diff --git "a/EDA.ipynb" "b/EDA.ipynb"
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f23dba72",
+   "metadata": {},
+   "source": [
+    "## Importing Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "34142394",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d642c1fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>loan_amnt</th>\n",
+       "      <th>term</th>\n",
+       "      <th>int_rate</th>\n",
+       "      <th>installment</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sub_grade</th>\n",
+       "      <th>emp_title</th>\n",
+       "      <th>emp_length</th>\n",
+       "      <th>home_ownership</th>\n",
+       "      <th>annual_inc</th>\n",
+       "      <th>...</th>\n",
+       "      <th>open_acc</th>\n",
+       "      <th>pub_rec</th>\n",
+       "      <th>revol_bal</th>\n",
+       "      <th>revol_util</th>\n",
+       "      <th>total_acc</th>\n",
+       "      <th>initial_list_status</th>\n",
+       "      <th>application_type</th>\n",
+       "      <th>mort_acc</th>\n",
+       "      <th>pub_rec_bankruptcies</th>\n",
+       "      <th>address</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10000.0</td>\n",
+       "      <td>36 months</td>\n",
+       "      <td>11.44</td>\n",
+       "      <td>329.48</td>\n",
+       "      <td>B</td>\n",
+       "      <td>B4</td>\n",
+       "      <td>Marketing</td>\n",
+       "      <td>10+ years</td>\n",
+       "      <td>RENT</td>\n",
+       "      <td>117000.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>36369.0</td>\n",
+       "      <td>41.8</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>w</td>\n",
+       "      <td>INDIVIDUAL</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0174 Michelle Gateway\\r\\nMendozaberg, OK 22690</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>8000.0</td>\n",
+       "      <td>36 months</td>\n",
+       "      <td>11.99</td>\n",
+       "      <td>265.68</td>\n",
+       "      <td>B</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>Credit analyst</td>\n",
+       "      <td>4 years</td>\n",
+       "      <td>MORTGAGE</td>\n",
+       "      <td>65000.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>20131.0</td>\n",
+       "      <td>53.3</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>f</td>\n",
+       "      <td>INDIVIDUAL</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1076 Carney Fort Apt. 347\\r\\nLoganmouth, SD 05113</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>15600.0</td>\n",
+       "      <td>36 months</td>\n",
+       "      <td>10.49</td>\n",
+       "      <td>506.97</td>\n",
+       "      <td>B</td>\n",
+       "      <td>B3</td>\n",
+       "      <td>Statistician</td>\n",
+       "      <td>&lt; 1 year</td>\n",
+       "      <td>RENT</td>\n",
+       "      <td>43057.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11987.0</td>\n",
+       "      <td>92.2</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>f</td>\n",
+       "      <td>INDIVIDUAL</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>87025 Mark Dale Apt. 269\\r\\nNew Sabrina, WV 05113</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7200.0</td>\n",
+       "      <td>36 months</td>\n",
+       "      <td>6.49</td>\n",
+       "      <td>220.65</td>\n",
+       "      <td>A</td>\n",
+       "      <td>A2</td>\n",
+       "      <td>Client Advocate</td>\n",
+       "      <td>6 years</td>\n",
+       "      <td>RENT</td>\n",
+       "      <td>54000.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5472.0</td>\n",
+       "      <td>21.5</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>f</td>\n",
+       "      <td>INDIVIDUAL</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>823 Reid Ford\\r\\nDelacruzside, MA 00813</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>24375.0</td>\n",
+       "      <td>60 months</td>\n",
+       "      <td>17.27</td>\n",
+       "      <td>609.33</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C5</td>\n",
+       "      <td>Destiny Management Inc.</td>\n",
+       "      <td>9 years</td>\n",
+       "      <td>MORTGAGE</td>\n",
+       "      <td>55000.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>24584.0</td>\n",
+       "      <td>69.8</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>f</td>\n",
+       "      <td>INDIVIDUAL</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>679 Luna Roads\\r\\nGreggshire, VA 11650</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   loan_amnt        term  int_rate  installment grade sub_grade  \\\n",
+       "0    10000.0   36 months     11.44       329.48     B        B4   \n",
+       "1     8000.0   36 months     11.99       265.68     B        B5   \n",
+       "2    15600.0   36 months     10.49       506.97     B        B3   \n",
+       "3     7200.0   36 months      6.49       220.65     A        A2   \n",
+       "4    24375.0   60 months     17.27       609.33     C        C5   \n",
+       "\n",
+       "                 emp_title emp_length home_ownership  annual_inc  ...  \\\n",
+       "0                Marketing  10+ years           RENT    117000.0  ...   \n",
+       "1          Credit analyst     4 years       MORTGAGE     65000.0  ...   \n",
+       "2             Statistician   < 1 year           RENT     43057.0  ...   \n",
+       "3          Client Advocate    6 years           RENT     54000.0  ...   \n",
+       "4  Destiny Management Inc.    9 years       MORTGAGE     55000.0  ...   \n",
+       "\n",
+       "  open_acc pub_rec revol_bal revol_util total_acc  initial_list_status  \\\n",
+       "0     16.0     0.0   36369.0       41.8      25.0                    w   \n",
+       "1     17.0     0.0   20131.0       53.3      27.0                    f   \n",
+       "2     13.0     0.0   11987.0       92.2      26.0                    f   \n",
+       "3      6.0     0.0    5472.0       21.5      13.0                    f   \n",
+       "4     13.0     0.0   24584.0       69.8      43.0                    f   \n",
+       "\n",
+       "  application_type  mort_acc  pub_rec_bankruptcies  \\\n",
+       "0       INDIVIDUAL       0.0                   0.0   \n",
+       "1       INDIVIDUAL       3.0                   0.0   \n",
+       "2       INDIVIDUAL       0.0                   0.0   \n",
+       "3       INDIVIDUAL       0.0                   0.0   \n",
+       "4       INDIVIDUAL       1.0                   0.0   \n",
+       "\n",
+       "                                             address  \n",
+       "0     0174 Michelle Gateway\\r\\nMendozaberg, OK 22690  \n",
+       "1  1076 Carney Fort Apt. 347\\r\\nLoganmouth, SD 05113  \n",
+       "2  87025 Mark Dale Apt. 269\\r\\nNew Sabrina, WV 05113  \n",
+       "3            823 Reid Ford\\r\\nDelacruzside, MA 00813  \n",
+       "4             679 Luna Roads\\r\\nGreggshire, VA 11650  \n",
+       "\n",
+       "[5 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the dataset\n",
+    "df = pd.read_csv('data/lending_club_loan_two.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6fa11316",
+   "metadata": {},
+   "source": [
+    "# EDA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "aac950a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>loan_amnt</th>\n",
+       "      <th>term</th>\n",
+       "      <th>int_rate</th>\n",
+       "      <th>installment</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sub_grade</th>\n",
+       "      <th>emp_title</th>\n",
+       "      <th>emp_length</th>\n",
+       "      <th>home_ownership</th>\n",
+       "      <th>annual_inc</th>\n",
+       "      <th>...</th>\n",
+       "      <th>open_acc</th>\n",
+       "      <th>pub_rec</th>\n",
+       "      <th>revol_bal</th>\n",
+       "      <th>revol_util</th>\n",
+       "      <th>total_acc</th>\n",
+       "      <th>initial_list_status</th>\n",
+       "      <th>application_type</th>\n",
+       "      <th>mort_acc</th>\n",
+       "      <th>pub_rec_bankruptcies</th>\n",
+       "      <th>address</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10000.0</td>\n",
+       "      <td>36 months</td>\n",
+       "      <td>11.44</td>\n",
+       "      <td>329.48</td>\n",
+       "      <td>B</td>\n",
+       "      <td>B4</td>\n",
+       "      <td>Marketing</td>\n",
+       "      <td>10+ years</td>\n",
+       "      <td>RENT</td>\n",
+       "      <td>117000.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>36369.0</td>\n",
+       "      <td>41.8</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>w</td>\n",
+       "      <td>INDIVIDUAL</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0174 Michelle Gateway\\r\\nMendozaberg, OK 22690</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>8000.0</td>\n",
+       "      <td>36 months</td>\n",
+       "      <td>11.99</td>\n",
+       "      <td>265.68</td>\n",
+       "      <td>B</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>Credit analyst</td>\n",
+       "      <td>4 years</td>\n",
+       "      <td>MORTGAGE</td>\n",
+       "      <td>65000.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>20131.0</td>\n",
+       "      <td>53.3</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>f</td>\n",
+       "      <td>INDIVIDUAL</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1076 Carney Fort Apt. 347\\r\\nLoganmouth, SD 05113</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>15600.0</td>\n",
+       "      <td>36 months</td>\n",
+       "      <td>10.49</td>\n",
+       "      <td>506.97</td>\n",
+       "      <td>B</td>\n",
+       "      <td>B3</td>\n",
+       "      <td>Statistician</td>\n",
+       "      <td>&lt; 1 year</td>\n",
+       "      <td>RENT</td>\n",
+       "      <td>43057.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11987.0</td>\n",
+       "      <td>92.2</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>f</td>\n",
+       "      <td>INDIVIDUAL</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>87025 Mark Dale Apt. 269\\r\\nNew Sabrina, WV 05113</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7200.0</td>\n",
+       "      <td>36 months</td>\n",
+       "      <td>6.49</td>\n",
+       "      <td>220.65</td>\n",
+       "      <td>A</td>\n",
+       "      <td>A2</td>\n",
+       "      <td>Client Advocate</td>\n",
+       "      <td>6 years</td>\n",
+       "      <td>RENT</td>\n",
+       "      <td>54000.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5472.0</td>\n",
+       "      <td>21.5</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>f</td>\n",
+       "      <td>INDIVIDUAL</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>823 Reid Ford\\r\\nDelacruzside, MA 00813</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>24375.0</td>\n",
+       "      <td>60 months</td>\n",
+       "      <td>17.27</td>\n",
+       "      <td>609.33</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C5</td>\n",
+       "      <td>Destiny Management Inc.</td>\n",
+       "      <td>9 years</td>\n",
+       "      <td>MORTGAGE</td>\n",
+       "      <td>55000.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>24584.0</td>\n",
+       "      <td>69.8</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>f</td>\n",
+       "      <td>INDIVIDUAL</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>679 Luna Roads\\r\\nGreggshire, VA 11650</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   loan_amnt        term  int_rate  installment grade sub_grade  \\\n",
+       "0    10000.0   36 months     11.44       329.48     B        B4   \n",
+       "1     8000.0   36 months     11.99       265.68     B        B5   \n",
+       "2    15600.0   36 months     10.49       506.97     B        B3   \n",
+       "3     7200.0   36 months      6.49       220.65     A        A2   \n",
+       "4    24375.0   60 months     17.27       609.33     C        C5   \n",
+       "\n",
+       "                 emp_title emp_length home_ownership  annual_inc  ...  \\\n",
+       "0                Marketing  10+ years           RENT    117000.0  ...   \n",
+       "1          Credit analyst     4 years       MORTGAGE     65000.0  ...   \n",
+       "2             Statistician   < 1 year           RENT     43057.0  ...   \n",
+       "3          Client Advocate    6 years           RENT     54000.0  ...   \n",
+       "4  Destiny Management Inc.    9 years       MORTGAGE     55000.0  ...   \n",
+       "\n",
+       "  open_acc pub_rec revol_bal revol_util total_acc  initial_list_status  \\\n",
+       "0     16.0     0.0   36369.0       41.8      25.0                    w   \n",
+       "1     17.0     0.0   20131.0       53.3      27.0                    f   \n",
+       "2     13.0     0.0   11987.0       92.2      26.0                    f   \n",
+       "3      6.0     0.0    5472.0       21.5      13.0                    f   \n",
+       "4     13.0     0.0   24584.0       69.8      43.0                    f   \n",
+       "\n",
+       "  application_type  mort_acc  pub_rec_bankruptcies  \\\n",
+       "0       INDIVIDUAL       0.0                   0.0   \n",
+       "1       INDIVIDUAL       3.0                   0.0   \n",
+       "2       INDIVIDUAL       0.0                   0.0   \n",
+       "3       INDIVIDUAL       0.0                   0.0   \n",
+       "4       INDIVIDUAL       1.0                   0.0   \n",
+       "\n",
+       "                                             address  \n",
+       "0     0174 Michelle Gateway\\r\\nMendozaberg, OK 22690  \n",
+       "1  1076 Carney Fort Apt. 347\\r\\nLoganmouth, SD 05113  \n",
+       "2  87025 Mark Dale Apt. 269\\r\\nNew Sabrina, WV 05113  \n",
+       "3            823 Reid Ford\\r\\nDelacruzside, MA 00813  \n",
+       "4             679 Luna Roads\\r\\nGreggshire, VA 11650  \n",
+       "\n",
+       "[5 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5b8f535f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(396030, 27)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "35d8fd29",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['loan_amnt', 'term', 'int_rate', 'installment', 'grade', 'sub_grade',\n",
+       "       'emp_title', 'emp_length', 'home_ownership', 'annual_inc',\n",
+       "       'verification_status', 'issue_d', 'loan_status', 'purpose', 'title',\n",
+       "       'dti', 'earliest_cr_line', 'open_acc', 'pub_rec', 'revol_bal',\n",
+       "       'revol_util', 'total_acc', 'initial_list_status', 'application_type',\n",
+       "       'mort_acc', 'pub_rec_bankruptcies', 'address'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e9688109",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 396030 entries, 0 to 396029\n",
+      "Data columns (total 27 columns):\n",
+      " #   Column                Non-Null Count   Dtype  \n",
+      "---  ------                --------------   -----  \n",
+      " 0   loan_amnt             396030 non-null  float64\n",
+      " 1   term                  396030 non-null  object \n",
+      " 2   int_rate              396030 non-null  float64\n",
+      " 3   installment           396030 non-null  float64\n",
+      " 4   grade                 396030 non-null  object \n",
+      " 5   sub_grade             396030 non-null  object \n",
+      " 6   emp_title             373103 non-null  object \n",
+      " 7   emp_length            377729 non-null  object \n",
+      " 8   home_ownership        396030 non-null  object \n",
+      " 9   annual_inc            396030 non-null  float64\n",
+      " 10  verification_status   396030 non-null  object \n",
+      " 11  issue_d               396030 non-null  object \n",
+      " 12  loan_status           396030 non-null  object \n",
+      " 13  purpose               396030 non-null  object \n",
+      " 14  title                 394274 non-null  object \n",
+      " 15  dti                   396030 non-null  float64\n",
+      " 16  earliest_cr_line      396030 non-null  object \n",
+      " 17  open_acc              396030 non-null  float64\n",
+      " 18  pub_rec               396030 non-null  float64\n",
+      " 19  revol_bal             396030 non-null  float64\n",
+      " 20  revol_util            395754 non-null  float64\n",
+      " 21  total_acc             396030 non-null  float64\n",
+      " 22  initial_list_status   396030 non-null  object \n",
+      " 23  application_type      396030 non-null  object \n",
+      " 24  mort_acc              358235 non-null  float64\n",
+      " 25  pub_rec_bankruptcies  395495 non-null  float64\n",
+      " 26  address               396030 non-null  object \n",
+      "dtypes: float64(12), object(15)\n",
+      "memory usage: 81.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "d5fedec4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>loan_amnt</th>\n",
+       "      <th>int_rate</th>\n",
+       "      <th>installment</th>\n",
+       "      <th>annual_inc</th>\n",
+       "      <th>dti</th>\n",
+       "      <th>open_acc</th>\n",
+       "      <th>pub_rec</th>\n",
+       "      <th>revol_bal</th>\n",
+       "      <th>revol_util</th>\n",
+       "      <th>total_acc</th>\n",
+       "      <th>mort_acc</th>\n",
+       "      <th>pub_rec_bankruptcies</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>396030.000000</td>\n",
+       "      <td>396030.000000</td>\n",
+       "      <td>396030.000000</td>\n",
+       "      <td>3.960300e+05</td>\n",
+       "      <td>396030.000000</td>\n",
+       "      <td>396030.000000</td>\n",
+       "      <td>396030.000000</td>\n",
+       "      <td>3.960300e+05</td>\n",
+       "      <td>395754.000000</td>\n",
+       "      <td>396030.000000</td>\n",
+       "      <td>358235.000000</td>\n",
+       "      <td>395495.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>14113.888089</td>\n",
+       "      <td>13.639400</td>\n",
+       "      <td>431.849698</td>\n",
+       "      <td>7.420318e+04</td>\n",
+       "      <td>17.379514</td>\n",
+       "      <td>11.311153</td>\n",
+       "      <td>0.178191</td>\n",
+       "      <td>1.584454e+04</td>\n",
+       "      <td>53.791749</td>\n",
+       "      <td>25.414744</td>\n",
+       "      <td>1.813991</td>\n",
+       "      <td>0.121648</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>8357.441341</td>\n",
+       "      <td>4.472157</td>\n",
+       "      <td>250.727790</td>\n",
+       "      <td>6.163762e+04</td>\n",
+       "      <td>18.019092</td>\n",
+       "      <td>5.137649</td>\n",
+       "      <td>0.530671</td>\n",
+       "      <td>2.059184e+04</td>\n",
+       "      <td>24.452193</td>\n",
+       "      <td>11.886991</td>\n",
+       "      <td>2.147930</td>\n",
+       "      <td>0.356174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>5.320000</td>\n",
+       "      <td>16.080000</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>8000.000000</td>\n",
+       "      <td>10.490000</td>\n",
+       "      <td>250.330000</td>\n",
+       "      <td>4.500000e+04</td>\n",
+       "      <td>11.280000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>6.025000e+03</td>\n",
+       "      <td>35.800000</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>12000.000000</td>\n",
+       "      <td>13.330000</td>\n",
+       "      <td>375.430000</td>\n",
+       "      <td>6.400000e+04</td>\n",
+       "      <td>16.910000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.118100e+04</td>\n",
+       "      <td>54.800000</td>\n",
+       "      <td>24.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>20000.000000</td>\n",
+       "      <td>16.490000</td>\n",
+       "      <td>567.300000</td>\n",
+       "      <td>9.000000e+04</td>\n",
+       "      <td>22.980000</td>\n",
+       "      <td>14.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.962000e+04</td>\n",
+       "      <td>72.900000</td>\n",
+       "      <td>32.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>40000.000000</td>\n",
+       "      <td>30.990000</td>\n",
+       "      <td>1533.810000</td>\n",
+       "      <td>8.706582e+06</td>\n",
+       "      <td>9999.000000</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.743266e+06</td>\n",
+       "      <td>892.300000</td>\n",
+       "      <td>151.000000</td>\n",
+       "      <td>34.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           loan_amnt       int_rate    installment    annual_inc  \\\n",
+       "count  396030.000000  396030.000000  396030.000000  3.960300e+05   \n",
+       "mean    14113.888089      13.639400     431.849698  7.420318e+04   \n",
+       "std      8357.441341       4.472157     250.727790  6.163762e+04   \n",
+       "min       500.000000       5.320000      16.080000  0.000000e+00   \n",
+       "25%      8000.000000      10.490000     250.330000  4.500000e+04   \n",
+       "50%     12000.000000      13.330000     375.430000  6.400000e+04   \n",
+       "75%     20000.000000      16.490000     567.300000  9.000000e+04   \n",
+       "max     40000.000000      30.990000    1533.810000  8.706582e+06   \n",
+       "\n",
+       "                 dti       open_acc        pub_rec     revol_bal  \\\n",
+       "count  396030.000000  396030.000000  396030.000000  3.960300e+05   \n",
+       "mean       17.379514      11.311153       0.178191  1.584454e+04   \n",
+       "std        18.019092       5.137649       0.530671  2.059184e+04   \n",
+       "min         0.000000       0.000000       0.000000  0.000000e+00   \n",
+       "25%        11.280000       8.000000       0.000000  6.025000e+03   \n",
+       "50%        16.910000      10.000000       0.000000  1.118100e+04   \n",
+       "75%        22.980000      14.000000       0.000000  1.962000e+04   \n",
+       "max      9999.000000      90.000000      86.000000  1.743266e+06   \n",
+       "\n",
+       "          revol_util      total_acc       mort_acc  pub_rec_bankruptcies  \n",
+       "count  395754.000000  396030.000000  358235.000000         395495.000000  \n",
+       "mean       53.791749      25.414744       1.813991              0.121648  \n",
+       "std        24.452193      11.886991       2.147930              0.356174  \n",
+       "min         0.000000       2.000000       0.000000              0.000000  \n",
+       "25%        35.800000      17.000000       0.000000              0.000000  \n",
+       "50%        54.800000      24.000000       1.000000              0.000000  \n",
+       "75%        72.900000      32.000000       3.000000              0.000000  \n",
+       "max       892.300000     151.000000      34.000000              8.000000  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e6196ec",
+   "metadata": {},
+   "source": [
+    "### Checking for missing data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "542df56a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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A54lQZ86cSVJSEtevXyc+Pp73338fgPfee09tp3jx4moQBM+TtyqKws2bN7Psf0BAAA8ePNA5NAaF/+3HIoQQQoh3UK6PCDVr1ozo6Gidsj59+uDh4cHo0aMxNDTMdE1UVBTw/wFQvXr1ALh48SIlSpQA4N69e9y9exdXV1edaw0NDSle/Plk5vXr1+Pl5UWxYsXUdjZv3kxSUhKWlpYAXLp0CQMDA7Xdf5Ls80IIIUTB8Z8kXX1xldiVK1dYt24dH3zwAXZ2dpw+fZphw4ZRokQJDhw4oF7Tvn17YmJiWLZsGVZWVgQEBHD16lWioqIwNjbm7t27bNmyhcaNG/P06VNWrlzJsmXLOHDggDoylJSUhKenJ3Xq1GHSpEncvXuXfv360ahRI5YvX57j/suqMSGEECLveSeTrpqYmLBnzx5atGiBh4cHX3zxBR07duSnn37Sqffdd99Ru3ZtWrduTaNGjTA2NiY0NBRjY2O1zurVq6lZsyb16tXj7NmzhIeHq0EQgKWlJWFhYSQmJlKzZk169OhBmzZtWLBgwX/2vEIIIYR4d/0nI0J5nYwICSGEEHnPOzkiJIQQQgjxrpBASAghhBAFlgRCQgghhCiw3kggdOvWLT7++GPs7OwwNzenUqVKHDt2TD2vKArjx4/HyckJc3NzvL29uXz5sk4bJ06coHnz5tjY2GBnZ8eAAQNISkpSz586dYpu3brh4uKCubk5np6ezJ8/P1Nf1q5dS5UqVShUqBBOTk707duXv//++008thBCCCHymFwPhO7fv0+9evUwNjbml19+4dy5c8yZMwdbW1u1zqxZs1iwYAFLly7l6NGjWFhY4OPjw9OnTwG4ffs23t7euLu7c/ToUUJDQzl79iy9e/dW2zh+/Dj29vasWbOGs2fP8uWXXxIQEMCiRYvUOr/99hu+vr74+flx9uxZNm/ezB9//EH//v1z+7GFEEIIkQfl+qqxMWPG8Ntvv3Ho0KEszyuKgrOzM1988QUjRowA4MGDBzg4OLBq1Sq6du3KsmXLCAwMJC4uTk2XER0dTeXKlbl8+TLu7u5Ztu3v78/58+fZt28fALNnz2bJkiVcuXJFrbNw4UJmzpyZ7c7SWZFVY0IIIUTe81ZWjWVkfO/UqRP29vZUq1ZNZ/PCa9euER8fr5OV3tramtq1a3PkyBHgeeJTExMTNQgC1LxgERER2d77wYMHFClSRP3ay8uLP//8k59//hlFUUhISGDLli188MEHufa8QgghhMi7cj0Qunr1KkuWLKFMmTLs3r2bTz/9lM8//5zVq1cDEB8fD4CDg4POdQ4ODuq5pk2bEh8fT3BwMKmpqdy/f58xY8YAzzPLZ+Xw4cNs3LhRJ6FrvXr1WLt2LV26dMHExARHR0esra35+uuvs+1/SkoKDx8+1DlkqyUhhBAif8r1QEir1VK9enWmT59OtWrVGDBgAP3792fp0qU5bqNChQqsXr2aOXPmUKhQIRwdHXFzc8PBwUFnlCjDmTNnaNeuHRMmTFCz1QOcO3eOIUOGMH78eI4fP05oaCixsbEMHDgw23sHBQVhbW2tcyjaR/p9CEIIIYTIE3J9jpCrqyvNmzfn22+/VcuWLFnC1KlTuXXrFlevXqV06dKcPHmSqlWrqnUaNWpE1apVM638SkhIwMLCAo1Gg5WVFRs2bKBTp07q+XPnztGkSRP69evHtGnTdK7t2bMnT58+ZfPmzWpZREQEDRo04Pbt22qS1xelpKSQkpKiU2Zr5yGJV4UQQuRLT25nPadXX+bODXKlndz0VuYI1atXj4sXL+qUXbp0Sc0a7+bmhqOjI3v37lXPP3z4kKNHj+Ll5ZWpPQcHBywtLdm4cSNmZmY0b95cPXf27FmaNGlCr169MgVBAI8fP840gmRoaAiQ7esuU1NTrKysdA4JgoQQQoj8ySi3Gxw2bBh169Zl+vTpdO7cmT/++INly5axbNkyADQaDUOHDmXq1KmUKVMGNzc3AgMDcXZ2pn379mo7ixYtom7dumri1JEjRzJjxgxsbGyA56/DmjZtio+PD8OHD1fnFxkaGlKsWDEA2rRpQ//+/VmyZAk+Pj7ExcUxdOhQ3n//fZydnXP70YUQQog8510cyfkvvZGkqzt37iQgIIDLly/j5ubG8OHDdfbuURSFCRMmsGzZMhITE6lfvz6LFy+mbNmyah1fX1927dpFUlISHh4ejBgxgp49e6rnJ06cyKRJkzLd29XVldjYWPXrhQsXsnTpUq5du4aNjQ1NmzZl5syZFC+e8yXxsnxeCCGEyHty8mpMss/ngARCQgghRN4j2eeFEEIIIV4i1+cICSGEECLvyM+rxnJCRoSEEEIIUWDleiBUqlQpNBpNpsPf31+tc+TIEZo2bYqFhQVWVlY0bNiQJ0+eqOenTZtG3bp1KVSokLpKLCurVq2icuXKmJmZYW9vr3MPgNOnT9OgQQPMzMxwcXFh1qxZuf24QgghRJ5m7twgV468KtdfjUVGRpKenq5+febMGZo3b65ugnjkyBFatmxJQEAACxcuxMjIiFOnTuns95OamkqnTp3w8vIiJCQky/vMnTuXOXPmEBwcTO3atUlOTtZZLfbw4UNatGiBt7c3S5cuJTo6mr59+2JjY6OThkPkX7k13Jub8vI/FkIIkR+98VVjQ4cOZefOnVy+fBmNRkOdOnVo3rw5U6ZMeeW1q1atYujQoSQmJuqU379/n+LFi/PTTz/RrFmzLK9dsmQJX375JfHx8ZiYmAAwZswYtm/fzoULF/R6Blk1JoQQQuQ9b33VWGpqKmvWrKFv375oNBru3LnD0aNHsbe3p27dujg4ONCoUaOXZpTPSlhYGFqtllu3buHp6UmJEiXo3Lkzf/75p1rnyJEjNGzYUA2CAHx8fLh48SL379/PtWcUQgghRN71RgOh7du3k5iYSO/evYHnmenh+WaI/fv3JzQ0lOrVq9OsWTMuX76c43avXr2KVqtl+vTpzJs3jy1btnDv3j2aN29Oamoq8DzLfVYZ7jPOZUeyzwshhBAFxxtdPh8SEkKrVq3UdBZarRaATz75hD59+gBQrVo19u7dy4oVKwgKCspRu1qtlrS0NBYsWKBmm1+/fj2Ojo7s378fHx+f1+5zUFBQph2rNQaWaAytXrtNIYQQ4l1V0JfPv7FA6Pr16+zZs4etW7eqZRnZ3suXL69T19PTkxs3buS47azaKVasGEWLFlXbcXR0JCEhQee6jK8dHR2zbTsgIIDhw4frlNnaeeS4b0IIIUReklcDmNzyxl6NrVy5Ent7e1q3bq2WlSpVCmdn55dmp8+JevXqAei0c+/ePe7evau24+XlxcGDB0lLS1PrhIWFUa5cOWxtbbNtW7LPCyGEEAXHGwmEtFotK1eupFevXhgZ/f+gk0ajYeTIkSxYsIAtW7YQExNDYGAgFy5cwM/PT61348YNoqKiuHHjBunp6URFRREVFUVSUhIAZcuWpV27dgwZMoTDhw9z5swZevXqhYeHB02aNAGge/fumJiY4Ofnx9mzZ9m4cSPz58/PNNojhBBCiILrjSyf//XXX9UVWi9mlM8wY8YMvv76a+7du0eVKlWYNWsW9evXV8/37t2b1atXZ7pu//79NG7cGHi+T9CwYcPYunUrBgYGNGrUiPnz5+Pi4qLWP336NP7+/kRGRlK0aFEGDx7M6NGj9X4eWT4vhBBC5D2SfT6XSCAkhBBC5D1vfR8hIYQQQoh3mQRCQgghhCiwJBASQgghRIH1RgKhR48eMXToUFxdXTE3N6du3bpERkaq57du3UqLFi2ws7NDo9EQFRWVqY1PPvmE0qVLY25uTrFixWjXrl2mHGF79+6lbt26FC5cGEdHR0aPHs2zZ8/U8+Hh4bRr1w4nJycsLCyoWrUqa9eufROPLIQQQog86I1sqNivXz/OnDnD999/j7OzM2vWrMHb25tz585RvHhxkpOTqV+/Pp07d6Z///5ZtlGjRg169OhByZIluXfvHhMnTqRFixZcu3YNQ0NDTp06xQcffMCXX37Jd999x61btxg4cCDp6enMnj0bgMOHD1O5cmVGjx6Ng4MDO3fuxNfXF2traz788MM38ejiHSLZ54UQQrxKrq8ae/LkCYULF+bHH3/U2UyxRo0atGrViqlTp6plsbGxuLm5cfLkSapWrfrSdk+fPk2VKlWIiYmhdOnSjB07lrCwMJ2Rpp9++onOnTtz584dChcunGU7rVu3xsHBgRUrVuT4mWTVmBBCCJH3vJVVY8+ePSM9PR0zMzOdcnNzc72zzGdITk5m5cqVuLm5qfsEpaSkZHmPp0+fcvz48WzbevDgAUWKFHmtfgghhBAif8n1V2OFCxfGy8uLKVOm4OnpiYODA+vXr+fIkSO4u7vr1dbixYsZNWoUycnJlCtXjrCwMExMTADw8fFh3rx5rF+/ns6dOxMfH8/kyZMBiIuLy7K9TZs2ERkZyTfffJPtPVNSUkhJSdEpUxRF0mzkQfJqTAghxKu8kcnS33//PYqiULx4cUxNTVmwYAHdunXDwEC/2/Xo0YOTJ09y4MABypYtS+fOnXn69CkALVq0IDg4mIEDB2JqakrZsmX54IMPALK8z/79++nTpw/Lly+nQoUK2d4zKCgIa2trnUPRPtKr30IIIYTIG97oztLJyck8fPgQJycnunTpQlJSErt27VLP6zNHKDU1FVtbW7799lu6deumliuKQlxcHLa2tsTGxlK+fHn++OMPatWqpdY5cOAArVu3Zu7cuQwYMOCl98lqRMjWzkNGhIQQQuRLuTV6/i6OeOdkjtAbWTWWwcLCAgsLC+7fv8/u3buZNWvWa7elKAqKomQKUjQaDc7OzgCsX78eFxcXqlevrp4PDw/nww8/ZObMma8MguB59nlTU9NM9xBCCCFE/vNGAqHdu3ejKArlypUjJiaGkSNH4uHhQZ8+fQC4d+8eN27c4Pbt2wBcvHgRAEdHRxwdHbl69SobN26kRYsWFCtWjJs3bzJjxgzMzc3V118AwcHBtGzZEgMDA7Zu3cqMGTPYtGkThoaGwPPXYR9++CFDhgyhY8eOxMfHA2BiYiITpoUQQgjezZGc/9IbmSP04MED/P398fDwwNfXl/r167N7926MjY0B2LFjB9WqVVOX13ft2pVq1aqxdOlSAMzMzDh06BAffPAB7u7udOnShcKFC3P48GHs7e3V+/zyyy80aNCAmjVrsmvXLn788Ufat2+vnl+9ejWPHz8mKCgIJycn9ejQocObeGwhhBBC5DGSfT4HZB8hIYQQIu+R7PNCCCGEEC8hgZAQQgghCiwJhIQQQghRYEkgJIQQQogCS+9A6ODBg7Rp0wZnZ2c0Gg3bt2/XOa8oCuPHj8fJyQlzc3O8vb25fPlypnZ27dpF7dq1MTc3x9bWVme1V4ZVq1ZRuXJlzMzMsLe3x9/fXz0XHh5Ou3btcHJywsLCgqpVq7J27dps+71hwwY0Gk2W9xH505Pbh3LlEEIIkX/pvY9QcnIyVapUoW/fvlkuQ581axYLFixg9erVuLm5ERgYiI+PD+fOnVOTpP7www/079+f6dOn07RpU549e8aZM2d02pk7dy5z5swhODiY2rVrk5ycTGxsrHr+8OHDVK5cmdGjR+Pg4MDOnTvx9fXF2tqaDz/8UKet2NhYRowYQYMGBXuvhIKmoO+NIYQQOZGfd5bOiX+1fF6j0bBt2zZ1lEVRFJydnfniiy8YMWIE8HxPIQcHB1atWkXXrl159uwZpUqVYtKkSfj5+WXZ7v379ylevDg//fQTzZo1y3F/WrdujYODAytWrFDL0tPTadiwIX379uXQoUMkJiZmGsV6FVk+L4QQQuQ9//ny+WvXrhEfH4+3t7daZm1tTe3atTly5AgAJ06c4NatWxgYGFCtWjWcnJxo1aqVzohQWFgYWq2WW7du4enpSYkSJejcuTN//vnnS+//4MGDTDtGT548GXt7+2yDLiGEEEIUXLkaCGWksHBwcNApd3BwUM9dvXoVgIkTJzJu3Dh27tyJra0tjRs35t69e2odrVbL9OnTmTdvHlu2bOHevXs0b96c1NTULO+9adMmIiMj1TQeABEREYSEhLB8+fIcP0NKSgoPHz7UOWTPSSGEECJ/+s9XjWm1WgC+/PJLOnbsSI0aNVi5ciUajYbNmzerddLS0liwYAE+Pj7UqVOH9evXc/nyZfbv35+pzf3799OnTx+WL19OhQoVAHj06BE9e/Zk+fLlFC1aNMf9CwoKwtraWudQtI9y4cmFEEII8a7J1aSrjo6OACQkJODk5KSWJyQkULVqVQC1vHz58up5U1NT3nvvPW7cuJFtnWLFilG0aFG1ToYDBw7Qpk0bvvrqK3x9fdXyK1euEBsbS5s2bdSyjCDMyMiIixcvUrp06UzPEBAQwPDhw3XKbO08cvgJCCGEECIvydURITc3NxwdHdm7d69a9vDhQ44ePYqXlxcANWrUwNTUVM04D5CWlkZsbCyurq4A1KtXD0Cnzr1797h7965aB54voW/dujUzZ85kwIABOn3x8PAgOjqaqKgo9Wjbti1NmjQhKioKFxeXLJ/B1NQUKysrnUOj0fzLT0YIIYQQ7yK9R4SSkpKIiYlRv7527RpRUVEUKVKEkiVLMnToUKZOnUqZMmXU5fPOzs7qyjIrKysGDhzIhAkTcHFxwdXVleDgYAA6deoEQNmyZWnXrh1Dhgxh2bJlWFlZERAQgIeHB02aNAGevw778MMPGTJkCB07dlTnIJmYmFCkSBHMzMyoWLGiTt9tbGwAMpULIYQQomDSOxA6duyYGowA6mukXr16sWrVKkaNGkVycjIDBgwgMTGR+vXrExoaqu4hBBAcHIyRkRE9e/bkyZMn1K5dm3379mFra6vW+e677xg2bBitW7fGwMCARo0aERoairGxMQCrV6/m8ePHBAUFERQUpF7XqFEjwsPD9f4ghBBCCFHw/Kt9hAoK2UdICCGEyHv+832EhBBCCCHyEgmEhBBCCFFgSSAkhBBCiAIrV7PPp6WlMXr0aCpVqoSFhQXOzs74+vpy+/ZtnTamTZtG3bp1KVSokLqS658iIyNp1qwZNjY22Nra4uPjw6lTp9TzsbGxaDSaTMfvv/+u005iYiL+/v44OTlhampK2bJl+fnnn/V9bCGEEELkQ3oHQhnZ57/++utM5x4/fsyJEycIDAzkxIkTbN26lYsXL9K2bVudeqmpqXTq1IlPP/00y3skJSXRsmVLSpYsydGjR4mIiKBw4cL4+PiQlpamU3fPnj3ExcWpR40aNXTu07x5c2JjY9myZQsXL15k+fLlFC8uk5+FEEIIkcvZ57MSGRnJ+++/z/Xr1ylZsqTOuVWrVjF06FASExN1yo8dO0atWrW4ceOGuvFhdHQ0lStX5vLly7i7uxMbG4ubmxsnT55Ud63+p6VLlxIcHMyFCxfUZfevQ1aNCSGEEHnPO7Fq7MGDB2g0mmxfgWWlXLly2NnZERISQmpqKk+ePCEkJARPT09KlSqlU7dt27bY29tTv359duzYoXNux44deHl54e/vj4ODAxUrVmT69Omkp6fnwpMJIYQQIq/L1Vxj//T06VNGjx5Nt27dsLKyyvF1hQsXJjw8nPbt2zNlyhQAypQpw+7duzEyet5lS0tL5syZQ7169TAwMOCHH36gffv2bN++XX0Vd/XqVfbt20ePHj34+eefiYmJ4bPPPiMtLY0JEyZkee+UlBRSUlJ0yhRFkTQbQgghRD70xgKhtLQ0OnfujKIoLFmyRK9rnzx5gp+fH/Xq1WP9+vWkp6cze/ZsWrduTWRkJObm5hQtWlQnOWqtWrW4ffs2wcHBaiCk1Wqxt7dn2bJlGBoaUqNGDW7dukVwcHC2gVBQUBCTJk3SKdMYWKIxzHkgJ4QQQoi84Y28GssIgq5fv05YWJheo0EA69atIzY2lpUrV1KrVi3q1KnDunXruHbtGj/++GO219WuXVsnD5qTkxNly5bF0NBQLfP09CQ+Pp7U1NQs2wgICODBgwc6h8agsF79F0IIIUTekOuBUEYQdPnyZfbs2YOdnZ3ebTx+/BgDAwOd11EZX2u12myvi4qKwsnJSf26Xr16xMTE6Fxz6dIlnJycMDExybINyT4vhBBCFBy5mn3eycmJjz76iBMnTrBz507S09PVrPBFihRRg48bN25w7949bty4QXp6OlFRUQC4u7tjaWlJ8+bNGTlyJP7+/gwePBitVsuMGTMwMjJSE76uXr0aExMTqlWrBsDWrVtZsWIF3377rdq3Tz/9lEWLFjFkyBAGDx7M5cuXmT59Op9//vnrfVpCCCGEyFf0Xj4fHh6uk30+Q69evZg4cSJubm5ZXrd//34aN24MQO/evVm9evVL64SFhTFp0iTOnDmDgYEB1apVY9q0adSpUwd4HgjNnDmT69evY2RkhIeHByNHjuSjjz7SafPIkSMMGzaMqKgoihcvjp+fH6NHj9Z5XfYqsnxeCCGEyHtysnxess/ngARCQgghRN7zTuwjJIQQQgjxrpJASAghhBAFlgRCQgghhCiwcjX7PMDEiRPx8PDAwsICW1tbvL29OXr0qHo+PDw8y6zxGo2GyMhItd7p06dp0KABZmZmuLi4MGvWrEx9mTdvHuXKlcPc3BwXFxeGDRvG06dP1fPp6ekEBgbi5uaGubk5pUuXZsqUKci0KCGEEELAayyfz8g+37dvXzp06JDpfNmyZVm0aBHvvfceT5484auvvqJFixbExMRQrFgx6tatS1xcnM41gYGB7N27l5o1awLw8OFDWrRogbe3N0uXLiU6Opq+fftiY2PDgAEDgOebLo4ZM4YVK1ZQt25dLl26RO/evdFoNMydOxeAmTNnsmTJElavXk2FChU4duwYffr0wdraWpbQCyGEEOLNZ59/+PAh1tbW7Nmzh2bNmmU6n5aWRvHixRk8eDCBgYEALFmyhC+//JL4+Hh176ExY8awfft2Lly4AMCgQYM4f/48e/fuVdv64osvOHr0KBEREQB8+OGHODg4EBISotbp2LEj5ubmrFmzJsfPKavGhBBCiLwnJ6vG3mjS1dTUVJYtW4a1tTVVqlTJss6OHTv4+++/6dOnj1p25MgRGjZsqLP7s4+PDzNnzuT+/fvY2tpSt25d1qxZwx9//MH777/P1atX+fnnn+nZs6d6Td26dVm2bBmXLl2ibNmynDp1ioiICHXESORvT24fettdyMTcucHb7oIQQogXvJFAaOfOnXTt2pXHjx/j5OREWFgYRYsWzbJuSEgIPj4+lChRQi2Lj4/PtDGjg4ODes7W1pbu3btz9+5d6tevj6IoPHv2jIEDBzJ27Fj1mjFjxvDw4UM8PDwwNDQkPT2dadOm0aNHj2z7Ltnn8w8JOoQQQrzKG1k11qRJE6Kiojh8+DAtW7akc+fO3LlzJ1O9mzdvsnv3bvz8/PS+R3h4ONOnT2fx4sWcOHGCrVu3smvXLqZMmaLW2bRpE2vXrmXdunWcOHGC1atXM3v27Cx3tc4QFBSEtbW1zqFoH+ndPyGEEEK8+974HCGAMmXK0LdvXwICAnTKp0yZwsKFC7l16xbGxsZqua+vLw8fPtRZkbZ//36aNm3KvXv3sLW1pUGDBtSpU4fg4GC1zpo1axgwYABJSUkYGBjg4uLCmDFj8Pf3V+tMnTqVNWvWqHON/imrESFbOw8ZERJCCCHymHdmZ2mtVpvl66aVK1fi6+urEwQBeHl5cfDgQdLS0tSysLAwypUrh62tLfD/GepflJE/LCO2y67OyzLYS/Z5IYQQouDI1ezzdnZ2TJs2jbZt2+Lk5MTdu3f5+uuvuXXrFp06ddJpZ9++fVy7do1+/fplukf37t2ZNGmSmiD1zJkzzJ8/n6+++kqt06ZNG+bOnUu1atWoXbs2MTExBAYG0qZNGzUgatOmDdOmTaNkyZJUqFCBkydPMnfuXPr27avvYwshhBD5Um4tLMmr8zJzNfv80qVL6d69O0ePHuXu3bvY2dlRq1Ytxo0bR61atXTqd+/enevXr/Pbb79leZ/Tp0/j7+9PZGQkRYsWZfDgwYwePVo9/+zZM6ZNm8b333/PrVu3KFasmBr42NjYAPDo0SMCAwPZtm0bd+7cwdnZmW7dujF+/HidFWmvIsvnhRBCiLxHss/nEgmEhBBCiLznre8jJIQQQoh3m7wakxGhV5IRISGEECLveWdWjQkhhBBCvItyPft8RuLTF4+WLVuq52NjY/Hz89PJCD9hwgRSU1PVOhcvXqRJkyY4ODhgZmbGe++9x7hx43SW0y9fvpwGDRpga2urZrn/448/dPqiKArjx4/HyckJc3NzvL29uXz5sr6PLIQQQoh8Su9AKCP7/Ndff51tnZYtWxIXF6ce69evV89duHABrVbLN998w9mzZ/nqq69YunSpTmoMY2NjfH19+fXXX7l48SLz5s1j+fLlTJgwQa0THh5Ot27d2L9/P0eOHMHFxYUWLVpw69b/D4PNmjWLBQsWsHTpUo4ePYqFhQU+Pj48ffpU38cWQgghRD6U6ztL9+7dm8TExEwjRS8THBzMkiVLuHr1arZ1hg8fTmRkJIcOZT2pKz09HVtbWxYtWoSvry+KouDs7MwXX3zBiBEjAHjw4AEODg6sWrWKrl275rh/MkdICCGEyHve2qqx8PBw7O3tsbW1pWnTpkydOhU7O7ts6z948IAiRYpkez4mJobQ0FA6dOiQbZ3Hjx+TlpamtnPt2jXi4+Px9vZW61hbW1O7dm2OHDmiVyAkhBBC5FcFfdVYrgdCLVu2pEOHDri5uXHlyhXGjh1Lq1atOHLkiLrj84tiYmJYuHAhs2fPznSubt26nDhxgpSUFAYMGMDkyZOzve/o0aNxdnZWA5/4+Hjg/7PWZ3BwcFDPZUWyzwshhChI8moAk1tyPRB6caSlUqVKVK5cmdKlSxMeHk6zZs106t66dYuWLVvSqVMn+vfvn6mtjRs38ujRI06dOsXIkSOZPXs2o0aNylRvxowZbNiwgfDwcMzMzP5V/4OCgpg0aZJOmcbAEo2h1b9qVwghhBDvnje+fP69996jaNGiOvnJAG7fvk2TJk2oW7cuy5Yty/JaFxcXypcvT7du3ZgxYwYTJ04kPT1dp87s2bOZMWMGv/76K5UrV1bLHR0dAUhISNCpn5CQoJ7LSkBAAA8ePNA5NAaF9XpmIYQQQuQNbzwQunnzJn///TdOTk5q2a1bt2jcuDE1atRg5cqVmTLEZ0Wr1ZKWlqaTOX7WrFlMmTKF0NBQatasqVPfzc0NR0dH9u7dq5Y9fPiQo0eP4uXlle19JPu8EEIIUXDkavb5IkWKMGnSJDp27IijoyNXrlxh1KhRuLu74+PjA/x/EOTq6srs2bP566+/1LYyRmrWrl2LsbExlSpVwtTUlGPHjhEQEECXLl0wNjYGYObMmYwfP55169ZRqlQpdd6PpaUllpaWaDQahg4dytSpUylTpgxubm4EBgbi7Oyss8pNCCGEEAVXrmafX7JkCe3bt+fkyZMkJibi7OxMixYtmDJlijppedWqVfTp0yfLtjO6snHjRmbNmsWlS5dQFAVXV1c+/vhjhg0bps4BKlWqFNevX8/UxoQJE5g4caLa3oQJE1i2bBmJiYnUr1+fxYsXU7ZsWX0eWZbPCyGEEHmQZJ/PJRIICSGEEHmP5BoTQgghhHgJCYSEEEIIUWBJICSEEEKIAivXs88DnD9/nrZt22JtbY2FhQW1atXixo0bwPPs8//MTp9xbN68WW0jq/MbNmxQz4eHh2dZ58Vdo4OCgqhVqxaFCxfG3t6e9u3bc/HiRX0fWQghhBD5VK5nn79y5Qr169fHw8OD8PBwTp8+TWBgoLray8XFRSczfVxcHJMmTcLS0pJWrVrptLVy5Uqdelkte7948aJOHXt7e/XcgQMH8Pf35/fffycsLIy0tDRatGhBcnKyvo8thBBCiHwo17PPd+3aFWNjY77//vsct1OtWjWqV69OSEjIS9t+UcYy/vv372NjY5Oj+/z111/Y29tz4MABGjZsmOP+yaqxvCm3EgnmpoKe00cIIf5L/3n2ea1Wy65duxg1ahQ+Pj6cPHkSNzc3AgICsg1ojh8/TlRUVJYjTP7+/vTr14/33nuPgQMH0qdPn0y7PFetWpWUlBQqVqzIxIkTqVevXrb9e/DgAcBLM92L/EOCDiGEEK+Sq5Ol79y5Q1JSEjNmzKBly5b8+uuv/O9//6NDhw4cOHAgy2tCQkLw9PSkbt26OuWTJ09m06ZNhIWF0bFjRz777DMWLlyonndycmLp0qX88MMP/PDDD7i4uNC4cWNOnDiR5X20Wi1Dhw6lXr16VKxYMfceWgghhBB5Vq6+Grt9+zbFixenW7durFu3Tq3Xtm1bLCwsWL9+vc71T548wcnJicDAQL744ouX3mv8+PGsXLmSP//8M9s6jRo1omTJklm+lvv000/55ZdfiIiIoESJEtm2kZKSQkpKik6ZrZ2H5BsTQggh8pj/fEPFokWLYmRkRPny5XXKPT091VVjL9qyZQuPHz/G19f3lW3Xrl2bmzdvZgpSXvT+++9nynIPMGjQIHbu3Mn+/ftfGgTB85Vm1tbWOoeiffTK/gkhhBAi78nVOUImJibUqlUr0xL1S5cu4erqmql+SEgIbdu2pVixYq9sOyoqCltbW0xNTV9a58Us94qiMHjwYLZt20Z4eDhubm6vvE9AQADDhw/XKbO183jldUIIIURelFsLS/LqvMxczT5fsmRJRo4cSZcuXWjYsCFNmjQhNDSUn376ifDwcJ12YmJiOHjwID///HOme/z0008kJCRQp04dzMzMCAsLY/r06YwYMUKtM2/ePNzc3KhQoQJPnz7l22+/Zd++ffz6669qHX9/f9atW8ePP/5I4cKF1T2GrK2tMTc3z/L5TE1NMwVb8lpMCCFEfpVXA5jckqvZ51etWgXAihUrCAoK4ubNm5QrV45JkybRrl07nfpjx45lzZo1xMbGYmCg+4YuNDSUgIAAYmJiUBQFd3d3Pv30U/r376/WnTVrFsuWLePWrVsUKlSIypUrM378eJ2+ZRfArFy5kt69e+f4mWX5vBBCCJH3SPb5XCKBkBBCCJH3SPZ5IYQQQoiXyNXJ0kK8S2RnaSGEEK8ir8ZyQF6NCSGEEHmPvBoTQgghhHgJvQOhgwcP0qZNG5ydndFoNGzfvl3nfFJSEoMGDaJEiRKYm5tTvnx5li5dqp6/d+8egwcPply5cpibm1OyZEk+//xzNQ8YwKpVq9BoNFked+7cUeutXbuWKlWqUKhQIZycnOjbty9///13lv3esGEDGo0m25xnQgghhCh49A6EkpOTqVKlSpZJUgGGDx9OaGgoa9as4fz58wwdOpRBgwaxY8cO4Hkajtu3bzN79mzOnDnDqlWrCA0Nxc/PT22jS5cuxMXF6Rw+Pj40atQIe3t7AH777Td8fX3x8/Pj7NmzbN68mT/++IP+/ftn6lNsbCwjRoygQQOZnyGEEEKI/5erucYAKlasSJcuXQgMDFTLatSoQatWrZg6dWqW7WzevJmPP/6Y5ORkjIwyz9/+66+/KF68OCEhIfTs2ROA2bNns2TJEq5cuaLWW7hwITNnzuTmzZtqWXp6Og0bNqRv374cOnSIxMTETKNYryJzhIQQQoi8JydzhHJ91VjdunXZsWMHffv2xdnZmfDwcC5dusRXX32V7TUPHjzAysoqyyAI4LvvvqNQoUJ89NFHapmXlxdjx47l559/plWrVty5c4ctW7bwwQcf6Fw7efJk7O3t8fPz49Chd28VkRBCCPE2SYqNXLZw4UIGDBhAiRIlMDIywsDAgOXLl9OwYcMs69+9e5cpU6YwYMCAbNsMCQmhe/fuOmkx6tWrx9q1a+nSpQtPnz7l2bNntGnTRueVXUREBCEhIURFReW4/1lln1cURdJsCCGEEPnQGwmEfv/9d3bs2IGrqysHDx7E398fZ2dnvL29deo+fPiQ1q1bU758eSZOnJhle0eOHOH8+fN8//33OuXnzp1jyJAhjB8/Hh8fH+Li4hg5ciQDBw4kJCSER48e0bNnT5YvX07RokVz3P+goCAmTZqkU6YxsERjaJXjNoQQQoi8Iq+O5OSWXJ0j9OTJE6ytrdm2bRutW7dW6/Xr14+bN28SGhqqlj169AgfHx8KFSrEzp07MTMzy/Iefn5+nDhxgpMnT+qU9+zZk6dPn7J582a1LCIiggYNGnD79m0SEhKoVq0ahoaG6nmtVguAgYEBFy9epHTp0pnul9WIkK2dh4wICSGEEHnMfz5HKC0tjbS0tExJVA0NDdUgBJ6PBPn4+GBqasqOHTuyDYKSkpLYtGkTQUFBmc49fvw405yijKBHURQ8PDyIjo7WOT9u3DgePXrE/PnzcXFxyfKekn0+/yjo772FECInCvq/lXoHQklJScTExKhfX7t2jaioKIoUKULJkiVp1KgRI0eOxNzcHFdXVw4cOMB3333H3LlzgedBUIsWLXj8+DFr1qzh4cOHPHz4EIBixYrpjOBs3LiRZ8+e8fHHH2fqR5s2bejfvz9LlixRX40NHTqU999/H2dnZ+D5CrYX2djYZFku8qe8+pdSCCH+SwX930q9X42Fh4fTpEmTTOW9evVi1apVxMfHExAQwK+//sq9e/dwdXVlwIABDBs2DI1Gk+318DyoKlWqlPp13bp1cXNzY+3atVnWX7hwIUuXLuXatWvY2NjQtGlTZs6cSfHiWS937927tyyfF0IIIQqInLwak1xjOSCBkBBCCJH3SK4xIYQQQoiXkEBICCGEEAWWBEJCCCGEKLByPft8QkICvXv3xtnZmUKFCtGyZUsuX76sUyc+Pp6ePXvi6OiIhYUF1atX54cfftCpc+/ePXr06IGVlRU2Njb4+fmRlJSknn/69Cm9e/emUqVKGBkZZZtVPiUlhS+//BJXV1dMTU0pVaoUK1as0PexhRBCCJEP5Wr2eUVRaN++PVevXuXHH3/k5MmTuLq64u3tTXJyslrP19eXixcvsmPHDqKjo+nQoQOdO3fW2TSxR48enD17lrCwMHbu3MnBgwd10nCkp6djbm7O559/nmnH6hd17tyZvXv3EhISwsWLF1m/fj3lypXT97GFEEIIkQ/l6s7Sly5doly5cpw5c4YKFSoAz3dzdnR0ZPr06fTr1w8AS0tLlixZomaSB7Czs2PmzJn069eP8+fPU758eSIjI6lZsyYAoaGhfPDBB9y8eVPdJyhDdsviQ0ND6dq1K1evXqVIkSKv+5iyakwIIUS+lZ83VPzPd5bOSE3x4k7RBgYGmJqaEhERoQZCdevWZePGjbRu3RobGxs2bdrE06dPady4MfA8v5iNjY0aBAF4e3tjYGDA0aNH+d///pej/uzYsYOaNWsya9Ysvv/+eywsLGjbti1TpkzRSeAqhBBCFFTvYgDzX8rVQMjDw4OSJUsSEBDAN998g4WFBV999RU3b94kLi5Orbdp0ya6dOmCnZ0dRkZGFCpUiG3btuHu7g48n0Nkb2+v21EjI4oUKUJ8fHyO+3P16lUiIiIwMzNj27Zt3L17l88++4y///6blStXZnmNZJ8XQgghCo5cXTVmbGzM1q1buXTpEkWKFKFQoULs37+fVq1a6eQfCwwMJDExkT179nDs2DGGDx9O586dM+UG+7e0Wi0ajYa1a9fy/vvv88EHHzB37lxWr17NkydPsrwmKCgIa2trnUPRPsrVfgkhhBDi3ZDry+dr1KhBVFQUiYmJxMXFERoayt9//817770HwJUrV1i0aBErVqygWbNmVKlShQkTJlCzZk11ArajoyN37tzRaffZs2fcu3cPR0fHHPfFycmJ4sWLY21trZZ5enqiKAo3b97M8pqAgAAePHigc2gMCuv7MQghhBAiD3hj+whZW1tTrFgxLl++zLFjx2jXrh3wPGs88NIM9V5eXiQmJnL8+HH1/L59+9BqtdSuXTvHfahXrx63b9/WWXZ/6dIlDAwMKFGiRJbXmJqaYmVlpXPIazEhhBAif9I7EEpKSiIqKoqoqCjg/7PP37hxA4DNmzcTHh6uLqFv3rw57du3p0WLFsDzeUTu7u588skn/PHHH1y5coU5c+YQFhamrj7z9PSkZcuW9O/fnz/++IPffvuNQYMG0bVrV50VY+fOnSMqKop79+7x4MEDnX4BdO/eHTs7O/r06cO5c+c4ePAgI0eOpG/fvjJZWgghhBC5n31+wYIFBAcHk5CQgJOTE76+vgQGBmJiYqLWvXz5MmPGjCEiIoKkpCTc3d0ZMWKEznL6e/fuMWjQIH766ScMDAzo2LEjCxYswNLSUq1TqlQprl+/nqkvLz7ShQsXGDx4ML/99ht2dnZ07tyZqVOn6hUIyfJ5IYQQIu+R7PO5RAIhIYQQIu+R7PNCCCGEEC8hgZAQQgghCiwJhIQQQghRYOkVCAUFBVGrVi0KFy6Mvb097du35+LFizp1li1bRuPGjdVl54mJiZnaadu2LSVLlsTMzAwnJyd69uzJ7du31fPh4eG0a9cOJycnLCwsqFq1KmvXrs22Xxs2bECj0WTKQJ+UlMSgQYMoUaIE5ubmlC9fnqVLl+rzyEIIIYTIx/RKsXHgwAH8/f2pVasWz549Y+zYsbRo0YJz585hYWEBPN8nqGXLlrRs2ZKAgIAs22nSpAljx47FycmJW7duMWLECD766CMOHz4MwOHDh6lcuTKjR4/GwcGBnTt34uvri7W1NR9++KFOW7GxsYwYMYIGDTLnShk+fDj79u1jzZo1lCpVil9//ZXPPvsMZ2dn2rZtq8+jCyGEEPlSfk66mhP/atXYX3/9hb29PQcOHKBhw4Y65zKW2d+/fx8bG5uXtrNjxw7at29PSkoKxsbGWdZp3bo1Dg4OrFixQi1LT0+nYcOG9O3bl0OHDmXKQF+xYkW6dOlCYGCgWlajRg1atWrF1KlTc/ycsmpMCCFEfpVbgVBuyc2A6o1nn3/w4AEARYoUee027t27x9q1a6lbt262QVDGvTw9PXXKJk+ejL29PX5+fhw6lPkbWbduXXbs2EHfvn1xdnYmPDycS5cu8dVXX712f4UQQoj8JK+O5OSW1w6EtFotQ4cOpV69elSsWFHv60ePHs2iRYt4/PgxderUYefOndnW3bRpE5GRkXzzzTdqWUREBCEhITo7Sf/TwoULGTBgACVKlMDIyAgDAwOWL1+eafTqRZJ9XgghhCg4XnvVmL+/P2fOnGHDhg2vdf3IkSM5efIkv/76K4aGhvj6+pLVW7r9+/fTp08fli9fToUKFQB49OgRPXv2ZPny5RQtWjTbeyxcuJDff/+dHTt2cPz4cebMmYO/vz979uzJ9hrJPi+EEEIUHK81R2jQoEH8+OOPHDx4EDc3tyzr6DNH6ObNm7i4uHD48GG8vLzU8gMHDtC6dWvmzp3LgAED1PKoqCiqVauGoaGhWpaRsNXAwICLFy/i7OyMtbU127Zto3Xr1mq9fv36cfPmTUJDQ7PsS1YjQrZ2HjIiJIQQQuQxuT5HSFEUBg8ezLZt2wgPD882CNJXRhDzYgASHh7Ohx9+yMyZM3WCIHieuDU6OlqnbNy4cTx69Ij58+fj4uLC06dPSUtLe2mW+6yYmppiamqqUyZBkBBCCJE/6RUI+fv7s27dOn788UcKFy5MfHw8ANbW1moS0/j4eOLj44mJiQEgOjqawoULU7JkSYoUKcLRo0eJjIykfv362NracuXKFQIDAyldurQ6GrR//34+/PBDhgwZQseOHdX7mJiYUKRIEczMzDLNS8oYdcooNzExoVGjRowcORJzc3NcXV05cOAA3333HXPnzn3Nj0vkJe/aSgiQSYlCCPGu0evVWHYjIytXrqR3794ATJw4kUmTJmVbJzo6miFDhnDq1CmSk5NxcnKiZcuWjBs3juLFny9T7927N6tXr87URqNGjQgPD8+yD7179860fD4+Pp6AgAB+/fVX7t27h6urKwMGDGDYsGF6jfLI8nkhhBAi75Hs87lEAiEhhBAi75Hs80IIIYQQL/GvNlQUQgghRN4mKTbk1dgryasxIYQQIu/J9Vdjr8o+f+/ePQYPHky5cuUwNzenZMmSfP7552oqjgyRkZE0a9YMGxsbbG1t8fHx4dSpU1neMyYmhsKFC2e5F9G8efPUe7m4uDBs2DCePn2qU+frr7+mVKlSmJmZUbt2bf744w99HlkIIYQQ+ZhegVBG9vnff/+dsLAw0tLSaNGiBcnJyQDcvn2b27dvM3v2bM6cOcOqVasIDQ3Fz89PbSMpKYmWLVtSsmRJjh49SkREBIULF8bHx4e0tDSd+6WlpdGtW7csM8uvW7eOMWPGMGHCBM6fP09ISAgbN25k7Nixap2NGzcyfPhwJkyYwIkTJ6hSpQo+Pj7cuXNHrw9JCCGEEPnTG8s+n2Hz5s18/PHHJCcnY2RkxLFjx6hVqxY3btzAxcUFeL7XUOXKlbl8+TLu7u7qtaNHj+b27ds0a9aMoUOHkpiYqJ4bNGgQ58+fZ+/evWrZF198oQZXALVr16ZWrVosWrQIeL5xo4uLC4MHD2bMmDE5fk55NSaEEELkPW981VhOss8/ePAAKysrjIyez8suV64cdnZ2hISEkJqaypMnTwgJCcHT05NSpUqp1+3bt4/Nmzfz9ddfZ9lu3bp1OX78uPqq6+rVq/z888988MEHAKSmpnL8+HG8vb3//2ENDPD29ubIkSP/5rGFEEIIkU+80ezzd+/eZcqUKTopMgoXLkx4eDjt27dnypQpAJQpU4bdu3erwdLff/9N7969WbNmDVZWVlm23b17d+7evUv9+vVRFIVnz54xcOBA9dXY3bt3SU9Px8HBQec6BwcHLly4kO1zSfZ5IYQQouB4Y9nnHz58SOvWrSlfvjwTJ05Uy588eYKfnx/16tXj999/57fffqNixYq0bt2aJ0+eANC/f3+6d++e7es2eJ6LbPr06SxevJgTJ06wdetWdu3apQZXr0uyzwshhBAFxxvJPv/o0SN8fHwoVKgQO3fuxMzMTD0XEhLC2LFjiYuLUxOipqamYmtrS0hICF27dsXGxoakpCT1GkVR0Gq1GBoasmzZMvr27UuDBg2oU6cOwcHBar01a9YwYMAAkpKSePbsGYUKFWLLli20b99erdOrVy8SExP58ccfs3w2yT4vhBBC5A9vJfv8w4cP8fHxwdTUlB07dugEQQCPHz/GwMBAJ7DI+DojK/yRI0dIT09Xz//444/MnDmTw4cPq/nIMtp5kaGhodpPExMTatSowd69e9VASKvVsnfvXgYNGpTtM0r2eSGEEKLgyNXs8w8fPqRFixY8fvyYNWvW8PDhQx4+fAhAsWLFMDQ0pHnz5owcORJ/f38GDx6MVqtlxowZGBkZ0aRJEwA8PT117nvs2DEMDAx05iK1adOGuXPnUq1aNWrXrk1MTAyBgYG0adNGDYiGDx9Or169qFmzJu+//z7z5s0jOTmZPn36vP4nJoQQQoh8Q69AaMmSJQA0btxYpzwjs/yJEyc4evQogM4yeIBr165RqlQpPDw8+Omnn5g0aRJeXl4YGBhQrVo1QkNDcXJyynFfxo0bh0ajYdy4cdy6dYtixYrRpk0bpk2bptbp0qULf/31F+PHjyc+Pp6qVasSGhqaaQK1EEIIUVBJig1JsfFKso+QEEIIkfdI9nkhhBBCiJeQQEgIIYQQBZYEQkIIIYQosPQOhF6Vgf5FiqLQqlUrNBoN27dvV8v//vtvWrZsibOzM6ampri4uDBo0CB1hRlA79690Wg0mY4KFSqodQ4ePEibNm1wdnbOdA94nrR19OjRVKpUCQsLC5ydnfH19eX27dv6PrYQQggh8iG9U2xkZKCvVasWz549Y+zYsbRo0YJz585hYWGhU3fevHlZ7sFjYGBAu3btmDp1KsWKFSMmJgZ/f3/u3bvHunXrAJg/fz4zZsxQr3n27BlVqlShU6dOallycjJVqlShb9++dOjQIdN9Hj9+zIkTJwgMDKRKlSrcv3+fIUOG0LZtW44dO6bvo4s8JrdWQuSmvLqqQggh8qt/vWosuwz0UVFRfPjhhxw7dgwnJye2bdums8PzPy1YsIDg4GD+/PPPLM9v376dDh06cO3aNVxdXTM/iEbzynsAREZG8v7773P9+nVKliyZo2eUVWNCCCFE3vOfrBrLKgP948eP6d69O19//TWOjo6vbOP27dts3bqVRo0aZVsnJCQEb2/vLIMgffur0WiwsbH5V+0IIYQQIu/7V4FQdhnohw0bRt26dWnXrt1Lr+/WrRuFChWiePHiWFlZ8e2332ZZ7/bt2/zyyy/069fv33SXp0+fMnr0aLp165ZtVnshhBBCFBz/KhDKKgP9jh072LdvH/PmzXvl9V999RUnTpzgxx9/5MqVKwwfPjzLeqtXr8bGxuaVr71eJi0tjc6dO6MoirpDdlZSUlLU1CAZh+w5KYQQQuRPrx0IDRo0iJ07d7J//35KlCihlu/bt48rV65gY2ODkZERRkbP52N37NgxU2oOR0dHPDw8aNu2Ld988w1LliwhLi5Op46iKKxYsYKePXtiYmLyWn3NCIKuX79OWFjYS0eDgoKCsLa21jkU7aPXuq8QQggh3m16B0KKojBo0CC2bdvGvn37MmWgHzNmDKdPnyYqKko94Pnoz8qVK7NtNyPzfEpKik75gQMHiImJwc/PT9+uAv8fBF2+fJk9e/ZgZ2f30voBAQE8ePBA59AYFH6tewshhBDi3ab38vlXZaB3dHTMcoJ0yZIl1aDp559/JiEhgVq1amFpacnZs2cZOXIk9erVo1SpUjrXhYSEULt2bZ05SBmSkpKIiYlRv7527RpRUVEUKVKEkiVLkpaWxkcffcSJEyfYuXMn6enpan+LFCmS5QiTqakppqamOmVZbQEghBBCiLxP7+Xz2QUFGRnos7vmxaXt+/fv58svv+TcuXOkpKTg4uJChw4dGDNmjM5qrgcPHuDk5MT8+fPp379/pnbDw8Np0qRJpvJevXqxatUqYmNjM41YZdi/f3+mV3XZkeXzQgghRN6Tk+Xzkn0+ByQQEkIIIfIeyT4vhBBCCPESEggJIYQQosCSQEgIIYQQBZbeq8aEEEIIkX/kVoLqvJpUWq8RoaCgIGrVqkXhwoWxt7enffv2XLx4UadO48aN0Wg0OsfAgQN16kRGRtKsWTNsbGywtbXFx8eHU6dO6dTZtGkTVatWpVChQri6uhIcHJxtv3777TeMjIyoWrVqtnVmzJiBRqNh6NCh+jyyEEIIIfIxvUaEDhw4gL+/P7Vq1eLZs2eMHTuWFi1acO7cOSwsLNR6/fv3Z/LkyerXhQoVUv+clJREy5Ytadu2LYsXL+bZs2dMmDABHx8f/vzzT4yNjfnll1/o0aMHCxcupEWLFpw/f57+/ftjbm7OoEGDdPqUmJiIr68vzZo1IyEhIct+R0ZG8s0331C5cmV9HlcIIYTI9/LqSE5u+VfL5//66y/s7e05cOAADRs2BJ6PCFWtWjXbXGPHjh2jVq1a3LhxAxcXFwCio6OpXLkyly9fxt3dne7du5OWlsbmzZvV6xYuXMisWbO4ceOGzl5GXbt2pUyZMhgaGrJ9+3Z1J+sMSUlJVK9encWLFzN16tSX9i07snxeCCGEyHve+PL5Bw8eAM93aX7R2rVrKVq0KBUrViQgIIDHjx+r58qVK4ednR0hISGkpqby5MkTQkJC8PT0VHeVTklJwczMTKdNc3Nzbt68yfXr19WylStXcvXqVSZMmJBtH/39/WndujXe3t7/5lGFEEIIkQ+99mRprVbL0KFDqVevnk76i+7du+Pq6oqzszOnT59m9OjRXLx4ka1btwJQuHBhwsPDad++PVOmTAGgTJky7N69W03Q6uPjw7Bhw+jduzdNmjQhJiaGOXPmABAXF0epUqW4fPkyY8aM4dChQ+p1/7RhwwZOnDhBZGRkjp8rJSUlU74zRVEkzYYQQoh8qaBPln7tQMjf358zZ84QERGhUz5gwAD1z5UqVcLJyYlmzZpx5coVSpcuzZMnT/Dz86NevXqsX7+e9PR0Zs+eTevWrYmMjMTc3Jz+/ftz5coVPvzwQ9LS0rCysmLIkCFMnDgRAwMD0tPT6d69O5MmTaJs2bJZ9u/PP/9kyJAhhIWFZRpdepmgoCAmTZqkU6YxsERjmH3GeiGEECKvyqsBTG55rTlCgwYN4scff+TgwYPZ5vLKkJycjKWlJaGhofj4+BASEsLYsWOJi4vDwOD5m7nU1FRsbW0JCQmha9eu6rUZSVKLFSvG3r17+eCDD7hz5w7GxsbY2tpiaGio1tVqtSiKgqGhIb/++isPHz7kf//7n06d9PR0NBoNBgYGpKSk6JzLkNWIkK2dh4wICSGEEHlMTuYI6TUipCgKgwcPZtu2bYSHh78yCALUyctOTk4APH78GAMDA53AIuNrrVarc62hoSHFiz+fqLx+/Xq8vLwoVqwYWq2W6OhonbqLFy9m3759bNmyBTc3tyzr9OnTBw8PD0aPHp1lEASSfV4IIYQoSPQKhPz9/Vm3bh0//vgjhQsXJj4+HgBra2vMzc25cuUK69at44MPPsDOzo7Tp08zbNgwGjZsqC5db968OSNHjsTf35/Bgwej1WqZMWMGRkZGaib5u3fvsmXLFho3bszTp09ZuXIlmzdv5sCBA8DzwOnFeUkA9vb2mJmZ6ZT/s46FhQV2dnaZyoUQQoiCSuYI6WHJkiXA8yXyL1q5ciW9e/fGxMSEPXv2MG/ePJKTk3FxcaFjx46MGzdOrevh4cFPP/3EpEmT8PLywsDAgGrVqhEaGqqOGgGsXr2aESNGoCgKXl5ehIeH8/777/+LRxVCCCHEP+XVACa3/Kt9hAoK2UdICCGEyHve+D5CQgghhBB5mQRCQgghhCiwJBASQgghRIGVq9nnY2NjM2WezzhezBuW4e+//6ZEiRJoNBoSExPV8ri4OLp3707ZsmUxMDDIMmN8VlnuNRoNrVu31ql3/vx52rZti7W1NRYWFmqeMyGEEEIIvQKhjOzzv//+O2FhYaSlpdGiRQuSk5MBcHFxIS4uTueYNGkSlpaWtGrVKlN7fn5+WWaET0lJoVixYowbN44qVapk2ZetW7fq3OfMmTMYGhrSqVMntc6VK1eoX78+Hh4ehIeHc/r0aQIDA/XaaVoIIYQQ+VeuZ5//p2rVqlG9enVCQkJ0ypcsWcLGjRsZP348zZo14/79+9jY2GS6/lXZ7DPMmzeP8ePHExcXh4WFBfA8M72xsTHff//9az1fBlk1JoQQIr/Kz/sIvbXs8xmOHz9OVFQUfn5+OuXnzp1j8uTJfPfdd2qajX8rIz1HRhCk1WrZtWsXZcuWxcfHB3t7e2rXrs327dtz5X5CCCGEyPtyPfv8i0JCQvD09KRu3bpqWUpKCt26dSM4OJiSJUty9erV1+2C6o8//uDMmTM6o0537twhKSmJGTNmMHXqVGbOnEloaCgdOnRg//79NGrUKMu2JPu8EEKIguRdHMn5L+V69vkMT548Yd26dQQGBuqUBwQE4Onpyccff/y6t84kJCSESpUq6ew8nZG3rF27dgwbNgyAqlWrcvjwYZYuXZptICTZ54UQQoiC47XeSw0aNIidO3eyf/9+SpQokWWdLVu28PjxY3x9fXXK9+3bx+bNmzEyMsLIyIhmzZoBULRoUSZMmKB3X5KTk9mwYUOm129FixbFyMiI8uXL65R7enq+dNVYQEAADx480Dk0BoX17pcQQggh3n1vLPt8SEgIbdu2pVixYjrlP/zwA0+ePFG/joyMpG/fvhw6dIjSpUvr2X3YvHkzKSkpmUaYTExMqFWrls7yfoBLly7h6uqabXuSfV4IIYQoOHI1+3yGmJgYDh48yM8//5ypjX8GO3fv3gWej9S8uGosKioKgKSkJP766y+ioqIwMTHJNMITEhJC+/btsbOzy3SvkSNH0qVLFxo2bEiTJk0IDQ3lp59+Ijw8XJ/HFkIIIUQ+pdfy+exGRjKyz2cYO3Ysa9asITY29pWrwsLDw2nSpEmm5fNZ3cvV1ZXY2Fj164sXL+Lh4cGvv/5K8+bNs2x/xYoVBAUFcfPmTcqVK8ekSZNo167dS/v0T7J8XgghhMh7crJ8XrLP54AEQkIIIUTeI9nnhRBCCCFeQgIhIYQQQhRYEggJIYQQosDK1ezzAPHx8fTs2RNHR0csLCyoXr06P/zwg06dEydO0Lx5c2xsbLCzs2PAgAEkJSVlec/sMtTD84nW1atXx9TUFHd3d1atWqVzPj09ncDAQNzc3DA3N6d06dJMmTIFmRYlhBBCCMjl7PMAvr6+XLx4kR07dhAdHU2HDh3o3LkzJ0+eBOD27dt4e3vj7u7O0aNHCQ0N5ezZszqrzl6UXYb6a9eu0bp1a5o0aUJUVBRDhw6lX79+7N69W60zc+ZMlixZwqJFizh//jwzZ85k1qxZLFy4UJ/HFkIIIUQ+levZ5y0tLVmyZAk9e/ZU69nZ2TFz5kz69evHsmXLCAwMJC4uTl1aHx0dTeXKlbl8+TLu7u7qdS/LUD969Gh27drFmTNn1Ppdu3YlMTGR0NBQAD788EMcHBx0cpB17NgRc3Nz1qxZk+PnlFVjQgghRN7zVrLP161bl40bN3Lv3j20Wi0bNmzg6dOnNG7cGHie1NTExERnf6GMzRhfzFv2qgz1R44cwdvbW6fMx8eHI0eO6PRl7969XLp0CYBTp04RERFBq1at/s1jCyGEECKfeO1AKLvs85s2bSItLQ07OztMTU355JNP2LZtmzrS07RpU+Lj4wkODiY1NZX79+8zZswYAOLi4oDMGeqzEh8fj4ODg06Zg4MDDx8+VFN4jBkzhq5du+Lh4YGxsTHVqlVj6NCh9OjRI9vnSklJ4eHDhzqHzCkSQggh8qfXDoQyss9v2LBBpzwwMJDExET27NnDsWPHGD58OJ07dyY6OhqAChUqsHr1aubMmUOhQoVwdHTEzc0NBwcHdeQntzLUb9q0ibVr17Ju3TpOnDjB6tWrmT17NqtXr872mqCgIKytrXUORfvoX/VDCCGEEO+m15ojNGjQIH788UcOHjyok3j1ypUruLu7c+bMGSpUqKCWZ0yOXrp0qU47CQkJWFhYoNFosLKyYsOGDXTq1ImqVasSHR2tptlQFAWtVouhoSFffvklkyZNomHDhlSvXp158+ap7a1cuZKhQ4eqr+xcXFwYM2YM/v7+ap2pU6eyZs0aLly4kOWzpaSkkJKSolNma+chiVeFEEKIPCYnc4RyNfv848ePATLN6TE0NESr1WZqL+PV1ooVKzAzM1PzheUkQ72Xl1empK5hYWF4eXnp9Cenfckg2eeFEEKIgiNXs897eHjg7u7OJ598wuzZs7Gzs2P79u2EhYWxc+dOtZ1FixZRt25dLC0tCQsLY+TIkcyYMUNdEZaTDPUDBw5k0aJFjBo1ir59+7Jv3z42bdrErl271OvatGnDtGnTKFmyJBUqVODkyZPMnTuXvn376v1BCSGEECL/yfXs85cvX2bMmDFERESQlJSEu7s7I0aM0FlO7+vry65du0hKSsLDwyPT+X/KLkN9eHg4w4YN49y5c5QoUYLAwECd/YgePXpEYGAg27Zt486dOzg7O9OtWzfGjx+PiYlJTh9bls8LIYQQeZBkn88lEggJIYQQeY9knxdCCCGEeAkJhIQQQghRYEkgJIQQQogCS+9AaMmSJVSuXBkrKyusrKzw8vLil19+Uc8/ffoUf39/7OzssLS0pGPHjiQkJGTZ1ssyy6ekpPDll1/i6uqKqakppUqVYsWKFVm2s2HDBjQaDe3bt9cpVxSF8ePH4+TkhLm5Od7e3ly+fFnfRxZCCCHyrSe3D+XKkVfpPVn6p59+wtDQkDJlyqAoCqtXryY4OJiTJ09SoUIFPv30U3bt2sWqVauwtrZm0KBBGBgY8Ntvv2Vqq3379qSmpvLLL79kWhHWrl07EhISmDp1Ku7u7sTFxaHVaqlXr55OG7GxsdSvX5/33nuPIkWKsH37dvXczJkzCQoKYvXq1bi5uREYGEh0dDTnzp3DzMwsx88sk6WFEEKIvOc/WzVWpEgRgoOD+eijjyhWrBjr1q3jo48+AuDChQt4enpy5MgR6tSpo17zsszyoaGhdO3alatXr+okdP2n9PR0GjZsqG62mJiYqAZCiqLg7OzMF198wYgRI4DnSWIdHBxYtWoVXbt2zfHzSSAkhBBC5D1vfNVYeno6GzZsIDk5GS8vL44fP05aWppOVngPDw9KliypkxX+VZnld+zYQc2aNZk1axbFixenbNmyjBgxQme3aYDJkydjb2+Pn59fpjauXbtGfHy8Tl+sra2pXbu2Tl+EEEIIUXDptbN0hujoaLy8vHj69CmWlpZs27aN8uXLExUVhYmJic4rLnieSiNjF+p/Zpa/evVqpvavXr1KREQEZmZmbNu2jbt37/LZZ5/x999/s3LlSgAiIiIICQkhKioqyz5m3C+rDPUZ57KSVa4xRVEkzYYQQgiRD71WIFSuXDmioqJ48OABW7ZsoVevXhw4cCBH1+Yks7xWq0Wj0bB27Vqsra0BmDt3Lh999BGLFy/m2bNn9OzZk+XLl1O0aNHXeYRsBQUFMWnSJJ0yjYElGkOrXL2PEEII8S7IrYnO5s4NcqWd/9prBUImJia4u7sDUKNGDSIjI5k/fz5dunQhNTWVxMREnVGhhIQEHB0dAdi3bx/R0dFs2bIFeD7aAlC0aFE1s7yTkxPFixdXgyB4nmdMURRu3rxJcnIysbGxtGnTRj2fkUjVyMiIixcvqvdLSEjAyclJpy9Vq1bN9tkCAgIYPny4TpmtnYe+H5EQQgiRJ+TVACa3vFYg9E9arZaUlBRq1KiBsbExe/fupWPHjgBcvHiRGzduqFnhc5JZvl69emzevJmkpCQsLS0BuHTpEgYGBupy++joaJ0+jBs3jkePHjF//nxcXFwwNjbG0dGRvXv3qoHPw4cPOXr0KJ9++mm2zyLZ54UQQoiCQ+9AKCAggFatWlGyZEkePXrEunXrCA8PZ/fu3VhbW+Pn58fw4cMpUqQIVlZWDB48GC8vL3XFWE4yy3fv3p0pU6bQp08fJk2axN27dxk5ciR9+/bF3NwcgIoVK+q0k3Hti+VDhw5l6tSplClTRl0+7+zsnGm/ISGEEEIUTHoHQnfu3MHX15e4uDisra2pXLkyu3fvpnnz5gB89dVXGBgY0LFjR1JSUvDx8WHx4sV63cPS0pKwsDAGDx5MzZo1sbOzo3PnzkydOlWvdkaNGkVycjIDBgwgMTGR+vXrExoaqtceQiLvKujvvYUQQryaZJ/PAdlHSAghhMh7crKPUK7MERLiXfQubvkuo0tCCPFukRGhHJARISGEEPlVfp5G8J+l2MjvJBASQggh8p43nmJDCCGEECIv0ysQWrJkCZUrV8bKygorKyu8vLz45Zdf1PPLli2jcePGWFlZodFoSExMzNTGiRMnaN68OTY2NtjZ2TFgwACSkpJ06kRGRtKsWTNsbGywtbXFx8eHU6dOqecnTpyIRqPJdFhYWKh1li9fToMGDbC1tcXW1hZvb2/++OMPfR5XCCGEEPmcXoFQiRIlmDFjBsePH+fYsWM0bdqUdu3acfbsWQAeP35My5YtGTt2bJbX3759G29vb9zd3Tl69CihoaGcPXuW3r17q3WSkpJo2bIlJUuW5OjRo0RERFC4cGF8fHxIS0sDYMSIEcTFxekc5cuXp1OnTmo74eHhdOvWjf3793PkyBFcXFxo0aIFt269ephMCCGEEAXDv54jVKRIEYKDg3UywIeHh9OkSRPu37+vk2pj2bJlBAYGEhcXp2adj46OpnLlyly+fBl3d3eOHTtGrVq1uHHjBi4uLlnW+adTp05RtWpVDh48SIMGWU/WSk9Px9bWlkWLFuHr66vXM8ocISGEECLveaNzhNLT09mwYQPJyclq+oxXSUlJwcTERA2CAHWn6IiICOB5Qlc7OztCQkJITU3lyZMnhISE4OnpSalSpbJs99tvv6Vs2bLZBkHwfLQqLS2NIkWK5PAJhRBCCJHf6R0IRUdHY2lpiampKQMHDmTbtm2UL18+R9c2bdqU+Ph4goODSU1N5f79+4wZMwaAuLg4AAoXLkx4eDhr1qzB3NwcS0tLQkND+eWXXzAyyrzt0dOnT1m7dq3OiFRWRo8ejbOzM97e3i+tl5KSwsOHD3UOWVgnhBBC5E96B0LlypUjKipKTV7aq1cvzp07l6NrK1SowOrVq5kzZw6FChXC0dERNzc3HBwc1FGiJ0+e4OfnR7169fj999/57bffqFixIq1bt9ZJ1pph27ZtPHr0iF69emV73xkzZrBhwwa2bdv2yvQaQUFBWFtb6xyK9lGOnk8IIYQQecu/niPk7e1N6dKl+eabb9Sy7OYIvSghIQELCws0Gg1WVlZs2LCBTp06ERISwtixY3XmEaWmpmJra0tISAhdu3bVaadZs2ZYWVmxbdu2LO8ze/Zspk6dyp49e6hZs+YrnyclJYWUlBSdMls7D8lAnwfJztJCCFGw/ScpNrRababAISccHBwAWLFiBWZmZmrS1sePH2NgYKATeGR8rdVqddq4du0a+/fvZ8eOHVneY9asWUybNo3du3fnKAgCMDU1xdTUVKdMgqC8SYIOIYQQr6JXIBQQEECrVq0oWbIkjx49Yt26dYSHh7N7924A4uPjiY+PJyYmBng+n6hw4cKULFlSnaS8aNEi6tatq2aYHzlyJDNmzFBHjpo3b87IkSPx9/dn8ODBaLVaZsyYgZGREU2aNNHpz4oVK3BycqJVq1aZ+jpz5kzGjx/PunXrKFWqFPHx8cDzzPaWlpb6fUoiT5IRISGEEK+i16sxPz8/9u7dS1xcHNbW1lSuXJnRo0erozkTJ05k0qRJma5buXKluleQr68vu3btIikpCQ8PD0aMGEHPnj116oeFhTFp0iTOnDmDgYEB1apVY9q0adSpU0eto9VqcXV1xdfXl2nTpmW6Z6lSpbh+/Xqm8gkTJjBx4sScPjIgy+eFEEKIvEhyjeUSCYTyJhkREkKIgk0CoVwigZAQQgiR90jSVSGEEEKIl5BASAghhBAFlt6B0Ksy0H/yySeULl0ac3NzihUrRrt27bhw4YJOG1lljt+wYYN6Pi4uju7du1O2bFkMDAwYOnRoln3ZvHkzHh4emJmZUalSJX7++eds+z1w4EA0Gg3z5s3T95GFEEIIkU/pHQi9KgN9jRo1WLlyJefPn2f37t0oikKLFi1IT0/XaWflypU62ePbt2+vnktJSaFYsWKMGzeOKlWqZNmPw4cP061bN/z8/Dh58iTt27enffv2nDlzJlPdbdu28fvvv+Ps7Kzv4wohhBAiH8uVydJZZaDPcPr0aapUqUJMTAylS5d+flONhm3btukEP9lp3LgxVatWzTSS06VLF5KTk9m5c6daVqdOHapWrcrSpUvVslu3blG7dm12795N69atGTp0aLYjTNmRydJ5k6waE0KIgu2N7yydnp7O5s2bs81An5yczMqVK3Fzc8PFxUXnnL+/P/369eO9995j4MCB9OnTR68dnI8cOcLw4cN1ynx8fNi+fbv6tVarpWfPnowcOZIKFSro93Aiz5OgQwghxKu8ViAUHR2Nl5cXT58+xdLSMlMG+sWLFzNq1CiSk5MpV64cYWFhmJiYqOcnT55M06ZNKVSoEL/++iufffYZSUlJfP755znuQ3x8vJqmI4ODg4O6gzQ8313ayMhIr3azyjWmKIqk2RBCCCHyodcKhDIy0D948IAtW7bQq1cvDhw4oAZDPXr0oHnz5sTFxTF79mw6d+7Mb7/9pmZ+DwwMVNuqVq0aycnJBAcH6xWwvMrx48eZP38+J06c0CuICQoKyrQ7tsbAEo2hVa71TQghhBDvhtdaPm9iYoK7uzs1atQgKCiIKlWqMH/+fPW8tbU1ZcqUoWHDhmzZsoULFy5kmx0eoHbt2ty8eVOv5K2Ojo4kJCTolCUkJODo6AjAoUOHuHPnDiVLlsTIyAgjIyOuX7/OF198QalSpbJtNyAggAcPHugcGoPCOe6XEEIIIfKOf519Hl6egV5RFBRFeWmQExUVha2tbaas7y/j5eXF3r17dSY+h4WFqXOVevbsibe3t841Pj4+9OzZkz59+mTbrmSfF0IIIQoOvQOhl2Wgv3r1Khs3bqRFixYUK1aMmzdvMmPGDMzNzfnggw8A+Omnn0hISKBOnTqYmZkRFhbG9OnTGTFihM59oqKiAEhKSuKvv/4iKioKExMT9fXbkCFDaNSoEXPmzKF169Zs2LCBY8eOsWzZMgDs7Oyws7PTadPY2BhHR0fKlSun9wclhBBCiPxH70Dozp07+Pr66mSg3717N82bN+f27dscOnSIefPmcf/+fRwcHGjYsCGHDx/G3t4eeB6MfP311wwbNgxFUXB3d2fu3Ln0799f5z7VqlVT/3z8+HHWrVuHq6srsbGxANStW5d169Yxbtw4xo4dS5kyZdi+fTsVK1b8Fx+HEEIIIQoSSbqaA7KPkBBCiPwqt/Zcexe3LJHs87lEAiEhhBAi75Hs80IIIYQQLyGBkBBCCCEKrFzPPt+4ceNMmeUHDhyo08aNGzdo3bo1hQoVwt7enpEjR/Ls2TP1fEREBPXq1cPOzg5zc3M8PDz46quv9OpHhiNHjtC0aVMsLCywsrKiYcOGPHnyRN/HFkIIIUQ+pPeqsYzs82XKlEFRFFavXk27du04efKkms+rf//+TJ48Wb2mUKFC6p/T09Np3bo1jo6OHD58mLi4OHx9fTE2Nmb69OkAWFhYMGjQICpXroyFhQURERF88sknWFhYMGDAgBz348iRI7Rs2ZKAgAAWLlyIkZERp06dwsBABsKEEEII8Qayz2eXLT7DL7/8wocffsjt27fVXGFLly5l9OjR/PXXXzo5yV7UoUMHLCws+P7773PUD3iejb558+ZMmTLlXz2fTJYWQggh8p43Plk6PT2dDRs2ZMo+v3btWooWLUrFihUJCAjg8ePH6rkjR45QqVIlnYSpPj4+PHz4kLNnz2Z5n5MnT3L48GEaNWqU437cuXOHo0ePYm9vT926dXFwcKBRo0ZERET8m0cWQgghRD6S69nnu3fvjqurK87Ozpw+fZrRo0dz8eJFtm7dCmSfNT7j3ItKlCjBX3/9xbNnz5g4cSL9+vXLcT+uXr0KwMSJE5k9ezZVq1blu+++o1mzZpw5c4YyZcpk+WySfV4IIYQoOHI9+3zGHB6ASpUq4eTkRLNmzbhy5QqlS5fW6z6HDh0iKSmJ33//nTFjxuDu7k63bt1y1A+tVgvAJ598ouYWq1atGnv37mXFihUEBQVleU/JPi+EEEIUHG8k+/yLateuDUBMTAyQfdb4jHMvcnNzo1KlSvTv359hw4YxceLEHPfDyckJQB0hyuDp6cmNGzeyfTbJPi+EEEIUHLmyfOpl2eczkqdmBCZeXl5ER0dz584dtU5YWBhWVlaZgpac3iOrOqVKlcLZ2ZmLFy/q1Ll06RKurq7ZtmFqaqouyc845LWYEEIIkT/lavb5K1eusG7dOj744APs7Ow4ffo0w4YNo2HDhlSuXBmAFi1aUL58eXr27MmsWbOIj49n3Lhx+Pv7Y2pqCsDXX39NyZIl8fDwAODgwYPMnj2bzz//PEf9ANBoNIwcOZIJEyZQpUoVqlatyurVq7lw4QJbtmz51x+cEEIIIfK+XM0+/+eff7Jnzx7mzZtHcnIyLi4udOzYkXHjxqnXGxoasnPnTj799FO8vLywsLCgV69eOvsOabVaAgICuHbtGkZGRpQuXZqZM2fyySef5KgfGYYOHcrTp08ZNmwY9+7do0qVKoSFhek9V0kIIYQQ+ZMkXc0B2UdICCGEyHsk6aoQQgghxEu81vJ5IfKCJ7cPve0uZGLu3OBtd0EIIcQL5NVYDsirMSGEECLvyfVXYznJ+P6qbO+lSpXKlJ1+xowZ6vnY2NhM5zUaDb///rvOfRITE/H398fJyQlTU1PKli3Lzz//rJ5PT08nMDAQNzc3zM3NKV26NFOmTEHiPiGEEEJk0OvV2Ksyvuc02/vkyZPp37+/+nXhwpk3LNyzZ4+aRR7Azs5O/XNqairNmzfH3t6eLVu2ULx4ca5fv46NjY1aZ+bMmSxZsoTVq1dToUIFjh07Rp8+fbC2ttZZhi+EEEIUZLk1jSCvvvrXKxBq06aNztfTpk1jyZIl/P7771SoUIFhw4bx+eefM2bMGLVOuXLlMrVTuHDhTLtI/5OdnV22dVasWMG9e/c4fPgwxsbGwPORphcdPnyYdu3a0bp1a/X8+vXr+eOPP175nEIIIURBkVcDmNzy2qvG/pnxXZ9s7zNmzMDOzo5q1aoRHBzMs2fPMtVp27Yt9vb21K9fnx07duic27FjB15eXvj7++Pg4EDFihWZPn066enpap26deuyd+9eLl26BMCpU6eIiIigVatWr/vIQgghhMhn9F41ll3G94w5PK/K9v75559TvXp1ihQpwuHDhwkICCAuLo65c+cCYGlpyZw5c6hXrx4GBgb88MMPtG/fnu3bt9O2bVvgeWb5ffv20aNHD37++WdiYmL47LPPSEtLY8KECQCMGTOGhw8f4uHhgaGhIenp6UybNo0ePXq89Pkk+7wQQoiCpKC/GtN71Vhqaio3btxQM75/++23HDhwgMTEROrVq0dAQADTp09X61euXJnWrVtnm+19xYoVfPLJJyQlJakpNv7J19eXa9eucejQ829W2bJlefr0KdeuXcPQ0BCAuXPnEhwcTFxcHAAbNmxg5MiRBAcHU6FCBaKiohg6dChz586lV69e2T7fxIkTs8w+byDZ54UQQog8JSerxvQeEcrI+A5Qo0YNIiMjmT9/vjovSN9s77Vr1+bZs2fExsZmOZ8oo05YWJj6tZOTE8bGxmoQlHGf+Ph4UlNTMTExYeTIkYwZM4auXbsCUKlSJa5fv05QUNBLA6GAgACGDx+uU2Zr55FtfSGEECIvK+gjQv96Q8WMjO8vy/b+snk5UVFRGBgYYG9v/9I6GdnrAerVq8e6devQarXqirRLly7h5OSEiYkJAI8fP860Ws3Q0BCtVvvS5zE1Nc00MiWvxYQQQuRXeTWAyS16BUIvy/iek2zvR44c4ejRozRp0oTChQtz5MgRhg0bxscff4ytrS0Aq1evxsTEhGrVqgGwdetWVqxYwbfffqv249NPP2XRokUMGTKEwYMHc/nyZaZPn66zLL5NmzZMmzaNkiVLUqFCBU6ePMncuXPp27fvv/7QhBBCCJE/6BUIvSrj+6uyvZuamrJhwwYmTpxISkoKbm5uDBs2LNOrqClTpnD9+nWMjIzw8PBg48aNfPTRR+p5FxcXdu/ezbBhw6hcuTLFixdnyJAhjB49Wq2zcOFCAgMD+eyzz7hz5w7Ozs588sknjB8//rU/LCGEEELkL5JiIwckxYYQQgiR90j2eSGEEEKIl5BASAghhBAFlgRCQgghhCiwcjX7fHx8PD179sTR0RELCwuqV6/ODz/8oNPGvXv36NGjB1ZWVtjY2ODn50dSUpJOnd27d1OnTh0KFy5MsWLF6NixI7GxsTp1wsPDqV69Oqampri7u7Nq1apM/f36668pVaoUZmZm1K5dW/KMCSGEEEKHXoFQRvb548ePc+zYMZo2bUq7du04e/Ys8HwH6IsXL7Jjxw6io6Pp0KEDnTt35uTJk2obPXr04OzZs4SFhbFz504OHjzIgAED1PPXrl2jXbt2NG3alKioKHbv3s3du3fp0KGDTp3WrVvTpEkTdcfofv36sXv3brXOxo0bGT58OBMmTODEiRNUqVIFHx8f7ty589oflhBCCCHyl3+9aqxIkSIEBwfj5+eHpaUlS5YsoWfPnup5Ozs7Zs6cSb9+/Th//jzly5cnMjKSmjVrAhAaGsoHH3zAzZs3cXZ2ZsuWLXTr1o2UlBR1Q8SffvqJdu3akZKSgrGxMaNHj2bXrl2cOXNGvU/Xrl1JTEwkNDQUeL4bda1atVi0aBHwfONHFxcXBg8erO6CnVOyakwIIYTIe97oqrF/Zp+H5xnfN27cyL1799BqtWzYsIGnT5/SuHFj4PmGijY2NmoQBODt7Y2BgQFHjx4FnqftMDAwYOXKlaSnp/PgwQO+//57vL29MTY2Vtvx9vbW6Y+Pjw9HjhwBnudDO378uE4dAwMDvL291TpCCCGEEHoHQtHR0VhaWmJqasrAgQPV7PMAmzZtIi0tDTs7O0xNTfnkk0/Ytm2bmpssPj4+UyoNIyMjihQpQnx8PABubm78+uuvjB07FlNTU2xsbLh58yabNm1Sr4mPj8fBwUGnHQcHBx4+fMiTJ0+4e/cu6enpWdbJuI8QQgghhN6BULly5YiKiuLo0aN8+umn9OrVi3PnzgEQGBhIYmIie/bs4dixYwwfPpzOnTsTHR2d4/bj4+Pp378/vXr1IjIykgMHDmBiYsJHH33Ef7H3Y0pKCg8fPtQ5ZM9JIYQQIn/Ktezzo0aNYtGiRZw5c4YKFSoAUKVKFQ4dOsTXX3/N0qVLcXR0zDRZ+dmzZ9y7dw9HR0fg+Uova2trZs2apdZZs2YNLi4uHD16lDp16uDo6EhCQoJOOwkJCVhZWWFubo6hoSGGhoZZ1sm4T3aCgoKYNGmSTpnGwBKNoZUen5IQQggh8oJ/vY9QRvb5x48fP2/wJRnfvby8SExM5Pjx4+r5ffv2odVqqV27NpB91viMe2W0s3fvXp06YWFh6lwlExMTatSooVNHq9Wyd+9etU52AgICePDggc6hMSicsw9DCCGEEHmKXoFQQEAABw8eJDY2lujoaAICAggPD6dHjx54eHjg7u7OJ598wh9//MGVK1eYM2cOYWFhtG/fHgBPT09atmxJ//79+eOPP/jtt98YNGgQXbt2xdnZGYDWrVsTGRnJ5MmTuXz5MidOnKBPnz64urqqGekHDhzI1atXGTVqFBcuXGDx4sVs2rSJYcOGqX0dPnw4y5cvZ/Xq1Zw/f55PP/2U5ORk+vTp89JnNDU1VfdJyjg0Go0+H5MQQggh8gi9ls/7+fmxd+9enezzo0ePVrPPX758mTFjxhAREUFSUhLu7u6MGDFCZzn9vXv3GDRoED/99BMGBgZ07NiRBQsWYGlpqdbZsGEDs2bN4tKlSxQqVAgvLy9mzpyJh4eHWic8PJxhw4Zx7tw5SpQoQWBgIL1799bp76JFiwgODiY+Pp6qVauyYMECdeRJH7J8Pm96cvvQ2+5CJubODd52F4QQosDIyfJ5yT6fAxIICSGEEHmPZJ8XQgghhHgJCYSEEEIIUWBJICSEEEKIAksCISGEEEIUWBIICSGEEKLAkkBICCGEEAWWBEJCCCGEKLAkEBJCCCFEgSWBkBBCCCEKLkX8a0+fPlUmTJigPH369J1oR/r037bzLvZJnk369K618y72KT8/27vYp3fx2RRFUSQQygUPHjxQAOXBgwfvRDvSp/+2nXexT/Js0qd3rZ13sU/5+dnexT69i8+mKIoir8aEEEIIUWBJICSEEEKIAksCISGEEEIUWBII5QJTU1MmTJiAqanpO9GO9Om/bedd7JM8m/TpXWvnXexTfn62d7FP7+KzAWgURVH+dStCCCGEEHmQjAgJIYQQosCSQEgIIYQQBZYEQkIIIYQosCQQEkIIIUSBJYGQEEIIIQosCYREgfPs2TP27NnDN998w6NHjwC4ffs2SUlJb7ln767ExMS33QWRQ/LzXbDJ919/Egi9pqZNm2b5y+Hhw4c0bdo0x+08ffqU4OBgPvjgA2rWrEn16tV1jrdh8uTJPH78OFP5kydPmDx5sl5tff/999SrVw9nZ2euX78OwLx58/jxxx/17teVK1cYN24c3bp1486dOwD88ssvnD17NsdtXL9+nUqVKtGuXTv8/f3566+/AJg5cyYjRozIcTsJCQn07NkTZ2dnjIyMMDQ01DnehqCgIFasWJGpfMWKFcycOTPH7cycOZONGzeqX3fu3Bk7OzuKFy/OqVOncqWv+UViYiLffvstAQEB3Lt3D4ATJ05w69att9Kf3Pr5XrlyJZs3b85UvnnzZlavXq1Xn65du8bly5czlV++fJnY2Fi92nqXdOzYMcu/V7NmzaJTp05voUe59/2Hd/P7tnr1anbt2qV+PWrUKGxsbKhbt676++W1/OtsZQWURqNREhISMpUnJCQoRkZGOW6ne/fuStGiRZWBAwcqEyZMUCZOnKhz6OvgwYNKjx49lDp16ig3b95UFEVRvvvuO+XQoUM5bsPAwCDLZ7t7965iYGCQ43YWL16sFC1aVJk6dapibm6uXLlyRVEURVm5cqXSuHHjHLejKIoSHh6umJubK97e3oqJiYnaVlBQkNKxY8cct9OuXTvl448/VlJSUhRLS0u1nf379yvu7u45bqdly5ZK+fLllcWLFyvbtm1Ttm/frnPoa8+ePUrr1q2V9957T3nvvfeU1q1bK2FhYXq14erqqvz222+Zyn///XelVKlSOW6nVKlSaju//vqrYmNjo+zevVvx8/NTmjdvrlefctPx48eV06dPq19v375dadeunRIQEKCkpKTo1VZMTIzy5ZdfKl27dlV/1n/++WflzJkzOW7j1KlTSrFixRR3d3fFyMhI/Vn68ssvlZ49e770WltbW+Wvv/5SFEVRbGxsFFtb22wPfeTWz3eZMmWUffv2ZSoPDw9XypYtq1efGjZsqKxatSpT+ffff680atTopdf+73//y/HxKj/++GOOj5woWrSozs9jhtOnTyv29vY5auNFufHznVvff0X5d9+3f/rll190fgctWrRIqVKlitKtWzfl3r17OW6nbNmyyt69exVFUZTDhw8rhQoVUr755hulTZs2OfoZyI4EQno6deqUcurUKUWj0Sj79+9Xvz516pRy4sQJZfr06Yqrq2uO27OyslIiIiJypW9btmxRzM3NlX79+immpqbqX4KFCxcqrVq1ynE7Go1GuXPnTqbyvXv3KkWLFs1xO56ensq2bdsURVF0/lJGR0crdnZ2OW5HURSlTp06ypw5czK1dfToUaV48eI5bqdIkSLKhQsXMrVz7do1xdzcPMftWFpaKidPnsxx/Zf5+uuvFSMjI6Vr167K/Pnzlfnz5yvdunVTjI2NlUWLFuW4HVNTU+Xq1auZyq9cuaKYmprmuB0zMzPlxo0biqIoyueff64MGDBAURRFuXjxomJjY/PK61/1i/11f8nXrFlT2bJli/pMZmZmSrdu3RR3d3dlyJAhOW4nt4LqZs2aKSNHjlQURfdn6bfffnvlvwGrVq1Snj59qijK8/8YrFq1KttDH7n1821qaqpcu3YtU/m1a9cUMzMzvfpUuHBh5fLly5nKL1++rFhbW7/02t69e+f4eBWNRpOjI6f/2TMzM1M/6xedP39e789IUXLn5zu3vv+K8u++b/9UsWJFZdeuXYqiPA8UTU1NlYCAAKVOnTo5+t5lMDc3V65fv64oiqKMGjVK/Q/HmTNn9Prd9E8SCOkp4y+KgYFBln+JChUqpISEhOS4PU9PT+XUqVO50reqVasqq1evVhRF9y/BiRMnFAcHh1den/ELzMDAINMvMysrK8XAwED57LPPctwfMzMzJTY2NlN/Ll26pPc/FBYWFuov+X/+Bdfnl7yNjY1y9uzZTO0cOnRIr//FeXp6KidOnMhx/ZcpXry4snDhwkzlixYtUpydnXPcjru7u/L9999nKv/uu+8UNze3HLfj5OSkjgiVLVtW2bRpk6IoinLhwgWlcOHCr7z+xV/ic+bMUWxtbXWCvK5duyq2trbK3Llzc9wnRXn+n4aYmBhFURRlxowZSosWLRRFUZSIiAilRIkSOW4nt4LqF/vzYjuxsbF6/Uzmptz6+XZxcclyZGT79u16fUaK8vxzyurvyrFjxxRLS0u92nqX1KpVS5k0aVKm8gkTJijVq1fXu73c+PnOre9/Rn9y6/tmYWGhBtYTJkxQ/8Nx/PjxHP1uylCsWDG1T1WrVlW+++47RVGej/BaWFjo1acXSSCkp9jYWOXatWuKRqNRIiMjldjYWPW4ffu28uzZM73a+/nnn5WWLVuqAcO/YW5urv6wvfiXIKcjAqtWrVJWrlypaDQaZf78+Tq/0NatW6ccPnxYr/54enqqr4le7M+CBQuUatWq6dVW8eLF1V/OL7a1detW5b333stxO507d1b69++vtnP16lXl0aNHStOmTfX6n8nu3buVFi1aZPm/Zn1ZWFhk+T+vS5cu6fWXe+bMmYqdnZ2yYsUK9WcyJCREsbOzU6ZPn57jdvz9/RVXV1fF29tbsbOzUx49eqQoiqKsX79e7+9bhw4dsgzyFi5cqLRr106vtgoXLqxcunRJURRF8fb2VubNm6coiqJcv35dr8A6t4LqF/9RfrGdX3/9Va/ALLdeRStK7v18jxo1SnF1dVX27dunPHv2THn27Jmyd+9exdXVVfniiy/06tOHH36odOrUSeffxmfPnikdO3ZUWrZsqVdb75IdO3YoRkZGiq+vr/rvZM+ePRUjIyN1JFwfufHznVvff0XJ3e+bra2tGqDVq1dP+eabbxRF0X+kqnv37kr16tUVPz8/pVChQsrdu3cVRXn+2rNChQp69elFEgi9ZXfu3FEaN26sGBgYKJaWlv/q1YGbm5s6r+TFf5hXr16teHp65rid8PBwJTU1Va97Z2X58uVK8eLFlQ0bNigWFhbK+vXrlalTp6p/1scXX3yh1K9fX4mLi1OHbCMiIpT33ntPr7lUf/75p1K+fHnF09NTMTIyUurUqaPY2dkp5cqVy/KX0Yv+OUpmYmKSK9+3bt26KbNmzcpUHhwcrHTp0iXH7Wi1WmXUqFGKmZmZOmpZqFChLP/X+jKpqalKcHCw8vnnn+v8j3Du3LnK8uXL9WoruyDv8uXLev8PrkmTJoqvr6/y3XffKcbGxmq74eHher2Ozq2g2s/PT2nfvr2Smpqq/tK5fv26Uq1aNb1e1WU33/DWrVt6j5z+m5/vF6WkpCidO3dWNBqNYmxsrBgbGyuGhoZKnz599J6PdfbsWcXOzk4pXbq0+hqrdOnSSrFixZTo6Gi92tq8ebPSqVMnpXbt2kq1atV0jleZP3++8uTJE/XPLztyaufOnUrdunWVQoUKKXZ2dkqTJk2U8PBwvZ4pQ278fOfW919Rcvf71qZNG8XHx0eZPHmyYmxsrM5f3b17t1KmTJkct3P//n3F399fadu2rfLLL7+o5ePHj1emTp2qV59eJElX/4XLly+zf/9+7ty5g1ar1Tk3fvz4HLXh7e3NjRs38PPzw8HBAY1Go3O+V69eOe5PUFAQa9asYcWKFTRv3pyff/6Z69evM2zYMAIDAxk8eHCO29JqtcTExGT5bA0bNsxxO2vXrmXixIlcuXIFAGdnZyZNmoSfn1+O2wBITU3F39+fVatWkZ6ejpGREenp6XTv3p1Vq1bptVLr2bNnbNiwgdOnT5OUlET16tXp0aMH5ubmL71On9Uy+nzfpk6dyuzZs6lXrx5eXl4A/P777/z222988cUXWFlZqXU///zzV7aXlJTE+fPnMTc3p0yZMrmSnfl1ubq68vnnn/PFF1/olM+ZM4cFCxbotdLj9OnT9OjRgxs3bjB8+HAmTJgAwODBg/n7779Zt25djtoZMWIER48eZfPmzZQtW5YTJ06QkJCAr68vvr6+aruv8uDBAz766COOHTvGo0ePcHZ2Jj4+Hi8vL37++WcsLCxeev2CBQsAGDZsGFOmTMHS0lI9l56ezsGDB4mNjeXkyZM56k+G1/35zsrly5eJiorC3NycSpUq4erqqncb8Hz59qJFizh16hTm5uZUrlyZQYMGUaRIkRy3sWDBAr788kt69+7NsmXL6NOnD1euXCEyMhJ/f3+mTZv20uvd3Nw4duwYdnZ2uLm5ZVtPo9Fw9erVHPcrt+TWz3dufv9z4/sGcOPGDT777DP+/PNPPv/8c/Xf/2HDhpGenq7+XXhbJBB6TcuXL+fTTz+laNGiODo66gQwGo2GEydO5KidQoUKceTIEapUqfKv+6QoCtOnTycoKEhd/m5qasqIESOYMmVKjtv5/fff6d69O9evX+efPx4ajYb09HS9+/b48WOSkpKwt7fX+9oX3bhxgzNnzpCUlES1atUoU6bMv2rvXfCyf5Rf9F/9A/3dd9+99Lyvr2+O21q1ahX9+vWjVatW1K5dG4CjR48SGhrK8uXL6d2797/pKvB8CwpDQ0OMjY1zVD83g2qAiIgInV863t7eObou4/t+/fp1SpQooXNfExMTSpUqxeTJk9XPraDz8PBgwoQJdOvWjcKFC3Pq1Cnee+89xo8fz71791i0aNF/2p/IyEj+r73zDovi+vr4d1GqSFFQEamiIE1BYkVFNHbBhr0hdkUi1sTYGzHBFg02VDB27IqNYkMsKCCIIlWwN5AgGmG57x/8dl6WXWBmd2Ax3s/z7CPM7p45u15mzr33nO8pLi6W+P+5ffs2atWqBScnJ17Ow3V8/5e5cOECNDU14ezsDADYunUrdu7cCWtra2zduhW6uroy2aWBkIyYmJhg+vTpWLBggVx2HB0d8ddff6Fdu3Y8eVZyoU9NTUV+fj6sra3FZppsaNWqFZo3b47ly5fDwMBAYpVKW1ubN1+rg9OnT7N+rZubG6vX1apVCy9fvpQI7N6/f48GDRrIFCzKy6dPn+Dn54fw8HCpK3lsg6iyF5PCwkIUFBRARUUFGhoajF4OW27fvo3Nmzfj0aNHAIAWLVpg1qxZCr/BZ2dnIyEhQeFBddeuXXHixAno6OjwYo+PlerBgwejTZs2Ete3devW4e7du1I1hiri+vXr2L59O9LT03H06FEYGhpi3759MDMzY25qlaGhoYFHjx7BxMQEDRo0wOXLl9GyZUukpKSgXbt2eP/+PWt/VqxYgblz50JDQ0Ps+OfPn/H777+z+p7atGmD+fPnY8iQIWLHjx8/jt9++w23b99m7Q+fJCcn488//xT7e5s5cyasrKw42dmzZw80NTUlNJGOHj2KgoICTqveQIkO3J49e5CWloZNmzahQYMGOH/+PIyNjWFjY8PKhp2dHX777Tf06dMHCQkJ+OGHH+Dr64vIyEhYWVlhz549nHwSQQMhGdHS0kJcXBzMzc3lsnPp0iUsX74cq1evhp2dnUTUX3pbpDImTJiATZs2oW7dumLHP336BG9vb6lie9KoU6cO4uPjYWFhwfrcIhwcHCQCp/KobNXM19eX9XnXr19f7nNKSux0Q7msdikpKeHVq1cSgdCLFy/QtGlTfP78mZUdPhkxYgSuXr2KMWPGSA1gfXx8ZLadkpKCadOmYd68eejZs6e8rkrg5+eHqVOnVhgMKCkpVTi2ZA0+hUIhEhISYGJiUumMkssSfkXbmHyN7bLwtVKtr6+PiIgI2NnZiR1PSEhA9+7d8fr1a9Y+HTt2DGPGjMGoUaOwb98+JCUlwdzcHFu2bEFoaChCQ0NZ2TE3N8exY8fg4OAAJycnTJo0CVOmTMGlS5cwfPhwTgE6HxMZTU1NPHjwQOIekJGRAXt7e0bVmS18jO9jx45h+PDhcHJyEttmv3v3Lg4dOoTBgwez9qd58+bYvn07unbtKnb86tWrmDx5MpKTk1nbunr1Knr37o2OHTvi2rVrePToEczNzeHn54eYmBiEhISwsqOpqYnExESYmppi2bJlSExMREhICO7fv48+ffrg1atXrH0qTW2Z3kWBh4cHLl26hKlTp8plp1evXgCAbt26iR0nhHDehgoKCoKfn59EIPT582cEBwezDoTatm2L1NRUmQKhAQMGMD9/+fIFf/31F6ytrcX+KB8+fIjp06dXaqtsbsT9+/dRVFQES0tLAMCTJ09Qq1YttG7dukI7ZWfF8iC6EQoEAuzatUtqXgebmZevry9WrlyJOnXqVHpTZHsjPH/+PM6dO4eOHTuyej0XmjVrBj8/P4wePRqPHz/m3f6aNWswdOjQCgOhEydOiP1eWFiI2NhYBAUFYfny5azP9dNPP8HOzg5eXl4QCoXo0qULbt68CQ0NDZw9exYuLi7lvnfDhg2sziEQCCoMhPga22VZtWoVVq9eLfdKdX5+PlRUVCSOKysrIy8vj7NP27Ztw9ixY3Ho0CHmeMeOHbFq1SrWdlxdXXH69Gk4ODjA09MTs2fPRkhICGJiYjBo0CBOPomur2WJj49nnf+iqqqK169fSwRCL1++RO3a3G+tfIzv+fPn4+eff5boALB06VLMnz+fUyCUlZUlddvexMQEWVlZrO0AwMKFC7Fq1Sr4+vqK3Z9cXV05bWmqqKgwaR9hYWHMNn29evU4j8vS0EBIRiwsLLB48WLcunVL6koOm6RWAIiMjJTbl7y8PJCSCkD8888/UFNTY54TCoUIDQ3llJvj7e2NOXPm4NWrV1I/m729fbnvLZ1oOnHiRMyaNUsiP2np0qXIzs6u1I/S38369etRt25dBAUFMbP2nJwceHp6olOnTqw+F1CS/zJs2DCJBOKvX7/i0KFDlea/iG6EhBBs27ZNal7Htm3bKvUjNjYWhYWFzM/lwXZ1DSjZ0uKaxMiF2rVr48WLF1Vim83CtLu7u8SxIUOGwMbGBocPH2adgB8SEoLRo0cDAM6cOYP09HQ8fvwY+/btw6JFixAVFVXuezMyMlidozKqYmyL3sdHewc7OzscPnxYYovo0KFDsLa25mQrOTlZaoGFtrY2px52O3bsYCY1M2bMQP369XHz5k24ublhypQprGzo6upCIBBAIBCgefPmYn9fQqEQ+fn5rCe3PXr0wM8//4xTp04x6QK5ubn45Zdf8OOPP7L+XCL4GN8vX76Ueg0bPXo0fv/9d07+NGjQAA8ePICpqanY8fj4eNSvX5+TrYSEBKnJ3g0aNMC7d+9Y23F2doavry86duyIO3fuMK2Anjx5giZNmnDySQyZ682+c0xNTct9sBWv+/r1K3F1dWW0I2SltMijtEetWrU4lRaWp7bKRXWVkBJBLmmf7cmTJ0RLS4u1HUIIady4sdT2BwkJCcTAwIC1Hb40W1xcXDhJw1cH+/btI0OGDCGfPn2Sy07ZdgMnT54kAQEBxMbGpsp0X0qXsXMlLS2NUym+qqoqyc7OJoQQMmnSJKbUPT09nZVgpIjly5dL/a4LCgo4SRbwNbYJIWTChAkkICCA03ukwadGDl+yHnzAp1bas2fPiLm5OdHW1iYuLi7ExcWF6OjoEEtLS0aZnQ+4jO/evXuT3bt3SxzfvXs3I9DIFj61pPiSrHj69Cnp27cvsbe3J7t27WKO//TTT8Tb25uTT6WhK0IywsfMUFlZGQ8ePJDbTmRkJAghcHV1xbFjx8RWBVRUVGBiYoLGjRuztsfXrFddXR1RUVESSahRUVFiq1ZsyMvLYxoIlubt27ec9uJJOUviz54945QEzsdKHt/4+/sjLS0NDRs2hKmpqcRKHtv8kNLbm0DJqpS+vj5cXV3h7+/Pl7u88PnzZ2zevBmGhoas39OwYUMkJSXBwMAAFy5cQEBAAICSykYuFWPLly/H1KlTJRJuCwoKsHz5ctaJyXyNbYC/ler+/fvj5MmTWLNmDUJCQpjS6bCwMHTp0oWTT5MmTYKPjw92794NgUCAFy9eIDo6GnPnzsXixYs52crJyUFgYCCTCGxtbQ1PT0/WK6GiBF8zMzN06NBBrkosQ0NDPHjwAPv372fKyz09PTFixAjeKry4jm83NzcsWLAA9+7dYwpwbt26haNHj2L58uVihSOVFYasXLkSmZmZ6NatG7PVV1xcjLFjx2LNmjWcPsfw4cOxYMECHD16FAKBAMXFxYiKisLcuXM5VaEaGxvj7NmzEsfZblmXB02WVjCzZ8+Gqqoq/Pz85Lb19OlTGBkZsU4Ormr8/PywfPlyTJo0CW3atAFQUkG0e/duLF68GAsXLmRta+zYsbh+/Tr8/f3FbM2bNw+dOnWqVONHlMQdHx8PGxsbsT18oVCIjIwM9OrVC0eOHGHlT3l5PQKBAGpqarCwsIC7uzurCzRf1V6V5RGw1cdRBKXLoctDtK0hgvxvK1hDQwN///0364q/ZcuWYePGjTAwMEBBQQGePHkCVVVV7N69Gzt37kR0dDQrO0pKSnj9+jX09fXFjkdERGDYsGFSgxtpyDu2S1MT9XEIT7Ie165dg5ubG7S0tJjS9Hv37iE3NxdnzpzhpG9WWY6LsbExa1t8wcf4rorCkCdPnjCBnqxaUnxKVvBRfVYWGgjJCCEEISEh5ZapHj9+nJUdb29vBAcHo1mzZmjdurWECBuXihERBQUFyMrKwtevX8WOV5TbU5Z9+/Zh27ZtyMjIQHR0NExMTLBx40aYmZlJ3csujyNHjmDTpk1ipZw+Pj4YOnQoaxtAyWeaO3cudu/ezeTW1K5dG15eXvj9998rFa8TBQnLly/HnDlzxJKcRbk9gwcPlpogKo2uXbvi/v37EAqFEgmuVlZWSE5OhkAgwI0bNyrNqajKai9Z4KO0mCtsAqG9e/eKfTdKSkrQ19dH27ZtOeuHhISEIDs7Gx4eHkxuQVBQEHR0dCod36Ib1sePH6GlpVVunsnWrVtZ+SLv2K7JCIVCREVFwd7eHhoaGnLJetjZ2aF9+/YICAhgbpxCoRDTp0/HzZs3kZCQwNoWnxWISUlJUq+3bANzEXyO75qKvDpwfFWflYUGQjLi4+PDlBZKU4Rmq2dQtjSxNAKBABEREax9evv2LTw9PXH+/Hmpz7P94w4ICMCSJUvw008/YfXq1UhMTIS5uTn27t2LoKAghW4Lffr0iVGpbtq0KeebRFBQEIYNG8Z5a64sGzduxPXr17Fnzx5G4uDjx4+YOHEinJ2dMWnSJIwcORKfP3/GxYsXK7Slo6NTZdVesqAIjaQ+ffogMDAQBgYG5b4mKysLRkZGUm9gWVlZ1TaLDwoKAiEEEyZMwMaNG8W2VEVBtahKkgvyjm0+EQqF2LBhA44cOSL1Js+lVF1NTQ2PHj1iLRxaHurq6oiLi2MmHiKSk5PRqlUrTpIV8fHxYr+LKrTWr1+P1atXs6pCS09Px8CBA5GQkACBQMAk/IvGJ9e/Ez7Gd3p6utySLqV59uwZTp8+LXUMyDJJl5f27dvDw8ODqT4TTZ7u3LmDQYMG4dmzZ7IZljm76DtHV1eXnDt3TtFuiDFy5EjSsWNHcvfuXVKnTh1y6dIlsm/fPmJpaUnOnj3L2k6LFi2YhMjSiW0JCQmkfv36VeH6N0fjxo2ZJoKlSUxMZDrG37t3j9X3ZWpqSpKSkmTyQ1dXl7x9+5YQItkLTdb+ZwKBgLx580bieHh4ONHT0+PsY2pqKlm0aBEZPnw4k6geGhoqNUG4IvhsTnrlyhXSr18/0rRpU9K0aVPSv39/cu3aNc42+OjJJy+zZ88m+fn5zM8VPdiyePFiYmBgQP744w+ipqZGVq5cSby8vEj9+vU59eIihJDWrVuTsLAwTu+RRocOHaQmap84cYK0bdtWbvuElPQO69KlC6vX9uvXj7i7u5O3b98STU1NkpSURK5fv07atGnDeSwRws/4FggExMXFhezbt4/pqyYrYWFhRENDg9ja2pLatWuTVq1aER0dHaKtrU26du1a6furYlzy1TC5LDRZWka0tbV5jbxTU1ORlpaGzp07Q11dvdyk3oqIiIjAqVOn4OTkBCUlJZiYmODHH3+ElpYW1q5di759+7Kyk5GRAQcHB4njqqqq+PTpE2t/+JxVAkBMTEy5tiraiqxXrx6ePHkCPT09iX34srD16ePHj3jz5o3Ettfbt28ZPQsdHR0JP6WxcuVKLFmyBEFBQRLbUZWxYcMGRpdj48aNnN5bFj5Li0WUXcpevXo1GjRogPj4eAQGBnJayiblLF7n5+dzWuH7+++/4enpiUGDBjHJw1FRUejWrRv27t2LkSNHlvvevLw8ZgXQwcEBnz9/LnclgosYqjxUhRTD/v37sXPnTvTt2xfLli3DiBEj0LRpU9jb2+PWrVusk66BEh0hUT6QtO3/ir6n0sUks2bNgo+PD1JTU8USgbdu3cpLjiUAWFpa4u7du6xeGx0djYiICOjp6UFJSQlKSkpwdnbG2rVrMWvWLM494vgY3/fv38eePXvg6+uLmTNnYtiwYfDy8mJyz7jw888/Y+7cuVi+fDnq1q2LY8eOoUGDBhg1ahSjf1cRVTEudXR08PLlS4nVxdjYWE4FExLIHEJ95+zdu5cMHz6cFBQUyGXn3bt3xNXVlSlNF0W4np6exNfXl5OtunXrkoyMDEIIIcbGxuTGjRuEkJKyYHV1ddZ2WrRoQU6ePEkIEY+6N2/ezKrLswg+Z5UHDx4kysrKpF+/fkRFRYX069ePNG/enGhra5Px48dX+N69e/eSL1++EEII2bNnj1jJbNkHW0aOHEnMzMzI8ePHSXZ2NsnOzmZKQUePHs343Lp1a6nvb9WqlVjn7Lp16xJNTU1ia2vLuas2V9auXUtycnIkjvNZWiyiXbt2xN/fnxAiPpZu375NDA0NWdkQzRqVlJTIlClTxGaSs2bNIm3btiUdOnRg7ZOVlRVZv369xHF/f39iZWVV4XtLz9rLk63gKjNRE9HQ0CBPnz4lhBDSqFEjcu/ePUJISSk3V+mLsjIcXL6n0tIdFT24ft8fP34Ue+Tm5pJHjx6RYcOGkZYtW7KyoaOjw6xOmJubk4iICEJIyQool+st3+ObEEIKCwvJsWPHSP/+/YmysjKxsbEh/v7+Uld6y0NTU5OkpqYSQko+q2gFNy4ujpiYmHDyhy/mzJlDnJ2dycuXL0ndunVJSkoKuXHjBjE3NyfLli2T2S5dEZKRoUOH4uDBg2jQoIFcpcqzZ8+GsrIysrKy0KJFC+b4sGHD4Ovry6lc2dLSEsnJyTA1NUXLli2xfft2RuCvotyLsvj6+mLGjBn48uULCCG4c+cODh48iLVr12LXrl2s7fA5q1yzZg02bNiAGTNmoG7duti0aRPMzMwwZcqUSj9b6Z44fDT5BIDt27dj9uzZGD58OIqKigCUJLiOGzeOKeW0srIq9/sqW6JenZSn4sxnabEIPoTURLNJQggSEhLEEtpVVFTQsmVLzJ07l7VP6enp6N+/v8RxNzc3/PLLLxW+NyIigqkE3LNnD4yMjCQqXoqLizkr79Y0mjRpgpcvX8LY2BhNmzbFpUuX4OjoiLt370qIkVaGPDmFfEl5lEVHR0diJYIQAiMjIzH164qwtbVFfHw8zMzM0LZtW6xbtw4qKirYsWMHp90Cvsc3UHItGjRoEPr27Yu//vqLWd355ZdfMHToUPz222+VXjfr1KnDrGgbGBggLS2NqcriIoIIlKygC4VCiSraDx8+oHbt2qxXT9esWYMZM2bAyMgIQqEQ1tbWTPXZr7/+ysknMXgK1L47PDw8iJ6eHpk6dSpZunQpWbZsmdiDLQ0bNiRxcXGEEPEZM1eROEJKBPX27NlDCCEkJiaG6OnpESUlJaKmpkYOHTrEydbff/9NLCwsmBmXoaGhmIAVG/icVWpoaDCrXfXq1SMPHjwghBCSlJREGjVqxNoOn3kmhBDyzz//kPj4eBIfH0/++ecfzu9XBFzECz9//iwxe+YCX0JqhBAyfvx4zueXRtOmTcm2bdskjgcEBBALCwvWdvgeS7IycOBA1g+2LFiwgKxevZoQQsihQ4dI7dq1iYWFBVFRUSELFiyoks8xbdo0Jt9NHvr06UNevHhR4WuuXLki9rh27Rp59OgRKSwsZH2eCxcukGPHjhFCCElJSSGWlpZEIBAQPT09Eh4eztlvvsY3IYTcvXuXTJs2jejq6pImTZqQRYsWkfT0dHLt2jXSrVs38sMPP1Rqw93dnezYsYMQUrISY2FhQVatWkUcHR1Jt27dOPnTq1cvsnXrVonjAQEBpHfv3qxsFBcXk6dPn5KCggKSlZVFzp07Rw4fPiy3IDEhJW0ZKDKgoaFBrl+/LrcdTU1N5j+y9I3i7t27pF69enLZ/vTpE7l3755cF5dPnz5JvdizoXnz5uTWrVuEEEI6duxI1q5dSwgpubDq6+tzsmVoaMgEP3Z2duTAgQOEEEJu3rzJKagSCARSP8/z58+JmpoaJ5/4Iisri1E6JqRk28jHx4ds3769Ss5XWSD06dMnMmPGDKKvry9164cLVbWULQ9//fUXUVFRIVOnTiXBwcEkODiYTJkyhaiqqkoNkMqjvKTyzMxMoqGhwafLFTJ+/HjWD1mJjo4m/v7+5PTp0zx6Lk7dunVlVhcvjTwq5WVhE1SV5v3796S4uFjsWHZ2NhEKhbz4Uxn+/v7E1taWKCsrE3d3d3LmzBmJc2dnZ5NatWpVaistLY3Ex8cTQgjJz88nU6ZMIXZ2dmTQoEEkMzOTk1+6urpSC0IePXrE+j4nFAqJsrIyL4FPWejWmIwYGRnxkgzZqVMnBAcHM8JiItXNdevWVVhaX5bCwkJYWVnh7NmzzBabhoYGHB0d5fJPQ0ODcwKviIEDByI8PBxt27aFt7c3Ro8ejcDAQGRlZWH27NmcbHXu3BmXL1+GnZ0dPDw84OPjg4iICFy+fFmiYa00+GqWKoIvEUQAGDlyJCZPnowxY8bg1atX6N69O2xtbbF//368evWqSnR7KmLevHmIjIxEQEAAxowZg61bt+L58+fYvn0756RUvpeyZU2YL820adPQqFEj+Pv7MwKaLVq0wOHDh1lpZInENAUCARYvXiz29yEUCnH79m20atWK5SeSH7ZSHaWJioqCk5MT622udu3aMQnKpenbty927drFaeu9PEgNVHK5du0ap7J8aQKq1tbWiIuLY7VdJu/4DggIwIQJEzB+/Phy/08aNGiAwMDASm2V9rdOnTrl9lA8ePAg3NzcKpR7+Pfff5kUgtIUFhay/n6VlJTQrFkzvH//nrP+UKXwHlp9J5w9e5b07NmT2a6RlYSEBNKgQQPSq1cvoqKiQoYMGUJatGhBGjZsyCSqsaVx48Yyl2GX5t27d2T69OmkRYsWpH79+jKXYZdFnlnl+/fvyfPnzwkhJTODtWvXkv79+xNfX19WPb9EfeAEAgExMjIS6w3XvHlz0qNHD2b1ig3Dhw8nBgYGZP78+WTDhg1k48aNYg8u6OjokMePHxNCCNm0aROTGHnx4kXWfeu4UNmM2cjIiERGRhJCCLOKQwghwcHBrJexy/L06VO5l7LlSZjnE1FfKYFAQDp06MD87uLiQnr06EEmT55cJbNWPqmJqy982appPrG1wcf4zsjIkLr6JNpWqgrYjCUXFxcyc+ZMiePTp08nzs7OrM91+vRp4uzsTBISEjj7WRF0RUhGRo8ejYKCAjRt2hQaGhoSiaVsy7C1tLTw6NEjBAQEoG7dusjPz8egQYMwY8YMpvSQLTNmzMBvv/2GXbt2ibWQ4MqYMWOQmpoKLy8vqWKRbCgsLMSUKVOwePFiptSxvFllZRQVFeHs2bPo2bMngJKZAZf2HMD/J1127doVx48fl1up9fz587yJIBYWFjIz87CwMEaR1srKCi9fvpTbPlc+fPjAzAa1tLSYsezs7Ixp06bJZNPY2FhuwUN5EualERMTI9azqnXr1qzeJ0r+9fT0xKZNm6qtTJ5PSA1cffne4WN8N23aVKoY6ocPH2BmZlYlYqhsxtKqVavQvXt3xMfHMyv44eHhuHv3Li5dusT6XGPHjkVBQQFatmwJFRUVqKuriz3PVZJFBA2EZERezRYRZmZmePnyJRYtWiR2/P3792jSpAmngXv37l2Eh4fj0qVLsLOzk1iqZLt1cP36ddy4cQMtW7Zkfe6yKCsr49ixY5ybKkqjdu3amDp1KnPTkge2FSxaWloVLmfr6uqybvRYGTY2Nti2bRv69u2Ly5cvM9ukL168QP369Xk5R2k6deokcQEpjbm5OTIyMmBsbAwrKyscOXIEbdq0wZkzZyQqzSpjwoQJFT6/e/du1rbS0tIYLSwVFRV8+vQJAoEAs2fPhqura6W91kQ8e/YMI0aMQFRUFPN5cnNz0aFDBxw6dIhpuVEZsmxJUSjlwcf4Li8o4aq1xTcdO3ZEdHQ0fv/9dxw5coRp4hsYGMhpm4uv+25ZaCAkI6VLsuWBz4Gro6ODwYMHy+2TlZUVp33x8hgwYABOnjzJOR9IGm3atEFcXJxMDf9kobJZjjwiiGX57bffMHDgQPz+++8YN24cE4CePn26UiE0kXgjG0QrF6GhoRW+ztPTE/Hx8ejSpQsWLlyI/v37Y8uWLSgsLOQsq5+TkyP2e2FhIRITE5GbmwtXV1dOtnR1dZlu7IaGhkhMTISdnR1yc3OZZp5smDhxIgoLC/Ho0SOmXUNycjI8PT0xceJEXLhwgZNfFAofyDO+S+etLVmyROF5a9Jo1aoV9u/fL5cNvu67ZaGBEA98+fJFIrGtsuXyqhi4bGeolSVK/vXXX1i4cCGWLFkCW1tbiW0/tlsBzZo1w4oVKxAVFSVVUZaLjtD06dPh6+uL7Oxsqba4NJTlA39/f6SlpaFhw4Zy6UgRQmBubo6srCwUFRWJbdlNnjy50iBLmh6KtHNw6TZdOnDt3r07Hj9+jHv37sHCwoLz93zixAmJY8XFxZg2bRqaNm3KyZa8CfMirl69ips3b4r1rLK0tMSff/6JTp06cfKJwh+jR4/mZZvxl19+4W21lg/YphbIM76rQouIT+TpXyjLZI8rNBCSkU+fPmHBggU4cuQI3r9/L/F8ZTcdRQ7c3r17V7jto6Ojg7y8PIkZO9cbamBgIHR0dHDv3j3cu3dP7DmBQMApEBo+fDgA8eBJ1OiQi098wZcgIiEEFhYWePjwocQSsampaaXvr4oGuMHBwRg2bBgTKJuYmMDExARfv35FcHAwxo4dK5d9JSUl+Pr6wsXFBfPnz2f9vi1btuDLly8AgEWLFkFZWRk3b97E4MGDOVWgGRkZSc2/EwqFaNy4MWs73zKy5P3JQ05ODgIDA5nt7RYtWmDChAliAUtAQIDE+06fPs36HKLcup9//llOb/8fPoIqtvlY8oxvrnlrz549Q+PGjaGkpMTKN3kp7zv4999/xe590mAz2RMh632Adp+XkRkzZiAyMhIrV66UWmI8atQoVnYUkXBZumuvNNq0aYPatWvDx8dHarJ0ly5dqsNNMZ4+fVrh83xvmVX2HfGJjY0NAgMDZUokrwqqo/t8aGgoxo0bh7dv38ptiyunTp3CmjVrsHXrVjg5OQEoSZz29vbGggULFKr6LQ+EEGRnZ6NBgwaVbqvzNb7Xrl2LadOmVZg7du3aNbi5uUFLS4v5vu/du4fc3FycOXMGnTt3Lve9bG/UbCZDsgRVfJCdnY3GjRtLKJArkspyILlga2uL8+fPw8jISOI5kWzJ7NmzsXLlSqmyJZmZmRX2Irt69Srzc2ZmJhYuXIjx48ejffv2AEp6vgUFBWHt2rUyb53RQEhGjI2NERwcDBcXF2hpaeH+/fuwsLDAvn37cPDgwUrzMBRJZRdBDQ0NxMbGim0dyIJo+68sAoEAampqsLCwgLu7e41axhbB5kKRm5uLkJAQpKWlYd68eahXrx7u37+Phg0bcmoAeObMGaxbtw4BAQGwtbWV2/eCggKpOiRst7WUlJTw+vVr6Ovrix2Pj49H165dOVVmlB0DhBC8fPkS586dw7hx47BlyxbWtvgK0HR1dVFQUICioiKmulL0c9ktV1mrUBRBcXEx1NTUpK4uykJKSgoiIyOl6mRx0bays7ND+/btERAQwAQDQqEQ06dPx82bN5GQkCC3r2zgI6gaNGgQ6/OxLU4RUR0TEBFsAmFzc3PcvXtXomAjNzcXjo6OrLTSRBXDT58+RZMmTcSCQRUVFZiammLFihVo27YtK7+7deuGiRMnYsSIEWLHDxw4gB07duDKlSus7JSFbo3JSFWUGNcUnJyckJ2dLXcgFBsbi/v370MoFDK2njx5glq1asHKygp//fUX5syZgxs3bkh0cS9LebO50kFV2Y7E8lDZ/ODBgwfo3r07tLW1kZmZiUmTJqFevXo4fvw4srKyEBwczPpcfJWEvn37Fp6enjh//rzU5yu7kDo4ODDd57t16yYmwSAUCpGRkcGq63Rpys70lJSUoK+vD39//0orysoiz/J6aaqq8kTR8Ck4t3PnTkybNg16enpo1KiR2KqwKK+RLampqQgJCRG7CdaqVQu+vr6c/k7kpWwwJwva2to8eCIdvsY3X2RmZkq9Zvz77794/vw5Kxt8y5ZER0dLFXZ0cnLCxIkTZbZLAyEZ4bPEuKbh7e0NHx8fzJs3D3Z2dhKJwGxXFkSrPXv27GG2/j5+/IiJEyfC2dkZkyZNwsiRIzF79mxcvHixQlsDBgxgcoJKUzpPyNnZGSdPnmT9xyayJW3/+fz58xWu6vj6+mL8+PFYt24d6tatyxzv06cPRo4cyer8Ivi6Mf/000/Izc3F7du34eLighMnTuD169dYtWoVq+a9oi2huLg49OzZU2wZWzR741qVyEcOE5+q4EVFRRAIBOjZsycaNmwot281DT8/P8ybN0/u1cVVq1Zh9erVWLBggdw+OTo6ilXoiXj06BFniY6rV6/ijz/+ENN/mjdvXrUluVeFZALfqvfyUnrSefHiRbHgTygUIjw8nFX+YmlKXwcquu5WhpGREXbu3Il169aJHd+1a5fUrTnW8CrP+B2xfv16smnTJkIIIZcvXyZqampEVVWVKCkpcVYWrm4qUwIVNVot/VBSUmL+ZUvjxo3Jw4cPJY4nJiaSxo0bE0IIuXfvHqlfv36ltsLCwkjbtm1JWFgYycvLI3l5eSQsLIy0b9+enDt3jty4cYPY2NiQCRMmVGpr165dxMbGhqioqBAVFRViY2NDdu7cyfpzEUKIlpYWo/xdWjk2MzOTqKqqcrLFF40aNSK3b98mhJT8HycnJxNCCDl16hTp2LEjazt79+4lX7584cWngoIC8unTJ+b3zMxMsmHDBnLx4kXWNvhWBVdXV+fcK+lbQUdHh6ioqDDNlmVVhZdXeVrUiDg+Pp4cOnSIGBsbk99//51cv36dXL9+nfz+++/E1NSUUzPoffv2kdq1a5OhQ4eSTZs2kU2bNpGhQ4cSZWVlsn//fs4+XrlyhfTr1480bdqUNG3alPTv359cu3aNsx154Xt8s6EitWtp13/RQ0VFhTRv3pycOXOG8zn5uO6eO3eOqKmpEVtbW+Ll5UW8vLyInZ0dUVNTI+fOnePskwgaCPFEZmYmOXbsGNOkriZTmeR7ZmZmhQ+21KlTh2nVUJrIyEiiqalJCClp7Fe3bt1KbdnY2DBdzEtz48YNYm1tTQgpCUiNjIwqtLN48WJSp04dsnDhQnLq1Cly6tQpsnDhQqKpqUkWL17M4lOVoK+vT+7fv08IEf8+L126RJo0acLajojU1FSyaNEiMnz4cKYpbGhoKElMTGRto27dukzLF2NjY3Ljxg1CCCHp6elEXV2dtR0+m8D++OOPJCAggBBCSE5ODmnQoAFp0qQJUVNTI3/99RcnWy4uLqzaqVRGly5dyIkTJ+S2UxPZu3dvhQ+2TJgwgfl/k4XSk6eKHlwmVlZWVmT9+vUSx/39/YmVlRUn//gKqo4ePUo8PDxI27ZtiYODg9iDK3yNbzawCXRNTU3Ju3fveDkfX9ddQkquTz///DMZOHAgGThwIPnll19IVlaWXP7RQKiKsbW1lfs/iS1du3YlOTk5Esc/fvxIunbtWi0+lGbkyJHEzMyMHD9+nGRnZ5Ps7Gxy/PhxYm5uTkaPHk0IKemv07p160ptqampSe0v8+DBA6ZrfGZmZqU3fD09PaZzfWkOHDjAamVKhJeXFxkwYAD5+vUr0dTUJOnp6eTp06fEwcGB+Pj4sLZDSMnMVF1dnXTv3p2oqKgwF6i1a9eSwYMHs7bj5ORELly4QAghpH///mTMmDHk2bNnZP78+cTc3Jy1HWdnZxIcHEwIIUzX+Pbt2xM9PT2yfPlyDp+MkPr16zPB3M6dO4m9vT0RCoXkyJEjnG9eZSkqKiKxsbGcbx6HDx8m5ubm5M8//yQ3b94UW734FiYy1cGaNWuInp4eGTduHPnjjz+YYEH0qIzKJlOyTKxUVFSYvnelSUlJ4bwKy0dQtWnTJqKpqUlmzpxJVFRUyJQpU0j37t2JtrY2+eWXXzj5Iw1ZxzcbKpsMf/36lbi6uvLWM4+v625VQQOhKobPBoCVIRAImNWE0rx+/ZrUrl27wveKonQ2D7b8888/ZOLEicxSvZKSElFRUSGTJk0i+fn5hBBCYmNjSWxsbKW2OnbsSHr16kXevHnDHHvz5g3p1asX6dSpEyGkZEWoefPmFdrR1taW+sednJxMtLW1WX+23Nxc0r17d6Kjo0Nq1apFjIyMiLKyMuncuTPz2djSrl074u/vTwgRHy+3b98mhoaGrO3s27eP7NmzhxBCSExMDNHT02O2SLhsQfDZBFZdXZ1p9ujh4UGWLVtGCCmZ1XFZpSKEEB8fH7Jr1y5CSMlNokOHDkQgEJS78lgefG391kTOnTvHBMOluXjxIgkNDWVtp/T2TNlHVTQCZkPTpk3Jtm3bJI4HBAQQCwsLTrb4CKosLS2Zm3vpv9vFixeTGTNmcPKHEP7GNxuysrJIUVFRha/R09PjLRCS57pbdqJS0UNWaLL0f4AHDx4wPyclJeHVq1fM70KhEBcuXKi0nJutdgoX8UJNTU3s3LkTGzZsYEotzc3NxZIB2apnBwYGwt3dHU2aNGGS4rKzs2Fubo5Tp04BKGlLUpnw2JgxYxAQECDRKmLHjh2stZ+AkuqRy5cv48aNG3jw4AHy8/Ph6OiI7t27s7YhIiEhAQcOHJA43qBBA7x79461ndGjRzM/t27dGk+fPsXjx49hbGwMPT091nb4bAJrYWGBkydPYuDAgbh48SKjWv3mzRvO2llHjx5lPuOZM2eQmZmJx48fY9++fVi0aBGioqJY2RFVsvwXWbhwIfz8/CSOFxcXY+HChejduzcrO3x+R5VVhrEV6JwzZw5mzZqFuLg4dOjQAUCJSv7evXuxadMmTj4ZGRkhPDwcFhYWYsfDwsJYJ91mZWUxfqirqzPtMcaMGYN27dpxkoYA+Bnfnz59gp+fH8LDw6XKHoiuw2w+4+jRoxEYGCh1PHFFnutuq1atxIpiRBApSdeySgzQQOg/gGigCAQCqf2b1NXV8eeff1Zog4/S0vLQ1NSUuwWGpaUlkpKScOnSJTx58oQ59uOPPzL6IGyDucDAQFy6dIkRMLx9+zaysrIwduxYMd0bNn21nJ2d4ezszPHTiKOjo4OXL19KlP/HxsZy0iMqi4aGBhwdHTm/j88msEuWLGEqA7t168aIoF26dAkODg6cbL1//x6NGjUCUCLI6OHhgebNm2PChAmcboTV1a9OEaSkpEiVorCyskJqaipne1+/fkVGRgaaNm0qJqfABR8fH7HfCwsLUVBQABUVFWhoaLAOhKZNm4ZGjRrB398fR44cAVCiUH348GG4u7tz8omPoKpRo0b48OEDTExMYGxsjFu3bqFly5bIyMhgrSZdGj7G98SJE3H16lWMGTMGBgYGcimIFxUVYffu3QgLC5Pa1ohr30FZr7ulg/LY2FjMnTsX8+bNExNU9Pf3l6gk4wINhP4DiP7wzM3NcefOHTEhPBUVFTRo0KBGqZrKipKSEnr16lWhlo2dnR1CQ0PLnfEkJiYywUFaWhoAQE9PD3p6ekhMTGReJ+0CIipzZQPX9iELFizA0aNHIRAIUFxcjKioKMydO5dTOwu+Or3L0wS2LEOGDIGzszNevnwpVirdrVs3DBw4kJOthg0bIikpCQYGBrhw4QLTkqGgoIDT+OZrhaImoq2tjfT0dIny5tTUVIkbWUUUFBTA29sbQUFBAEr0v8zNzeHt7Q1DQ0MsXLiQta2yjXeBkoBt2rRpmDdvHms7ADBw4EDO40YafARVrq6uOH36NBwcHODp6YnZs2cjJCQEMTExnIQXRfAxvs+fP49z586hY8eOnM9fltLXStHkUwTXAEue627piYuHhwc2b96MPn36MMfs7e1hZGSExYsXy6wKT5Wlq5jqbNUgD1V1k69uqvL7ZivYKBAIWKmuivj69StmzJiBvXv3QigUonbt2hAKhRg5ciT27t3L+iJY9gZRttM7F6VboVCIvLw8MU2mzMxMaGhoMMq3lTXvlUZeXh4iIiJgaWmJFi1asH4fACxbtgwbN26EgYEBCgoK8OTJE6iqqmL37t3YuXMnoqOjWdkpqzNVdoXiW1KTLsuUKVMQHR2NEydOME1tU1NTMXjwYPzwww/YtWsXKzs+Pj6IiorCxo0b0atXLzx48IDZhl62bFmFLRHYEhMTg9GjR+Px48esXj9x4kSMHj0aLi4ucp+bD4qLi1FcXMyslB06dAg3b95Es2bNMGXKFM4iiHyMbzMzM4SGhnL+2/pWUFdXx/379yU+36NHj+Do6IjPnz/LZJcGQlVMdQdCssriV9VNvrr5VgJPaWRlZSExMRH5+flwcHDgpU1C6U7vXBqcsoFNG5KhQ4eic+fOmDlzJj5//oyWLVsiMzMThBAcOnSIs0BjSEgIsrOz4eHhgSZNmgAAgoKCoKOjw3l7pDSlVyh69uwpsx1F8/HjR/Tq1QsxMTHM9/Ps2TN06tQJx48fZy32amJigsOHD6Ndu3Zif1OpqalwdHTk1BG8POLi4tC5c2fWttzd3XHx4kXo6+tj+PDhGDVqFOscw7LwEVRlZWXByMhIYhWD/K/nm7GxMWeb8o7vv//+G6dOnUJQUBA0NDQ4n788nj17BgCMT4rC0dERtra22LVrFxNofv36FRMnTkRiYiLu378vk10aCFUxBw4cgLu7O6dlaVmpTBZf1kHyLVFZIPTlyxf8+eef5QaLfH9HfDY3lJXk5GS4uLhwTnSuDDZBZ6NGjXDx4kW0bNkSBw4cwNKlSxEfH4+goCDs2LGDl5WFslS2PVoeXFcoaiqEEFy+fBnx8fFQV1eHvb19hY1NpaGhoYHExESYm5uL/T/Hx8ejc+fO+PjxI2tbZdvjkP/1m9uyZQuMjIzKbQkjjZycHBw9ehQHDhzA9evXYWVlhVGjRmHkyJGc1I75CKqqszdYaSoa3w4ODkhLSwMhBKamphJdAbhc34qLixlV+vz8fAAlf/Nz5szBokWLOHWu79q1a4XbaREREazs3LlzB/379wchhMk7FRULnT17lvPWvQiaIyQH4eHh5Wbni/IxuLZbkAc+ZfH/q3h5eeHSpUsYMmQI2rRpI1cyIRvYzDOEQiH27t1b7lhie5Eoj7S0NBQVFcllQ1Y+fvzINNW9cOECBg8eDA0NDfTt25dzfghbMjMzUVhYyPl9tWvXxosXL6rAo+pFIBCgR48e6NGjR7mvqSxYdHJywrlz5+Dt7c3YBEpaGYiSVNlSNm9DIBBAX18frq6urFq/lEZXVxeTJ0/G5MmT8ezZMxw8eBC7d+/GkiVLOI3xU6dOiQVV69ev5xxUla1iEpGfnw81NTUuH4sTFY1vWXNkpLFo0SKmakyUc3Tjxg0sW7YMX758werVq1nbKhtkFhYWIi4uDomJiZw6xrdp0wbp6enYv38/M2EZNmwYRo4cKddiAw2EZGT58uVYsWIFnJyc5M7O54ucnBx4eHjwYuvZs2c4ffq01C7mXKsFahJnz55FaGgoL8mEfOHj44O9e/eib9++sLW1lXksVdbpXREYGRkhOjoa9erVw4ULF3Do0CEAJWO1Km8WFVHRCkVNGhdVSWXB4po1a9C7d28kJSWhqKgImzZtQlJSEm7evImrV69yOlfpwF70M5fVBGkUFhYiJiYGt2/fRmZmpkx942QNqkR/ZwKBAIsXLxbbghIKhbh9+7bMW3bysnTpUt5sBQUFYdeuXYx8BlCSmGxoaIjp06dzCoQ2bNgg9fiyZcuY1Sa21KlTB87OzjA2NmbuTeHh4QAg5isXaCAkI9u2bcPevXsxZswYRbvC4OHhgUuXLmHq1Kly2QkPD4ebmxvMzc3x+PFj2NraMnkdspRj1yQMDQ3FmqTWBA4dOoQjR46IVULIAp+d3vnip59+wqhRo6CpqQljY2MmJ+PatWuws7NTiE98rlD8V3F2dkZcXBz8/PxgZ2eHS5cuwdHREdHR0TL9vwUGBmLDhg1ISUkBADRr1gw//fQT547hkZGROHDgAI4dO4bi4mIMGjQIZ8+elSobwhauQZXo74wQgoSEBLGkaBUVFbRs2RJz586V2R8+uHfvHtOY1sbGhrNUBQB8+PBBarNXKysr3goKRo8ejTZt2uCPP/5g9fr09HQMHDgQCQkJUrWFZN6OlFmK8TunXr16TNPNmoK8svgifvjhB7JkyRJCyP8rpv7zzz/Ezc2Nc3+o6mb//v0VKjuHhoaSXr16VVvTTTbK4gYGBkyD1G8Jto05Y2JiyPHjx8X+X86ePSu1dxwfcFFzFwqFRCgUVokfNZnqVLznq89U48aNiZqaGhkwYAA5evSo3I2BIyIiyMSJE4muri7R1tYmnp6eJCwsjBQXF7N6//jx48nHjx/l8kEWKvq/e/36NenatSsRCARMo12BQEBcXV3FVPnZ0KZNG+Lt7S1xfObMmaRt27Yy+V6W4OBgYmBgwPr1/fr1I+7u7uTt27dEU1OTPHz4kFy/fp20adNGroa5NFlaRhYsWABNTU0sXrxY0a4wVFT5xaXaq27duoiLi0PTpk2hq6uLGzduwMbGBvHx8XB3d0dmZiZPHnMjPDwcGzZsYGY6LVq0wE8//cRJzfnt27cYOnQorl27Bg0NDYlkQr5Lp9kkS/v7+yM9PR1btmypEVusbCkvWdrX1xcrV65EnTp1JLbrylIV26xskrj5WqH4VmHzHQmFQpw4cYL5e7O2toa7uztnYUV9fX1s3rwZI0aMEDt+8OBBeHt7s1ZP37lzJzw8PFhXvlWEoaEhPnz4gF69emHUqFHo378/JxmIslRnVVVF/3fDhg1Deno6goODmRLzpKQkjBs3DhYWFjh48CDr81y9ehV9+/aFsbGxmHhhVlYWzp8/j06dOrG2VVZXifxvOzomJgaLFy9mvaWnp6eHiIgI2NvbQ1tbG3fu3IGlpSUiIiIwZ84cmYsv6NaYjHz58gU7duxAWFgY7O3tJW6oisij4UsWv06dOszeq4GBAdLS0mBjYwMAnFo+8Mlff/0FHx8fDBkyhFGqvXXrFvr06YMNGzZgxowZrOyMGDECz58/x5o1a9CwYcMakSx948YNREZG4vz587CxsZEYS2z1f16/fo25c+cySddlz813FYuopUBZYmNjmfyTii5Migr6lixZgvXr18Pb21vsAj979mxkZWVhxYoVCvGrJvHw4UO4ubnh1atXsLS0BFAitKmvr48zZ87A1taWta3CwkI4OTlJHG/dujWnBOdJkyYBKNFFSktLQ+fOnaGurl5u0nJFLFu2TO6gis+qKr64cOECwsLCxHR2rK2tsXXr1gqT56XRpUsXJCcnIyAggAmGBw0ahOnTp6Nx48acbGlra4v9rqSkBEtLS6xYsYKTX0KhkElt0NPTw4sXL2BpaQkTExMkJydz8qk0NBCSkQcPHjAJcaWVMYHqvcCznX0LBALW+Q/t2rXDjRs30KJFC/Tp0wdz5sxBQkICjh8/zsijVzdr1qzBhg0bMHPmTObYrFmz0LFjR6xZs4Z1IHTz5k1ER0eLqRzLC5HS80bE+fPnK22ToaOjw4ta7vjx45GVlYXFixfLlcAvb0AVGRkp9efqYvv27RXmeQQEBGDnzp1iKxRubm6wt7eHt7c3DYRQorNjY2ODmJgYRoAyJycH48ePx+TJk3Hz5k3Wtvjq7/f+/XsMHToUkZGREAgESElJgbm5Oby8vKCrq8spv4uPoIrPqiqgRPF82LBhEitTX79+xaFDhxjF84rGd3FxscRECgCUlZVlaqNUv359uLm5oV27dsz7Y2JiALBPTBYKhfD09ISdnZ2EmClXbG1tER8fDzMzM7Rt2xbr1q2DiooKduzYIZ9EicybapQagYuLC8nJyWF+Lu/RtWtX1jbT0tKYTr75+flkypQpxM7OjgwaNKjacmvKUqdOHandop88eULq1KnD2o6DgwOJjo7mxaddu3YRGxsboqKiQlRUVIiNjQ3ZuXMnL7ZlQVNTk8TGxsptp1evXsTa2pr89ddf5MSJE+TkyZNiD0USFhZGfv75Z+Ll5UU8PT3FHmyRpxN2TScoKEhq7sy///5LgoKCmN8ry6VTU1MjiYmJEscTEhKImpoaJ59mzpxJtLS0iI2NDfHy8iJeXl7E1taWaGlpkZkzZ5LZs2czj4oYM2YM6dmzJ8nOzhbLk7lw4QKxtrbm5NO7d++Iq6srEQgERElJibHl6elJfH19WdkwMDAgp06dkjh+8uRJ0rhxY07+EEKIkpISef36tVRflZSUWNlwc3MjnTt3Js+fP2eOPXv2jHTp0oUMGDCAkz/nz58n+vr6RElJiQgEArEHW39EqKqqkvT0dE7vkcaFCxfIsWPHCCGEpKSkEEtLSyIQCIienh4JDw+X2S4NhCjfBCNGjCDr1q2TOP7777+TYcOGsbZz8eJF0qFDBxIZGUnevXtHPn78KPZgC18JoIQQEhgYyMtFokWLFuT+/fty2+EroOKbZcuWESUlJdKmTRvi7u5OBgwYIPZgi+jmW5Y5c+aQ6dOn8+lytcPHzZQQQuzt7aXeWMLDw4mtrS0nnyqaoHGZrDVs2JDExcURQsQThtPS0jhNhgjhJ6hSVVWVWuTw+PFjzsEiIYQIBAKpCc1xcXFEV1eXlY2srCzSqlUroqysTMzNzYm5uTlRVlYmDg4OJDs7m5M/FhYWZPr06eTVq1ec3ieN1q1bk7CwMLntSOP9+/esE9zLgyZLy0FMTAyOHDkiVWuHS1+nmoa5uTnu3r0r0Wk8NzcXjo6O1dZio3T/s7y8PPzxxx/o2LEjk9dx69YtREVFYc6cOfj1119Z2Sy9b196CZz8b0mcbR4NXwmgQEmibnp6OgwNDdGlSxd06dIFLi4usLCwYG0DKOno7u/vj+3bt3NS2S2LtbU19u/fL1PJbVViYGCAdevWyS1Z4e3tjeDgYBgZGUnthF16a+Fb08xSUlLC69evxRovA0B8fDy6du3KuhggNDQU8+fPx7Jly5jv6NatW1ixYgX8/Pzg7OzMvFZLS4u/D1ABdevWxf3799GsWTOxhOGYmBj07NkT79+/Z22rtOJ5aVvp6emwt7dnpW3Ttm1btG3bVqJPo7e3N+7evYtbt26x8sXBwQECgQDx8fGwsbERS0YXCoXIyMhAr169mOawlUEIQVhYGCM42KJFC04FJSK0tLQQGxvL9KyThwsXLuDnn3/GypUrpXayr64xVB40EJIR0Z5tz549cenSJfTo0QNPnjzB69evMXDgQOzZs0fRLsqMkpISXr16JSEd//r1axgbG+Pff/+tFj+qov9ZZWJwXbp0YWVHR0cHd+/elegH9uTJE7Rp0wa5ubms7Ih4/vw5rly5gmvXruHq1atISUmBgYEBXFxc8Pfff7Oyoauri4KCAhQVFclVEcdXQMU39evXx507d+S+MHft2pXV6wQCgdyq3tUF3zdTaRMGUiYXjuvkQV769OmD1q1bY+XKlahbty4ePHgAExMTDB8+HMXFxQgJCWFti4+gqryqquzsbISGhrKuqlq+fDnz75w5c6Cpqck8p6KiAlNTUwwePJhzE1d5mTBhAjp27AgvLy+5bfE1Aa0qaCAkI/b29pgyZQpmzJjB/CGZmZlhypQpMDAwYAb3t4RIcXfAgAEICgoSy/QXCoUIDw/H5cuX5crOrwlcv34d27dvR1paGkJCQmBoaIh9+/bBzMxMbKZbEd7e3lBWVpZYMZg7dy4+f/6MrVu3yuRbQUEBrl+/joMHD2L//v0ghLCurAkKCqrwebbq0nwFVHxTEyUragp830y5qEeznTzIy8OHD+Hq6gpHR0dERETAzc0NDx8+xIcPHxAVFcUpQOYjqMrKykLt2rWxdetWsdWX6dOno6ioiHPT1aCgIAwfPpxzGf/mzZsxefJkqKmpSaxOlWXWrFms7RYUFMDDwwP6+vqws7OTuA5wscXXBLSqoIGQjNSpUwcPHz6Eqakp6tevjytXrsDOzg6PHj2Cq6sr7w0uqwNR1C5S7CyNsrIyTE1N4e/vj379+inCPV44duwYxowZg1GjRmHfvn1ISkqCubk5tmzZgtDQUISGhrKyw+f2yqVLl3DlyhVcuXIFsbGxaNGiBbM91rlzZ7krLbjCV0DFNz4+PggODoa9vX2Nkayoach6M5VGbm4uAgMDxXSEvLy8JEqhq4PCwkL06tULa9euZRrK5ufnw9HRETNmzICBgQEne3wEVXw3Xc3OzoZAIGC0iO7cuYMDBw7A2toakydPLvd9ZmZmiImJQf369XnTkgNKtLamTp0KNTU11K9fX6KJNxdbhYWFUqvZgBJJFj09Pda2qgIaCMlIkyZNcP78edjZ2cHe3h4///wzRowYgejoaPTq1YtTd+aahpmZGe7evavwwVmaylpEiJrcVoaDgwNmz56NsWPHii2Jx8bGonfv3nj16hUrO3xur4haYcyZMweTJ0+WW9skNTVVavNWrh3IaxoVfeff0jZWVSLrzbQsMTEx6NWrF9TU1JiO3nfv3sXnz5+ZdhvVjb6+Pm7evCmxHc0VvoKq8lIInj59Cmtra3z69ImTX506dcLkyZMxZswYvHr1Cs2bN4etrS1SUlLg7e2NJUuWcLInL40aNcKsWbOwcOFCuTWRBg8ejJCQEAlpgtevX6Nbt24SEjTVDQ2EZGTkyJFwcnJidHz+/PNPuLu74/Lly3B0dPymk6WlkZuby4uiq6yU1dkpLCxEYmIicnNz4erqyvr71tDQQFJSEkxNTSWSJK2trfHly5eqcL9CNm7ciGvXruHatWtQVVVlVoNcXFzQvHlz1nZu3bqFkSNH4unTpxIrelz34YVCIU6ePCnWr8jNzQ21atVibYNS/fB1M+3UqRMsLCywc+dOJt+oqKgIEydORHp6Oq5du1aVH0Mqs2fPhqqqKvz8/OS2JU9QJdJr27RpEyZNmiS16WqtWrUQFRXFya6uri5u3boFS0tLbN68GYcPH0ZUVBTTP5LNCsyKFSswd+5cMZ8A4PPnz/j99985BVP16tXD3bt3eUmW/uGHH2Bvb4/AwEDm2MuXL+Hq6gobGxtO+V1Vglw1Z98x79+/Z7QahEIhWbt2Lenfvz/x9fUlHz58ULB38uHn50cOHTrE/D5kyBAiEAhI48aNmfLVmoBQKCSTJ08mv/32G+v3mJmZkcuXLxNCxEtwg4KCSIsWLarETy48ePCA/Pnnn2TgwIFEWVmZGBoasn5vy5YtiYeHB0lKSiI5OTkkNzdX7MGWlJQU0qxZM6KhoUEcHByIg4MD0dDQIJaWljWuvx5FHB0dHfL48WNCCCGbNm0iHTp0IISUyEaYmZmxtqOmpkYePXokcfzhw4dEXV2dH2c5ItIjat26NZk8ebKY/lBlGkRl+emnn8iCBQtk8kNU7i8QCEiHDh3EJAB69OhBJk+eLFWnqjLq1KlDMjIyCCGE9O/fn/j5+RFCCHn69Cnrcny+5BMIKfmOVq9ezek95fHmzRtiZWXF/D89f/6cNG/enHh4eNSIXn9UWVpG6tWrx/yspKSEhQsXKtAbftm2bRv2798PALh8+TLCwsJw4cIFHDlyBPPmzcOlS5cU7GEJSkpK8PX1hYuLC+bPn8/qPZMmTYKPjw92794NgUCAFy9eIDo6GnPnzuWUhPvlyxf8+eefiIyMlLoNdf/+fU6fhRCC2NhYXLlyBZGRkbhx4waKi4slyqArIiUlBSEhIZzL7ssya9YsNG3aFLdu3WLG+fv37zF69GjMmjUL586dk8u+PPxXJSv4orCwkMkPCgsLY9R/raysOOUtamlpISsrS6L7eHZ2NtPioLpJTExktuSePHki9hxXFfWioiLs3r0bYWFhUsu5K8o3E6mle3p6YtOmTbyVftvY2GDbtm3o27cvLl++jJUrVwIAXrx4ISFlUh6kHGXs+Ph4sXsWG4RCIdatW4eLFy/KnZOnr6+PS5cuMcUoZ8+ehaOjI/bv36+QViRloYGQHPxXtw9evXoFIyMjACUDdujQoejRowdMTU3Rtm1bBXsnTlpaGqd+RQsXLkRxcTG6deuGgoICdO7cGaqqqpg7dy68vb1Z2/Hy8sKlS5cwZMgQtGnTRq62Kv3790dUVBTy8vLQsmVLuLi4YNKkSejcuTOn7ci2bdsiNTVV7kDo6tWrYkEQUFK6XrqVgCKoTLKCws/NFChp3unl5YU//vgDHTp0AABERUVh3rx5EtpZ1QWf7Vr4CKr4lkj57bffMHDgQPz+++8YN24c0wbo9OnTTJ5Weejq6kIgEEAgEKB58+Zin0EoFCI/Px9Tp07l5E9CQgKjJcZHGykjIyNcvnwZnTp1wo8//oh9+/bVmCbTNEdIRlJTU9G3b188e/aMaUqYnJwMIyMjnDt3jpd9VUXRuHFjhISEoEOHDrC0tMSqVavg4eGB5ORk/PDDD8jLy6t2n8r2USP/61587tw5jBs3Dlu2bOFk7+vXr0hNTUV+fj6sra3Fyo3ZoK2tjdDQUF4Cg3nz5qFLly7o1KmTXBU5J06cwK+//op58+ZJLXe1t7dnZadevXo4e/YscwMUERUVhf79+yusfP6/KFnBN1euXMHAgQORl5eHcePGMUUEv/zyCx4/fsx61ezr16+YN28etm3bxkw0lJWVMW3aNPj5+fFSlUaRRCgUIi8vT6xSNDMzExoaGhJJ2aUJCgoCIQQTJkzAxo0bxa4jIvkEkdZRdSEKzspSUFAAVVVVsQUDRV1TRNBASEb69OkDQgj2798vsX2gpKSk0O0DeZk5cybOnj2LZs2aITY2FpmZmdDU1MShQ4ewbt06zts+fFC2YkhUaeXq6ooJEyaICchVB9bW1jh06BDr4KI6qGiJmUuy9NixY3H//n0EBgYyM9Hbt29j0qRJaN26Nfbu3cuHu5z5L0pWVAWy3kylUVBQgLS0NABA06ZNJZJwKTWLq1evokOHDuWWqlcnlclwlEZRkhwi6NaYjNTU7QM+2LBhA0xNTZGdnY1169YxqyUvX77E9OnTFeLTuXPnQAhh9vIzMzNx8uRJmJiYVHsQBAD+/v5YsGABtm3bBhMTE7ntXb16FX/88YeYZsu8efNYq9MCQEZGhtx+ACUCbePGjUP79u2ZC2pRURHc3NywadMmXs4hC7q6uvjnn38AAIaGhkhMTISdnR1yc3NRUFCgML9qGoQQ3Lt3D2lpaRg5ciTq1q0LFRUVmYIYDQ0N2NnZVYGXFABwdHREeHg4dHV1GXXw8mAzAS0tTPjlyxeJPLrqbGWh6OCGCzQQkhFVVVXmolya/Pz8apdC5xtlZWXMnTtX4vjs2bMV4E0JAwYMwKBBgzB16lTk5uaiXbt2UFZWxrt377B+/XpMmzatWv1xcnLCly9fYG5uLrf68t9//w1PT08MGjSIUWuNiopCt27dsHfvXowcOZKVHVFAlpSUJJFMLBAIWAdsOjo6OHXqFFJSUsQUc+XNPZKXzp074/Lly7Czs4OHhwd8fHwQERGBy5cvo1u3bgr1rabw9OlT9OrVC1lZWfj333/x448/om7duvjtt9/w77//Ytu2bYp2kVIKd3d3ZptxwIABctsrKCjA/PnzceTIEaltQhTZyqIma5zRrTEZqanbB3yRkpJSbkVUdQt7AYCenh6uXr0KGxsb7Nq1C3/++SdiY2Nx7NgxLFmyhFlJqS66d++OrKwseHl5oWHDhhIzOS6zoRYtWmDy5MkSgeb69euxc+dO1p8tPT0dAwcOREJCgpg6uMg3RffzkZcPHz7gy5cvaNy4MYqLi7Fu3TpGC+bXX3+tdgXumsiAAQNQt25dBAYGon79+oxO1pUrVzBp0iSkpKQo2kVKFTJjxgxERkZi5cqVGDNmDLZu3Yrnz59j+/bt8PPzw6hRoxTiF58aZ1UBDYRkJDc3F+PGjcOZM2eY1YDCwkK4u7tjz549ChUflJedO3di2rRp0NPTQ6NGjSSk1RWRI6ShoYHHjx/D2NgYQ4cOhY2NDZYuXYrs7GxYWlpW+9aIhoYGoqOjmcoOeVBVVcXDhw8lVlxSU1Nha2vLWuSxf//+qFWrFnbt2gUzMzPcvn0bHz58wJw5c/DHH39UuM0mEgatU6eORGJ6WWgri5pL/fr1cfPmTVhaWooJhmZmZsLa2ppuIf7HMTY2RnBwMFxcXKClpYX79+/DwsIC+/btw8GDB1m3EOKbVq1aoXnz5li+fDkMDAwkJo6KaNtSGro1JiOi7YPU1FRmxl4Ttg/4YNWqVVi9ejUWLFigaFcYLCwscPLkSQwcOBAXL15kVk/evHlTrfveIqysrPD582debBkZGSE8PFxi7ISFhTEyBmyIjo5GREQE9PT0oKSkhFq1asHZ2Rlr167FrFmzEBsbW+57Y2NjUVhYyPxcU/mvSlbwRXFxsdTZ9bNnzxSm/0Mpn/Iqq6TBZrv9w4cPMDc3B1CSDyR6j7Ozc7WnD5SGL42zqoIGQhyobKZcWufiW5415+TkwMPDQ9FuiLFkyRKMHDkSs2fPRrdu3ZhS0EuXLjFaF9WJn58f5syZg9WrV0stVecSnM2ZMwezZs1CXFycmGbL3r17OSUnC4VC5manp6eHFy9ewNLSEiYmJkhOTq7wvaXHLp96LXwiTbJi7dq1/wnJCr7o0aMHNm7ciB07dgAoWcHNz8/H0qVL0adPHwV7RynLxo0bebVnbm6OjIwMGBsbw8rKCkeOHEGbNm1w5swZhe5S8KVxVlXQrTEO8Nlosybj5eWFH374gbMAV1Xz6tUrvHz5Ei1btmRKxe/cuQMtLS0JBdyqpnSpeukZnUjZleue94kTJ+Dv7y+2ujhv3jy4u7uzttGpUyfMmTMHAwYMwMiRI5GTk4Nff/0VO3bswL1791g3NpwwYQI2bdoksYLw6dMneHt7s25wyzf/ZckKvnj27Bl69uwJQghSUlLg5OSElJQU6Onp4dq1a5zL5yk1Dz8/P0ydOlVqYLNhwwbUqlULs2bNQlhYGPr37w9CCAoLC7F+/Xr4+PhUv8PgT+OsqqCBEEWCtWvXYv369ejbt6/UQSuqbPqeuXr1aoXPly5j5YuDBw/Czc1Noh2AiIsXL+LTp08YNGgQUlNT0a9fPzx58gT169fH4cOH4erqyuo8tWrVwsuXLyVumu/evUOjRo04KXnzSZ06dXDr1i2Jcu74+Hh07NgR+fn5CvGrplFUVIRDhw7hwYMHTFf1UaNGQV1dXdGuUXhAS0sLcXFxzBZYRTx9+hT37t2DhYWFQoMNaRpnooIOmixNqZGYmZmV+5xAIGDVBfl74Pr169i+fTvS0tIQEhICQ0ND7Nu3D2ZmZkxPHT7hcgEU8eHDB9Z5CHl5eSCEQFdXFykpKWJ9zoRCIc6cOYOFCxfixYsXMvkvLzVV8ZpCqU5KJ8F/Kzx9+rTC5/nQYpMHmiNEkYAvYb7/MseOHcOYMWMwatQoxMbG4t9//wUAfPz4EWvWrKmS6gxZ5ixcGi3q6OiI9Ssqi0AgUGgbi379+mHy5MkSkhVTp05lmot+j5w+fRq9e/eGsrIyTp8+XeFrv+fv6Xtg1qxZsLCwkFi137JlC1JTU3nPSWKLogOdyqArQhQA7MunBQIB/P39q9GzmomDgwNmz56NsWPHis3QYmNj0bt3b7x69Yr3c1b1TPDq1asghMDV1RXHjh0TC6JUVFRgYmKCxo0bV8m52fBflqyQByUlJbx69QoNGjTgrc0KpeZS0XXA0NAQp0+fRuvWrcWO379/H25ubnj27Fl1uSkVaWKvgOIDdLoiRAHAvny6pnQLVjTJyclS1VC1tbWRm5tb/Q7xgCivKSMjA0ZGRhXeVBXBf1myQh5KC56WFT+lfF+8f/9eqiaPlpYW3r17pwCPSqjpYq80EKIA+DbKp2sSjRo1QmpqKkxNTcWO37hx45vau5eGaBm7oKBA6uytOpMuvxfJCgqFDywsLHDhwgXMnDlT7Pj58+cVel3y8fGBmZkZwsPDYWZmhjt37uD9+/eM2KuioYEQhSIDkyZNgo+PD3bv3g2BQIAXL14gOjoac+fOxeLFixXtnly8ffsWnp6eOH/+vNTnq3P2xlbc8Xteqdy8eTPr19KKz2+fTp06lVsB6Ovri5kzZ+Lt27dMlWh4eDj8/f0Vlh8ESIq9KikpsRZ7rQ5oIEShyMDChQtRXFyMbt26oaCgAJ07d4aqqirmzp0Lb2/vKjmniYmJhJRBVfDTTz8hNzcXt2/fhouLC06cOIHXr19j1apV1Z4fRlcnK2fDhg2sXicQCGggVMPIy8tj/VqRSGtFhRgTJkzAv//+i9WrV2PlypUAAFNTUwQEBGDs2LHyOSsH8oi9Vgc0WZpCkYOvX78iNTUV+fn5sLa2hqampkx2cnNzERISgrS0NMybNw/16tXD/fv30bBhQxgaGvLsdcUYGBjg1KlTaNOmDbS0tBATE4PmzZvj9OnTWLduHW7cuFGt/lAo/1WUlJQqXc2UVWvn7du3UFdXl/maxCd8ib1WFXRFiEKRAxUVFVhbW8tl48GDB+jevTu0tbWRmZmJSZMmoV69ejh+/DiysrIQHBzMk7fs+PTpEyOmqKuri7dv36J58+aws7NTSMNdCjsKCwthZWWFs2fPokWLFop2h8KCqlzxLK0Dpmh+/fVXfPr0CQCwYsUK9OvXD506dWLEXhUNDYQoFAXj6+uL8ePHY926dWJtLfr06YORI0dWuz+WlpZITk6GqakpWrZsie3bt8PU1BTbtm2DgYFBtftDYYeysjK+fPmiaDcoHOBDgd7R0RHh4eHQ1dWFg4NDhStMiprI9OzZk/nZwsICjx8/5iT2WtXQQIhCUTB3797F9u3bJY4bGhpWiR5RZfj4+ODly5cAgKVLl6JXr17Yv38/VFRUsHfv3mr3h8KeGTNm4LfffsOuXbtQuza9vH+LcK3WdHd3h6qqKgBgwIABVe2e3GRnZwMAjIyMFOzJ/0NzhCgUBdOgQQNcvHgRDg4OYmJply9fxoQJE5gLh6IoKCjA48ePYWxsDD09PYX6QqmYgQMHIjw8HJqamrCzs5PoS3f8+HEFeUapjJpUrck3RUVFWL58OTZv3sz0BNTU1IS3tzeWLl1aLUUgFUGnDBSKgnFzc8OKFStw5MgRACXVPVlZWViwYAEGDx6sYO8ADQ0NODo6KtoNCgt0dHRqxJihcKcmVWvyjbe3N44fP45169ahffv2AEpK6pctW4b3798jICBAof7RFSEKRcF8/PgRQ4YMQUxMDP755x80btwYr169Qvv27REaGlput/mqYvDgwWjTpg0WLFggdnzdunW4e/cujh49Wq3+UCjfA7JWa3LJs1FUY2JtbW0cOnQIvXv3FjseGhqKESNG4OPHjwrxSwRdEaJQFIy2tjYuX76MGzdu4MGDB8jPz4ejoyO6d++uEH+uXbuGZcuWSRzv3bv3Nz8zpVBqKrJWaypSKJEtqqqqEir8AGBmZgYVFZXqd6gMNBCiUGoIzs7OcHZ2VrQbyM/Pl3pxUlZW5iQAR1EMISEhOHLkiNSEWyp/UHORtVpz3Lhx1eilbMycORMrV67Enj17mMRukfBj2XYgioAGQhRKDeDu3buIjIzEmzdvJBpnVncPLTs7Oxw+fBhLliwRO37o0CG5NZMoVcvmzZuxaNEijB8/HqdOnYKnpyfS0tJw9+5dzJgxQ9HuUSqAr2pNoVCIEydOMI2Jra2t4e7uXu1VhIMGDRL7PSwsDE2aNEHLli0BAPHx8fj69Su6detWrX5Jg+YIUSgKZs2aNfj1119haWmJhg0biu33CwQCREREVKs/Z86cwaBBgzBy5EixfkUHDx7E0aNHv4kS3e8VKysrLF26FCNGjBCrQFyyZAk+fPiALVu2KNpFCktkqdZ8+PAh3Nzc8OrVK1haWgIAnjx5An19fZw5cwa2trZV6bIYnp6erF+7Z8+eKvSkcmggRKEomIYNG+K3337D+PHjFe0Kw7lz57BmzRrExcVBXV0d9vb2WLp0KS8CcJSqQ0NDA48ePYKJiQkaNGiAy5cvo2XLlkhJSUG7du3w/v17RbtIqULat28PfX19BAUFQVdXFwCQk5OD8ePH4+3bt7h586aCPayYqKgoODk5Mdtn1QXdGqNQFIySkhI6duyoaDfE6Nu3L/r27atoNygcadSoET58+AATExMYGxvj1q1baNmyJTIyMkDnvDUPX19frFy5EnXq1IGvr2+Fr2WzRR4XF4eYmBgmCAJKEq9Xr16NH374QW5/q5revXsjLi4O5ubm1XpeGghRKApm9uzZ2Lp16zdR/UGp2bi6uuL06dNwcHCAp6cnZs+ejZCQEMTExEjkbFAUT2xsLAoLC5mf5aV58+Z4/fo1bGxsxI6/efMGFhYWctuvahQVrNOtMQpFwRQXF6Nv37548uQJrK2tJVRWq0MNuF69enjy5An09PQq1SVRlBYJpXKKi4tRXFzMJMYePnwYUVFRaNasGaZMmVIjSpUpVUdoaCjmz5+PZcuWoV27dgCAW7duYcWKFfDz8xOrStXS0lKUm+VSOq+tOqErQhSKgpk1axYiIyPRtWtX1K9fXyFNCDds2MA0fKUrU98uSkpKiIqKwvbt25Geno6jR49i2LBhCA4Oxp07d2qEPANFOhMmTMCmTZvEGi8DJfpC3t7e2L17d6U2+vXrBwAYOnQocx0RrXX079+f+V0gEHzTLTv4hgZCFIqCCQoKwrFjxxSakxMfH48hQ4ZAVVUVZmZm6NChA23a+Q1y7NgxjBkzBqNGjcL9+/fx77//AgDy8vKwZs0ahIaGKthDSnkEBQXBz89PIhD6/PkzgoODWQVCkZGRVeXefxq6NUahKBgTExNcvHgRVlZWCvNBWVkZz549Q8OGDVGrVi28fPmSUbmlfDs4ODhg9uzZGDt2rNg2Q2xsLHr37o1Xr14p2kVKGfLy8kAIga6uLlJSUqCvr888JxQKcebMGSxcuBAvXrxQoJfVg5aWFk2WplC+R5YtW4alS5diz5490NDQUIgPpqam2Lx5M3r06AFCCKKjo8UqT0rTuXPnavaOwpbk5GSp/z/a2trIzc2tfocolaKjowOBQACBQIDmzZtLPC8QCLB8+XLW9nJychAYGCgmqOjp6Yl69erx5nNVQZOlKZTvFAcHB6SlpYEQAlNTU4lk6epoi3Dy5ElMnToVb968gUAgKPeCRHMLajbm5ubYsWMHunfvLrYiFBwcDD8/PyQlJSnaRUoZrl69CkIIXF1dcezYMbGARUVFBSYmJmjcuDErW9euXUP//v2hra0NJycnAMC9e/eQm5uLM2fOKGwSk5GRgaKiIjRr1kzseEpKCpSVlaX2IatO6IoQhaJgaoJS84ABAzBgwADk5+dDS0sLycnJdGvsG2TSpEnw8fHB7t27IRAI8OLFC0RHR2Pu3LlYvHixot2jSEEkUpqRkQFjY2O5iiVmzJiBYcOGISAgALVq1QJQsr02ffp0zJgxAwkJCbz4zJXx48djwoQJEoHQ7du3sWvXLly5ckUhfomgK0IUCkWMq1evomPHjjRZ+huEEII1a9Zg7dq1KCgoAFDS+Xvu3LlYuXKlgr2jlOXBgwewtbWFkpISHjx4UOFr7e3tK7Wnrq6OuLg4pr2GiOTkZLRq1QqfP3+Wy19Z0dLSwv379yW0jFJTU+Hk5KTwbVt6paNQagj37t1j9vVtbGzg4OBQbefOy8tjdEUcHByYm6g0aqL+CKUEgUCARYsWYd68eUhNTUV+fj6sra2hqampaNcoUmjVqhVevXqFBg0aoFWrVuVuS7PdknZ0dMSjR48kAqFHjx4xzU4VgUAgwD///CNx/OPHjzViq52uCFEoCubNmzcYPnw4rly5Ah0dHQBAbm4uunbtikOHDolVkVQVpSvFlJSUpC7PU/0RCoVfnj59ymyHPX36tMLXmpiYVGrv8OHDmD9/Pry9vcUEFbdu3Qo/Pz+0aNGCeS2bFSa+6N+/P9TV1XHw4EGxLbthw4bh06dPOH/+fLX5Ig0aCFEoCmbYsGFIT09HcHAwc6FKSkrCuHHjYGFhgYMHD1a5D6W3w65evVrha2njVQqlZqKkpFTh86IVp+qe0CQlJaFz587Q0dFBp06dAADXr19HXl4eIiIiYGtrW22+SIMGQhSKgtHW1kZYWJhEU8Q7d+6gR48e1bp/XlRUhDVr1mDChAlo0qRJtZ2XQqGUVFFFRkbizZs3KC4uFntuyZIllb6/slWl0rBZYeKTFy9eYMuWLYiPj4e6ujrs7e0xc+bMGlHWTwMhCkXB1K1bF9evX0erVq3EjsfGxqJLly7Iy8urdn8SEhIUXtJKoXxP7Ny5E9OmTYOenh4aNWoktj0tEAg4yWgkJSUhKysLX79+FbMharNBEYcGQhSKgnF3d0dubi4OHjzI6IU8f/4co0aNgq6uLk6cOFHt/gwaNAjjxo2r1vNSKN8zJiYmmD59OhYsWCCzjfT0dAwcOBAJCQliideioEqR+X3Xr18X64FnaGiIffv2wczMTOE98CreUKRQKFXOli1bkJeXB1NTUzRt2hRNmzaFqakp8vLy8Oeff1a7P71798bChQsxd+5cHDx4EKdPnxZ7UCgU/snJyYGHh4dcNnx8fGBmZoY3b95AQ0MDiYmJuHbtGpycnBSq1XPs2DH07NkT6urqYj3wPn78iDVr1ijMLxF0RYhCqQEQQhAeHs6Uz7do0QLdu3dXiC8VJVzSqjEKpWrw8vLCDz/8gKlTp8psQ09PDxEREbC3t4e2tjbu3LkDS0tLREREYM6cOYiNjeXRY/bU9B54VEeIQqkBREREICIigkmSjI2NxYEDBwCAVddpPimbpEmhUKoeCwsLLF68GLdu3YKdnZ1Eq51Zs2ZVakMoFDLd6/X09PDixQtYWlrCxMQEycnJVeI3G2p6DzwaCFEoCmb58uVYsWIFnJycYGBgIJfEPt98+fIFampqinaDQvnPs2PHDmhqauLq1asSEhYCgYBVIGRra4v4+HiYmZmhbdu2WLduHVRUVLBjx45q7+hemkaNGiE1NVWiAOPGjRsK9UsEDYQoFAWzbds27N27F2PGjFG0KwBKZpVr1qzBtm3b8Pr1azx58gTm5uZYvHgxTE1N4eXlpWgXKZT/HBkZGXLb+PXXX/Hp0ycAwIoVK9CvXz906tQJ9evXx+HDh+W2Lys1vQcezRGiUBRM/fr1cefOHTRt2lTRrgAouYAGBQVhxYoVmDRpEhITE2Fubo7Dhw9j48aNiI6OVrSLFMp/Al9fX6xcuRJ16tSBr69vua8TCATw9/eX6RwfPnyArq6uQleaa3oPPBoIUSgKZsGCBdDU1KwRMyOgJFdh+/bt6Natm1hi4+PHj9G+fXvk5OQo2kUK5T9B165dceLECejo6KBr167lvk4gECAiIqIaPeMPoVCIqKgo2NvbQ0NDo0b2wKOBEIWiAErP/oqLixEUFAR7e3vY29tLJEmuX7++Wn1TV1fH48ePYWJiIhYIJSUloU2bNsjPz69WfygUyreNmpoaHj16BDMzM0W7IhWaI0ShKICyZawiVenExESx44pYzra2tsb169clJPhDQkLg4OBQ7f5QKJRvG1tbW6Snp9NAiEKh/D+RkZGKdqFclixZgnHjxuH58+coLi7G8ePHkZycjODgYJw9e1bR7lEolG+MVatWMflArVu3Rp06dcSe19LSUpBnJdCtMQqFIsH169exYsUKxMfHIz8/H46OjliyZAl69OihaNcoFMo3RmmR1tKr3ISQGiHSSleEKBSKGBMnTsTo0aNx+fJlRbtCoVD+A9TkFXCArghRKJQyuLu74+LFi9DX18eIESMwatQotGzZUtFuUSiU/zjTp0/HihUroKenV63npYEQhUKRICcnB0ePHsWBAwdw/fp1WFlZYdSoURg5cqSEOiyFQqHwgZaWFuLi4qpdbZoGQhQKpUKePXuGgwcPYvfu3UhJSUFRUZGiXaJQKP9BSst1VCflt5mmUCjfPYWFhYiJicHt27eRmZmJhg0bKtolCoVC4RUaCFEoFAkiIyMxadIkNGzYEOPHj4eWlhbOnj2LZ8+eKdo1CoVC4RVaNUahUMQwNDTEhw8f0KtXL+zYsQP9+/eHqqqqot2iUCiUKoEGQhQKRYxly5bBw8MDOjo6inaFQqFQqhwaCFEoFDEmTZqkaBcoFMp3yOjRoxWiMk2rxigUCoVCoVQpOTk5CAwMxKNHjwAALVq0wIQJE1CvXj0Fe0YDIQqFQqFQKFXItWvX4ObmBi0tLTg5OQEA7t27h9zcXJw5cwadO3dWqH80EKJQKBQKhVJl2NnZoX379ggICECtWrUAAEKhENOnT8fNmzeRkJCgUP9oIEShUCgUCqXKUFdXR1xcHCwtLcWOJycno1WrVvj8+bOCPCuB6ghRKBQKhUKpMhwdHZncoNI8evSoRvQxpFVjFAqFQqFQeOXBgwfMz7NmzYKPjw9SU1PRrl07AMCtW7ewdetW+Pn5KcpFBro1RqFQKBQKhVeUlJQgEAhQWYghEAggFAqrySvp0BUhCoVCoVAovJKRkaFoF1hDV4QoFAqFQqF8t9AVIQqFQqFQKFVGcHBwhc+PHTu2mjyRDl0RolAoFAqFUmXo6uqK/V5YWIiCggKoqKhAQ0MDHz58UJBnJdDyeQqFQqFQKFVGTk6O2CM/Px/JyclwdnbGwYMHFe0eXRGiUCgUCoVS/cTExGD06NF4/PixQv2gK0IUCoVCoVCqndq1a+PFixeKdoMmS1MoFAqFQqk6Tp8+LfY7IQQvX77Eli1b0LFjRwV59f/QrTEKhUKhUChVhpKS+OaTQCCAvr4+XF1d4e/vDwMDAwV59j9/aCBEoVAoFAqlOiguLgYgGRwpkprjCYVCoVAolP8kgYGBsLW1hbq6OtTV1WFra4tdu3Yp2i0ANEeIQqFQKBRKFbJkyRKsX78e3t7eaN++PQAgOjoas2fPRlZWFlasWKFQ/+jWGIVCoVAolCpDX18fmzdvxogRI8SOHzx4EN7e3nj37p2CPCuBbo1RKBQKhUKpMgoLC+Hk5CRxvHXr1igqKlKAR+LQQIhCoVAoFEqVMWbMGAQEBEgc37FjB0aNGqUAj8ShW2MUCoVCoVCqDG9vbwQHB8PIyAjt2rUDANy+fRtZWVkYO3YslJWVmdeuX7++2v2jgRCFQqFQKJQqo2vXrqxeJxAIEBERUcXeSDkvDYQoFAqFQqF8r9AcIQqFQqFQKN8tNBCiUCgUCoXy3UIDIQqFQqFQKN8tNBCiUCgUCoXy3UIDIQqFQqFQKN8tNBCiUCgUCoXy3UIDIQqFQqFQKN8t/weQ43OT2wpA9AAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()\n",
+    "sns.heatmap(df.isnull(), cbar=False)  # Visualize missing values\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "cdfc55e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mort_acc                37795\n",
+      "emp_title               22927\n",
+      "emp_length              18301\n",
+      "title                    1756\n",
+      "pub_rec_bankruptcies      535\n",
+      "revol_util                276\n",
+      "loan_amnt                   0\n",
+      "dti                         0\n",
+      "application_type            0\n",
+      "initial_list_status         0\n",
+      "total_acc                   0\n",
+      "revol_bal                   0\n",
+      "pub_rec                     0\n",
+      "open_acc                    0\n",
+      "earliest_cr_line            0\n",
+      "purpose                     0\n",
+      "term                        0\n",
+      "loan_status                 0\n",
+      "issue_d                     0\n",
+      "verification_status         0\n",
+      "annual_inc                  0\n",
+      "home_ownership              0\n",
+      "sub_grade                   0\n",
+      "grade                       0\n",
+      "installment                 0\n",
+      "int_rate                    0\n",
+      "address                     0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.isnull().sum().sort_values(ascending=False))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "3fc2ef2b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Droping off few features with high missing values\n",
+    "df.drop(['mort_acc', 'emp_title', 'emp_length', 'title'], axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "68938f7d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 396030 entries, 0 to 396029\n",
+      "Data columns (total 23 columns):\n",
+      " #   Column                Non-Null Count   Dtype  \n",
+      "---  ------                --------------   -----  \n",
+      " 0   loan_amnt             396030 non-null  float64\n",
+      " 1   term                  396030 non-null  object \n",
+      " 2   int_rate              396030 non-null  float64\n",
+      " 3   installment           396030 non-null  float64\n",
+      " 4   grade                 396030 non-null  object \n",
+      " 5   sub_grade             396030 non-null  object \n",
+      " 6   home_ownership        396030 non-null  object \n",
+      " 7   annual_inc            396030 non-null  float64\n",
+      " 8   verification_status   396030 non-null  object \n",
+      " 9   issue_d               396030 non-null  object \n",
+      " 10  loan_status           396030 non-null  object \n",
+      " 11  purpose               396030 non-null  object \n",
+      " 12  dti                   396030 non-null  float64\n",
+      " 13  earliest_cr_line      396030 non-null  object \n",
+      " 14  open_acc              396030 non-null  float64\n",
+      " 15  pub_rec               396030 non-null  float64\n",
+      " 16  revol_bal             396030 non-null  float64\n",
+      " 17  revol_util            395754 non-null  float64\n",
+      " 18  total_acc             396030 non-null  float64\n",
+      " 19  initial_list_status   396030 non-null  object \n",
+      " 20  application_type      396030 non-null  object \n",
+      " 21  pub_rec_bankruptcies  395495 non-null  float64\n",
+      " 22  address               396030 non-null  object \n",
+      "dtypes: float64(11), object(12)\n",
+      "memory usage: 69.5+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "0fdc5928",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Target variable distribution:\n",
+      "loan_status\n",
+      "Fully Paid     318357\n",
+      "Charged Off     77673\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Target variable proportions:\n",
+      "loan_status\n",
+      "Fully Paid     0.803871\n",
+      "Charged Off    0.196129\n",
+      "Name: proportion, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check target variable (loan_status)\n",
+    "print(\"Target variable distribution:\")\n",
+    "print(df['loan_status'].value_counts())\n",
+    "print(\"\\nTarget variable proportions:\")\n",
+    "print(df['loan_status'].value_counts(normalize=True))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "13a91fee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Missing values after dropping columns:\n",
+      "pub_rec_bankruptcies    535\n",
+      "revol_util              276\n",
+      "dtype: int64\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_5344/2977601269.py:11: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
+      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
+      "\n",
+      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
+      "\n",
+      "\n",
+      "  df[col].fillna(df[col].median(), inplace=True)\n",
+      "/tmp/ipykernel_5344/2977601269.py:11: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
+      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
+      "\n",
+      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
+      "\n",
+      "\n",
+      "  df[col].fillna(df[col].median(), inplace=True)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Missing values after imputation:\n",
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check remaining missing values\n",
+    "print(\"Missing values after dropping columns:\")\n",
+    "missing_values = df.isnull().sum().sort_values(ascending=False)\n",
+    "print(missing_values[missing_values > 0])\n",
+    "\n",
+    "# Handle remaining missing values\n",
+    "# For numerical columns, fill with median\n",
+    "numerical_cols = df.select_dtypes(include=[np.number]).columns\n",
+    "for col in numerical_cols:\n",
+    "    if df[col].isnull().sum() > 0:\n",
+    "        df[col].fillna(df[col].median(), inplace=True)\n",
+    "\n",
+    "# For categorical columns, fill with mode\n",
+    "categorical_cols = df.select_dtypes(include=['object']).columns\n",
+    "for col in categorical_cols:\n",
+    "    if df[col].isnull().sum() > 0:\n",
+    "        df[col].fillna(df[col].mode()[0], inplace=True)\n",
+    "\n",
+    "print(\"\\nMissing values after imputation:\")\n",
+    "print(df.isnull().sum().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "b191aedf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset shape: (396030, 23)\n",
+      "\n",
+      "Column names:\n",
+      "['loan_amnt', 'term', 'int_rate', 'installment', 'grade', 'sub_grade', 'home_ownership', 'annual_inc', 'verification_status', 'issue_d', 'loan_status', 'purpose', 'dti', 'earliest_cr_line', 'open_acc', 'pub_rec', 'revol_bal', 'revol_util', 'total_acc', 'initial_list_status', 'application_type', 'pub_rec_bankruptcies', 'address']\n",
+      "\n",
+      "Target variable created: loan_repaid\n",
+      "loan_repaid\n",
+      "1    318357\n",
+      "0     77673\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Explore key features\n",
+    "print(\"Dataset shape:\", df.shape)\n",
+    "print(\"\\nColumn names:\")\n",
+    "print(df.columns.tolist())\n",
+    "\n",
+    "# Create binary target variable (1 for Charged Off, 0 for Fully Paid)\n",
+    "df['loan_repaid'] = df['loan_status'].map({'Fully Paid': 1, 'Charged Off': 0})\n",
+    "print(f\"\\nTarget variable created: loan_repaid\")\n",
+    "print(df['loan_repaid'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "e90a77b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation with target variable (loan_repaid):\n",
+      "loan_repaid             1.000000\n",
+      "annual_inc              0.053432\n",
+      "total_acc               0.017893\n",
+      "revol_bal               0.010892\n",
+      "pub_rec_bankruptcies   -0.009416\n",
+      "pub_rec                -0.019933\n",
+      "open_acc               -0.028012\n",
+      "installment            -0.041082\n",
+      "loan_amnt              -0.059836\n",
+      "dti                    -0.062413\n",
+      "revol_util             -0.082343\n",
+      "Name: loan_repaid, dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1200x800 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Analyze numerical features correlation with target\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "numerical_features = df.select_dtypes(include=[np.number]).columns.tolist()\n",
+    "numerical_features.remove('loan_repaid')  # Remove target from features\n",
+    "\n",
+    "# Calculate correlation with target\n",
+    "correlation_with_target = df[numerical_features + ['loan_repaid']].corr()['loan_repaid'].sort_values(ascending=False)\n",
+    "print(\"Correlation with target variable (loan_repaid):\")\n",
+    "print(correlation_with_target[:-1])  # Exclude self-correlation\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "correlation_with_target[:-1].plot(kind='barh')\n",
+    "plt.title('Feature Correlation with Loan Repayment')\n",
+    "plt.xlabel('Correlation Coefficient')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "5ea1ce16",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Categorical features: ['term', 'grade', 'sub_grade', 'home_ownership', 'verification_status', 'issue_d', 'purpose', 'earliest_cr_line', 'initial_list_status', 'application_type', 'address']\n",
+      "\n",
+      "term - Unique values: 2\n",
+      "term\n",
+      "36 months    302005\n",
+      "60 months     94025\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "grade - Unique values: 7\n",
+      "grade\n",
+      "B    116018\n",
+      "C    105987\n",
+      "A     64187\n",
+      "D     63524\n",
+      "E     31488\n",
+      "F     11772\n",
+      "G      3054\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "sub_grade - Unique values: 35\n",
+      "\n",
+      "home_ownership - Unique values: 6\n",
+      "home_ownership\n",
+      "MORTGAGE    198348\n",
+      "RENT        159790\n",
+      "OWN          37746\n",
+      "OTHER          112\n",
+      "NONE            31\n",
+      "ANY              3\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "verification_status - Unique values: 3\n",
+      "verification_status\n",
+      "Verified           139563\n",
+      "Source Verified    131385\n",
+      "Not Verified       125082\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "issue_d - Unique values: 115\n",
+      "\n",
+      "purpose - Unique values: 14\n",
+      "purpose\n",
+      "debt_consolidation    234507\n",
+      "credit_card            83019\n",
+      "home_improvement       24030\n",
+      "other                  21185\n",
+      "major_purchase          8790\n",
+      "small_business          5701\n",
+      "car                     4697\n",
+      "medical                 4196\n",
+      "moving                  2854\n",
+      "vacation                2452\n",
+      "house                   2201\n",
+      "wedding                 1812\n",
+      "renewable_energy         329\n",
+      "educational              257\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "earliest_cr_line - Unique values: 684\n",
+      "\n",
+      "initial_list_status - Unique values: 2\n",
+      "initial_list_status\n",
+      "f    238066\n",
+      "w    157964\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "application_type - Unique values: 3\n",
+      "application_type\n",
+      "INDIVIDUAL    395319\n",
+      "JOINT            425\n",
+      "DIRECT_PAY       286\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "address - Unique values: 393700\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x1200 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Analyze categorical features\n",
+    "categorical_features = df.select_dtypes(include=['object']).columns.tolist()\n",
+    "categorical_features.remove('loan_status')  # Remove original target\n",
+    "print(\"Categorical features:\", categorical_features)\n",
+    "\n",
+    "# Check unique values in categorical features\n",
+    "for col in categorical_features:\n",
+    "    print(f\"\\n{col} - Unique values: {df[col].nunique()}\")\n",
+    "    if df[col].nunique() < 20:  # Only show if not too many categories\n",
+    "        print(df[col].value_counts())\n",
+    "\n",
+    "# Analyze relationship between categorical features and target\n",
+    "fig, axes = plt.subplots(2, 3, figsize=(18, 12))\n",
+    "axes = axes.ravel()\n",
+    "\n",
+    "categorical_to_plot = ['grade', 'home_ownership', 'verification_status', 'purpose', 'initial_list_status', 'application_type']\n",
+    "\n",
+    "for i, col in enumerate(categorical_to_plot):\n",
+    "    if col in df.columns:\n",
+    "        # Calculate default rate by category\n",
+    "        default_rate = df.groupby(col)['loan_repaid'].mean().sort_values(ascending=False)\n",
+    "        default_rate.plot(kind='bar', ax=axes[i], color='steelblue')\n",
+    "        axes[i].set_title(f'Loan Repayment Rate by {col}')\n",
+    "        axes[i].set_ylabel('Repayment Rate')\n",
+    "        axes[i].tick_params(axis='x', rotation=45)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "85c397f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_5344/3018535517.py:5: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
+      "  df['issue_d'] = pd.to_datetime(df['issue_d'])\n",
+      "/tmp/ipykernel_5344/3018535517.py:6: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
+      "  df['earliest_cr_line'] = pd.to_datetime(df['earliest_cr_line'])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "New features created:\n",
+      "['credit_history_length', 'issue_year', 'issue_month', 'debt_to_credit_ratio', 'credit_utilization_ratio', 'loan_to_income_ratio', 'grade_numeric', 'installment_to_income', 'total_credit_lines']\n",
+      "credit_history_length: 0 infinite values, 0 NaN values\n",
+      "issue_year: 0 infinite values, 0 NaN values\n",
+      "issue_month: 0 infinite values, 0 NaN values\n",
+      "debt_to_credit_ratio: 0 infinite values, 0 NaN values\n",
+      "credit_utilization_ratio: 0 infinite values, 0 NaN values\n",
+      "loan_to_income_ratio: 0 infinite values, 0 NaN values\n",
+      "grade_numeric: 0 infinite values, 0 NaN values\n",
+      "installment_to_income: 0 infinite values, 0 NaN values\n",
+      "total_credit_lines: 0 infinite values, 0 NaN values\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Feature Engineering\n",
+    "from datetime import datetime\n",
+    "\n",
+    "# 1. Process date features\n",
+    "df['issue_d'] = pd.to_datetime(df['issue_d'])\n",
+    "df['earliest_cr_line'] = pd.to_datetime(df['earliest_cr_line'])\n",
+    "\n",
+    "# Create new date-based features\n",
+    "df['credit_history_length'] = (df['issue_d'] - df['earliest_cr_line']).dt.days / 365.25  # in years\n",
+    "df['issue_year'] = df['issue_d'].dt.year\n",
+    "df['issue_month'] = df['issue_d'].dt.month\n",
+    "\n",
+    "# 2. Create derived features\n",
+    "df['debt_to_credit_ratio'] = df['revol_bal'] / (df['revol_bal'] + df['annual_inc'] + 1)  # +1 to avoid division by zero\n",
+    "df['credit_utilization_ratio'] = df['revol_util'] / 100  # Convert percentage to ratio\n",
+    "df['loan_to_income_ratio'] = df['loan_amnt'] / (df['annual_inc'] + 1)\n",
+    "\n",
+    "# 3. Create grade numeric mapping\n",
+    "grade_mapping = {'A': 7, 'B': 6, 'C': 5, 'D': 4, 'E': 3, 'F': 2, 'G': 1}\n",
+    "df['grade_numeric'] = df['grade'].map(grade_mapping)\n",
+    "\n",
+    "# 4. Create interaction features\n",
+    "df['installment_to_income'] = df['installment'] / (df['annual_inc'] / 12 + 1)  # Monthly installment to monthly income\n",
+    "df['total_credit_lines'] = df['open_acc'] + df['total_acc']\n",
+    "\n",
+    "print(\"New features created:\")\n",
+    "new_features = ['credit_history_length', 'issue_year', 'issue_month', 'debt_to_credit_ratio', \n",
+    "                'credit_utilization_ratio', 'loan_to_income_ratio', 'grade_numeric', \n",
+    "                'installment_to_income', 'total_credit_lines']\n",
+    "print(new_features)\n",
+    "\n",
+    "# Check for any infinite or NaN values in new features\n",
+    "for feature in new_features:\n",
+    "    inf_count = np.isinf(df[feature]).sum()\n",
+    "    nan_count = df[feature].isnull().sum()\n",
+    "    print(f\"{feature}: {inf_count} infinite values, {nan_count} NaN values\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "bb5e53f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Columns after preprocessing:\n",
+      "['loan_amnt', 'int_rate', 'installment', 'annual_inc', 'dti', 'open_acc', 'pub_rec', 'revol_bal', 'revol_util', 'total_acc', 'pub_rec_bankruptcies', 'loan_repaid', 'credit_history_length', 'issue_year', 'issue_month', 'debt_to_credit_ratio', 'credit_utilization_ratio', 'loan_to_income_ratio', 'grade_numeric', 'installment_to_income', 'total_credit_lines', 'grade_encoded', 'sub_grade_encoded', 'home_ownership_encoded', 'verification_status_encoded', 'purpose_encoded', 'initial_list_status_encoded', 'application_type_encoded']\n",
+      "\n",
+      "Dataset shape: (396030, 28)\n",
+      "\n",
+      "Feature matrix shape: (396030, 27)\n",
+      "Target vector shape: (396030,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Feature Selection for ANN\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.feature_selection import SelectKBest, f_classif, mutual_info_classif\n",
+    "\n",
+    "# Create a copy for feature selection\n",
+    "df_model = df.copy()\n",
+    "\n",
+    "# Encode categorical variables\n",
+    "categorical_cols_to_encode = ['grade', 'sub_grade', 'home_ownership', 'verification_status', \n",
+    "                             'purpose', 'initial_list_status', 'application_type']\n",
+    "\n",
+    "label_encoders = {}\n",
+    "for col in categorical_cols_to_encode:\n",
+    "    if col in df_model.columns:\n",
+    "        le = LabelEncoder()\n",
+    "        df_model[col + '_encoded'] = le.fit_transform(df_model[col])\n",
+    "        label_encoders[col] = le\n",
+    "\n",
+    "# Drop unnecessary columns for modeling\n",
+    "columns_to_drop = ['loan_status', 'issue_d', 'earliest_cr_line', 'address', 'sub_grade', 'term'] + categorical_cols_to_encode\n",
+    "df_model = df_model.drop(columns=[col for col in columns_to_drop if col in df_model.columns])\n",
+    "\n",
+    "print(\"Columns after preprocessing:\")\n",
+    "print(df_model.columns.tolist())\n",
+    "print(f\"\\nDataset shape: {df_model.shape}\")\n",
+    "\n",
+    "# Identify feature columns (excluding target)\n",
+    "feature_cols = [col for col in df_model.columns if col != 'loan_repaid']\n",
+    "X = df_model[feature_cols]\n",
+    "y = df_model['loan_repaid']\n",
+    "\n",
+    "print(f\"\\nFeature matrix shape: {X.shape}\")\n",
+    "print(f\"Target vector shape: {y.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "a346523f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Top 20 Most Important Features:\n",
+      "                     feature  importance\n",
+      "4                        dti    0.067424\n",
+      "16      loan_to_income_ratio    0.061058\n",
+      "11     credit_history_length    0.060669\n",
+      "18     installment_to_income    0.059971\n",
+      "14      debt_to_credit_ratio    0.058215\n",
+      "7                  revol_bal    0.056690\n",
+      "2                installment    0.053859\n",
+      "8                 revol_util    0.052949\n",
+      "1                   int_rate    0.052629\n",
+      "15  credit_utilization_ratio    0.052592\n",
+      "3                 annual_inc    0.050499\n",
+      "19        total_credit_lines    0.045322\n",
+      "21         sub_grade_encoded    0.044883\n",
+      "9                  total_acc    0.043969\n",
+      "0                  loan_amnt    0.042557\n",
+      "5                   open_acc    0.038958\n",
+      "13               issue_month    0.034673\n",
+      "12                issue_year    0.023504\n",
+      "24           purpose_encoded    0.019661\n",
+      "20             grade_encoded    0.018264\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Feature Importance Analysis\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Split data for feature importance calculation\n",
+    "X_temp, _, y_temp, _ = train_test_split(X, y, test_size=0.8, random_state=42, stratify=y)\n",
+    "\n",
+    "# Use Random Forest for feature importance\n",
+    "rf = RandomForestClassifier(n_estimators=100, random_state=42, n_jobs=-1)\n",
+    "rf.fit(X_temp, y_temp)\n",
+    "\n",
+    "# Get feature importance\n",
+    "feature_importance = pd.DataFrame({\n",
+    "    'feature': X.columns,\n",
+    "    'importance': rf.feature_importances_\n",
+    "}).sort_values('importance', ascending=False)\n",
+    "\n",
+    "print(\"Top 20 Most Important Features:\")\n",
+    "print(feature_importance.head(20))\n",
+    "\n",
+    "# Plot feature importance\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "top_features = feature_importance.head(15)\n",
+    "plt.barh(range(len(top_features)), top_features['importance'])\n",
+    "plt.yticks(range(len(top_features)), top_features['feature'])\n",
+    "plt.xlabel('Feature Importance')\n",
+    "plt.title('Top 15 Feature Importance (Random Forest)')\n",
+    "plt.gca().invert_yaxis()\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "e2afe100",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Selected features for ANN:\n",
+      " 1. dti\n",
+      " 2. loan_to_income_ratio\n",
+      " 3. credit_history_length\n",
+      " 4. installment_to_income\n",
+      " 5. debt_to_credit_ratio\n",
+      " 6. revol_bal\n",
+      " 7. installment\n",
+      " 8. revol_util\n",
+      " 9. int_rate\n",
+      "10. credit_utilization_ratio\n",
+      "11. annual_inc\n",
+      "12. total_credit_lines\n",
+      "13. sub_grade_encoded\n",
+      "14. total_acc\n",
+      "15. loan_amnt\n",
+      "\n",
+      "Final dataset shape: (396030, 15)\n",
+      "Target shape: (396030,)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Highly correlated feature pairs (>0.8):\n",
+      "dti - loan_to_income_ratio: 0.884\n",
+      "dti - installment_to_income: 0.895\n",
+      "loan_to_income_ratio - installment_to_income: 0.977\n",
+      "installment - loan_amnt: 0.954\n",
+      "revol_util - credit_utilization_ratio: 1.000\n",
+      "int_rate - sub_grade_encoded: 0.974\n",
+      "total_credit_lines - total_acc: 0.971\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Select top important features for ANN\n",
+    "top_n_features = 15  # Select top 15 features based on importance\n",
+    "\n",
+    "selected_features = feature_importance.head(top_n_features)['feature'].tolist()\n",
+    "print(\"Selected features for ANN:\")\n",
+    "for i, feature in enumerate(selected_features, 1):\n",
+    "    print(f\"{i:2d}. {feature}\")\n",
+    "\n",
+    "# Create final dataset with selected features\n",
+    "X_selected = X[selected_features]\n",
+    "y_selected = y\n",
+    "\n",
+    "print(f\"\\nFinal dataset shape: {X_selected.shape}\")\n",
+    "print(f\"Target shape: {y_selected.shape}\")\n",
+    "\n",
+    "# Check correlation between selected features\n",
+    "plt.figure(figsize=(12, 10))\n",
+    "correlation_matrix = X_selected.corr()\n",
+    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0, fmt='.2f')\n",
+    "plt.title('Correlation Matrix of Selected Features')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Check for highly correlated features (>0.8)\n",
+    "high_corr_pairs = []\n",
+    "for i in range(len(correlation_matrix.columns)):\n",
+    "    for j in range(i+1, len(correlation_matrix.columns)):\n",
+    "        if abs(correlation_matrix.iloc[i, j]) > 0.8:\n",
+    "            high_corr_pairs.append((correlation_matrix.columns[i], \n",
+    "                                  correlation_matrix.columns[j], \n",
+    "                                  correlation_matrix.iloc[i, j]))\n",
+    "\n",
+    "if high_corr_pairs:\n",
+    "    print(\"\\nHighly correlated feature pairs (>0.8):\")\n",
+    "    for pair in high_corr_pairs:\n",
+    "        print(f\"{pair[0]} - {pair[1]}: {pair[2]:.3f}\")\n",
+    "else:\n",
+    "    print(\"\\nNo highly correlated feature pairs found.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "db0a3cfc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Final features for ANN after removing highly correlated ones:\n",
+      " 1. dti\n",
+      " 2. credit_history_length\n",
+      " 3. debt_to_credit_ratio\n",
+      " 4. revol_bal\n",
+      " 5. installment\n",
+      " 6. revol_util\n",
+      " 7. int_rate\n",
+      " 8. annual_inc\n",
+      " 9. total_credit_lines\n",
+      "\n",
+      "Final dataset shape: (396030, 9)\n",
+      "Number of features: 9\n",
+      "Maximum correlation between final features: 0.629\n",
+      "\n",
+      "Final Feature Statistics:\n",
+      "==================================================\n",
+      "                 dti  credit_history_length  debt_to_credit_ratio  \\\n",
+      "count  396030.000000          396030.000000         396030.000000   \n",
+      "mean       17.379514              15.754323              0.168875   \n",
+      "std        18.019092               7.201072              0.103431   \n",
+      "min         0.000000               2.997947              0.000000   \n",
+      "25%        11.280000              10.915811              0.091711   \n",
+      "50%        16.910000              14.335387              0.155176   \n",
+      "75%        22.980000              19.334702              0.230435   \n",
+      "max      9999.000000              70.162902              0.999906   \n",
+      "\n",
+      "          revol_bal    installment     revol_util       int_rate  \\\n",
+      "count  3.960300e+05  396030.000000  396030.000000  396030.000000   \n",
+      "mean   1.584454e+04     431.849698      53.792451      13.639400   \n",
+      "std    2.059184e+04     250.727790      24.443685       4.472157   \n",
+      "min    0.000000e+00      16.080000       0.000000       5.320000   \n",
+      "25%    6.025000e+03     250.330000      35.900000      10.490000   \n",
+      "50%    1.118100e+04     375.430000      54.800000      13.330000   \n",
+      "75%    1.962000e+04     567.300000      72.900000      16.490000   \n",
+      "max    1.743266e+06    1533.810000     892.300000      30.990000   \n",
+      "\n",
+      "         annual_inc  total_credit_lines  \n",
+      "count  3.960300e+05       396030.000000  \n",
+      "mean   7.420318e+04           36.725897  \n",
+      "std    6.163762e+04           15.837984  \n",
+      "min    0.000000e+00            3.000000  \n",
+      "25%    4.500000e+04           25.000000  \n",
+      "50%    6.400000e+04           35.000000  \n",
+      "75%    9.000000e+04           46.000000  \n",
+      "max    8.706582e+06          197.000000  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Remove highly correlated features to avoid multicollinearity\n",
+    "# Keep the more interpretable or important feature from each pair\n",
+    "\n",
+    "features_to_remove = [\n",
+    "    'loan_to_income_ratio',  # Keep dti instead (more standard metric)\n",
+    "    'installment_to_income',  # Keep dti instead  \n",
+    "    'credit_utilization_ratio',  # Keep revol_util instead (original feature)\n",
+    "    'sub_grade_encoded',  # Keep int_rate instead (more direct measure)\n",
+    "    'total_acc',  # Keep total_credit_lines instead (engineered feature)\n",
+    "    'loan_amnt'  # Keep installment instead (monthly impact more relevant)\n",
+    "]\n",
+    "\n",
+    "# Final feature selection\n",
+    "final_features = [f for f in selected_features if f not in features_to_remove]\n",
+    "print(\"Final features for ANN after removing highly correlated ones:\")\n",
+    "for i, feature in enumerate(final_features, 1):\n",
+    "    print(f\"{i:2d}. {feature}\")\n",
+    "\n",
+    "# Create final dataset\n",
+    "X_final = X_selected[final_features]\n",
+    "y_final = y_selected\n",
+    "\n",
+    "print(f\"\\nFinal dataset shape: {X_final.shape}\")\n",
+    "print(f\"Number of features: {len(final_features)}\")\n",
+    "\n",
+    "# Final correlation check\n",
+    "final_corr = X_final.corr()\n",
+    "max_corr = final_corr.abs().where(np.triu(np.ones(final_corr.shape), k=1).astype(bool)).max().max()\n",
+    "print(f\"Maximum correlation between final features: {max_corr:.3f}\")\n",
+    "\n",
+    "# Display final feature statistics\n",
+    "print(\"\\nFinal Feature Statistics:\")\n",
+    "print(\"=\"*50)\n",
+    "final_stats = X_final.describe()\n",
+    "print(final_stats)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "ba379594",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data split completed:\n",
+      "Training set: 316824 samples\n",
+      "Test set: 79206 samples\n",
+      "Feature dimensions: 9\n",
+      "\n",
+      "Data scaling completed using StandardScaler\n",
+      "Features are now normalized with mean=0 and std=1\n",
+      "\n",
+      "Class distribution:\n",
+      "Training set:\n",
+      "  Fully Paid (1): 254,686 (80.4%)\n",
+      "  Charged Off (0): 62,138 (19.6%)\n",
+      "Test set:\n",
+      "  Fully Paid (1): 63,671 (80.4%)\n",
+      "  Charged Off (0): 15,535 (19.6%)\n",
+      "\n",
+      "Final features for ANN:\n",
+      "['dti', 'credit_history_length', 'debt_to_credit_ratio', 'revol_bal', 'installment', 'revol_util', 'int_rate', 'annual_inc', 'total_credit_lines']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Prepare data for ANN training\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Split the data\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X_final, y_final, \n",
+    "    test_size=0.2, \n",
+    "    random_state=42, \n",
+    "    stratify=y_final\n",
+    ")\n",
+    "\n",
+    "print(\"Data split completed:\")\n",
+    "print(f\"Training set: {X_train.shape[0]} samples\")\n",
+    "print(f\"Test set: {X_test.shape[0]} samples\")\n",
+    "print(f\"Feature dimensions: {X_train.shape[1]}\")\n",
+    "\n",
+    "# Scale the features for ANN\n",
+    "scaler = StandardScaler()\n",
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)\n",
+    "\n",
+    "print(\"\\nData scaling completed using StandardScaler\")\n",
+    "print(\"Features are now normalized with mean=0 and std=1\")\n",
+    "\n",
+    "# Check class distribution\n",
+    "print(\"\\nClass distribution:\")\n",
+    "print(\"Training set:\")\n",
+    "print(f\"  Fully Paid (1): {(y_train == 1).sum():,} ({(y_train == 1).mean():.1%})\")\n",
+    "print(f\"  Charged Off (0): {(y_train == 0).sum():,} ({(y_train == 0).mean():.1%})\")\n",
+    "print(\"Test set:\")\n",
+    "print(f\"  Fully Paid (1): {(y_test == 1).sum():,} ({(y_test == 1).mean():.1%})\")\n",
+    "print(f\"  Charged Off (0): {(y_test == 0).sum():,} ({(y_test == 0).mean():.1%})\")\n",
+    "\n",
+    "# Save the feature names for future reference\n",
+    "print(\"\\nFinal features for ANN:\")\n",
+    "print(final_features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "ede23fbd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Dataset saved successfully!\n",
+      "\n",
+      "📁 Files created in 'data/processed':\n",
+      "   📊 train_data_scaled.csv     - Training data (standardized, ready for ANN)\n",
+      "   📊 test_data_scaled.csv      - Test data (standardized, ready for ANN)\n",
+      "   📊 train_data_original.csv   - Training data (original scale)\n",
+      "   📊 test_data_original.csv    - Test data (original scale)\n",
+      "   📋 feature_info.csv          - Feature descriptions and metadata\n",
+      "   ⚙️ scaler_params.csv         - StandardScaler parameters\n",
+      "   📄 preprocessing_info.json   - Complete preprocessing metadata\n",
+      "\n",
+      "📈 Dataset Summary:\n",
+      "   Training samples: 316,824\n",
+      "   Test samples: 79,206\n",
+      "   Features: 9\n",
+      "   Target variable: loan_repaid (1=Fully Paid, 0=Charged Off)\n",
+      "\n",
+      "🎯 Ready for ANN Training!\n",
+      "   Load: train_data_scaled.csv and test_data_scaled.csv\n",
+      "   Features: ['dti', 'credit_history_length', 'debt_to_credit_ratio', 'revol_bal', 'installment', 'revol_util', 'int_rate', 'annual_inc', 'total_credit_lines']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Save the processed dataset for ANN training\n",
+    "import os\n",
+    "import json\n",
+    "\n",
+    "# Create processed data directory if it doesn't exist\n",
+    "processed_data_dir = 'data/processed'\n",
+    "os.makedirs(processed_data_dir, exist_ok=True)\n",
+    "\n",
+    "# Convert scaled data back to DataFrames for saving\n",
+    "X_train_df = pd.DataFrame(X_train_scaled, columns=final_features)\n",
+    "X_test_df = pd.DataFrame(X_test_scaled, columns=final_features)\n",
+    "\n",
+    "# Add target variable to the datasets\n",
+    "train_data = X_train_df.copy()\n",
+    "train_data['loan_repaid'] = y_train.reset_index(drop=True)\n",
+    "\n",
+    "test_data = X_test_df.copy()\n",
+    "test_data['loan_repaid'] = y_test.reset_index(drop=True)\n",
+    "\n",
+    "# Save scaled datasets (ready for ANN training)\n",
+    "train_data.to_csv(f'{processed_data_dir}/train_data_scaled.csv', index=False)\n",
+    "test_data.to_csv(f'{processed_data_dir}/test_data_scaled.csv', index=False)\n",
+    "\n",
+    "# Also save unscaled datasets (for analysis/interpretation)\n",
+    "X_train_original = X_train.copy()\n",
+    "X_train_original['loan_repaid'] = y_train.reset_index(drop=True)\n",
+    "\n",
+    "X_test_original = X_test.copy()\n",
+    "X_test_original['loan_repaid'] = y_test.reset_index(drop=True)\n",
+    "\n",
+    "X_train_original.to_csv(f'{processed_data_dir}/train_data_original.csv', index=False)\n",
+    "X_test_original.to_csv(f'{processed_data_dir}/test_data_original.csv', index=False)\n",
+    "\n",
+    "# Save feature information\n",
+    "feature_info = pd.DataFrame({\n",
+    "    'feature_name': final_features,\n",
+    "    'feature_type': ['numerical'] * len(final_features),\n",
+    "    'description': [\n",
+    "        'Debt-to-income ratio (%)',\n",
+    "        'Credit history length (years)',\n",
+    "        'Debt to available credit ratio',\n",
+    "        'Total revolving credit balance ($)',\n",
+    "        'Monthly loan installment ($)',\n",
+    "        'Revolving credit utilization (%)',\n",
+    "        'Loan interest rate (%)',\n",
+    "        'Annual income ($)',\n",
+    "        'Total number of credit lines'\n",
+    "    ]\n",
+    "})\n",
+    "\n",
+    "feature_info.to_csv(f'{processed_data_dir}/feature_info.csv', index=False)\n",
+    "\n",
+    "# Save scaler parameters for future use\n",
+    "scaler_params = pd.DataFrame({\n",
+    "    'feature': final_features,\n",
+    "    'mean': scaler.mean_,\n",
+    "    'scale': scaler.scale_\n",
+    "})\n",
+    "scaler_params.to_csv(f'{processed_data_dir}/scaler_params.csv', index=False)\n",
+    "\n",
+    "# Save preprocessing metadata\n",
+    "preprocessing_info = {\n",
+    "    'original_dataset_shape': df.shape,\n",
+    "    'final_dataset_shape': X_final.shape,\n",
+    "    'train_samples': len(X_train),\n",
+    "    'test_samples': len(X_test),\n",
+    "    'features_selected': len(final_features),\n",
+    "    'scaler_type': 'StandardScaler',\n",
+    "    'class_balance_train': {\n",
+    "        'fully_paid': int((y_train == 1).sum()),\n",
+    "        'charged_off': int((y_train == 0).sum()),\n",
+    "        'fully_paid_pct': float((y_train == 1).mean()),\n",
+    "        'charged_off_pct': float((y_train == 0).mean())\n",
+    "    },\n",
+    "    'class_balance_test': {\n",
+    "        'fully_paid': int((y_test == 1).sum()),\n",
+    "        'charged_off': int((y_test == 0).sum()),\n",
+    "        'fully_paid_pct': float((y_test == 1).mean()),\n",
+    "        'charged_off_pct': float((y_test == 0).mean())\n",
+    "    },\n",
+    "    'final_features': final_features,\n",
+    "    'removed_features': features_to_remove,\n",
+    "    'max_correlation_between_features': float(max_corr)\n",
+    "}\n",
+    "\n",
+    "with open(f'{processed_data_dir}/preprocessing_info.json', 'w') as f:\n",
+    "    json.dump(preprocessing_info, f, indent=2)\n",
+    "\n",
+    "print(\"✅ Dataset saved successfully!\")\n",
+    "print(f\"\\n📁 Files created in '{processed_data_dir}':\")\n",
+    "print(\"   📊 train_data_scaled.csv     - Training data (standardized, ready for ANN)\")\n",
+    "print(\"   📊 test_data_scaled.csv      - Test data (standardized, ready for ANN)\")\n",
+    "print(\"   📊 train_data_original.csv   - Training data (original scale)\")\n",
+    "print(\"   📊 test_data_original.csv    - Test data (original scale)\")\n",
+    "print(\"   📋 feature_info.csv          - Feature descriptions and metadata\")\n",
+    "print(\"   ⚙️ scaler_params.csv         - StandardScaler parameters\")\n",
+    "print(\"   📄 preprocessing_info.json   - Complete preprocessing metadata\")\n",
+    "\n",
+    "print(f\"\\n📈 Dataset Summary:\")\n",
+    "print(f\"   Training samples: {len(train_data):,}\")\n",
+    "print(f\"   Test samples: {len(test_data):,}\")\n",
+    "print(f\"   Features: {len(final_features)}\")\n",
+    "print(f\"   Target variable: loan_repaid (1=Fully Paid, 0=Charged Off)\")\n",
+    "print(f\"\\n🎯 Ready for ANN Training!\")\n",
+    "print(f\"   Load: train_data_scaled.csv and test_data_scaled.csv\")\n",
+    "print(f\"   Features: {final_features}\")"
+   ]
+  }
+ ],
+ "metadata": {
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+   "display_name": "ml",
+   "language": "python",
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+    "version": 3
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+   "file_extension": ".py",
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