feature_engineering(H).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import scipy\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv(\"./Dataset/train.csv\")\n",
    "test_df = pd.read_csv(\"./Dataset/test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_c = train_df.copy()\n",
    "test_df_c = test_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6548 entries, 0 to 6547\n",
      "Data columns (total 24 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   Student ID                      6548 non-null   object \n",
      " 1   Age                             6456 non-null   float64\n",
      " 2   Gender                          6548 non-null   object \n",
      " 3   Home Region                     6546 non-null   object \n",
      " 4   Home City                       6546 non-null   object \n",
      " 5   Program ID                      6548 non-null   object \n",
      " 6   Program Main Category Code      6548 non-null   object \n",
      " 7   Program Sub Category Code       5613 non-null   object \n",
      " 8   Technology Type                 3566 non-null   object \n",
      " 9   Program Skill Level             4902 non-null   object \n",
      " 10  Program Presentation Method     6548 non-null   object \n",
      " 11  Program Start Date              6548 non-null   object \n",
      " 12  Program End Date                6548 non-null   object \n",
      " 13  Program Days                    6548 non-null   int64  \n",
      " 14  Completed Degree                6548 non-null   object \n",
      " 15  Level of Education              6522 non-null   object \n",
      " 16  Education Speaciality           6271 non-null   object \n",
      " 17  College                         2658 non-null   object \n",
      " 18  University Degree Score         6467 non-null   float64\n",
      " 19  University Degree Score System  6467 non-null   float64\n",
      " 20  Employment Status               5982 non-null   object \n",
      " 21  Job Type                        1981 non-null   object \n",
      " 22  Still Working                   1981 non-null   object \n",
      " 23  Y                               6548 non-null   int64  \n",
      "dtypes: float64(3), int64(2), object(19)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engieerning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First feature is is gpa_grade\n",
    "# in gpa system of 5 the grading is as follows \n",
    "# 2.00- 2.74 Acceptable\n",
    "# 2.75 - 3.74 Good\n",
    "# 3.75 - 4.49 Very Good\n",
    "# 4.5 - 5 Excellent\n",
    "\n",
    "# in gpa system of 4 the grading is as follows \n",
    "# 1.00- 1.74 Acceptable\n",
    "# 1.75 - 2.74 Good\n",
    "# 2.75 - 3.49 Very Good\n",
    "# 3.5 - 4 Excellent\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>University Degree Score</th>\n",
       "      <th>University Degree Score System</th>\n",
       "      <th>GPA_Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.44</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Excellent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.55</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Very Good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   University Degree Score  University Degree Score System  GPA_Grade\n",
       "0                     2.44                             4.0       Good\n",
       "1                     5.00                             5.0  Excellent\n",
       "2                     3.50                             5.0       Good\n",
       "3                     3.55                             5.0       Good\n",
       "4                     4.00                             5.0  Very Good"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def classify_gpa(score, system):\n",
    "    if system == 5:\n",
    "        if 2.00 <= score <= 2.74:\n",
    "            return \"Acceptable\"\n",
    "        elif 2.75 <= score <= 3.74:\n",
    "            return \"Good\"\n",
    "        elif 3.75 <= score <= 4.49:\n",
    "            return \"Very Good\"\n",
    "        elif 4.5 <= score <= 5.00:\n",
    "            return \"Excellent\"\n",
    "        else:\n",
    "            return \"Not Applicable\"\n",
    "    elif system == 4:\n",
    "        if 1.00 <= score <= 1.74:\n",
    "            return \"Acceptable\"\n",
    "        elif 1.75 <= score <= 2.74:\n",
    "            return \"Good\"\n",
    "        elif 2.75 <= score <= 3.49:\n",
    "            return \"Very Good\"\n",
    "        elif 3.5 <= score <= 4.00:\n",
    "            return \"Excellent\"\n",
    "        else:\n",
    "            return \"Not Applicable\"\n",
    "    else:\n",
    "        return \"Unknown System\"\n",
    "\n",
    "train_df_c['GPA_Grade'] = train_df_c.apply(lambda x: classify_gpa(x['University Degree Score'], x['University Degree Score System']), axis=1)\n",
    "\n",
    "train_df_c[['University Degree Score', 'University Degree Score System', 'GPA_Grade']].head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How ***far*** each city is from riyadh is\n",
    "## that could play a big factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unq_cites=train_df[\"Home City\"].unique()\n",
    "len(unq_cites)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the list into two halves\n",
    "half = len(unq_cites) // 2\n",
    "first_half = unq_cites[:half]\n",
    "second_half = unq_cites[half:]\n",
    "\n",
    "# Write the first half to a file\n",
    "with open('cities_1.txt', 'w', encoding='utf-8') as f:\n",
    "    for city in first_half:\n",
    "        f.write(str(city) + '\\n')\n",
    "\n",
    "# Write the second half to a file\n",
    "with open('cities_2.txt', 'w', encoding='utf-8') as f:\n",
    "    for city in second_half:\n",
    "        f.write(str(city) + '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a dict with the distance to riyadh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>cities</th>\n",
       "      <th>distances</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>الرياض</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>خميس مشيط</td>\n",
       "      <td>822.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>حفر الباطن</td>\n",
       "      <td>422.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>جدة</td>\n",
       "      <td>849.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>نجران</td>\n",
       "      <td>838.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>مكة المكرمة</td>\n",
       "      <td>793.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ينبع</td>\n",
       "      <td>879.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>المدينة المنورة</td>\n",
       "      <td>720.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>الهفوف</td>\n",
       "      <td>383.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>بريدة</td>\n",
       "      <td>330.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>تبوك</td>\n",
       "      <td>1089.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>الأحساء</td>\n",
       "      <td>286.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>الجبيل</td>\n",
       "      <td>390.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>عرعر</td>\n",
       "      <td>895.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>أبها</td>\n",
       "      <td>842.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>أبو عريش</td>\n",
       "      <td>947.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>الدمام</td>\n",
       "      <td>389.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>الطائف</td>\n",
       "      <td>749.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>الباحة</td>\n",
       "      <td>750.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>أحد المسارحة</td>\n",
       "      <td>830.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>حائل</td>\n",
       "      <td>592.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>الدلم</td>\n",
       "      <td>114.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>القطيف</td>\n",
       "      <td>389.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>الدرعية</td>\n",
       "      <td>17.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>جيزان</td>\n",
       "      <td>842.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>الخبر</td>\n",
       "      <td>393.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>المجمعة</td>\n",
       "      <td>192.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>الخرج</td>\n",
       "      <td>85.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>الظهران</td>\n",
       "      <td>387.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>الدوادمي</td>\n",
       "      <td>237.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>المزاحمية</td>\n",
       "      <td>50.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>عنيزة</td>\n",
       "      <td>315.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>القنفذة</td>\n",
       "      <td>849.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>الليث</td>\n",
       "      <td>832.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>شرورة</td>\n",
       "      <td>802.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>الرس</td>\n",
       "      <td>409.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>صامطة</td>\n",
       "      <td>981.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>شقراء</td>\n",
       "      <td>160.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>سيهات</td>\n",
       "      <td>387.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>صفوى</td>\n",
       "      <td>387.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>سكاكا</td>\n",
       "      <td>872.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>البدائع</td>\n",
       "      <td>331.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>رابغ</td>\n",
       "      <td>809.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>رفحاء</td>\n",
       "      <td>635.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             cities  distances\n",
       "0            الرياض       0.00\n",
       "1         خميس مشيط     822.50\n",
       "2        حفر الباطن     422.67\n",
       "3               جدة     849.76\n",
       "4             نجران     838.30\n",
       "5       مكة المكرمة     793.92\n",
       "6              ينبع     879.45\n",
       "7   المدينة المنورة     720.19\n",
       "8            الهفوف     383.63\n",
       "9             بريدة     330.55\n",
       "10             تبوك    1089.64\n",
       "11          الأحساء     286.11\n",
       "12           الجبيل     390.41\n",
       "13             عرعر     895.48\n",
       "14             أبها     842.43\n",
       "15         أبو عريش     947.89\n",
       "16           الدمام     389.73\n",
       "17           الطائف     749.34\n",
       "18           الباحة     750.82\n",
       "19     أحد المسارحة     830.20\n",
       "20             حائل     592.76\n",
       "21            الدلم     114.00\n",
       "22           القطيف     389.83\n",
       "23          الدرعية      17.01\n",
       "24            جيزان     842.85\n",
       "25            الخبر     393.04\n",
       "26          المجمعة     192.60\n",
       "27            الخرج      85.33\n",
       "28          الظهران     387.14\n",
       "29         الدوادمي     237.85\n",
       "30        المزاحمية      50.13\n",
       "31            عنيزة     315.70\n",
       "32          القنفذة     849.00\n",
       "33            الليث     832.22\n",
       "34            شرورة     802.25\n",
       "35             الرس     409.00\n",
       "36            صامطة     981.68\n",
       "37            شقراء     160.07\n",
       "38            سيهات     387.74\n",
       "39             صفوى     387.62\n",
       "40            سكاكا     872.30\n",
       "41          البدائع     331.26\n",
       "42             رابغ     809.47\n",
       "43            رفحاء     635.88"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's create a dictionary with two keys: \"cities\" and \"distances\", where each key corresponds to a list.\n",
    "cities_distance = {\n",
    "    \"cities\": [\n",
    "        \"الرياض\", \"خميس مشيط\", \"حفر الباطن\", \"جدة\", \"نجران\",\n",
    "        \"مكة المكرمة\", \"ينبع\", \"المدينة المنورة\", \"الهفوف\", \"بريدة\",\n",
    "        \"تبوك\", \"الأحساء\", \"الجبيل\", \"عرعر\",\n",
    "        \"أبها\", \"أبو عريش\", \"الدمام\", \"الطائف\", \"الباحة\",\n",
    "        \"أحد المسارحة\", \"حائل\", \"الدلم\", \"القطيف\", \"الدرعية\",\n",
    "        \"جيزان\",  \"الخبر\", \"المجمعة\", \"الخرج\",\n",
    "        \"الظهران\", \"الدوادمي\", \"المزاحمية\", \"عنيزة\", \"القنفذة\",\n",
    "        \"الليث\", \"شرورة\", \"الرس\", \"صامطة\", \"شقراء\",\n",
    "        \"سيهات\", \"صفوى\", \"سكاكا\", \"البدائع\", \"رابغ\", \"رفحاء\"\n",
    "    ],\n",
    "    \"distances\": [\n",
    "        0, 822.50, 422.67, 849.76, 838.30,\n",
    "        793.92, 879.45, 720.19, 383.63, 330.55,\n",
    "        1089.64, 286.11, 390.41, 895.48,\n",
    "        842.43, 947.89, 389.73, 749.34, 750.82,\n",
    "        830.20, 592.76, 114, 389.83, 17.01,\n",
    "        842.85, 393.04, 192.60, 85.33,\n",
    "        387.14, 237.85, 50.13, 315.70, 849,\n",
    "        832.22, 802.25, 409, 981.68, 160.07,\n",
    "        387.74, 387.62, 872.30, 331.26, 809.47, 635.88\n",
    "    ]\n",
    "}\n",
    "\n",
    "\n",
    "\n",
    "# Convert the dictionary to a DataFrame\n",
    "df_cities_distance = pd.DataFrame(cities_distance)\n",
    "\n",
    "df_cities_distance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>City</th>\n",
       "      <th>Distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>بيشة</td>\n",
       "      <td>672.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>محايل</td>\n",
       "      <td>390.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>المذنب</td>\n",
       "      <td>284.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>الخفجي</td>\n",
       "      <td>450.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>رأس تنورة</td>\n",
       "      <td>403.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>صفوى</td>\n",
       "      <td>387.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>سكاكا</td>\n",
       "      <td>872.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>البدائع</td>\n",
       "      <td>331.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>رابغ</td>\n",
       "      <td>809.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>رفحاء</td>\n",
       "      <td>635.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>91 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         City  Distance\n",
       "0        بيشة    672.65\n",
       "1       محايل    390.10\n",
       "2      المذنب    284.82\n",
       "3      الخفجي    450.32\n",
       "4   رأس تنورة    403.60\n",
       "..        ...       ...\n",
       "86       صفوى    387.62\n",
       "87      سكاكا    872.30\n",
       "88    البدائع    331.26\n",
       "89       رابغ    809.47\n",
       "90      رفحاء    635.88\n",
       "\n",
       "[91 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Your original data\n",
    "cities_1 = ['بيشة', 'محايل', 'المذنب', 'الخفجي', 'رأس تنورة', 'ضمد', 'القريات', 'حوطة بني تميم', 'ضبا', 'مهد الذهب', 'بارق', 'بيش', 'دومة الجندل', 'صبيا', 'العرضيات', 'الحناكية', 'سراة عبيدة', 'أحد رفيدة', 'تربة', 'العلا', 'الحريملاء', 'بلجرشي', 'بقيق', 'القرى', 'رجال ألمع', 'المجاردة', 'وادي الدواسر', 'وادي الفرع', 'محايل عسير', 'الزلفي', 'بدر', 'تثليت', 'عنك', 'البكيرية', 'طريف', 'بني حسن', 'رياض الخبراء', 'طبرجل', 'ثادق', 'الجموم', 'الغاط', 'تيماء', 'ظهران الجنوب', 'أملج', 'ابها', 'الدرب', 'بحرة']\n",
    "distances_1 = [672.65, 390.1, 284.82, 450.32, 403.6, 936.61, 850.27, 89.49, 1140.38, 608.47, 19.43, 774.62, 883.95, 938.15, 17.9, 626.83, 823.43, 824.88, 649.55, 904.66, 77.5, 756.95, 390.94, 349.8, 856.22, 794.9, 845.47, 509.48, 954.17, 262.24, 812.27, 661.43, 387.84, 357.35, 897.82, 762.45, 350.45, 14.6, 108.64, 794.73, 231.97, 879.41, 848.66, 949.86, 842.43, 899.74, 829.66]\n",
    "\n",
    "# New data\n",
    "cities_2 = [\"الرياض\", \"خميس مشيط\", \"حفر الباطن\", \"جدة\", \"نجران\", \"مكة المكرمة\", \"ينبع\", \"المدينة المنورة\", \"الهفوف\", \"بريدة\", \"تبوك\", \"الأحساء\", \"الجبيل\", \"عرعر\", \"أبها\", \"أبو عريش\", \"الدمام\", \"الطائف\", \"الباحة\", \"أحد المسارحة\", \"حائل\", \"الدلم\", \"القطيف\", \"الدرعية\", \"جيزان\", \"الخبر\", \"المجمعة\", \"الخرج\", \"الظهران\", \"الدوادمي\", \"المزاحمية\", \"عنيزة\", \"القنفذة\", \"الليث\", \"شرورة\", \"الرس\", \"صامطة\", \"شقراء\", \"سيهات\", \"صفوى\", \"سكاكا\", \"البدائع\", \"رابغ\", \"رفحاء\"]\n",
    "distances_2 = [0, 822.50, 422.67, 849.76, 838.30, 793.92, 879.45, 720.19, 383.63, 330.55, 1089.64, 286.11, 390.41, 895.48, 842.43, 947.89, 389.73, 749.34, 750.82, 830.20, 592.76, 114, 389.83, 17.01, 842.85, 393.04, 192.60, 85.33, 387.14, 237.85, 50.13, 315.70, 849, 832.22, 802.25, 409, 981.68, 160.07, 387.74, 387.62, 872.30, 331.26, 809.47, 635.88]\n",
    "\n",
    "# Combine the two lists\n",
    "cities = cities_1 + cities_2\n",
    "distances = distances_1 + distances_2\n",
    "\n",
    "# Create the DataFrame\n",
    "df_cities_distance = pd.DataFrame({'City': cities, 'Distance': distances})\n",
    "\n",
    "df_cities_distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge\n",
    "distance_map = df_cities_distance.set_index('City')['Distance'].to_dict()\n",
    "train_df_c['distance'] = train_df_c['Home City'].map(lambda x: distance_map.get(x))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Student ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Home Region</th>\n",
       "      <th>Home City</th>\n",
       "      <th>Program ID</th>\n",
       "      <th>Program Main Category Code</th>\n",
       "      <th>Program Sub Category Code</th>\n",
       "      <th>Technology Type</th>\n",
       "      <th>Program Skill Level</th>\n",
       "      <th>...</th>\n",
       "      <th>Education Speaciality</th>\n",
       "      <th>College</th>\n",
       "      <th>University Degree Score</th>\n",
       "      <th>University Degree Score System</th>\n",
       "      <th>Employment Status</th>\n",
       "      <th>Job Type</th>\n",
       "      <th>Still Working</th>\n",
       "      <th>Y</th>\n",
       "      <th>GPA_Grade</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1926</th>\n",
       "      <td>36ed263b-22fb-491d-8cc3-509f0ab9a5d0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>أنثى</td>\n",
       "      <td>منطقة الرياض</td>\n",
       "      <td>حوطة بني تميم</td>\n",
       "      <td>9f5abff4-6dc1-4ac0-8769-7dad61963b6d</td>\n",
       "      <td>CAUF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>مبتدئ</td>\n",
       "      <td>...</td>\n",
       "      <td>نظم المعلومات</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.99</td>\n",
       "      <td>5.0</td>\n",
       "      <td>غير موظف</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>89.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Student ID   Age Gender   Home Region  \\\n",
       "1926  36ed263b-22fb-491d-8cc3-509f0ab9a5d0  22.0   أنثى  منطقة الرياض   \n",
       "\n",
       "          Home City                            Program ID  \\\n",
       "1926  حوطة بني تميم  9f5abff4-6dc1-4ac0-8769-7dad61963b6d   \n",
       "\n",
       "     Program Main Category Code Program Sub Category Code Technology Type  \\\n",
       "1926                       CAUF                       NaN             NaN   \n",
       "\n",
       "     Program Skill Level  ... Education Speaciality College  \\\n",
       "1926               مبتدئ  ...         نظم المعلومات     NaN   \n",
       "\n",
       "     University Degree Score  University Degree Score System  \\\n",
       "1926                    4.99                             5.0   \n",
       "\n",
       "     Employment Status Job Type Still Working  Y  GPA_Grade  distance  \n",
       "1926          غير موظف      NaN           NaN  0  Excellent     89.49  \n",
       "\n",
       "[1 rows x 26 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_c[train_df_c['Home City'] == \"حوطة بني تميم\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Encoding "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0.00\n",
       "1       822.50\n",
       "2         0.00\n",
       "3         0.00\n",
       "4         0.00\n",
       "         ...  \n",
       "6543      0.00\n",
       "6544    330.55\n",
       "6545      0.00\n",
       "6546      0.00\n",
       "6547      0.00\n",
       "Name: distance, Length: 6548, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_c['distance']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# le = LabelEncoder()\n",
    "# categorical_features = ['Gender', 'Home Region', 'Home City', 'Program ID', \n",
    "#                         'Program Main Category Code', 'Program Sub Category Code', \n",
    "#                         'Technology Type', 'Program Skill Level', \n",
    "#                         'Program Presentation Method', 'Completed Degree', \n",
    "#                         'Level of Education', 'Education Speaciality', 'College', \n",
    "#                         'Employment Status', 'Job Type','Still Working',\"Student ID\",\"GPA_Grade\"]\n",
    "\n",
    "# for feature in categorical_features:\n",
    "#     train_df_c[feature] = le.fit_transform(train_df_c[feature])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Student ID</th>\n",
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       "      <th>Program ID</th>\n",
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       "      <th>...</th>\n",
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       "      <th>distance</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4f14c50d-162e-4a15-9cf0-ec129c33bcf0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>ذكر</td>\n",
       "      <td>منطقة الرياض</td>\n",
       "      <td>الرياض</td>\n",
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       "      <td>PCRF</td>\n",
       "      <td>PCRF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>متوسط</td>\n",
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       "      <td>5.00</td>\n",
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       "      <td>طالب</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>Excellent</td>\n",
       "      <td>822.5</td>\n",
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       "      <td>38a11c0e-4afc-4261-9c64-e94cc0a272fb</td>\n",
       "      <td>24.0</td>\n",
       "      <td>ذكر</td>\n",
       "      <td>منطقة الرياض</td>\n",
       "      <td>الرياض</td>\n",
       "      <td>e006900d-05a9-4c2b-a36f-0ffb9fce44cd</td>\n",
       "      <td>APMR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>متوسط</td>\n",
       "      <td>...</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>موظف</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Good</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1693e85b-f80e-40ce-846f-395ddcece6d3</td>\n",
       "      <td>23.0</td>\n",
       "      <td>ذكر</td>\n",
       "      <td>منطقة الرياض</td>\n",
       "      <td>الرياض</td>\n",
       "      <td>2ec15f6b-233b-428a-b9f5-e40bc8d14cf9</td>\n",
       "      <td>TOSL</td>\n",
       "      <td>TOSL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>حوسبة تطبيقية - (مسار شبكات الحاسب)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.55</td>\n",
       "      <td>5.0</td>\n",
       "      <td>خريج</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Good</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>98a0e8d0-5f80-4634-afd8-322aa0902863</td>\n",
       "      <td>23.0</td>\n",
       "      <td>ذكر</td>\n",
       "      <td>منطقة الرياض</td>\n",
       "      <td>الرياض</td>\n",
       "      <td>d32da0e9-1aed-48c3-992d-a22f9ccc741e</td>\n",
       "      <td>CAUF</td>\n",
       "      <td>SWPS</td>\n",
       "      <td>تقليدية</td>\n",
       "      <td>متوسط</td>\n",
       "      <td>...</td>\n",
       "      <td>نظم المعلومات الحاسوبية</td>\n",
       "      <td>تكنولوجيا الاتصالات والمعلومات</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Student ID   Age Gender   Home Region  Home City  \\\n",
       "0  4f14c50d-162e-4a15-9cf0-ec129c33bcf0  37.0    ذكر  منطقة الرياض     الرياض   \n",
       "1  0599d409-876b-41a5-af05-749ef0e77d32  21.0    ذكر    منطقة عسير  خميس مشيط   \n",
       "2  38a11c0e-4afc-4261-9c64-e94cc0a272fb  24.0    ذكر  منطقة الرياض     الرياض   \n",
       "3  1693e85b-f80e-40ce-846f-395ddcece6d3  23.0    ذكر  منطقة الرياض     الرياض   \n",
       "4  98a0e8d0-5f80-4634-afd8-322aa0902863  23.0    ذكر  منطقة الرياض     الرياض   \n",
       "\n",
       "                             Program ID Program Main Category Code  \\\n",
       "0  453686d8-4023-4506-b2df-fac8b059ac26                       PCRF   \n",
       "1  cc8e4e42-65d5-4fa1-82f9-6c6c2d508b60                       APMR   \n",
       "2  e006900d-05a9-4c2b-a36f-0ffb9fce44cd                       APMR   \n",
       "3  2ec15f6b-233b-428a-b9f5-e40bc8d14cf9                       TOSL   \n",
       "4  d32da0e9-1aed-48c3-992d-a22f9ccc741e                       CAUF   \n",
       "\n",
       "  Program Sub Category Code Technology Type Program Skill Level  ...  \\\n",
       "0                      PCRF             NaN                 NaN  ...   \n",
       "1                      SWPS             NaN               متوسط  ...   \n",
       "2                       NaN             NaN               متوسط  ...   \n",
       "3                      TOSL             NaN                 NaN  ...   \n",
       "4                      SWPS         تقليدية               متوسط  ...   \n",
       "\n",
       "                 Education Speaciality                         College  \\\n",
       "0                     هندسة حاسب الالي                             NaN   \n",
       "1           الإذاعة والتلفزيون والفيلم        الفنون والعلوم الإنسانية   \n",
       "2               Information Technology                             NaN   \n",
       "3  حوسبة تطبيقية - (مسار شبكات الحاسب)                             NaN   \n",
       "4              نظم المعلومات الحاسوبية  تكنولوجيا الاتصالات والمعلومات   \n",
       "\n",
       "  University Degree Score  University Degree Score System Employment Status  \\\n",
       "0                    2.44                             4.0          غير موظف   \n",
       "1                    5.00                             5.0              طالب   \n",
       "2                    3.50                             5.0              موظف   \n",
       "3                    3.55                             5.0              خريج   \n",
       "4                    4.00                             5.0               NaN   \n",
       "\n",
       "  Job Type Still Working  Y  GPA_Grade  distance  \n",
       "0      NaN           NaN  0       Good       0.0  \n",
       "1      NaN           NaN  0  Excellent     822.5  \n",
       "2      NaN           NaN  0       Good       0.0  \n",
       "3      NaN           NaN  0       Good       0.0  \n",
       "4      NaN           NaN  0  Very Good       0.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_c.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis after encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Student ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Home Region</th>\n",
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       "      <th>Program Main Category Code</th>\n",
       "      <th>Program Sub Category Code</th>\n",
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       "      <th>Program Skill Level</th>\n",
       "      <th>...</th>\n",
       "      <th>Education Speaciality</th>\n",
       "      <th>College</th>\n",
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       "      <th>Y</th>\n",
       "      <th>GPA_Grade</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4f14c50d-162e-4a15-9cf0-ec129c33bcf0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>ذكر</td>\n",
       "      <td>منطقة الرياض</td>\n",
       "      <td>الرياض</td>\n",
       "      <td>453686d8-4023-4506-b2df-fac8b059ac26</td>\n",
       "      <td>PCRF</td>\n",
       "      <td>PCRF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>هندسة حاسب الالي</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.44</td>\n",
       "      <td>4.0</td>\n",
       "      <td>غير موظف</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Good</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0599d409-876b-41a5-af05-749ef0e77d32</td>\n",
       "      <td>21.0</td>\n",
       "      <td>ذكر</td>\n",
       "      <td>منطقة عسير</td>\n",
       "      <td>خميس مشيط</td>\n",
       "      <td>cc8e4e42-65d5-4fa1-82f9-6c6c2d508b60</td>\n",
       "      <td>APMR</td>\n",
       "      <td>SWPS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>متوسط</td>\n",
       "      <td>...</td>\n",
       "      <td>الإذاعة والتلفزيون والفيلم</td>\n",
       "      <td>الفنون والعلوم الإنسانية</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>طالب</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>822.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38a11c0e-4afc-4261-9c64-e94cc0a272fb</td>\n",
       "      <td>24.0</td>\n",
       "      <td>ذكر</td>\n",
       "      <td>منطقة الرياض</td>\n",
       "      <td>الرياض</td>\n",
       "      <td>e006900d-05a9-4c2b-a36f-0ffb9fce44cd</td>\n",
       "      <td>APMR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>متوسط</td>\n",
       "      <td>...</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>موظف</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Good</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1693e85b-f80e-40ce-846f-395ddcece6d3</td>\n",
       "      <td>23.0</td>\n",
       "      <td>ذكر</td>\n",
       "      <td>منطقة الرياض</td>\n",
       "      <td>الرياض</td>\n",
       "      <td>2ec15f6b-233b-428a-b9f5-e40bc8d14cf9</td>\n",
       "      <td>TOSL</td>\n",
       "      <td>TOSL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>حوسبة تطبيقية - (مسار شبكات الحاسب)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.55</td>\n",
       "      <td>5.0</td>\n",
       "      <td>خريج</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Good</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>98a0e8d0-5f80-4634-afd8-322aa0902863</td>\n",
       "      <td>23.0</td>\n",
       "      <td>ذكر</td>\n",
       "      <td>منطقة الرياض</td>\n",
       "      <td>الرياض</td>\n",
       "      <td>d32da0e9-1aed-48c3-992d-a22f9ccc741e</td>\n",
       "      <td>CAUF</td>\n",
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       "      <td>متوسط</td>\n",
       "      <td>...</td>\n",
       "      <td>نظم المعلومات الحاسوبية</td>\n",
       "      <td>تكنولوجيا الاتصالات والمعلومات</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Very Good</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>6543</th>\n",
       "      <td>cd196579-9590-441b-8787-41078f3cee25</td>\n",
       "      <td>31.0</td>\n",
       "      <td>أنثى</td>\n",
       "      <td>منطقة الرياض</td>\n",
       "      <td>الرياض</td>\n",
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       "      <td>CAUF</td>\n",
       "      <td>SWPS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>تقنية المعلومات</td>\n",
       "      <td>تكنولوجيا الاتصالات والمعلومات</td>\n",
       "      <td>4.40</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>6544</th>\n",
       "      <td>37bfc11c-ff8c-42dc-9cf9-0d13bb8f7131</td>\n",
       "      <td>27.0</td>\n",
       "      <td>أنثى</td>\n",
       "      <td>منطقة القصيم</td>\n",
       "      <td>بريدة</td>\n",
       "      <td>e94942dd-8684-4746-97ae-df567b9b0a4a</td>\n",
       "      <td>PCRF</td>\n",
       "      <td>PCRF</td>\n",
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       "      <td>مبتدئ</td>\n",
       "      <td>...</td>\n",
       "      <td>علوم الحاسب</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.46</td>\n",
       "      <td>5.0</td>\n",
       "      <td>موظف</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>330.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6545</th>\n",
       "      <td>fc114302-a79f-439f-a08b-fe0a51cf839e</td>\n",
       "      <td>24.0</td>\n",
       "      <td>أنثى</td>\n",
       "      <td>منطقة الرياض</td>\n",
       "      <td>الرياض</td>\n",
       "      <td>02ae0b47-64a6-47a1-b3c5-c0e4df393c30</td>\n",
       "      <td>PCRF</td>\n",
       "      <td>PCRF</td>\n",
       "      <td>تقليدية</td>\n",
       "      <td>مبتدئ</td>\n",
       "      <td>...</td>\n",
       "      <td>نظم المعلومات</td>\n",
       "      <td>تكنولوجيا الاتصالات والمعلومات</td>\n",
       "      <td>4.93</td>\n",
       "      <td>5.0</td>\n",
       "      <td>موظف</td>\n",
       "      <td>دوام كامل</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6546</th>\n",
       "      <td>4b6d9a36-4402-4c75-bc3a-fca927dbaf65</td>\n",
       "      <td>25.0</td>\n",
       "      <td>ذكر</td>\n",
       "      <td>منطقة الرياض</td>\n",
       "      <td>الرياض</td>\n",
       "      <td>9b4cedaa-fac0-4eac-aa4b-b05b6a0c97ff</td>\n",
       "      <td>PCRF</td>\n",
       "      <td>PCRF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>متوسط</td>\n",
       "      <td>...</td>\n",
       "      <td>تقنية المعلومات</td>\n",
       "      <td>تكنولوجيا الاتصالات والمعلومات</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>غير موظف</td>\n",
       "      <td>تدريب</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6547</th>\n",
       "      <td>008f3386-0d43-45a4-8372-b282e5a0101a</td>\n",
       "      <td>37.0</td>\n",
       "      <td>أنثى</td>\n",
       "      <td>منطقة الرياض</td>\n",
       "      <td>الرياض</td>\n",
       "      <td>d8fbc53c-e042-4e45-b8c7-42b4ad0528ee</td>\n",
       "      <td>CAUF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>مبتدئ</td>\n",
       "      <td>...</td>\n",
       "      <td>دراسات وقضايا معاصرة</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.32</td>\n",
       "      <td>5.0</td>\n",
       "      <td>موظف</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6548 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Student ID   Age Gender   Home Region  \\\n",
       "0     4f14c50d-162e-4a15-9cf0-ec129c33bcf0  37.0    ذكر  منطقة الرياض   \n",
       "1     0599d409-876b-41a5-af05-749ef0e77d32  21.0    ذكر    منطقة عسير   \n",
       "2     38a11c0e-4afc-4261-9c64-e94cc0a272fb  24.0    ذكر  منطقة الرياض   \n",
       "3     1693e85b-f80e-40ce-846f-395ddcece6d3  23.0    ذكر  منطقة الرياض   \n",
       "4     98a0e8d0-5f80-4634-afd8-322aa0902863  23.0    ذكر  منطقة الرياض   \n",
       "...                                    ...   ...    ...           ...   \n",
       "6543  cd196579-9590-441b-8787-41078f3cee25  31.0   أنثى  منطقة الرياض   \n",
       "6544  37bfc11c-ff8c-42dc-9cf9-0d13bb8f7131  27.0   أنثى  منطقة القصيم   \n",
       "6545  fc114302-a79f-439f-a08b-fe0a51cf839e  24.0   أنثى  منطقة الرياض   \n",
       "6546  4b6d9a36-4402-4c75-bc3a-fca927dbaf65  25.0    ذكر  منطقة الرياض   \n",
       "6547  008f3386-0d43-45a4-8372-b282e5a0101a  37.0   أنثى  منطقة الرياض   \n",
       "\n",
       "      Home City                            Program ID  \\\n",
       "0        الرياض  453686d8-4023-4506-b2df-fac8b059ac26   \n",
       "1     خميس مشيط  cc8e4e42-65d5-4fa1-82f9-6c6c2d508b60   \n",
       "2        الرياض  e006900d-05a9-4c2b-a36f-0ffb9fce44cd   \n",
       "3        الرياض  2ec15f6b-233b-428a-b9f5-e40bc8d14cf9   \n",
       "4        الرياض  d32da0e9-1aed-48c3-992d-a22f9ccc741e   \n",
       "...         ...                                   ...   \n",
       "6543     الرياض  4f8c696a-b783-4d40-9776-105f6d3bd624   \n",
       "6544      بريدة  e94942dd-8684-4746-97ae-df567b9b0a4a   \n",
       "6545     الرياض  02ae0b47-64a6-47a1-b3c5-c0e4df393c30   \n",
       "6546     الرياض  9b4cedaa-fac0-4eac-aa4b-b05b6a0c97ff   \n",
       "6547     الرياض  d8fbc53c-e042-4e45-b8c7-42b4ad0528ee   \n",
       "\n",
       "     Program Main Category Code Program Sub Category Code Technology Type  \\\n",
       "0                          PCRF                      PCRF             NaN   \n",
       "1                          APMR                      SWPS             NaN   \n",
       "2                          APMR                       NaN             NaN   \n",
       "3                          TOSL                      TOSL             NaN   \n",
       "4                          CAUF                      SWPS         تقليدية   \n",
       "...                         ...                       ...             ...   \n",
       "6543                       CAUF                      SWPS             NaN   \n",
       "6544                       PCRF                      PCRF             NaN   \n",
       "6545                       PCRF                      PCRF         تقليدية   \n",
       "6546                       PCRF                      PCRF             NaN   \n",
       "6547                       CAUF                       NaN             NaN   \n",
       "\n",
       "     Program Skill Level  ...                Education Speaciality  \\\n",
       "0                    NaN  ...                     هندسة حاسب الالي   \n",
       "1                  متوسط  ...           الإذاعة والتلفزيون والفيلم   \n",
       "2                  متوسط  ...               Information Technology   \n",
       "3                    NaN  ...  حوسبة تطبيقية - (مسار شبكات الحاسب)   \n",
       "4                  متوسط  ...              نظم المعلومات الحاسوبية   \n",
       "...                  ...  ...                                  ...   \n",
       "6543                 NaN  ...                      تقنية المعلومات   \n",
       "6544               مبتدئ  ...                          علوم الحاسب   \n",
       "6545               مبتدئ  ...                        نظم المعلومات   \n",
       "6546               متوسط  ...                      تقنية المعلومات   \n",
       "6547               مبتدئ  ...                 دراسات وقضايا معاصرة   \n",
       "\n",
       "                             College University Degree Score  \\\n",
       "0                                NaN                    2.44   \n",
       "1           الفنون والعلوم الإنسانية                    5.00   \n",
       "2                                NaN                    3.50   \n",
       "3                                NaN                    3.55   \n",
       "4     تكنولوجيا الاتصالات والمعلومات                    4.00   \n",
       "...                              ...                     ...   \n",
       "6543  تكنولوجيا الاتصالات والمعلومات                    4.40   \n",
       "6544                             NaN                    4.46   \n",
       "6545  تكنولوجيا الاتصالات والمعلومات                    4.93   \n",
       "6546  تكنولوجيا الاتصالات والمعلومات                    4.00   \n",
       "6547                             NaN                    4.32   \n",
       "\n",
       "      University Degree Score System Employment Status   Job Type  \\\n",
       "0                                4.0          غير موظف        NaN   \n",
       "1                                5.0              طالب        NaN   \n",
       "2                                5.0              موظف        NaN   \n",
       "3                                5.0              خريج        NaN   \n",
       "4                                5.0               NaN        NaN   \n",
       "...                              ...               ...        ...   \n",
       "6543                             5.0               NaN        NaN   \n",
       "6544                             5.0              موظف        NaN   \n",
       "6545                             5.0              موظف  دوام كامل   \n",
       "6546                             4.0          غير موظف      تدريب   \n",
       "6547                             5.0              موظف        NaN   \n",
       "\n",
       "     Still Working  Y  GPA_Grade  distance  \n",
       "0              NaN  0       Good      0.00  \n",
       "1              NaN  0  Excellent    822.50  \n",
       "2              NaN  0       Good      0.00  \n",
       "3              NaN  0       Good      0.00  \n",
       "4              NaN  0  Very Good      0.00  \n",
       "...            ... ..        ...       ...  \n",
       "6543           NaN  0  Very Good      0.00  \n",
       "6544           NaN  0  Very Good    330.55  \n",
       "6545           Yes  1  Excellent      0.00  \n",
       "6546            No  0  Excellent      0.00  \n",
       "6547           NaN  0  Very Good      0.00  \n",
       "\n",
       "[6548 rows x 26 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df['Age'].hist(figsize=(14,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split train data into validation and training datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train_df_c.drop([\"Y\",\"Program End Date\",\"Program Start Date\"],axis=1)\n",
    "y = train_df_c[\"Y\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Student ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Home Region</th>\n",
       "      <th>Home City</th>\n",
       "      <th>Program ID</th>\n",
       "      <th>Program Main Category Code</th>\n",
       "      <th>Program Sub Category Code</th>\n",
       "      <th>Technology Type</th>\n",
       "      <th>Program Skill Level</th>\n",
       "      <th>...</th>\n",
       "      <th>Level of Education</th>\n",
       "      <th>Education Speaciality</th>\n",
       "      <th>College</th>\n",
       "      <th>University Degree Score</th>\n",
       "      <th>University Degree Score System</th>\n",
       "      <th>Employment Status</th>\n",
       "      <th>Job Type</th>\n",
       "      <th>Still Working</th>\n",
       "      <th>GPA_Grade</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4f14c50d-162e-4a15-9cf0-ec129c33bcf0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>ذكر</td>\n",
       "      <td>منطقة الرياض</td>\n",
       "      <td>الرياض</td>\n",
       "      <td>453686d8-4023-4506-b2df-fac8b059ac26</td>\n",
       "      <td>PCRF</td>\n",
       "      <td>PCRF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>البكالوريوس</td>\n",
       "      <td>هندسة حاسب الالي</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.44</td>\n",
       "      <td>4.0</td>\n",
       "      <td>غير موظف</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Good</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0599d409-876b-41a5-af05-749ef0e77d32</td>\n",
       "      <td>21.0</td>\n",
       "      <td>ذكر</td>\n",
       "      <td>منطقة عسير</td>\n",
       "      <td>خميس مشيط</td>\n",
       "      <td>cc8e4e42-65d5-4fa1-82f9-6c6c2d508b60</td>\n",
       "      <td>APMR</td>\n",
       "      <td>SWPS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>متوسط</td>\n",
       "      <td>...</td>\n",
       "      <td>البكالوريوس</td>\n",
       "      <td>الإذاعة والتلفزيون والفيلم</td>\n",
       "      <td>الفنون والعلوم الإنسانية</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>طالب</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>822.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38a11c0e-4afc-4261-9c64-e94cc0a272fb</td>\n",
       "      <td>24.0</td>\n",
       "      <td>ذكر</td>\n",
       "      <td>منطقة الرياض</td>\n",
       "      <td>الرياض</td>\n",
       "      <td>e006900d-05a9-4c2b-a36f-0ffb9fce44cd</td>\n",
       "      <td>APMR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>متوسط</td>\n",
       "      <td>...</td>\n",
       "      <td>البكالوريوس</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>موظف</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Good</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1693e85b-f80e-40ce-846f-395ddcece6d3</td>\n",
       "      <td>23.0</td>\n",
       "      <td>ذكر</td>\n",
       "      <td>منطقة الرياض</td>\n",
       "      <td>الرياض</td>\n",
       "      <td>2ec15f6b-233b-428a-b9f5-e40bc8d14cf9</td>\n",
       "      <td>TOSL</td>\n",
       "      <td>TOSL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>البكالوريوس</td>\n",
       "      <td>حوسبة تطبيقية - (مسار شبكات الحاسب)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.55</td>\n",
       "      <td>5.0</td>\n",
       "      <td>خريج</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Good</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>98a0e8d0-5f80-4634-afd8-322aa0902863</td>\n",
       "      <td>23.0</td>\n",
       "      <td>ذكر</td>\n",
       "      <td>منطقة الرياض</td>\n",
       "      <td>الرياض</td>\n",
       "      <td>d32da0e9-1aed-48c3-992d-a22f9ccc741e</td>\n",
       "      <td>CAUF</td>\n",
       "      <td>SWPS</td>\n",
       "      <td>تقليدية</td>\n",
       "      <td>متوسط</td>\n",
       "      <td>...</td>\n",
       "      <td>البكالوريوس</td>\n",
       "      <td>نظم المعلومات الحاسوبية</td>\n",
       "      <td>تكنولوجيا الاتصالات والمعلومات</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Student ID   Age Gender   Home Region  Home City  \\\n",
       "0  4f14c50d-162e-4a15-9cf0-ec129c33bcf0  37.0    ذكر  منطقة الرياض     الرياض   \n",
       "1  0599d409-876b-41a5-af05-749ef0e77d32  21.0    ذكر    منطقة عسير  خميس مشيط   \n",
       "2  38a11c0e-4afc-4261-9c64-e94cc0a272fb  24.0    ذكر  منطقة الرياض     الرياض   \n",
       "3  1693e85b-f80e-40ce-846f-395ddcece6d3  23.0    ذكر  منطقة الرياض     الرياض   \n",
       "4  98a0e8d0-5f80-4634-afd8-322aa0902863  23.0    ذكر  منطقة الرياض     الرياض   \n",
       "\n",
       "                             Program ID Program Main Category Code  \\\n",
       "0  453686d8-4023-4506-b2df-fac8b059ac26                       PCRF   \n",
       "1  cc8e4e42-65d5-4fa1-82f9-6c6c2d508b60                       APMR   \n",
       "2  e006900d-05a9-4c2b-a36f-0ffb9fce44cd                       APMR   \n",
       "3  2ec15f6b-233b-428a-b9f5-e40bc8d14cf9                       TOSL   \n",
       "4  d32da0e9-1aed-48c3-992d-a22f9ccc741e                       CAUF   \n",
       "\n",
       "  Program Sub Category Code Technology Type Program Skill Level  ...  \\\n",
       "0                      PCRF             NaN                 NaN  ...   \n",
       "1                      SWPS             NaN               متوسط  ...   \n",
       "2                       NaN             NaN               متوسط  ...   \n",
       "3                      TOSL             NaN                 NaN  ...   \n",
       "4                      SWPS         تقليدية               متوسط  ...   \n",
       "\n",
       "  Level of Education                Education Speaciality  \\\n",
       "0        البكالوريوس                     هندسة حاسب الالي   \n",
       "1        البكالوريوس           الإذاعة والتلفزيون والفيلم   \n",
       "2        البكالوريوس               Information Technology   \n",
       "3        البكالوريوس  حوسبة تطبيقية - (مسار شبكات الحاسب)   \n",
       "4        البكالوريوس              نظم المعلومات الحاسوبية   \n",
       "\n",
       "                          College University Degree Score  \\\n",
       "0                             NaN                    2.44   \n",
       "1        الفنون والعلوم الإنسانية                    5.00   \n",
       "2                             NaN                    3.50   \n",
       "3                             NaN                    3.55   \n",
       "4  تكنولوجيا الاتصالات والمعلومات                    4.00   \n",
       "\n",
       "  University Degree Score System Employment Status  Job Type  Still Working  \\\n",
       "0                            4.0          غير موظف       NaN            NaN   \n",
       "1                            5.0              طالب       NaN            NaN   \n",
       "2                            5.0              موظف       NaN            NaN   \n",
       "3                            5.0              خريج       NaN            NaN   \n",
       "4                            5.0               NaN       NaN            NaN   \n",
       "\n",
       "   GPA_Grade distance  \n",
       "0       Good      0.0  \n",
       "1  Excellent    822.5  \n",
       "2       Good      0.0  \n",
       "3       Good      0.0  \n",
       "4  Very Good      0.0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5238, 23)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1310, 23)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Student ID', 'Age', 'Gender', 'Home Region', 'Home City', 'Program ID',\n",
       "       'Program Main Category Code', 'Program Sub Category Code',\n",
       "       'Technology Type', 'Program Skill Level', 'Program Presentation Method',\n",
       "       'Program Days', 'Completed Degree', 'Level of Education',\n",
       "       'Education Speaciality', 'College', 'University Degree Score',\n",
       "       'University Degree Score System', 'Employment Status', 'Job Type',\n",
       "       'Still Working', 'GPA_Grade', 'distance'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "categorical_features = ['Gender', 'Home Region', 'Home City', 'Program ID', \n",
    "                        'Program Main Category Code', 'Program Sub Category Code', \n",
    "                        'Technology Type', 'Program Skill Level', \n",
    "                        'Program Presentation Method', 'Completed Degree', \n",
    "                        'Level of Education', 'Education Speaciality', 'College', \n",
    "                        'Employment Status', 'Job Type','Still Working',\"Student ID\",\"GPA_Grade\"]\n",
    "\n",
    "for feature in categorical_features:\n",
    "    X_train[feature] = le.fit_transform(X_train[feature])\n",
    "    X_test[feature] = le.fit_transform(X_test[feature])\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'xgboost'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[25], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mxgboost\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m XGBClassifier\n\u001b[0;32m      3\u001b[0m xg \u001b[38;5;241m=\u001b[39m XGBClassifier(objective \u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbinary:logistic\u001b[39m\u001b[38;5;124m'\u001b[39m, learning_rate \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m,\n\u001b[0;32m      4\u001b[0m                           max_depth \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m, n_estimators \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m)\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'xgboost'"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "\n",
    "xg = XGBClassifier(objective ='binary:logistic', learning_rate = 1,\n",
    "                          max_depth = 2, n_estimators = 2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score: 0.64\n",
      "Recall: 0.70\n",
      "Precision: 0.60\n",
      "Accuracy: 0.89\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score, recall_score, precision_score, accuracy_score\n",
    "\n",
    "xg.fit(X_train, y_train)\n",
    "preds = xg.predict(X_test)\n",
    "\n",
    "accuracy = accuracy_score(y_test, preds)\n",
    "\n",
    "accuracy_percentage = accuracy * 100\n",
    "f1 = f1_score(y_test, preds)\n",
    "recall = recall_score(y_test, preds)\n",
    "precision = precision_score(y_test, preds)\n",
    "accuracy = accuracy_score(y_test, preds)\n",
    "\n",
    "print(f\"F1 Score: {f1:.2f}\")\n",
    "print(f\"Recall: {recall:.2f}\")\n",
    "print(f\"Precision: {precision:.2f}\")\n",
    "print(f\"Accuracy: {accuracy:.2f}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y\n",
       "0    5509\n",
       "1    1039\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_c['Y'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# light gradeint boosting machine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fine tuning\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightgbm as lgb\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 1728 candidates, totalling 8640 fits\n",
      "[LightGBM] [Warning] Unknown parameter: is_unbalanced\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Warning] Unknown parameter: is_unbalanced\n",
      "[LightGBM] [Info] Number of positive: 847, number of negative: 4391\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001666 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 1244\n",
      "[LightGBM] [Info] Number of data points in the train set: 5238, number of used features: 23\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.161703 -> initscore=-1.645612\n",
      "[LightGBM] [Info] Start training from score -1.645612\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "Best parameters: {'learning_rate': 0.1, 'max_bin': 255, 'max_depth': 2, 'n_estimators': 128, 'num_leaves': 12}\n",
      "Best F1 score: 0.6120729235310531\n"
     ]
    }
   ],
   "source": [
    "import shap\n",
    "# Define the parameter grid\n",
    "param_grid = {\n",
    "    'max_depth': [2, 4, 6, 8, 10, 12],\n",
    "    'num_leaves': [12, 15, 22, 35],\n",
    "    'learning_rate': [ 0.1, 0.5, 1.5, 2.5, 3, 6],\n",
    "    'n_estimators': [200, 128, 64,255],\n",
    "    'max_bin':[64, 128, 255 ],\n",
    "}\n",
    "\n",
    "# Create the LightGBM estimator\n",
    "lgbm = lgb.LGBMClassifier(objective='binary', metric=['f1', 'accuracy', 'recall'], is_unbalanced=True)\n",
    "\n",
    "# Create the grid search object\n",
    "grid_search = GridSearchCV(\n",
    "    estimator=lgbm,\n",
    "    param_grid=param_grid,\n",
    "    cv=5,\n",
    "    scoring='f1',\n",
    "    n_jobs=-1,\n",
    "    verbose=2,\n",
    ")\n",
    "\n",
    "# Fit the grid search object to the data\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Print the best parameters and score\n",
    "print(\"Best parameters:\", grid_search.best_params_)\n",
    "print(\"Best F1 score:\", grid_search.best_score_)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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Qu(t),null;if(r=0!=(128&t.flags),null===(l=o.rendering))if(r)qu(o,!1);else{if(0!==Fl||null!==e&&0!=(128&e.flags))for(e=t.child;null!==e;){if(null!==(l=lo(e))){for(t.flags|=128,qu(o,!1),null!==(r=l.updateQueue)&&(t.updateQueue=r,t.flags|=4),t.subtreeFlags=0,r=n,n=t.child;null!==n;)e=r,(o=n).flags&=14680066,null===(l=o.alternate)?(o.childLanes=0,o.lanes=e,o.child=null,o.subtreeFlags=0,o.memoizedProps=null,o.memoizedState=null,o.updateQueue=null,o.dependencies=null,o.stateNode=null):(o.childLanes=l.childLanes,o.lanes=l.lanes,o.child=l.child,o.subtreeFlags=0,o.deletions=null,o.memoizedProps=l.memoizedProps,o.memoizedState=l.memoizedState,o.updateQueue=l.updateQueue,o.type=l.type,e=l.dependencies,o.dependencies=null===e?null:{lanes:e.lanes,firstContext:e.firstContext}),n=n.sibling;return 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t=void 0;\"string\"==typeof this.props.plot_cmap?this.props.plot_cmap in je.colors?t=je.colors[this.props.plot_cmap]:(console.log(\"Invalid color map name, reverting to default.\"),t=je.colors.RdBu):Array.isArray(this.props.plot_cmap)&&(t=this.props.plot_cmap),this.colors=t.map((function(e){return q(e)})),this.brighterColors=[1.45,1.6].map((function(t,n){return e.colors[n].brighter(t)}));var n=ze(\",.4\");null!=this.props.ordering_keys&&null!=this.props.ordering_keys_time_format?(this.parseTime=Tr(this.props.ordering_keys_time_format),this.formatTime=Cr(this.props.ordering_keys_time_format),this.xtickFormat=function(e){return\"object\"==Za(e)?this.formatTime(e):n(e)}):(this.parseTime=null,this.formatTime=null,this.xtickFormat=n),this.xscale=De(),this.xaxis=dn().scale(this.xscale).tickSizeInner(4).tickSizeOuter(0).tickFormat((function(t){return e.xtickFormat(t)})).tickPadding(-18),this.ytickFormat=n,this.yscale=De(),this.yaxis=pn(an,undefined).scale(this.yscale).tickSizeInner(4).tickSizeOuter(0).tickFormat((function(t){return e.ytickFormat(e.invLinkFunction(t))})).tickPadding(2),this.xlabel.node().onchange=function(){return e.internalDraw()},this.ylabel.node().onchange=function(){return e.internalDraw()},this.svg.on(\"mousemove\",(function(t){return e.mouseMoved(t)})),this.svg.on(\"click\",(function(){return alert(\"This original index of the sample you clicked is \"+e.nearestExpIndex)})),this.svg.on(\"mouseout\",(function(t){return e.mouseOut(t)})),window.addEventListener(\"resize\",this.redraw),window.setTimeout(this.redraw,50)}},{key:\"componentDidUpdate\",value:function(){this.draw()}},{key:\"mouseOut\",value:function(){this.hoverLine.attr(\"display\",\"none\"),this.hoverx.attr(\"display\",\"none\"),this.hoverxOutline.attr(\"display\",\"none\"),this.hoverxTitle.attr(\"display\",\"none\"),this.hovery.attr(\"display\",\"none\"),this.hoveryOutline.attr(\"display\",\"none\"),this.hoverGroup1.attr(\"display\",\"none\"),this.hoverGroup2.attr(\"display\",\"none\")}},{key:\"mouseMoved\",value:function(e){var t,n,r=this;this.hoverLine.attr(\"display\",\"\"),this.hoverx.attr(\"display\",\"\"),this.hoverxOutline.attr(\"display\",\"\"),this.hoverxTitle.attr(\"display\",\"\"),this.hovery.attr(\"display\",\"\"),this.hoveryOutline.attr(\"display\",\"\"),this.hoverGroup1.attr(\"display\",\"\"),this.hoverGroup2.attr(\"display\",\"\");var a=function(e,t){if(e=function(e){let t;for(;t=e.sourceEvent;)e=t;return e}(e),void 0===t&&(t=e.currentTarget),t){var n=t.ownerSVGElement||t;if(n.createSVGPoint){var r=n.createSVGPoint();return r.x=e.clientX,r.y=e.clientY,[(r=r.matrixTransform(t.getScreenCTM().inverse())).x,r.y]}if(t.getBoundingClientRect){var a=t.getBoundingClientRect();return[e.clientX-a.left-t.clientLeft,e.clientY-a.top-t.clientTop]}}return[e.pageX,e.pageY]}(e,this.svg.node())[0];if(this.props.explanations){for(t=0;t<this.currExplanations.length;++t)(!n||Math.abs(n.xmapScaled-a)>Math.abs(this.currExplanations[t].xmapScaled-a))&&(n=this.currExplanations[t]);this.nearestExpIndex=n.origInd,this.hoverLine.attr(\"x1\",n.xmapScaled).attr(\"x2\",n.xmapScaled).attr(\"y1\",0+this.topOffset).attr(\"y2\",this.height),this.hoverx.attr(\"x\",n.xmapScaled).attr(\"y\",this.topOffset-5).text(this.xtickFormat(n.xmap)),this.hoverxOutline.attr(\"x\",n.xmapScaled).attr(\"y\",this.topOffset-5).text(this.xtickFormat(n.xmap)),this.hoverxTitle.attr(\"x\",n.xmapScaled).attr(\"y\",this.topOffset-18).text(n.count>1?n.count+\" averaged samples\":\"\"),this.hovery.attr(\"x\",this.leftOffset-6).attr(\"y\",n.joinPointy).text(this.ytickFormat(this.invLinkFunction(n.joinPoint))),this.hoveryOutline.attr(\"x\",this.leftOffset-6).attr(\"y\",n.joinPointy).text(this.ytickFormat(this.invLinkFunction(n.joinPoint)));for(var i,o,u=[],l=this.currPosOrderedFeatures.length-1;l>=0;--l){var s=this.currPosOrderedFeatures[l],c=n.features[s];o=5+(c.posyTop+c.posyBottom)/2,(!i||o-i>=15)&&c.posyTop-c.posyBottom>=6&&(u.push(c),i=o)}var f=[];i=void 0;var p,d=Ga(this.currNegOrderedFeatures);try{for(d.s();!(p=d.n()).done;){var h=p.value,v=n.features[h];o=5+(v.negyTop+v.negyBottom)/2,(!i||i-o>=15)&&v.negyTop-v.negyBottom>=6&&(f.push(v),i=o)}}catch(e){d.e(e)}finally{d.f()}var g=function(e){var t=\"\";return null!==e.value&&void 0!==e.value&&(t=\" = \"+(isNaN(e.value)?e.value:r.ytickFormat(e.value))),n.count>1?\"mean(\"+r.props.featureNames[e.ind]+\")\"+t:r.props.featureNames[e.ind]+t},y=this.hoverGroup1.selectAll(\".pos-values\").data(u);y.enter().append(\"text\").attr(\"class\",\"pos-values\").merge(y).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.posyTop+e.posyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"stroke\",\"#fff\").attr(\"fill\",\"#fff\").attr(\"stroke-width\",\"4\").attr(\"stroke-linejoin\",\"round\").attr(\"opacity\",1).text(g),y.exit().remove();var m=this.hoverGroup2.selectAll(\".pos-values\").data(u);m.enter().append(\"text\").attr(\"class\",\"pos-values\").merge(m).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.posyTop+e.posyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"fill\",this.colors[0]).text(g),m.exit().remove();var b=this.hoverGroup1.selectAll(\".neg-values\").data(f);b.enter().append(\"text\").attr(\"class\",\"neg-values\").merge(b).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.negyTop+e.negyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"stroke\",\"#fff\").attr(\"fill\",\"#fff\").attr(\"stroke-width\",\"4\").attr(\"stroke-linejoin\",\"round\").attr(\"opacity\",1).text(g),b.exit().remove();var _=this.hoverGroup2.selectAll(\".neg-values\").data(f);_.enter().append(\"text\").attr(\"class\",\"neg-values\").merge(_).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.negyTop+e.negyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"fill\",this.colors[1]).text(g),_.exit().remove()}}},{key:\"draw\",value:function(){var e=this;if(this.props.explanations&&0!==this.props.explanations.length){(0,Re.each)(this.props.explanations,(function(e,t){return e.origInd=t}));var t,n={},r={},a={},i=Ga(this.props.explanations);try{for(i.s();!(t=i.n()).done;){var o=t.value;for(var u in o.features)void 0===n[u]&&(n[u]=0,r[u]=0,a[u]=0),o.features[u].effect>0?n[u]+=o.features[u].effect:r[u]-=o.features[u].effect,null!==o.features[u].value&&void 0!==o.features[u].value&&(a[u]+=1)}}catch(e){i.e(e)}finally{i.f()}this.usedFeatures=(0,Re.sortBy)((0,Re.keys)(n),(function(e){return-(n[e]+r[e])})),console.log(\"found \",this.usedFeatures.length,\" used features\"),this.posOrderedFeatures=(0,Re.sortBy)(this.usedFeatures,(function(e){return n[e]})),this.negOrderedFeatures=(0,Re.sortBy)(this.usedFeatures,(function(e){return-r[e]})),this.singleValueFeatures=(0,Re.filter)(this.usedFeatures,(function(e){return a[e]>0}));var l=[\"sample order by similarity\",\"sample order by output value\",\"original sample ordering\"].concat(this.singleValueFeatures.map((function(t){return e.props.featureNames[t]})));null!=this.props.ordering_keys&&l.unshift(\"sample order by key\");var s=this.xlabel.selectAll(\"option\").data(l);s.enter().append(\"option\").merge(s).attr(\"value\",(function(e){return e})).text((function(e){return e})),s.exit().remove();var c=this.props.outNames[0]?this.props.outNames[0]:\"model output value\";(l=(0,Re.map)(this.usedFeatures,(function(t){return[e.props.featureNames[t],e.props.featureNames[t]+\" effects\"]}))).unshift([\"model output value\",c]);var f=this.ylabel.selectAll(\"option\").data(l);f.enter().append(\"option\").merge(f).attr(\"value\",(function(e){return e[0]})).text((function(e){return e[1]})),f.exit().remove(),this.ylabel.style(\"top\",(this.height-10-this.topOffset)/2+this.topOffset+\"px\").style(\"left\",10-this.ylabel.node().offsetWidth/2+\"px\"),this.internalDraw()}}},{key:\"internalDraw\",value:function(){var e,t,n=this,r=Ga(this.props.explanations);try{for(r.s();!(e=r.n()).done;){var a,i=e.value,o=Ga(this.usedFeatures);try{for(o.s();!(a=o.n()).done;){var u=a.value;i.features.hasOwnProperty(u)||(i.features[u]={effect:0,value:0}),i.features[u].ind=u}}catch(e){o.e(e)}finally{o.f()}}}catch(e){r.e(e)}finally{r.f()}var l=this.xlabel.node().value,s=\"sample order by key\"===l&&null!=this.props.ordering_keys_time_format;if(this.xscale=s?Ya():De(),this.xaxis.scale(this.xscale),\"sample order by similarity\"===l)t=(0,Re.sortBy)(this.props.explanations,(function(e){return e.simIndex})),(0,Re.each)(t,(function(e,t){return e.xmap=t}));else if(\"sample order by output value\"===l)t=(0,Re.sortBy)(this.props.explanations,(function(e){return-e.outValue})),(0,Re.each)(t,(function(e,t){return e.xmap=t}));else if(\"original sample ordering\"===l)t=(0,Re.sortBy)(this.props.explanations,(function(e){return e.origInd})),(0,Re.each)(t,(function(e,t){return e.xmap=t}));else if(\"sample order by key\"===l)t=this.props.explanations,s?(0,Re.each)(t,(function(e,t){return e.xmap=n.parseTime(n.props.ordering_keys[t])})):(0,Re.each)(t,(function(e,t){return e.xmap=n.props.ordering_keys[t]})),t=(0,Re.sortBy)(t,(function(e){return e.xmap}));else{var c=(0,Re.findKey)(this.props.featureNames,(function(e){return e===l}));(0,Re.each)(this.props.explanations,(function(e,t){return e.xmap=e.features[c].value}));var f=(0,Re.sortBy)(this.props.explanations,(function(e){return e.xmap})),p=(0,Re.map)(f,(function(e){return e.xmap}));if(\"string\"==typeof p[0])return void alert(\"Ordering by category names is not yet supported.\");var d,h,v=(0,Re.min)(p),g=((0,Re.max)(p)-v)/100;t=[];for(var y=0;y<f.length;++y){var m=f[y];if(d&&!h&&m.xmap-d.xmap<=g||h&&m.xmap-h.xmap<=g){h||((h=(0,Re.cloneDeep)(d)).count=1);var b,_=Ga(this.usedFeatures);try{for(_.s();!(b=_.n()).done;){var w=b.value;h.features[w].effect+=m.features[w].effect,h.features[w].value+=m.features[w].value}}catch(e){_.e(e)}finally{_.f()}h.count+=1}else if(d)if(h){var x,k=Ga(this.usedFeatures);try{for(k.s();!(x=k.n()).done;){var S=x.value;h.features[S].effect/=h.count,h.features[S].value/=h.count}}catch(e){k.e(e)}finally{k.f()}t.push(h),h=void 0}else t.push(d);d=m}d.xmap-t[t.length-1].xmap>g&&t.push(d)}this.currUsedFeatures=this.usedFeatures,this.currPosOrderedFeatures=this.posOrderedFeatures,this.currNegOrderedFeatures=this.negOrderedFeatures;var E=this.ylabel.node().value;if(\"model output value\"!==E){var C=t;t=(0,Re.cloneDeep)(t);for(var T=(0,Re.findKey)(this.props.featureNames,(function(e){return e===E})),M=0;M<t.length;++M){var N=t[M].features[T];t[M].features={},t[M].features[T]=N,C[M].remapped_version=t[M]}this.currUsedFeatures=[T],this.currPosOrderedFeatures=[T],this.currNegOrderedFeatures=[T]}this.currExplanations=t,\"identity\"===this.props.link?this.invLinkFunction=function(e){return n.props.baseValue+e}:\"logit\"===this.props.link?this.invLinkFunction=function(e){return 1/(1+Math.exp(-(n.props.baseValue+e)))}:console.log(\"ERROR: Unrecognized link function: 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e.effect<0})),(function(e){return-e.effect})))||0;A=Math.max(A,2.2*Math.max(R,j))}this.yscale.domain([-A/2,A/2]).range([this.height-10,this.topOffset]),this.yaxisElement.attr(\"transform\",\"translate(\"+this.leftOffset+\",0)\").call(this.yaxis);for(var U=0;U<O;++U){var I,$=t[U].features,B=-((0,Re.sum)((0,Re.map)((0,Re.filter)($,(function(e){return e.effect<0})),(function(e){return-e.effect})))||0),W=void 0,V=Ga(this.currPosOrderedFeatures);try{for(V.s();!(I=V.n()).done;)$[W=I.value].posyTop=this.yscale(B),$[W].effect>0&&(B+=$[W].effect),$[W].posyBottom=this.yscale(B),$[W].ind=W}catch(e){V.e(e)}finally{V.f()}var H,q=B,Q=Ga(this.currNegOrderedFeatures);try{for(Q.s();!(H=Q.n()).done;)$[W=H.value].negyTop=this.yscale(B),$[W].effect<0&&(B-=$[W].effect),$[W].negyBottom=this.yscale(B)}catch(e){Q.e(e)}finally{Q.f()}t[U].joinPoint=q,t[U].joinPointy=this.yscale(q)}var Y=En().x((function(e){return e[0]})).y((function(e){return e[1]})),G=this.mainGroup.selectAll(\".force-bar-array-area-pos\").data(this.currUsedFeatures);G.enter().append(\"path\").attr(\"class\",\"force-bar-array-area-pos\").merge(G).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].posyTop]})),r=(0,Re.map)((0,Re.rangeRight)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].posyBottom]}));return Y(n.concat(r))})).attr(\"fill\",this.colors[0]),G.exit().remove();var K=this.mainGroup.selectAll(\".force-bar-array-area-neg\").data(this.currUsedFeatures);K.enter().append(\"path\").attr(\"class\",\"force-bar-array-area-neg\").merge(K).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].negyTop]})),r=(0,Re.map)((0,Re.rangeRight)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].negyBottom]}));return Y(n.concat(r))})).attr(\"fill\",this.colors[1]),K.exit().remove();var Z=this.mainGroup.selectAll(\".force-bar-array-divider-pos\").data(this.currUsedFeatures);Z.enter().append(\"path\").attr(\"class\",\"force-bar-array-divider-pos\").merge(Z).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].posyBottom]}));return Y(n)})).attr(\"fill\",\"none\").attr(\"stroke-width\",1).attr(\"stroke\",(function(){return n.colors[0].brighter(1.2)})),Z.exit().remove();var X=this.mainGroup.selectAll(\".force-bar-array-divider-neg\").data(this.currUsedFeatures);X.enter().append(\"path\").attr(\"class\",\"force-bar-array-divider-neg\").merge(X).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].negyTop]}));return Y(n)})).attr(\"fill\",\"none\").attr(\"stroke-width\",1).attr(\"stroke\",(function(){return n.colors[1].brighter(1.5)})),X.exit().remove();for(var J=function(e,t,n,r,a){var i,o,u,l;\"pos\"===a?(i=e[n].features[t].posyBottom,o=e[n].features[t].posyTop):(i=e[n].features[t].negyBottom,o=e[n].features[t].negyTop);for(var s=n+1;s<=r;++s)\"pos\"===a?(u=e[s].features[t].posyBottom,l=e[s].features[t].posyTop):(u=e[s].features[t].negyBottom,l=e[s].features[t].negyTop),u>i&&(i=u),l<o&&(o=l);return{top:i,bottom:o}},ee=[],te=0,ne=[\"pos\",\"neg\"];te<ne.length;te++){var re,ae=ne[te],ie=Ga(this.currUsedFeatures);try{for(ie.s();!(re=ie.n()).done;)for(var oe=re.value,ue=0,le=0,se=0,ce={top:0,bottom:0},fe=void 0;le<O-1;){for(;se<100&&le<O-1;)++le,se=t[le].xmapScaled-t[ue].xmapScaled;for(ce=J(t,oe,ue,le,ae);ce.bottom-ce.top<20&&ue<le;)++ue,ce=J(t,oe,ue,le,ae);if(se=t[le].xmapScaled-t[ue].xmapScaled,ce.bottom-ce.top>=20&&se>=100){for(;le<O-1;){if(++le,!((fe=J(t,oe,ue,le,ae)).bottom-fe.top>20)){--le;break}ce=fe}se=t[le].xmapScaled-t[ue].xmapScaled,ee.push([(t[le].xmapScaled+t[ue].xmapScaled)/2,(ce.top+ce.bottom)/2,this.props.featureNames[oe]]);var pe=t[le].xmapScaled;for(ue=le;pe+100>t[ue].xmapScaled&&ue<O-1;)++ue;le=ue}}}catch(e){ie.e(e)}finally{ie.f()}}var de=this.onTopGroup.selectAll(\".force-bar-array-flabels\").data(ee);de.enter().append(\"text\").attr(\"class\",\"force-bar-array-flabels\").merge(de).attr(\"x\",(function(e){return e[0]})).attr(\"y\",(function(e){return e[1]+4})).text((function(e){return e[2]})),de.exit().remove()}},{key:\"componentWillUnmount\",value:function(){window.removeEventListener(\"resize\",this.redraw)}},{key:\"render\",value:function(){var t=this;return e.createElement(\"div\",{ref:function(e){return t.wrapper=Jt(e)},style:{textAlign:\"center\"}},e.createElement(\"style\",{dangerouslySetInnerHTML:{__html:\"\\n          .force-bar-array-wrapper {\\n            text-align: center;\\n          }\\n          .force-bar-array-xaxis path {\\n            fill: none;\\n            opacity: 0.4;\\n          }\\n          .force-bar-array-xaxis .domain {\\n            opacity: 0;\\n          }\\n          .force-bar-array-xaxis paths {\\n            display: none;\\n          }\\n          .force-bar-array-yaxis path {\\n            fill: none;\\n            opacity: 0.4;\\n          }\\n          .force-bar-array-yaxis paths {\\n            display: none;\\n          }\\n          .tick line {\\n            stroke: #000;\\n            stroke-width: 1px;\\n            opacity: 0.4;\\n          }\\n          .tick text {\\n            fill: #000;\\n            opacity: 0.5;\\n            font-size: 12px;\\n            padding: 0px;\\n          }\\n          .force-bar-array-flabels {\\n            font-size: 12px;\\n            fill: #fff;\\n            text-anchor: middle;\\n          }\\n          .additive-force-array-xlabel {\\n            background: none;\\n            border: 1px solid #ccc;\\n            opacity: 0.5;\\n            margin-bottom: 0px;\\n            font-size: 12px;\\n            font-family: arial;\\n            margin-left: 80px;\\n            max-width: 300px;\\n          }\\n          .additive-force-array-xlabel:focus {\\n            outline: none;\\n          }\\n          .additive-force-array-ylabel {\\n            position: relative;\\n            top: 0px;\\n            left: 0px;\\n            transform: rotate(-90deg);\\n            background: none;\\n            border: 1px solid #ccc;\\n            opacity: 0.5;\\n            margin-bottom: 0px;\\n            font-size: 12px;\\n            font-family: arial;\\n            max-width: 150px;\\n          }\\n          .additive-force-array-ylabel:focus {\\n            outline: none;\\n          }\\n          .additive-force-array-hoverLine {\\n            stroke-width: 1px;\\n            stroke: #fff;\\n            opacity: 1;\\n          }\"}}),e.createElement(\"select\",{className:\"additive-force-array-xlabel\"}),e.createElement(\"div\",{style:{height:\"0px\",textAlign:\"left\"}},e.createElement(\"select\",{className:\"additive-force-array-ylabel\"})),e.createElement(\"svg\",{ref:function(e){return t.svg=Jt(e)},style:{userSelect:\"none\",display:\"block\",fontFamily:\"arial\",sansSerif:!0}}))}}])&&Xa(n.prototype,r),Object.defineProperty(n,\"prototype\",{writable:!1}),u}(e.Component);ni.defaultProps={plot_cmap:\"RdBu\",ordering_keys:null,ordering_keys_time_format:null};const ri=ni;window.SHAP={SimpleListVisualizer:He,AdditiveForceVisualizer:Ln,AdditiveForceArrayVisualizer:ri,React:e,ReactDom:t}})()})();\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Hamad\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\shap\\explainers\\_tree.py:448: UserWarning: LightGBM binary classifier with TreeExplainer shap values output has changed to a list of ndarray\n",
      "  warnings.warn('LightGBM binary classifier with TreeExplainer shap values output has changed to a list of ndarray')\n"
     ]
    }
   ],
   "source": [
    "# Load JS visualization code for SHAP in the notebook\n",
    "shap.initjs()\n",
    "\n",
    "# Create the explainer with the best estimator\n",
    "explainer = shap.TreeExplainer(grid_search.best_estimator_)\n",
    "\n",
    "# Generate SHAP values\n",
    "shap_values = explainer.shap_values(X_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable Importance Plot - Global Interpretation\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x950 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Variable Importance Plot - Global Interpretation\")\n",
    "figure = plt.figure()\n",
    "shap.summary_plot(shap_values, X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "Summary plots need a matrix of shap_values, not a vector.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[37], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mshap\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msummary_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mshap_values\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\Hamad\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\shap\\plots\\_beeswarm.py:518\u001b[0m, in \u001b[0;36msummary_legacy\u001b[1;34m(shap_values, features, feature_names, max_display, plot_type, color, axis_color, title, alpha, show, sort, color_bar, plot_size, layered_violin_max_num_bins, class_names, class_inds, color_bar_label, cmap, show_values_in_legend, auto_size_plot, use_log_scale)\u001b[0m\n\u001b[0;32m    516\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m plot_type \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    517\u001b[0m         plot_type \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdot\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# default for single output explanations\u001b[39;00m\n\u001b[1;32m--> 518\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(shap_values\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSummary plots need a matrix of shap_values, not a vector.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    520\u001b[0m \u001b[38;5;66;03m# default color:\u001b[39;00m\n\u001b[0;32m    521\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m color \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[1;31mAssertionError\u001b[0m: Summary plots need a matrix of shap_values, not a vector."
     ]
    }
   ],
   "source": [
    "shap.summary_plot(shap_values[1], X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "Summary plots need a matrix of shap_values, not a vector.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[38], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mshap\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msummary_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mshap_values\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\Hamad\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\shap\\plots\\_beeswarm.py:518\u001b[0m, in \u001b[0;36msummary_legacy\u001b[1;34m(shap_values, features, feature_names, max_display, plot_type, color, axis_color, title, alpha, show, sort, color_bar, plot_size, layered_violin_max_num_bins, class_names, class_inds, color_bar_label, cmap, show_values_in_legend, auto_size_plot, use_log_scale)\u001b[0m\n\u001b[0;32m    516\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m plot_type \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    517\u001b[0m         plot_type \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdot\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# default for single output explanations\u001b[39;00m\n\u001b[1;32m--> 518\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(shap_values\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSummary plots need a matrix of shap_values, not a vector.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    520\u001b[0m \u001b[38;5;66;03m# default color:\u001b[39;00m\n\u001b[0;32m    521\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m color \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[1;31mAssertionError\u001b[0m: Summary plots need a matrix of shap_values, not a vector."
     ]
    }
   ],
   "source": [
    "shap.summary_plot(shap_values[0], X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFik8HQByUF4r4V/+zGODqn1GCoBAAAA4GmcSQIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwYHS7ZIRR9gAAAADP40wSAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFh4NCSFh4fL4XAkunXs2FGSdOPGDXXs2FFp0qRRUFCQGjVqpKioKE+WDAAAAOAJ59GQtGnTJp05c8Z5W7p0qSSpcePGkqTu3bvrxx9/1Pfff69Vq1bp9OnTatiwoSdLBgAAAPCES+HJxtOlS+dyf8iQIcqVK5cqVKigK1euaOLEifrmm29UuXJlSdLkyZNVoEABbdiwQaVKlUryMW/evKmbN28678fExLjvFwAAAADwxEk21yTdunVLM2bMUNu2beVwOLRlyxbdvn1bVatWdW6TP39+Zc+eXevXr7/v4wwePFihoaHOW7Zs2ewoHwAAAMATItmEpPnz5ys6OlqtW7eWJJ09e1YpU6ZUWFiYy3YZMmTQ2bNn7/s4vXv31pUrV5y3EydOuLFqAAAAAE8aj3a3s5o4caJq1KihzJkz/63H8fX1la+v72OqCgAAAMC/TbIISceOHdOyZcs0d+5c57KMGTPq1q1bio6OdjmbFBUVpYwZM3qgSgAAAAD/Bsmiu93kyZOVPn161apVy7msRIkS8vHx0fLly53L9u/fr+PHj6t06dKeKBMAAADAv4DHzyTFx8dr8uTJatWqlVKk+L9yQkND1a5dO/Xo0UOpU6dWSEiIOnfurNKlS993ZDsAAAAA+Ls8HpKWLVum48ePq23btonWffrpp/Ly8lKjRo108+ZNVatWTWPHjvVAlQAAAAD+LTwekl588UUZY5Jc5+fnpzFjxmjMmDE2VwUAAADg3ypZXJMEAAAAAMkFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwMLjIenUqVN69dVXlSZNGvn7+6tIkSLavHmzc70xRh988IEyZcokf39/Va1aVZGRkR6sGAAAAMCTzKMh6fLlyypbtqx8fHz0888/a8+ePRo+fLhSpUrl3GbYsGEaPXq0xo8fr40bNyowMFDVqlXTjRs3PFg5AAAAgCdVCk82PnToUGXLlk2TJ092LouIiHD+bIzRyJEj9f7776tevXqSpGnTpilDhgyaP3++mjVrZnvNAAAAAJ5sHj2TtGDBAj3zzDNq3Lix0qdPr+LFi+vLL790rj9y5IjOnj2rqlWrOpeFhobqueee0/r165N8zJs3byomJsblBgAAAAAPy6Mh6fDhwxo3bpzy5MmjxYsX680331SXLl00depUSdLZs2clSRkyZHD5fxkyZHCuu9fgwYMVGhrqvGXLls29vwQAAACAJ4pHQ1J8fLyefvppffTRRypevLg6dOig1157TePHj3/kx+zdu7euXLnivJ04ceIxVgwAAADgSefRkJQpUyYVLFjQZVmBAgV0/PhxSVLGjBklSVFRUS7bREVFOdfdy9fXVyEhIS43AAAAAHhYHg1JZcuW1f79+12WHThwQDly5JB0dxCHjBkzavny5c71MTEx2rhxo0qXLm1rrQAAAAD+HTw6ul337t1VpkwZffTRR2rSpIl+++03ffHFF/riiy8kSQ6HQ926ddPAgQOVJ08eRUREqE+fPsqcObPq16/vydIBAAAAPKE8GpKeffZZzZs3T71791b//v0VERGhkSNHqnnz5s5t3nnnHV2/fl0dOnRQdHS0ypUrp0WLFsnPz8+DlQMAAAB4Unk0JElS7dq1Vbt27fuudzgc6t+/v/r3729jVQAAAAD+rTx6TRIAAAAAJDeEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFh4NCT17dtXDofD5ZY/f37n+hs3bqhjx45KkyaNgoKC1KhRI0VFRXmwYgAAAABPOo+fSSpUqJDOnDnjvK1du9a5rnv37vrxxx/1/fffa9WqVTp9+rQaNmzowWoBAAAAPOlSeLyAFCmUMWPGRMuvXLmiiRMn6ptvvlHlypUlSZMnT1aBAgW0YcMGlSpVyu5SAQAAAPwLePxMUmRkpDJnzqycOXOqefPmOn78uCRpy5Ytun37tqpWrercNn/+/MqePbvWr19/38e7efOmYmJiXG4AAAAA8LA8GpKee+45TZkyRYsWLdK4ceN05MgRPf/887p69arOnj2rlClTKiwszOX/ZMiQQWfPnr3vYw4ePFihoaHOW7Zs2dz8WwAAAAB4kni0u12NGjWcPxctWlTPPfeccuTIoVmzZsnf3/+RHrN3797q0aOH835MTAxBCQAAAMBD83h3O6uwsDDlzZtXBw8eVMaMGXXr1i1FR0e7bBMVFZXkNUwJfH19FRIS4nIDAAAAgIeVrELStWvXdOjQIWXKlEklSpSQj4+Pli9f7ly/f/9+HT9+XKVLl/ZglQAAAACeZB7tbtezZ0/VqVNHOXLk0OnTp/Xhhx/K29tbL7/8skJDQ9WuXTv16NFDqVOnVkhIiDp37qzSpUszsh0AAAAAt/FoSDp58qRefvllXbx4UenSpVO5cuW0YcMGpUuXTpL06aefysvLS40aNdLNmzdVrVo1jR071pMlAwAAAHjCeTQkzZw584Hr/fz8NGbMGI0ZM8amigAAAAD82yWra5IAAAAAwNMISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgMUjh6Tp06erbNmyypw5s44dOyZJGjlypH744YfHVhwAAAAA2O2RQtK4cePUo0cP1axZU9HR0YqLi5MkhYWFaeTIkY+zPgAAAACw1SOFpM8++0xffvml3nvvPXl7ezuXP/PMM9q1a9djKw4AAAAA7PZIIenIkSMqXrx4ouW+vr66fv363y4KAAAAADzlkUJSRESEtm/fnmj5okWLVKBAgb9bEwAAAAB4TIpH+U89evRQx44ddePGDRlj9Ntvv+nbb7/V4MGD9dVXXz3uGgEAAADANo8Uktq3by9/f3+9//77io2N1SuvvKLMmTNr1KhRatas2eOuEQAAAABs80ghSZKaN2+u5s2bKzY2VteuXVP69OkfZ10AAAAA4BGPFJKOHDmiO3fuKE+ePAoICFBAQIAkKTIyUj4+PgoPD3+cNQIAAACAbR5p4IbWrVtr3bp1iZZv3LhRrVu3/rs1AQAAAIDHPFJI2rZtm8qWLZtoealSpZIc9Q4AAAAA/ikeKSQ5HA5dvXo10fIrV64oLi7ubxcFAAAAAJ7ySCGpfPnyGjx4sEsgiouL0+DBg1WuXLnHVhwAAAAA2O2RBm4YOnSoypcvr3z58un555+XJK1Zs0YxMTH65ZdfHmuBAAAAAGCnRzqTVLBgQe3cuVNNmjTRuXPndPXqVbVs2VL79u1T4cKFH3eNAAAAAGCbR54nKXPmzProo48eZy0AAAAA4HGPHJKio6P122+/6dy5c4qPj3dZ17Jly79dGAAAAAB4wiOFpB9//FHNmzfXtWvXFBISIofD4VzncDgISQAAAAD+sR7pmqS33npLbdu21bVr1xQdHa3Lly87b5cuXXrcNQIAAACAbR4pJJ06dUpdunRRQEDA464HAAAAADzqkUJStWrVtHnz5sddCwAAAAB43CNdk1SrVi29/fbb2rNnj4oUKSIfHx+X9XXr1n0sxQEAAACA3R4pJL322muSpP79+yda53A4FBcX9/eqAgAAAAAPeaSQdO+Q3wAAAADwpHika5IAAAAA4En1yJPJXr9+XatWrdLx48d169Ytl3VdunT524UBAAAAgCc8Ukjatm2batasqdjYWF2/fl2pU6fWhQsXFBAQoPTp0xOSAAAAAPxjPVJ3u+7du6tOnTq6fPmy/P39tWHDBh07dkwlSpTQJ5988rhrBAAAAADbPFJI2r59u9566y15eXnJ29tbN2/eVLZs2TRs2DD997//fdw1AgAAAIBtHikk+fj4yMvr7n9Nnz69jh8/LkkKDQ3ViRMnHl91AAAAAGCzR7omqXjx4tq0aZPy5MmjChUq6IMPPtCFCxc0ffp0FS5c+HHXCAAAAAC2eaQzSR999JEyZcokSRo0aJBSpUqlN998U+fPn9eECRMeqZAhQ4bI4XCoW7duzmU3btxQx44dlSZNGgUFBalRo0aKiop6pMcHAAAAgIfxSGeSnnnmGefP6dOn16JFi/5WEZs2bdKECRNUtGhRl+Xdu3fXwoUL9f333ys0NFSdOnVSw4YN9euvv/6t9gAAAADgfh7pTFLlypUVHR2daHlMTIwqV678lx7r2rVrat68ub788kulSpXKufzKlSuaOHGiRowYocqVK6tEiRKaPHmy1q1bpw0bNjxK2QAAAADwpx4pJK1cuTLRBLLS3e5xa9as+UuP1bFjR9WqVUtVq1Z1Wb5lyxbdvn3bZXn+/PmVPXt2rV+//r6Pd/PmTcXExLjcAAAAAOBh/aXudjt37nT+vGfPHp09e9Z5Py4uTosWLVKWLFke+vFmzpyprVu3atOmTYnWnT17VilTplRYWJjL8gwZMri0e6/BgwerX79+D10DAAAAAFj9pZBUrFgxORwOORyOJLvV+fv767PPPnuoxzpx4oS6du2qpUuXys/P76+U8UC9e/dWjx49nPdjYmKULVu2x/b4AAAAAJ5sfykkHTlyRMYY5cyZU7/99pvSpUvnXJcyZUqlT59e3t7eD/VYW7Zs0blz5/T00087l8XFxWn16tX6/PPPtXjxYt26dUvR0dEuZ5OioqKUMWPG+z6ur6+vfH19/8qvBQAAAABOfykk5ciRQ7dv31arVq2UJk0a5ciR45EbrlKlinbt2uWyrE2bNsqfP7/effddZcuWTT4+Plq+fLkaNWokSdq/f7+OHz+u0qVLP3K7AAAAAPAgf3kIcB8fH82bN08ffPDB32o4ODg40cSzgYGBSpMmjXN5u3bt1KNHD6VOnVohISHq3LmzSpcurVKlSv2ttgEAAADgfh5pdLt69epp/vz5j7mUxD799FPVrl1bjRo1Uvny5ZUxY0bNnTvX7e0CAAAA+Pd6pMlk8+TJo/79++vXX39ViRIlFBgY6LK+S5cuj1TMypUrXe77+flpzJgxGjNmzCM9HgAAAAD8VY8UkiZOnKiwsDBt2bJFW7ZscVnncDgeOSQBAAAAgKc9Ukg6cuTI464DAAAAAJKFR7omycoYI2PM46gFAAAAADzukUPStGnTVKRIEfn7+8vf319FixbV9OnTH2dtAAAAAGC7R+puN2LECPXp00edOnVS2bJlJUlr167VG2+8oQsXLqh79+6PtUjYK7zXwr/9GEeH1HoMlQAAAAD2e6SQ9Nlnn2ncuHFq2bKlc1ndunVVqFAh9e3bl5AEAAAA4B/rkbrbnTlzRmXKlEm0vEyZMjpz5szfLgoAAAAAPOWRQlLu3Lk1a9asRMu/++475cmT528XBQAAAACe8kjd7fr166emTZtq9erVzmuSfv31Vy1fvjzJ8AQAAAAA/xSPdCapUaNG2rhxo9KmTav58+dr/vz5Sps2rX777Tc1aNDgcdcIAAAAALZ5pDNJklSiRAnNmDHjcdYCODHCHgAAADzlkUNSXFyc5s2bp71790qSChYsqHr16ilFikd+SAAAAADwuEdKNLt371bdunV19uxZ5cuXT5I0dOhQpUuXTj/++KMKFy78WIsEAAAAALs80jVJ7du3V6FChXTy5Elt3bpVW7du1YkTJ1S0aFF16NDhcdcIAAAAALZ5pDNJ27dv1+bNm5UqVSrnslSpUmnQoEF69tlnH1txAAAAAGC3RzqTlDdvXkVFRSVafu7cOeXOnftvFwUAAAAAnvJIIWnw4MHq0qWLZs+erZMnT+rkyZOaPXu2unXrpqFDhyomJsZ5AwAAAIB/kkfqble7dm1JUpMmTeRwOCRJxhhJUp06dZz3HQ6H4uLiHkedAAAAAGCLRwpJK1aseNx1AAAAAECy8EghqUKFCo+7DgAAAABIFh555tcbN25o586dOnfunOLj413W1a1b928XBgAAAACe8EghadGiRWrZsqUuXLiQaB3XIQEAAAD4J3uk0e06d+6sxo0b68yZM4qPj3e5EZAAAAAA/JM9UkiKiopSjx49lCFDhsddDwAAAAB41COFpJdeekkrV658zKUAAAAAgOc90jVJn3/+uRo3bqw1a9aoSJEi8vHxcVnfpUuXx1IcAAAAANjtkULSt99+qyVLlsjPz08rV650Tigr3R24gZAEAAAA4J/qkULSe++9p379+qlXr17y8nqkHnsAAAAAkCw9UsK5deuWmjZtSkACAAAA8MR5pJTTqlUrfffdd4+7FgAAAADwuEfqbhcXF6dhw4Zp8eLFKlq0aKKBG0aMGPFYigMAAAAAuz1SSNq1a5eKFy8uSfr9998fa0EAAAAA4EmPFJJWrFjxuOsAAAAAgGThL4Wkhg0b/uk2DodDc+bMeeSCAAAAAMCT/lJICg0NdVcdAAAAAJAs/KWQNHnyZHfVAQAAAADJAhMdAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYOHRkDRu3DgVLVpUISEhCgkJUenSpfXzzz8719+4cUMdO3ZUmjRpFBQUpEaNGikqKsqDFQMAAAB40nk0JGXNmlVDhgzRli1btHnzZlWuXFn16tXT7t27JUndu3fXjz/+qO+//16rVq3S6dOn1bBhQ0+WDAAAAOAJl8KTjdepU8fl/qBBgzRu3Dht2LBBWbNm1cSJE/XNN9+ocuXKkqTJkyerQIEC2rBhg0qVKuWJkgEAAAA84ZLNNUlxcXGaOXOmrl+/rtKlS2vLli26ffu2qlat6twmf/78yp49u9avX3/fx7l586ZiYmJcbgAAAADwsDweknbt2qWgoCD5+vrqjTfe0Lx581SwYEGdPXtWKVOmVFhYmMv2GTJk0NmzZ+/7eIMHD1ZoaKjzli1bNjf/BgAAAACeJB4PSfny5dP27du1ceNGvfnmm2rVqpX27NnzyI/Xu3dvXblyxXk7ceLEY6wWAAAAwJPOo9ckSVLKlCmVO3duSVKJEiW0adMmjRo1Sk2bNtWtW7cUHR3tcjYpKipKGTNmvO/j+fr6ytfX191lAwAAAHhCefxM0r3i4+N18+ZNlShRQj4+Plq+fLlz3f79+3X8+HGVLl3agxUCAAAAeJJ59ExS7969VaNGDWXPnl1Xr17VN998o5UrV2rx4sUKDQ1Vu3bt1KNHD6VOnVohISHq3LmzSpcuzch2AAAAANzGoyHp3Llzatmypc6cOaPQ0FAVLVpUixcv1gsvvCBJ+vTTT+Xl5aVGjRrp5s2bqlatmsaOHevJkgEAAAA84TwakiZOnPjA9X5+fhozZozGjBljU0UAAAAA/u2S3TVJAAAAAOBJhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAIsUni4ASM7Cey38249xdEitx1AJAAAA7MKZJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABYeDUmDBw/Ws88+q+DgYKVPn17169fX/v37Xba5ceOGOnbsqDRp0igoKEiNGjVSVFSUhyoGAAAA8KTzaEhatWqVOnbsqA0bNmjp0qW6ffu2XnzxRV2/ft25Tffu3fXjjz/q+++/16pVq3T69Gk1bNjQg1UDAAAAeJKl8GTjixYtcrk/ZcoUpU+fXlu2bFH58uV15coVTZw4Ud98840qV64sSZo8ebIKFCigDRs2qFSpUp4oGwAAAMATLFldk3TlyhVJUurUqSVJW7Zs0e3bt1W1alXnNvnz51f27Nm1fv36JB/j5s2biomJcbkBAAAAwMNKNiEpPj5e3bp1U9myZVW4cGFJ0tmzZ5UyZUqFhYW5bJshQwadPXs2yccZPHiwQkNDnbds2bK5u3QAAAAAT5BkE5I6duyo33//XTNnzvxbj9O7d29duXLFeTtx4sRjqhAAAADAv4FHr0lK0KlTJ/3vf//T6tWrlTVrVufyjBkz6tatW4qOjnY5mxQVFaWMGTMm+Vi+vr7y9fV1d8kAAAAAnlAePZNkjFGnTp00b948/fLLL4qIiHBZX6JECfn4+Gj58uXOZfv379fx48dVunRpu8sFAAAA8C/g0TNJHTt21DfffKMffvhBwcHBzuuMQkND5e/vr9DQULVr1049evRQ6tSpFRISos6dO6t06dKMbAcAAADALTwaksaNGydJqlixosvyyZMnq3Xr1pKkTz/9VF5eXmrUqJFu3rypatWqaezYsTZXCgAAAODfwqMhyRjzp9v4+flpzJgxGjNmjA0VAQAAAPi3Szaj2wEAAABAckBIAgAAAAALQhIAAAAAWBCSAAAAAMAiWUwmC+DBwnst/NuPcXRILY/X8ThqAAAAcDfOJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGCRwtMFAMBfEd5r4d9+jKNDaj0xdQAAgMePM0kAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMAihacLAAA8uvBeC//W/z86pJbHa3hcdQAA8LhwJgkAAAAALDwaklavXq06deooc+bMcjgcmj9/vst6Y4w++OADZcqUSf7+/qpataoiIyM9UywAAACAfwWPhqTr16/rqaee0pgxY5JcP2zYMI0ePVrjx4/Xxo0bFRgYqGrVqunGjRs2VwoAAADg38Kj1yTVqFFDNWrUSHKdMUYjR47U+++/r3r16kmSpk2bpgwZMmj+/Plq1qyZnaUCAAAA+JdIttckHTlyRGfPnlXVqlWdy0JDQ/Xcc89p/fr19/1/N2/eVExMjMsNAAAAAB5Wsg1JZ8+elSRlyJDBZXmGDBmc65IyePBghYaGOm/ZsmVza50AAAAAnizJNiQ9qt69e+vKlSvO24kTJzxdEgAAAIB/kGQbkjJmzChJioqKclkeFRXlXJcUX19fhYSEuNwAAAAA4GEl25AUERGhjBkzavny5c5lMTEx2rhxo0qXLu3BygAAAAA8yTw6ut21a9d08OBB5/0jR45o+/btSp06tbJnz65u3bpp4MCBypMnjyIiItSnTx9lzpxZ9evX91zRAAAAAJ5oHg1JmzdvVqVKlZz3e/ToIUlq1aqVpkyZonfeeUfXr19Xhw4dFB0drXLlymnRokXy8/PzVMkAgGQqvNfCv/0YR4fUegyVAAD+6TwakipWrChjzH3XOxwO9e/fX/3797exKgAAAAD/Zsn2miQAAAAA8ARCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFik8HQBAAA8ScJ7Lfzbj3F0SK3HUAkA4FFxJgkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAACLFJ4uAAAAPF7hvRb+7cc4OqTWY6gEAP6ZOJMEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsGLgBAAC4RXIZQCK51AHgn4MzSQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFik8HQBAAAAT7rwXgv/9mMcHVLriakDSO44kwQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgweh2AAAAsBWj7CG540wSAAAAAFj8I0LSmDFjFB4eLj8/Pz333HP67bffPF0SAAAAgCdUsg9J3333nXr06KEPP/xQW7du1VNPPaVq1arp3Llzni4NAAAAwBMo2V+TNGLECL322mtq06aNJGn8+PFauHChJk2apF69eiXa/ubNm7p586bz/pUrVyRJMTEx920j/mbs367zQY//sKgjedVAHY+/juRQA3Ukvxqo4/HXkRxqoI7kVwN1AP/3vjHGPHA7h/mzLTzo1q1bCggI0OzZs1W/fn3n8latWik6Olo//PBDov/Tt29f9evXz8YqAQAAAPyTnDhxQlmzZr3v+mR9JunChQuKi4tThgwZXJZnyJBB+/btS/L/9O7dWz169HDej4+P16VLl5QmTRo5HI5HqiMmJkbZsmXTiRMnFBIS8kiP8XclhxqoI3nWkRxqoI7kVwN1JL8aqCP51UAdybOO5FADdSS/Gh5XHcYYXb16VZkzZ37gdsk6JD0KX19f+fr6uiwLCwt7LI8dEhLi0TdGcqmBOpJnHcmhBupIfjVQR/KrgTqSXw3UkTzrSA41UEfyq+Fx1BEaGvqn2yTrgRvSpk0rb29vRUVFuSyPiopSxowZPVQVAAAAgCdZsg5JKVOmVIkSJbR8+XLnsvj4eC1fvlylS5f2YGUAAAAAnlTJvrtdjx491KpVKz3zzDMqWbKkRo4cqevXrztHu7ODr6+vPvzww0Td+OyUHGqgjuRZR3KogTqSXw3UkfxqoI7kVwN1JM86kkMN1JH8arC7jmQ9ul2Czz//XB9//LHOnj2rYsWKafTo0Xruuec8XRYAAACAJ9A/IiQBAAAAgF2S9TVJAAAAAGA3QhIAAAAAWBCSAAAAAMCCkAQAAAAAFoSk+zDG6MKFC7p48aKnS5EkxcTEaP78+dq7d69H2r9165b279+vO3fueKR9JO3GjRsebT86OlpfffWVevfurUuXLkmStm7dqlOnTnm0LnjW5MmTFRsb6+kyYHH9+nVPl4Bk7M6dO1q2bJkmTJigq1evSpJOnz6ta9euebgyXLt2TTExMS43OxhjdPz4cY/vZyTwRB2MbnePs2fP6p133tGCBQucXxQhISFq0KCBBg8erAwZMthSR5MmTVS+fHl16tRJf/zxh5566ikdPXpUxhjNnDlTjRo1sqWO2NhYde7cWVOnTpUkHThwQDlz5lTnzp2VJUsW9erVy+01xMfHa8qUKZo7d66OHj0qh8OhiIgIvfTSS2rRooUcDofba7jXrVu3dOTIEeXKlUspUtg73Vh8fLwGDRqk8ePHKyoqyvma9OnTR+Hh4WrXrp0tdezcuVNVq1ZVaGiojh49qv379ytnzpx6//33dfz4cU2bNs2WOiRp+vTpGj9+vI4cOaL169crR44cGjlypCIiIlSvXj23tDl69OiH3rZLly5uqUGSGjZs+NDbzp071211WGXIkEF//PGHGjdurHbt2qlMmTK2tHs/nvy8Tp06VWnTplWtWrUkSe+8846++OILFSxYUN9++61y5MhhSx1BQUFq0qSJ2rZtq3LlytnS5v1ER0dr9uzZOnTokN5++22lTp1aW7duVYYMGZQlSxZba/Hke6N48eJJ/v1yOBzy8/NT7ty51bp1a1WqVMmtdRw7dkzVq1fX8ePHdfPmTefflK5du+rmzZsaP368W9uXpKioKPXs2VPLly/XuXPndO+uaVxcnNtrSLB8+XJnHfHx8S7rJk2aZEsNR44cUadOnbRy5UqXcGCMkcPhsOX5iI+Pl5+fn3bv3q08efK4vb371eDR/R0DpytXrpiIiAiTLl06061bNzN+/Hgzbtw407lzZ5M2bVqTJ08ec/XqVVtqyZAhg9m+fbsxxpivv/7a5M6d21y/ft2MHTvWFCtWzJYajDGmS5cupkSJEmbNmjUmMDDQHDp0yBhjzPz5822pIz4+3tSqVcs4HA5TrFgx06xZM9O0aVNTtGhR43A4TL169dxeg9X169dN27Ztjbe3t/H29nY+H506dTKDBw+2pYZ+/fqZnDlzmhkzZhh/f39nDTNnzjSlSpWypQZjjKlSpYp5++23jTHGBAUFOev49ddfTY4cOWyrY+zYsSZt2rRm4MCBLs/H5MmTTcWKFd3Wbnh4+EPdIiIi3FaDMca0bt3aeWvVqpUJCQkx2bJlMw0aNDANGjQw2bNnNyEhIaZ169ZurcPq9u3bZu7cuaZu3brGx8fH5MuXzwwZMsScOXPGthqMSR6f17x585rly5cbY4xZt26dCQgIMBMmTDB16tQxDRo0sKUGY4yZN2+eqVevnvHx8TF58uQxgwcPNqdOnbKt/QQ7duww6dKlM7lz5zYpUqRwvibvvfeeadGihW11JIf3Rq9evUxoaKgpV66c6dGjh+nRo4d5/vnnTWhoqOnatat54YUXjJeXl5k/f75b66hXr5559dVXzc2bN12+y1esWGFy587t1rYTVK9e3RQsWNCMHTvWzJs3z8yfP9/lZpe+ffsaLy8vU7JkSVOvXj1Tv359l5tdypQpY0qXLm1mzpxpVqxYYVauXOlys0vBggXN+vXrbWvvXp7e3yEkWfTv39/kzp3bnDt3LtG6qKgokzt3bjNo0CBbavHz8zPHjx83xhjTokUL8+677xpjjDl27JgJDAy0pQZjjMmePbvzA2L98oyMjDTBwcFub3/SpEkmODjY/PLLL4nWLV++3AQHB5upU6e6vY4Eng6NxhiTK1cus2zZMmOM62uyd+9eExYWZksNxhgTEhJiDh48mKiOo0ePGl9fX9vqKFCggJk3b16iOnbt2mXSpEljWx3JwTvvvGPat29v7ty541x2584d06FDB9OzZ0+P1HT27FnzySefmCJFihgfHx9Tp04dM3/+fBMXF+f2tpPD59Xf398cO3bMGHP39UkIAr///rtJmzatLTVYnTt3zgwfPtwUKVLEpEiRwtSqVcvMmTPH3L5925b2k8vBleTw3mjfvr3p379/ouUDBgww7du3N8YY88EHH5gSJUq4tY7UqVObffv2GWNcX5MjR44Yf39/t7adICgoyGzbts2Wth4kY8aMZtq0aZ4uwwQGBjpfE09asGCBKVeunNm1a5dH2vf0/g4hyeK5554zkyZNuu/6iRMn2nakPk+ePOa7774z165dM+nSpXMeidy+fbutO37W5G59g27fvt2EhIS4vf0XXnjhgUf1Bg0aZF588UW315HA06HRmLsB+ujRo4lq2L17t60BOl26dGbr1q2J6liyZInJmjWrbXXc7/k4cOCA8fPzs60OY4y5efOm2bdvn207nPdKmzZtkn9Y9+3bZ1KnTu2Biu7asGGD6dChg/H19TXh4eEmNDTUhIeHmxUrVri13eTwebV+TooVK+bcATt48KCtn9ekjB492vj6+hqHw2HSpUtn+vTpY65fv+7WNpPLwZXk8N4ICQkxkZGRiZZHRkY6/77u3bvXBAUFubWOsLAws3v3bmOM63OxZs0akz59ere2naBAgQLOz4knpU6d2vn+9KSKFSuapUuXeroMExYWZlKmTGm8vLyMn5+fSZUqlcvN3Ty9v8PADRYHDhx4YN/5MmXKaP/+/bbU0q1bNzVv3lxZs2ZV5syZVbFiRUnS6tWrVaRIEVtqkKRnnnlGCxcudN5P6D/91VdfqXTp0m5vf+fOnapevfp919eoUUM7duxwex0Jzp8/r/Tp0ydafv36dduujSpYsKDWrFmTaPns2bNVvHhxW2qQpLp166p///66ffu2pLvvjePHj+vdd9+17Zo5SYqIiND27dsTLV+0aJEKFChgSw2xsbFq166dAgICVKhQIR0/flyS1LlzZw0ZMsSWGqS7F1/v27cv0fJ9+/Yl6lvvblFRUfrkk09UqFAhVaxYUTExMfrf//6nI0eO6NSpU2rSpIlatWrl1hqSw+f1hRdeUPv27dW+fXsdOHBANWvWlCTt3r1b4eHhttRgFRUVpWHDhqlgwYLq1auXXnrpJS1fvlzDhw/X3LlzVb9+fbe27+vrm+SF5wcOHFC6dOnc2rZVcnhv+Pn5ad26dYmWr1u3Tn5+fpL+77oQd3rxxRc1cuRI532Hw6Fr167pww8/dL5f3W3kyJHq1auXjh49akt799O+fXt98803Hq1BuruPNXToUE2dOlVbtmzRzp07XW52GTlypL744gtNmjRJ48eP16effupyczeP7++4PYb9g3h7e5uzZ8/ed/2ZM2eMt7e3bfVs2rTJzJ071+U6qP/9739m7dq1ttWwZs0aExQUZN544w3j5+fn7CcdGBhoNm/e7Pb2fXx8zOnTp++7/tSpUyZlypRuryPB888/b0aPHm2MuXtU4/Dhw8aYu/3Yq1WrZksN8+fPN6GhoWbIkCEmICDAfPzxx6Z9+/YmZcqUZsmSJbbUYIwx0dHRpmrVqiYsLMx4e3ubbNmyGR8fH1O+fHlz7do12+r48ssvTZYsWczMmTNNYGCg+fbbb83AgQOdP9shOXTdMcaY7t27mzRp0pjhw4ebNWvWmDVr1phPPvnEpE2b1nTv3t22OmrXrm18fHxMoUKFzKeffmouXryYaJuoqCjjcDjcWkdy+LxevnzZdOzY0dStW9f8/PPPzuUffPCBGThwoC01GGPMnDlznK/LU089ZT777DNz+fJll20OHjxofHx83FpHu3btTP369c2tW7ecr8mxY8dM8eLFTdeuXd3atlVyeG8MGDDA+Pv7my5dupjp06eb6dOnmy5dupiAgADne2PEiBGmatWqbq3jxIkTpmDBgqZAgQImRYoUplSpUiZNmjQmX758Jioqyq1tJ7CesQgKCrL9jEWCLl26mLCwMFO+fHnTqVMn0717d5ebXdavX28iIiKMw+Fw3ry8vJz//lt4en+HkGTh5eWV5PVICc6ePWvbmzOpa3A85eDBg6Z9+/bm2WefNQUKFDDNmzc3O3futKXt5PSaGOP50Jhg9erVpmrVqiZdunTG39/flC1b1ixevNi29q3Wrl1rxowZY4YOHeqx7gEzZswwuXPndv4xyZIli/nqq69saz85dN0xxpi4uDgzdOhQkzlzZudzkTlzZjN06FCX65TcrW3btmbdunUP3CY+Pt7ZjcJdksvnNTkICQkxHTp0ML/99tt9t4mNjTV9+/Z1ax3J5eBKcnlvzJgxw5QqVcoZBkqVKmW+/vpr5/rY2Fjzxx9/uL2O27dvm+nTp5u3337bvPnmm+bLL780sbGxbm83wZQpUx54s0vFihXve6tUqZJtdRQoUMA0bNjQbNiwwRw5csQcPXrU5WangwcPmvfee880a9bMGZp/+ukn8/vvv9vSvif3dxgC3MLLy0uhoaH3PdVujFFMTIwtQy/6+voqa9asatOmjVq1aqVs2bK5vc3kyMvLSzVq1JCvr2+S62/evKlFixbZOjzooUOHNGTIEO3YsUPXrl3T008/rXfffdfWbpCedvv2bfn7+2v79u0qXLiwp8txio2N1bVr15LsRuNOAQEB+v3335UzZ04FBwdrx44dypkzp3bs2KHy5cvrypUrttYjydmlKSQkxPa2p02bpqZNmyb63N66dUszZ85Uy5YtbavF05/X1atXP3B9+fLlbakjNjZWAQEBtrT1MNauXaudO3c6X5OqVavaXoOn3xvA/QQGBmrHjh3KnTu3R+tYtWqVatSoobJly2r16tXau3evcubMqSFDhmjz5s2aPXu2R+tzN0KSRcJcQH/G3f3oJenChQuaPn26pk6dqt27d6ty5cpq166d6tevr5QpU7q9fatDhw5p8uTJOnz4sEaOHKn06dPr559/Vvbs2VWoUCG3tt2mTZuH2m7y5MlurQOJ5cyZU/PmzdNTTz3l6VI8rnz58mrcuLE6d+6s4OBg7dy5UxEREercubMiIyO1aNEiW+s5f/688/rJ/PnzK23atLa27+3trTNnziQKqxcvXlT69OltPajhaV5eiS/9tR6Is+u54DVJvm7dupXknDzZs2e3rYb9+/frs88+c05YX6BAAXXq1En58+e3rYa4uDjNnz/fWUOhQoVUt25deXt721aD1cmTJyVJWbNmtb3tOnXqqHXr1rZe35uU0qVLq3HjxurRo4fLAcDffvtNDRs2dD5H7rZ582bn+6JgwYIqUaKELe0Skv4Btm7dqsmTJ+vbb7+VJL3yyitq166dLTun//ajCPe630zXDodDvr6+bguwqVKleuiLiS9duuSWGu41ceJEzZ07V9OnT1fq1KltaTPB/SZhTMrWrVvdXM3do+I1atTQq6++qilTpuj111/Xnj17tG7dOq1atcq2L/Tr16+rc+fOmjZtmnOHy9vbWy1bttRnn31m25kELy8vRUVFJboQf8eOHapUqZJt71FPfV6t7j2LePv2bW3btk19+vTRoEGDVKVKFbfXIN19Tc6ePZsoJJ0+fVq5cuXSH3/84ba2k8vEy1Y//fSTvL29Va1aNZflixcvVnx8vGrUqOH2GiIjI9W2bdtEgzcYGycMlaQ5c+aoWbNmeuaZZ5wDMm3YsEGbNm2ybfL6gwcPqmbNmjp16pTy5csn6W5wy5YtmxYuXKhcuXK5vQbp7kAZAwcO1PDhw3Xt2jVJUnBwsN566y299957SR70cIcvvvhCAwcOVNu2bVWkSBH5+Pi4rK9bt64tdQQFBWnXrl2KiIhwCUlHjx5V/vz5XSa6dYeTJ0/q5Zdf1q+//qqwsDBJdyejLlOmjGbOnOn2AEtI+oc4ffq0vvjiCw0ZMkQpUqTQjRs3VLp0aY0fP96tZ3OSy1GE5MLLy+uBO+dZs2ZV69at9eGHHz7WL1PrWc6LFy9q4MCBqlatmvMP2vr167V48WL16dNH3bt3f2ztPkjx4sV18OBB3b59Wzly5FBgYKDLeneGk379+jl/vnHjhsaOHauCBQu6/IHfvXu3/vOf/2jw4MFuq8MqOXTdef3117Vs2TJ9/vnnKlu2rKS7Aa5Lly564YUXNG7cOLe2nxBed+zYoUKFCilFihTOdXFxcTpy5IiqV6+uWbNmubWOBJ76vD6MVatWqUePHtqyZYtb20kIKN27d9eAAQMUFBTkXBcXF6fVq1fr6NGj2rZtm9tqiIiIcLl//vx5xcbGuuz0BAQEKH369Dp8+LDb6rAqWrSohgwZkmj0tkWLFundd9+1ZdTUsmXLKkWKFOrVq5cyZcqU6L1q11n6XLlyqXnz5urfv7/L8g8//FAzZszQoUOH3F5DzZo1ZYzR119/7TzodvHiRb366qvy8vJyGWXXnXr37q2JEyeqX79+Lt+hffv21WuvvaZBgwbZUseDvo/sDNBZs2bVrFmzVKZMGZd9wHnz5qlnz55uf29Ur15d0dHRmjp1qkt4btOmjUJCQtzeS4OQZPGwR+vtOgp6+/Zt/fDDD5o0aZKWLl2qZ555Ru3atdPLL7+s8+fP6/3339fWrVu1Z88et9Xg6aMIDRs2fKjt5s6d69Y6EkybNk3vvfeeWrdurZIlS0qSfvvtN02dOlXvv/++zp8/r08++URvv/22/vvf/7qlhkaNGqlSpUrq1KmTy/LPP/9cy5Yt0/z5893S7r2sQSUpH374oS11tG/fXpkyZdKAAQMStX/ixAlNmjTJljqSg7Rp02r27NnOKQMSrFixQk2aNNH58+fd2n7Ce6Jfv3566623XHbIU6ZMqfDwcDVq1Mi2LsPJ4fN6P/v27dMzzzzjPFrtLgkB5dixY8qaNatL16WE16R///567rnn3FpHgm+++UZjx47VxIkTXXZ6XnvtNb3++utq3ry5LXX4+/tr7969iYZhP3r0qAoVKqTr16+7vYbAwEBt2bLF1i5tSQkICNDOnTsTXf8SGRmpp556SrGxsW6vITAwUBs2bEh0UGnHjh0qW7as2z8nCTJnzqzx48cnOlPzww8/6D//+Y9OnTplSx3JRc+ePbVx40Z9//33yps3r7Zu3aqoqCi1bNlSLVu2dPvfeX9/f61bty7RcN9btmzR888/7/b3Zoo/3+TfwzpPgKd17txZ3377rYwxatGihYYNG+ZygXxgYKA++eQTZc6c2a11hIWF6cyZM4mOBG7btk1ZsmRxa9uSFBoa6vY2/oqpU6dq+PDhatKkiXNZnTp1VKRIEU2YMEHLly9X9uzZNWjQILftdC1evFhDhw5NtLx69erq1auXW9pMil0h6M98//332rx5c6Llr776qp555hlbQlLVqlX16quvqmHDhh4ZKCFBbGysMmTIkGh5+vTpbdnRSXhPhIeHq2nTpm6f3+XPJIfP671zmhhjdObMGQ0ZMkTFihVzS5tWR44ckSRVqlRJc+fOVapUqdze5oP06dNHs2fPdgYkScqXL58+/fRTvfTSS7aFpNDQUB0+fDhRSDp48GCis+LuUrBgQV24cMGWth6kYsWKWrNmTaKQtHbtWj3//PO21ODr66urV68mWn7t2jVbr8O+dOlSkqE1f/78th0gv9eNGzc89l360UcfqWPHjsqWLZvi4uJUsGBBxcXF6ZVXXtH777/v9vazZcvmnIvRKi4uzu37v5KYJym5qly5svnmm2/MjRs37rvN7du3zcqVK91ax1tvvWXKlStnzpw5Y4KDg01kZKRZu3atyZkzp9uHik2O/Pz8zIEDBxItP3DggPH39zfGGHP48GHnz+6QPXt288knnyRa/sknn5js2bO7rd372bx5s3OOD0/MmJ4hQwYzefLkRMsnT55s22zxXbp0MRkzZjT+/v7mpZdeMvPnzze3bt2ypW2rypUrm8aNG7sMGRwbG2saN25sqlSpYns9npYcPq/W+U2st9KlS5u9e/e6rd3kyt/fP8lhyDdu3OjW1+FeHTp0MEWKFDEHDx50LouMjDRFixY17dq1s6WG5cuXm9KlS5sVK1aYCxcumCtXrrjc7DJu3DiTLl0607FjR+d3eceOHU369OnNuHHjzA8//OC8uUuLFi1MoUKFzIYNG0x8fLyJj48369evN4ULFzatWrVyW7v3KlmypOncuXOi5Z06dTLPPfecbXXcuXPH9O/f32TOnNl4e3s7p5V4//33bZ3eIsGxY8fMwoULzXfffZfkd6q7zJ8/35QsWdJs2rTJuWzTpk2mVKlSZt68eW5vn5CEB7p586Zp3769SZEihXE4HMbHx8d4eXmZV1991dZ5V5KLPHnymHfffTfR8nfffdfkzZvXGHP3A5w5c2a31TB58mTj7e1tateubQYMGGAGDBhgateubVKkSJFkWHCXqKgoU6lSJeNwOJxzfDgcDlO5cuUHzm31uA0ePNj4+fmZzp07O//Ad+rUyQQEBJjBgwfbVkdcXJxZvHixadWqlQkJCTGpUqUyr732mtsPZFjt2rXLZM6c2aRJk8ZUrlzZVK5c2aRJk8ZkyZLF7XNapEqVypw/f94Yc3diyHsng/TExJDJ4fN67/wmx48ft2XeG2PuTi6cMPfQvZNiemqSzNq1a5vixYubLVu2OJdt3rzZPP3006ZOnTq21REdHW1KlSplUqRIYcLDw014eLhJkSKFqVSpUqJJdt3FOkmo9Wb3hKH3Bvj73dxZ0+XLl03dunWNw+EwKVOmdE4sW79+fRMdHe22du+1cuVKExgYaAoUKGDatm1r2rZtawoUKGCCgoLM6tWrbaujX79+JmfOnGbGjBnG39/fGZJmzpxpSpUqZVsdnmadZNj6vkiZMqUtf1e4JimZ27Nnj44fP65bt265LLdjZBNjjE6cOKF06dLpwoUL2rVrl65du6bixYsrT548bm8/OVqwYIEaN26s/Pnz69lnn5X0f0NTzpkzR7Vr19a4ceMUGRmpESNGuK2OjRs3avTo0S7DtXbp0sW26wokqWnTpjp8+LCmTZumAgUKSLr7fm3VqpVy587tHI3RDrNmzdKoUaNcno+uXbu6dLOy040bN/Tjjz9q0KBB2rVrl63DK8fGxurrr7/Wvn37JN19Lpo3by5/f3+3tjt16lQ1a9ZMvr6+mjJlygOv77RjGgXp/p/Xffv2afbs2bZ8Xj05Z1SlSpU0b948hYWFqVKlSvfdzuFw6JdffnFbHVbnz59Xq1attGjRIueIXXfu3FG1atU0ZcoUW+c4M8Zo6dKl2rFjh/z9/VW0aFHb5q2S7g7e8SAVKlSwqZLkIzIy0uW7yxPzBJ0+fVpjxoxxqeM///mPPd27/r/cuXNrwoQJqlKlisv14Pv27VPp0qV1+fJlt7Xdo0ePh97Wnfs5kv70b4mVO/6uEJKSqcOHD6tBgwbatWuXHA6HEl6mhDeLHTtd8fHx8vPz0+7du/+1oSgpR48e1fjx43XgwAFJd/vTv/7667p27VqymljV3UJDQ7Vs2TLnzmeC3377TS+++KKio6M9U5iHnT17VjNnztSMGTO0detWlSxZUhs2bPB0Wf9aR44c0YQJExJ9Xu+9FsVdmJ8oaQcOHHDuhObPn1958+b1cEX/XocPH1bOnDk9XQYs/P39tW/fPuXIkcMlJO3Zs0clS5Z060AW9x5Q2bp1q+7cueO8jvDAgQPy9vZWiRIlbDu44ikM3JBMde3aVREREVq+fLkiIiL022+/6eLFi3rrrbf0ySef2FKDl5eX8uTJo4sXLxKSLMLDwzVkyBBJd+dh+fbbb9W0aVNt3rzZth2e5DDpXnx8fKK5GyTJx8cn0aSIdtiyZYvL83HvaDjuFBMTozlz5uibb77RypUrlTNnTjVv3lzfffedbfN7JIiMjNSKFSuSnJzygw8+cFu795uTKCl2Dm4RERHh/Lx6gvn/c97c6+TJk7YMTBMXF+c80HXv2cQ//vhDkZGRKly4sO1DoOfNm9fjwWj58uVavnx5kp8Vdw34snPnTufzfe+gHvcqWrSoW2q4V+7cuVWhQgW1a9dOL730km2DBPTo0UMDBgxQYGDgn569cOcZi+T4mhQsWFBr1qxRjhw5XJbPnj3b7X/bVqxY4fx5xIgRCg4O1tSpU52Dvly+fFlt2rSxZVCPhPdl48aN3d4bIklu6cSHvy1NmjRmx44dxhhjQkJCzL59+4wxdy/0LFasmG11LFiwwJQrV87s2rXLtjaTsmrVKnP79u1Ey2/fvm1WrVrlkXpatmxpAgMDndc9JHUxsjtERkaavHnzmoCAAFO8eHFTvHhxExAQYPLly+dyEbK71a1b15QvX96cOnXKuezkyZOmQoUKpn79+rbVkRyujfLz8zOZMmUy3bp1c7nA1G5ffPGF8fb2NhkyZDBPPfWUKVasmPNWvHhxt7ad1LUVnr7WwhhjVq9ebZo3b25Kly5tTp48aYwxZtq0aWbNmjVubTfhOffy8jJFihRxflaLFy9uihYtaoKDg03jxo3dWoMxd69hLFGiRJLXkN6+fduUKFHCTJ8+3e11WJ04ccKMGTPGvPvuux67Nqpv377Gy8vLlCxZ0tSrV8/Ur1/f5eYuDofDREVFOX9OalAPuz8n27ZtM126dDHp0qUzoaGhpkOHDmbjxo1ub7dixYrO678qVqz4wJs7JcfXZP78+SY0NNQMGTLEBAQEmI8//ti0b9/epEyZ0ixZssS2OjJnzpzk9ay7du0ymTJlcnv7Xbt2NenSpTMhISGmffv2Zv369W5v04rudkkwxmj27Nn3PRprx5w8qVKl0tatWxUREaFcuXLpq6++UqVKlXTo0CEVKVLEluF8E+qIjY3VnTt3lDJlykRJ3q4hMZNDl5WzZ89qypQpmjhxomJiYtSkSRONHz9eO3bsUMGCBd3efoLkMuneiRMnVLduXe3evVvZsmVzLitcuLAWLFjg9pmwEySHa6OWLl2qKlWq2H40/l45cuTQf/7zH7377ru2t/1n11dY2XWtxZw5c9SiRQs1b95c06dP1549e5QzZ059/vnn+umnn/TTTz+5re3kMmfU888/r44dO6pZs2ZJrp81a5Y+//xzrV692q11JFi+fLnq1q3rvL6icOHCOnr0qIwxevrpp23rvpMpUyYNGzZMLVq0sKW9BMeOHVP27NnlcDh07NixB25771kEd7tz544WLFigKVOmaNGiRcqbN6/atm2rFi1aKF26dLbWYqfk+pqsWbNG/fv3d5mg/IMPPtCLL75oWw3BwcH68ccfk5x7r27dukkO2/64Jbwvp06dqp9//lm5c+d2vi+TmvLisbI1kv1DdOnSxfj6+prq1aubVq1amdatW7vc7FCuXDnn8IYvv/yyqV69ulm7dq1p2bKlKVSokC01GGPMlClTHnizi8PhSPKswP79+01wcLDb269du7YJCQkxL7/8svnf//7nPCqbIkUKs3v3bre3bxUQEGB27tyZaPn27dtNYGCgrbXEx8ebJUuWmNGjR5vRo0ebpUuX2tq+MXfPtN5vSOHQ0FDb6rh9+7ZZunSpGT9+vImJiTHGGHPq1Clz9epV22oIDg52joKEu2dzpk6daowxJigoyPncbN261WTIkMGWGqZMmWLbaHZJSZcunTly5Mh91x8+fNikTZvWtnqeffZZ88EHHxhj/u81uXr1qqlbt64ZO3asbXWkTp3a1jPvSXnQMN+RkZE2VuLqxo0bZsSIEcbX19c4HA7j6+trWrRoYU6fPu22Ntu0aeP83rS6du2aadOmjdvaxYO1aNHChIeHmzlz5pgTJ06YEydOmNmzZ5uIiAjTsmVL2+uJiooyAwYMMH5+fsbHx8fUq1fPLF++3G3tEZKSkCpVKrNw4UKP1rBo0SIzZ84cY8zdL8t8+fIZh8Nh0qZN69Y3RHLToEED06BBA+Pl5WVq1qzpvN+gQQNTt25dEx4ebqpVq+b2Ory9vU337t0TzQ/giZCUKlUq8+uvvyZavnbtWluHV04ugoKCzLZt2xIt37p1qy0B2pi7wzznz5/fBAQEuMxp0aVLF/P666/bUoMxxrRt29aMGzfOtvb+zPXr183evXvNjh07XG528ff3dwYEa0g6dOiQ8fX1ta0OTwoICHjgc75jxw4TEBBgWz1BQUHOcBIWFubsyrN9+3aTI0cO2+p45513TP/+/W1rLynlypVLMkDv27fPZMmSxfZ6Nm3aZN58802TKlUqkzVrVvPee++Zw4cPm9WrV5sqVaqYZ5991m1te3l5Obu8WZ0/f954e3u7rd17WeeEst4WLFhglixZYg4fPmxLHREREebChQuJll++fNlERETYUoMxd7/D33zzTePr6+vsNp0yZUrz5ptvOqcXsMvGjRvNG2+8YcLCwkz27NnNBx98YNq1a2f8/f3NW2+95ZY2GbghCaGhoR4f6aVatWrOn3Pnzq19+/bp0qVLSpUq1UMPh/g43O9ibIfDIV9fX7d3FUm4sNkYo+DgYJfufilTplSpUqX02muvubUG6e7M4xMnTlSJEiVUoEABtWjR4r7dV9ytdu3a6tChgyZOnKiSJUtKujsk+BtvvGHL0PAJunTpoty5c6tLly4uyz///HMdPHhQI0eOtKWOypUrq2vXrvr222+dQ7SeOnVK3bt3V5UqVWypoWvXrnrmmWe0Y8cOpUmTxrm8QYMGbn9/jh492vlz7ty51adPH23YsEFFihRJNLDGva+Vu5w/f15t2rTRzz//nOR6uwY4yZgxow4ePJhoJLu1a9fa9h0fFxenTz/9VLNmzUpyOgd3d1nOkyeP1q1bd98LzteuXWvrwDyBgYHO5yBTpkw6dOiQChUqJEm6cOGCbXXcuHFDX3zxhZYtW6aiRYsm+qy4e2hjSQoKClLDhg21YMECpUhxd3ds7969qly5si3TF7Rt21ajRo3Sl19+qcmTJ2v//v2qWbOmpk2bppo1azq7D0dERGjKlCluGREyJiZG5u4Be129etVl0Ii4uDj99NNPtg4LX79+fZcRhRMkLHM4HCpXrpzmz5/vHMjAHY4ePZrk9+TNmzd16tQpt7V7r4CAAI0dO1Yff/yxDh06JEnKlSuXAgMD3dru6tWrVaZMGV26dEnTp0/X5MmTFRkZqTp16ujbb79VtWrVnPvCrVu3VvXq1d0zqJlbotc/3JQpU0yzZs1MbGysR+uIj48358+fT/Jogl3+7GLshDQfFxfn1jr69u1r+1GLpFy7ds1MnDjRlC1b1jmx7siRI5PsJuAuyWXSvcyZM5vNmzcnWr5lyxZbj4IeP37cFCtWzPj4+JicOXOanDlzGh8fH1O8eHFz4sQJW2pInTq1c3AV6xmLI0eOGH9/f7e2nTAR5p/d7Dz6+Morr5iyZcuaTZs2mcDAQLNkyRIzffp0ky9fPvO///3Ptjo++ugjU7BgQbNhwwYTHBxs1qxZY2bMmGHSpUtnRo8ebUsNffr0MZkyZTKffPKJ8fPzMwMGDDDt2rUzadKkMaNGjXJ7+0OHDnUZCMhq+/btJk2aNGbo0KFuryNBvXr1zBdffGGMMeatt94yuXPnNgMHDjRPP/20qVKlim11PGiQgEqVKtlSQ2xsrClTpoxp0qSJiY+PN7t27TLp06e3bQCLhLM3uXPnNh999NEDu9PdvHnTLV3s/2wfw9vb2wwcOPCxt3s/y5YtM88995xZtmyZiYmJMTExMWbZsmWmdOnSZuHChWbt2rWmUKFCpm3btm5pP+HMlcPhMNOmTXM5mzV37lzTsWNH50TYT7KE96aPj4/Jnz+/GTZs2H0HYrpy5YrbBvcgJCUhNjbWVKtWzQQFBZnChQu7jErk7hGijDHmzJkzpkWLFiY0NNT5RREWFmbatGljzp496/b2raZOnWqyZs1q3n//fbNgwQKzYMEC8/7775ts2bKZCRMmmIEDB5qwsDAzaNAgW+tKDvbt22fefvttkzFjRuPn52frbPHG3O2GmfCaeKL/uq+vb5LtRkZG2t6VydPXRoWFhTm7XVpD0po1a0z69OltrSU5yJgxo3N0rODgYLN//35jzN0dgLJly9pWR3x8vBk4cKAJDAx0jlDl5+dn3n//fdtqyJkzpzMYWruajRo1yrz88stub//WrVumYsWKJkWKFKZ69eqmW7duplu3bqZ69eomRYoUpkKFCubWrVturyPBoUOHnIHt2rVr5vXXXzdFihQxDRs2NEePHrWtjuTi8uXL5qmnnjIvvfSSSZ8+venZs6dtbVtHdfOUlStXmhUrVhiHw2Hmzp1rVq5c6bytW7fOZfRUOxQqVOi+3dkLFixojDFm6dKlJlu2bG5p3zqS3r2j66VMmdLkzZvX/Pjjj25pOynXrl0z77//vildurTJlSuXiYiIcLm5S8J7c/Xq1W5r46HqMIbR7e7VpEkTrVixQi+99JIyZMiQqHvbhx9+6La2Y2JiVKxYMV27dk3NmzdX/vz5ZYzRnj179O233zpHvbOOlOROVapU0euvv57o1P+sWbM0YcIELV++XNOnT9egQYOcEwO6Q1RUlHr27Omc0+Let60nJ2SMi4vTjz/+qEmTJmnBggVubSsmJkZBQUGJRlGLj4/XtWvXbJ1/pnDhwnrjjTfUqVMnl+WfffaZxo0bpz179thWi6c1bdpUoaGh+uKLLxQcHKydO3cqXbp0qlevnrJnz67JkyfbUkf//v3Vs2dPBQQEuCz/448/9PHHH7t1niSrkJAQ7dy5U+Hh4cqRI4e++eYblS1bVkeOHFGhQoVsG50zwa1bt3Tw4EFdu3ZNBQsWtO37U7rbvWzv3r3Knj27MmXKpIULF+rpp5/W4cOHVbx4cV25csXtNdy+fVuffvqpvvnmG0VGRsoYo7x58+qVV15Rt27d3N5tOkFcXJx+/fVXFS1aVGFhYba0mdwk1YX9zJkzeuGFF1S7dm2XOb3c/X3u5eWlyMjIPx25zo6/K8eOHVO2bNk8PkKov7+/Nm3alGhi+F27dqlkyZL6448/dOzYMRUoUMCt32MRERHatGmT0qZN67Y2HsbLL7+sVatWqUWLFsqUKVOi/eGuXbu6pV0vLy9FRUV5fFRFQlISAgMDtXjxYpUrV872tgcMGKBp06Zp3bp1id4c586dU9myZdWmTRv997//taUef39/7dy5M1Gf9cjISD311FOKjY21ZcenRo0aOn78uDp16pTkB7VevXpuazu5mDdvnt59911t37490U7w9evX9fTTT+uTTz5RnTp1bKln0qRJ6tSpk95++21VrlxZ0t3hfYcPH66RI0e6/VqcX375RZ06ddKGDRsS/RG/cuWKypQpo/Hjx9sy4d3JkydVrVo1GWMUGRmpZ555RpGRkUqbNq1Wr15tW5/65DBUviQ9++yzGjhwoKpVq6a6desqLCxMgwcP1ujRozV79mxn3/Z/g3z58mnatGl67rnnVK5cOdWuXVu9evXSd999p86dO+vcuXOeLtFWfn5+2rt3ryIiIjxdijZv3nzfa8XcNdWHl5dXktcVJ+yKWa99cffn9X61WGuyow6r2NjYJF8PuyZxLVeunIKDgzVt2jTnPtj58+fVsmVLXb9+XatXr9ayZcvUsWNH7d+/35aaEkRHR9t+cCEsLEwLFy5U2bJlbW3Xy8tLNWrUkK+v7wO3c/eUPAzckIRs2bLZekTeauHChfrvf/+bZHpOnz69evfurS+//NK2kJQtWzZNnDgx0Yz1EydOdM6Nc/HiRbdewCjdvbh4zZo1KlasmFvbSc7GjRund955J1FAku4G+3fffVeff/65bSGpbdu2unnzpgYNGqQBAwZIksLDwzVu3Di1bNnS7e0nBLGkPquhoaF6/fXXNWLECFtCUtasWbVjxw599913zjkt2rVrp+bNm9s6S3jCTs29duzY4ZxTyw5du3bVmTNnJN098169enV9/fXXSpkypaZMmeLWths2bPjQ29ox512DBg20fPlyPffcc+rcubNeffVVTZw4UcePH1f37t3d3n5yU7hwYR0+fNjjIWnmzJlq2bKlqlWrpiVLlujFF1/UgQMHFBUVpQYNGrit3RUrVrjtsR/F7Nmzbf1uuJ/kMtjLxIkTVa9ePWXNmtVl/r+cOXPqhx9+kCRdu3ZN77//vlvrGDp0qMLDw9W0aVNJUuPGjTVnzhxlypRJP/30k5566im3tp8gVapUHnt/3DtYlydwJikJCxcu1Geffabx48e7ZTSXB0mdOrXWr1+vfPnyJbl+3759zhE/7LBgwQI1btxY+fPn17PPPivp7tG3ffv2afbs2apdu7bGjRunyMhIt44GVLBgQX399dcqXry429pI7jJnzqzVq1crd+7cSa4/ePCgypcvr9OnT9tc2d0/cP7+/rZ2Y8qRI4cWLVrknED2Xvv27dOLL76o48eP21bTvQ4fPqw33nhDS5YscWs7CaNeXrlyRSEhIS5BKS4uTteuXdMbb7yhMWPGuLWO+4mNjdW+ffuUPXt2t3cfadOmzUNva1c3SKsNGzZo3bp1ypMnj20HNJKTRYsWqXfv3howYIBKlCiRaJQsuw5QFi1aVK+//ro6duyo4OBg7dixQxEREXr99deVKVMm52TA7nLnzh199NFHatu2rW0Tb9/Ly8tLZ8+etXX0uPtp3ry5jh07ppEjR6pixYqaN2+eoqKiNHDgQA0fPly1atWyrZb4+HgtWbJEBw4ckHT3bPALL7xga1fAiIgIff311ypTpoyWLl2qJk2a6LvvvnOe+XT335QEM2bM0A8//KCpU6cmeYDWXZLLe5OQlIRUqVIpNjZWd+7cUUBAQKKhQd0ZUFKkSKFTp07ddxbhs2fPKmvWrLpz547barjXkSNHNGHCBJcvjNdff93WALlkyRINHz5cEyZMsD24Jhf+/v7atm2b8ufPn+T6vXv36umnn9Yff/xhc2V3rVq1SrGxsSpVqpTbzyxKd7vt/P777w8MjUWKFPHY8yHdPYPz9NNPu/0o6NSpU2WMUdu2bTVy5Ejn0PnS3aHyw8PDVbp0abfWgKQlDGWbMMRzgjt37mjdunUqX768hyrzDOuOpjXM2921KzAwULt371Z4eLjSpEmjlStXqkiRIs4huBPOhLpTcHCwdu3a5bG/acllR1S6Oxz8Dz/8oJIlSyokJESbN29W3rx5tWDBAg0bNkxr1661vaYbN27I19fX1mlXEvj7++vAgQPKli2bunbtqhs3bjj3w5577jldvnzZljqKFy+uQ4cOyRij8PDwRPvDW7dudUu79+s6bje62yXBrvldkmKMeeDRiqTG73e3iIiIRN3t7Na0aVPFxsYqV65ctgfX5CI8PFybN2++b0javHmzcuTI4fY6hg4dqmvXrjm72BljVKNGDeeRrfTp02v58uXOuU/cJUuWLA8MSTt37lSmTJncWkNy0apVK0l3P6tly5ZNtENuhx49emjAgAEKDAxUjx49HritHXPQJBeVKlVK8o/9lStXVKlSJY8OOuMJyaW7WapUqXT16lVJ//ddUqRIEUVHR9s2sEjlypW1atUqj4WkHDlyyNvb2yNt3+v69evOz0iqVKl0/vx55c2bV0WKFHHbjnhS4uPjNWjQII0fP15RUVE6cOCAcubMqT59+ig8PFzt2rWzpY5UqVLpxIkTypYtmxYtWqSBAwdKuvv31s7vjPr169vWllVyOX9DSEpCwg6HJySMOnS/IxeeeOOsWbNGEyZM0OHDh/X9998rS5Ysmj59uiIiImwb3MKTwTW5aNiwod577z298MILic40nj17Vu+//75effVVt9fx3Xff6d1333Xenz17tlavXq01a9aoQIECatmypfr166dZs2a5tY6aNWuqT58+ql69ussEhNLd0dw+/PBD1a5d2601JDcVKlTQoUOHNHnyZB06dEijRo1S+vTp9fPPPyt79uxuDa7btm3T7du3nT/fj7uPyhYvXvyh27Bj5+t+14ldvHjR7RMyJuXWrVs6cuSIcuXK5ZEwXaFCBdvbTEr58uW1dOlSFSlSRI0bN1bXrl31yy+/aOnSpbZNQl2jRg316tVLu3btSrLrobsnBz9y5IhbH/+vyJcvn/bv36/w8HA99dRTzl4j48ePt/Vg18CBAzV16lQNGzbMZfChwoULa+TIkbaFpIYNG+qVV15Rnjx5dPHiRdWoUUPS3e/W+x0YdAd3jub8ICtWrEgW18rR3e5P3LhxI9EoK+7sMz116tSH2s6uIDdnzhy1aNFCzZs31/Tp07Vnzx7lzJlTn3/+uX766Sf99NNPttQB6erVqypdurSOHz+uV1991Xnd2r59+/T1118rW7Zs2rBhg4KDg91aR6pUqbRu3TrntUBt2rRRXFycpk2bJunuNReNGzfWiRMn3FpHVFSUnn76aXl7e6tTp04uz8eYMWMUFxenrVu33rfrqh3s6m6XYNWqVapRo4bKli2r1atXa+/evcqZM6eGDBmizZs3a/bs2bbU4Ul/5VoSd+4AJAwg8cMPP6h69eouozTFxcVp586dypcvnxYtWuS2GqxiY2PVuXNn59+YhCPknTt3VpYsWdSrVy+31xATE+P8+/nTTz+5dBv39va29bqTS5cu6caNG8qcObPi4+M1bNgw57Vi77//vi1dhv+s18i/6SzjjBkzdOfOHbVu3VpbtmxR9erVdenSJedgLwkDGLhb7ty5NWHCBFWpUsV5rVrOnDm1b98+lS5d2rZubrdv39aoUaN04sQJtW7d2nk99qeffqrg4GC1b9/eljqku6PqJYxK+vbbbyt16tTOv61ZsmSxrQ6PsGU2pn+Ya9eumY4dO5p06dIlOQP0v0mxYsXM1KlTjTGuk2Ru3brVZMiQwdZaDh48aN577z3TrFkz5wR4P/30k/n9999trcOToqOjzZtvvmlSp07tnGAuVapU5s033zSXLl2ypQbr+8AYY/Lly2fGjRvnvH/s2DHj5+dnSy1Hjx41NWrUcJl4z8vLy9SoUcMcPnzY7e0XK1Ys0WTT1lu+fPls/c4oVaqUGT58uDHG9XXauHGjyZIli211REdHm4sXLyZafvHiRXPlyhXb6vCk1q1bm9atWxuHw2GaNm3qvN+6dWvToUMH89FHH5nz58/bVk+XLl1MiRIlzJo1a0xgYKDzvTF//nxTrFgxt7f/448/urQTFBTkMlGml5eX+f77791eB/4Zrl+/brZs2WLrZ8QYY/z8/JyTGlu/Q3fv3m0CAwNtq+PatWu2tfUgO3bsMOnSpTO5c+c2KVKkcD4f7733nmnRooWHq3M/utsl4Z133tGKFSs0btw4tWjRQmPGjNGpU6c0YcIEj1+bY7f9+/cneWFxaGiooqOjbavj3iPkgwYNUvr06bVjxw5NnDjxX3GEXLr7vI8dO1ZjxozRhQsXZIxRunTpbL2wNFeuXFq9erVy5syp48eP68CBAy7vkZMnTypNmjS21JIjRw799NNPunz5sg4ePChjjPLkyWPLUWDJc/2172fXrl365ptvEi1Pnz69Lly4YFsdzZo1U506dfSf//zHZfmsWbO0YMEC289Ab9myRXv37pUkFSpUyJZRMhNGzgsPD1fPnj090rXOav78+fruu+9UqlQpl++LQoUK2TJv1RdffKHOnTu7LDt48KBy5swpSRo2bJgmTZqkl156ye21SMlnTjEkLSAgQE8//bTt7RYsWFBr1qxJdH3v7NmzbR1dN0OGDGrSpInatm3rkTk7E/To0UOtW7fWsGHDXHqp1KxZU6+88orH6rKNp1NacpQtWzazYsUKY4wxwcHBJjIy0hhjzLRp00yNGjU8WJn9IiIizNKlS40xrkdVpk6dagoUKGBbHcnlCDmM+eKLL0xgYKBp27atKViwoClTpozL+gEDBpjatWt7qLp/tyxZsphff/3VGOP6OZk7d67JmTOnbXWkSpXK7NmzJ9HyvXv3mtSpU9tWR1RUlKlUqZLzjGuqVKmMw+EwlStXNufOnbOtDquVK1eahQsX2nbmN4G/v7/z/WB9b2zfvt2EhIS4vf3w8HCzb98+5/17z0jv3LnTpEuXzu11JHA4HM4eCVanTp2y7Uy4MXffD7Vr1za5cuUyuXLlMnXq1DGrV6+2rX1jjMvr4CkNGzY0Q4YMSbR86NCh5qWXXrKtjvnz55vQ0FAzZMgQExAQYD7++GPTvn17kzJlSrNkyRLb6pg3b56pV6+e8fHxMXny5DGDBw82p06dsq39BCEhIebgwYPGGNfP7NGjR42vr6/t9djNvkHf/0EuXbrkPLoVEhLiHDmtXLlyWr16tSdLs91rr72mrl27auPGjXI4HDp9+rS+/vpr9ezZU2+++aZtdezatSvJCf7sPkKOu++J0aNH69KlSypfvrzmzJnjsv706dNq27ath6r7d2vWrJneffddnT17Vg6HQ/Hx8fr111/Vs2dPWyb4TXDz5s0kpym4ffu2rUOyd+7cWVevXtXu3bt16dIlXbp0Sb///rtiYmLUpUsXt7Y9dOhQ9enTx3nfGKPq1aurUqVKql27tgoUKKDdu3e7tQarZ555RgsXLnTeTzib9NVXX9kyPPyZM2dcrstasWKFc7JOSQoKCtKVK1fcXsfo0aM1evRoORwOffXVV877o0eP1qeffqqOHTvedwTRx23GjBmqWrWqAgIC1KVLF3Xp0kX+/v6qUqVKkmeE3SV37tyqVKmSZsyYoRs3btjWrtXq1atVs2bNRMtr1Khh635XvXr19OOPP2rZsmUKDAzUBx98oL179+rHH3/UCy+8YFsd9evX1/z583Xq1Cm98cYb+uabb5QjRw7Vrl1bc+fOtW0aGF9fX8XExCRafuDAAaVLl86WGuLi4jR79mwNGDBAAwYM0OzZs+2bBsfTKS05KlKkiFm5cqUxxpgqVaqYt956yxhjzKhRo/51Zy3i4+PNwIEDTWBgoLPvuJ+fn3n//fdtrSO5HCEHkrObN2+a9u3bmxQpUhiHw2F8fHyMl5eXefXVV82dO3dsq6NixYqmU6dOiZb/5z//MeXKlbOtjpCQEPPbb78lWr5x40YTGhrq1raLFy9uZs6c6bw/a9Ys4+/vb9auXWsuXrxoatWqZRo3buzWGqzWrFljgoKCzBtvvGH8/PxM165dzQsvvGACAwPN5s2b3d5+pkyZnL0SkrJ48WKTMWNGt9cRHh5uwsPDjcPhMNmyZXPeDw8PN3nz5jUvvvii2bBhg9vrMMaY/PnzmxEjRiRaPnz4cJM/f35bajDGmG3btpkuXbqYdOnSmdDQUNOhQwezceNG29o35u61QNYzjQn27t1r65m9B9m0aZNH2x89erTx9fU1DofDpEuXzvTp08dcv37drW22a9fO1K9f39y6dcsEBQWZw4cPm2PHjpnixYubrl27urVtY4z5/fffTc6cOU1AQIDzWt/AwEATHh5udu3a5fb2CUlJGDFihBk1apQxxpilS5caPz8/4+vra7y8vMzIkSM9XJ1n3Lx50+zevdts3LjRXL161fb233rrLVOuXDlz5swZZxfItWvXmpw5c5q+ffvaXg+QnB07dswsXLjQfPfdd+bAgQO2t7927Vrj5+dnnn/+edO3b1/Tt29f8/zzzxs/Pz9buxIFBQWZbdu2JVq+detWExwc7Na2w8LCXLoctm7d2uVC5/Xr15usWbO6tYZ7HTx40LRv3948++yzpkCBAqZ58+Zm586dtrTdtGlTU6dOnfuur1WrlmnSpIkttRhzN8jb3eXxXilTpnR257eKjIz0SFem27dvmzlz5pg6deoYHx8fU6hQITN8+HBbuqY+++yzpl+/fomWf/jhh+bpp592e/sJrl69amJjY12Wbdu2zdSuXdsjA3edPXvWDB061BQoUMAEBASY5s2bm19++cVMmzbNFCpUyLzwwgtubT86OtpUrVrVhIWFGW9vb5MtWzbj4+Njnn/+eVsGlyhVqpSpU6eOy2f10qVLpm7duqZ06dJub5+Q9BCOHj1q5syZY3bs2GFbm/Hx8WbWrFnmzTffNI0aNTINGjRwudkpPj7enD9/3ly4cMHWdq2SyxHy5CA59B9PTpLLKEBwtW3bNvPKK6+YggULmhIlSpg2bdrYHtjq1q1rypcv79KX/+TJk6ZChQqmfv36bm07OY0CmRxs3brV+Pr6mpdeesn89ttvJjo62kRHR5uNGzeahg0bGl9fX7NlyxaP1Xfnzh2zbds2W4NTrly5zPjx4xMtHzdunMmdO7dtddzrxo0bZsSIEc6zFr6+vqZFixbm9OnTbmtzwYIFJkWKFKZly5ZmypQpZsqUKaZFixYmRYoUZt68eW5rN8Hx48dNqVKljJeXl/Hx8THdu3c3169fNy1atDApU6Y0TZs2te0MozHGzJkzx9SuXdv4+PiYp556ynz22Wfm8uXLLtscPHjQ+Pj42FLP2rVrzZgxY8zQoUMfeEb4cfPz80tyBONdu3bZ8v3J6Hb3uH37tqpXr67x48crT548ku6OoHXvSCfu1q1bN02YMEGVKlVShgwZbB29LMHZs2f1zjvvaMGCBc6ZyUNCQtSgQQMNHjzY1vlnUqZMqS+//FJ9+vTR77//rmvXrql48eLO1+jfJHfu3KpQoYLatWunl156KdFEqv82yWUUIE/q37//Q233wQcfuLmS/1OsWDF9/fXXtrWXlM8//1x169ZVeHi48/qXEydOqHDhwpoxY4Zb205Oo0BanTt3TufOnVN8fLzL8qJFi7q13eLFi+u7775T+/btNXfuXJd1qVKl0syZM20dzaxbt24qUqSI2rVrp7i4OJUvX17r169XQECA/ve//6lixYpur+Gtt95Sly5dtH37dpUpU0aS9Ouvv2rKlCkaNWqU29u/1+bNmzVp0iTNnDlTgYGB6tmzp9q1a6eTJ0+qX79+qlevnn777Te3tF2nTh3Nnz9fH330kWbPni1/f38VLVpUy5Yts2UC4rfffls3btzQqFGjNHfuXI0aNUpr1qzRc889p0OHDilr1qxur8GqTZs2atasmX799Vc9++yzSW6TOXNmvffee25p/48//tDy5cudE7L/73//082bNyXdneNsyZIl6t+/v9v3P/LmzauoqKhEE6GfO3fOlkl1mUw2CenSpXNOKucpqVOn1owZM5K8kNEOMTExKlasmK5du6bmzZsrf/78MsZoz549+vbbb5UqVSpt3bpVQUFBHqnv32z79u2aPHmyvv32W926dUtNmzZVu3btVLJkSU+X5hHz58/XlClT9NNPPyk8PFxt27ZVy5YtlTlzZre2O3r06Ife1t2DBHh5eSlz5sxKnz697veV7nA4tHXrVrfWkRS7J+S+lzFGy5Yt0759+yRJBQoUUNWqVd3e7pdffqnu3buradOm2rBhg8LCwvTrr7861w8cOFAbN27Ujz/+6PZapLvDoLdq1Up79+5N9B6xc+LS2NhYLV68WJGRkZKkPHny6MUXX7R9iPQsWbLohx9+0DPPPKP58+erY8eOWrFihaZPn65ffvnF5bVyp3nz5mn48OHOIeoLFCigt99+W/Xq1bOlfUkaMWKEJk+erP3796tmzZpq3769atas6TLZ7cmTJxUeHm7fBfM2y5w5s+bOnatSpUrp3Llzypgxo0aMGKFu3bp5pJ7Y2FgFBAR4pG1JGj9+vBYuXOj8fgoODlahQoXk7+8v6e6k7e+88466d+/+2Nu2DhSxdu1avfPOO+rbt69KlSol6e6E9f3799eQIUPcvo9MSEpC9+7d5evr69E5kSIiIvTzzz/bNsrOvQYMGKBp06Zp3bp1iUYwOXfunMqWLas2bdrov//9r9tq6NGjx0NvO2LECLfVkVzduXNHCxYs0JQpU7Ro0SLlzZtXbdu2VYsWLWwbdUa6O9fJoUOHVL58efn7+8sY45Ezn+fPn9f06dM1ZcoU7d27V9WqVVPbtm1Vt25dpUjx+E+aR0REPNR2DodDhw8ffuztW9WqVUu//PKL83euXbu2yw6O3WJjY/XOO+9o1qxZunjxYqL17t4h/+WXX9SpUydt2LAhUSC7cuWKypQpo/Hjx+v55593ax2TJk3Sjz/+qIwZM+rDDz9UxowZnev+85//6IUXXkhy1E53eOqpp5QrVy69++67SfZOsLu3xJ8pUqSIfvrpJ5cR8B4nPz8/HTx4UFmzZlWHDh0UEBCgkSNH6siRI3rqqaeSHNHrcZk8ebIqV66cbJ7zPHnyqG3btmrdurUyZcqU5Da3bt3St99+q1atWtlcnT28vb11+vRpZw+ZoKAgbdmyRfny5bO1jjt37iguLs5lJMioqCiNHz9e169fV926dW3pMfH888/rnXfeUZ06dSTdDUk7duxwjvw8Y8YMjRkzRuvXr3/sbXt5ebl8PyXElIRl1vvu/ltCSEpC586dNW3aNOXJk0clSpRIdITLjh3yqVOnatGiRZo0aZIzudupVKlSev3119WmTZsk10+aNElffvmlWz4gCSpVquRyf+vWrbpz547zS+vAgQPy9vZWiRIl9Msvv7itjuTu5s2bGjt2rHr37q1bt24pZcqUatKkiYYOHXrfP3iPw8WLF9W0aVP98ssvcjgcioyMVM6cOdW2bVulSpVKw4cPd1vbf+azzz7T22+/rVu3bilt2rR644031KtXL48emXO306dPa+rUqZoyZYpiYmLUsmVLtW3b1vY/8pKcR+UHDBiQ5ITczZs3d2v7devWVaVKle57lHP06NFasWKF5s2b59Y6kpPg4GBt27bNli4qj8O9O2WPW44cOfTll1+qSpUqioiI0Lhx41SrVi3t3r1b5cqV0+XLl93SriT5+/vr1q1bypEjhypVqqTKlSurUqVKbj/7nZzdu2N8L3fvDHt7e+vs2bPOA4whISHasWPHQx8Me1zatGmjlClTasKECZKkq1evqlChQrpx44YyZcqkPXv26IcffnD7GZRMmTJp/fr1Cg8Pl3S3h9WmTZuc9w8cOKBnn33WLcP2r1q16qG3dXdXTK5JSsLvv//u7Bt94MABl3V2HSFv0qSJvv32W6VPn17h4eHy8fFxWe/ubjMHDhxw9pFOSpkyZdSzZ0+31rBixQrnzyNGjFBwcLCmTp2qVKlSSZIuX76sNm3auP1ocHLlyf7j0t0zrilSpNDx48dVoEAB5/KmTZuqR48etoekqKgoZ0g4duyYXnrpJefzMXToUG3YsEFLliyxtSY7Zc6cWb1791bv3r21evVqTZ48Wc8++6yKFCmiZcuW2Xqw5ccff9S0adNUsWJF52c0d+7cypEjh77++mu3h6QdO3Zo6NCh913/4osv6pNPPnFrDclNlSpVtGPHjn9MSHK3Nm3aqEmTJsqUKZMcDoezC+bGjRvd3oMjOjpa69at06pVq7RixQp98803unXrlnO+okqVKqlixYq2Xfe7c+fOJJc7HA75+fkpe/bsLmc23OHeAxa3b9/Wtm3bNHXqVPXr18+tbUt3z07kzZvXuY+XcN3zvWfkE+bNdJdff/1Vn3/+ufP+tGnTFBcXp8jISIWGhurdd9/Vxx9/7PaQFB0d7bwGSbrbU8MqPj7eZf3jZMc1aA+LkJQE6865p7Rq1UpbtmzRq6++6pGBG2JiYhQWFnbf9WFhYW7tjnCv4cOHa8mSJc6AJN292HfgwIF68cUX9dZbb9lWi6fd23982rRpLv3HIyIiNGXKFOcRH3dZsmSJFi9enOiC1jx58ujYsWNubdtq7ty5mjx5shYvXqyCBQvqP//5j1599VWX92+ZMmVcgtzjkJy7gz777LM6evSo9uzZo23btun27du2hqQHTchtxyTUUVFRiQ4sWaVIkSLRH/0n3VdffaVWrVrp999/V+HChRM9P3Xr1vVQZZ7Rt29fFS5cWCdOnFDjxo2dIcDb21u9evVya9u+vr7OMNS3b1/duHFD69ev14oVK7Ry5UpNnTpVt2/ftu36n2LFij1wH8PHx0dNmzbVhAkT3HahflLXYL300ksqVKiQvvvuO7Vr184t7SaYPHmyWx//YZ06dcrlevjly5erUaNGCg0NlXR339COWrNmzarff//9vj0Rdu7cadtgFtHR0Zo4caLzur1ChQqpbdu2zufEnQhJydTChQu1ePFij43WZYx54DUNDofjvheIu0NMTEySOzXnz593jrz3bzFu3Lg/7T+ePn16TZw40a11XL9+Pcnua5cuXXL7UUerNm3a6OWXX7Z9FKBt27Y91HZ2HuBYv369Jk2apFmzZilv3rxq06aNXnnlFVsHSpCknDlz6siRI8qePbvy58+vWbNmqWTJkvrxxx8fePDlccmSJYt+//33+5412blzp1u7oiZH69ev16+//qqff/450To7B25ITl566aVEyzxxzY2Xl5ezu1nC39bs2bPb1v68efP07rvv6u2333YOAPTbb79p+PDh+vDDD3Xnzh316tVL77//vu1nYEuVKqUOHTq4vZ3kcq2Vn5+f/vjjD+f9DRs26OOPP3ZZf+3aNbfXUbNmTX3wwQeqVatWomD8xx9/qF+/fqpVq5bb69i8ebOqVasmf39/53tzxIgRGjRokJYsWeL2ETG5JikJDRo0SHLHJuHUc+7cufXKK6+4ta9/wo6Fu4dlvR8vLy+FhobedwfPGKOYmBjb/rC2bNlSa9as0fDhw50flI0bN+rtt9/W888/r6lTp9pSh6fduXNHAwcOVPv27W0fkvReNWvWVIkSJTRgwAAFBwdr586dypEjh5o1a6b4+HjNnj3b7TXcuXNHX3zxhRo1amTrkPTJzbBhwzRlyhRduHBBzZs3V5s2bTz23SFJn376qby9vdWlSxctW7ZMderUkTFGt2/f1ogRI9S1a1e3tt+5c2etXLlSmzZtSvIPfMmSJVWpUqW/NELhP114eLhq166tPn36/CM+K+64Jmn06NHq0KGD/Pz8/vS1d+eIlLdu3dKGDRu0cuVK/fLLL9q4caNy5Mih8uXLq3z58qpQoYLbBqxISsmSJTVgwABVq1bNZfnixYvVp08f/fbbb5o/f77eeustHTp0yLa6/vjjD/Xu3Vs///yz9u/fb1u7nlSlShWVLFlSgwcP1po1a1SxYkWdPHnSeVBn6dKlevPNN3Xw4EG31hEVFaVixYopZcqU6tSpk/LmzStJ2r9/vz7//HPduXNH27Ztc/t3SUJX7S+//NI5ANOdO3fUvn17HT58WKtXr3Zr+4SkJLRu3Vrz589XWFiYSpQoIenuNUDR0dF68cUXtWPHDh09elTLly9X2bJl3VLDwoUL9dlnn2n8+PFu7zaVlIcNHXYdfYmNjVXPnj01adIk3b59W9LdLjPt2rXTxx9/bPvwsZ4UHBysXbt2eeR9YfX777+rSpUqevrpp/XLL7+obt262r17ty5duqRff/1VuXLlsqWOgIAA7d27N9mMFOUJXl5eyp49u2rXrq2UKVPedztPjQJ57NgxbdmyRblz57YlvEVFRenpp5+Wt7e3OnXq5DygtW/fPo0ZM0ZxcXHaunWrLWGhbdu2GjVqlIKDg12WX79+XZ07d9akSZPcXoN093tj+/bttn0u/y53hKSIiAht3rxZadKkeeAF+e4ekdLf31/p06dXnTp1VKFCBT3//PMuIx/azd/fX9u2bUt0Lda+fftUvHhx/fHHHzp69KgKFiyo2NhYt9SQKlWqRCOaXb16VQEBAZoxY8a/pjvoqlWrVKNGjf/X3p3H1Zj2fwD/nI72HS2WtCKRiDG2xlJIJkvzWEshS8bSRGTGkEKyJWSE0MLYsmaMKFkKY4mKpFKEJ1lDJamu3x/9OtNxYuZ5njn3fXK+79er16P77nF/JnXOfd3XdX2/aNasGQoLCzF27FixVSHff/89SktLOXkwnJ+fj+nTp+P06dNiFeUGDBiAX375RWpFVer61M9mZmYmunbtKrWfx1o0SKrHggUL8ObNG4SFhYmWnFVXV8Pb2xuamppYvnw5vLy8cPv2bSQnJ0slg66uLsrKylBZWQk1NTWJ9ePS3jwoq0pLS0VPsszNzeVqcFRr2LBhcHFxkYnlAa9fv8bGjRuRnp6OkpIS2NraYsaMGZwuZerbty9++OEHDB8+nLNrAoCLiwsiIyOhpaUFFxeXz37tx80z/2l9+/b9y2V9AoGAlyqQ5eXlvDQ8fvDgAaZPn474+HixN/hBgwZh06ZNnFWtEgqFKCwshL6+vtjx58+fw9DQkLN9Jx4eHrCzs8PkyZM5ud5/qri4GLt27cLMmTMBAL/++iuGDRv2Rb7Gd+/eHTdu3EDbtm3Rt29f9OnTB3379uWluTBQ0+jXxsYGW7duFT1k+fDhA6ZMmYK0tDTcuHEDKSkpcHNzQ35+vlQyREZGir2GKSgoQE9PD19//bXYXmR5cOfOHZw6dQqGhoYYOXKk2NaHrVu3olu3bujUqRNneV6+fCmaubKwsEDjxo05u7aBgQFiYmIwcOBAsePx8fFwd3dHUVGRVK9Pg6R66OnpISUlRTS9WKu24tvz58+RkZEBOzs7FBcXSyXDXz0lkIUbZMKP8PBwBAQEwNXVtd4S9fLyxK3W/v378eOPP8LHx6fe74e0Zi4mTpyIDRs2QFNT85Ol8mvJyqZgrlRVVSEoKAjh4eEoKipCdnY2zMzMsGjRIpiYmEh9E3Zdr169Qm5uLhhjaN26NWc3XG/evAFjDLq6usjJyRHrXVZVVYW4uDgsWLAA//73vznJs3z5coSGhmLIkCGwtraWePAm7YbHn5KYmIjt27fj8OHDUFNTq7ev1j/l7xZbEQgEUq/OWVJSguTkZFGxhhs3bqBNmzaiQVOfPn0kBtbScvHiRQwdOhQKCgqi18uMjAxUVVXh+PHj6N69O2JiYvDkyRPMmzePk0yEADWvS4cPH8aaNWtEFZdTUlIwb948fPfddwgNDZXq9WmQVA9dXV1ERUVJ3GweO3YMHh4eePXqFXJyctCtWzep9lIgf+rXr99nn5TLU5+kvyqoweUG7AsXLmDLli3Iy8vDgQMH0KJFC8TExMDU1JSzoiP1fT9qNz/L64Z0vgUGBiIqKgqBgYGYMmUKbt26BTMzM+zbtw+hoaFS7a8mK/6q74tAIEBAQMA/XlDkU/hcXvaxhw8fYufOndi5cycKCgowZswYjB8/Hvb29p+tSvi/kuXee2/fvsWFCxdw+vRp7Ny5EyUlJZzNMtZef/fu3aK2J23btsW4ceMklon+kz5Verw+XO+xrKioQH5+PszNzaXSjJz8PRUVFZg3bx7Cw8NRWVkJxhiUlJQwffp0BAcHS71IFP3L12P8+PHw9PTETz/9JKqWdfXqVQQFBcHd3R1AzbrR9u3bc5KnvLwcFRUVYse4rlbFt4+nlj98+ICbN2/i1q1bcjerVl1dzXcEAMDBgwcxfvx4uLq6IjU1VdQz4fXr1wgKCsKJEyc4ySGt5R//iT179mDs2LH1nps3b55YdSJ5EB0dja1bt8Le3h5eXl6i4zY2NsjKyuIxGXeSkpLAGEP//v1x8OBBsSUqSkpKMDY25rR5KN+/Jx8+fMCRI0cQERGBCxcuwNHREatXr8bYsWOxcOFCWFlZST2DLPbeq66uxtWrV3H27FkkJSUhJSUFpaWlnO+x1NTUFPtd5UJt6fG6y2E/hauHXWVlZZg1a5ZoNU/tLPisWbPQokULqZeHJ+KUlJSwfv16rFixQmyrBVeN4WkmqR5VVVUIDg5GWFiYaL2jgYEBZs2aBT8/PwiFQhQUFEBBQUFqFcZKS0vh5+eH/fv317v8gJ6O11iyZAlKSkrkrjGkLOjcuTN8fHzg7u4utsn6xo0bGDx4MJ48ecJ3RM7o6Ohgz549GDx4sNhxHx8f7N27F4WFhTwl44eqqiqysrJgbGws9rORmZmJbt26cVLCVlY8ePAARkZGn50B5trfuSn9p+nr68PS0hJubm4YOXKkaGCiqKiItLQ0TgZJdbVo0QKnTp2SeNh569YtDBw4UKrLIK9cuYKzZ8/i7NmzSE5ORklJCVq2bIm+ffuK+idxXZjn3r17CA0NFetFM3v2bKkW+qjbT+/GjRvw9fXFvHnz0KNHDwA1ZevXrl2LVatWcbbn1NvbGykpKQgNDYWjoyPS09NhZmaGo0ePYsmSJX+79QP53/zVPl+gpniXoaEhBgwYAGdnZ6nkoJmkegiFQixcuBALFy4UNUz9eOZG2j0M5s+fj6SkJGzevBnjx4/Hpk2b8PjxY2zZsgXBwcFSvXZD4ubmhm7dusndIOncuXNYs2aN6A3NyspKVA6dK3fv3sU333wjcVxbW1tqe/U+5eM3eCsrK3h7e3NWyWv37t0YO3Ysjh8/LlpmOGvWLBw6dEgmmlNzzcrKChcuXJB4Gh4bG4vOnTvzlIofxsbGKC4uxpUrV/D06VOJmeDa1QlciI6OxurVq5GTkwMAaNOmDebNm4fx48dL/dqVlZWiPkBCoVDq1/srfPbe6969OwwNDdGvXz+EhISgX79+vFYdjI+Px9ChQ9GpUydRxd6UlBRs2bIFcXFxGDBggFSuW/f1YeTIkdiwYQOcnJxExzp27AgjIyMsWrSIs0HSkSNHsG/fPnTv3l3sIUL79u05LX8u7/5Oo9jq6mrk5OQgIiICvr6+CAwM/Mdz0CDpM549eyaqzW9paYmmTZtydu24uDhER0ejb9++oul/CwsLGBsbY/fu3XB1dZXatf/u5laAv5LCtS5dusRL5Sw+7dq1CxMnToSLi4tos3VKSgrs7e0RGRmJcePGcZLD0NAQubm5Ek88k5OTOSkNWutTb/Dt27eX6ht8XUOGDMEvv/yCoUOH4vTp09i+fTuOHj2KpKQkiQIw8mDx4sXw8PDA48ePUV1djUOHDuHu3buIjo7G8ePH+Y7Hqbi4OLi6uqKkpARaWlpiN14CgYCzQVJISAgWLVqEmTNnin5PkpOT4eXlhefPn8PHx0eq1//3v/+NgwcPYvv27fD29sbgwYPh5ubG6WxWXSNGjMDEiRPr7b33d55i/y/u3Lkj1T6L/6kFCxbAx8dH4gHsggUL4Ofnx8lraEZGRr375kxNTZGZmSn169d69uxZvQUzSktLOf9ZLS4uRmxsLO7du4d58+ahcePGotYFLVq04DQL1/6TYkfHjx/H999/L5VBEhiRUFJSwiZOnMiEQiETCARMIBCwRo0asUmTJrHS0lJOMqirq7MHDx4wxhhr0aIF++OPPxhjjOXl5TF1dXWpXrtv375iH1paWkxNTY117tyZde7cmamrqzMtLS3Wr18/qeaoa8SIEWIfw4cPZ19//TUTCoVsyZIlnOWQBZaWliwkJETi+Nq1a5mlpSVnOYKCgpiVlRW7fPky09TUZBcuXGC7du1ienp6bMOGDZzl6NSpE/Pz85M47ufnxzp37sxZDsYY27RpE1NWVmYtW7ZkOTk5nF671qtXr1h8fDyLiYlhUVFRYh9cOn/+PHNwcGB6enpMVVWV9erVi8XHx3OaQRa0bt2aeXt7c/be8SkmJib1/gxERkYyExMTTrPk5uayn376ibVs2ZIJBAI2btw4durUKVZZWclZhtLSUjZ9+nSmrKzMFBQUmIKCAlNSUmLTp09nJSUlnOWQBcrKyiw7O1vi+N27d5mysjInGTp37szGjx/P3r9/Lzr2/v17Nn78eE5fx+3s7ETvXxoaGiwvL48xxtjMmTPZoEGDOMuRlpbG9PT0mIWFBWvUqBG7d+8eY4yxhQsXsvHjx3OWoyF49eoVGzFihFT+btqTVI9p06YhISEBYWFhYk/cZs+ejQEDBmDz5s1Sz9CxY0ds3LgRffr0gYODAzp16oQ1a9Zgw4YNWLVqFR49eiT1DEDN08ezZ89+cnPr3LlzOckxYcKEenso9O/fX6J+/pdOWVkZt2/fhoWFhdjx3NxcdOjQAeXl5ZzkYIwhKCgIK1asEDV0U1ZWhq+vL5YuXcpJBgBQUVFBRkYGWrduLXY8OzsbHTt2lNr341MzrgcOHICtra3Y8hmuZlz/atZCXvur8UldXR0ZGRmczq7WR0VFBbdu3ZJ43cjJyYG1tTVnrxt1VVdXIz4+Htu3b0dcXBw0NTXx/PlzTjNQ7z3AyMgIISEhGDlypNjx/fv3w9fXFwUFBVLPcOXKFTg7O4MxJqpkl56eDoFAgLi4ONFsn7QlJyeLZjkjIyMxbdo0ZGZm4uLFizh37hy6dOnCSQ4HBwfY2tpi1apVYvs6L168iHHjxuH+/fuc5JB3tNyuHgcPHkRsbCz69u0rOubk5ARVVVWMGjWKk0HSxIkTkZaWhj59+mDBggVwdnZGWFgYPnz4wOkSt7Vr1+LUqVNivUV0dXWxbNkyDBw4kLNBUmRkJCfXaQiMjIyQmJgocbOTkJAAIyMjTjJUVVUhJSUFM2bMwLx585Cbm4uSkhJYWVlBQ0ODkwy19PT0cPPmTYlB0s2bN6XaZ+RTG3gtLCzw5s0b0Xkul2jMnTsXkyZNQlBQEGfVfz7n2rVrYvvEuLrBkCWDBg3CtWvXeB8kWVhYYP/+/fjpp5/Eju/bt0/id0daLl++jLi4OFRUVMDe3h6Ojo4YPHgwBg8ejGfPniEmJoaTHHWpq6tzXl5a1kyZMgVTp05FXl6eWC+alStX/kfL7/8X3bp1Q15eHnbv3i2qgDl69GiMGzeO04Fr7969cfPmTQQHB8Pa2hqnTp2Cra0tLl26BGtra85yXL16FVu2bJE43qJFC7kqisQ7qcxPNXCqqqosMzNT4vitW7eYmpoaD4kYu3//Pjt48CBLS0vj9LoaGhosKSlJ4viZM2eYhoYGZzlMTU3Z8+fPJY6/evWKmZqacpZDFvzyyy9MSUmJeXl5sejoaBYdHc2mTZvGlJWVWXh4OGc5lJWVRUsR+BQQEMB0dHRYcHAwO3/+PDt//jxbsWIF09HRYYGBgXzH45SamppoWQafHj58yHr37s0EAgHT1dVlurq6TCAQsF69erGHDx/yHY9TERERrFWrVszf35/Fxsayo0ePin1wJTY2lgmFQjZo0CAWGBjIAgMD2aBBg1ijRo3YoUOHpH79AwcOMAUFBaaurs50dHSYgoICW716tdSvS/5adXU1CwkJYS1atBBtMWjRogULDQ1l1dXVfMeTS3p6eiw1NZUxVnMfVvu6furUKdayZUs+o8kVGiTVo3///mzkyJHs3bt3omNlZWVs5MiRzN7eXurXr6ioYP379693jTDXxo8fz0xMTNjBgwfZw4cP2cOHD1lsbCwzNTVl7u7unOUQCASsqKhI4viTJ0+YkpISZzlkxaFDh1ivXr1Y48aNWePGjVmvXr3YkSNHOM3QpUsXlpCQwOk16yOLb/D3799nt2/fZlVVVZxed8SIEWzfvn2cXrM+gwYNYl9//TXLysoSHcvKymI9evTgdF2/LKj9mazvQ0FBgdMs165dY66urszW1pbZ2toyV1dX0Y2YtNna2rJp06aJ9h0FBQUxXV1dTq5N/r43b96wN2/ecHa96dOns7dv34o+//XXX8X2hL169YoNHjyYszy1ioqKWEZGBktLSxP74IqnpycbPnw4q6ioEO2NevDgAevcuTPz9vbmLIe8oz1J9cjIyICjoyPev38PGxsbAEBaWhpUVFQQHx/PSRNZPT09XLx4kbNlEJ9SVlYGX19f7NixAx8+fABQU5ve09MTq1evlvo0+LFjxwAAw4cPR1RUlFhZyKqqKiQmJuL06dOiKoSEOydPnsSPP/6IpUuXokuXLhI/C3w0PK4t3SvNLvF17dixA8XFxWJLUqZOnYrt27cDqOlaHx8fz9kyyO3btyMwMBATJ06EtbU1FBUVxc4PHTqUkxyqqqq4ePGiRLnv69evw87OTrSHjcgPDQ0N3Lx5U7RMuKKiAurq6nj8+LFUl8XKmoZUPZYLQqEQhYWFop8BLS0t3Lx5U7Q8taioCM2bN+esN+T169fh4eGBO3fu4OPbY4FAwFmO169f41//+heuXbuGt2/fonnz5njy5Al69OiBEydOyOXeOT7QIOkTysrKxNbGtmvXDq6urlBVVeXk+j4+PlBWVpaZnkh8bW6tbcBYtyt3LUVFRZiYmGDt2rX49ttvOclD/lS3OWbdfTeMMU7fTPjUvXt3TJs2DRMnTgRQM3B0dnZGZGQk2rVrh5kzZ8LKygoRERGc5Plcw1Iu/03atGmDXbt2SWy2vnLlCsaNG4fc3FxOcsia8vJyzlsW1Pb6+yvSfqihoKCAJ0+eiA2I6m5Ilxf9+vX7W18nEAhw5swZqeXo3Lnz394vmZqaKrUcH/9cfPwzwfUgycbGBubm5vDz84OBgYHE9+jj3m/SlpKSgrS0NJSUlMDW1hYODg6cXl/eUeGGj3z48AGWlpY4fvw4pkyZwluOyspK7NixAwkJCfU+pef6CVNhYSEKCwvxzTffQFVVVXQjLG21jRdNTU1x9epVTntVySpdXd16v/cCgQAqKiqwsLDAhAkTRDfu0iIrTVI/9Wb/8ffj796c/CdycnLQtWtX0edHjx7FsGHDRH3MgoKCpP7vUNfHjUr5snr1asyaNQubNm0SfX+uXbsGb29vuWv8XFVVhaCgIISHh6OoqAjZ2dkwMzPDokWLYGJiAk9PT6leX0dH57Ov1Vw+1IiIiBAr7FJZWYnIyEix1/Xa3m9fKll53eSqOWtDk5eXh4MHD0oURuLShw8foKqqips3b6JXr16iKsuEezRI+oiioiIvpVA/duvWLdja2gKoKWVcF5fVsl68eIFRo0YhKSkJAoEAOTk5MDMzg6enJ3R1dbF27VpOcuTn53NynYZg8eLFWL58OQYPHix6Un/lyhWcPHkSM2bMQH5+PqZPn47KykqpDvT79Okjtb/7P+Ho6IjNmzfD2tpa9P24evUq0tPTMWHCBGRmZsLBwQGHDh3CsGHD/tFrv3v3TuwJ/MWLF8Vues3MzOSyEtGECRNQVlaGr7/+Go0a1bzNVFZWolGjRpg0aRImTZok+tovvSz58uXLERUVhVWrVon9Pnbo0AGhoaFSHyTVvSlnjMHJyQkRERGcN6Ns1aoVtm3bJnbM0NBQrKKdQCD44gdJssLf35/vCDLJ3t4eaWlpvA6SFBUV0apVK7lYjSHraJBUjxkzZmDlypWIiIgQvcFzTVaeNvn4+EBRUREFBQVo166d6Pjo0aMxZ84cqQ+SLl26hBcvXogtp4uOjoa/vz9KS0sxfPhwbNy4EcrKylLNIUuSk5OxbNkyeHl5iR3fsmULTp06hYMHD6Jjx47YsGGDVAdJ6enp9R6vncFp1aoVJ/8uz58/x9y5c7Fo0SKx48uWLcODBw9w6tQp+Pv7Y+nSpf/4IMnY2BjXr1+HsbExnj9/jtu3b4s99Xvy5InYPjounDt3DmvWrBErvT1v3jzY2dlxliE0NJSza8m66OhobN26Ffb29mK/szY2NqLl3NL08cMMoVCI7t27c77Ejfq61HBxcfnbX3vo0CEpJpF0/fp10etG+/btJfYUSsvixYtFLQsqKiqwfPly0esm1/sXIyIi4OHhgVu3bqFDhw687etcuHAhfvrpJ8TExKBx48acXJNIoj1J9RgxYgQSExOhoaEBa2triaVuXL9w8cnQ0BDx8fGwsbERWyucl5eHjh07oqSkRKrXHzx4MPr27Qs/Pz8ANUU1bG1tMWHCBLRr1w6rV6/GtGnTsGTJEqnmkCUfb4CulZubi06dOqGkpAT37t1Dx44dUVpaKrUcCgoKn53VVFRUxOjRo7Flyxap7sPQ1tbG9evX6/1+dOnSBa9fv0ZWVha++uorUWGHf0pwcDDWr1+P77//HmfOnMGzZ89w69Yt0fnQ0FAcP34cCQkJ/+h1P2XXrl2YOHEiXFxcRIO1lJQUHD58GJGRkRg3bhwnOcifVFVVkZWVBWNjY7HX0MzMTHTr1k3qr6Ef42sfUHl5ORISEkQPvH788Ue8f/9edL5Ro0YIDAzkfM8W1/6T5bc7d+6UYpI/PX36FGPGjMHZs2eho6MDACguLka/fv2wd+9e6OnpSe3affv2/VurY7h6cBwXF4fx48fXu5ePy32dnTt3Rm5uLj58+ABjY2OJ+1Bp7hMjf6KZpHro6Ojgu+++4zXDiBEj/nKfxbhx49C2bVup5igtLa23KeXLly85mSW4efMmli5dKvp87969+Prrr0XLNoyMjODv7y9Xg6TGjRsjLi4OPj4+Ysfj4uJET5xKS0ulXuHt8OHD8PPzw7x588SW/a1duxb+/v6orKzEggUL8PPPP0t1H4qKigouXrwoMUi6ePGi6IarurpaKjdf8+fPR1lZGQ4dOgRDQ0McOHBA7HxKSgrGjh37j1/3U5YvX45Vq1aJ/WzMnj0bISEhWLp0qdQHSZWVlaiqqhJ7bSgqKkJ4eDhKS0sxdOhQ9O7dW6oZZI2VlRUuXLggseE7NjaWsyf1siAyMhK//fabaJAUFhaG9u3bi4ohZWVlwdDQkLPmpXzhauDzn5g1axbevn2L27dvi1aMZGZmwsPDA7Nnz8aePXukdu2zZ89K7e/+b8yaNQtubm5YtGgRDAwMeMtBe8ZkBD+Vx8lf8fDwYNra2szY2Ji5uLgwFxcXZmJiwnR0dNioUaNY27ZtmbKyMktOTpZqjsGDB7Off/6ZMcZEtfqrqqrYyJEj2XfffSfVazNW07C0oKBA9HmvXr3YsmXLRJ/n5+dz2tRWFmzdupUJhULm7OzMli5dypYuXcqGDh3KGjVqxCIiIhhjjK1Zs4aNGjVKqjm++uordvLkSYnjJ0+eZF999RVjjLHDhw8zMzMzqeZYunQpU1VVZbNnz2YxMTEsJiaGzZ49m6mpqYl+VkJCQpiDg4NUc8gCJSUllpOTI3E8JyeHKSsrS/36EyZMYFOnThV9/ubNG2ZkZMT09PRYx44dWaNGjdhvv/0m9Ryy5MiRI0xbW5sFBwczNTU1tnr1ajZ58mSmpKTETp06xXme2tdxrvXu3ZsdO3ZMLEfdxscxMTGse/funOcijGlpabErV65IHP/jjz+YtrY294F4pKGhwXJzc/mOQWQEzSTVUV1djdWrV+PYsWOoqKiAvb09/P39OSv7XZehoSHGjRuHsLAwUVnf6upqeHt7Q1NTE3v37oWXlxf8/PyQnJwstRyrVq2Cvb09rl27hoqKCsyfPx+3b9/Gy5cvkZKSIrXr1jIwMEB+fj6MjIxQUVGB1NRUBAQEiM6/fftWYs3wl27KlCmwsrJCWFiYaOln27Ztce7cOfTs2RMAMHfuXKnnyMjIqLccqrGxMTIyMgAAnTp1QmFhoVRz/PzzzzA1NUVYWJhoE3jbtm2xbds20cyJl5cXpk+fLtUcssDIyAiJiYkSs2oJCQmc9GpKSUlBWFiY6PPo6GhUVVUhJycH2tra8PPzw+rVq+Hk5CT1LLJi2LBhiIuLQ2BgINTV1bF48WLY2toiLi4OAwYMkPr1P94DU15eDi8vL86Xkefm5sLa2lr0uYqKiljJ+m7dumHGjBlSzSALbG1tkZiYCF1d3b8sw83Vkqrq6up630cVFRVlpmImV1xcXJCUlARzc3O+owDgb58YqUGDpDqWL1+OJUuWwMHBAaqqqli/fj2ePn2KHTt2cJ5l+/btSElJEXsTUVBQwKxZs9CzZ08EBQVh5syZUt+M3aFDB2RnZyMsLAyampooKSmBi4sLZsyYgWbNmkn12gDg5OSEBQsWYOXKlThy5AjU1NTE/pvT09Nl5sWMS7JQFtTS0hLBwcHYunUrlJSUANSULg0ODoalpSUA4PHjx5wsWXB1dRWV3a4PHw86+DB37lzMnj0bN2/eFA2YU1JSEBkZifXr10v9+o8fPxZrgJ2YmIjvvvtOtAnbw8NDJpcbSZudnR1Onz7Ny7U/Lhzi5ubGS47i4mKxPUjPnj0TO19dXS12/ks1bNgw0XLUYcOGcVqt9lP69+8Pb29v7NmzB82bNwdQ87vs4+MDe3t7ntNxq02bNvjxxx+RnJxcb0Nurqov8rlPjNTB91SWLLGwsGDh4eGiz0+fPs2UlJRYVVUV51l0dHTY0aNHJY4fPXqU6ejoMMYYy87OFv1ZWh48eMCqq6s/eU7anj17xuzs7JhAIGCamprs0KFDYuf79+/PfvrpJ6nnkDW5ubls4cKFbOzYsayoqIgxxtiJEyfYrVu3OMuQkpLCmjRpwvT09Ji9vT2zt7dn+vr6rEmTJuzSpUuMMcaio6PZqlWrpJ7l1atXbNu2bezHH39kL168YIwxdv36dfbo0SOpX1vWHDp0iPXq1Ys1btyYNW7cmPXq1YsdOXKEk2s3btyY3b59W/R5s2bN2K5du0Sf37t3j6mqqnKSRRa9ffuWvX79WuxDXlhYWLDY2NhPnt+3bx8zNzfnMBGpVVBQwDp16sQUFRWZmZkZMzMzY4qKiqxz587s4cOHfMfjlImJySc/TE1NOcsxatQo1rVrV5aZmSk6dvv2bda1a1c2ZswYznLIOxok1aGkpCS2/4Wxmj0xfLxIzJo1izVt2pSFhISwCxcusAsXLrCQkBDWtGlTNnv2bMYYY9u2bWO9evWSag4FBQXRTXhdz58/ZwoKClK9dl3FxcWssrJS4viLFy/Y+/fvOcshC86ePctUVVWZg4MDU1JSEq3rX7FiBSf7xOp68+YN27x5M/Px8WE+Pj4sPDycvXnzhtMMaWlpTE9Pj1lYWLBGjRqJvh8LFy5k48eP5zSLvOvfvz9bsGABY4yx8+fPMwUFBfbvf/9bdP7UqVNydyOcl5fHnJycmJqaGlNQUBB9CAQCTl9D+TZ79mxmZWXF3r17J3GurKyMWVlZid7b5IWpqSl7/vy5xPFXr15xekPOGGPV1dXs1KlTbMOGDWzDhg3s9OnTUr/miBEjRA8KoqKiWHl5udSv2VDQPjHZQCXA6xAKhXjy5InYNKampibS09NhamrKaZaqqioEBwcjLCwMRUVFAGr258yaNQt+fn4QCoUoKCiAgoICWrZsKbUcCgoKKCoqkpjaffDgAaysrKRaYprUr0ePHhg5ciTmzJkjVs73ypUrcHFxwaNHj/iOyCkHBwfY2tpi1apVYt+PixcvYty4cdSfhUPnzp3D4MGD0axZMxQWFmLs2LHYvn276Pz333+P0tJSREVF8ZiSW7169QJjDN7e3jAwMJBYXiUrTZmlraioCJ06dYKSkhJmzpyJNm3aAADu3r2LsLAwVFZW4saNG7xWFOOagoICnjx5An19fbHjRUVFon24XzIlJSU8ePAAzZo1g1AoRGFhocT3gk+1t8d8LInU1NTEhQsX0KlTJ7HjN27cQJ8+feotUU7+ebQnqQ7GGCZMmCBWvra+Ta5c9EkSCoVYuHAhFi5cKPpl0NLSEvuaVq1aSe36tWVYBQIBFi1aJFYGvKqqCn/88YfELy/hRkZGBn799VeJ4/r6+nj+/DmnWWJiYrBlyxbk5eXh0qVLMDY2xrp162BmZvaPN279lKtXr2LLli0Sx1u0aIEnT55wkoExhtjYWCQlJeHp06cSm52l+ZrRuHFjZGdno2nTptDV1f3sG/rLly+llgOoueG/fv06Tp06BUNDQ4wcOVLsfKdOnUTl4uVFWloarl+/LvV2DbLOwMAAFy9exPTp07FgwQKxG9ABAwbgl19+kZsB0rFjx0R/jo+PF9s3VlVVhcTERM4fzCYmJiIxMbHe1y9p7cu2tLTEjz/+iH79+oExhv3790vc59Ryd3eXSob6REdHY/Xq1cjJyQFQs09p3rx5GD9+PGcZaJ+YbKBBUh0eHh4Sx/ja5Frr2bNnuHv3LoCaF5SmTZtyct0bN24AqLn5y8jIEG3MB2qe/tjY2MDX15eTLEScjo4OCgsLJd5Eb9y4gRYtWnCWY/PmzVi8eDF++OEHLFu2TNRkT1dXF6GhoZwNkpSVlet9qpadnc3Z5tYffvgBW7ZsQb9+/eqdLZCmdevWiXpirVu3jveN4O3atRP1WvnY1KlTOU7Dv6+++goPHz6U+0ESAJiamuLkyZN4+fIlcnNzAQAWFhai/m7yorYHjkAgkLjvUFRUhImJCdauXctZnoCAAAQGBqJr165o1qwZZ68h4eHhmDNnDn777TcIBAL8/PPPn+wPydUgKSQkBIsWLcLMmTNFxZGSk5Ph5eWF58+fS/QnlJawsDAMHToUJiYmosqkDx8+RIcOHbBr1y5OMhBQ4QZZVVJSwiZOnMiEQiETCARMIBCwRo0asUmTJrHS0lLOckyYMEGuNhc3BHPnzmW9e/dmhYWFTFNTk+Xk5LDk5GRmZmbGlixZwlmOdu3ascOHDzPGxHueZGRksCZNmnCWw9PTkw0fPpxVVFSIesA8ePCAde7cmXl7e3OSQVdXV+76/5C/Jzc3lzk4OLDIyEh27do1lpaWJvZB5JeJiQl79uwZ3zGYoaEhi46O5jWDQCCod/8z10xMTFhUVJTE8cjISGZiYsJpFj72iRFxNEiSUVOnTmVmZmbsxIkToipIv/32GzM3N2deXl6c58nJyWEnT55kZWVljDH2yYp3RPrev3/PJk+ezBo1asQEAgFTVFRkCgoKzM3Nrd7iFtKioqLC7t+/zxgTHyRlZ2czFRUVznIUFxczBwcHpqOjw4RCITMyMmKKiorsm2++YSUlJZxkMDExYXfu3OHkWp8jK4VWyJ8uXbrETE1NRQ+7ags2yFvhBiK7GjduzHsD1fv378vEfYWysnK9Dbmzs7M5achNZIvCX881ET4cPHgQ27dvx+DBg6GlpQUtLS04OTlh27ZtiI2N5SzHy5cvYW9vjzZt2sDJyUnUGNTT05OThqVEkpKSErZt24a8vDwcP34cu3btQlZWFmJiYiAUCjnLYWpqips3b0ocP3ny5CeXW0mDtrY2Tp8+jePHj2PDhg2YOXMmTpw4gXPnzkk0zJSWJUuWICAgAO/evePkep/CPlGH5/3792JLZgl3Jk2ahM6dO+PSpUvIy8tDfn6+2P8S+XPp0iUcP35c7Fh0dDRMTU2hr6+PqVOnctozavLkyfXuc+WSsbExXr9+jbVr12Ly5MmYPHkyQkJC8Pr1a05zWFhYYP/+/RLH9+3bJ9YDTtpmz56NDRs2SBwPCwvDDz/8wFkOeUd7kmRUWVlZvZtY9fX1UVZWxlmOH374AYqKiigoKBC78R09ejTmzJnD6bppUiMwMBC+vr4wMjISrVUGgHfv3mH16tVYvHgxJznmzJmDGTNmoLy8HIwxXLlyBXv27MGKFSsQERHBSQag5uZi9OjREg12KyoqsHfvXk7Wso8aNQp79uyBvr4+TExMJBoQpqamSvX6tW+mAoEAERER0NDQEJ2rqqrC+fPnRQ1+CbcePHiAY8eOwcLCgu8oREYEBgaib9+++PbbbwHUFOPx9PTEhAkT0K5dO6xevRrNmzfHkiVLOMlTXl6OrVu3IiEhAR07dpR4/QoJCZF6hmvXrmHQoEFQVVUVFXdZt24dgoKCcOrUKdja2ko9A1CzP2v06NE4f/686P0kJSUFiYmJ9Q6epOXgwYNiBT5q9ezZE8HBwQgNDeUsizyjEuAyyt7eHk2aNEF0dDRUVFQA1NwEe3h44OXLl0hISOAkh6GhIeLj42FjYyNWXjkvLw8dO3ZESUkJJznInz5VKvXFixfQ19cXFVDgwu7du7FkyRLcu3cPANC8eXMEBATA09OTswyy8P0YNWoUkpKS8K9//avewg3+/v5SvX5tEY8HDx6gZcuWYjOKSkpKMDExQWBgIL7++mup5vhYRUVFvdWypFmZU9Y4OztjwoQJ+O677/iOQmREs2bNEBcXh65duwIAFi5ciHPnziE5ORkAcODAAfj7+yMzM5OTPP369fvkOYFAgDNnzkg9g52dHSwsLLBt2zY0alTz/L6yshKTJ09GXl4ezp8/L/UMta5fv45169bhzp07AGqK0cydOxedO3fmLIOKigpu3bol8XAlNzcXHTp0QHl5OWdZ5BnNJMmo0NBQODo6omXLlrCxsQFQU0pWRUUF8fHxnOUoLS0VK/9d6+XLl2Kl0gl3GGP1VgBKS0vjvEqUq6srXF1dUVZWhpKSEl56XHzq+/Ho0SOx0rrS9NtvvyE+Ph69e/fm5Hofy8/PB1Bzs3Po0CHo6urykqNWTk4OJk2ahIsXL4odr/234nIgzzdnZ2f4+PggIyMD1tbWEk/phw4dylMywpdXr16JrRSp7S9Wq7YiIleSkpI4u9anXLt2TWyABACNGjXC/PnzRYNJrnTp0oX3CnIWFhY4efIkZs6cKXb8999/h5mZGU+p5A8NkmSUtbU1cnJysHv3bmRlZQEAxo4dC1dXV6iqqnKWw87ODtHR0Vi6dCmAmqdK1dXVWLVq1WefPpF/Xm0PHIFAgDZt2ogNDKqqqlBSUgIvLy9OMz1//hz379+HQCCAiYkJp9fu3Lmz6Pthb28v9uZaVVWF/Px8ODo6cpLFyMjok/09uCQLNzsAMGHCBDRq1AjHjx/ntKSwLKr9nQwMDJQ4J28DRlLDwMAA+fn5ooaxqampCAgIEJ1/+/atxGD6S6elpYWCggKJZcEPHz4UtTiQpr/bnJWr1/k5c+Zg5syZePbsGfr37w+gppfV2rVraakdh2iQJIM+fPgAS0tLHD9+HFOmTOE1y6pVq2Bvb49r166hoqIC8+fPx+3bt/Hy5UukpKTwmk3ehIaGgjGGSZMmISAgQGyWpHZJVY8ePTjJcvv2bUyfPl3iZ6BPnz7YvHkzJz1hanuN3Lx5E4MGDRLbh1P7/eBqidPatWsxf/58hIeHcz5Y/NijR49w7NgxFBQUoKKiQuwcF3sLgJp/k+vXr9M+KEBiqSEhTk5OWLBgAVauXIkjR45ATU0NdnZ2ovPp6ekwNzfnLE9paSmCg4M/2UyWiwIjo0ePhqenJ9asWYOePXsCqNkLNG/ePIwdO1bq19fR0fnswxyuZ8EnTZqE9+/fY/ny5aKH1CYmJti8eTOnjXXlHQ2SZJCioqLMrDft0KEDsrOzERYWBk1NTZSUlMDFxQUzZsxAs2bN+I4nV2qbDpqamqJnz568PWl88uQJ+vTpAz09PYSEhMDS0hKMMWRmZmLbtm2ws7PDrVu3pL70rnafj4mJCUaPHi3au8cHNzc3lJWVwdzcHGpqahL/Ni9fvuQkR2JiIoYOHQozMzNkZWWhQ4cOuH//PhhjnG18BgArKys8f/6cs+vJsry8PFoeQ8QsXboULi4u6NOnDzQ0NBAVFSVWfXLHjh0YOHAgZ3kmT56Mc+fOYfz48bzN/K5Zs0bUNLayshJAzb3Q9OnTERwcLPXr152FZ4zByckJERERnDZo/9j06dMxffp0PHv2DKqqqmIPAgk3qHCDjAoKCkJ2djYiIiLElhERUld5ebnEbIG0lwP4+fkhISEBKSkpEgOTd+/eoXfv3hg4cCBWrFgh1RyyJCoq6rPnawe40tatWzcMHjwYAQEBokIr+vr6cHV1haOjI6ZPn85JjjNnzuDnn39GUFBQvftwZGFpIlcUFBTQp08feHp64l//+hevg3kiW16/fg0NDQ2J1g0vX76EhoYGZ2X7dXR08Ntvv4lVB+VLWVmZqBBQ7UMnPtQtVCULzp07h7KyMnTv3p33PafyhAZJMmrEiBFITEyEhoYGrK2tJfq9HDp0SGrXTk9P/9tf27FjR6nlIPUrKyvD/PnzsX//frx48ULivLSXA9ja2mLBggUYNWpUvef37t2LVatWSb3sda2qqiqsW7cO+/fvr3eJGVezOLJAU1MTN2/ehLm5OXR1dZGcnIz27dsjLS0Nw4YNw/379znJoaBQ04Lv4yfS8li44ebNm9i5cyf27NmDiooK0bKi2jLHhPDN1NQUJ06c4LS/nazja5C0cuVKlJSUiJbYMcYwePBgnDp1CkBNG5jExES0b9+e01zyiqYoZJSOjg5vJWM7deoEgUDwycaUteTtZkdWzJs3D0lJSdi8eTPGjx+PTZs24fHjx9iyZQsnyxLy8vI+u3Sra9eunDbJDAgIQEREBObOnYuff/4ZCxcuxP3793HkyBHOekbVxcfsXi11dXXRtZs1a4Z79+6J3ky5XP4mKwUkZEGnTp2wfv16rF27FseOHUNkZCR69+6NNm3aYNKkSRg/fjz09PT4jknk2NKlS7F48WJERUXxNnNDauzbtw9+fn6iz2NjY3H+/HlcuHAB7dq1g7u7OwICAjjt2STPaCaJSHjw4MHf/lpjY2MpJiH1adWqFaKjo9G3b19oaWkhNTUVFhYWiImJwZ49e3DixAmpXv9TfYlqFRUVoUWLFqJ15dJmbm6ODRs2YMiQIWIzKRs2bMDly5c56SRfWloKPz8/3mb3ag0fPhxDhgzBlClT4Ovri6NHj2LChAmisuBc9Vcjn/b+/Xv88ssv+PHHH1FRUQElJSWMGjUKK1eupH2ehDO11UFr5ebmgjHGSzNsWaSpqYn09HRRDzqu6Orq4uLFi6JZvYkTJ6KqqgrR0dEAgMuXL2PkyJGcloiXZzSTJGOqq6uxevVqHDt2DBUVFbC3t4e/vz+nZb9p4CPbXr58KVoCoKWlJVpO1rt3b872nLx9+/aTeyvevHnzl7OQ/6QnT57A2toaAKChoYHXr18DAL799lssWrSIkwzz58/ndXavVkhIiKjBc0BAAEpKSrBv3z60bt2as8p2tYqLi7F9+3ZRQ8b27dtj0qRJnPWukjXXrl3Djh07sHfvXqirq8PX1xeenp549OgRAgICMGzYMFy5coXvmERO1FYHJTVcXFzEPi8vL4eXlxenWx2Amga6dXtQXrp0CT/88IPo8+bNm1NRHA7RIEnGLF++HEuWLIGDgwNUVVWxfv16PH36FDt27OAt0927d7Fx40ax7tOzZs3ipMwzkWRmZob8/Hy0atUKlpaW2L9/P7p164a4uDjo6OhI/fqMMbRp0+az57msjtSyZUsUFhaiVatWMDc3x6lTp2Bra4urV69y1vA4Li5ONLs3ceJEUfd4Y2Nj7N69G66urpzkqLt+Xl1dHeHh4Zxc92PXrl3DoEGDoKqqKtp7ExISguXLl4v+feRFSEgIdu7cibt378LJyQnR0dFwcnIS7dsyNTVFZGQk76XjiXyprQ5Kanz88MbNzY2XHObm5jh//jzMzMxQUFCA7OxsfPPNN6Lzjx49QpMmTXjJJo9ouZ2Mad26NXx9fTFt2jQAQEJCAoYMGYJ3796J3lS5dPDgQYwZMwZdu3YV9eC5fPkyrl69ir179/K2b0qerVu3DkKhELNnz0ZCQgKcnZ3BGMOHDx8QEhICb29vqV7/3Llzf+vr+vTpI9UctRYsWAAtLS389NNP2LdvH9zc3GBiYoKCggL4+PhwMpOjoaGBzMxMtGrVCi1btsShQ4fQrVs35Ofnw9raWjS7I21mZma4evWqxJtocXExbG1tOdsrVjtI3LZtm6g6Z2VlJSZPnoy8vDycP3+ekxyyoHXr1pg0aRImTJjwyeV0FRUV2LNnD2dVEAmp6+rVq6iursbXX38tdvyPP/6AUChE165dpXLdY8eO/e2vHTp0qFQyyJpt27bBx8cHo0ePxuXLl6GjoyPWj3DZsmX4448/EBcXx2NK+UGDJBmjrKyM3NxcGBkZiY6pqKggNzcXLVu25DyPubk5XF1dJbrF+/v7Y9euXaJSnYQ/Dx48wPXr12FhYUHVBlEziL948SJat24NZ2dnTq7ZsWNHbNy4EX369IGDgwM6deqENWvWYMOGDVi1ahUePXrESQ4FBQU8efJEYr9YUVERWrVqhffv33OSQ1VVFTdu3JBoJpuZmYmuXbuirKyMkxyEkL/WrVs3zJ8/H//617/Ejh86dAgrV67EH3/8IZXr/t0Hv/JWJGrHjh2Ii4uDoaEh/P39YWhoKDr3/fffY8CAARgxYgSPCeUHDZJkjFAoxJMnT8SqHfG1gRAA1NTUkJ6eDgsLC7HjOTk5sLGxoZsdQsD/7F7tE9nhw4cjKipKbOlIVVUVEhMTcfr0ady9e1eqOWoZGBggJiZGoiFmfHw83N3dUVRUxEkOWUH7s4gs09DQQHp6ukS56/z8fHTs2BFv377lKRkh/KI9STKGMYYJEyaI7aWobwOhtDcP1urbty8uXLggMUhKTk6GnZ0dJxlIjTNnzmDmzJm4fPmyREnp169fo2fPnggPD5ebf5fr16+LKrjV9/0YPnw4QkNDYWNjI/UsPj4+oj87ODggKyuL09m92k3YAoFAYsmWoqIiTExMsHbtWqnnqFXbC2jNmjXo2bMnACAlJQXz5s3D2LFjOcshC2h/FpF1ysrKKCoqkhgkFRYW8tLMvry8nJouE5lAM0kyZuLEiX/r63bu3CnlJDXCw8OxePFijBo1Ct27dwdQs5zpwIEDCAgIQPPmzUVfKy9rhvkydOhQ9OvXT+yGvK4NGzYgKSkJhw8f5jgZP8aNG4d27dp9soLd8uXLcefOHezatUuqOT58+ABHR0eEh4ejdevWUr3WXzE1NcXVq1fRtGlTXnNUVFRg3rx5CA8PF5WCV1RUxPTp0xEcHMxZQQ1ZQPuziKwbO3YsCgsLcfToUdHsZnFxMYYPHw59fX1OevJUVVUhKCgI4eHhKCoqQnZ2NszMzLBo0SKYmJjA09NT6hkI+RgNkshn0Zph2WFsbIyTJ09+sit6VlYWBg4ciIKCAo6T8cPc3ByHDx/+5ExNRkYGhg0bxkmxAj09PdE+KPKnsrIy0b5Fc3NzuWxUSfuziKx7/PgxvvnmG7x48QKdO3cGANy8eRMGBgY4ffq02B5paQkMDERUVBQCAwMxZcoU3Lp1C2ZmZti3bx9CQ0Nx6dIlqWcg5GO03I58VnV1Nd8RyP8rKiqSaPJXV6NGjfDs2TPO8uzcuROjR4/m7cb38ePH0NTU/OR5DQ0NFBYWcpLFzc0N27dv57Qn0qckJiYiMTERT58+lfj95bqVgJqamqiHlbzS0tJCQUGBxCDp4cOHn/35JYQrLVq0QHp6Onbv3o20tDSoqqpi4sSJGDt27Gffc/5J0dHR2Lp1K+zt7eHl5SU6bmNjg6ysLE4yEPIxGiQR0kC0aNECt27dktgfVis9Pf2TJYalYcGCBfD29sbIkSPh6ekp2nvCFT09Pdy9e/eTBU2ysrI4W3ZWWVmJHTt2ICEhAV26dJFoQMhVI9eAgAAEBgaia9euaNasGaf9qlxcXBAZGQktLS2Jxowf42pPpSyg/VmkIVBXV8fUqVN5u/7jx4/rfW+rrq7Ghw8feEgkG3Jzc3Hv3j188803UFVV5bwPobyjQRL5S1evXkVSUlK9T6a5uvkjgJOTExYtWgRHR0eJTa3v3r2Dv78/vv32W87yPH78GHFxcYiMjETfvn1hZmaGiRMnwsPDQ6xkqbQ4ODhg+fLlcHR0lDjHGMPy5cvh4OAg9RwAcOvWLdEG/OzsbLFzXL6hhYeHIzIyEuPHj+fsmrW0tbVF/61aWlr0Rv7/1qxZA4FAAHd393r3ZxEiC2JiYrBlyxbk5eXh0qVLMDY2xrp162BmZoZhw4ZJ/fpWVla4cOECjI2NxY7HxsaKlgDKkxcvXmD06NE4c+YMBAIBcnJyYGZmBk9PT+jq6nJaiEee0Z4k8llBQUH4+eef0bZtWxgYGIjd+AgEApw5c4bHdPKlqKgItra2EAqFmDlzJtq2bQugZsZk06ZNqKqqQmpqKgwMDHjJtmvXLkRFRSErKwuOjo7w9PSEs7Oz1Jog37t3D126dEHbtm0xd+5cse/H2rVrkZ2djWvXrn1y5u1L1KRJE1y5cgXm5uZ8RyEfof1ZRFZt3rwZixcvxg8//IBly5bh9u3bMDMzQ2RkJKKiopCUlCT1DEePHoWHhwd+/PFHBAYGIiAgAHfv3kV0dDSOHz+OAQMGSD2DLHF3d8fTp08RERGBdu3aIS0tDWZmZoiPj8ecOXNw+/ZtviPKB0bIZ+jr67OdO3fyHYP8v/v377PBgwczBQUFJhAImEAgYAoKCmzw4MEsLy+P12yXL19mU6dOZcrKyszExIRpa2szExMTlpSUJLVrXr16lbVv3170faj9vrRv355duXJFateVVfPnz2eBgYF8x2D9+vVjr169kjj++vVr1q9fP+4DEUI+qV27duzw4cOMMcY0NDTYvXv3GGOMZWRksCZNmnCW4/z588zBwYHp6ekxVVVV1qtXLxYfH8/Z9WWJgYEBu3nzJmNM/N/k3r17TF1dnc9ocoWW25HPUlBQQK9evfiOQf6fsbExTpw4gVevXiE3NxeMMbRu3Rq6urq85CkqKkJMTAx27tyJvLw8DB8+HMePH4eDgwNKS0sRGBgIDw8PPHjwQCrX79q1K27duoWbN28iJycHjDG0adMGnTp1ksr1PmXEiBH1Li8TCARQUVGBhYUFxo0bJ5rtkpby8nJs3boVCQkJ6Nixo8Sma66Wx549exYVFRX15rtw4QInGfj0V3uy6pKn/VlENuXn59e7pE1ZWRmlpaWc5bCzs8Pp06c5u54sKy0trXe2+eXLl3LVQoFvNEgin+Xj44NNmzYhNDSU7yikDl1dXXz11Ve8ZnB2dkZ8fDzatGmDKVOmwN3dHY0bNxadV1dXx9y5c7F69WqpZ+nUqRPnA6O6tLW1ceTIEejo6KBLly4AgNTUVBQXF2PgwIHYt28fVq5cicTERKk+dEhPTxd9H27duiV2jos9Qunp6aI/Z2Zm4smTJ6LPq6qqcPLkSbRo0ULqOfhW22uGkIbA1NQUN2/elNgP9LmWE/+0yZMnw83NDX379uXkerLOzs4O0dHRWLp0KYCa1+/q6mqsWrUK/fr14zmd/KBBEvksX19fDBkyBObm5rCyspJ4Mk1PQeWXvr4+zp07hx49enzya/T09JCfn89hKn4YGhpi3LhxCAsLE+3Bqq6uhre3NzQ1NbF37154eXnBz88PycnJUsvBxd6Bz+nUqRMEAgEEAgH69+8vcV5VVRUbN27kIRm3uGr2Tcj/IjAwEL6+vpgzZw5mzJiB8vJyMMZw5coV7NmzBytWrEBERAQnWZ49ewZHR0fo6elhzJgxcHV15fXBF99WrVoFe3t7XLt2DRUVFZg/fz5u376Nly9fIiUlhe94coMKN5DPmjlzJiIiItCvXz+Jwg0A3QzIs+joaIwePVpi6r+iogJ79+6Fu7s7T8m4p6enh5SUFLRp00bseHZ2Nnr27Innz58jIyMDdnZ2KC4ulnoevsrGPnjwAIwxmJmZ4cqVK9DT0xOdU1JSgr6+PoRCodRzyKKnT5/i7t27AIC2bdtCX1+f50RE3gmFQhQWFkJfXx+7d+/GkiVLRMVFmjdvjoCAAHh6enKW59WrVzhw4AB+/fVXXLhwAZaWlnB1dcW4ceNgYmLCWQ5Z8fr1a2zcuBHp6ekoKSmBra0tZsyYwWmrD3lHgyTyWbVPwYcMGcJ3FCJj6r7B1vXixQvo6+ujqqqKp2Tc09XVRVRUFIYOHSp2/NixY/Dw8MCrV6+Qk5ODbt264dWrV1LL8eLFC4waNQpJSUliZWMnTZpEZWN58ubNG8yYMQN79+4V/U4IhUKMHj0amzZtoqV5hDcKCgp48uSJ2Gt4WVkZSkpKeB/EP3r0CHv27MGOHTuQk5MjKp9PCJekU5uXfDEaN25M5YRJvT41O/Ho0SO5u/EbP348PD09sW7dOiQnJyM5ORnr1q2Dp6enaEbt3LlzaN++vVRz+Pj4QFFREQUFBWKbfkePHo2TJ09K9dr1yczMxMmTJ3Hs2DGxD3kyZcoU/PHHHzh+/DiKi4tRXFyM48eP49q1a5g2bRrf8Yic+/g1XE1NjfcB0ocPH3Dt2jX88ccfuH//Pi9tLWTBhQsX4Obmhp49e+Lx48cAavpZSXPJNhFHM0nks3bu3ImTJ09i586d1NdDhty9excbN27EnTt3AADt2rXDrFmzpF49DQA6d+4MgUCAtLQ0tG/fHo0a/bm1saqqCvn5+XB0dMT+/fulnqVWcXExrly5Um/DYy6W/VVVVSE4OBhhYWEoKioCABgYGGDWrFnw8/ODUChEQUEBFBQU0LJlS6nlMDQ0RHx8PGxsbKCpqSnqrZGXl4eOHTuipKREateuKy8vDyNGjEBGRgYEAgFq32Zqb8jkaZZRXV0d8fHx6N27t9jxCxcuwNHRkdPqYYTUpaCgINYE+lNevnzJSZ6kpCT8+uuvOHjwIKqrq+Hi4gJXV1f0799f7ppTHzx4EOPHj4erqytiYmKQmZkJMzMzhIWF4cSJEzhx4gTfEeUCFW4gn7Vhwwbcu3cPBgYGMDExkSjckJqaylMy+XXw4EGMGTMGXbt2FRVNuHz5Mjp06IC9e/fiu+++k+r1hw8fDgC4efMmBg0aBA0NDdE5JSUlmJiYSD1DXXFxcXB1dUVJSQm0tLQkGh5zMUgSCoVYuHAhFi5ciDdv3gAAtLS0xL6mVatWUs8hK2Vjvb29YWpqisTERJiamuLKlSt48eIF5s6dizVr1nCWQxY0adKk3plVbW1t3kr3E1IrICBAJmb+W7RogZcvX8LR0RFbt26Fs7OzXJe6XrZsGcLDw+Hu7o69e/eKjvfq1QvLli3jMZl8oZkk8lkBAQGfPe/v789RElLL3Nwcrq6uCAwMFDvu7++PXbt2iTbeSltUVBRGjx4NFRUVTq73KW3atIGTkxOCgoJ4n+189uyZaHO+paUlmjZtyun1nZyc0KVLFyxduhSamppIT0+HsbExxowZg+rqasTGxnKSo2nTpjhz5gw6duwIbW1tXLlyBW3btsWZM2cwd+5c3Lhxg5McsmDr1q04cOAAYmJiYGhoCAB48uQJPDw84OLiQkvuCG/q25PEl23btmHkyJHQ0dHhO4pMUFNTQ2ZmJkxMTCRWBVhZWaG8vJzviHKBBkmENDBqampIT0+HhYWF2PGcnBzY2NigrKyMp2T8UFdXR0ZGBszMzHjLUFpailmzZiE6Olq03E8oFMLd3R0bN27kbPB269Yt2Nvbw9bWFmfOnMHQoUPFysZytb9QV1cXqampMDU1hbm5uahC5r1792BtbS1XP6OdO3dGbm4u3r9/L5pNLCgogLKyMlq3bi32tTQzT7j0qeI7fOKrMqesMTMzw9atW+Hg4CA2SIqOjkZwcDAyMzP5jigXaLkd+VuuX78u2v/Svn37ertzE2707dsXFy5ckBgkJScnw87OTqrXbty4MbKzs9G0aVPo6up+9s2Lq3XsgwYNwrVr13gdJM2ZMwfnzp1DXFycqFlscnIyZs+ejblz52Lz5s2c5OjQoQOys7MRFhYGTU1NlJSUwMXFhfOysR06dEBaWhpMTU3x9ddfY9WqVVBSUsLWrVt5/XfiQ+3yVEJkjSw9I/9UZU5PT0+5rMw5ZcoUeHt7Y8eOHRAIBPj3v/+NS5cuwdfXF4sWLeI7ntygmSTyWU+fPsWYMWNw9uxZ0TR4cXEx+vXrh71794r1QSHSU7ci2L///W8sXrwYo0aNQvfu3QHU7Ek6cOAAAgIC4OXlJbUcUVFRGDNmDJSVlREZGfnZQZKHh4fUctS1fft2BAYGYuLEibC2tpbYN/dxWW5paNq0KWJjYyW6xSclJWHUqFF49uyZ1DPIkvj4eJSWlsLFxQW5ubn49ttvkZ2djSZNmmDfvn31NpolhMgvd3d3PH36FBEREWjXrp1o5iQ+Ph5z5szB7du3+Y7IKcYYgoKCsGLFCtHMu7KyMnx9fbF06VKe08kPGiSRzxo9ejTy8vIQHR2Ndu3aAagp6+vh4QELCwvs2bOH54TyQUHh71XrFwgEclU5DPj894ar74eamhquX78u+h2pdfv2bXTr1o2zCmYnT56EhoaGqJLapk2bsG3bNlhZWWHTpk28Fgp4+fLlX84+fulKSkokqi9+XOCDEHkkK5U5ZUFVVRVSUlLQsWNHqKmpITc3FyUlJbCyshIrlESkj/okkc86efIkfvnlF7Gbv9obrt9//53HZPKlurr6b31wOUBKTU1FRkaG6POjR49i+PDh+Omnn1BRUcFZDln4fvTo0QP+/v5im2nfvXuHgIAAUQVCLsybN09UXS8jIwNz5syBk5MT8vPzMWfOHM5yvH79WmK5ZePGjfHq1StRPnmRn5+PIUOGQF1dXVTRTldXFzo6OlTdjpD/JyuVOWWBUCjEwIED8erVKygpKcHKygrdunWjARIPaE8S+azq6mqJ5UsAoKioKPFElMiXadOmYcGCBbC2tkZeXh5Gjx4NFxcXHDhwAGVlZQgNDeU7ImdCQ0Ph6OiIli1bwsbGBgCQlpYGFRUVxMfHc5YjPz8fVlZWAGpKxTs7OyMoKAipqalwcnLiLMeYMWPg7OyM77//Xuz4/v37cezYMbnq8eHm5gbGGHbs2AEDAwO5nkkj5FPs7OwQHR0tWkomEAhQXV2NVatWoV+/fjyn416HDh2Ql5cHU1NTvqPINVpuRz5r2LBhKC4uxp49e9C8eXMAwOPHj+Hq6gpdXV0cPnyY54Ty5+PS3x9bvHgxJzm0tbWRmpoKc3NzrFy5EmfOnEF8fDxSUlIwZswYPHz4UGrX3rBhA6ZOnQoVFRVs2LDhs187e/ZsqeWoq6ysDLt370ZWVhaAmga/rq6uUFVV5eT6QM1sTXJyMqysrNC7d2+4u7tj6tSpuH//PqysrDirKte4cWOkpKRILD/MyspCr1698OLFC05yyAINDQ1cv36dk0bPhDRUslKZU1acPHkSP/74I5YuXYouXbpAXV1d7Dwt0+UGDZLIZz18+FD0YmVkZCQ61qFDBxw7dgwtW7bkOaH8+biy4IcPH5Cfn49GjRrB3NycszLCWlpauH79Olq3bo0BAwbg22+/hbe3NwoKCtC2bVu8e/dOatc2NTXFtWvX0KRJk88+aRMIBMjLy5NaDqDm+29paYnjx49LDAq4NnToUFRUVKBXr15YunQp8vPz0aJFC5w6dQozZ85EdnY2JznU1dVx+fJlWFtbix3PyMjA119/LVclwPv164eFCxfCwcGB7yiEyLTXr18jLCwMaWlpKCkpga2tLeeVOWVF3b22dWefa0uiy9veY77QcjvyWUZGRkhNTUVCQoLYE3J6w+dPfY0437x5gwkTJmDEiBGc5ejatSuWLVsGBwcHnDt3TlTmOj8/HwYGBlK9dn5+fr1/5oOioqLMNPYLCwvD999/j9jYWGzevBktWrQAAPz+++9wdHTkLEe3bt2wdetWbNy4Uex4eHg4unTpwlkOWRAREQEvLy88fvwYHTp0kFi+3LFjR56SESJbtLW1sXDhQrFjjx49wtSpU7F161aeUvEjKSmJ7wgENJNEyBcjIyMDzs7OuH//PifXS09Ph6urKwoKCjBnzhz4+/sDAGbNmoUXL17g119/5SSHLAgKCkJ2djYiIiLQqBE9e0pJSYGDgwO++uor2NvbAwASExNx9epVnDp1Sur9vGTJ5cuXMW7cOLHfS4FAQE+ECfkb0tLSYGtrS78nhBf0bk7qdebMGcycOROXL1+WWPv6+vVr9OzZE+Hh4XJ1syPrXr9+jdevX3NyraqqKhQXF+P8+fMSFbpWr14NoVDISY5ajx49wrFjx1BQUCBRWS8kJETq17969SoSExNx6tQpWFtbS6wfP3TokNQz1Lp37x527tyJe/fuYf369dDX18fvv/+OVq1aoX379pxk6NWrFy5duoTVq1dj//79UFVVRceOHbF9+3a0bt2akwyyYtKkSejcuTP27NlDhRsIIX9Lenp6vccFAgFUVFTQqlUruav6xweaSSL1Gjp0KPr16wcfH596z2/YsAFJSUlUuIEHHxcqYIyhsLAQMTEx6NOnD2czOCoqKrhz5w7v1XcSExMxdOhQmJmZISsrCx06dMD9+/fBGBNtApa2iRMnfvb8zp07pZ4BAM6dO4fBgwejV69eOH/+PO7cuQMzMzMEBwfj2rVriI2N5SQH+ZO6ujrS0tJgYWHBdxRCGhx5nUlSUFD47AMVRUVFjB49Glu2bIGKigqHyeQLDZJIvYyNjXHy5MlPbkTPysrCwIEDUVBQwHEy8vGgREFBAXp6eujfvz9+/PFHaGpqcpKja9euWLlypWg5FV+6deuGwYMHIyAgQNSEUF9fH66urnB0dMT06dN5zcelHj16YOTIkZgzZ45YQ8YrV67AxcUFjx494iTHX70utGrVipMcssDZ2RkTJkzAd999x3cUQhoceR0kHT16FH5+fpg3bx66desGALhy5QrWrl0Lf39/VFZWYsGCBRg9ejTWrFnDc9ovFy23I/UqKiqqtz9SrUaNGuHZs2ccJiK1+C5UUGvZsmXw9fXlvUTpnTt3sGfPHgA1P5fv3r2DhoYGAgMDMWzYMKkOkqqrq7F69WocO3YMFRUVsLe3h7+/P6dlv+vKyMiodyZRX18fz58/5yyHiYnJZ5+CytMNj7OzM3x8fJCRkQFra2uJ19WhQ4fylIwQ/rm4uHz2fHFxMTdBZMzy5cuxfv16DBo0SHTM2toaLVu2xKJFi3DlyhWoq6tj7ty5NEiSIhokkXq1aNECt27d+uQSkfT0dLksyymLHjx4gNLSUlhaWoqVDZW22uakQ4cO5bVEqbq6umgfUrNmzXDv3j3R3htpDwyWL1+OJUuWwMHBAaqqqli/fj2ePn2KHTt2SPW6n6Kjo4PCwkKJ2cYbN26IKt1x4eMKjB8+fMCNGzcQEhKC5cuXc5ZDFnh5eQGov78ZFW4g8k5bW/svz7u7u3OURnZkZGTA2NhY4rixsTEyMjIAAJ06dUJhYSHX0eQKDZJIvZycnLBo0SI4OjpKrHd99+4d/P398e233/KUTj7t2LEDxcXFmDNnjujY1KlTsX37dgBA27ZtER8fL+pnJW2yUqK0e/fuSE5ORrt27eDk5IS5c+ciIyMDhw4dQvfu3aV67ejoaPzyyy+YNm0aACAhIQFDhgxBREQEpwPWWmPGjIGfnx8OHDgg6lifkpICX19fTm80bGxsJI517doVzZs3x+rVq//y6fGXpLq6mu8IhMgsrvZrNjSWlpYIDg7G1q1boaSkBKDmYVNwcDAsLS0BAI8fP5Z6uw15R3uSSL2Kiopga2sLoVCImTNnirrFZ2VlYdOmTaiqqkJqair9gnKoe/fumDZtmqhIwMmTJ+Hs7IzIyEi0a9cOM2fOhJWVFSIiInhOyq28vDyUlJSgY8eOKC0txdy5c3Hx4kW0bt0aISEh9T6N+6coKysjNzdXbGCqoqKC3NxcXhotV1RUYMaMGYiMjERVVRUaNWqEqqoqjBs3DpGRkZxXHfxYbm4ubGxsUFpaymsOQgiRZRcvXsTQoUOhoKAg6qWWkZGBqqoqHD9+HN27d0dMTAyePHmCefPm8Zz2y0WDJPJJDx48wPTp0xEfH4/aHxOBQIBBgwZh06ZNvFc1kzdNmjTB2bNnYW1tDQCYPn06nj17JqpYdvbsWUycOJHTPUsXLlzAli1bkJeXhwMHDqBFixaIiYmBqakpevfuzVkOvgiFQjx58gR6enqiY5qamkhPT+f894MxhocPH0JPTw/Pnz9HRkYGSkpK0LlzZ87Lbr9580YiW2FhIZYsWYKsrCzcvHmT0zx8cHJywp49e0TLiYKDg+Hl5QUdHR0AwIsXL2BnZ4fMzEweUxJCZNXbt2+xe/duZGdnA6hZLTJu3DjOijMRGiSRv+HVq1fIzc0FYwytW7eW6ItDuKGmpoY7d+6IZkZsbGzg6emJ2bNnA6ipKNa2bVu8e/eOkzwHDx7E+PHj4erqipiYGGRmZsLMzAxhYWE4ceIETpw4wUkOPikoKGDw4MFi/Sri4uLQv39/sUIWXPRJqq6uhoqKCm7fvs17L6L6ytcyxmBkZIS9e/eiR48ePCXjjlAoRGFhIfT19QHUFDK5efMmzMzMANTM1jdv3pz2JBFCiIyiPUnkL+nq6uKrr77iO4bcMzY2xvXr12FsbIznz5/j9u3b6NWrl+j8kydP/nIT7D9p2bJlCA8Ph7u7O/bu3Ss63qtXLyxbtkzq16+92fwreXl5Usvg4eEhcczNzU1q1/scBQUFtG7dGi9evOB9kPTxfrXaMvUWFhZo1Eg+3nY+fv5IzyMJIf+JmJgY0UqNS5cuwdjYGOvWrYOZmRmGDRvGdzy5IB/vVoR8ATw8PDBjxgzcvn0bZ86cgaWlJbp06SI6f/HiRXTo0IGzPHfv3sU333wjcVxbW5uTsq3379+HsbExxo0bJ3pazzVZ23QcHByMefPmYfPmzZz+LHysT58+vF2bEEIaus2bN2Px4sX44YcfsGzZMtGMs66uLkJDQ2mQxBEaJBHSQMyfPx9lZWU4dOgQDA0NceDAAbHzKSkpGDt2LGd5DA0NkZubCxMTE7HjycnJf3uW53+xb98+7NixAyEhIRg8eDAmTZoEJycnXqrKyQp3d3eUlZXBxsYGSkpKEv2aXr58KbVrHzt27G9/rTz0BhIIBBJLDj/XO4oQQmpt3LgR27Ztw/DhwxEcHCw63rVrV/j6+vKYTL7QniRCyH9lxYoV2LVrF3bs2IEBAwbgxIkTePDgAXx8fLBo0SLMmjWLkxyPHz9GZGQkIiMjUVZWhvHjx8PT05P3JWd8iIyM/OyNeH3LA/8pHw9OBQKB2BKzurnkYR/Ox/vVPt6r9v79e5w8eVIuvheEkP+MqqoqsrKyYGxsDE1NTaSlpcHMzAw5OTno2LEjZ3uP5R0Nkggh/xXGGIKCgrBixQqUlZUBqCmJ7evri6VLl/KS6dy5c1iyZAnOnz+P58+fy12Rkffv36OyslKsaAQfEhIS4Ofnh6CgIFGRhkuXLuHnn39GUFAQBgwYwGs+LtSW6v8rsrZkkxDCPysrK6xYsQLDhg0TGyRt3LgRO3fuRGpqKt8R5QINkggh/5OKigrk5uaipKQEVlZW0NDQ4DxDeXk5YmNjsWPHDly+fBlDhw5FVFSUWNW5L9mzZ8/g7u6OhIQEVFdX46uvvsKuXbtgYWHBS54OHTogPDxcogz8hQsXMHXqVNy5c4eXXIQQ0hBERERgyZIlWLt2LTw9PREREYF79+5hxYoViIiIwJgxY/iOKBdokEQI+a/s2rULLi4uUFNT4y3DH3/8ge3bt2P//v0wMzPDpEmT4OrqKnczSJMmTcLvv/+O2bNnQ0VFBVu2bEGzZs0kqsxxRVVVFVevXpUoHpGeno6vv/6alooQQshf2L17N5YsWYJ79+4BAJo3b46AgAB4enrynEx+0CCJEPJf0dPTw7t37zB06FC4ublh0KBBEAqFnF2/ffv2ePr0KcaNG4dJkybBxsaGs2vLGiMjI0RERGDQoEEAgJycHLRr1w6lpaW8zKZ98803UFFRQUxMDAwMDADU9AVyd3dHeXk5zp07x3kmQghpiMrKylBSUsJbFVd5RoMkQsh/pbKyEidPnsSePXtw9OhRqKmpYeTIkXB1dUXPnj2lfn0FBQWoq6ujUaNGny1WIM2KbrJCKBTi8ePHMDQ0FB1TV1fH7du3JaoPciE3NxcjRoxAdnY2jIyMAAAPHz5E69atceTIEd6WARJCSEPx/Plz3L9/HwKBACYmJmjSpAnfkeQODZIIaWAYY4iNjUVSUhKePn2K6upqsfOHDh3iPFNZWRkOHz6MX3/9FQkJCWjZsqVoiYC0REVF/a2vk2ZFN1khFArx5MkT6OnpiY5paWkhLS0NpqamvGRijOH06dPIysoCALRr1w4ODg5UBpsQQj7j9u3bmD59OlJSUsSO9+nTB5s3b0bbtm15SiZ/aJBESAPj7e2NLVu2oF+/fjAwMJC46eSrWtbz58+xd+9ehIeH486dO1TamEMKCgrQ1tYW+1koLi6GlpaWWGlueZhVI4SQhurJkyfo0KED9PT04OXlBUtLSzDGkJmZiW3btuHFixe4desWLb3jCA2SCGlgGjdujF27dsHJyYnvKKIZpN27dyMxMRFGRkYYO3YsXF1dYWlpyXc8uSErs2pOTk7Ys2cPtLW1AQDBwcHw8vKCjo4OAODFixews7NDZmamVHMQQkhD5Ofnh4SEBKSkpEBFRUXs3Lt379C7d28MHDgQK1as4CmhfKFBEiENjKmpKX7//XfeByFjxozB8ePHoaamhlGjRsHV1VXUE4fIJ6FQiMLCQtFTTi0tLdy8eRNmZmYAaoo3NG/enGYZCSGkHra2tliwYAFGjRpV7/m9e/di1apV1CeJI434DkAI+c8sWbIEAQEB2LFjB1RVVXnLIRQKsX//fs6r2hHZ9fEzN3oGRwghf19eXh5sbW0/eb5r167Iy8vjMJF8o0ESIQ3MqFGjsGfPHujr68PExASKiopi57l6wrR7925OrkMIIYTIg7dv30JLS+uT5zU1NVFSUsJhIvlGgyRCGhgPDw9cv34dbm5u9RZukKYNGzZg6tSpUFFRwYYNGz77tbNnz+YoFZEVAoFA4ueRqtkRQsjf9/btW4n9SLXevHlDM/Qcoj1JhDQw6urqiI+PR+/evTm/tqmpKa5du4YmTZp8trS0QCDgbEmALJZEl1cKCgoYPHiwqIFtXFwc+vfvD3V1dQDA+/fvcfLkSdqTRAgh9VBQUPjsgyXGGAQCAb2GcoRmkghpYIyMjD47HS9N+fn59f6ZTz/88MNnS6IT7nxcPc/NzU3ia9zd3bmKQwghDUpSUhLfEUgdNJNESAPz22+/YePGjQgPD4eJiQnfcXgnSyXR+UazaoQQQsg/g2aSCGlg3NzcUFZWBnNzc6ipqUkUbuCqYWhVVRUiIyORmJhY7w35mTNnOMmhra0tKjEt72hWjRBCCPln0CCJkAYmNDSU7wgAAG9vb0RGRmLIkCHo0KEDbzfkslISXRbExMTg0KFDNKtGCCGE/I9ouR0h5L/StGlTREdH835D/u7dO4wYMQIpKSm8lkSXBbLSaJgQQghp6GgmiZAGrLy8HBUVFWLHuCrqoKSkBAsLC06u9Tl8lkSXNTSrRgghhPwzaCaJkAamtLQUfn5+2L9/P168eCFxnqvSoGvXrkVeXh7CwsJ4HZjwWRJd1tCsGiGENHw7d+7E6NGjoaamxncUuUYzSYQ0MPPnz0dSUhI2b96M8ePHY9OmTXj8+DG2bNmC4OBgznIkJycjKSkJv//+O9q3by9xQ85VJTU+S6LLGppVI4SQhm/BggXw9vbGyJEj4enpiZ49e/IdSS7RTBIhDUyrVq0QHR2Nvn37QktLC6mpqbCwsEBMTAz27NmDEydOcJJj4sSJnz2/c+dOTnJQSfQ/0awaIYQ0fJWVlYiLi0NkZCR+//13mJmZYeLEifDw8IChoSHf8eQGDZIIaWA0NDSQmZmJVq1aoWXLljh06BC6deuG/Px8WFtbo6SkhO+InNLV1UVZWRkqKyt5LYkuCywtLbF//3507NiR7yiEEEL+AUVFRdi1axeioqKQlZUFR0dHeHp6wtnZGQoKCnzH+6LRcjtCGhgzMzPk5+ejVatWopvibt26IS4uDjo6OnzH45yslESXBWvXrsX8+fNpVo0QQr4QBgYG6N27N7Kzs5GdnY2MjAx4eHhAV1cXO3fuRN++ffmO+MWimSRCGph169ZBKBRi9uzZSEhIgLOzMxhj+PDhA0JCQuDt7S3V6+vq6ta710VbWxtt2rSBr68vBgwYINUMpH40q0YIIV+GoqIixMTEYOfOncjLy8Pw4cPh6ekJBwcHlJaWIjAwEHv37sWDBw/4jvrFokESIQ3cgwcPcP36dVhYWHCyzCoqKqre48XFxbh+/Tr27duH2NhYODs7Sz3Lx/gsiS4LPvVvU8vDw4OjJIQQQv5bzs7OiI+PR5s2bTB58mS4u7ujcePGYl/z9OlTGBoaorq6mqeUXz4aJBHSgHz48AGOjo4IDw9H69at+Y5Tr5CQEMTGxuLixYucXE9WSqITQggh/wRPT09MnjwZPXr0+OTXMMZQUFAAY2NjDpPJF9rxRUgDoqioiPT0dL5jfNa3336LrKwszq43f/58nDlzBps3b4aysjIiIiIQEBCA5s2bIzo6mrMcsqa8vBxv3rwR+yCEECL7+vTpA1tbW4njFRUVovc1gUBAAyQpo5kkQhoYHx8fKCsrc9oT6T+RkZGBAQMG4MmTJ5xcT1ZKossCmlUjhJCGTygUorCwEPr6+mLHX7x4AX19fXot5whVtyOkgamsrMSOHTuQkJCALl26QF1dXex8SEgIT8lqbN++HZ06deLsei9fvoSZmRmAmv1HtcUJevfujenTp3OWQxbISqNhQggh/z3GWL0Fkh49egRtbW0eEsknGiQR0sDcunVLNA2fnZ0tdq6+F9V/2pw5c+o9/vr1a6SmpiI7Oxvnz5+Xeo5aVBL9T3FxcaJZtYkTJ8LOzg4WFhYwNjbG7t274erqyndEQgghn9C5c2cIBAIIBALY29ujUaM/b9OrqqqQn58PR0dHHhPKFxokEdLAJCUl8Xr9Gzdu1HtcS0sLAwYMwKFDh2BqaspZnokTJyItLQ19+vTBggUL4OzsjLCwMFFJdHlCs2qEENJwDR8+HABw8+ZNDBo0CBoaGqJzSkpKMDExwXfffcdTOvlDgyRCyH+E70Hax3x8fER/dnBwQFZWFqcl0WUJzaoRQkjD5e/vDwAwMTHB6NGjoaKiwnMi+UaFGwhpYEaMGFHvsjqBQAAVFRVYWFhg3LhxaNu2LQ/puNUQSqJzie9Gw4QQQsiXggZJhDQwEyZMwJEjR6Cjo4MuXboAAFJTU1FcXIyBAwciLS0N9+/fR2JiInr16sVzWunT09PDxYsXaZBUD64bDRNCCPnvNG7cGNnZ2WjatCl0dXU/u8e4dik1kS4aJBHSwCxYsABv3rxBWFgYFBRqWp1VV1fD29sbmpqaWL58Oby8vHD79m0kJyfznFb6ZL0kOldoVo0QQhquqKgojBkzBsrKyoiMjPzsIMnDw4PDZPKLBkmENDB6enpISUlBmzZtxI5nZ2ejZ8+eeP78OTIyMmBnZ4fi4mJ+QnJo1qxZiI6ORuvWrWWyJDqXaFaNEEII+Wco8B2AEPKfqaysRFZWlsTxrKwsUYM5FRUVTsqBy4LakuiamprIzs7GjRs3RB83b97kOx6n3NzcsH37dr5jEEII+R+kpqYiIyND9PnRo0cxfPhw/PTTT6ioqOAxmXyh6naENDDjx4+Hp6cnfvrpJ3z11VcAgKtXryIoKAju7u4AgHPnzqF9+/Z8xuSMrFXb45OsNxomhBDy16ZNm4YFCxbA2toaeXl5GD16NFxcXHDgwAGUlZUhNDSU74hygZbbEdLAVFVVITg4GGFhYSgqKgIAGBgYYNasWfDz84NQKERBQQEUFBTQsmVLntMSLvXr1++T5wQCAc6cOcNhGkIIIf8NbW1tpKamwtzcHCtXrsSZM2cQHx+PlJQUjBkzBg8fPuQ7olygQRIhDdibN28A1DQOlVdUEp0QQsiXREtLC9evX0fr1q0xYMAAfPvtt/D29kZBQQHatm2Ld+/e8R1RLtCeJEIaqGfPniE9PR3p6el4/vw533F4o62tjTNnziA1NRUCgQACgQA3btzAmTNnUFlZiX379sHGxgYpKSl8RyWEEEL+UteuXbFs2TLExMTg3LlzGDJkCAAgPz8fBgYGPKeTH7QniZAGprS0VFTRrbq6GgAgFArh7u6OjRs3Qk1NjeeE3DI0NMS4ceM+WRJ979698PLygp+f3xdfEp1m1QghpOELDQ2Fq6srjhw5goULF8LCwgIAEBsbi549e/KcTn7QcjtCGphp06YhISEBYWFhomaxycnJmD17NgYMGIDNmzfznJBbVBL9T9RomBBCGraqqiqkpKTA2toaurq6YufKy8shFAqhqKjIUzr5QsvtCGlgDh48iO3bt2Pw4MHQ0tKClpYWnJycsG3bNsTGxvIdj3NUEv1PtbNqeXl5OHjwIA4ePIh79+7Bzc0N5ubmuHPnDjw8PODn58d3VEIIIfUQCoUYOHBgvQ/1VFRUaIDEIVpuR0gDU1ZWVu+aZH19fZSVlfGQiF9UEv1P27dvR0pKimjZIQAoKChg1qxZ6NmzJ4KCgjBz5kzY2dnxmJIQQsjndOjQAXl5eTA1NeU7ilyjQRIhDUyPHj3g7++P6OhoqKioAADevXuHgIAA9OjRg+d03Fu3bh0MDAywatUqsZLoPj4+ohmTgQMHwtHRkc+YnKidVft46aE8zqoRQkhDtWzZMvj6+mLp0qX19ryT54q2XKI9SYQ0MBkZGXB0dMT79+9hY2MDAEhLS4OKigri4+PlYsbkU+S9JPrs2bOxZ8+eemfVxo0bh/Xr1yMiIgKRkZFffBELQghpqOquBqj7UIsxBoFAIHroRaSLBkmENEBlZWXYvXu3aC9Ou3bt4OrqClVVVZ6T8efZs2e4e/cuAMDS0hJNmzblORH3qNEwIYQ0fOfOnfvs+T59+nCURL7RIImQBuTDhw+wtLTE8ePH0a5dO77jyAQqiV4/eZ9VI4QQQv4XVN2OkAZEUVER5eXlfMeQKXPmzMG5c+cQFxeH4uJiFBcX4+jRozh37hzmzp3LdzxeUKNhQghp2C5cuAA3Nzf07NkTjx8/BgDExMTQUmkO0SCJkAZmxowZWLlyJSorK/mOIhOoJPqfSktLMWnSJDRr1gzffPMNvvnmGzRr1gyenp5yWfmQEEIaooMHD2LQoEFQVVVFamoq3r9/DwB4/fo1goKCeE4nP2iQREgDc/XqVRw6dAitWrXCoEGD4OLiIvYhb6gk+p9oVo0QQhq+ZcuWITw8HNu2bRPri9SrVy+kpqbymEy+0J4kQhqYiRMnfvb8zp07OUoiG+zt7dGkSROJkugeHh54+fIlEhISeE7InaZNmyI2NhZ9+/YVO56UlIRRo0bh2bNn/AQjhBDyt6mpqSEzMxMmJibQ1NREWloazMzMkJeXBysrK1p2zxHqk0RIAyNvg6C/EhoaCkdHR7Rs2bLekujyhGbVCCGk4TM0NERubi5MTEzEjicnJ8PMzIyfUHKIltsR0kBUV1dj5cqV6NWrF7766issWLAA79694zsW76ytrZGTk4MVK1agU6dO6NSpE4KDg5GTkyN3PaNqGw3Xfcooz42GCSGkIZoyZQq8vb3xxx9/QCAQ4N///jd2794NX19fTJ8+ne94coOW2xHSQCxduhRLliyBg4MDVFVVER8fj7Fjx2LHjh18R+MNlUQXR42GCSGk4WOMISgoCCtWrBCtAlBWVoavry+WLl3Kczr5QYMkQhqI1q1bw9fXF9OmTQMAJCQkYMiQIXj37p1Yd25506JFCyQkJNAg6f9Ro2FCCPkyVFRUIDc3FyUlJbCysoKGhgbfkeQKDZIIaSCUlZWRm5sLIyMj0TEVFRXk5uaiZcuWPCbjV1BQELKzsxEREYFGjeR3myXNqhFCyJdh165dcHFxkdtm6LJCfu8oCGlgKisrRdXbaikqKuLDhw88JZINV69eRWJiIk6dOgVra2uoq6uLnT906BBPybhFjYYJIeTL4OPjAy8vLwwdOhRubm4YNGgQhEIh37HkDg2SCGkgGGOYMGEClJWVRcfKy8vh5eUlNjCQl0FBLR0dHXz33Xd8x5AJtY2G5X1WjRBCGrLCwkKcPHkSe/bswahRo6CmpoaRI0fC1dUVPXv25Due3KDldoQ0EH/VH6kWlQiXXyNGjEBiYiI0NDTkelaNEEK+FGVlZTh8+DB+/fVXJCQkoGXLlrh37x7fseQCPWokpIGgwY+46upqrF69GseOHUNFRQXs7e3h7+8v1wUKaFaNEEK+LGpqahg0aBBevXqFBw8e4M6dO3xHkhs0k0QIaZCoJDohhJAvVe0M0u7du5GYmAgjIyOMHTsWrq6usLS05DueXKBBEiGkQaKS6H+iWTVCCPlyjBkzBsePH4eamhpGjRoFV1dXagjOA1puRwhpkAoKCuDk5CT63MHBQdSZXN5Koi9fvlxsVm39+vV4+vQpzaoRQkgDJBQKsX//fqpqxzOaSSKENEhCoRBPnjyBnp6e6JimpibS09NhamrKYzLu0awaIYQQ8s+iQRIhpEFSUFDA4MGDxUqix8XFoX///nJXEp0aDRNCSMO2YcMGTJ06FSoqKtiwYcNnv3b27NkcpZJvNEgihDRIVBL9TzSrRgghDZupqSmuXbuGJk2afPZ1WyAQIC8vj8Nk8osGSYQQ0sDRrBohhBDyz6LCDYQQ0sB5eHhIHHNzc+MhCSGEEPJloJkkQgghhBBCZERVVRUiIyORmJiIp0+forq6Wuz8mTNneEomX2gmiRBCCCGEEBnh7e2NyMhIDBkyBB06dIBAIOA7klyimSRCCCGEEEJkRNOmTREdHS3WC5BwjxpoEEIIIYQQIiOUlJRgYWHBdwy5R4MkQgghhBBCZMTcuXOxfv160GIvftFyO0IIIYQQQmTEiBEjkJSUhMaNG6N9+/ZQVFQUO0/tHLhBhRsIIYQQQgiRETo6OhgxYgTfMeQezSQRQgghhBBCSB20J4kQQgghhBBC6qDldoQQQgghhPBMV1e33p5I2traaNOmDXx9fTFgwAAeksknWm5HCCGEEEIIz6Kiouo9XlxcjOvXr2Pfvn2IjY2Fs7Mzx8nkEw2SCCGEEEIIkXEhISGIjY3FxYsX+Y4iF2iQRAghhBBCiIzLzs5G9+7d8fLlS76jyAUq3EAIIYQQQoiMe//+PZSUlPiOITdokEQIIYQQQoiM2759Ozp16sR3DLlB1e0IIYQQQgjh2Zw5c+o9/vr1a6SmpiI7Oxvnz5/nOJX8okESIYQQQgghPLtx40a9x7W0tDBgwAAcOnQIpqamHKeSX1S4gRBCCCGEEELqoD1JhBBCCCGEEFIHDZIIIYQQQgghpA4aJBFCCCGEEEJIHTRIIoQQQgghhJA6aJBECCGEEEIIIXXQIIkQQgghhBBC6qBBEiGEEN5NmDABAoFA4iM3N/d//rsjIyOho6Pzv4ckhBAiN6iZLCGEEJng6OiInTt3ih3T09PjKU39Pnz4AEVFRb5jEEIIkTKaSSKEECITlJWVYWhoKPYhFApx9OhR2NraQkVFBWZmZggICEBlZaXo/xcSEgJra2uoq6vDyMgI33//PUpKSgAAZ8+excSJE/H69WvR7NSSJUsAAAKBAEeOHBHLoKOjg8jISADA/fv3IRAIsG/fPvTp0wcqKirYvXs3ACAiIgLt2rWDiooKLC0t8csvv0j9+0MIIYQ7NJNECCFEZl24cAHu7u7YsGED7OzscO/ePUydOhUA4O/vDwBQUFDAhg0bYGpqiry8PHz//feYP38+fvnlF/Ts2ROhoaFYvHgx7t69CwDQ0ND4jzIsWLAAa9euRefOnUUDpcWLFyMsLAydO3fGjRs3MGXKFKirq8PDw+Of/QYQQgjhBQ2SCCGEyITjx4+LDWAGDx6MV69eYcGCBaLBh5mZGZYuXYr58+eLBkk//PCD6P9jYmKCZcuWwcvLC7/88guUlJSgra0NgUAAQ0PD/yrXDz/8ABcXF9Hn/v7+WLt2reiYqakpMjMzsWXLFhokEULIF4IGSYQQQmRCv379sHnzZtHn6urq6NixI1JSUrB8+XLR8aqqKpSXl6OsrAxqampISEjAihUrkJWVhTdv3qCyslLs/P+qa9euoj+Xlpbi3r178PT0xJQpU0THKysroa2t/T9fixBCiGygQRIhhBCZoK6uDgsLC7FjJSUlCAgIEJvJqaWiooL79+/j22+/xfTp07F8+XI0btwYycnJ8PT0REVFxWcHSQKBAIwxsWMfPnyoN1fdPACwbds2fP3112JfJxQK//o/khBCSINAgyRCCCEyy9bWFnfv3pUYPNW6fv06qqursXbtWigo1NQi2r9/v9jXKCkpoaqqSuL/q6enh8LCQtHnOTk5KCsr+2weAwMDNG/eHHl5eXB1df1P/3MIIYQ0EDRIIoQQIrMWL16Mb7/9Fq1atcK//vUvKCgoIC0tDbdu3cKyZctgYWGBDx8+YOPGjXB2dkZKSgrCw8PF/g4TExOUlJQgMTERNjY2UFNTg5qaGvr374+wsDD06NEDVVVV8PPz+1vlvQMCAjB79mxoa2vD0dER79+/x7Vr1/Dq1SvMmTNHWt8KQgghHKIS4IQQQmTWoEGDcPz4cZw6dQpfffUVunfvjnXr1sHY2BgAYGNjg5CQEKxcuRIdOnTA7t27sWLFCrG/o2fPnvDy8sLo0aOhp6eHVatWAQDWrl0LIyMj2NnZYdy4cfD19f1be5gmT56MiIgI7Ny5E9bW1ujTpw8iIyNhamr6z38DCCGE8ELAPl6QTQghhBBCCCFyjGaSCCGEEEIIIaQOGiQRQgghhBBCSB00SCKEEEIIIYSQOmiQRAghhBBCCCF10CCJEEIIIYQQQuqgQRIhhBBCCCGE1EGDJEIIIYQQQgipgwZJhBBCCCGEEFIHDZIIIYQQQgghpA4aJBFCCCGEEEJIHTRIIoQQQgghhJA6/g8HhBFfn3L5+gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# Assuming grid_search.best_estimator_ is your fitted LightGBM model\n",
    "feature_importances = grid_search.best_estimator_.feature_importances_\n",
    "\n",
    "# Creating a pandas series to hold the feature importances and sort them\n",
    "feature_importance_series = pd.Series(feature_importances, index=X_train.columns).sort_values(ascending=False)\n",
    "\n",
    "# Plotting\n",
    "feature_importance_series.plot(kind='bar', figsize=(10, 6))\n",
    "plt.title('Feature Importances')\n",
    "plt.xlabel('Feature')\n",
    "plt.ylabel('Importance')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Unknown parameter: is_unbalanced\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "\n",
    "# Predict on the test set\n",
    "y_pred = grid_search.best_estimator_.predict(X_test)\n",
    "\n",
    "# Confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(7, 5))\n",
    "sns.heatmap(cm, annot=True, fmt=\"d\", cmap='Blues')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.xlabel('Predicted Labels')\n",
    "plt.ylabel('True Labels')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Unknown parameter: is_unbalanced\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr, tpr, _ = roc_curve(y_test, grid_search.best_estimator_.predict_proba(X_test)[:, 1])\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (area = {roc_auc:0.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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