhMvu98MPP9TkyZMlSV5eXpo9e7YaNWpk17+vr2+u/f/222+X7f9iBH8AAAAAwDXp5h8vH7BLqs6dO6t27do6d+6cNmzYoEcffVRz585Vhw4dCnxfBH8AAAAAwHUrryv6l+Pn5yeLxeJQnpCQIH9//8u2DwoKUlBQkKTsh/slJCTopZdesgV/Pz8/RUdH57v/i3GPPwAAAAAATsrtXnuLxaIzZ8443Pt/JerWrasjR47Y9X/o0CFZrVa7eocOHXK6f4I/AAAAAABOat++vbZv367ExERb2YYNG2Q2m+2exH+lfv75Z1WqVMmu/4SEBH3//fe2skOHDun3339X+/btneqbpf4AAAAAADhpwIABWr58uUaNGqWRI0cqJiZGM2fO1IABAxQaGmqrN2TIEJ08eVIbN26UJH377bdau3atOnbsqHLlyikhIUHr16/Xd999p1deecXWrlGjRmrbtq2eeuopPfnkk/Lw8NDs2bMVHh6ubt26OTVWgj8AAAAAAE7y9/fX0qVL9eyzz2rUqFHy9vZWv379NHbsWLt6WVlZyszMtH2uVKmS0tLSNGvWLJ07d06BgYEKDw/X8uXL1bx5c7u2r776qqZPn64pU6YoIyNDbdu21eTJk+Xq6lyUN1n/e8MAgCJzLT+FtKita+7PfDlhXXPnHvgCAABwLSqI3w+vh9+buMcfAAAAAAADI/gDAAAAAGBgBH8AAAAAAAyM4A8AAAAAgIER/AEAAAAAMDCCPwAAAAAABkbwBwAAAADAwFyLewDIW2RkpObOnWv77O7urooVK+q2227T8OHDZTabdfz4cXXu3FmvvfaaevToUSTjev7557Vp0yZ9/fXXkqQdO3Zo8ODBtu1eXl6qUqWK7r77bt1+++0ymUxFMi5ky0pPU+zqhUrc9rmyLljkUammgvs9IO+IFpdsZ9n5jSw7Nio16ndlJMTKNShUPo3aKqjPcLl4+9rX/WGjzu/aqpSD+5Qec0ylajdWpUkLCvOwCkVhz1WmJV4JW9bpwq6tSjt5WNbMDLmXq6rAHgPl27JrYR8eAAAAIIngX+J5enpq6dKlkqSUlBTt2LFDs2bNktVq1f3331/Mo7M3ffp0Va9eXYmJifrwww81adIkZWRkaMCAAcU9tOtKzBvTZNm5SYHdB8ottJISt67XiVmPqtLE11UqvGHe7Ra/INfAMvJt01NuwaFKPXZQ8RtX6cLu7ar87DKZ3T1tdeM3rVbK4f3yrHaDMs8nFMFRFY7Cnqvkv/fq7KrX5d2gtYL6DJPJ7CLLzq91at4kpZ44pNK3l6y/wwAAADAmgn8JZzab1bBhQ9vnli1b6sCBA/ryyy9LXPCvVauWIiIiJElt2rRRr169tGLFijyDf0pKijw9PXPdVlJcC2O8WPLBfbL88KVKDxijoN53S5L82vbSkYkDdWZlpCo/vSjPtuXHzJDXDU3syjyr1Vb0wqmybN8g/459beVlH5gq18AyMpnNOjzh2vxipyjmyqNCdVV7ebXcSpez1fPv0k/HZ4zSuU+XKaj3PTJ7lir4gwMAAAAuwj3+1yBvb29lZGTkuT0rK0vz589Xp06dVK9ePfXo0UMrV650qLdz504NGDBA9evXV4sWLTRx4kTFx8fb1YmJidEDDzygBg0aqF27dnrzzTevaIwuLi664YYbdPz4cUnSmjVrFB4erl27dmno0KFq2LChZs6cKUmKjo7W+PHj1aJFC9WvX1+DBg3Sb7/9Ztffpk2bdNttt6lRo0Zq2rSpbrvtNm3evPmKt4eHh2vRIvsgt2TJEoWHh9s+79ixQ+Hh4fr22281ZswYNW7cWI888ogkKTExUc8884zatm2revXq6bbbbtN33313RXNRlM7/uEkyu8i/U19bmdndQ/4dblHK33uVHhuTZ9v/BllJ8mnSUZKUdvKwXblbcKhM5mv7n4+imCu3kAp2oV+STCaTfJp0kDU9TelnTlzVMQAAAABXgiv+14CckJ+z1P/LL7/UyJEj86w/c+ZMLVu2TA8++KAaNWqkb7/9Vk8//bQyMjJ0993ZVzZ/++03DR06VC1atNBrr72ms2fPatasWfr777+1cuVKubi4SJIeeughxcTE6JlnnpGvr6/efPNNnTp1Sq6ulz91jh8/rpCQELuyxx57TP3799fIkSNVqlQpJSQk6K677pKXl5f+97//ydfXV8uXL9eQIUP05ZdfKjg4WEePHtUjjzyi3r1767HHHlNWVpb279+vhITsJeaX2+6s//3vf7rllls0b948mc1mpaWlaejQoYqNjdWjjz6q0NBQffLJJxo5cqTtC42SIvXIAbmXrSyXUj525Z416ti2uwWHXnF/GQmxkiQXn4ACG2NJUZxzlZlT1/fydQEAAICrRfAv4ZKSklS3bl27sl69euW5zD8uLk4rVqzQ8OHDNXr0aElS27Ztde7cOc2bN08DBw6Ui4uLFixYoDJlymjBggVyc3OTJJUrV07Dhw/X5s2b1alTJ23ZskW//fablixZolatWkmSWrRooQ4dOiggIMBh31lZWcrIyJDFYtH777+vvXv3OnxBMWDAALuxz5kzR4mJiVq1apWCg4MlSa1atVL37t21aNEiPfHEE/r999+Vnp6u//3vf/LxyQ5p7dq1s/Vxue3O6tSpkx5//HHb59WrV2v//v36+OOPVbNmTVv/R44c0fz58/Xaa6/le18FLSP+rFwCgh3KXQJK/7P9jFP9xa1fJpld5NO8U4GMryQprrnKPJ+ghG8/UanwhnL9Z18AAABAYbq21+peBzw9PfXhhx/qww8/1LvvvqtJkyZp69atmjx5cq719+zZo/T0dIcn/Pfs2VNxcXE6fPiwJOmnn35S586dbaFfyv6CwM/PTz///LOtL19fX1volyRfX1+1bt06133feeedqlu3rlq2bKnIyEgNGDBAo0aNsqvTsWNHu8/btm1TixYt5O/vr4yMDGVkZMhsNqtZs2bau3evpOxl+i4uLho/fry+/vprWSwWuz4ut91ZuY0xLCxMVatWtY0xIyNDrVu3to2xpLCmp8rk6u5QbnLzyN6elnrFfSVu36DEzZ8osOddci9bucDGWFIUx1xZs7J06vUpykqyKOSe8c4PGgAAAMgHrviXcGaz2fbAPElq0qSJMjMzNWPGDA0dOlReXl529XOWt5cubX8lMedzzj38iYmJtivsFwsODrb1cfr0aQUFBeVaJzcvvviiatSoIR8fH1WoUEHu7o6h6r/jOnfunH799VeHVQ2SVLlydoCqVq2aFixYoIULF+rhhx+W2WxW27ZtNWXKFJUvX/6y25313+M7d+6cfv/991zHmHNLRElhcvOQNSPNodyanh1iTe4eV9RP0p+7FPPW8/KKaKnSdzxYoGMsKYpjrk4ve1lJe75X2ZHPyKNKmPODBgAAAPKB4H8Nql69uiTp77//Vv369e225SzBj42NVWjov/cnnz171m67v7+/YmNjHfqOjY2Vv7+/JCkkJERxcXG51slNjRo17L6kuBL+/v5q166d7SF6F7v4i4P27durffv2On/+vLZs2aLp06dr4sSJtlcdXm67u7u70tPT7fpPTEzMdUwmk8lhjOHh4Xr++eedOrbi4BpQWhnnHJeoZ8af/Wd7mcv2kXrkgE6+Ml7uFaur/JgZMrkY85+Jop6r2DVvKmHThyrdf5T82vbK/8ABAAAAJxnzN3qD++uvvyRJgYGBDtsiIiLk5uamDRs2qE6dOrbyzz//XMHBwapataqk7JUDmzZt0oQJE2wP6tu2bZsSExPVpEkTW18Wi0Xff/+9bbm/xWLR9u3bc73HPz9at26tTz75RDVq1HBYvZAbHx8f9erVS3v27NH69euveHvZsmV18OBBu7rbt2+/4jFu3rxZISEhdl+mlEQeVcKU9MfPykw+b/fQuuSD+2zbLyUt5riOv/SIXPwCVWH8qzJ7Xv5ncq0qyrmK37hKsR+9qYDuAxR005CCOQAAAADgChH8S7isrCz9+uuvkqT09HTt27dPr7/+umrWrKmmTZsqJsb+lWNBQUG6++67tWjRIrm7u6thw4bavHmz1q9fr//973+2pekPPPCABgwYoJEjR+qee+6xPdW/fv366tChg6Tsq+h169bV448/rvHjx8vX11dvvPGG7QF6BeHee+/VunXrdPfdd2vw4MEqX7684uLitHv3boWGhuree+/VypUr9euvv6pdu3YqU6aMjh8/rk8++URt2rSRpMtul6Tu3btr6dKlioiIULVq1fTJJ584zF1e+vbtq5UrV2rw4MEaNmyYqlatKovFYnuo4GOPPVZg83G1fJp10rnPVijh67W2d9Nnpacpcct6edaoZ3tKffrZaFnTUuRevqqtbUb8WZ2YOVoym1XxiTly9XP8YslIimquLD9s1Onls+TbuofKDBpbqMcEAAAA5IbgX8KlpKSof//+kiRXV1eVLVtWt9xyix5++GG7B/Nd7IknnpCvr68+/PBDLViwQBUqVNDUqVM1YMAAW5169epp8eLFeuWVVzR69Gh5eXmpU6dOevLJJ21fDphMJs2fP19PP/20pkyZIj8/P9uXBJs2bSqQ4wsMDNT777+vV199VS+//LLi4+MVHBysBg0aqGvXrpKyH973zTffaPr06YqPj1eZMmXUu3dv2+0Bl9suZb+WMDY2VvPmzZPJZFL//v01ePBgzZgx47JjdHd317JlyxQZGakFCxbozJkzCggIUJ06dXTXXXcVyDwUlFI168mneWedXTVPmYlxcgutpMTvPlX62ZMKHTHJVi964TNK3v+Lwpb/aCs78dIjSj99QoG971Hygd1KPrDbts3FL0jeES1sn5P2/6Lk/bskSZmWeGWlJit27aLsMdRuJK/ajQv7UK9aUcxV8sF9il74jFx8/OVVt5ks2zfYjcGzVn25h1Qo5CMFAADA9c5ktVqtxT0I4Hp1848JBd5nVlqqYlcvVOK2z5WVZJF7pZoqfftIedf/9+0Mx55/wCHMHrineZ59lqrdWJUmLbB9PrvmDcV99FaudYNuHaHSt+X+usmrsa65f4HPV2HPVcKW9Yp5c1qedUPvmyL/9jcVwJE4Wtfcv1D6BQAAKEkK4vfD6+H3JoI/UIwKI/gbVWEEfyO7Hv4DBgAAQPC/MubiHgAAAAAAACg8BH8AAAAAAAyM4A8AAAAAgIER/AEAAAAAMDCCPwAAAAAABkbwBwAAAADAwAj+AAAAAAAYGMEfAAAAAAADI/gDAAAAAGBgBH8AAAAAAAyM4A8AAAAAgIER/AEAAAAAMDCCPwAAAAAABkbwBwAAAADAwAj+AAAAAAAYGMEfAAAAAAADI/gDAAAAAGBgBH8AAAAAAAzMtbgHAAAAAADAtejgwYN67rnntGvXLnl7e6tPnz569NFH5e7unmeb06dPa8mSJdq2bZuOHj0qX19fNWvWTOPGjVOFChVs9Xbs2KHBgwc7tO/Vq5dmz57t1DgJ/gAAAAAAOCkhIUFDhgxR1apVFRkZqZiYGM2YMUMpKSmaMmVKnu327dunjRs36vbbb1eDBg107tw5vf7667rjjju0fv16BQUF2dWfPn26qlevbvscGBjo9FgJ/gAAAAAAOGnlypW6cOGC5s6dq4CAAElSZmampk6dqpEjRyo0NDTXdk2aNNHnn38uV9d/43jjxo3VsWNHrV27VsOGDbOrX6tWLUVERFzVWLnHHwAAAAAAJ23ZskWtWrWyhX5J6tmzp7KysrRt27Y82/n5+dmFfkkqW7asgoKCdPr06UIZK8EfAAAAAAAnRUVF2S3Bl7JDfZkyZRQVFeVUX4cOHVJsbKxq1KjhsO3+++/XDTfcoPbt2+vFF19USkqK02NlqT8AAAAA4LrVuXPnS27ftGlTruWJiYny8/NzKPf391dCQsIV799qteq5555TSEiIevfubSv39fXViBEj1KxZM3l4eOiHH37Q4sWLFRUVpYULF15x/xLBHwAAAACAYhMZGakffvhBb731lry8vGzlderUUZ06dWyfW7VqpZCQEE2bNk179uxR/fr1r3gfBH8AAAAAwHUrryv6l+Pn5yeLxeJQnpCQIH9//yvq44MPPtC8efP0/PPPq1WrVpet37NnT02bNk2//fabU8Gfe/wBAAAAAHBS9erVHe7lt1gsOnPmjMO9/7nZuHGjnnnmGY0ZM0b9+vUrrGFKIvgDAAAAAOC09u3ba/v27UpMTLSVbdiwQWazWW3atLlk2x07dmjcuHG64447NGrUqCve56effipJTr/ej6X+AAAAAAA4acCAAVq+fLlGjRqlkSNHKiYmRjNnztSAAQMUGhpqqzdkyBCdPHlSGzdulCQdPHhQo0aNUtWqVdWnTx/9+uuvtrpBQUGqXLmyJGn8+PGqUqWK6tSpY3u435IlS9SlSxeCPwAAAAAAhc3f319Lly7Vs88+q1GjRsnb21v9+vXT2LFj7eplZWUpMzPT9nn37t2yWCyyWCwaOHCgXd1bb71VM2bMkCTVqlVL69at0+LFi5Wenq4KFSrogQce0P333+/0WE1Wq9Waj2MEUABu/vHKX/NxvVvX3J/5csK65lf2QBkAAIBrWUH8fng9/N7EFX+gGF0P/8gUJOYLAAAAcB7BHyhGXYadK+4hXDO+WhzIFX8nsELCOXypBAAAjIyn+gMAAAAAYGAEfwAAAAAADIzgDwAAAACAgRH8AQAAAAAwMII/AAAAAAAGRvAHAAAAAMDACP4AAAAAABgYwR8AAAAAAAMj+AMAAAAAYGAEfwAAAAAADIzgDwAAAACAgRH8AQAAAAAwMII/AAAAAAAGRvAHAAAAAMDACP4AAAAAABgYwR8AAAAAAAMj+AMAAAAAYGAEfwAAAAAADIzgDwAAAACAgRH8AQAAAAAwMII/AAAAAAAGRvAHAAAAAMDACP4AAAAAABgYwR8AAAAAAAMj+AMAAAAAYGAEfwAAAAAADIzgDwAAAACAgRH8AQAAAAAwMII/AAAAAAAG5lrcA8ClffLJJ1q2bJkOHTokq9Wq0NBQNW7cWOPGjVNwcLAkqVOnTurYsaOmTJmSZz/h4eF64oknNHz48Cvab2RkpObOnXvJOhUqVNDXX3+te+65R15eXlq4cGGedY8fP67OnTvrtddeU48ePa5oDGvWrNHEiRP1/fffKygo6IraQHJzlYb0LaUurd3l62VS1PFMvb0mWb/8nnHZtsEBJj000EtN6rrKZDJp9/50vb4yWafOZNnVC/AzaUS/UmpR301eniYdPZWp9z5N0Zaf0gvrsApNVnqaYlcvVOK2z5V1wSKPSjUV3O8BeUe0uGQ7y85vZNmxUalRvysjIVauQaHyadRWQX2Gy8XbN892aTHHdWTiAFnT01R56hJ5Vq9T0IdUaIpqrs7/skWxa95U2slDcvELlF+7mxXcd5hMLvwnCwAAID/4LaoEe/PNNzVr1izde++9GjNmjKxWq/766y+tW7dOp0+ftgX/K/H++++rfPnyV1z/jjvuULt27WyfV61apfXr12vp0qW2Mnd39yvuLyQkRO+//76qVq16xW2QP48P91b7Jm5aszFVJ05nqlsbD73wqI/Gv2TRb39l5tnO00Oa9YSvvEuZ9O76FGVmSrd389SsJ331wNOJSrxglSR5eUqvTvRVoJ9ZH32VorgEqzo0c9OUh3z0wsLz+nrHtRX+Y96YJsvOTQrsPlBuoZWUuHW9Tsx6VJUmvq5S4Q3zbrf4BbkGlpFvm55yCw5V6rGDit+4Shd2b1flZ5fJ7O6Za7sz78yWzC6FdDSFqyjm6sLu7Tr56uMqVbuxytwzXmnH/lbcx4uVmRin0KETiuAoAQAAjIfgX4ItX75ct956qyZM+PeX3Q4dOmjEiBHKysq6REtHDRs2dKp+2bJlVbZsWdvnrVu3ymw2O91PDnd393y3xZULr+aiTi3ctfD9JK36IlWS9OW2NL31rJ/uu8NLj7xgybPtLTd6qGJZF42alqg/D2d/QfDj3nS99ayf+nX30OI1KZKkmzp6qGKoi8bPtOjX/dmrCNZ9k6rISb4a2d9LW35KUEbe3y+UKMkH98nyw5cqPWCMgnrfLUnya9tLRyYO1JmVkar89KI825YfM0NeNzSxK/OsVlvRC6fKsn2D/Dv2dWhzYc/3Str7gwJ736O4jxcX6LEUtqKaqzPvvSaPSjVV8clI2xV+cylvxa1bosDuA+RevmqBHxsAAIDRcY9/CZaYmKiQkJBct5nNef/ozp07p9tvv1233Xab4uLiJGUv9V+06N9fzO+55x6NHDlSGzZsUPfu3dWoUSMNHjxYR48ezfd4L9XX8ePHFR4erg0bNti1Wbt2rfr27auIiAi1aNFC9913n06cOJHnPlavXq169epp1apVkrJvSWjUqJH+/PNPDRw4UA0aNNBNN92krVu3OrRds2aNbr75ZkVERKhdu3aaPXu2MjP/TaiJiYmaPHmy2rVrp4iICHXo0EFjx4694u0lQfum7srMtOrTzam2svQMacPWNNWt6aoygaZLtt0flWEL/ZJ0LDpLu/7IUIdm/67uqFfLVecSs2yhX5KsVmnzzjQFB5hVP/za+T7x/I+bJLOL/Dv1tZWZ3T3k3+EWpfy9V+mxMXm2/W+QlSSfJh0lSWknDztss2Zk6PSKVxTQrb/cQipe7dCLXFHMVeqJKKWdOCT/G2+1W9Yf0KWfZLXK8uOmqz4OAACA6xHBvwSrW7euVq5cqVWrVunMmTNX1ObMmTMaPHiw3N3dtXTp0kveG//HH39o0aJFGj9+vKZPn66jR4/q8ccfz9dY89PXW2+9pSeffFJ169bV3Llz9fzzz6tKlSq2Lyv+a/ny5Xr66af14osv6o477rCVp6ena/z48brttts0d+5cBQUFacyYMTp37pytzttvv63Jkyerbdu2WrBgge677z4tW7ZMs2fPttWZPn26vv32W40bN06LFi3SE088YXc7w+W2lwQ1K7voeEyWklLsy/cfyg7pNSrnHspNJql6JRcdOOz4HID9hzJUIdRFpf5Zje3uZlJamtWhXmpa9v+GVb12lrGnHjkg97KV5VLKx67cs0Yd23ZnZCTESpJcfAIctp374j1lXbAoqO+w/A22mBXFXKUezu7Do9oNdnVdA8vINSjE6X0AAAAg27Vzae469PTTT+vhhx/W5MmTJUkVK1bUjTfeqHvvvVcVKzpeMTx58qTuvfdeVahQQfPmzZOXl9cl+7dYLFq7dq3ty4GkpCRNnDhR0dHRdsv8r4SzfVksFs2dO1f9+/fXtGnTbOVdunTJtf+FCxdq7ty5eu2119S5c2e7bTnBv0OHDpKkatWqqXPnztqyZYv69Omj8+fPa86cORoxYoTGjRsnSWrTpo3c3Nw0Y8YMDR8+XIGBgdq7d69uuukm3Xrrrba+e/fubfv/l9teEgT5mxUb73gbSGxCdllwQO5X/H29TXJ3Myk2wTHQx8Vb/2lr1vHoLB2LzlSjOq4KCTbrdOy/+6oXlv3PSemAa+f7xIz4s3IJcHxWhktA6X+2X9kXbjni1i+TzC7yad7JYT9xaxer9MAxDsH5WlEUc5URf1aS5PpPn//dj7P7AAAAQLZr5zf061BYWJjWr1+vN954Q4MHD5avr6+WL1+uW265RX/88Ydd3aNHj2rQoEGqUaOGFi5ceNnQL0m1a9e2WxFQs2ZNSVJ0dLTTY3W2r127dik5OVn9+vW7bN+vvvqqFixYoIULFzqEfin7todWrVrZPlesWFGenp6KiYmx7SspKUk9evRQRkaG7U/r1q2VkpKiv/76S5JUp04dffTRR1q0aJEOHHC8sni57SWBh3v20v7/Sk/P2Z578Pdwy6nnGPzT/inzcMtu+9mWNGVlSf970Ft1arioXBmzBvbyVNvG2Z2457GPksianiqTq+OqDZObR/b2tFSHbXlJ3L5BiZs/UWDPu+RetrLdtrPvz5VbSHn5d+xzdQMuRkUxV9b07D5Mrm4Obcxu7k7tAwAAAP/iin8J5+7urg4dOtiuZm/dulUjR47UvHnz7F63t3fvXsXHx2vSpElXvPzcz8/P7rObW/Yv26mpzv9y7Wxf8fHxkpTnMwwu9sUXXygsLExNmjjeJyxJnp6eDsfs5uZm23fOkv+Lr9Rf7NSpU5Kk//3vf/L399fbb7+tmTNnqly5crr//vt11113XdH2kiA1Lft1fv/1z49Dqbks0Zek1PSceo6h3f2fstR/vgA4dDxTLyy8oEcHe2nOpOyfe2x8lua/l6RHB3srOTX3fZREJjcPWTPSHMptAdTd44r6Sfpzl2Leel5eES1V+o4H7bYl/71Xids+V8UJ82S6xLM5SrqimCvblwgZjm+GyEpPu+J9AAAAwB7B/xrTrl071a5dWwcPHrQr7927t1xcXDRu3DgtXLjQ7gp4SRQQECBJOn369GVvK5g/f75Gjx6t0aNHa968ebYvFa6Uv7+/JGnu3Lm57ivntglfX19NmjRJkyZN0p9//qlly5Zp6tSpCgsLU9OmTS+7vSSIS8hS6UDHcBnsn10WG597KLdcsCot3apgf8fgH/TP7QEX30Kw9ed0ff9rgmpUcpHZLP11JFMNamf/c3Ii2rk3ThQn14DSyjjnuHw807bkvMxl+0g9ckAnXxkv94rVVX7MDId3zZ9dGalSYQ3lVqa80s+czO7fEi9JyoiPVfrZaLmVdu7WmuJQFHPlartt4KzcgkMd9uNZvW5+hw8AAFAoDh48qOeee067du2St7e3+vTpo0cfffSSF2NPnz6tJUuWaNu2bTp69Kh8fX3VrFkzjRs3ThUqVLCrGxMTo+eee07fffed3Nzc1LVrV02cOFE+Ps7dPnrtXn66Dpw9e9ahLCUlRadOnVLp0o73wE6aNEl9+/bVQw89pJ9//rkohphvjRo1UqlSpbR69erL1q1WrZrefvtt7d69W4899pjdk/id2Vd0dLQiIiIc/gQGBjq0CQ8P18SJEyXJ4UuWK9leXA4ezVTFULO8/vMK+drVXf/Znst9AMp+Kv+h45kKq+r4XWDtaq46eTpTyf95YGBGpvTn4Uz9EZWpjEypcZ3sL2R+/t3xam1J5VElTGnRR5WZfN6uPPngPtv2S0mLOa7jLz0iF79AVRj/qsyejrfYpMfGKPnPXTo0rq/tz9mVcyRJJ2c/piOTSs6KkUspirnyqFJLkpR6yP5WpoxzZ5QRd/qy+wAAAChKCQkJGjJkiNLT0xUZGamxY8fqgw8+0IwZMy7Zbt++fdq4caN69uyp+fPna8KECTpw4IDuuOMOuwedp6ena8SIETp8+LBmzZqlZ555Rt99950ee+wxp8fKFf8S7Oabb9aNN96otm3bKiQkRDExMVqxYoXOnTunIUOG5Npm6tSpSk1N1f3336+3335b9evXL+JRXxlfX1+NGjVKL7/8sqxWqzp37qysrCzt2LFDvXv3VkREhF398PBwLV68WEOGDNGTTz6pmTNnXvKVhhfz8/PTmDFj9NJLLyk6OlrNmzeXi4uLjh07pk2bNikyMlKlSpXSgAED1LVrV9WqVUsuLi5au3at3NzcbFfzL7e9JNjyc5ru7Omp3h08tOqL7CXYbq5S97bu+uNghs6cy77iHxJkkoe7Sccuujq/5ac03XeHl8KquujAP6/0q1jWrEY3uNr6ykuFELNu6uih739N04mYa+eKv0+zTjr32QolfL3W9m76rPQ0JW5ZL88a9WxXndPPRsualmL3DvmM+LM6MXO0ZDar4hNz5Orn+AWSJIUOmyhrqv23Jkm//6T4jR+o9MBH5F6+SuEcXAErirnyqFhD7uWrKuGbj+Tf6VaZzNlviIjftFoymeTTrFOu7QAAAIrDypUrdeHCBc2dO9e2ojkzM1NTp07VyJEjFRoammu7Jk2a6PPPP5er679xvHHjxurYsaPWrl2rYcOy3wL1xRdf6K+//tJnn32m6tWrS8rONsOHD9eePXucynoE/xLs4Ycf1jfffKMZM2YoLi5OgYGBCg8P15IlS9SyZctc25hMJr3wwgtKS0vTiBEjtGzZMtWuXbuIR35l7rvvPgUFBWnJkiVas2aNvL291ahRIwUHOz45XMp+veFbb72loUOHasqUKXr22WeveF/Dhg1TaGio3n77ba1YsUKurq6qXLmyOnbsaLt1oHHjxlq7dq2OHz8us9mssLAwLViwQDVq1Lii7SXB/qhMbd6ZpuG3l1KAn1knT2eqa2sPlQ02a9bb/16pfXKEtxrUdlOXYf++8vCTb1LVq72Hnn/ER6u+SFFGptSvm6fOJVq16gv74LroOT9t3pmm03FZKlfarJtv9JDlQpZeXZZUZMdaEErVrCef5p11dtU8ZSbGyS20khK/+1TpZ08qdMQkW73ohc8oef8vClv+o63sxEuPKP30CQX2vkfJB3Yr+cBu2zYXvyB5R7SQJHlHOP5dzUzK/ll41W4kz+p1CuvwClRRzJUklR4wWidnj9fxF0fLt2U3pR0/qPiNq+TfoY88KlQrmoMFAAC4Alu2bFGrVq1soV+Sevbsqaefflrbtm3Tbbfdlmu7/z4fTZLKli2roKAgnT592q7/8PBwW+iXst9OFhAQoM2bNzsV/E1Wq/XaeRIXYDAXB++C4uYqDb21lDq3cpevt0lRxzK15KNk/bTv32X+s57wcQj+klQ60KQHB3ipaV03mczS7v3pen1lsk6etr+K/9RIb9Wr6aoAP5MSz1v1/a/pWro2WfGWwvvn5KvFgbr5x4QC7zcrLVWxqxcqcdvnykqyyL1STZW+faS86//7nIxjzz/gEGYP3NM8zz5L1W6sSpMW5Lk9Yct6xbw5TZWnLim04L+uuX+Bz1dRzdX5n75V7Nq3lHbysFx8A+TX7iYF9x0hk2vhfVe9rrl/ofUNAAAKT0H8vpM0MfeAnmPTpk25lrdq1Uq33367xo8fb1ferl079enTx6H8Ug4dOqQePXroueee0x133CFJ6tevnypVqqTZs2fb1R0wYIDKlSvnUH4pXPEHDCY9Q3pjVbLeWJWcZ53HZp7PtfzsOaueff3CZffxwsLL17lWmN09VGbgGJUZOCbPOrmF+IuDrbP8298k//Y35bt9cSmqufJp2lE+TTs6OzwAAIAilZiYmOvVe39/fyUkXPkXElarVc8995xCQkLUu3dvu/59fX2vun+J4A8AAAAAuI7ldUW/qERGRuqHH37QW2+9JS8vxwcgFwSe6g8AAAAAgJP8/PxksVgcyhMSEmyvFL+cDz74QPPmzdPUqVMdXsnu5+en8+cdV+o6038Ogj8AAAAAAE6qXr26oqKi7MosFovOnDlj90C+vGzcuFHPPPOMxowZo379+l1R/1arVYcOHbqi/i9G8AcAAAAAwEnt27fX9u3blZiYaCvbsGGDzGaz2rRpc8m2O3bs0Lhx43THHXdo1KhRefa/f/9+HT582Fb2/fffKz4+Xh06dHBqrAR/AAAAAACcNGDAAHl7e2vUqFH67rvvtHr1as2cOVMDBgxQaGiord6QIUPUtWtX2+eDBw9q1KhRqlq1qvr06aNff/3V9ufo0aO2et27d1etWrU0evRoffPNN/rss8/01FNPqWPHjk69yk/i4X4AAAAAADjN399fS5cu1bPPPqtRo0bJ29tb/fr109ixY+3qZWVlKTMz0/Z59+7dslgsslgsGjhwoF3dW2+9VTNmzJAkubm56a233tJzzz2ncePGydXVVV27dtVTTz3l9FhNVqu18F68DeCSugw7V9xDuGZ8tTiwwN9Lb2TrmvszX05Y19y5B+QAAICSoSB+37kefg9gqT8AAAAAAAZG8AcAAAAAwMAI/gAAAAAAGBjBHwAAAAAAAyP4AwAAAABgYAR/AAAAAAAMjOAPAAAAAICBEfwBAAAAADAwgj8AAAAAAAZG8AcAAAAAwMAI/gAAAAAAGBjBHwAAAAAAAyP4AwAAAABgYK7FPQAAAAAAAPIjeUHW1XfS/Oq7KOm44g8AAAAAgIER/AEAAAAAMDCCPwAAAAAABkbwBwAAAADAwAj+AAAAAAAYGMEfAAAAAAADI/gDAAAAAGBgBH8AAAAAAAyM4A8AAAAAgIER/AEAAAAAMDCT1Wq1FvcgAAAAAABwVpdh5666j68WBxbASEo21+IeAHA9K4h/qK4XXy0O1M0/JhT3MK4Z65r7c345gfPryq1r7l/cQwAAAE5iqT8AAAAAAAZG8AcAAAAAwMAI/gAAAAAAGBjBHwAAAAAAAyP4AwAAAABgYAR/AAAAAAAMjOAPAAAAAICBEfwBAAAAADAwgj8AAAAAAAZG8AcAAAAAwMAI/gAAAAAAGBjBHwAAAAAAAyP4AwAAAABgYAR/AAAAAADy4eDBgxo6dKgaNmyoNm3aaObMmUpLS7tsu3feeUcjR45Uy5YtFR4erg0bNjjU2bFjh8LDwx3+jB071ulxujrdAgAAAACA61xCQoKGDBmiqlWrKjIyUjExMZoxY4ZSUlI0ZcqUS7b9+OOPJUkdOnTQ2rVrL1l3+vTpql69uu1zYGCg02Ml+AMAAAAA4KSVK1fqwoULmjt3rgICAiRJmZmZmjp1qkaOHKnQ0NBLtjWbzTp+/Phlg3+tWrUUERFxVWNlqT8AAAAAAE7asmWLWrVqZQv9ktSzZ09lZWVp27Ztl2xrNhdtFOeKPwAAAADgutW5c+dLbt+0aVOu5VFRUbr99tvtyvz8/FSmTBlFRUUV2Pjuv/9+xcfHq0yZMurdu7ceeeQReXp6OtUHwR8AAAAAACclJibKz8/Podzf318JCQlX3b+vr69GjBihZs2aycPDQz/88IMWL16sqKgoLVy40Km+CP4AAAAAgOtWXlf0i1udOnVUp04d2+dWrVopJCRE06ZN0549e1S/fv0r7ot7/AEAAAAAcJKfn58sFotDeUJCgvz9/Qtlnz179pQk/fbbb061I/gDAAAAAOCk6tWrO9zLb7FYdObMGbvX75UEBH8AAAAAAJzUvn17bd++XYmJibayDRs2yGw2q02bNoWyz08//VSSnH69H/f4AwAAAADgpAEDBmj58uUaNWqURo4cqZiYGM2cOVMDBgxQaGiord6QIUN08uRJbdy40Va2d+9enThxQnFxcZKk3bt3S5KCgoLUvHlzSdL48eNVpUoV1alTx/ZwvyVLlqhLly4EfwAAAAAACpu/v7+WLl2qZ599VqNGjZK3t7f69eunsWPH2tXLyspSZmamXdk777yjjz76yPZ58eLFkqTmzZtr+fLlkqRatWpp3bp1Wrx4sdLT01WhQgU98MADuv/++50eq8lqtVqdbgWgQHQZdq64h3DN+GpxoG7+8epfi3K9WNfcn/PLCZxfV25d88J5WBEAAPlREL/vfLU4sABGUrJxjz8AAAAAAAZG8AcAAAAAwMC4xx8wGDdXaUjfUurS2l2+XiZFHc/U22uS9cvvGZdtGxxg0kMDvdSkrqtMJpN270/X6yuTdepMll29AD+TRvQrpRb13eTladLRU5l679MUbfkpvbAOq9BkpacpdvVCJW77XFkXLPKoVFPB/R6Qd0SLS7Y7u+YNxX30lkO5yc1dtRZ/51Ce8O3HOvf5O0o/c1KuQaEK6HanArv1L7DjKAr5PbcqljXr5o4eql3dVbWquMjdzaRBjycoJjYr1/qtGrppcB9PVSnvovhEqzZ8l6oV61KUlXv1Eiu/55Zl5zey7Nio1KjflZEQK9egUPk0aqugPsPl4u1rX/eHjTq/a6tSDu5TeswxlardWJUmLSjMwwIAANcgrvijRIiMjFSjRo2KZd+dOnXStGnTbJ8nTJigm266qVjGUhAeH+6tft089PX3aZr/XpKysqQXHvVRvVoul2zn6SHNesJX9cNc9e76FC1bm6yalV0160lf+XmbbPW8PKVXJ/qqXRN3fbo5VQs/SFZSilVTHvJRpxZuhX14BS7mjWk6t+Fd+bXuoTJ3j5PMZp2Y9aiS//z1itqH3Pukyj4w1fYn9L7/OdSJ/3qNYhY9L/cK1VXmnvHyrFlPZ5bPUtz6pQV8NIUrv+dWnRqu6tvFI/tLopOZl6zbLMJVUx/21vkkq+a+k6Rtu9I06GZPjR7kVZCHUiTye27FLH5BaScPy7dNT4Xc85i867dS/MZVOjZtuLLSUuzqxm9arfO/bJFrUIjM3n6FeDQAAOBaxhV/4D8eeughJSUlFfcw8iW8mos6tXDXwveTtOqLVEnSl9vS9NazfrrvDi898oIlz7a33OihimVdNGpaov48nB3Oftybrree9VO/7h5avCY7cNzU0UMVQ100fqZFv+7PvtK77ptURU7y1cj+XtryU4IyLp3tSozkg/tk+eFLlR4wRkG975Yk+bXtpSMTB+rMykhVfnrRZfvwbd5ZLr4BeW7PSkvR2VWvy7thG5UfM0OSFHBjX8lqVezaxfK/8Va5XAOB7WrOre9/TVffh+OVnCLd0d1DNavk/Z+ekXd6Kep4pp6cdd52hT8p2aqBvT21ZmOKjkVfG5f9r+bcKj9mhrxuaGJX5lmttqIXTpVl+wb5d+xrKy/7wFS5BpaRyWzW4QkDCuVYAADAtY8r/sB/VK5cWbVr1y7uYeRL+6buysy06tPNqbay9Axpw9Y01a3pqjKBpku23R+VYQv9knQsOku7/shQh2butrJ6tVx1LjHLFvolyWqVNu9MU3CAWfXDr53vE8//uEkyu8i/U19bmdndQ/4dblHK33uVHhtz2T6sVqsyk88rrxekJP3+s7LOJ8i/cz+78oAu/WRNTdaFX7dd1TEUlas5tywXrEpOyXOzTeXyZlWt4KLPNqfZLev/5JtUmc0mtW/qnnfjEuZqzq3/hn5J8mnSUZKUdvKwXblbcKhMZv5TDgAALo3fFlDiHD9+XOHh4fr44481bdo0NWvWTG3bttWLL76ojIx/w2Z0dLQeeeQRtW7dWhEREerUqZNeeOEF2/bcluwnJiYqPDxca9asyXP//223Zs0ahYeH6/fff9eIESPUsGFDdevWTWvXri24gy4gNSu76HhMlpL+E7L2H8qetxqVcw/lJpNUvZKLDhx2vFd7/6EMVQh1USnP7M/ubialpTmG3NS07P8Nq3rpZd8lSeqRA3IvW1kupXzsyj1r1LFtv5xDj92qg/d30t/3ddSp16coIyH2P/v4M7vPajfY76PaDZLJbNte0uX33HJuH9l9/Pmf8zA23qrTcVmqWfn6OrculnNeufgEFMj4AADA9eXauTSH686rr76qzp0769VXX9WuXbsUGRmpypUra+DAgZKkJ554QqdPn9bkyZMVHBysU6dO6bfffiu08YwfP1533nmnhg4dqg8++EATJkxQRESEatSoUWj7dFaQv1mx8Y5LoWMTssuCA3K/KuvrbZK7m0mxCY6BPi7e+k9bs45HZ+lYdKYa1XFVSLBZpy96OFu9sOx/TkoHXDvfJ2bEn5VLQLBDuUtA6X+2n8mzrYu3nwK63iHPmhEyubkr+c9fFf/VKqVE7VPlaUttgS8jPlYyu8jVP8iuvcnVTS4+/so4d7YAj6jw5Pfcckawf3YfcbnsJy4+S8HXybmVm7j1yySzi3yadyqQ8QEAgOsLwR8lVv369TV58mRJUps2bbRjxw598cUXtuC/d+9ejRs3Tr169bK16du3b6GNZ9CgQRo0aJAkqVGjRtq8ebO++OILPfTQQ4W2T2d5uGcvv/6v9PSc7bmHMw+3nHqOwT/tnzIPt+y2n21J000dPfS/B731+ntJOpdoVcdm7mrbOLsT9zz2URJZ01NlcnVcPm5y88jenpbqsC1HYHf7+6l9m3WSZ/U6in59ihK+Wq2gm4f800eKTK55rLRwd3d4WFtJld9zyxk5505u+0lLt8qr1PVxbv1X4vYNStz8iQJ73yP3spULbIwAAOD6ce1cPsF1p23btnafa9SooejoaNvnOnXqaPHixXr33Xd15MiRIh2Pl5eXypcvbzeekiA1LfuVa//l5pazPff70FPTc+o5Biv3f8pS//kC4NDxTL2w8ILKlzFrziQ/LX/RX327eGj+e9kPRExOzX0fJZHJzUPWjDSHcmt6digzuXs41Z9f6x5y8Q9W0r4f/92Hu6esGbm/7s6aliazu6dT+ygu+T23nJFzC0lu+8m+xeSqd1FkCurcSvpzl2Leel5eES1V+o4HC3SMAADg+kHwR4nl62v/vmo3NzelXfSb/+zZs9WyZUu9+uqr6tatm3r06KEvv/yy2MZTEsQl5L4cOtg/uyw2PvdwZrlgVVq61bbU+mJB/yzhvniZ99af09V/XIJGTUvU6OcSNejxBJ06k739xDXy1HVJcg0orcz4WIfyzPiz/2wv43yfwaHKPJ940T6CpaxMZSTE2dWzZqQr83yCXANLO72P4pDfc8sZObeaBOWyn6CA3G81KKkK4txKPXJAJ18ZL/eK1VV+zAyZXFikBwAA8ofgj2tWSEiIpk+frh9++EGrVq1StWrVNHbsWB07dkyS5O7urvScdcj/SEhIKI6hFpmDRzNVMdQsr/9cRK5d3fWf7XlcebZmX8kPq+oYLGpXc9XJ05kOT2XPyJT+PJypP6IylZEpNa6Tfen359/THfooqTyqhCkt+qgyk8/blScf3Gfb7gyr1aqMM6fk4hdgtw9JSjn0h13dlKg/JGuWPCo7t4/ikt9zy7l9ZPcR/p/zMDjApJAgs/4+do28J1JXf26lxRzX8ZcekYtfoCqMf1VmT69CGysAADA+gj+ueWazWfXr19ejjz6qjIwM27L/smXLKjo6WhcuXLDV3bbt2nh1Wn5t+TlNLi4m9e7w7zJiN1epe1t3/XEwQ2fOZV9RDQkyqVJZ+7/+W35KU+3qrnZP5a9Y1qxGN7hqy0+XDvMVQsy6qaOHvv81TSdirp2rsj7NOklZmUr4eq2tLCs9TYlb1suzRj25BYdKktLPRju8Ri0j8ZxDfwmbVivTck7eEa1sZV51msrs46eETavt6sZ/vVomd095N2xTcAdUiK7m3LpSR05m6cjJTPXq4C7zRYtPbr7RQ1lZVm39qWStsLmUqzq34s/qxMzRktmsik/MkatfYBGOHAAAGBHrBnFNslgsGj58uPr06aNq1aopPT1dy5cvl5+fn+rUyX5dVrdu3TRnzhw99dRTuvPOO/XXX3/pww8/LOaRF679UZnavDNNw28vpQA/s06ezlTX1h4qG2zWrLf/vfL45AhvNajtpi7D/g2vn3yTql7tPfT8Iz5a9UWKMjKlft08dS7RqlVf2F/uX/ScnzbvTNPpuCyVK23WzTd6yHIhS68uSyqyYy0IpWrWk0/zzjq7ap4yE+PkFlpJid99qvSzJxU6YpKtXvTCZ5S8/xeFLf/33v1DY2+Rb4uu8qhUQyY3DyUf+FWWHzbKo0qY/DvdZqtndvdU6dsf0OmlM3VyzgR512+p5D9/lWXb5wq+40G5+PgX6THn19WcW96lpL6ds5cK1K2V/Z+dPp09dCHJqvNJVn389b8PuntjVbKeHe2tGY/56Nsf01S1gov6dPbQ51vTdPTUtfOl0tWcWydeekTpp08osPc9Sj6wW8kHdtu2ufgFyTuihe1z0v5flLx/lyQp0xKvrNRkxa5dlD2G2o3kVbtxYR8qAAC4BhD8cU3y8PBQWFiYli9frlOnTsnT01P16tXTokWLFBSU/dq0mjVrasaMGZo/f74eeughNWnSRC+//LL69OlTzKMvXDPevKCht5ZSl1bu8vU2KepYpia/dl57D1x6KXZyivTYTIseHOClQTeVksks7d6frtdXJivBYn//9sFjmerR1kMBfiYlnrdq8850LV2brHjLtfNgvxxlRz6j2NULlbjtc2UlWeReqaYqjHvlsoHJt3UPpfy1R+d3fqOs9FS5lS6nwN73KPiWoTJ72K+HD+jST3Jx1bnP39GFXVvlGhSqMoPGKuA/bwYo6fJ7bvl4mTX0tlJ2ZXf2yJ6j6LOZdsF/x+50PTPvggbf4qmHB3kp3mLVe5+maPkn18bbDy6W33Mr9ehfkqRzny532FaqdmP74P/7T4r76C27OrGrF0qSgm4dQfAHAACSJJPVar32flMHDOLiq6K4tK8WB+rmH439jIaCtK65P+eXEzi/rty65tfGKhUAwPWhIH7f+Wqx8W+r4x5/AAAAAAAMjOAPAAAAAICBEfwBAAAAADAwgj8AAAAAAAZG8AcAAAAAwMAI/gAAAAAAGBjBHwAAAAAAAyP4AwAAAABgYAR/AAAAAAAMjOAPAAAAAICBEfwBAAAAADAwgj8AAAAAAAZG8AcAAAAAwMAI/gAAAAAAGBjBHwAAAAAAAyP4AwAAAABgYAR/AAAAAAAMjOAPAAAAAICBEfwBAAAAADAwgj8AAAAAAAZG8AcAAAAAwMAI/gAAAAAAGBjBHwAAAAAAAyP4AwAAAACQDwcPHtTQoUPVsGFDtWnTRjNnzlRaWtpl273zzjsaOXKkWrZsqfDwcG3YsCHXejExMRo9erQaNWqk5s2ba9KkSTp//rzT4yT4AwAAAADgpISEBA0ZMkTp6emKjIzU2LFj9cEHH2jGjBmXbfvxxx/r3Llz6tChQ5510tPTNWLECB0+fFizZs3SM888o++++06PPfaY02N1dboFAAAAAADXuZUrV+rChQuaO3euAgICJEmZmZmaOnWqRo4cqdDQ0Eu2NZvNOn78uNauXZtrnS+++EJ//fWXPvvsM1WvXl2S5Ofnp+HDh2vPnj2qX7/+FY+VK/4AAAAAADhpy5YtatWqlS30S1LPnj2VlZWlbdu2XbKt2Xz5KL5lyxaFh4fbQr8ktWnTRgEBAdq8ebNTY+WKPwAAAADgutW5c+dLbt+0aVOu5VFRUbr99tvtyvz8/FSmTBlFRUVd9biioqLsQr8kmUwmVatWzen+ueIPAAAAAICTEhMT5efn51Du7++vhISEAunf19e3QPrnij8AAAAA4LqV1xV9I+GKPwAAAAAATvLz85PFYnEoT0hIkL+/f4H0n9ur+/LTP8EfAAAAAAAnVa9e3eFee4vFojNnzjjcm19Q/VutVh06dMjp/lnqDxSjrxYHFvcQrinrml/9N6fXE84v53B+AQAAZ7Rv314LFiywu9d/w4YNMpvNatOmTYH0/8knn+jw4cOqWrWqJOn7779XfHy8OnTo4FRfBH+gGLXxXVHcQ7hmbLPcrZt/vPqHpFwv1jX3Z76cwHxdOebKOXyhBADGNWDAAC1fvlyjRo3SyJEjFRMTo5kzZ2rAgAEKDQ211RsyZIhOnjypjRs32sr27t2rEydOKC4uTpK0e/duSVJQUJCaN28uSerevbsWLlyo0aNHa9y4cUpOTtbMmTPVsWNH1a9f36mxEvwBAAAAAHCSv7+/li5dqmeffVajRo2St7e3+vXrp7Fjx9rVy8rKUmZmpl3ZO++8o48++sj2efHixZKk5s2ba/ny5ZIkNzc3vfXWW3ruuec0btw4ubq6qmvXrnrqqaecHivBHwAAAACAfKhRo4aWLFlyyTo5Qf5iM2bM0IwZMy7bf2hoqCIjI/M7PBse7gcAAAAAgIER/AEAAAAAMDCCPwAAAAAABkbwBwAAAADAwAj+AAAAAAAYGMEfAAAAAAADI/gDAAAAAGBgBH8AAAAAAAyM4A8AAAAAgIER/AEAAAAAMDCCPwAAAAAABkbwBwAAAADAwAj+AAAAAAAYGMEfAAAAAAADI/gDAAAAAGBgBH8AAAAAAAyM4A8AAAAAgIER/AEAAAAAMDCCPwAAAAAABkbwBwAAAADAwAj+AAAAAAAYGMEfAAAAAAADI/gDAAAAAGBgBH8AAAAAAAyM4A8AAAAAgIER/AEAAAAAMDDX4h4AgILl5m7WiMkN1GNANfkGuOvv3+L15rO/auc30ZdtW7pcKY2Z0VTNO5WT2Sz9sjVGcyb8rJOHz9vqhFTwUu97aqh19wqqWMNXWVlWRf2eoKUz9+qnby+/j5ImKz1NsasXKnHb58q6YJFHpZoK7veAvCNaXLKdZec3suzYqNSo35WRECvXoFD5NGqroD7D5eLtm2e7tJjjOjJxgKzpaao8dYk8q9cp6EMqNEUxV1kpSTr74QKd//FrZVrOyS2kggK63qmALv0K89AKRX7n6+yaNxT30VsO5SY3d9Va/J3tc8KW9Yp5c1qe/ZR9YJr82vTI/wEUsaI4v06veEXJ+3cp/ewpWdNT5Va6nHxadFFQr7tl9vQqzMMDAKBYEfxhWJGRkZo7d67ts7u7uypWrKjbbrtNw4cPl9mcveAlPDxckvTMM89o4MCBdn1s27ZNw4YNkyRt2rRJFStWlCR16tRJHTt21JQpU4riUJwyaUFr3di3sj6Yv1/HDiaq16Aaenl1J43uvVF7vj+TZ7tS3q6K/KyrfPzctGzWb8pMz9Kdo27Q3M+76t42nyoxLk2S1K53Rd09tq62rD+mz9+NkourST0GVtdr67ro+Qe367MVUUV1qAUi5o1psuzcpMDuA+UWWkmJW9frxKxHVWni6yoV3jDvdotfkGtgGfm26Sm34FClHjuo+I2rdGH3dlV+dpnM7p65tjvzzmzJ7FJIR1O4CnuurFmZOj5zjFIP/SH/Lv3kHlpJF/b+oNNLZyozyaLgW4YW0ZEWjPzOV46Qe5+0D6Nm+0V6pWo3UtkHpjq0O7fhPaUe/UtedZte7SEUqaL4u5hy6A+VCm8ov/Y3yeTmodQjf+rc+mVK2rdTlSYtlMnMQkgAgDER/GFonp6eWrp0qSQpJSVFO3bs0KxZs2S1WnX//ffb6nl5eemzzz5zCP7r16+Xl5eXkpKSinTc+XVDk2B1vaOq5k76We/N+UOStOHdKC3fcbMeeraxHujyRZ5tb70vTJVr+ml4h8+1/5dYSdL3G09q+Y6bNHB0HS2c+qsk6ZctMbqtzkdKiE21tV276C8t2d5bIyY1uKaCf/LBfbL88KVKDxijoN53S5L82vbSkYkDdWZlpCo/vSjPtuXHzJDXDU3syjyr1Vb0wqmybN8g/459Hdpc2PO9kvb+oMDe9yju48UFeiyFrSjm6vzOb5Ty1x6Fjpgs/w63SJICuvTTyTkTFLd2sfw79JGrf1DhHGABu5r5yuHbvLNcfAPy3O4eUkHuIRXsyrLSUnR6yUx51Wkq14DSV3UMRamo/i5W/t+bDu3dQirq7HuvKSVqn0rVjCiYAwIAoIThq20YmtlsVsOGDdWwYUO1bNlSjzzyiDp37qwvv/zSrl7nzp31008/KSYmxlaWlpamjRs3qkuXLkU97Hy7sW9lZWRk6eO3/7aVpaVmaf3yvxXRooxCKuS9lPXGPpX1+09nbaFfko4eSNTP30ar061VbGWH9ifYhX5JSk/L0vdfnlBoRW95+Vw73yee/3GTZHaRf6e+tjKzu4f8O9yilL/3Kj02Js+2/w0akuTTpKMkKe3kYYdt1owMnV7xigK69ZdbSMWrHXqRK4q5Sv7zV0mSb8tudnV9W3aVNT1V53/Zku/xF7Wrma8cVqtVmcnnZbVar3i/F3ZtVVbKBfm27p6fYRebovy7+F9uZcpJkrKSzl+mJgAA1y6CP6473t7eysjIsCu74YYbVLVqVX322We2ss2bN8tqtapjx45FPML8C6sfpGN/JyrJkm5X/vtP2WG+Vv3AXNuZTFKNeoHavyvWYdsfP8eqYg3fywb64JBSSr6QoZSkzHyOvuilHjkg97KV5VLKx67cs0Yd23ZnZCRkz5+LT4DDtnNfvKesCxYF9R2Wv8EWs6KYK2tGumR2kcnV/lwz/bNUO/XwH84Ou9gUxHwdeuxWHby/k/6+r6NOvT7FNmeXkrj9C5ncPeTb9Mb8DbyYFOXfRWtmhjIt8co4d0YX9v6g2A8XyOzpfU09bwMAAGddO5fmgHzKCfk5S/2//PJLjRw50qFe7969tX79eg0dmn0f8fr169W1a1d5eHgU6XivRnDZUoqNTnYozykrXTb3K/5+QR7y8HRRbHSKw7azOW3LeenoX4m5tq9Q3Ucdbqmkrz86qqysK786Wdwy4s/KJSDYodzlnyXSGfF5PxMhN3Hrl0lmF/k07+Swn7i1i1V64BiHYHOtKIq5ci9XWcrKVMrfv9nd052zEiAjzrl9FKermS8Xbz8FdL1DnjUjZHJzV/Kfvyr+q1VKidqnytOW5nkOZZ5PUNKe7+XdpIPMpbwL5kCKSFH9XZSy7/M/NnW47bNbuSoqP+5lufj4OzlqAACuHQR/GFpSUpLq1q1rV9arVy+7+/tz3HTTTYqMjNTRo0cVHBysb7/9VvPmzVNKimMYLqk8PF2UnprlUJ6Wmn0V3qNU7g+V8/DMLk9Lc7xab2vrmUfbUi56bll7pSZnasHTu/I17uJiTU+VydXdodzklv1ljzUt1WFbXhK3b1Di5k8U2PseuZetbLft7Ptz5RZSXv4d+1zdgItRUcyVb6vuil27SNFvPauQwU/IvWwlXdi7QwmbPrSN4VpxNfMV2H2A3WffZp3kWb2Ool+fooSvVivo5iG5trP8+LWsGenya33tPMk/R1H9XZQk9wrVVOHJubKmJiv5rz1K2rdTWSnXxnNcAADIL5b6w9A8PT314Ycf6sMPP9S7776rSZMmaevWrZo8ebJD3apVq6pu3bpav369vvrqK3l7e6tVq1bFMOr8S03JlJuH419rd4/s0J6anPsy/NSU7HJ3d8dwb2ub4tjWbDZp2pJ2qlrbX5Pu2WJbHXCtMLl5yJqR5lCeEzBN7le22iPpz12Keet5eUW0VOk7HrTblvz3XiVu+1xlBo29pp8YXhRz5RpQWuXHvixrerpOzBytQ+P66uzKOQoZPD57Hx6lrvIoik5BzVcOv9Y95OIfrKR9P+ZZx7J9g8w+fvKu39q5wZYARXF+5XAp5SPves3l06SDygwYrcCed+nk7Medvp0AAIBrCVf8YWhms1kREf8+pblJkybKzMzUjBkzNHToUIWFhdnVv+mmm7R69WqVL19ePXv2lIvLtfXatdjoZJUu7xiOgstml52Nzv2qVmJcqlJTMhVc1vEVdKVz2p5ybPvk3BZq3aOCpg7fpl+2XP5hZSWNa0BpZZxzXEKcGX/2n+1lLttH6pEDOvnKeLlXrK7yY2bI5GL/z+rZlZEqFdZQbmXKK/3Myez+LfGSpIz4WKWfjZZb6bJXeSSFryjmSpK8ajdWtVc+Uuqxg7KmJsujci3bfnO7eltSFcR8OfQZHKrM87nfbpN+NlrJB36V/419HZ6RcC0oqvMrNz5Nb5T0jBJ/2KgyVcIuWx8AULIkr/r06jtZfPfV91HCXXu/HQBXqXr16pKkv//+2yH49+rVSzNnzlRUVJTeeeed4hjeVflr7zk1ah8qL183uwf81W2Wfe/sX3vO5drOapWi9sWrdiPHe2zrNA3WiSiLks7bPxBx1HONddM9NfXqEzv11YeHC+4gipBHlTAl/fGzMpPP2903nXxwn237paTFHNfxlx6Ri1+gKox/1f6d6/9Ij41RxtlTOjSur8O2k7Mfk9nLRzUXfn11B1IEimKucpjMLvK8qL+kfTslSV71ml/NIRSpq52v/7Jarco4c0oeVXNvZ/nhC8lqle81uMxfKtrz67+sGemSNUtZyTzVHwBgXNfuulMgn/766y9JUmCg4xPuy5YtqyFDhuimm25S48aNi3poV+2btUfk6mpWn6E1bWVu7mb1GlRD+3ae0ekT2VftQyt6qXKYn33bj4+oTtPSqt3o3/ekV67lp8YdyurrtUfs6t71SB3d9UgdLX1pr1a9/mchHlHh8mnWScrKVMLXa21lWelpStyyXp416sktOFRS9tXU/74WLCP+rE7MHC2Zzar4xBy5+uX+xoTQYRNV/pGZdn8Cut4pSSo98BGVfXBaoRxbQSuKucpNRuI5xa1fJvdKNeVV99oJ/lc1X4mOX9AlbFqtTMs5eUfkfvtR4vYv5BpcVqXCGhbUIRSpoji/Mi9YZP3PG10kKeHbjyVJntVuKJiDAQCgBOKKPwwtKytLv/76qyQpPT1d+/bt0+uvv66aNWuqadOmubaZOHFiEY6wYP3+U6w2rTmiB55ppMAynjoeZVHPu6qrXBUfTX/4B1u9yW+0UeN2oWrju8JW9tGbB3TLkFp66cMb9d6cP5SRnqX+D9+gc6dTtDLy39eotb+5kkY911hH/07U4T8T1a1/Nbsx7Pz6lM6duTYeiFiqZj35NO+ss6vmKTMxTm6hlZT43adKP3tSoSMm2epFL3xGyft/Udjyf++vPvHSI0o/fUKBve9R8oHdSj6w27bNxS9I3hEtJEneES0d9pv5z/vCvWo3umZeIVYUcyVJx54bKc9aEXIPraiM+FglfLtWWSnJqvDYK9fUMxKuZr4Ojb1Fvi26yqNSDZncPJR84FdZftgojyph8u90m8O+Uo8dVNqxvxV40xCZTKYiOb6CVhTnV/IfP+v0ilnyadZJ7qGVZM3MUPKfv+r8T9/Io9oN8mvTs+gOGACAIkbwh6GlpKSof//+kiRXV1eVLVtWt9xyix5++GG5ubkV8+gKx3P3b1PM/xqq+4Dq8g1w18HfzunxO77R7m2nL9ku6XyGHu61UWNmNNGQx+vJbDbpl+9iNGfCT4o/++8TtWvWy76aVrmmn55+q41DPw/33HjNBH9JKjvyGcWuXqjEbZ8rK8ki90o1VWHcK/KqfekVH6lHs1eOnPt0ucO2UrUb24VZoyiKufKoVlvnf9ykjHNnZPb0lle95gru94DcQyoU7MEUgfzOl2/rHkr5a4/O7/xGWempcitdToG971HwLUNl9nB8Dkfi9g2SJL/W3QvlOIpKYZ9f7pVqyuuGJrrwyxYlxJ+VrJJbSAUF9R2uoF73yORqzP8mAAAgSSar1XrtvHQbMJiLr7jj0rZZ7tbNPyYU9zCuGeua+zNfTmC+rhxz5Zx1zf2LewgAYGgF8fv0Nkv+H+538OBBPffcc9q1a5e8vb3Vp08fPfroo3J3d3xN7cWsVqvefPNNvfvuu4qLi9MNN9ygiRMnqmHDhrY6O3bs0ODBgx3a9urVS7Nnz3ZqnFzxBwAAAADASQkJCRoyZIiqVq2qyMhIxcTEaMaMGUpJSdGUKVMu2fbNN9/UnDlzNH78eIWHh+udd97RsGHD9PHHH6tSpUp2dadPn257QLmU+7PKLofgDwAAAACAk1auXKkLFy5o7ty5CggIkCRlZmZq6tSpGjlypEJDQ3Ntl5qaqoULF2rYsGG69957JWW/drxHjx5atGiRnnnmGbv6tWrVsntFeX5cO09KAgAAAACghNiyZYtatWplC/2S1LNnT2VlZWnbtm15tvvll190/vx59ez574Nl3d3d1bVrV23ZsqVQxsoVfwAAAADAdatz586X3L5p06Zcy6OionT77bfblfn5+alMmTKKiorKs7+cbRcv35ekGjVqaOnSpUpJSZGn578P9L3//vsVHx+vMmXKqHfv3nrkkUfstl8Jgj8AAAAAAE5KTEyUn5+fQ7m/v78SEvJ+EG5iYqLc3d3l4eFhV+7n5yer1aqEhAR5enrK19dXI0aMULNmzeTh4aEffvhBixcvVlRUlBYuXOjUWAn+AAAAAIDrVl5X9ItbnTp1VKdOHdvnVq1aKSQkRNOmTdOePXtUv379K+6Le/wBAAAAAHCSn5+fLBaLQ3lCQoL8/fN+naufn5/S0tKUmppqV56YmCiTyXTJtjnPBfjtt9+cGivBHwAAAAAAJ1WvXt3hXn6LxaIzZ8443L//33aSdOjQIbvyqKgolS9f3un7968EwR8AAAAAACe1b99e27dvV2Jioq1sw4YNMpvNatOmTZ7tGjduLB8fH33++ee2svT0dH355Zdq3779Jff56aefSpLTr/fjHn8AAAAAAJw0YMAALV++XKNGjdLIkSMVExOjmTNnasCAAQoNDbXVGzJkiE6ePKmNGzdKkjw8PDRy5EhFRkYqKChIYWFheu+99xQfH6/hw4fb2o0fP15VqlRRnTp1bA/3W7Jkibp06ULwBwAAAACgsPn7+2vp0qV69tlnNWrUKHl7e6tfv34aO3asXb2srCxlZmbald13332yWq1avHix4uLidMMNN2jRokWqVKmSrU6tWrW0bt06LV68WOnp6apQoYIeeOAB3X///U6P1WS1Wq35O0wAV6uN74riHsI1Y5vlbt38Y96vRYG9dc39mS8nMF9XjrlyzrrmeT+gCQBw9Qri9+ltlrsLYCQlG/f4AwAAAABgYAR/AAAAAAAMjOAPAAAAAICBEfwBAAAAADAwgj8AAAAAAAZG8AcAAAAAwMAI/gAAAAAAGBjBHwAAAAAAAyP4AwAAAABgYAR/AAAAAAAMjOAPAAAAAICBEfwBAAAAADAwgj8AAAAAAAZG8AcAAAAAwMAI/gAAAAAAGBjBHwAAAAAAAyP4AwAAAABgYAR/AAAAAAAMjOAPAAAAAICBmaxWq7W4BwEAAAAAgLPa+K646j62We4ugJGUbK7FPQDgelYQ/1BdL7ZZ7laXYeeKexjXjK8WB3J+OYHz68p9tTiQuXIC8+WcrxYHFvcQAMCQWOoPAAAAAICBEfwBAAAAADAwgj8AAAAAAAZG8AcAAAAAwMAI/gAAAAAAGBjBHwAAAAAAAyP4AwAAAABgYAR/AAAAAAAMjOAPAAAAAICBEfwBAAAAADAwgj8AAAAAAAZG8AcAAAAAwMAI/gAAAAAAGBjBHwAAAAAAAyP4AwAAAABgYAR/AAAAAAAMjOAPAAAAAICBEfwBAAAAADAwgj8AAPg/e/cdFcXVhgH8WRaW3qtYsYGAHQugYtSI0diiscSusWGLvX62GDVGjVHsvcdu7D021Nhr7BWV3ntZ9vuDsLouqAjsbHl+5+QE7p3Zfec6u8w7cwsRERF9gadPn6JXr16oVq0afH19MWfOHKSnp39yP5lMhhUrVqBhw4aoUqUKOnbsiJs3byptFxYWhiFDhqB69eqoXbs2Jk6ciMTExHzHycSfiIiIiIiIKJ/i4uLQo0cPZGRkYNGiRRg+fDi2b9+O2bNnf3LflStXYuHChejZsyeWL18Oe3t79O7dG8HBwfJtMjIy8OOPP+LFixeYN28epk6divPnz2PkyJH5jlU/33sQERERERER6bg///wTSUlJCAwMhJWVFQBAKpVi2rRp6N+/PxwdHXPdLy0tDcuXL0fv3r3Rs2dPAEDNmjXRrFkzrF69GlOnTgUAHD16FI8fP8ahQ4dQtmxZAICFhQX69OmD27dvo0qVKp8dK5/4ExEREREREeXT2bNn4e3tLU/6AeCbb75BVlYWgoKC8tzv+vXrSExMxDfffCMvk0gk+Prrr3H27FmF13d1dZUn/QDg6+sLKysrnDlzJl+x8ok/ERERERER6azGjRt/tP7kyZO5lj979gzt2rVTKLOwsIC9vT2ePXuW5+vl1L2f0ANAuXLlsH79eqSmpsLIyAjPnj1T2kYkEsHFxeWjr58bPvEnIiIiIiIiyqf4+HhYWFgolVtaWiIuLu6j+0kkEhgaGiqUW1hYQCaTyfeNj4+Hubl5vl8/N3ziT0RERERERDorryf62oRP/ImIiIiIiIjyycLCAgkJCUrlcXFxsLS0/Oh+6enpSEtLUyiPj4+HSCSS72thYZHr0n2fev3cMPEnIiIiIiIiyqeyZcsqjbVPSEhARESE0tj8D/cDgOfPnyuUP3v2DM7OzjAyMsrz9WUyGZ4/f/7R188NE38iIiIiIiKifGrQoAEuXLiA+Ph4edmRI0egp6cHX1/fPPerUaMGzMzMcPjwYXlZRkYGjh07hgYNGii8/oMHD/DixQt52cWLFxEbGws/P798xcrEn4iIiIiIiCifOnXqBFNTUwwaNAjnz5/Hrl27MGfOHHTq1AmOjo7y7Xr06IGvv/5a/ruhoSH69++PNWvWYP369bh48SJGjhyJ2NhY9OnTR76dv78/KlSogCFDhuDvv//GoUOHMGHCBDRs2BBVqlTJV6z5ntxv0aJFCAwMBJC9lICpqSmcnZ1Rq1YtdOnSBeXKlZNv26hRIzRs2BCTJ0/+7Nf/559/0L17d+zcuROVK1fOb3i5xuvr64saNWoU+LU+tHv3bowfP17+u7m5OcqVK4e+ffuiSZMmBX79gIAAJCQkYOPGjQCyj2XNmjW4ceNGgV+7sH3YFjlMTEwEiXfdunVwcXFRuhP2JeekpjKzNEDAzzXQoGVJGBnr4/61SCyacB2PbkV/1v6lXS0wdJYXqnjbIzM9CxeOvsGiCdcQG/luLJKdkzECfq6BSjVtYedkDKlUhuAn8di98hEOb8nfEiNCMdAHerQxRhMfCcxNRHj2Woq1u1Nw/d/Mj+5XwkkPLRsawq2sPiqUFkNiIEKX0XEIi8r66H7F7PWweoYFJAYiBEyPx6MX0sI8HJVRxfkFACIR0HmYO9r2qQhbJ2MEP4nHxnn3cGLniyI4qsKlqnPLu5oBurc2QmlnMWLjZThyPg2b9qci6+Onotop6vayMBWhWX0J6lY1QClnMfTFQHBIFnYdS8XpKxlFeWhFQhXn16Y5FnCyEyuV7/87DX9sTC60YyEi0lSWlpZYv349fv75ZwwaNAimpqZo3749hg8frrBdVlYWpFLFa76+fftCJpNhzZo1iI6ORqVKlbB69WqULFlSvo2BgQFWrVqFGTNmYMSIEdDX18fXX3+NCRMm5DvWL5rV38jICOvXrwcAJCUl4dGjR9i2bRu2b9+OX375Ba1btwYABAYG5rq8gSoFBgbCxMSkSBL/HKtWrYK5uTmio6Oxdu1aDBo0CKtWrUL9+vUL9X2+//77fHfpULWctsihpydMp5INGzagYcOGSu2lDuekKohEwG87v0J5T2ts+eNfxEWl4bu+FRF4qAl6NziM10+VJyF5n72zCRYfaYqkuAwsn3YTJmb66DzEHeU8rPBjwyPIzMi+QLS0NYR9cRP8vfclwoKToW8gQq1GxTBpuQ9KVbDA8mk3VXC0BTO6jyka1DTA7uNpeBMuRVNfQ8z8yQyjfkvA3cd5J+Xu5fTRpokhXr3Nwqu3UpQv/XlfpwM7GUMqBWBQSAcgAFWdXwDQf0o1dBvpib/WPsb961Go36IEpq2tB5lMhpO7Xhb1oRaIKs6tWpX1MW2wKW49zETg5mS4lBCjS0sjWFvoaVxiVtTtVam8Pnp9Z4zLtzOweX8qpFlA/ZoGmDTQDKWcU7Dhr9SiOrQioarvricvM7HjqOINuddhmnnDkoioKJQrVw7r1q376DY5D3LfJxKJ0L9/f/Tv3/+j+zo6OmLRokUFCRHAFyb+enp6qFatmvx3X19f/PDDD+jXrx8mTpyIGjVqoGTJknB3dy9wgJrAw8MDNjY2AIDatWujYcOG2LRpU6En/k5OTnByciqU10pNTZVPGlGY3m8LdaQr5+RXbUqhSl0HTOx6Fqf/egUAOLXnJbZeb4U+E6pgWp+gj+7ffZQHjE300af+IYS9zk4e/r0ahT/2N0HzrmWxb+0TAMDTe7EY0vy4wr67VjzCr9sbov0AV6z8+RaysmRFcISFw9VFjEZ1JFi+LVl+YXssKB2rfrZA3+9NMGxm3gnsxZsZaDM4FimpwPf+hp+V+Ht56MPL0wDbj6Sia0vjQjsOVVPV+WVXzBidhlTCruUPMX/UFQDA/nVPsPjI1xg0owb+3vNKbc8vVZ1b/TuY4NlrKcbOS5Q/4U9OkaFzCyPsPp6K4FDNeOyvivZ6+UaKHuPjEf7ek+19p9IwZ5QZOjU3wvbDqUhNL9zjKiqq/O6KjM3CyUsa0jBERJSnQnsca2hoiP/973/IyMjAjh07AGR3q54+fbp8mxs3bmDAgAGoV68eqlWrhtatW2Pv3r25vl50dDQGDx6MatWqoV69eli2bJnSNk+fPsXAgQNRs2ZNVKtWDf369cOrV6/k9a6urgCAOXPmwNXVFa6urvjnn38AZM+GuHr1avj7+8PT0xONGzdWulMTGhqKYcOGwcfHB5UrV0ajRo0wc+bMj7aDmZkZXFxc8Pr1a3nZ6dOn8f3336NKlSqoW7cupkyZguRkxScxT58+RdeuXVG5cmU0adIEe/bsUXrtRYsWoXr16gpljx8/RpcuXVC5cmU0bdoU+/btQ0BAALp166a03+3bt9GxY0dUrlwZmzdv/qw2/Ny2+pRu3bop3c26f/++wr8JkP1vtnLlSixatAg+Pj6oU6cOxo8fr9ReYWFhGDNmDHx8fFClShU0a9ZM3gulUaNGePPmDTZv3iz/d9+9e7e87v1zEgCOHTuG1q1bo3LlyqhXrx5mzZqlsLTGP//8A1dXVwQFBWHkyJGoXr06vvrqK6xcuTJfbaBKDduURlRYCs7se/dvGRuZhlN7XqF+i5IwkHz8o9+wdSkEHXkjT8oA4OrpULx6HI9GbUt/8v1DXybCyEQf+p94H6E18JJAKpXh4Jl3/94ZmcCRc+nwKK8Pe2tRnvsmJMmQko8HhGIxEPCDCfacSMPbcM1IxvKiqvMr+7XE2L3ykcL+e1Y9hmMJU3jWsSukIyp8qji3SjnroUxxMQ6dSVfo1r/v7zTo6YnQwEtSkENQKVW0V2hklkLSn+PCjQxIDEQoZq/e31fvU+V3FwDoiwEjzTmdiIgoF1/0xD8v5cuXh6OjY55jut++fYsaNWqgc+fOkEgkuH79OiZNmgSZTIa2bdsqbPu///0PLVq0wKJFi3DhwgX8/vvvsLS0ROfOnQEAwcHB6NSpEypUqIDZs2dDJBJh2bJl6NmzJ44cOQKJRIJt27ahY8eO6NatG7799lt5jADwyy+/YMeOHRgwYACqVq2K69evY+7cuTA0NJS/x5gxYxAeHo5JkybB1tYWISEhuHv37kfbQCqVIiQkBBUqVACQPavj8OHD8d1332HIkCGIiIjAvHnzEB8fj99//x0AkJaWht69e8PY2Bhz5swBACxcuBCJiYkoU6ZMnu+VmpqK3r17w8LCAr/99hsAYPHixYiPj0epUqUUts3IyMDIkSPRs2dPDB8+HFZWVp/Vhp/bVjmysrKQmflufKFYLIZIlPcFSG42b96MmjVrYvbs2Xjx4gXmzJkDW1tbjBo1CgAQExODjh07AgCGDx+OEiVK4OXLl/IbFoGBgejXrx9q1KiB3r17A4BSe+Q4efIkhg4dihYtWmDkyJF49uwZfv/9d4SEhGDhwoUK206ZMgWtW7fG4sWLceLECcydOxeurq4KM2+qi4pVrPHoZjRkHzwMvX8tEm16V0DJ8hZ49m9srvvaFTOGjYMxHtyIUqr792okvP2LK5VLjMQwNtWHsak+qtdzRPOu5XD3cgTSU9W7O2j5UmK8DstC8gcXwQ+eZ5/D5UrpIyKmcMb+tvvaEOYmImzen4J6NTX7ClpV51fFqtZITszAi4dxSu+THYcNbl+MKNjBFBFVnFvlS2X/CX/4QnFMd1SsDOHRWShfSnlstrpS5WfxQ9aW2X+j4hLVs/dIblTZXtXcDHBwmRXEYhFCI6XYdSwNe06kfXpHIiJSK4Wa+ANAsWLFEBkZmWtdixYt5D/LZDLUqlULYWFh2LZtm1LiX7duXYwdOxYAUL9+fURFRWHp0qXo2LEj9PT0EBgYCEtLS6xduxaGhoYAspdFaNy4MXbs2IEuXbrIhyMUK1ZMYWjCq1evsGnTJkybNk2eQPr4+CA1NRWLFy+Wv8edO3cwYsQING/eXL5vmzZtlI4rJ9mNjo7G0qVLERERgSFDhkAmk2HOnDlo3rw5fvnlF/n29vb26NevHwICAlChQgXs3r0b4eHhOHz4sDzRd3d3R7NmzT6a+O/atQtRUVHYunUrSpQoAQDw9PRE06ZNc038hw8frnAsY8eO/WQbfm5b5fhw2Yphw4YhICAgz2PIjb29PebNmwcgewmLf//9F0ePHpUn/uvWrUNUVBQOHz4sP25vb2/5/u7u7pBIJLCzs1P4d89NYGAgqlWrpvB+xsbGmDx5Mh4+fCjvNQIATZs2xZAhQ+Tvd/r0aRw9elQtE39bJ2PcvBCuVB4VmgIgO/nKMzFzMlbYVmH/sBRY2hjCQKKHjPR3T846BLhh4LR3vVGu/B2CmQMvFuQQVMLGUg9RscpPAKPisstsrfJ30yov1hYidGlpjBXbk5Uu1DWRqs4vWydjxIQrN1jke++jrlRxbtn+l7BG5/I+0bFZsLXSnCfYqvosfsjcVITm9Q1x+2EGouM0J/FXVXs9C5bi7uM0vA7NgoWZCE19JRj0gwlsrfSwaqfyZ5iIiNRXoSf+Mpkszye8cXFxWLRoEU6ePImwsDD5zIZWVlZK276/3AGQvZTBX3/9hdDQUDg7OyMoKAjNmzeHWCyWP2G2sLCAu7v7J5/KX7hwAUB2Ivf+02kfHx+sXLkSISEhKF68ONzd3bFmzRqIxWL4+vqidOncuzi/n+waGRlh4MCB6NChA54/f443b95gwoQJCu9Tu3Zt6Onp4e7du6hQoQJu376NChUqKCT5pUuXhpub20eP4+7du6hYsaI8+QWAEiVK5LnfhxPdfU4bfm5b5Vi3bh3MzMzkv7+/jMXn8vHxUfi9XLlyOHjwoPz3ixcvom7dugrH/SWSkpJw//59+Q2mHM2bN8fkyZNx7do1hcS/Xr168p9FIhHKlSuH0NDQAsVQVAyNxchIU37anvbfE3hD47yfBObU5bZ/+nv7v5/4H9/xAg+uR8HKzgi+zYrD2sHoo++hLgwl2d1jP5SRkVNfOBfPfb83RkiEFIfOasc4WVWdX4ZGYqSnf2Q7I/U9x1Rxbkn+e43c3ic9QwYT46JJlouCqj6L7xOJgPH9TGFqIkLgFs1KYlXVXpMXJSn8fuR8OmYNN0P7pobYezIVkTGac7OEiEjXFXriHxoamudT6nHjxuHGjRsYNGgQypcvDzMzM2zduhWHDx9W2vbDCeLs7LLHckZERMDZ2RkxMTFYv369fFz3+wwMPj5ddkxMDGQyGerWrZtrfU4y+/vvv+P333/HggULMG3aNLi4uGDEiBFo2rSpwvY5ya6lpSWcnZ2hr68vfx8AGDRoUJ7vAwDh4eGwtbVVqre1tVUYa/6h8PDwXCfSs7GxUdrP2NgYpqamCmWf04af21Y5XF1dCzy534ez7hsYGCA9/V3CFBsbKx9KURAJCQmQyWRKbW9ubg6JRIK4uDil8g/jSkj4+OzlRU3fQA8W1ordxmMj05CWIoWBoXJSlJMopaXk3QU/py63/SV57B8WnISw4OwLxBM7X2DMwjpYsK8JOtfYp9bd/dPSs5fE+lDOV0haesEvaiuVFaOJtwSj5yYqdY1Xd0KfX2mpUkgkH9lOx8+t9P9eI7f3kRiIkK5B95lU0V4fGtzFGLUrG2D2yiQ8C1bfcyk3QrRXjl3HUlGrsjmquhpw0j8iIg1SqIn/48ePERYWptRtH8gex3769GmMGzdOYeK5LVu25Ppa0dGKa0HnDB+wt7cHkL1mop+fH3744QelfT9McD9kaWkJkUiELVu25HqTwMXFBQDg4OCAWbNmISsrC3fv3sXSpUsxfPhwHDlyRGF9xbyS3ZyeDJMnT0aVKlWU6h0cHOT/v3fvnlJ9VFSUwtPz3Pa/f/++Unl0dLRSG+TWC+Nz2vBz2+pTJBIJMjIUxxt+mFh/LisrK4SHK3czzi9zc3OIRCKlcy0hIQHp6emwtLQs8HsUtcp17BF4WLF3TDuPPYgKTYGdo3I3aNv/ullHhuT9dCunG3XOtgr7OxojLjpN4Wl/bk7vfYXWvSqgmq8DLp8M+eRxCCU6Lgt21srdoW0ts8uiYgt+8dz3e2PceZyJ0IgsONpmv66lWfbn0cZSDw42WQiPVs87AkKfX1GhKahRX3klE7vPeB+hqeLcivqva7qNlR4iYhQTVxsrPTx89vH13NWJKtrrfd1aGaF1IyOs3JGMExc1L3lVdXu9Lzwm+/NpbqY5PUqIiKgQE/+0tDT8/PPPkEgk+P7775Xq09PTkZWVpZA8JiYm4tSpU7m+3vHjxxW6+x89ehQODg7y5ey8vb3x+PFjuLu7QyzOu7ungYGB0tPvnPHgsbGxaNSo0SePTU9PD1WqVMFPP/2EU6dO4eXLlwqJf17Kli0LJycnBAcHo0uXLnluV7lyZezduxcvX76UDyd4+fIlHjx4AC8vrzz38/T0xN69exEcHCyP5/Xr13jw4AFq1qz5yfg+pw3z21Z5cXJywoULFxSGggQFfXzJr7x4e3tjzZo1ePv2LZydnXPdJrd/9w+ZmpqiUqVKOHLkCHr27Ckvz+mB8jltKLQnd2MwrOUJhbLosBQ8vhODqj4OEImg8JTZ3csOKUmZCH4Sn+drRoakICYiFW7VlXuhuHvZ4fHt6Fz2UiT5rzu3mYV6L1b/9JUU1dz0YWIEhbH3bmX1/6sveOLkYKsHJzsxNv+mfCNpxjAzJCZnoc3gL7sJVtSEPr8e345Bq54VUMbVUmGCP3ev7B5gj+98+lwUiirOrZzXcC2jj4fP3yX+tlYiONjo4eAZzXmKrYr2ytGqkSF6tDHGrmOp2HZYMyepU2V7faiYffb3e1yCZq9OQkSka74o8c/KysLNmzcBAMnJyXj06BG2bduG4OBgzJ49O9ex1+bm5qhcuTJWrlwJGxsb6OvrY8WKFTAzM1N64goAly5dwq+//gpfX18EBQXhr7/+wuTJk+UTyQ0dOhTt27dHnz590KFDB9jZ2SEyMhKXL1+Gl5eXfBb/smXL4uTJk/Dy8oKxsTFcXFzg4uKCLl26YMyYMejTpw+qVq2KjIwMvHjxAv/88w+WLFmChIQE9OnTB61bt4aLiwsyMjKwceNG+Rj4zyESiTBu3DiMGjUKycnJaNiwIYyNjfH27VucOXMGw4cPh4uLC7777jssXboU/fv3x7BhwwBkz+qfM7whL+3atcOyZcswYMAA+aRzgYGBsLOz+6yZ9D+nDT+nrT6Hv78/du7ciZ9//hlNmjTB9evXcfTo0c/a90M9e/bEX3/9ha5du2LgwIEoWbIkgoOD8eLFC4wePRpA9r/7pUuXEBQUBAsLC5QoUQLW1tZKrzV48GAMGjQIo0aNQqtWrfD8+XP8/vvv8Pf3Vxjfr64SYtNx9bTyPAOn975Co7al4deqlHyddUtbQ3zVphSCDr9WeGJf3CW7V8mb54nv9v/rFb75oSwcipsg/E32kms1/ZxQqoIFtgW+62ViZWeI2EjlC+eW3csjK0uGh7fUNzEDgLPX0tHhGyO08DOUr4VtoA/415Pg/tNMRPw3ftXBRgRDieiL1kT/fX2y0njb6pX00baJEZZtS0ZwiPomZ0KfX+cOvsbQ2TXxXd+KmD/qiry8TZ8KCH+ThDuXcp9IVh2o4tx6+TYLL99K0dxPggOn05D1302Yll8ZIitLhnNXNedJtiraCwAa1jLAoB+MceJiGpb+qb49Rj5FFe1lbipCUrJMfl4B2cuSdm5uhPQMGW7e15weJURE9IWJf2pqqnyGdxMTE5QoUQLe3t4IDAxEuXLl8txv3rx5mDx5MsaNGwcrKyt069YNycnJWLNmjdK206dPx7Zt27B161aYmppi2LBhCk/NS5cujR07dsjH3ycnJ8Pe3h61atVSSNgmT56MmTNnom/fvkhNTcWGDRtQp04dTJo0CS4uLti2bRsWL14MU1NTuLi4oFmzZgAAQ0NDVKxYERs3bkRISAiMjIzg6emJ1atX52sM+zfffAMLCwssW7YM+/fvBwAUL14c9evXlyf2RkZGWLNmDaZOnYrRo0fD0dERAQEBOHny5EfHkOfsN2XKFIwaNUq+3969e5XGo+fmc9vwU231ORo0aIDRo0dj06ZN2LNnDxo0aIBp06YpPGn/XNbW1ti6dSvmzZuHuXPnIiUlBcWLF1cYsjBixAhMnToVQ4YMQVJSEmbNmoXvvvtO6bUaN26MP/74A4sXL0ZAQACsrKzQoUMHjBw5Mt9xqZO/977C3csRmLjUGy5uloiNSsN3fStCLBZh9czbCtv+sb8JAKC951552Ya5d/FV21JYdPBr7Fj6AMZm+vhhqDue3I3BwU1P5dv1GOWJynUd8M+Jtwh9nQQLawkatioFdy877Fj6AG+eJUKdPXgmxZkr6ejTzhhWFnp4Gy7F1z6GcLLVw7y172If+6MpqroZoEnvGHmZqTHQprERAMCjQvZXaevGhkhKliExWYa/TmVfjF+7p3xxbGaSfSPg9sNMPHqhvol/XlR1fkW8Tcb2JQ/Q5ScPiA308OB6FOp/WwLVfB0xtfd5ZGWp5xAJQDXnFgCs2JGCn4eYYvZIM5y+nI4yxcVo3dgQh8+l41WI5jyRVUV7ubqIMeZHU8QnynDjfiYa11Wcv+Lfp5kIidCMNlNFe3lXM0CXb41w7loGQiKksDDVQ6O6EriUEGP1zhTExKvv54+IiJSJZDJNm26KPiY2NhZNmjRBz549MXjwYKHDoU/wNd9UZK9tbiXBoBk1UP/bEjA00sf961FYPPEaHtxQfAq/824bAIqJGQC4uFliyKyaqOLtgIx0KS4efYtFE64hJuJdv9JaXzmh/UA3uFa1gZWdIdJTpXhyLxYH1j/Boc3PCvV4ghK6Kly8FhYDfaBXW2M09pbA3FSEZ8FSrNuTgqvvJezzxpgpXTw72url2n0fAEIjpeg6Ju/u7k19JRjTxxQB0+OLLPE/scZa488vIHvm9a4jPNC6VwXYOhnj9dMEbJx3F8e2vyjU4ymK80tV55ZPdQN0b2WEUs5ixCbIcCwoDRv3pUJaRPeUTqyx1sjPYs7nLi9zVifhWFDh95LQ1PaqUFqMbq2MUKG0PizNRciUZg8h2HMiDWevZuS6f2E4sUa5dx4R0ccUxvVOUELXQohEvTHx13ArVqyAnZ0dihcvjoiICKxZswbPnz/HoUOHUKxYMaHDo08oysRM2xRV4q+tijrx1zY8vz5fUSWy2ortlT9M/Ikov5j4f55CX86PVEtPTw9Lly5FWFgYxGIxqlativXr1zPpJyIiIiIiIgBM/DXejz/+iB9//FHoMIiIiIiIiEhNKS8CS0RERERERERag4k/ERERERERkRZj4k9ERERERESkxZj4ExEREREREWkxJv5EREREREREWoyJPxEREREREZEWY+JPREREREREpMWY+BMRERERERFpMSb+RERERERERFqMiT8RERERERGRFmPiT0RERERERKTFmPgTERERERERaTEm/kRERERERERaTF/oAIiIiIiIiIi+RFBCV6FD0Ah84k9ERERERESkxZj4ExEREREREWkxJv5EREREREREWoyJPxEREREREZEWY+JPREREREREpMWY+BMRERERERFpMSb+RERERERERFqMiT8RERERERGRFmPiT0RERERERKTFmPgTERERERERaTEm/kRERERERERajIk/ERERERERkRZj4k9ERERERESkxZj4ExEREREREWkxJv5EREREREREWkwkk8lkQgdBREREREREREVDX+gAiHRZy8txQoegMfbXtmR75QPbK3/217ZEk94xQoehEU6ssWZb5cOJNdbwNd8kdBgaIyihK9srH4ISugodAhFpCHb1JyIiIiIiItJiTPyJiIiIiIiItBgTfyIiIiIiIiItxsSfiIiIiIiISIsx8SciIiIiIiLSYkz8iYiIiIiIiLQYE38iIiIiIiIiLcbEn4iIiIiIiEiLMfEnIiIiIiIi0mJM/ImIiIiIiIi0GBN/IiIiIiIiIi3GxJ+IiIiIiIhIizHxJyIiIiIiItJiTPyJiIiIiIiItBgTfyIiIiIiIiItxsSfiIiIiIiISIsx8SciIiIiIiLSYkz8iYiIiIiIiLQYE38iIiIiIiIiLcbEn4iIiIiIiEiLMfEnIiIiIiIi0mJM/ImIiIiIiIi0GBN/IiIiIiIiIi3GxJ+IiIiIiIhIizHxJyIiIiIiItJiTPyJiIiIiIiItBgTfyIiIiIiIiItxsSfiIiIiIiISIsx8SciIiIiIiLSYvpCB0BUlFq1aoWHDx9i8+bN8PLyEjoclcjKSEfUruWIDzqMrKQEGJYsD9v2A2Bauc5H94vcvQLRe1YplYsMJKiw5nye+6U8vIngGf0AAOWWHIPY3KpA8aval7ZXwpW/kfDPcaQ9+xeZcVHQt3GEWfV6sGndB2JTc/l2yfev4fXMgXm+jm37AbBt3bvQjqcofXFbXf0bcad2Iy34KbIS4yA2t4ZReU/Ytu0Lw5Ll5NtJE2IRd3Y/km6cQ/rbF5BJMyEpVgbWzTrDvO7XRX14hc5AH+jRxhhNfCQwNxHh2Wsp1u5OwfV/Mz+6XwknPbRsaAi3svqoUFoMiYEIXUbHISwq66P7FbPXw+oZFpAYiBAwPR6PXkgL83CKXFG3V1VXfcwba57HqwBrdqdgy4HUQjkWVTCQ6OHHSVXRrJMLzK0keHI3Fit/vokrf4d+cl+7YsYYOtsLtRsVg54ecP1cGBaOu4a3LxLl20iMxBgxrxY8vOzgUNwEemIR3jxPxMGNT7B75SNIM2VFeXiFim1FRMTEn7TY48eP8fDhQwDA/v37dSbxD1sxHQlXTsLavzMMHEsi/twBvJn3E0qOXwpj12qf3N+h51joGZm8K9DLu2OQLCsL4RvnQmRoDFlaSiFEr3pf2l5ha2ZC39oe5r7fwMDWEWnBTxF7fAeSbl1AqZ83QE9iBACQOJeB04BpSvvHBx1C8p1/YFq5blEdWqH70rZKD34KsYkFrP07Qmxmhcy4KMSf3Y9XU3ui1OTVMCxdEQCQ8uQOIncshWlVH9i07g2RnhgJV04hZPFEpL15Drt2/VR0pIVjdB9TNKhpgN3H0/AmXIqmvoaY+ZMZRv2WgLuP807K3cvpo00TQ7x6m4VXb6UoX/rz/lQP7GQMqRSAQSEdgIoVdXu9DJFi1ookpfKvfSTw8jTAtbsZhXYsqjBxmQ++alMK25c8QPDTeDTvUg5zdzXCkBbHcftiRJ77GZvqY9Ghr2FmYYAN8+5CmpGFDoMqIfDw1+jpexDx0ekAAENjMcpWssTFY28Q8jIJsiwZPOvYY+hsL7h72WFanyBVHWqBsa2IiJj4kxbbv38/9PT0UKtWLRw5cgSTJk2CgYGGXhF/ppSn95Bw6RjsOg2FTYuuAACLes3xcnxnRPy5CKWmrP7ka5jXbvzZT+3j/t6DjKgwWDZsjdijfxYkdEEUpL2ch86GSaWaCmVGLm4IXT4NCReOwLJhGwCAvqUtLHy/Udo/as8qGDiVhFFZ98I7oCJUkLaybfujUpllw9Z4NuxbxJ7aBcde4wEAhsXLwmXuLhjYFXu3XZP2eD17EGIOboBNi27QMzIu5CMrGq4uYjSqI8HybcnYcTQNAHAsKB2rfrZA3+9NMGxmQp77XryZgTaDY5GSCnzvb/hZib+Xhz68PA2w/UgqurbUjDZ6nyraKzZehpOX0pXKu7U2wutQKR5qUA+JSjVt8fX3ZRA48Rq2LrwPADiy5Rk2/tMSAT/XwIAmR/Pct23fiihV3gJ9/A7jwfUoAMDF42+x8Z9v0XmIO5ZPuwkASIhJR79Giq+zd81jJMWno/0ANywafw3R4erfQ4JtRUSUjWP8SSvJZDIcOHAAdevWRa9evRAbG4tz584pbPP48WN06dIFlStXRtOmTbFv3z4EBASgW7duCts9ffoUAwcORM2aNVGtWjX069cPr169UuXhfLbEyycBPTEsG7WRl+lJDGHp1wqpT+4gIyrsk68hk8kgTUmETPbxronSxDhE7lwGu3b9oWdiVtDQBVGQ9vow6QcAs5oNAQDpb1989H1Tnt5DRlgwLLybfUnYgiiMc+t9Ygsb6EmMkJX0rrusgUNxhaQfAEQiEcxq+kGWkY6MiDcFOgZVauAlgVQqw8EzafKyjEzgyLl0eJTXh721KM99E5JkSMlHjiAWAwE/mGDPiTS8Df/4cAB1pcr2ep+rixglHMW53hBQZ1+1KYXMzCz8tfaJvCw9LQsHNj5B5Tr2cChukve+rUvh36uR8kQWAF49ise106Fo1Lb0J9875FV2rwkzK0kBjkB12FZERNmY+JNWun79Ot68eYNvv/0W9erVg5WVFQ4cOCCvT01NRe/evREbG4vffvsNI0aMwMqVK3Hv3j2F1wkODkanTp0QFxeH2bNnY+7cuYiOjkbPnj2Rnq5+F4ppLx9B4lQKYmPFRNyonLu8/lOej2yLp/0a4UnfhghZOhmZcVG5bhe5czn0LW1h2ahtwQMXSGG01/ty2kpsZvXR7RIuHAEAmPtqTuJfGG0lTUpAZnwM0oKfIGzVDGSlJMHYo9an98tpVw2aP6J8KTFeh2Uh+YOE9MHz7PHq5UoVXoe7dl8bwtxEhM37NXO4DaDa9npf47rZCZmmJf4Vq9gg+Ek8khMUhyf8ezX7s1KhinWu+4lEQDlPazy4ofy9fv9aFEqUM4eJmWJb6xvowdLWEA7FTdCgZUl0HuqOkJeJePM0714Y6oRtRUSUjV39SSsdOHAAhoaGaNq0KQwMDODv7499+/YhKSkJpqam2LVrF6KiorB161aUKFECAODp6YmmTZuiVKlS8tcJDAyEpaUl1q5dC0NDQwBAjRo10LhxY+zYsQNdunQR5PjykhkbCbGVrVK52Mruv/q8xzKKTS1g9fX3MCpfGSIDCVIe3kTsiR1IfXYPpaavV0j40l49Rtzfe1B81O8Q6YkL/0BUpCDtlZvoAxsAPTHMajfKcxtZlhQJ/xyHUVkPSBxL5i9gARVGW72a1hsZIS8BACIjE9i07g1Lv1Yf3UeaGIe40/tg7FoN+v+9lyawsdRDVKzy0/eouOwyW6u8n2Dnh7WFCF1aGmPF9mSlpFmTqKq93qcnAhrWluD+s0yN6ylh62SMqFDlGz05ZXZOuT/FtrAxhKGRGFGhyidLZM6+xUzw6nG8vNyvVUlMX1df/vv9a1GYGXARUqlmTFjHtiIiysbEn7ROZmYmjhw5Aj8/P5ibZ8/g3LJlS2zbtg3Hjx9HmzZtcPfuXVSsWFGe9ANAiRIl4ObmpvBaQUFBaN68OcRiMTIzs588WVhYwN3dHXfv3lXdQX0mWUYaRPrKXQpFBtk3LWTpaUp1Oaz9Oyn8bl6rEYzKuiN06WTEndgFm5Y95HXhG+fBtIq3Rk1Ml5uCtNeH4i8cQfyZfbBu0Q0Sp1J5bpd87wqkcdGwadkr/wELqDDayqnv/5CVkoSM8DeIO3cge5+srDwnkJRlZSFk6WRkJSfAoduogh2AihlKsruqfygjI6e+cBLZvt8bIyRCikNnNeuJ9YdU1V7vq+6uDxtLPWw5qHl3TAyNxMhIU75ZkZ6WPU+BoXHuN2QNjbLL09OV5zOQ72ukuO/1s2EY1vIEzCwl8GrohPKVrWFsqjmXj2wrIqJs/DYirRMUFITo6Gh89dVXiI/PvhNfsWJF2Nvb48CBA2jTpg3Cw8NhY2OjtK+NjQ3S0t4lMDExMVi/fj3Wr1+vtK06ThQoMjCELFM5AZBlZB+TSGKYr9ez8GmGiC1/IPneZXnin3DpOFIe30aZWVsLHrDACqu9kh/eQNiqX2BSuS7svs976T7gv27+emKY122S/4AFVBhtZVyhivxnc++meDG2AwDA/odhuW4fvmEukm9fhFP/qfKZ/zVFWnr28nQfyvnaSEsv+BPASmXFaOItwei5ifjElBxqTxXt9aHGdbPnFTh9WfNumqSlSmFgqHzDTGKYnYimpeQ+UWFaana5RKKc7Mr3TVXcNyYiFVdPZy97d/qvV+g+ygML/mqMjtX+0ogJ69hWRETZmPiT1tm/fz8AYPz48Rg/frxCXUxMDKKiouDg4ID79+8r7RsdHQ1TU1P575aWlvDz88MPP/ygtO3726kLfSs7ZMYod7mWxkb+V2+f/9e0dYQ08V1Xxog/F8K8dmOI9A2QEfEWAJCVnD1BW0ZUGGSZGdC3zv/7CKEw2ivt5SO8nT8KkhJl4Tx0NkTivL9Ws9JTkXj1DEw8akHfUrnbvDor7HNLbGoBE3cvxF84kmviH7V7JeJO7oRdx0GwqNf8y4IWUHRcFuyslZMNW8vssqjYgieyfb83xp3HmQiNyIKjbfbrWpplPxm3sdSDg00WwqM1446AKtrrfRIDwLeGBNf/zURsvGa00fuiQlNg56y8eoOtU3ZZZGhyrvvFR6chLVUKWycjpTq7nH1Dct83x997X6H/lOqo36Ik/lr7OL+hqxzbiogoGxN/0iopKSk4efIkmjRpgu7duyvURUZGYsSIETh06BA8PT2xd+9eBAcHo2TJ7HHWr1+/xoMHD1Cz5rvZ2r29vfH48WO4u7tDLFb/seyGpSsi+f41SFMSFcbkpzy9J6/PD5lMhsyIEBiWebdfZlQYEi4eRcJF5SWQXv2vGwxLVUDpXzZ/4RGoVkHbKz3sNV7/NgxiC2sUH7UAekZ5zw4NAEnXzyErNQkWPpozqV+Owj63ACArPQ1ZKYlK5bHHdyBqz0pY+XeCzbc9ctlT/T19JUU1N32YGEFh7L1bWf3/6nPp155PDrZ6cLITY/Nvlkp1M4aZITE5C20GxxX4fVRBFe31Pp9qBjA1FmncpH45Ht+JQfUGjjAxN1CYtM6jVvYNxce3Y3LdTyYDnt2LhVt15RuP7l62ePMsAcmJH2/rnO7tppbq1+stN2wrIqJsnNWftMrJkyeRnJyMbt26oU6dOgr/tWjRAu7u7jhw4ADatWsHOzs7DBgwAEeOHMGRI0cwYMAA2NnZQSR6N5Z06NChePnyJfr06YNDhw7h8uXLOHToEKZOnaqwSoC6MKvVCMiSIu7UXnlZVkY64s8egFE5TxjYOgIAMiJDlZacy4xXvviJO7kL0oQYmFb2lpc5D5uj9J95na8BAE79p8K+y/DCP7AiUqD2io3EmzlDAD09lBizEPoWuc8M/b74i0chkhjBzKthIR6FahSoreKilV4vI+ItUu5dhZFLJYXyhEvHEb5xHsx9mmnUufShs9fSIRaL0MLv3RAIA33Av54E959mIiIm+ymzg40IJZ2+7E/x7+uTMXlRosJ/e05kZ83LtiVj1oqkgh+Iiqiivd7XqK4EKWkynL+umYn/33tfQl9fD617lZeXGUj00LxLOdy7EoHwN9lPoh1LmKBURQvFff96CXcvO7hVfzfcrVQFC9Twc8KpvS/lZZa2uQ/fadkj+z3fX+JOnbGtiIiy8Yk/aZUDBw7A2dkZderUybW+TZs2mDlzJsLDw7FmzRpMmTIFo0aNgqOjIwICArB37175hIAAULp0aezYsQMLFizAtGnTkJycDHt7e9SqVQuurq6qOqzPZlzeE2a1GyNyx2JI46Nh4FgS8ecPIiPyLRx/nCjfLnT5VKQ8uI6KGy/Ly54PbwXzOl/DsGQ5iAwMkfLoJhIuHYdh6YqwbPSdfLvcktbUV9lLuZlW9dGoJdcK0l5vfhuGjPA3sG7RDSmPbiHl0S15ndjCBqaVFc9BaWIckm5dgHmtRp/sGaCOCtJWLyf8ABMPLxiWqgg9UwtkhL5C3Nl9kEkzYddhsHy7lKf3ELp8KsRmljDxqCVf9jCHUYUqkDgUL/qDLQQPnklx5ko6+rQzhpWFHt6GS/G1jyGcbPUwb+27Xg5jfzRFVTcDNOn97sabqTHQpnF292KPCtl/pls3NkRSsgyJyTL8dSp7XoVr95SfNpqZZN+4vP0wE49e5D52WR2por1ymJuKUKuyAc5dy0Dq58/fqVb+vRqFk7tfYsDU6rC2N8LrZwn45oeyKFbaDLMGX5JvN2mFL2rUd4Sv+SZ52Z6Vj9CqRwX8tvMrbF14H5kZWeg4uBJiwlPx56J3Q+D8O7qgTZ8KOHsgGG9fJMLEzAB1GhdD7cbOOH/oNa6fDVPpMX8pthURUTYm/qRVli1b9tH6Hj16oEePd12Ht2zZIv85NjYWs2bNQs+ePRX2KVOmDBYsWFCYYRYpp/5TEbVrOeKDDiMrOQGSkuVRfMR8mLjV+Oh+5j7NkPr4NhKv/I2sjDQY2BWDdYtusG3VC3qGymMctcWXtlfaq+zxmjEHNyrVGbvVUEr8Ey6fBKSZMPf2L7zgVexL28qy8XdIuhmEpNuXkJWalH1jxLMubFr1hGHJd0/h0t88hywzA9KEGISt/FnpdRz7TtaYxB8AZq9MQq+2xmjiLYG5qQjPgqWY9Eci7jz6ePdgMxM99PpOcUxyh2bZn8HQSKlSIqstVNVeDbwMYKAvwikN7eafY0a/IIT9rxr8O5WFuZUET+/GYPT3f+NWUPhH90tOzMTg5scxdHZN9BjtCT09Ea6fD8PCcVcRG/murW5fDIdnHXt83b4MrB2MIc3MwqvH8Vg47ip2LntY1IdXqNhWRESASCbT9LmAib7MihUrYGdnh+LFiyMiIgJr1qzB8+fPcejQIRQrVkwlMbS8rBnjb9XB/tqWbK98YHvlz/7algpPkSlvJ9ZYs63y4cQaa4WnyPRxQQld2V75EJTQVegQiEhD8Ik/6Sw9PT0sXboUYWFhEIvFqFq1KtavX6+ypJ+IiIiIiEgVmPiTzvrxxx/x448/Ch0GERERERFRkeKs/kRERERERERajIk/ERERERERkRZj4k9ERERERESkxZj4ExEREREREWkxJv5EREREREREWoyJPxEREREREZEWY+JPREREREREpMWY+BMRERERERFpMSb+RERERERERFqMiT8RERERERGRFmPiT0RERERERKTFmPgTERERERERaTEm/kRERERERERajIk/ERERERERkRZj4k9ERERERESkxZj4ExEREREREWkxJv5EREREREREWoyJPxEREREREZEWY+JPREREREREpMWY+BMRERERERFpMSb+RERERERERFqMiT8RERERERGRFmPiT0RERERERKTFmPgTERERERERaTEm/kRERERERERajIk/ERERERERkRZj4k9ERERERESkxZj4ExEREREREWkxJv5EREREREREWkwkk8lkQgdBREREREREREVDX+gAiHRZy8txQoegMfbXtmR75cP+2pZo0jtG6DA0xok11jy/PhM/i/nD9soffnflD7+7Pt/+2pZCh0AkKHb1JyIiIiIiItJiTPyJiIiIiIiItBgTfyIiIiIiIiItxsSfiIiIiIiISIsx8SciIiIiIiLSYkz8iYiIiIiIiLQYE38iIiIiIiIiLcbEn4iIiIiIiEiLMfEnIiIiIiIi0mJM/ImIiIiIiIi0GBN/IiIiIiIiIi3GxJ+IiIiIiIhIizHxJyIiIiIiItJiTPyJiIiIiIiItBgTfyIiIiIiIiItxsSfiIiIiIiISIsx8SciIiIiIiLSYkz8iYiIiIiIiLQYE38iIiIiIiIiLcbEn4iIiIiIiEiLMfEnIiIiIiIi0mJM/ImIiIiIiIi0GBN/IiIiIiIiIi3GxJ+IiIiIiIhIizHxJyIiIiIiItJiTPyJiIiIiIiItBgTfyIiIiIiIiItxsSfiIiIiIiISIvpCx0AERWurIx0RO1ajvigw8hKSoBhyfKwbT8AppXrfHS/hCt/I+Gf40h79i8y46Kgb+MIs+r1YNO6D8Sm5vLtpAmxiDu7H0k3ziH97QvIpJmQFCsD62adYV7366I+vEL3pe0VuXsFovesUioXGUhQYc15pfK4038h5vBmZES8hb6NI6yadoB1046FdhyqYKAP9GhjjCY+EpibiPDstRRrd6fg+r+ZH92vhJMeWjY0hFtZfVQoLYbEQIQuo+MQFpWV63u0b2qEJj4SONrqITFZhntPMrHhrxS8fKu8vTr74s/i1b8Rd2o30oKfIisxDmJzaxiV94Rt274wLFlOcdtLx5F44xxSn95DRlgwjN1qoOTEZUV5WEXmS9vrQ69nD0byvcuwbPI9HHuMVqiLPbETyf9eReqze8iMCoNFvRZw6j+lMA9DZYr6/NKm7/qi/u6q6qqPeWPN83gVYM3uFGw5kFoox6IKRX0d8aH0sNd4Ob4TZBnpKDVtHYzKuhf2IRER+MSfCmjfvn3o1KkTqlevjurVq6Njx47Yu3fvF73WokWLcP369cINUAeFrZiOmCNbYOHTDPZdRwB6engz7yekPLz58f3WzET62xcw9/0GDt1GwrSKN2KP70Dw9D7ISn93wZLy5A4idyyFnqkFbFr3hl37gRBJDBGyeCIid60o4qMrfF/aXjkceo6F04Bp8v8c+/5PaZvYU7sRtvoXSIqXhX23UTAq74mIjfMQfWB9IR9N0RrdxxTtmxri1MV0LNmajKwsYOZPZvCsIP7ofu7l9NGmiSFMjER49Vb60W0n9DNFjzZGuPUgA4u3JOPA6TRUqaiPhRMt4GCrWX+yvvTcSg9+CrGJBaz9O8KhxxhYNv4OaS8f4tXUnkh7+Uhh29iTu5B4/Sz0bRygZ2pRhEdT9Ar6WQSyE4+UJ3fyrI8+uBHJ969CUrwsIP74eavuivr80qbv+qL+7noZIsWsFUlK/129mwEAuPbf/zVFUV9HfChi8++AnmZ/Hok0AZ/40xf7+eefsXnzZrRr1w4BAQEQiUQ4evQoxo0bhzt37uB//1NOgD4mMDAQJiYmqFGjRhFFrP1Snt5DwqVjsOs0FDYtugIALOo1x8vxnRHx5yKUmrI6z32dh86GSaWaCmVGLm4IXT4NCReOwLJhGwCAYfGycJm7CwZ2xeTbWTZpj9ezByHm4AbYtOgGPSPjwj+4IlCQ9sphXrsxxOZWedZnpacicsdSmFbzhfPQ2QAAq6/aADIZovaugeVXbSHWgITN1UWMRnUkWL4tGTuOpgEAjgWlY9XPFuj7vQmGzUzIc9+LNzPQZnAsUlKB7/0NUb507n96bK1EqO8lwfbDqVixI0VefudxJuaNMUf9GgbYdTytcA+siBTk3LJt+6NSmWXD1ng27FvEntoFx17j5eVOA6ZB39oeIj09vBjXqfAPREUK47OYlZ6GiC0LYPNtd0TtWp7rNiUnLoO+rRNEIhEe/+hXqMegSqo4v7Tlu14V312x8TKcvJSuVN6ttRFeh0rx8MXHb3iqE1VcR7wv6fZFJN+5BOsW3RD915pCPRYiUqRZj09IbZw8eRKbNm3CoEGD8Msvv6BBgwaoX78+ZsyYgYCAAGzatAmnTp0SOkydk3j5JKAnhmWjNvIyPYkhLP1aIfXJHWREheW574d/rAHArGZDAED62xfyMgOH4goXggAgEolgVtMPsox0ZES8KdAxqFJB2iuHTCaDNCURMpks1/rkf68hKzEOlo3bK5RbNWkPWVoKkm4GFegYVKWBlwRSqQwHz7xLvDMygSPn0uFRXh/21qI8901IkiHlM3q5mhhlv0ZMvGI32ujY7N/TMnJvY3VUGOfW+8QWNtCTGCErKVGh3MDWESI9zf9TXhjtFXNwIyCTwbp5lzy3MbArBpEo73NVU6ji/NKW73pVfHflxtVFjBKO4lxvCKgzVVxH5JBlZiJ803xYNe0IA4cSBQ2diD5B868WSBDr16+HpaUlevfurVTXp08fWFpaYv367G7M3bp1Q//+/RW2uX//PlxdXfHPP/8AAFxdXQEAc+bMgaurq0JdVlYW1q5di2+++Qaenp7w9fXF0KFDkZDw7i79lStX0KlTJ1SpUgV16tTB+PHjERsbK69//fo1XF1dsXfvXkyePBleXl7w9vbG2rVrAQAHDx6Ev78/atSogcGDByM+Pl4h3vj4eEydOhX16tWDp6cnvvvuO5w/rzyOW2hpLx9B4lQKYmMzhXKjcu7y+vzIjIsCAIjNrD65rTRn2488/VY3hdFez0e2xdN+jfCkb0OELJ0sb7N37/Ew+zVdKim+h0slQKQnr1d35UuJ8TosC8kfXAQ/eJ49RrZcqYJ3IHsbkYXw6Cy09zdC3aoGsLMWwdVFjJ+6myAkQoq//9Gc7rKFcW5JkxKQGR+DtOAnCFs1A1kpSTD2qFUk8QqtoO2VERmK6APrYddxMPQkRkUWp7oQ8vzStO96VXx35aZxXQkAaFzir8rriJijW5GVlACbNsrXkkRU+NjVn/ItMzMTN27cQMOGDWFqaqpUb2pqijp16uDMmTPIzPz4xDk5tm3bho4dO6Jbt2749ttvAQDly5cHkD2kYNu2bejRowd8fX2RlJSE06dPIzk5Gebm5rh79y569eqFOnXq4I8//kBkZCTmzZuHJ0+e4M8//4T4vXGcCxYsQNOmTfHHH3/gxIkTmD17NqKjo3H58mWMHj0aiYmJmDFjBn777Tf8/PPPAID09HT06tULUVFR+Omnn+Do6Ih9+/ahf//+2L17t/ymhTrIjI2E2MpWqVxsZfdffUS+Xi/6wAZATwyz2o0+up00MQ5xp/fB2LUa9P97L01QkPYSm1rA6uvvYVS+MkQGEqQ8vInYEzuQ+uweSk1fL79oyoyNAvTE0Le0UdhfpG8AsZklMmMiC/GIio6NpR6iYpUn14uKyy6ztSr4U1SpFJi2OBET+plixrB3F50PX2Ri6C8JSErRnCf+hfFZfDWtNzJCXgIAREYmsGndG5Z+rQo3UDVR0PaK2LIAhqVdYeHdtEjiUzdCnV+a+F2viu+uD+mJgIa1Jbj/LBNvwzVrUlJVXUdkxkYieu8a2HUeqnSTgYiKBhN/yreYmBikp6ejWLFieW5TrFgxpKWlKTx1/5hq1arJ98v5GQCeP3+OrVu3Yvjw4Qq9Bvz9/eU/L1u2DPb29li2bBkMDAzkr9OnTx+cOXMGjRq9+2NTrVo1TJgwAQBQt25dHDt2TD4swdraGgDw8OFD7Ny5U57479+/Hw8ePMBff/0lvxlRv359vHz5EkuWLMEff/zxWceoCrKMNIj0JUrlIgPD7Pr0zx8fHX/hCOLP7IN1i26QOJXK+z2zshCydDKykhPg0G1U/oMWUEHay9pfcTy1ea1GMCrrjtClkxF3YhdsWvb47zVSIdLP/atWJJF8dMIjdWIoye4e+6GMjJz6wrl4TkyS4WmwFGevpuP+UymcHfXQubkRJgeYYszcxFxjUEeF8Vl06vs/ZKUkISP8DeLOHcjeJysL0IKu/R8qSHsl/3sViVf/Rqmpa4ssPnUjxPmlqd/1qvruel91d33YWOphy0HN+H5/n6quIyK3BcLAwRmWDVsXLGAi+mxM/EmtXbp0CTKZDO3bt89zm6tXr+Lbb7+VJ/0AUK9ePVhYWODatWsKib+vr6/8Z7FYjJIlS0IkEsmTfgAoU6YM4uPjkZSUBFNTUwQFBaFixYooU6aMQg8GHx8f7Nu3r7AOtVCIDAwhy1TuVijLyP5DLZIYftbrJD+8gbBVv8Ckcl3YfT/wo9uGb5iL5NsX4dR/KgxLV8x/0AIqrPbKYeHTDBFb/kDyvcvyxF8kMYIsj54vsvR0jemWnJaevSTWh3I+dmnpBX8ab2oM/D7eHNuPpGLn0XcXl4+eSzF/nDma1ZNg/2nN6DZbGOeWcYUq8p/NvZvixdgOAAD7H4YVUpTq40vbSybNRPjGebDw/UanlgAT4vzS1O96VXx3fahx3ex5BU5f1ozvq/ep4joi5ckdxAcdRolxi7VijhIiTcHEn/LN2toaEokEISEheW4TEhICQ0NDWFlZFei9YmNjoa+vD1tb5W5nOeLj43Ott7W1RVxcnEKZubniOrIGBgYwMTFRKgOAtLQ0mJqaIiYmBv/++y88PDyU3kOsZstB6VvZITNGuRueNDbyv3r7T75G2stHeDt/FCQlysJ56GyIxHl/TUTtXom4kzth13EQLOo1//LABVIY7aX0mraOkCa+myNC38oWyJIiMy5aobu/LDMD0sQ46FtrRnfZ6Lgs2FkrX6DZWmaXRcUW/OK5fk0JbCz1cPGG4lj+248ykZQsg0cFfY1J/Av73BKbWsDE3QvxF45oZeL/pe0Vf/4Q0kNewrHXeGREvFWok6UmISPibfbEdYaacYPtc6n6/NLk73pVfHe9T2IA+NaQ4Pq/mYiN15zhSTlUcR0R+eciGFesBgN7Z/nnVpoQCyB7eFxGZCgM7JwKeCRE9CEm/pRv+vr6qF69Oi5fvozk5GSlxDk5ORmXL19G9erVoa+vD4lEgowMxQv5DxPyvFhZWSEzMxNRUVF5Jv+WlpaIiopSKo+KioKlpeVnHlXeLC0t4erqil9++aXAr1XUDEtXRPL9a5CmJCqMmUt5ek9e/zHpYa/x+rdhEFtYo/ioBdAzMslz29jjOxC1ZyWs/DvB5tsehXMAKlbQ9vqQTCZDZkQIDMu82y/nNVKf34dZtXc9TlKf3QdkWTAspRlPzp6+kqKamz5MjKAwSZZbWf3/6gveB9/aIvtCPLcHQHp6gFhPc2ZjL+xzC8heri4rJfHTG2qgL22vjKhQQJqJ4J+Vl6iLP38I8ecPwXnYHJh5NSySuIWiyvNL07/rVfHd9T6fagYwNRZp3KR+OVRxHZERFYbMyBA8H9FGqe7t7yOhZ2KG8su5MhRRYWP/GvoiPXr0QGxsLNasUV5zdc2aNYiNjUWPHtkXCE5OTnj+/LnCcmdBQcpLmBkYGCAtTXHsWN26dSESibBr1648Y6lZsyZOnjyp0A0/KCgI8fHxqFlTeWmZ/PLx8UFwcDAcHBxQuXJlpf/UiVmtRkCWFHGn9srLsjLSEX/2AIzKecLA1hFA9gzYHy6tkxkbiTdzhgB6eigxZiH0LayRl4RLxxG+cR7MfZrBvsvwojgUlShQe8XHKL1e3MldkCbEwLSyt7zMxN0LemYWiDupeA7HntoFkcQIpu/dDFBnZ6+lQywWoYXfu26eBvqAfz0J7j/NRERM9ufbwUaEkk5f9qfldVj2Wtdf1VEcX+pTzQDGRiI8KeQL9KJUoHMrLlrp9TIi3iLl3lWl1SG0xZe2l0XdpnAeNkfpPwAwreoL52FzYFTeU5WHohKqOr+04bteFd9d72tUV4KUNBnOX9fMxF8V1xGOvccrfWatvs4eamLXeRicBk4vkmMj0nV84k9fpHHjxujatSsCAwMRGhqKZs2aAQCOHTuG7du3o2vXrvKx9f7+/vLJ8po0aYLr16/j6NGjSq9ZtmxZnDx5El5eXjA2NoaLiwtcXFzQqVMn/PHHH4iLi4O3tzdSU1Nx+vRpDBkyBI6OjhgwYAA6deqE/v37o1u3bvJZ/atUqQI/P78CH2ubNm3w559/onv37ujduzfKlCmDhIQE/Pvvv8jIyMDIkSML/B6Fxbi8J8xqN0bkjsWQxkfDwLEk4s8fREbkWzj+OFG+XejyqUh5cB0VN16Wl735bRgywt/AukU3pDy6hZRHt+R1YgsbmFauAyD7rn/o8qkQm1nCxKMWEi4cUYjBqEIVSByKF/GRFo6CtNfz4a1gXudrGJYsB5GBIVIe3UTCpeMwLF0Rlo2+k2+nJzGCXbsBCF8/B28XjoNplbpIeXgTCUGHYfv9QIjNCt4rRRUePJPizJV09GlnDCsLPbwNl+JrH0M42eph3tp3TwnH/miKqm4GaNL73Y0RU2OgTePsrtYeFbL/7LRubIikZBkSk2X461T2Db+LNzPw/LUUXVsawcFWD/efZqK4gxitGxsiMjYLh89pzoV0Qc6tlxN+gImHFwxLVYSeqQUyQl8h7uw+yKSZsOswWOF9kh9cR8qDGwCyu8pmpaUgau/q7BjcqsPErYYKjrbgvrS9JM5lIHEuk+tr6ts7Kz3pT7x+Dmmv/luOTJqJtOAn8vYyq9EAhqUqFPqxFQVVnF/a8l2viu+uHOamItSqbIBz1zKQ+vlz4KkVVVxHmFauq/S+0uTsfwsTt+o6NV8HkSox8acv9r///Q9Vq1bFli1bMGTIEABAxYoVMXv2bLRp00a+XYMGDTB69Ghs2rQJe/bsQYMGDTBt2jT07NlT4fUmT56MmTNnom/fvkhNTcWGDRtQp04dTJ48GSVKlMCOHTuwfv16WFlZoVatWvKlBD09PbFmzRrMnz8fQ4YMgYmJCRo1aoSxY8cWyhh8iUSCDRs2YNGiRVi2bBkiIiJgZWUFd3d3/PDDDwV+/cLm1H8qonYtR3zQYWQlJ0BSsjyKj5j/yQQg7dVjAEDMwY1KdcZuNeR/sNPfPM8en54Qg7CVPytt69h3skZcDOb40vYy92mG1Me3kXjlb2RlpMHArhisW3SDbateSuOJrZq0B8T6iDm8GUk3zkHfxhH2XYbD6oOVAdTd7JVJ6NXWGE28JTA3FeFZsBST/kjEnUcffxJvZqKHXt8ZK5R1aJbdRqGRUvnFc6YUGD47AV1bGqFOVQM0qiNBcqoMQTcysGZXCuITNWu87JeeW5aNv0PSzSAk3b6ErNSk7Atmz7qwadUThiXLK2yb/O9VRO9ZpVAWtWs5AMCm7Y8ak/gDX95e+ZF45RTizx+U/5728iHSXj4EAOjbOGpM4g8U/fmlTd/1Rf3dlaOBlwEM9EU4paHd/HMU9XUEEQlDJHu//zURqVTLy5831wEB+2tbsr3yYX9tS4UnV/RxJ9ZY8/z6TPws5g/bK3/43ZU//O76fPtra0YPO6KiwjH+RERERERERFqMiT8RERERERGRFmPiT0RERERERKTFmPgTERERERERaTEm/kRERERERERajIk/ERERERERkRZj4k9ERERERESkxZj4ExEREREREWkxJv5EREREREREWoyJPxEREREREZEWY+JPREREREREpMWY+BMRERERERFpMSb+RERERERERFqMiT8RERERERGRFmPiT0RERERERKTFmPgTERERERERaTEm/kRERERERERajIk/ERERERERkRZj4k9ERERERESkxZj4ExEREREREWkxJv5EREREREREWoyJPxEREREREZEWY+JPREREREREpMWY+BMRERERERFpMSb+RERERERERFqMiT8RERERERGRFmPiT0RERERERKTFmPgTERERERERaTEm/kRERERERERajIk/ERERERERkRZj4k9ERERERESkxZj4ExEREREREWkxkUwmkwkdBBEREREREREVDT7xJyIiIiIiItJiTPyJiIiIiIiItBgTfyIiIiIiIiItxsSfiIiIiIiISIsx8SciIiIiIiLSYkz8iYiIiIiIiLQYE38iIiIiIiIiLcbEn4iIiIiIiEiLMfEnIiIiIiIi0mJM/ImIiIiIiIi0GBN/IiIiIiIiIi3GxJ+IiIiIiIhIizHxJyIiIiIiItJiTPyJiIiIiIiItBgTfyIiIiIiIiItxsSfiOgLhISE4Pr160hOThY6FNJi165dw44dO/Ds2TOhQyEiylVGRga2bt2KCRMmoHfv3njx4gUA4NChQ3j69Kmwwampx48fY/jw4WjSpAk8PT1x7949AMDvv/+OM2fOCBwdaSt9oQMgIvXz+vVrvHr1Cu7u7rCyshI6HLWybds2BAYGIjIyEgCwc+dOeHh4YNCgQahduzZ69OghcITq5/Hjx1iyZAnu3LmD0NBQbNu2DR4eHvj9999Ro0YN+Pn5CR2iWhg5ciQkEglmzZoFANi6dSumTZsGAJBIJFi+fDm8vb2FDFHtPHv2DMeOHUNoaCjS0tIU6kQiEWbOnClQZES6ITg4GD179kRMTAzc3d1x7do1JCUlAQCuXLmCc+fOyb/TKFtQUBD69+8PDw8PtGzZEkuXLpXX6evrY+vWrfy7SEWCiT+Rjps9ezakUikmTpwIADh+/DiGDx+OzMxMWFpaYvXq1fD09BQ4SvWwbt06zJ07F7169YK3tzd69+4tr6tduzaOHDnCxP8DvMD5fNeuXcOYMWPkv69YsQLff/89xo0bh6lTpyIwMJCJ/3v27t2LCRMmwNDQEM7OzjAwMFCoF4lEAkWmPmbMmJGv7SdNmlREkWim8+fP4+jRo3neWFq/fr1AkamPGTNmwMbGBjt27ICFhYXC9UKtWrUwf/58AaNTT/PmzUPz5s0xZ84cZGZmKvxdrFSpEnbs2CFgdKTNmPgT6bjjx49j6NCh8t/nz58PPz8/DBs2DHPmzMGCBQuwatUqASNUH5s2bUJAQAACAgIglUoV6lxcXPD8+XOBIlNfvMD5fNHR0XBwcACQ3UsiJCQE3bt3h6mpKdq2bYthw4YJHKF6Wbp0Kfz9/TFz5kwYGxsLHY5aOnXq1GdvKxKJmPi/Z9WqVZg7dy6KFy+OcuXKwdzcXOiQ1NLly5cxb9482NjYKP1dtLe3R0REhECRqa/Hjx9j5MiRAJRvUFpYWCAmJkaIsEgHMPEn0nERERFwdnYGALx69QrPnz/Hb7/9hooVK6Jbt24YO3aswBGqj7CwMFSvXj3XOgMDA473zwUvcD6flZUV3rx5Ay8vL5w7dw729vaoUKECAEAqlSIrK0vgCNVLeHg4pk6dyqT/I/KT+JOiLVu2oGvXrrwZ8glisRgymSzXusjISJiYmKg4IvVnaWmJ8PDwXOtevHgBe3t7FUdEuoKT+xHpOHNzc0RFRQHI7pZtaWkp76onkUiUujfqMmdnZ9y5cyfXulu3bqFMmTKqDUgD8ALn8zVo0ABz587Fr7/+ihUrVqB58+byusePH6NEiRICRqd+vLy88OjRI6HDIC0VGxuLxo0bCx2G2qtVqxbWrl2LjIwMeZlIJIJMJsP27ds5PCkXTZo0waJFixQmbRWJRIiIiMDq1avh7+8vYHSkzfjEn0jHeXl5YeHChYiKisLq1avRpEkTed2zZ89QrFgxAaNTLx06dEBgYCCsra3RtGlTAEBmZiZOnz6N1atX46effhI2QDWUc4FTtWpVlC5dGgAvcPIyduxYSKVSnD9/Hn5+fhgyZIi87vjx46hfv76A0amfESNGYPTo0TA0NISvr2+uXbF1fXLSt2/f5mv7nN5fBHz11Ve4du0aE9dPGDVqFDp37owWLVqgUaNGEIlE2Lx5Mx4/foyXL19yOFcuRo4ciTt37qBVq1aoWLEiAGDChAkIDg6Gi4sLBg8eLHCEpK1Esrz65xCRTggLC8Po0aNx584deHh4YMGCBbCzswMAdOzYEa6urpg+fbrAUaqPGTNmYPPmzRCJRMjKyoKeXnbHqR9++IFdQnORkJCAnj174uHDh6hYsSL+/fdfuLm5yS9w1q9fD1NTU6HDJA3k5uYm/zmvifzu37+vqnDUUqVKlT5rO5lMBpFIpPPt9b4LFy5g6tSp8Pf3h4+PDywsLJS28fDwECAy9RMcHIzAwEAEBQUhNjYWlpaW8Pb2xtChQ1GqVCmhw1NLGRkZ2LdvHy5cuICYmBhYWlrCx8cHrVu3hkQiETo80lJM/IkoT4mJiZBIJPwj9IHg4GCFP9be3t7s5v8RvMD5cteuXcOzZ8/g5eUFFxcXocNRK7t37/7kzP1t27ZVUTTqyc3NDaampmjSpAn8/Pw++Xl7v8eXrnv/xhKgeHOJN0qISBMx8SciuZCQEISEhMDNzY0T8lCBpaWlYdiwYejTpw9q1aoldDhqb+TIkZBIJPI1r7du3Ypp06YByJ5vY/ny5ex2TPly4cIFHDhwAMePHweQndi3bNkS3t7eXO7wEy5fvvzJbWrXrq2CSEibpaSk5DqXkq4PU6KiwcSfiLBt2zYEBgYiIiICIpEIO3fuhIeHBwYNGoTatWtzbfr/3L17FwkJCfLkKz4+HnPmzMHTp0/h4+ODQYMGybv+U7aaNWti8eLFqFu3rtChqL2GDRtizJgx8kn9vvrqK9SrVw/jxo3D1KlT8fbtW2zevFngKEkTpaen48yZMzh48CBOnz4NMzMzNGvWDN9++y2qVasmdHikwbp3755nnZ6eHszNzVGpUiW0a9cOjo6OKoxMfSUmJmLOnDk4evQo4uPjc92GvUmoKHByPyIdt27dOsydOxe9evWCt7c3evfuLa+rXbs2jhw5wsT/P7NmzYK3t7c88f/ll19w4sQJ+Pr6Ys2aNdDT08OgQYMEjlK9+Pr6IigoiIn/Z4iOjoaDgwOA7Fn8Q0JC0L17d5iamqJt27YYNmyYwBEKr2XLlpg3bx4qVqyIli1bfnRbkUiEffv2qSgy9SaRSPD111/j66+/RnJyMk6cOIEDBw6gS5cuaNmyJWbPni10iGrr8ePHuHbtGuLi4mBpaYmaNWvKl9mk7JWB7t27h4iICLi6usLW1hZRUVF4+PAh7O3tUapUKaxduxarV6/Ghg0bOC8CgPHjx+PSpUto3749XFxcYGBgIHRIpCOY+BPpuE2bNiEgIAABAQGQSqUKdS4uLnj+/LlAkamfJ0+eoF+/fgCA1NRUHD16FP/73//Qrl07bN68GRs2bGDi/4F27dph8uTJSEpKgp+fH2xtbZW6GPNCMJuVlRXevHkDLy8vnDt3Dvb29vIEQyqVIisrS+AIhefp6QljY2MA2ecNu6vnX1RUFN6+fYu3b98iKysL1tbWQoekltLT0zF69GgcO3YMMpkMEokE6enpEIlE8Pf3x5w5czhHCYBmzZrh1atX2Lx5M4oXLy4vf/36NQYOHIi2bdti0aJF6NWrF+bPn4/Vq1cLGK16uHDhAqZMmYJWrVoJHQrpGCb+RDouLCwM1atXz7XOwMAAycnJKo5IfaWmpsqTjuvXryM9PV2+zrOrqytCQ0OFDE8t9e/fHwCwZcsWbNmyhRNkfUSDBg0wd+5cPHjwAHv27EHr1q3ldY8fP0aJEiUEjE495Mx/AIBPqfMhPDwchw4dwoEDB3D37l14eHigXbt2aN68Obtf52H+/Pk4c+YMpk2bhubNm8PMzAyJiYk4dOgQZs2ahd9//x1jx44VOkzBBQYGYuTIkQpJPwCUKFECgwYNwrx589C2bVv07t0bU6ZMEShK9WJvb5/r8qNERY2JP5GOc3Z2xp07d3KdNOzWrVucrf49JUuWxNmzZ1G7dm3s378fHh4e8gl4oqKiYGZmJmyAamjDhg1Ch6Axxo4dC6lUivPnz8PPzw9DhgyR1x0/fhz169cXMDrSRNu2bcPBgwdx7do1lClTBs2bN8e8efNQunRpoUNTewcPHsSIESPQoUMHeZmZmRk6dOiAlJQUrFq1iok/sicFzqvnjUgkQlhYGADAwcFBqVehrhoyZAiWL1+OmjVr5rpMJFFRYeJPpOM6dOiAwMBAWFtbo2nTpgCAzMxMnD59GqtXr8ZPP/0kbIBqpGfPnpg0aRJ27tyJuLg4zJkzR153+fJluLq6ChideuKs15/P3Nxc4Yn2+7Zu3ariaNTTjBkz8rX9pEmTiigSzTBlyhSYmpqiRYsWqFSpEgDg1KlTuW4rEonQs2dPFUan3uLi4lC2bNlc68qWLYu4uDgVR6SeKleujIULF8LT0xPFihWTl7958waLFi1ClSpV5L+zd0m2Fi1a4OHDh2jYsCEqVaqk9PRfJBJh6dKlAkVH2oyz+hMRZsyYgc2bN0MkEiErK0s+M/0PP/yg8xfOH7py5Qru3LkDd3d3hQnrFi1ahMqVK6Nhw4bCBUdaIS4uTj65X4MGDWBpaYm0tDQYGBjo/KoRjRo1+uxtRSIRTp48WYTRqL8P16L/GA67UdSmTRtUqFABv/32m1Ld6NGj8fjxY+zdu1f1gamZJ0+eoFevXoiJiUHFihVhbW2NmJgYPHz4ELa2tli7di3KlSuHlStXQk9PD3369BE6ZMGtW7cOs2fPhp2dHUqUKJHr5H4bN24UIDLSdkz8iQgAEBwcjKCgIMTGxsLS0hLe3t7s5k8F5ubm9skJ2JhsZJPJZPj999+xceNGpKSkKCyt2bdvX1StWhWDBw8WOkwinXDs2DEMGzYM1atXR9OmTWFnZ4eoqCgcPXoUN2/exB9//IGvv/5a6DDVQlpaGnbu3Im7d+8iIiIC9vb2qFy5Mtq3b4+wsDCULFlS6BDVio+PD7755htMnDhR52/mkmox8Sciyofk5GTs2bNHaXmntm3bwsTEROjw1M66deuUEv/4+HgEBQUhPDwc3bt3Z/fi/+Qk/WPGjIG3tzf8/f2xa9cueHh4YOvWrdixYwd2794tdJikJZ48eYLHjx/DxsYGXl5eEIvFQoekdk6ePInFixfj/v378slIK1WqhMGDB+er94muiY6OxuHDh7F//37cunWLN3c/UKtWLSxcuDDXuZWIihLH+BPpoHv37qFcuXIwMjLCvXv3Prk9l1vLFhISgm7duuHNmzdwc3ODra0tnj9/jiNHjmDdunXYsGGDwhhHQp5J/ZAhQzBmzBiOk33Pnj17MGLECHTq1ElpEqxSpUohODhYoMjU0+d0s27Tpk2Rx6HOZDIZVq1ahePHjyMzMxPNmjVD3759MXHiROzZs0eezJYvXx7r16+HjY2N0CGrlcaNG6Nx48ZITk5GQkICzM3NeYM3DykpKTh+/DgOHDiACxcuQCqVolKlShg/frzQoamd5s2b48yZM0z8SeWY+BPpoHbt2mH79u2oUqUK2rVrl2dXbC63pihn4rWDBw8qTPr07NkzDBgwALNnz8Yff/whVHgap1WrVhgzZgyGDRsmdChqITY2FuXKlcu1TiqVIjMzU8URqbdx48blWv7+95muJ/6rV6/G/Pnz0bhxY5iammLp0qV4+PAhzp49i7Fjx6Js2bJ49OgRli1bhiVLlnBOlzyYmJgw4c+FVCrFuXPnsH//fpw6dQqpqamws7ODVCrFvHnz0Lx5c6FDVEs1atTAH3/8gYiICHh7e+c6s3/OZMtEhYmJP5EO2rBhgzzB4HJrn+/ChQuYPn260kzPZcuWxbBhw7hGcT49f/4cWVlZQoehNsqUKYOgoKBcnwJdvnwZFSpUECAq9XXlyhWlsri4OJw/fx6bN2/G3LlzBYhKvezevRsBAQHypSGbNm2KwYMHY+LEiejatSsAoEGDBtDX18fmzZt1PvGfMWMGevfuDWdn589aQUJX2+vatWs4cOAAjhw5gpiYGFhZWaFVq1Zo2bIlKlSogDp16sDe3l7oMNVWzjKQb9++xcGDB5Xq+cCFigoTfyId9P4Sa1xu7fNJpVIYGhrmWmdoaMg1inOxdu1apbKMjAw8ffoUR44cwbfffitAVOqpZ8+e+N///gd9fX00a9YMABAaGoqbN29i48aNeS71p6s+XAIrp6xTp05IS0vDb7/9hlWrVgkQmfp4/fo16tSpI/+9bt26kMlkSsO3PD09ERISourw1M6pU6fQvn17ODs757nsYQ6RSKSziX+XLl0gEolQp04d9OrVC76+vtDXz04pEhISBI5O/en6aiMkHCb+RDruwYMHCAsLg5+fn1LdmTNn4OjomK8lobRZjRo1sHTpUtSuXVsh6UhISMCyZctQo0YNAaNTT7/++qtSmUQigZOTE7p3746AgAABolJP3333HeLi4rBo0SIsX74cADBo0CAYGxvjp59+YrfZfKhQoQIWLFggdBiCS09Ph5GRkfz3nJ8lEonCdgYGBrxxCSgk+59K/HVZxYoV8ejRI1y5cgVisRgxMTFo0qQJzMzMhA5NIxQvXlzoEEhHMfEn0nEzZ85EjRo1ck38b9++jevXr+f61FYXjR07Fl27doWfnx/q1q0rX97p4sWLMDAwwMyZM4UOUe08ePBA6BA0Sq9evdChQwdcv35dvrRm9erVc326TblLSUnB9u3b4eDgIHQoautTS2xS9uSRfn5+sLa2VqqLjY3F6dOndXYOiX379uHJkyfYt28fDh48iHHjxsHIyAh+fn746quveH59BplMhjNnziisEOTl5YUGDRqw/ajIcDk/Ih1Xu3ZtzJ07Fw0aNFCqO3fuHEaNGoV//vlHgMjUU2hoKNauXYtr164hPj5evpxfz5494eTkJHR4RDqjZcuWSmUZGRkICwtDamoqfv31V7Rq1UqAyNSHm5sbjI2NFRKJ5ORkpTKZTIbU1FSOK35PpUqVsG3bNlSpUkWp7u7du/j+++/ZXv/JGfN/9OhRREdHQyQSoUmTJujevTtq1aoldHhqJy4uDv369cOtW7dgYWEBW1tbREVFIT4+HtWqVcOKFStynfCPqKD4xJ9Ix6WnpyMjIyPPurS0NBVHpN6cnJy4PFE+nD17FvHx8fKx/CEhIZgwYQKePn0KHx8fTJ48mbNlvycuLg5nz55FaGio0mdPJBJh0KBBAkWmfjw8PJSejOUMI2natGmeKyToksGDBwsdgsb62HOx+Ph4mJqaqjAa9VazZk3UrFkTkyZNwvnz53HgwAGcPHkSJ06cgLOzM8e0f+DXX3/Fq1evsHr1avj6+srLg4KCMHr0aPz666/45ZdfBIyQtBWf+BPpuM6dO8Pe3h4LFy5Uqhs6dCjCw8Px559/ChCZ+gkJCUF0dLTSxFgAcO/ePdja2vKp/wfat2+Pb775Bn369AEADBw4EPfu3UOLFi2wb98+tGzZMs9l2XTN+fPnMXToUCQnJ8PIyAgGBgYK9SKRCJcvXxYoOiLtd+bMGZw7dw4AsGnTJjRr1gx2dnYK26SlpeHSpUtwcnLCxo0bhQhTI6SmpuLEiRM4cOAAli1bJnQ4aqVu3boYPXo02rVrp1S3c+dOzJ07F5cuXRIgMtJ2fOJPpOP69++PgQMHol+/fvjuu+/g4OCA8PBw7N69G+fPn8eSJUuEDlFtTJ06FaVLl8418T9w4ABevHiBpUuXChCZ+nr58qV8csjExEScO3cOc+fORbNmzVChQgUEBgYy8f/Pr7/+isqVK2PmzJmc/OkLvX79Gq9evYK7uzusrKyEDoc0zIsXL+ST+olEIly9ejXXiRArVKiAESNGCBGixjAyMsK3337LlVtykZKSonRDKYe9vT1SUlJUHBHpCib+RDquYcOGmDdvHubMmYOffvoJIpEIMpkMTk5OmDt3Lho2bCh0iGrj1q1b6NixY651derUwd69e1UbkAbIzMyEnp4egHfrrtevXx8AULJkSURGRgoWm7oJDg7GuHHjmPR/ptmzZ0MqlWLixIkAgOPHj2P48OHIzMyEpaUlVq9eDU9PT4GjJE3So0cP9OjRAwDQqFEjLF68GJUqVRI4KtI2lSpVwqZNm1CvXj2IxWJ5eVZWFjZu3Ah3d3cBoyNtxsSfiNC8eXM0b94cz549Q2xsLKysrFC2bFmhw1I7ycnJ8rWKPyQSiZCUlKTiiNRf2bJlsW/fPlStWhXbtm1D9erV5WNjIyIi+FT2Pe7u7lxLPR+OHz+OoUOHyn+fP38+/Pz8MGzYMMyZMwcLFizAqlWrBIyQNBmX86OiMnLkSPTu3Rtff/01GjduLF8h6MSJE4iMjMSaNWuEDpG0lJ7QARCR+ihbtixq1KjBpD8P5cqVw4kTJ3KtO3nyJFxcXFQckfoLCAjA/v37UbNmTZw/fx79+/eX1507d45PNt4zdepUbNy4EefOnUNmZqbQ4ai9iIgIODs7AwBevXqF58+fY+DAgahYsSK6deuGu3fvChwhabKNGzdi7ty5udbNnTsXmzdvVnFEpC1q1aqFrVu3wt3dHQcOHMDChQtx4MABeHh4YOvWrfDy8hI6RNJSfOJPRHj27BmOHTuW50ziXJ8+W48ePTBu3Djo6emhXbt2CvMh7Nixg+2Ui8aNG+Pw4cP4999/4erqijJlysjrqlWrBldXV+GCUzMdO3ZEZmYm+vXrBz09PRgaGirUi0QiXLt2TaDo1I+5uTmioqIAZM+GbWlpKe/aL5FIuCIJFciWLVvQq1evXOvKlCmDtWvXokuXLiqOirSFp6cnAgMDhQ6DdAwTfyIdt3fvXkyYMAGGhoZwdnbOdSZxytamTRtERkZi8eLF2LZtm7zcyMgII0eORNu2bQWMTn2VLFkSJUuWVCrPa74EXdW7d29+3vLBy8sLCxcuRFRUFFavXo0mTZrI6549e4ZixYoJGB1purdv36J06dK51pUsWRJv3rxRcUSkLRITE5GcnAwHBweluvDwcJiamnK5SCoSTPyJdNzSpUvh7++PmTNnwtjYWOhw1N6PP/6ITp064caNG/L5EKpXrw4zMzOhQ1NLZ8+eRXx8vHxm55CQEEyYMAFPnz6Fj48PJk+eDBMTE4GjVA9DhgwROgSNMmHCBIwePRpz586Fh4cHhg8fLq/bt28fu8tSgZiZmeH169eoU6eOUl1wcDCMjIwEiIq0waRJk2BqaopffvlFqW7RokVITk7GvHnzBIiMtB3H+BPpuPDwcHTo0IFJfz6YmZmhfv36aNmyJerXr8+k/yMWLlyIsLAw+e/Tp0/H06dP0aJFC5w7dw4LFy4UMDr1smvXrjzrZDIZJk+erMJo1J+joyM2bNiAGzduYNOmTQrLY61evRqTJk0SMDrSdL6+vli8eLHShJuhoaFYsmQJGjRoIFBkpOmuXr2a54pJfn5+uHz5smoDIp3BJ/5EOs7LywuPHj2Ct7e30KGovc9Zrq9NmzZFHocmefnyJdzc3ABkd288d+4c5s6di2bNmqFChQoIDAzEuHHjBI5SPUyZMgUmJib45ptvFMplMhnGjBmDU6dOYfr06QJFp1l4M44KauTIkejYsSOaNWuGunXryud0uXTpEmxsbDBy5EihQyQNFRcXl2dXfmNjY8TGxqo2INIZTPyJdNyIESMwevRoGBoawtfXF+bm5krbcMm1bHklqO+Py2birygzMxN6etmdy65cuQIAqF+/PoDscbKRkZGCxaZupk2bhrFjx8LY2Fj+NEgqlWL48OG4ePEil3j6QFZWFnbs2IGjR4/mOjEpkL3aBtGXcHR0xN69e7F27VpcunQJL168gJWVFXr16oWePXvy7yJ9sZIlS+LChQvw8fFRqrt48SKKFy8uQFSkC5j4E+m4nAnppk6dmufEYvfv31dlSGorJ3F9X1xcHM6fP4/NmzfnufSTLitbtiz27duHqlWrYtu2bahevbr8SUdERAQvnt/Trl07JCcnY9iwYVi+fDlq1KiBIUOG4NatW1i/fj2XPvzAb7/9hrVr16JWrVqoU6eO0sSkRAVlZWWlMHcEUWH4/vvvMW/ePFhaWqJdu3awsbFBdHQ0du/ejXXr1mHEiBFCh0haSiSTyWRCB0FEwtm9e/cnZxLnbPWftn79epw7dw6rVq0SOhS1cvLkSQwbNgxSqRRisRjLli1DvXr1AADjx49HTEwMli1bJnCU6mX58uVYvnw5KlasiODgYKxduxYVK1YUOiy1U69ePXTu3BmDBg0SOhQios8mk8kwffp0/PnnnwAAsVgMqVQKAOjUqROmTJkiZHikxZj4ExEVggsXLmDQoEG4ceOG0KGoneDgYPz7779wdXVFmTJl5OXbtm2Dq6srqlWrJlhsQstrLOf8+fNx7NgxLF26FC4uLvJy9pB4p3bt2vjjjz84PwkVidTUVCxZskQ+lCQ9PV1pG/aGo4J48eIFLl26JF8hqG7dugp/I4kKGxN/IqICSklJwfjx43H//n0cPXpU6HBIg7i5ueXZ40YmkynVMdF45+eff4aenh4mTpwodCikhcaPH48DBw7g22+/Rbly5XIdStKjRw8BIiNNd+XKFbi7u+c6wV9ycjLu3buHWrVqCRAZaTuO8SciXLlyBdu2bcOLFy9ynSBr//79AkSlflq2bKlUlpGRgbCwMKSmpuLXX38VICr1Fx0djTVr1uDOnTsIDQ1FYGAgKlSogPXr16Nq1ao6/cR/5syZnxxqQ7mrWrUqFixYgKioKPj4+MDCwkJpm6ZNmwoQGWmDv//+G2PHjkXXrl2FDoW0TPfu3bFt2zZUqVJFqe7Zs2fo3r07b/JSkWDiT6Tjzp07h/79+8Pb2xt3795FgwYNkJqaiuvXr8PJyYl3nd/j4eGhlKRJJBI4OTmhadOmKFeunECRqa979+6hZ8+eMDc3R61atXD58mV5l9mwsDCsW7cOCxYsEDZIAX333XdCh6CxxowZAwB4+/YtDh06pFQvEol48UxfTCwWs9s1FYmPdbZOSUmBkZGRCqMhXcLEn0jHLVq0CD169MCoUaPg4eGBYcOGwcPDA2/evEGfPn1Qt25doUNUG7NnzxY6BI0za9YsVKtWDUuWLIFIJMJff/0lr6tatSoOHz4sYHTqJSQkBNHR0fDw8FCqu3fvHmxtbeHk5CRAZOqJS/VRUercuTP++usv+WSkRAVx8+ZNhTmA9u/fj2vXrilsk5aWhpMnT6Js2bKqDo90BBN/Ih339OlTDB8+HHp6ehCJREhJSQEAFC9eHEOGDMGiRYvQunVrgaMkTXXnzh0sWrQIBgYG8lmLc9jY2CAqKkqgyNTP1KlTUbp06VwT/wMHDuDFixdYunSpAJGpJ651TUXJyMgI165dQ6dOneDt7a00lEQkEqFnz57CBEca5/z58wgMDASQfe5s3LhRaRt9fX2UK1eOs/pTkWHiT6TjDA0NkZWVBZFIBHt7e7x69QpeXl4AAFNTU4SGhgocobAGDBjw2duKRCImZh8wNjZGYmJirnVv377lLPXvuXXrFjp27JhrXZ06dbB3717VBqSGqlev/tlzIohEIqUnakSfa+7cuQCyv6du3rypVM/En/Jj8ODBGDx4MIDsSV23b9+e6xh/oqLExJ9Ix7m5ueH58+fw9fWFt7c3li1bBmtra+jr62PBggU6v354UlKS0CFotHr16mHp0qUKT8xEIhFSU1OxYcMG+Pn5CRyh+khOToa+fu5/lkUiEc9FAL179+ZkiKQSDx48EDoE0lI8t0goXM6PSMedOXMGr1+/RpcuXRAWFoYBAwbIJ8RycnJCYGAgPD09BY6SNFVYWBg6d+6MxMRE1KlTBydOnED9+vXx5MkTiEQibN++Hba2tkKHqRbatm2LypUrY/r06Up1kydPxq1btxTmSCAiIs1z5cqVT27DiZWpKDDxJyIFMpkML1++RGpqKsqWLQuJRCJ0SIILCgqCra0t3NzcAGS30S+//KKwjampKYYPHy5EeGovPj4e69atw4ULFxATEwNLS0t4e3ujV69e7Or/nr1792LcuHHo1KkT2rVrBwcHB4SHh2P37t34888/MXPmTLRt21boMIl0ApMzKipubm4QiUQKs/t/2JOJK5JQUWDiT0T0EQcPHsSYMWPw559/onLlygAAqVQKDw8P2Nvbw8DAAAAQFRWFWbNmoXnz5kKGq1bS0tLw22+/oVWrVhzL+JlWrVqFxYsXIzU1VV5mZGSEQYMG4ccffxQwMiLdwuSMikpuXf3j4uJw/vx5HDt2DNOmTeOKSlQkmPgTEZ49e4Zjx44hNDQUaWlpCnUikQgzZ84UKDLh9e/fHxYWFvjtt9/kZTmJ/65du+QzsP/222948uQJli9fLlSoaql69epYvnw5ateuLXQoGiMxMRE3btxAbGwsrKysUL16dZiZmQkdFpFOYXJGQli8eDGePHmC33//XehQSAtxcj8iHbd3715MmDABhoaGcHZ2lj/BzqHrE2ndvXsXkydP/uR2tWrV4vjrXFSvXh03b95k4p8PZmZmqF+/vtBhEOm0nKFdH6pTpw6MjIywbds2Jv5U6GrUqIHVq1cLHQZpKSb+RDpu6dKl8Pf3x8yZM2FsbCx0OGonLi5OafI5sViMP/74A6VKlZKXmZmZITY2VsXRqb+hQ4di1KhREIvF8PPzg62trdLNJF0e53/s2DHUrVsXFhYWOHbs2Ce3b9q0qQqiIqKPYXJGReXEiRM6/TeRihYTfyIdFx4ejqlTpzLpz4O5uTkiIiKUyv39/RV+j4iIgLm5uarC0hidOnUCkD0UImdd7A/p8jjZoUOHytdzHjp06Ee3FYlEOt1WROqCyRkVxIABA5TKMjIy8Pz5c4SEhGD06NECREW6gIk/kY7z8vLCo0eP4O3tLXQoaqly5co4fPgwvvnmm49ud/jwYU5gl4uZM2fq/HCRjzl58iTs7e3lPxORemByRkUlKSlJqczQ0BA+Pj7w9/fnUC8qMpzcj0jH3b9/H6NHj0bXrl3h6+ub61NrXX6ycebMGfTv3x9DhgzBwIEDoaenp1Avk8mwdOlSLFq0CMuWLYOfn59AkRIRUWHp1q2bUpmhoSGcnJyYnFGBZWVlyYcHWllZKV1bEBUFJv5EOu79CYzyejKr692L58+fjxUrVsDBwQF169ZFsWLFAABhYWG4ePEiwsPD0bdvX4wYMULgSNXH9u3bsXHjRrx+/RoODg745ptvEBAQAIlEInRoai8sLAxhYWFKK2wAXDeciEiTnT59GuvXr8eNGzfk3/GGhoaoUaMGevTowYcHVKSY+BPpuN27d3+yK3bbtm1VFI36On36NNauXYsbN24gPT0dACCRSFCjRg306tWLf6zfs2vXLkycOBGlS5eGu7s7Xr9+jTt37uCHH374rBUSdFVwcDBGjx6NW7duAQA+/PPMMf5ERatGjRrYsGEDPD095WXLli1D+/btYWdnJ2BkpA1mzJiBTZs2wdLSEnXq1JE/RAgJCcE///yD+Ph4dO3aFRMnThQ4UtJWTPyJiPJBKpUqdM8Ti8XCBqSG2rZti9KlS2P+/Pny7ovLli1DYGAgbt68CX19Ti+Tmx49euDVq1cYOnQoypUrl2vviLyWGCOignNzc5NPtglkf997enpi586d8PDwEDg60mR//fUXxo0bh4CAAPTp0wcmJiYK9SkpKVi9ejWWLFmCX3/9FS1bthQoUtJmvPoiIsoHsVistLwfKXr58iVGjhypMGaxU6dOWLBgAd68eYPSpUsLGJ36un37Nn799Vcu2UekRvh8jArD1q1b8f3332PIkCG51hsbG2Pw4MEIDw/H5s2bmfhTkWDiT6TjGjVqlGdXfz09PZibm8PNzQ1dunThEw/6LMnJybCwsFAoy5k0MjExUYiQNIKjoyMneCIi0kIPHz7MM+l/n7+/Pw4cOKCCiEgXMfEn0nGNGzfGiRMnkJiYCG9vb9jY2CA6OhoXL16EmZkZXF1dcfXqVezbtw8rVqyAj4+P0CGTBnj27JnCMAipVCov/xBvKGUbPnw4Vq5cCS8vL51eSYNISImJifLhXDnfW++XvY+fU/pcIpGIvUdIcBzjT6Tj1q1bh+PHj2PlypUKY86SkpLQr18/NGzYED179kS/fv2QkpKCP//8U8BoSRO4ubnl2osk589NTp1MJtP5Ces+XCv8/v37SEhIQKVKlZSW1hSJRFi6dKkqwyPSKbl9d+V8T+VGl7+7KH86deqEihUrYvr06R/d7n//+x+ePHmCrVu3qigy0iV84k+k49avX4/JkycrTTRjamqKPn36YOrUqejbty86d+6MsWPHChQlaZINGzYIHYLGSEpKUvi9VKlSedYRUdGaNWuW0CGQlvrhhx8wduxYODo6onfv3jA2NlaoT01NxZo1a7Br1y7MmTNHoChJ2zHxJ9JxMTExeY67TkhIQHx8PADA0tJSlWGRBqtdu7bQIWiMjRs3Ch0CEf2HS9dSUWnVqhVu3bqFRYsWYcOGDahTpw6cnZ0BAG/fvsXly5cRFxeHLl264NtvvxU4WtJWTPyJdFydOnUwb948FC9eHDVq1JCXX716FfPnz0fdunUBAM+fP0fx4sWFCpNIKzVu3BiLFy/mMn1EaiwkJAQhISFwc3NT6h1H9Ln+97//wcfHB+vXr8fp06eRnp4OAJBIJKhevTp69OiBRo0aCRwlaTOO8SfScWFhYRg4cCDu378PCwsLWFtbIyYmBvHx8ahUqRKWLl0KR0dHbN++HRKJBG3atBE6ZFJzH1spIjcnT54swmjU24frhhOR+ti2bRsCAwMREREBkUiEnTt3wsPDA4MGDULt2rXRo0cPoUMkDSWVShETEwMAsLa2VpgMl6io8Ik/kY5zdHTE7t27cebMGdy5cwcRERGwt7dH5cqV4efnJ9+uQ4cOAkZJmqRx48YKif/Ro0eRmJgIHx8f2NraIioqChcuXIC5uTn8/f0FjJSIKHfr1q3D3Llz0atXL3h7e6N3797yutq1a+PIkSNM/OmLicVi2NnZCR0G6Rgm/kQEAPDz81NI9Im+1MSJE+U/r1q1CsWKFcOqVatgZmYmL09ISEDfvn1ha2srRIhERB+1adMmBAQEICAgQL6sXw4XFxc8f/5coMiIiL4ME38iAgCcPXsWd+7cQWhoKAYOHAhnZ2dcuXIFpUqVgqOjo9DhkYbauHEjpkyZopD0A4C5uTn69u2LadOmoV+/fgJFpx569OjxWUMjRCIRrl27poKIiCgsLAzVq1fPtc7AwADJyckqjoiIqGCY+BPpuOjoaAQEBODWrVsoVqwYQkJC0KlTJzg7O2PXrl0wNjbGlClThA6TNFRcXBwSEhJyrXt/1Qhd1qJFCzg5OQkdBhG9x9nZGXfu3IG3t7dS3a1bt1CmTBnVB0VEVABM/Il03C+//IKYmBgcOHAApUuXhqenp7zO29sbS5cuFTA60nR169bF3LlzUaxYMYVl/v755x/MmzdPvmqELuvQoQMn9yNSMx06dEBgYCCsra3RtGlTAEBmZiZOnz6N1atX46effhI2QCKifGLiT6Tjzpw5g59//hnlypVTGsdYrFgxhIWFCRQZaYPp06dj4MCB6NGjB8zNzeWrRiQkJKBSpUqYNm2a0CESESnp06cPQkJCMHnyZHmvt86dOwMAfvjhB3Tp0kXI8IiI8o2JP5GOk0qlea5LHB8fDwMDAxVHRNrEwcEBu3btwtmzZ3H79m35qhFVqlRBgwYNhA6PiChPkyZNQvfu3XHhwgXExsbC0tIS3t7e7OZPRBqJiT+RjqtSpQp27dqV64z+Bw8eRI0aNQSIirRNgwYNmOjnYvDgwZw8k0iNlSpVCqVKlRI6DCKiAhPJZDKZ0EEQkXBu3LiB7t27o0qVKvD398esWbMwYMAAPH36FGfOnMGWLVvg4eEhdJikwWQyGc6cOYNr164hLi4OlpaW8PLyQoMGDT5rNntdExYWhrCwMKSlpSnV1apVS4CIiHRTRkYGdu7cKV/xZvLkyShTpgwOHToEV1dXlCtXTugQiYg+GxN/IsKNGzcwb9483LhxA1KpFCKRCNWqVcOYMWPyXM6I6HPExcWhX79+uHXrFiwsLGBra4uoqCjEx8ejWrVqWLFiBSwsLIQOUy0EBwdj9OjRuHXrFoDsGybvE4lEuH//vhChEemc4OBg9OzZEzExMXB3d8e1a9ewc+dOeHh4YNq0aUhNTcWsWbOEDpOI6LOxqz+RDktPT8fp06dRqVIlbNq0CampqYiLi4OFhQWMjY2FDo+0wK+//opXr15h9erV8PX1lZcHBQVh9OjR+PXXX/HLL78IGKH6mDRpEsLCwjBz5kyUK1cOEolE6JCIdNaMGTNgY2ODHTt2wMLCQmHFm1q1amH+/PkCRkdElH96QgdARMKRSCQYOXIk3r59CwAwMjKCo6Mjk34qNKdOncKoUaMUkn4A8PX1xYgRI3Dy5EmBIlM/t2/fxvjx49G2bVtUqVIFbm5uSv8RkWpcvnwZAwcOhI2NjdKQJHt7e0RERAgUGRHRl2HiT6TjypYti5CQEKHDIC2VkpICOzu7XOvs7e2RkpKi4ojUl6OjI/T0+GeZSB2IxWKl4TY5IiMj81wNh4hIXfEKg0jHjRgxAkuXLsWdO3eEDoW0UM4wEqlUqlCelZWFjRs3wt3dXaDI1M/w4cOxcuVKxMbGCh0Kkc6rVasW1q5di4yMDHmZSCSCTCbD9u3b4e3tLWB0RET5x8n9iHRcy5YtER4ejvj4eFhZWSk9nRWJRNi3b59A0ZGmu3LlCnr37g17e3s0btwYdnZ2iIqKwokTJxAZGYk1a9bAy8tL6DDVwoABA3D//n0kJCSgUqVKMDc3V6gXiURYunSpQNER6ZanT5+ic+fOsLKyQqNGjbB+/Xp89913ePz4MV6+fIkdO3ZwmT8i0iic3I9Ix70/YRFRYatVqxa2bt2KZcuW4cCBA4iPj4elpSVq1qyJAQMGcKnI9yQlJSkkEklJSQJGQ6TbypUrh127diEwMBAHDhyAWCzG6dOn4e3tjblz5zLpJyKNwyf+RDrqyZMn+PPPP/H69Ws4ODjA399faQI2IiIiXZOWloYtW7bA19cXFStWFDocIqJCwcSfSAddvXoVvXr1QmZmJmxsbBAbG4usrCxMnjwZnTt3Fjo80kIymQzPnz9HXFwcrKysUKZMGaWZsomI1EXVqlWxcuVK1K5dW+hQiIgKBbv6E+mgRYsWoWzZsli2bBmKFSuGxMREjB8/HgsWLGDiT4Vu8+bNWLJkCaKjoyGTySASiWBra4uAgAD88MMPQocnqLVr16Jly5aws7PD2rVrP7qtSCRCz549VRMYkY6rVKkSnjx5wsSfiLQGn/gT6SBvb29MmzYNTZs2lZe9fv0aTZo0wd9//41ixYoJGB1pk23btmHKlClo0aIFmjdvDjs7O0RGRuLQoUM4dOgQpk+fju+//17oMAXj5uaG7du3o0qVKnBzc/votiKRCPfv31dRZES67fbt2xg9ejSGDx8OPz8/GBsbCx0SEVGBMPEn0kHvJxs5pFIpPDw8sHv3bi6xRoXmm2++Qb169TBx4kSlul9++QXnzp3DkSNHBIiMiChv1atXR0ZGhnwpUiMjI4XhSSKRCNeuXRMqPCKifGNXfyIiKjKvX7/GV199lWtdw4YN8eeff6o4IvUVGxsLKyurPOsfPXrEicaIVKR3796ch4SItAoTfyId1aNHj1wvarp06cKnGlRo7O3tcePGDfj4+CjV3bx5E/b29gJEpZ769OmDDRs2wNTUVKnuzp07+PHHH/HPP/8IEBmR7hkyZIjQIRARFSom/kQ6aPDgwUKHQDqiffv2WLJkCdLT09GsWTPY2toiOjoahw8fxurVqzFo0CChQ1Qr/fv3x6pVq2BkZCQvu3HjBvr27YsGDRoIGBkRERFpMo7xJyKiIiOTyfDrr79i06ZN8rGyACAWi9GtWzeMHTtWwOjUS0xMDLp37w4HBwcsW7YMBgYGuHTpEgYOHAh/f3/MnDkTenp6QodJpBO6d++eZ52enh7Mzc1RqVIltGvXDo6OjiqMjIjoyzDxJyKiIhcTE4Pbt28jLi4OlpaWqFKlCqytrYUOS+1ERESga9euqFChAr777jsMHz4cbdq0wbRp04QOjUinDBo0CPfu3UNERARcXV1ha2uLqKgoPHz4EPb29ihVqhTu37+PrKwsbNiwAR4eHkKHTET0UUz8iYiI1EhISAi6dOmCkJAQdO/eHePHjxc6JCKds3//fqxYsQLLli1D8eLF5eWvX7/GwIED0bt3bzRq1Ai9evWCtbU1Vq9eLWC0RESfxsSfiIgK1bFjx/K1fdOmTYsoEvU3Y8aMXMtfvnyJO3fu4Ntvv1UonzRpkirCItJ5/v7+GDlyZK7fT0eOHMG8efNw/PhxHDhwAFOmTOEkuESk9ji5HxERFaqhQ4d+9rYikQj3798vwmjU26lTp/KsMzExUagXiURM/IlUJCQkJM/l/EQiEcLCwgAADg4OCvOXEBGpKyb+RERUqE6ePCl0CBrjY4k/EQmncuXKWLhwITw9PVGsWDF5+Zs3b7Bo0SJUqVJF/jsn9yMiTcCu/kREpDJPnjzB48ePYW1tjVq1akEsFgsdEhGRkidPnqBXr16IiYlBxYoVYW1tjZiYGDx8+BC2trZYu3YtypUrh5UrV0JPTw99+vQROmQioo9i4k9ERIVKJpNh1apVOH78ODIzM9GsWTP07dsXEydOxJ49e+TblS9fHuvXr4eNjY2A0QorOjoa4eHhcHNzUyh/8OABlixZgqdPn8LOzg49evRAo0aNBIqSSDelpaVh586duHv3LiIiImBvb4/KlSujXbt2MDQ0FDo8IqJ8YeJPRESFatWqVZg3bx4aN24MU1NTHDt2DI0aNcLZs2cxaNAglC1bFo8ePcKyZcvQpk0bnR63PmnSJNy7d0/hhsibN2/QqlUrpKamwtXVFaGhoYiNjcX69etRq1YtAaMlIiIiTcUx/kREVKh2796NgIAADBkyBED2rP2DBw/GxIkT0bVrVwBAgwYNoK+vj82bN+t04n/9+nW0b99eoWzdunVITk7GypUrUa9ePaSmpqJXr15YuXIlE38iIiL6Ikz8iYioUL1+/Rp16tSR/163bl3IZDJ4eHgobOfp6YmQkBBVh6dWwsLCUKFCBYWyv//+G5UqVUK9evUAAEZGRujatSvmzJkjRIhEOqNGjRrYsGEDPD09Ub169Txn9QeyZ/bnEn5EpEmY+BMRUaFKT0+HkZGR/PecnyUSicJ2BgYGOr8MlkgkUkguIiMj8fr1a/To0UNhO0dHR8TExKg6PCKd0rt3b9jb28t//ljiT0SkaZj4OYEI+QAADTtJREFUExGRSvAiWpmLiwsuXLggf7r/999/QyQSwdfXV2G7iIgInZ4EkUgVBg8eLP85Z6gSEZG2YOJPRESFrkePHkqJfpcuXRTKOLcs0K1bN4wdOxbx8fGws7PD1q1bUapUKfj4+Chsd/78eVSsWFGgKImIiEjTMfEnIqJC9f5TM/q4Vq1aISwsDJs2bUJ8fDw8PDwwZcoU6Ou/+/McFRWFv//+m08giYpY9+7d87X9hg0biigSIqLCx+X8iIiIiEjnBQQEKPRKunPnDiIjI+Hm5gZbW1tERUXhwYMHsLe3R+XKlREYGChgtERE+cMn/kRERESk85YsWSL/ee/evXj+/Dk2bdqEUqVKyctfvnyJgQMHonHjxkKESET0xfSEDoCIiIiISJ0sWbIEw4YNU0j6AaB06dIYMmSIwk0CIiJNwMSfiIiIiOg9oaGhea5EIhKJEBYWpuKIiIgKhok/Ef2/vfsP9aq+/wD+/PjjOrzKDa830+nIxHkJdVvTgUOJuUGufwqbrjXWSBjDWxsE+4U/EFFw1dbacEqpszILFaFyhJsKjdKFYnO2iOVuZjdTU6dz6sVr+tkf3y/y8UfLj7fdk/c+Hn/dc17nj+dfl/u87/N+HwCgwujRo/PII4+kpaXlvPstLS359a9/nc997nMFJQO4Mg73AwCACs3NzZk2bVoOHTqU4cOHnzvcb9euXamvr8/y5cszbNiwomMCXDbFHwAALtDW1pa1a9dm586dOXjwYBoaGjJ69OhMnjw5vXr1KjoeQFUUfwAAAOjEfM4PAAAuobm5Oa+99lr279+fO+64Iw0NDdmzZ0/q6+vTp0+fouMBXDbFHwAAKrS2tmbWrFl54YUX0q1bt5w9ezYTJkxIQ0NDfvnLX2bw4MH5yU9+UnRMgMvmVH8AAKjwwAMP5JVXXsmSJUuyffv2VO6Mvfnmm/PSSy8VmA6geoo/AABU+MMf/pAf/ehHGT9+fHr27Hne7NOf/nT27t1bUDKAK6P4AwBAhZMnT6ahoeGSs9bW1g5OA9B+ij8AAFQYMWJE/vjHP15y9uKLL2bkyJEdnAigfRzuBwAAFZqamtLU1JTW1tZMmjQppVIpO3fuzO9///usXbs2S5YsKToiQFVK5crTSgAAgKxfvz4PPvhg3nvvvXP3rrvuuvzsZz/LpEmTCkwGUD3FHwAAPsTu3btz5MiR1NXVZdiwYUXHAbgiij8AACTZvHlzVqxYkb179+baa6/NpEmTMmXKlKJjAbSbPf4AAHR5mzZtyr333pu+fftm6NChefPNN7Nly5bs378/P/jBD4qOB9AuVvwBAOjy7rzzzvTq1SuLFi1KbW1tzp49m3nz5uXZZ5/N9u3b062bj2EBVy+/wQAA6PKam5tzzz33pLa2NknSrVu3TJ8+Pa2trdm7d2/B6QDaR/EHAKDL+/e//51+/fqdd6++vj5JcuzYsSIiAXxs7PEHAIAkx48fz9GjR89dnzlz5pL3k+Saa67puGAA7WSPPwAAXV5jY2NKpdJF98vl8iXvv/HGGx0RC+BjYcUfAIAub8GCBUVHAPifseIPAAAAnZjD/QAA4CO8++672bJly0V7/QGuBl71BwCACj//+c9z5syZzJw5M0myYcOG3H///fnggw9SV1eXZcuWZeTIkQWnBLh8VvwBAKDChg0bziv2Dz/8cG6++eY8//zzGTVqVB555JHiwgFcAcUfAAAqHDx4MIMGDUqSvPPOO9m9e3emT5+ez372s/nOd76Tv/3tbwUnBKiO4g8AABX69u2bw4cPJ0k2b96curq6c28A1NTU5NSpU0XGA6iaPf4AAFBhzJgx+c1vfpPDhw9n2bJl+drXvnZu9tZbb2XgwIEFpgOonhV/AACoMGPGjPTv3z+/+MUvMmjQoNx///3nZs8//3zGjBlTYDqA6pXK5XK56BAAAHA1OH78eGpqalJTU1N0FIDLpvgDAMCH2LdvX/bt25fGxsb07t276DgAV8Sr/gAAcIFVq1ZlwoQJ+cpXvpJvf/vb2b17d5Lk3nvvzRNPPFFwOoDqKP4AAFDh8ccfz7x583L77bfnd7/7XSpfkP3Sl76U9evXF5gOoHpO9QcAgApPPfVUmpqa0tTUlDNnzpw3Gzp06LnVf4CrhRV/AACocODAgXzhC1+45Kxnz545efJkBycCaB/FHwAAKgwaNCivvfbaJWd//etfc/3113dsIIB2UvwBAKDC1KlTs3jx4qxZsybHjx9PknzwwQd58cUXs2zZsnzzm98sOCFAdXzODwAALjB//vysXLkypVIpZ8+eTbdu/7dedtddd2XWrFkFpwOojuIPAACX0NLSki1btuTIkSOpq6vLuHHjvOYPXJUUfwAAqLBt27bceOONqa2tvWh28uTJvP766xk7dmwByQCujD3+AABQ4e67705zc/MlZ2+99VbuvvvuDk4E0D6KPwAAVPhvL8S2trbmU5/6VAemAWi/HkUHAACAou3YsSN/+ctfzl2vW7cu27dvP++ZU6dOZdOmTbnhhhs6Oh5Auyj+AAB0eS+//HIWLlyYJCmVSlmxYsVFz/To0SPDhg3LnDlzOjoeQLs43A8AACo0NjZm9erVGT16dNFRAD4Wij8AAAB0Yg73AwCAC5w+fTrPPPNMZsyYkWnTpuXtt99OkrzwwgsfeuI/wCeV4g8AABVaWloyadKkPPTQQ3nnnXfy5z//OSdOnEiSbNu2LUuXLi04IUB1FH8AAKgwf/789OvXLxs3bszjjz9+3uf9xo4dm23bthWYDqB6ij8AAFTYunVrpk+fnn79+qVUKp03a2hoyMGDBwtKBnBlFH8AAKjQvXv3fNj514cOHUrv3r07OBFA+yj+AABQYezYsVm+fHlOnz597l6pVEq5XM7q1aszbty4AtMBVM/n/AAAoEJzc3O+9a1v5ZprrsnEiRPzxBNPZPLkydm1a1f27NmTNWvW5DOf+UzRMQEum+IPAAAXaGlpycKFC7N58+YcPXo0dXV1GTduXH74wx8q/cBVR/EHAACATqxH0QEAAOCTav/+/Xn//fczYMCADBgwoOg4AFdE8QcAgAusWrUqixcvzoEDB1Iul1MqlXLttddm+vTpufPOO4uOB1AVxR8AACo8+uij+dWvfpXbbrstt9xyS/r3759Dhw5l/fr1mTt3bv71r3/l+9//ftExAS6bPf4AAFBh/Pjxue222/LjH//4otkDDzyQdevW5eWXXy4gGcCV6VZ0AAAA+CQ5ceJEvvzlL19yNn78+Jw4caKDEwG0j+IPAAAVxo8fny1btlxytnnz5owbN66DEwG0jz3+AAB0ea+//vq5n7/xjW9kzpw5+ec//5mvfvWrqa+vz+HDh7Nx48a88sormTt3boFJAapnjz8AAF1eY2NjSqXSuevKP5FLpdJF12+88UaH5gNoDyv+AAB0eU8++WTREQD+Z6z4AwAAQCfmcD8AAADoxLzqDwAAF3j22WezatWqvP322zl16tRF81dffbWAVABXxoo/AABUeO655zJ79uwMHz48R44cyde//vXccsst6dmzZ+rr6zNt2rSiIwJURfEHAIAKy5cvT1NTU+bMmZMkueuuu7JgwYJs2rQp/fr1S21tbcEJAaqj+AMAQIU9e/bkpptuSvfu3dO9e/ccP348SdKnT59873vfy4oVKwpOCFAdxR8AACr06dMnbW1tSZIBAwbkH//4x7nZmTNncuTIkaKiAVwRh/sBAECFkSNH5u9//3smTJiQiRMn5re//W3K5XJ69OiRxx57LJ///OeLjghQlVK5XC4XHQIAAD4pduzYkffeey+33nprjh07lp/+9Kf505/+lLNnz2bUqFF5+OGHM2TIkKJjAlw2xR8AAD5CW1tb2tra0qdPn6KjAFRN8QcAAIBOzB5/AAC6vPnz52fatGkZNGhQ5s+f/5HPz5o1qwNSAXw8rPgDANDlTZw4MYsWLUpjY2MmTpz4X58tlUrZtGlTByUDaD/FHwAAADoxr/oDAMD/e/fdd7NmzZrs2LEjhw4dSqlUSv/+/XPTTTdlypQpGThwYNERAapmxR8AAJKsW7cuM2fOTFtbWwYMGJCBAwemXC5n//79OXDgQHr16pUFCxbk1ltvLToqQFUUfwAAurzm5ubcfvvt+eIXv5jZs2dn2LBh58137dqVefPmZceOHXnuuecydOjQgpICVK9b0QEAAKBoTz/9dIYMGZLHHnvsotKfJMOHD8/SpUszePDgrFy5soCEAFdO8QcAoMvbunVrpk6dmpqamg99pqamJlOnTs3WrVs7MBlA+yn+AAB0efv27cuIESM+8rkRI0Zk7969HZAI4OOj+AMA0OWdOHEitbW1H/lc7969c/LkyQ5IBPDxUfwBAOjynHcNdGY9ig4AAACfBN/97ndTKpX+6zP+QQBcjRR/AAC6vPvuu6/oCAD/M6Wyf1sCAABAp2WPPwAAAHRiij8AAAB0Yoo/AAAAdGKKPwAAAHRiij8AAAB0Yoo/AAAAdGKKPwAAAHRi/wFL5+JuOyk/HwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1100x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define my custom color palette with hex codes\n",
    "custom_palette = [\"#f72585\", \"#7209b7\", \"#3a0ca3\", \"#4361ee\", \"#4cc9f0\"]\n",
    "\n",
    "\n",
    "sns.set(style=\"white\")\n",
    "plt.figure(figsize=(11, 9))\n",
    "mask = np.triu(np.ones_like(df.corr(), dtype=bool))\n",
    "cmap = sns.color_palette(custom_palette, as_cmap=True)\n",
    "sns.heatmap(df.corr(), mask=mask, cmap=cmap, vmax=.3, center=0,\n",
    "            annot=True, fmt=\".2f\", square=True, linewidths=.5,\n",
    "            cbar_kws={\"shrink\": .5})\n",
    "\n",
    "plt.title('Correlation Heatmap', fontsize=16)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x1200 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\", palette=\"muted\")\n",
    "plt.figure(figsize=(20, 12))\n",
    "ax = df.hist(grid=False, alpha=0.7, bins=20, edgecolor='black')\n",
    "for a in ax.flatten():\n",
    "    a.set_title('')\n",
    "    a.set_xlabel(\"Values\", fontsize=12)\n",
    "    a.set_ylabel(\"Frequency\", fontsize=12)\n",
    "    a.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.suptitle('Histograms for Each Column', fontsize=18, y=1.02)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building the ML model\n",
    "\n",
    "Logistic Regression, Decision Trees, Random Forest, and Gradient Boosting are all supervised machine learning algorithms used for different types of tasks, such as classification and regression. Let's briefly explain each of these algorithms:\n",
    "\n",
    "1. **Logistic Regression:**\n",
    "   - **Type:** Classification\n",
    "   - **Use Case:** Used for binary or multiclass classification problems.\n",
    "   - **How it works:** Despite its name, logistic regression is a classification algorithm. It models the probability that an instance belongs to a particular category. It's especially useful for binary classification tasks (e.g., spam or not spam).\n",
    "\n",
    "2. **Decision Trees:**\n",
    "   - **Type:** Classification and Regression\n",
    "   - **Use Case:** Used for both classification and regression problems.\n",
    "   - **How it works:** Decision Trees split the dataset into subsets based on the most significant attribute at each step. Each node in the tree represents a decision based on an attribute, and each leaf node represents the outcome.\n",
    "\n",
    "3. **Random Forest:**\n",
    "   - **Type:** Ensemble method (Bagging)\n",
    "   - **Use Case:** Used for classification and regression tasks.\n",
    "   - **How it works:** Random Forest is an ensemble of multiple decision trees. Each tree is trained on a random subset of the data, and the final prediction is the result of combining the predictions of all individual trees. This helps to reduce overfitting and improve accuracy.\n",
    "\n",
    "4. **Gradient Boosting:**\n",
    "   - **Type:** Ensemble method (Boosting)\n",
    "   - **Use Case:** Used for classification and regression tasks.\n",
    "   - **How it works:** Gradient Boosting builds an ensemble of decision trees sequentially. Each tree corrects the errors of the previous one. It focuses on instances that were misclassified by the previous trees, gradually improving the model's performance.\n",
    "\n",
    "In summary, Logistic Regression is a simple linear model for classification, Decision Trees are versatile for both classification and regression, Random Forest is an ensemble of decision trees for improved accuracy, and Gradient Boosting is an ensemble method that builds trees sequentially to correct errors and enhance predictive performance. The choice of which algorithm to use depends on the specific characteristics of the data and the problem at hand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting columns\n",
    "X = df.drop(columns='Outcome')\n",
    "y = df['Outcome']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"background: linear-gradient(to right, #cfbaf0, #98f5e1); padding: 20px; border-radius: 10px; color: #14213d; text-align: justify; font-size: 16px; max-width:80%\">\n",
    "    <p style=\"margin-bottom: 15px;\">The predictor variables undergo a crucial step known as Standardization. The main goal of this process is to ensure that all features share a common scale, maintaining the differences in their value ranges.</p>\n",
    "    <p style=\"margin-bottom: 15px;\">Standardization, a feature scaling technique, employs a mean of 0 and a standard deviation of 1 to scale features. It transforms features by subtracting the mean and dividing by the standard deviation, as depicted in the formula:</p>\n",
    "    <p style=\"margin-bottom: 15px;\"><strong>X<sub>new</sub> = (X - μ)/σ</strong></p>\n",
    "    <p style=\"margin-bottom: 15px;\">Where <strong>μ</strong> represents the mean and <strong>σ</strong> represents the standard deviation. The features are scaled using the <code>StandardScaler()</code> function from sklearn.</p>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scaling the columns\n",
    "scaler = StandardScaler()\n",
    "X =  pd.DataFrame(scaler.fit_transform(X), columns=['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the dataset into 70% Training set and 30% Testing set\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The datasets are then split into a training and testing set in a 70:30 ratio.\n",
    "\n",
    "We’d use the confusion matrix, accuracy score, f1 score, and cross-validation score to evaluate performance.\n",
    "**Confusion matrix**:- used to describe the performance of a classification model.\n",
    "- **True Positive (TP)**:- people with diabetes that are correctly predicted\n",
    "- **False Positive (FP)**:- non-diabetics that are incorrectly predicted as diabetic\n",
    "- **True Negative (TN)**:- non-diabetics that are correctly predicted\n",
    "- **False Negative (FN)**:- people with diabetes that are incorrectly predicted as non-diabetic\n",
    "\n",
    "**Accuracy**:- fraction of correct predictions by the model — (TP +TN) / (TP + TN + FP +FN)\n",
    "\n",
    "**F1 score**:- harmonic average of precision and recall — 2 x ((Precision x Recall) / (Precision + Recall))\n",
    "\n",
    "**Cross-validation score**:- trains ML models on a subset of the dataset and evaluates another subset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the dataset into 70% Training set and 30% Testing set\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def classification_model_performance(y_test, prediction, model, x_train, y_train):\n",
    "    accuracy = metrics.accuracy_score(y_test, prediction)\n",
    "    print(\"Accuracy Score: {:.3%}\".format(accuracy))\n",
    "    f1 = metrics.f1_score(y_test, prediction)\n",
    "    print(\"F1 Score: {:.3%}\".format(f1))\n",
    "    kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
    "    accuracy_model = []\n",
    "    for train_index, test_index in kf.split(x_train):\n",
    "        X_train, X_test = x_train.iloc[train_index], x_train.iloc[test_index]\n",
    "        Y_train, Y_test = y_train.iloc[train_index], y_train.iloc[test_index]\n",
    "        model.fit(X_train, Y_train)\n",
    "        accuracy_model.append(accuracy_score(Y_test, model.predict(X_test)))\n",
    "    print(\"Cross-Validation Score: {:.3%}\".format(np.mean(accuracy_model)))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "def confusion_matrix_plot(y_test, prediction):\n",
    "    cm = confusion_matrix(y_test, prediction)\n",
    "    classes = ['0', '1']\n",
    "    fig, ax = plt.subplots(figsize=(8, 6))\n",
    "    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=classes)\n",
    "    disp.plot(cmap=plt.cm.Blues, values_format='d', ax=ax)\n",
    "    plt.title('Confusion Matrix', fontsize=16)\n",
    "    plt.xlabel('Predicted Label', fontsize=14)\n",
    "    plt.ylabel('True Label', fontsize=14)\n",
    "    cbar = disp.im_.colorbar\n",
    "    cbar.set_label('Count', rotation=270, labelpad=15, fontsize=12)\n",
    "    \n",
    "    # Adjust tick labels and font size\n",
    "    ax.set_xticklabels(classes, fontsize=12)\n",
    "    ax.set_yticklabels(classes, fontsize=12)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 80.519%\n",
      "F1 Score: 69.799%\n",
      "Cross-Validation Score: 77.293%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "logmodel = LogisticRegression()\n",
    "logmodel.fit(x_train, y_train)\n",
    "prediction = logmodel.predict(x_test)\n",
    "classification_model_performance(y_test, prediction, logmodel, x_train, y_train)\n",
    "\n",
    "confusion_matrix_plot(y_test, prediction)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 82.684%\n",
      "F1 Score: 77.011%\n",
      "Cross-Validation Score: 84.360%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dec_tree = DecisionTreeClassifier()\n",
    "dec_tree.fit(x_train, y_train)\n",
    "prediction2 = dec_tree.predict(x_test)\n",
    "\n",
    "classification_model_performance(y_test, prediction2, dec_tree, x_train, y_train)\n",
    "\n",
    "confusion_matrix_plot(y_test, prediction2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 89.610%\n",
      "F1 Score: 85.185%\n",
      "Cross-Validation Score: 87.349%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rfmodel = RandomForestClassifier(random_state=1)\n",
    "rfmodel.fit(x_train, y_train)\n",
    "prediction3 = rfmodel.predict(x_test)\n",
    "classification_model_performance(y_test, prediction3, rfmodel, x_train, y_train)\n",
    "confusion_matrix_plot(y_test, prediction3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Boosting Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 89.610%\n",
      "F1 Score: 85.714%\n",
      "Cross-Validation Score: 87.534%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gb = GradientBoostingClassifier()\n",
    "gb.fit(x_train, y_train)\n",
    "prediction4 = gb.predict(x_test)\n",
    "classification_model_performance(y_test, prediction4, gb, x_train, y_train)\n",
    "confusion_matrix_plot(y_test, prediction4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import accuracy_score, f1_score, make_scorer\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "def evaluate_models(models, X_train, y_train, X_test, y_test):\n",
    "    \"\"\"\n",
    "    Evaluate multiple machine learning models and plot their performance metrics.\n",
    "\n",
    "    Parameters:\n",
    "    - models (dict): Dictionary containing model names as keys and corresponding model instances as values.\n",
    "    - X_train, y_train, X_test, y_test: Training and testing data.\n",
    "\n",
    "    Returns:\n",
    "    - None (plots the performance metrics).\n",
    "    \"\"\"\n",
    "\n",
    "    metrics = ['Accuracy', 'F1 Score', 'Cross-Validation']\n",
    "\n",
    "    # Initialize lists to store performance metrics for each model\n",
    "    accuracy_scores = []\n",
    "    f1_scores = []\n",
    "    cv_scores = []\n",
    "\n",
    "    # Define a stratified k-fold cross-validation strategy\n",
    "    cv_strategy = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
    "\n",
    "    # Evaluate each model\n",
    "    for model_name, model in models.items():\n",
    "        # Train the model\n",
    "        model.fit(X_train, y_train)\n",
    "\n",
    "        # Make predictions\n",
    "        y_pred = model.predict(X_test)\n",
    "\n",
    "        # Calculate performance metrics\n",
    "        accuracy = accuracy_score(y_test, y_pred)\n",
    "        f1 = f1_score(y_test, y_pred)\n",
    "        cv = np.mean(cross_val_score(model, X_train, y_train, cv=cv_strategy, scoring=make_scorer(accuracy_score)))\n",
    "\n",
    "        # Append metrics to lists\n",
    "        accuracy_scores.append(accuracy)\n",
    "        f1_scores.append(f1)\n",
    "        cv_scores.append(cv)\n",
    "\n",
    "    # Plot the performance metrics with annotations\n",
    "    fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(18, 6))\n",
    "\n",
    "    for i, metric in enumerate(metrics):\n",
    "        bars = axes[i].bar(models.keys(), [accuracy_scores, f1_scores, cv_scores][i], color=['skyblue', 'lightcoral', 'lightgreen'])\n",
    "        axes[i].set_ylabel(metric)\n",
    "        axes[i].set_title(f'{metric} Comparison')\n",
    "\n",
    "        # Annotate the bars with their values\n",
    "        for bar in bars:\n",
    "            yval = bar.get_height()\n",
    "            axes[i].text(bar.get_x() + bar.get_width()/2, yval, round(yval, 3), ha='center', va='bottom')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the Metrics score of every model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "models_to_evaluate = {'Logistic Regression': logmodel, 'Decision Tree': dec_tree, 'Random Forest': rfmodel, 'Gradient Boosting': gb}\n",
    "evaluate_models(models_to_evaluate, x_train, y_train, x_test, y_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"background-color: #e5989b; padding: 20px; border-radius: 10px; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1); max-width:80%\">\n",
    "    <p style=\"font-size: 18px; color: #333; line-height: 1.6;\">\n",
    "        After thorough exploration of four distinct machine learning models, the <strong>Gradient Boosting</strong> model emerged as the most effective performer. With its superior predictive capabilities, we have decided to leverage Gradient Boosting for the development of our web application.\n",
    "    </p>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importances = gb.feature_importances_\n",
    "sorted_features = sorted(zip(feature_importances, x_train.columns), reverse=True)\n",
    "sorted_importances, sorted_labels = zip(*sorted_features)\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.barh(sorted_labels, sorted_importances, color='skyblue')\n",
    "plt.xlabel('Feature Importances')\n",
    "plt.ylabel('Feature Labels')\n",
    "plt.title('Feature Importance of Variables')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"background-color: #bb3e03; padding: 20px; border-radius: 10px; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1); max-width:80%\">\n",
    "    <p style=\"font-size: 18px; color: #fff; line-height: 1.6;\">\n",
    "        <strong>Insulin</strong>, <strong>Glucose</strong>, <strong>BMI</strong>, <strong>Age</strong>, and <strong>DiabetesPedigreeFunction</strong> stand out as the top five critical variables, as depicted in the insightful bar chart above. These key features align with the patterns observed in the correlation plot we examined earlier. To further enhance our model's performance, we will focus on analyzing the gradient boosting model using only these influential features.\n",
    "    </p>\n",
    "    <ul style=\" color: #ffba08; list-style-type: default; padding: 0; margin: 10px 0;\">\n",
    "        <li><strong>Insulin:</strong> Critical for understanding 2-hour serum insulin levels.</li>\n",
    "        <li><strong>Glucose:</strong> Indicates the glucose concentration in the blood.</li>\n",
    "        <li><strong>BMI:</strong> Measures body mass index for assessing body fat.</li>\n",
    "        <li><strong>Age:</strong> A crucial factor that contributes to diabetes risk.</li>\n",
    "        <li><strong>DiabetesPedigreeFunction:</strong> Scores likelihood of diabetes based on family history.</li>\n",
    "    </ul>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 90.476%\n",
      "F1 Score: 87.059%\n",
      "Cross-Validation Score: 87.909%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_tr = x_train.loc[:,['Insulin','Glucose','BMI','Age','SkinThickness']]\n",
    "x_te = x_test.loc[:,['Insulin','Glucose','BMI','Age','SkinThickness']]\n",
    "gb2 = GradientBoostingClassifier()\n",
    "gb2.fit(x_tr, y_train)\n",
    "prediction5 = gb2.predict(x_te)\n",
    "classification_model_performance(y_test, prediction5, gb2, x_tr, y_train)\n",
    "confusion_matrix_plot(y_test, prediction5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The modified model produces essentially the same outcome, indicating that these five variables are sufficient for our predictions. As a result, only these variables would be used in the model deployment stage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['gradient_boosting_model.pkl']"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save the model to a file\n",
    "import joblib\n",
    "joblib.dump(gb, 'gradient_boosting_model.pkl')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}