diff --git a/EksplorasiData_Farrel/.gitignore b/EksplorasiData_Farrel/.gitignore
deleted file mode 100644
index 16f2dc5..0000000
--- a/EksplorasiData_Farrel/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-*.csv
\ No newline at end of file
diff --git a/EksplorasiData_Farrel/README.md b/EksplorasiData_Farrel/README.md
deleted file mode 100644
index b62afd1..0000000
--- a/EksplorasiData_Farrel/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
-# DSCDataScience
-# Analyzing US Presidential Candidate Tweet's Sentiment Score
-## Repo ini ditujukkan untuk Tugas 1 DSC
-
-- Nama Lengkap : Muhammad Farrel Mahendra
-- NIM : 13317027
-- Asal Universitas : Institut Teknologi Bandung
-- Tanggal : 7 November 2020
-- Sumber Dataset : [US Election 2020 Tweets](https://www.kaggle.com/manchunhui/us-election-2020-tweets) (dataset terlalu besar ~ 800mb sehingga tidak dimasukkan ke repo)
-
diff --git a/EksplorasiData_Farrel/US Election Tweet Score.ipynb b/EksplorasiData_Farrel/US Election Tweet Score.ipynb
deleted file mode 100644
index 638dcb6..0000000
--- a/EksplorasiData_Farrel/US Election Tweet Score.ipynb	
+++ /dev/null
@@ -1,1624 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-07T16:31:42.825853Z",
-     "start_time": "2020-11-07T16:31:42.818105Z"
-    }
-   },
-   "source": [
-    "- Nama Lengkap : Muhammad Farrel Mahendra\n",
-    "- NIM : 13317027\n",
-    "- Asal Universitas : Institut Teknologi Bandung\n",
-    "- Tanggal : 7 November 2020\n",
-    "- Sumber Dataset : [US Election 2020 Tweets](https://www.kaggle.com/manchunhui/us-election-2020-tweets)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 193,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-07T18:19:34.827727Z",
-     "start_time": "2020-11-07T18:19:34.822661Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# importing library\n",
-    "import sklearn\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "import emoji\n",
-    "import re\n",
-    "import string\n",
-    "from wordcloud import WordCloud\n",
-    "import nltk\n",
-    "from nltk.stem import WordNetLemmatizer\n",
-    "import geopandas as gpd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-07T17:05:38.103573Z",
-     "start_time": "2020-11-07T17:05:26.217731Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "#load data\n",
-    "biden = pd.read_csv(\"hashtag_joebiden.csv\", lineterminator='\\n')\n",
-    "trump = pd.read_csv(\"hashtag_donaldtrump.csv\", lineterminator='\\n')\n",
-    "\n",
-    "biden['data'] = 'biden'\n",
-    "trump['data'] = 'trump'\n",
-    "\n",
-    "df = pd.concat([biden, trump])\n",
-    "df = df.reset_index()\n",
-    "del biden\n",
-    "del trump"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Dataset Information"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-07T17:06:10.759242Z",
-     "start_time": "2020-11-07T17:06:09.007206Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "Int64Index: 1362377 entries, 0 to 810985\n",
-      "Data columns (total 22 columns):\n",
-      " #   Column                Non-Null Count    Dtype  \n",
-      "---  ------                --------------    -----  \n",
-      " 0   created_at            1362377 non-null  object \n",
-      " 1   tweet_id              1362377 non-null  float64\n",
-      " 2   tweet                 1362377 non-null  object \n",
-      " 3   likes                 1362377 non-null  float64\n",
-      " 4   retweet_count         1362377 non-null  float64\n",
-      " 5   source                1360958 non-null  object \n",
-      " 6   user_id               1362377 non-null  float64\n",
-      " 7   user_name             1362345 non-null  object \n",
-      " 8   user_screen_name      1362377 non-null  object \n",
-      " 9   user_description      1219731 non-null  object \n",
-      " 10  user_join_date        1362377 non-null  object \n",
-      " 11  user_followers_count  1362377 non-null  float64\n",
-      " 12  user_location         946724 non-null   object \n",
-      " 13  lat                   637519 non-null   float64\n",
-      " 14  long                  637519 non-null   float64\n",
-      " 15  city                  324159 non-null   object \n",
-      " 16  country               633971 non-null   object \n",
-      " 17  continent             633994 non-null   object \n",
-      " 18  state                 465271 non-null   object \n",
-      " 19  state_code            438402 non-null   object \n",
-      " 20  collected_at          1362377 non-null  object \n",
-      " 21  data                  1362377 non-null  object \n",
-      "dtypes: float64(7), object(15)\n",
-      "memory usage: 239.1+ MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "df.info()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Data Cleaning"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-07T17:06:30.256291Z",
-     "start_time": "2020-11-07T17:06:30.240581Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "810981    MISMANAGED BALLOTS, Michigan Pennsylvania #Tru...\n",
-       "810982    TRUTH #Trump is a fraud, among many other trai...\n",
-       "810983    Vivement que Kamala Harris soit présidente ! #...\n",
-       "810984    What a deadbeat. He’s just going to humiliate ...\n",
-       "810985    Tem louco pra tudo! #JornalNacional #JornalNac...\n",
-       "Name: tweet, dtype: object"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Before cleaning\n",
-    "df.tail(5)['tweet']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-07T17:08:21.651911Z",
-     "start_time": "2020-11-07T17:08:21.477985Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Take only tweet from US\n",
-    "df = df[df['country'].isin(['United States of America', 'United States'])]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 162,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-07T17:54:11.970211Z",
-     "start_time": "2020-11-07T17:53:44.227552Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[nltk_data] Downloading package wordnet to /home/mahendra/nltk_data...\n",
-      "[nltk_data]   Unzipping corpora/wordnet.zip.\n"
-     ]
-    }
-   ],
-   "source": [
-    "def emoji_cleaning(text):\n",
-    "    \n",
-    "    # Change emoji to text\n",
-    "\n",
-    "    text = emoji.demojize(text).replace(\":\", \" \")\n",
-    "    \n",
-    "    # Delete repeated emoji\n",
-    "    tokenizer = text.split()\n",
-    "    repeated_list = []\n",
-    "    \n",
-    "    for word in tokenizer:\n",
-    "        if word not in repeated_list:\n",
-    "            repeated_list.append(word)\n",
-    "    \n",
-    "    text = ' '.join(text for text in repeated_list)\n",
-    "    text = text.replace(\"_\", \" \").replace(\"-\", \" \")\n",
-    "    return text\n",
-    "\n",
-    "\n",
-    "def normalizer(text):\n",
-    "    # delete lowercase and newline\n",
-    "    text = text.strip().lower()\n",
-    "    text = re.sub(r'\\n', '', text)\n",
-    "        # delete punctuation\n",
-    "    text = re.sub('[^a-z ]', ' ', text)\n",
-    "    \n",
-    "    # remove long word (more than 15)\n",
-    "    text = re.sub(r'\\W*\\b\\w{15,}\\b', ' ', text)\n",
-    "    \n",
-    "    #delete multiple space\n",
-    "    text = re.sub(' +', ' ', text).strip()\n",
-    "    \n",
-    "    for char in string.ascii_lowercase:\n",
-    "        char = char*3\n",
-    "        text = re.sub('{}+'.format(char), char[0], text)\n",
-    "    return text\n",
-    "\n",
-    "df['tweet'] = df['tweet'].apply(emoji_cleaning)\n",
-    "df['tweet'] = df['tweet'].apply(normalizer)\n",
-    "\n",
-    "lemmatizer = WordNetLemmatizer()\n",
-    "nltk.download('wordnet')\n",
-    "df['tweet'] = df['tweet'].apply(lemmatizer.lemmatize)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Wordcloud"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 163,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-07T17:54:24.277665Z",
-     "start_time": "2020-11-07T17:54:21.621800Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'t': 274819,\n",
-       " 'trump': 269424,\n",
-       " 'co': 223288,\n",
-       " 'https': 207019,\n",
-       " 'the': 161123,\n",
-       " 'biden': 154544,\n",
-       " 'to': 118381,\n",
-       " 'a': 99878,\n",
-       " 's': 91619,\n",
-       " 'is': 91482,\n",
-       " 'and': 88643,\n",
-       " 'of': 84280,\n",
-       " 'joebiden': 84233,\n",
-       " 'in': 80108,\n",
-       " 'for': 73488,\n",
-       " 'election': 58710,\n",
-       " 'i': 57668,\n",
-       " 'you': 55836,\n",
-       " 'it': 55402,\n",
-       " 'vote': 54823,\n",
-       " 'he': 51531,\n",
-       " 'that': 50926,\n",
-       " 'this': 49390,\n",
-       " 'on': 46952,\n",
-       " 'with': 42639,\n",
-       " 'are': 35818,\n",
-       " 'not': 34623,\n",
-       " 'be': 33892,\n",
-       " 'we': 31539,\n",
-       " 'his': 30721,\n",
-       " 'will': 30214,\n",
-       " 'bidenharris': 29982,\n",
-       " 'amp': 29870,\n",
-       " 'if': 28187,\n",
-       " 'donaldtrump': 27834,\n",
-       " 'all': 26730,\n",
-       " 'have': 26136,\n",
-       " 'covid': 25681,\n",
-       " 'has': 24299,\n",
-       " 'they': 23844,\n",
-       " 'what': 23446,\n",
-       " 'as': 22279,\n",
-       " 'but': 21746,\n",
-       " 'about': 21714,\n",
-       " 'can': 21282,\n",
-       " 'from': 21163,\n",
-       " 'maga': 21144,\n",
-       " 'face': 21085,\n",
-       " 'president': 20944,\n",
-       " 'who': 20707,\n",
-       " 'by': 20491,\n",
-       " 'so': 20433,\n",
-       " 'out': 20025,\n",
-       " 'at': 19891,\n",
-       " 'just': 19406,\n",
-       " 'no': 19092,\n",
-       " 'was': 18483,\n",
-       " 'like': 18271,\n",
-       " 'people': 18230,\n",
-       " 'your': 18068,\n",
-       " 'up': 17668,\n",
-       " 'more': 17140,\n",
-       " 'or': 16912,\n",
-       " 'do': 16435,\n",
-       " 'america': 16290,\n",
-       " 'm': 15501,\n",
-       " 'how': 15244,\n",
-       " 'my': 14661,\n",
-       " 'now': 14633,\n",
-       " 'joe': 14349,\n",
-       " 'our': 14220,\n",
-       " 'us': 14028,\n",
-       " 'states': 14005,\n",
-       " 'gop': 13806,\n",
-       " 'him': 13646,\n",
-       " 'don': 13642,\n",
-       " 'get': 13496,\n",
-       " 'elections': 13459,\n",
-       " 'an': 12789,\n",
-       " 'when': 12572,\n",
-       " 're': 12378,\n",
-       " 'one': 11949,\n",
-       " 'would': 11708,\n",
-       " 'there': 11638,\n",
-       " 'their': 11449,\n",
-       " 'why': 11143,\n",
-       " 'know': 11089,\n",
-       " 'debates': 10689,\n",
-       " 'over': 10628,\n",
-       " 'debate': 10607,\n",
-       " 'united': 10315,\n",
-       " 'democrats': 10243,\n",
-       " 'than': 10002,\n",
-       " 'me': 9983,\n",
-       " 'win': 9935,\n",
-       " 'because': 9842,\n",
-       " 'time': 9632,\n",
-       " 'right': 9580,\n",
-       " 'going': 9550,\n",
-       " 'go': 9439,\n",
-       " 'votes': 9381,\n",
-       " 'only': 9261,\n",
-       " 'u': 9135,\n",
-       " 'down': 9128,\n",
-       " 'electionday': 9098,\n",
-       " 'd': 9051,\n",
-       " 'usa': 8878,\n",
-       " 'did': 8807,\n",
-       " 'been': 8775,\n",
-       " 'potus': 8547,\n",
-       " 'via': 8502,\n",
-       " 'kamalaharris': 8466,\n",
-       " 'k': 8466,\n",
-       " 'w': 8452,\n",
-       " 'y': 8400,\n",
-       " 'think': 8391,\n",
-       " 'voting': 8384,\n",
-       " 'should': 8358,\n",
-       " 'even': 8340,\n",
-       " 'news': 8315,\n",
-       " 'country': 8310,\n",
-       " 'them': 8269,\n",
-       " 'after': 8141,\n",
-       " 'n': 8059,\n",
-       " 'years': 7943,\n",
-       " 'let': 7916,\n",
-       " 'want': 7847,\n",
-       " 'see': 7734,\n",
-       " 'make': 7731,\n",
-       " 'man': 7701,\n",
-       " 'said': 7602,\n",
-       " 'campaign': 7577,\n",
-       " 'coronavirus': 7504,\n",
-       " 'americans': 7445,\n",
-       " 'selector': 7409,\n",
-       " 'need': 7328,\n",
-       " 'here': 7328,\n",
-       " 'say': 7289,\n",
-       " 'china': 7259,\n",
-       " 'still': 7146,\n",
-       " 'new': 7048,\n",
-       " 'won': 7008,\n",
-       " 'day': 6872,\n",
-       " 'never': 6865,\n",
-       " 'good': 6857,\n",
-       " 'take': 6805,\n",
-       " 'had': 6762,\n",
-       " 'republican': 6643,\n",
-       " 'please': 6638,\n",
-       " 'cnn': 6636,\n",
-       " 'voteblue': 6626,\n",
-       " 'obama': 6545,\n",
-       " 'american': 6535,\n",
-       " 'voted': 6520,\n",
-       " 'pennsylvania': 6503,\n",
-       " 'tone': 6477,\n",
-       " 'blue': 6463,\n",
-       " 'skin': 6449,\n",
-       " 'says': 6440,\n",
-       " 'then': 6430,\n",
-       " 'doesn': 6424,\n",
-       " 'republicans': 6350,\n",
-       " 'donald': 6314,\n",
-       " 'again': 6312,\n",
-       " 'does': 6239,\n",
-       " 'harris': 6234,\n",
-       " 'many': 6222,\n",
-       " 'any': 6201,\n",
-       " 'o': 6192,\n",
-       " 'today': 6167,\n",
-       " 'landslide': 6159,\n",
-       " 'way': 6062,\n",
-       " 'these': 6054,\n",
-       " 'back': 6015,\n",
-       " 'politics': 6011,\n",
-       " 'florida': 5994,\n",
-       " 'being': 5939,\n",
-       " 'supporters': 5871,\n",
-       " 'c': 5844,\n",
-       " 'heart': 5817,\n",
-       " 'last': 5815,\n",
-       " 'wins': 5796,\n",
-       " 'white': 5795,\n",
-       " 'r': 5790,\n",
-       " 'b': 5766,\n",
-       " 'watch': 5739,\n",
-       " 'democrat': 5724,\n",
-       " 'de': 5702,\n",
-       " 'were': 5666,\n",
-       " 'could': 5638,\n",
-       " 'votehimout': 5536,\n",
-       " 'off': 5504,\n",
-       " 'voters': 5494,\n",
-       " 'some': 5490,\n",
-       " 'family': 5458,\n",
-       " 'foxnews': 5430,\n",
-       " 'stop': 5410,\n",
-       " 'every': 5407,\n",
-       " 'really': 5278,\n",
-       " 'most': 5246,\n",
-       " 'l': 5217,\n",
-       " 'great': 5211,\n",
-       " 'hunterbiden': 5188,\n",
-       " 'michigan': 5150,\n",
-       " 'f': 5147,\n",
-       " 'hunter': 5113,\n",
-       " 'blm': 5097,\n",
-       " 'too': 5093,\n",
-       " 'plan': 5085,\n",
-       " 'll': 5076,\n",
-       " 'media': 5074,\n",
-       " 'h': 5046,\n",
-       " 'where': 5002,\n",
-       " 'msnbc': 4986,\n",
-       " 'twitter': 4982,\n",
-       " 'p': 4952,\n",
-       " 'e': 4913,\n",
-       " 'other': 4903,\n",
-       " 'got': 4884,\n",
-       " 'before': 4841,\n",
-       " 'georgia': 4835,\n",
-       " 've': 4819,\n",
-       " 'well': 4810,\n",
-       " 'much': 4809,\n",
-       " 'light': 4778,\n",
-       " 'lies': 4777,\n",
-       " 'against': 4754,\n",
-       " 'love': 4732,\n",
-       " 'x': 4723,\n",
-       " 'ballot': 4689,\n",
-       " 'money': 4683,\n",
-       " 'state': 4661,\n",
-       " 'red': 4605,\n",
-       " 'didn': 4581,\n",
-       " 'am': 4579,\n",
-       " 'electionnight': 4567,\n",
-       " 'better': 4561,\n",
-       " 'dumptrump': 4516,\n",
-       " 'she': 4515,\n",
-       " 'j': 4504,\n",
-       " 'kag': 4471,\n",
-       " 'into': 4470,\n",
-       " 'black': 4462,\n",
-       " 'q': 4456,\n",
-       " 'those': 4451,\n",
-       " 'pandemic': 4417,\n",
-       " 'pence': 4416,\n",
-       " 'g': 4401,\n",
-       " 'v': 4398,\n",
-       " 'pa': 4376,\n",
-       " 'done': 4368,\n",
-       " 'care': 4350,\n",
-       " 'while': 4319,\n",
-       " 'first': 4305,\n",
-       " 'support': 4303,\n",
-       " 'la': 4287,\n",
-       " 'believe': 4263,\n",
-       " 'house': 4263,\n",
-       " 'look': 4256,\n",
-       " 'very': 4249,\n",
-       " 'tosaveamerica': 4234,\n",
-       " 'world': 4211,\n",
-       " 'ever': 4201,\n",
-       " 'rally': 4194,\n",
-       " 'nothing': 4167,\n",
-       " 'real': 4149,\n",
-       " 'texas': 4134,\n",
-       " 'must': 4123,\n",
-       " 'video': 4113,\n",
-       " 'keep': 4099,\n",
-       " 'medium': 4095,\n",
-       " 'en': 4078,\n",
-       " 'tears': 4040,\n",
-       " 'live': 4037,\n",
-       " 'joy': 3998,\n",
-       " 'arizona': 3992,\n",
-       " 'another': 3986,\n",
-       " 'help': 3971,\n",
-       " 'which': 3954,\n",
-       " 'left': 3925,\n",
-       " 'big': 3924,\n",
-       " 'come': 3911,\n",
-       " 'democracy': 3897,\n",
-       " 'show': 3871,\n",
-       " 'z': 3801,\n",
-       " 'trumpmeltdown': 3798,\n",
-       " 'corruption': 3792,\n",
-       " 'presidential': 3759,\n",
-       " 'polls': 3748,\n",
-       " 'also': 3729,\n",
-       " 'anyone': 3719,\n",
-       " 'rolling': 3713,\n",
-       " 'person': 3710,\n",
-       " 'already': 3695,\n",
-       " 'days': 3659,\n",
-       " 'ballots': 3658,\n",
-       " 'same': 3616,\n",
-       " 'her': 3609,\n",
-       " 'party': 3567,\n",
-       " 'trying': 3543,\n",
-       " 'tonight': 3535,\n",
-       " 'work': 3530,\n",
-       " 'under': 3529,\n",
-       " 'office': 3521,\n",
-       " 'made': 3519,\n",
-       " 'away': 3511,\n",
-       " 'doing': 3493,\n",
-       " 'own': 3468,\n",
-       " 'needs': 3466,\n",
-       " 'racist': 3465,\n",
-       " 'elecciones': 3461,\n",
-       " 'youtube': 3461,\n",
-       " 'end': 3448,\n",
-       " 'hands': 3443,\n",
-       " 'thing': 3438,\n",
-       " 'hope': 3435,\n",
-       " 'wants': 3434,\n",
-       " 'everyone': 3418,\n",
-       " 'wisconsin': 3406,\n",
-       " 'fraud': 3395,\n",
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-       " 'crying': 958,\n",
-       " 'event': 957,\n",
-       " 'especially': 956,\n",
-       " 'majority': 956,\n",
-       " 'false': 955,\n",
-       " 'dangerous': 953,\n",
-       " 'bank': 951,\n",
-       " 'class': 946,\n",
-       " 'ppl': 946,\n",
-       " 'sense': 945,\n",
-       " 'operationmaga': 941,\n",
-       " 'child': 940,\n",
-       " 'wake': 940,\n",
-       " 'miami': 937,\n",
-       " 'amazing': 936,\n",
-       " 'candidates': 935,\n",
-       " 'hasn': 932,\n",
-       " 'wh': 928,\n",
-       " 'along': 927,\n",
-       " 'fist': 925,\n",
-       " 'vice': 922,\n",
-       " 'self': 921,\n",
-       " 'sounds': 921,\n",
-       " 'across': 921,\n",
-       " 'killing': 920,\n",
-       " 'trumprally': 919,\n",
-       " 'fair': 919,\n",
-       " 'article': 918,\n",
-       " 'followers': 918,\n",
-       " 'doubt': 918,\n",
-       " 'idiot': 917,\n",
-       " 'join': 917,\n",
-       " 'usaelections': 916,\n",
-       " 'vetsforscience': 916,\n",
-       " 'su': 915,\n",
-       " 'asking': 914,\n",
-       " 'cent': 914,\n",
-       " 'ftrumps': 913,\n",
-       " 'bc': 911,\n",
-       " 'seeing': 910,\n",
-       " 'trending': 908,\n",
-       " 'music': 907,\n",
-       " 'nice': 907,\n",
-       " 'oct': 905,\n",
-       " 'yrs': 905,\n",
-       " 'according': 903,\n",
-       " 'boy': 903,\n",
-       " 'lo': 903,\n",
-       " 'walkaway': 901,\n",
-       " 'shitler': 900,\n",
-       " 'failure': 897,\n",
-       " 'decide': 896,\n",
-       " 'icecube': 892,\n",
-       " 'john': 892,\n",
-       " 'reporting': 890,\n",
-       " 'needed': 890,\n",
-       " 'nobody': 889,\n",
-       " 'patriots': 889,\n",
-       " 'knowing': 888,\n",
-       " 'clearly': 888,\n",
-       " 'link': 886,\n",
-       " 'safe': 886,\n",
-       " 'fall': 885,\n",
-       " 'clapping': 885,\n",
-       " 'americadecides': 884,\n",
-       " 'simply': 883,\n",
-       " 'senatemajldr': 883,\n",
-       " 'uselections': 880,\n",
-       " 'mike': 878,\n",
-       " 'photo': 877,\n",
-       " 'pathetic': 876,\n",
-       " 'key': 872,\n",
-       " 'continues': 872,\n",
-       " 'guys': 870,\n",
-       " ...}"
-      ]
-     },
-     "execution_count": 163,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Find frequency every word\n",
-    "text = ' '.join(df['tweet'].to_list())\n",
-    "def word_count(str_):\n",
-    "    counts = dict()\n",
-    "    words = str_.split()\n",
-    "\n",
-    "    for word in words:\n",
-    "        if word in counts:\n",
-    "            counts[word] += 1\n",
-    "        else:\n",
-    "            counts[word] = 1\n",
-    "\n",
-    "    return counts\n",
-    "\n",
-    "count = word_count(text)\n",
-    "count = {k: v for k, v in sorted(count.items(), key=lambda item: item[1], reverse=True)}\n",
-    "count"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 164,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-07T17:54:25.216313Z",
-     "start_time": "2020-11-07T17:54:25.178617Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[nltk_data] Downloading package stopwords to\n",
-      "[nltk_data]     /home/mahendra/nltk_data...\n",
-      "[nltk_data]   Package stopwords is already up-to-date!\n"
-     ]
-    }
-   ],
-   "source": [
-    "nltk.download('stopwords')\n",
-    "stopwords = nltk.corpus.stopwords.words('english')\n",
-    "stopwords.extend(['trump', 'biden', 'https','joebiden','bidenharris', 'vote','president', 't', 'trump', 't', \n",
-    "                  'trump', 'co', 'https', 'the', 'biden', 'to', 'a', 's', 'is', 'and', 'of', 'joebiden', 'in', \n",
-    "                  'for', 'election', 'i', 'you', 'it', 'vote', 'he', 'that', 'this', 'on', 'with', 'are', \n",
-    "                  'not', 'be', 'we', 'his', 'will', 'bidenharris', 'amp', 'if', 'donaldtrump', 'joe',\n",
-    "                  'kamalaharris', 'harris'])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 165,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-07T17:54:37.585588Z",
-     "start_time": "2020-11-07T17:54:25.944158Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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BEyA0GDicZ/qT+4hnLdyay82XFlk83z1uumkkmRp6nGR8+xhFTafEe/PPsFOxW9MMQCClhxAammYQBJ1nG0LoDOceoD8zTaA8lovvUqqve/52Rnb4MLW1m6Ag3T9JZeVqKy2XmmRq+DEgDDtQqFxlYe2tjvUox8ObL+DdFm/+3iGoVmm+fQHl3RZL6b5gT6Trl5ssf+1tCi9cJj7WR+70JKN/9QxCE5TOzVB56xaNm4W7Vj3oGOS1YVzVpKo2e8TGRYqUyFKTpZY6ICv6AUFNlUiIFHGRQqDhKpu6KhOweZAt4mS1gRa5+HiU5MptOl2BRlKkiYs0w/o+DGHRr4+RUjkAFJKiXL6tfgOLlMhgijgKia0aNFS1o85YxyQtcpjCIiCgLvcS/rUzwn5kMaPoggE+tmpgq3qrL+E1ZkiIFEPaBKawGNDHcVVIUpKAolxGsiGxa+gkRRpLJNDRkV2uUccgKbIkRZq8NoRAY0jb11LPeMqhpNrDvoaIkSApsujCQBLQkFXsHUfP3EDgScbPDLHw5iqNwiJ6TGf6kxOoQKEUDB8f4JH/6gTz55apzNWY/sQ4yYE4b/7OJdyGx/KFNcbODFG5VWXmhQX8po8fkeq+J0Y5/vmDzL6yhAKOf/4goLj+7BxWyuTsTx6ndLPK4vlVkgMJ9Fi3XYOisdJMBrIHyae3C5cL5fo8V+efRdMMNM3AD8LNlw09hu87KBSWkSSQLoH0yKUniZlpFgvnsYwUIwMnmV36NiCQKojWIUApSS41weGJT5GI9YFSZBIjvDPzJ9hep01RBLFknr7RYxhWElBYidwm0k1YOcb6w232pAzw/EZn0hUCY6AfY2AAhMBfWcEvrKHnc+jZDMI0CSpV9FwOf3mZoFJFxONYE2NoloW3soq/GpK1OTKM8jz0vjzCMHDnFpC1GsZAP+bYKEG1Fi3CRNA0zNERjHwO5fu4C4vIag09l8UcGUYYBn5hDW95BWHomMNDKARGNkNQr+POL4K/MxLf+3Y9UqG8gKBu49cdUAotESN9ZJTcw/tZ++ZlVp+/hPL2Pq2OixQnzSdZCWa54G/eymtQG+eQcYp3vVdZkmG0uv3GMSzirMklhvRxYqTQhY6vPJblLNf9t/FwWnUkRJr9+oPERYq4SNJQFV51v74pD4TkfMg4RVrkiYsUOjqHjJPINtJ4032eJq0oeqRFngP6CbJaP4awUChcZbMY3OBWcGkTQcdIctA4yaA2jhb1t6qKeMq5TW+8W6RFngPGSbKiH0NE0g4BTVnjanCeolyKxjrJEeMMSZElLpLRNZ5qte+qJm+4z7VecAYmB42H6NdGMUUMDQ1QuMphIbjOzeBSi6Azoo/D5hniIkmcJAp40Hy0dWUVuUrJWw+YFkrEA9o4U/oDpEQWTehIJWmqKjeDS6zIuV0tdjYKNvNvrDD5+Cjz55ZJ9scZOT7AK7/6NtKTHPrUJKWZCm/81rtIT1KZq/HY336I68/NUrpZZfmdAvWVJsWZKrOvLKKCsJ9W2uTI90yx+HaBa8/OIjRBajDBgU/sY/bbSwgNzKSJU3GZf30Fu+Qg/e37rZTE9Ru4Xj0iQg0hRCidCqNFjuFACcaGHsZxypTrc2SSo8SsDI5bpVJfIJ/dj2kkmFs+R9MukrDyADheFaUkIEglhpDKxzLT1JsruF6NZKyfZKwPITQQkIoPE7MyXUgXhKajZICmmwS+Q2lxj+EudI3Y1CR6PocwTVKnT1L68tdIHj9G7MA0SkmEphE0GsjKFNUXXyb90SfQYjFko0nioRNUv/Ei3uISqcceQU+n8BaXUEoRlCvIWg10HWtsDOuxcQq/+4fIRgM0jeSpEySPH8NdWoYgIKjVkfUG5sgwsalJlILUw2cof/0ZpG3T9/nvx11aJiiVSe0bp/7qOZoX3t3RZe6JdPWERfrYGLkz+0kdGMIt1il84zKVC3MEDYfMA2OMfP/DlM/P4i7vZMuwe4esNoApYswH1yjJFUxhMaU/wD79EGW5wpK82cpbUWuc917AEnEeMB7B6hJn2qXJJe8chjA4EJHjW+43aUZSl1IKt23xLUaCQ8YpUiLLbHCFklzBEBbj+kGmjKN42MwH11AoNHQmjSMM65MsBTdYDG6ggBF9klF9OiKz2yEMLeSo9WcwOlaB3DQL3KcfoU8bZsa/QEkutyTalJbFbttr0VYNLnqvYAqTg8Yp8toQb7rPt0hWKYnLxsKDQuGoJkvBTcqqgKdskiLDtHGcffoRinKFciS9VtQab3svEifJg+ZjSALOey+0CF2q9V3Ko/9Q9HPUeBhX2Vz2X6epaiREiinjQY4aZ3G8JmW13dZmmyE9yfxry5z58WMk8jGGHuzDrXuUZsJ7MzWUYPniGtILCbG62MBIGMSy2+v8dFMjM54mN5lh5MQAAGbSoHg9rNepeZz/vcs88H3TjD08xNxry1z7i1vUV7ov1LpejYs3/wzTSGLqcUwjgaHHSceHmBx+DF1s3q7O0CwWKtfRNINseiKSbH1iZhpN6CQTgyF5doEfNOnLHgAEldpsdM4hkB6GHot+25Ek3QkKu7bK4tUXUEqipMR3d7dg3EIQ4Ny8hVGvo6WSxKb2offlQdNw5+bwiyXihw7SfOsdkqdPYg4PkXjwKI03zhPUasSmp4gfPYy3GAoSQaVK9cVvoaRszbr95RWaFy9ijW/EndeSSZIPnaD+2hvYl98Lr0pKkBJvMdw8RMRixKb2YY4M49wId7hvnn8b+/oNMh9/Emti/P0l3eTBIcb/xmNUL8xx67depDGzirQ3FqDq11bwy40PZNHBwGA+uMat4FL4UCsIlE/eGianDbEi51oSmCIkEqXUpmnzVigUDg18ZeArr0U43aa6g/o4eW2Qq/555oL3Wv1oyApnrKcY0w+wEszj0iQuUgxrU9Rlmff8N1sLeU2/SkrkyGmDHdvIHBvDzCeRjo/QRLiYKQT2QonGjQIqkqjiIomvXIpymZoqolAhYW0RuBQShwaBMgnwUEriqAY2nR+gAJ8bweYdwKuqiBXEOWycJinSLdKVBCHBi/BYqiBSbdwuxQsEY/pBTBHjgvdyS+1QUQV83+OM+V2M6vsp+zsnXQj17V7DY/yRYcZODbLw1gqNtfAl4jV8zKQRvsAUmAkDJWVr8VOhQKpNs1EAJRVu3WP25UWuPTe78WA7AV7dQym4+dICS+8UGD7Wz0M/fIRkf5yXf7mzPnO9Lcer4nib9wvNJEaZGDyLrm0mXS9oEkgXpST15gq6ZlBvrmBZaUwzhe/baJpBJjVGMjFAzMpi6DESsT7SyWHqdgFDj9OwCwQynH2VajeYW32dwdxh/MBhduU1ms7t6xXtiCX7SPdPghDU1m5RXb1+x/9kK4y+PrJPfRJ/tUDQqIPQEIYOUiJtH+W4yGYT6bqAQMRj6KkUWiIOmsC+eg33xkZ8fL9QQLl3tngQho4wTPxyGdWmItCSCTKf/BjKdfELa6E5qBGqh4J6g6BcAd9H2TYicVsc/e7XufMh2UDj+grXfumruKU6qsOqvF9pMvvbL+Gu7V7/drfwcCnLlU0PtINNgI+5p01tdwtBVgwg0DCwGNA2Nu7V0VEoUiKLIQxcBZaIkxApVuXcJssJF5uqWiPHQMdWAtvDcINQ91V3ELqGChRmNhFaUERYlXMcNE5x1HiYRTlDSa7SVFXkHm2Rt15rjAQxkcDAQAiNmEgg0NC67ni+PTR0ctognnJJahkMtSFtJkQKSUBa5Hddr9f0WXq7wKFPTaJbOhf/5FpLuJ5/fZlDn55i9OQgzaLNoc9MUrpRpRqZ+ClfYZcccvszZMdS+E5Ao2DjNX3mX1ti6Fg/C2+u0FxzsDImUjooBWbKID+Zwam4lGerlGdrJPvvoUWIUiwW3gZAKp9C6Qq6HiMIXBp2gVpjCaUkQeBSrFynXLuJ59t4fpOZhW8SBA6GkcDzG9SbG+od129wdf5Zbiy9hELi+c1oNtIdydwYxYVQ0suNHNkT6er5HHomTeXZ50P1wsNnts0vm038Ygnn+g3cuXlEzELZbarBrmtKYpM+V7khmVsTEwRrJRAiVJckEljjY5S+/BWCap3kqZO0ppZq74q/PZGukgrp+iiv84OrJKiyIBEbBEvRbBZQ8l6bTHWWoj3l4rNVoa1CHeBWUeV9gIaGJWKYWBwwTnSU5oI2cjUwEWg46nabQU+5XXWX9SvL1FlGWBZm/wBCgJFLIT0QQkNFkvtCMIOnPMb1gxw2zuCqJiW5ylxwlapa27PO2MBiXD/AkLYPS8SjEZaYmGjsjXDDek0MTBIixQPGI7f1rv23EU8RywzSKMyGAbZVFDt7fTqtNo/drW8vcvRz06xeLrZUAADXn5sj3hfnzE88iEDQLNq8+aV3cSrhlNp3A649P8fZnzjGk//dGSrzNV791Xdwax7v/sfrnPwhg7M/eRyUIvAkF/74GvWVJvFcjJM/dIR4LoYMFF7D5+KfXOVeIgg2SCaQHoFse3G3HfuBzfpkTqmglWaSpFKfo+lsXqj2pYMvN69tbNsPzyY7FO7LGrh7c+Tx14oE1RrZpz6JbDYJikUIApTvoaRCBUEo5UbffmGN2rdfJXn2NMmHTyMbDWqvnEOurKBcd5PUCqGKIPXwaWIHpzEGB8h99lM0376IffUatZe+TeqJR4kd2I+yHepvvIW/vIx7a5bMR58kqNUIanWUG/omKNsBGd5fyvNR3s5tiPdEuqnDwwx+4kFu/cYL+LXbySKZGmRo7AyeW0VKH3ehii/vxqPqdrLU0Xdn0nTfDNpD8rFpctl7jZrqpNOWNCN96jo56h10t+H13fkaleejJZOAiVcpg6ZBENYb4LMkb1KUS6S1PCPaFP3aKFltgHe9V3alG23HmH6AaeMEa8Ei14K3acoaCsmQPsER4+E91Qnh6CkkZbXKe/6buOp2XaIkIJ4fIZ4fxYwlcesl4rkhpO/i23XMVB7dtLBLyzjVDdOj0kyFL//0N/DtAL/NO9Cte7z9B1e4+vRNhC5wax5Ota1dBSsX13juX72KEdeRnsRrhA90s+jw+m++SzxrIXSB9CR22QEFtcUG3/p3b2HGdYx0Dt8VVG/ebqXxQcJ2742VTHHxIsncKCBoVpbvmL8TgnKF0pe/Ei6MuR5IiXJd3MWlaF8UiTs3FxLuXzyHbDRonH8b59p1hGmiPJ+gXgelqH7zJVSwWdBTrkvzwkXsK1cjVZIKF9KUwrl5C69QQLNiKBkg6w2U51F+5nm0ZCJUI/g+yg9Qvk/xy19FNsOZUOPtC7vyVdjbQlrMDPfjkJ2lME0zqFcXKa5eCvWlwd48SRQKqSTmlsUDDZ2ESH9ABvbRCrvQOhK5RNJQVfqjDWLrqrRtba5y8XBIaTkIRKt+DT0yedv+GpXr4hVW8LY1a1S42KzJRYpyiRFtP0fNswzrkx11oyq8wK7XqKHRr40gVcDN4F0qaq0tzehow7wJQiAQHaVsH4+mqpESOVzlUFe3k4JuJegbnsZtlBGJDOnRgyQHJsKHp7KK9D1iuSGE0HFqa5EEDDJQXb0CAyegttR9AUhJRXOtsweTlugn0OLYy/Mkxw+g2SskpyYwUhmclUXqxVX69j1IMpMj4AKNm++RnDyElR/ALa7QmJshffAYQgiUlFSvvtPq83cChGagZEC9OIemW6TyY5SX39t9RUoh6w1kfcv/0CaxrkuU0o9Ul4EiqGzWfwMhmXaov1PeVtu1OnLLOo2ybYIOnmuytmGppBxnVzLdnkjXXiyj/IDYcJbGjcJtN0gQuGT7pkik+gl8l9XSJaQBfrHYEsl3gkB52NRIiz5yYpBaRGB92ggD2tjuhNcNPtszwoU3J9Q7ioHWYpBA26QyWAnmGNammNAP46gmdVWOLBUMYiJOoAIa0TZmjqpTlqvkxCCD2hhFGUoJOW2Qfm3krvqroZMWOTxcXOUgCdAxWkTeifQkEk856NE1usq+7RrXVQma0LBEHKFCEk2LPEPaPvQut1Wg/FBXK9KkRV/r/xSIlgmdRLIY3OBB81Em9aPcDC7hqg0ztZhI4eJH5kM6Skmk72IXl7ArK5jJLIHTQDfjLQO093uaozyX5IFjSMcmPjyO9FzMTA6KVpoAACAASURBVJ7azBXSBx/ErRRxiisEThN78RZGNk9q+ij24iypycO4xVXiw+NUr17AK6/tiXB1YWJoMZygjkBDiNAqxNQS+NIGBIZm4cl76/qqG3EG959tqQ81w8Iw43sj3fcJQujEzAyWkcTQLQQaEkkQODheHder3rVp5m6wN51uIDHzKab/m0/RvLWGdP3WjVL4xmXql5e5dTXcGsswE4hcgsT0JLVzr+/K39nFZiG4ziHjFCfMj1CLpJ64SFJTZbL07aX7QGiwP6xPkSCNJWKktTw6BoeN07jKxsVmObi1KQ6DRLIWLDGmHeCg8RCDagKFJFB+OBWOTKqqao1r/nkOG6c4YT5JQ1WQyIg0kiwE15kJLoS2uzjc9C/xoPkoDxqPUlUlJJKYiNNQ1a4EthMYmBwxHkYXJo5qtEg3LXI0VY2l4OZtZUKX6AVGtCkOGacZVlPhYopyI+uKMObESnCLPm2Iw8ZpRuR+hIC4SNOUtdvsnNfh4bIq5zlonOSY+TgNFUoddVXmmn++lW9FzpIM0kzoh8lrg9iqEV2PRUKkuOB/m9ryDGYiS61axG2USPaPoaRPY3UWFfh4jUo4w1Lvv3ek36jhN6rkjj9CbeZyy3RKunYr+JF0mkgrhvRc9GQa3bQQQtCYn0H6HtJ18EoFAntv5lYJM0c+PsZy/T3S1hCGZrHauE4uPkKpOY8QOtnYCGvNm611AkOPMTX0OHErt23ddXuV2dVzBLKD2ZiAwHeor4VWA7oZI5kbvz3fHqBrJiN9J8ilJlqCghc0mV09d5sOulv5bHKCkb5jZJPjJKwchpFAExpSBXh+k6ZTpFS/xVLxAtXmUmS//P5ibwFvdA1nsYS7Vov2/dx4SxhGAk03MYxwlTbXf5CKv4Rf3r3uSKGYD67jKZdBbQJLxHCUzfXgbRxlM6Ef2mQfW5MlpAgI1GYFeqBCb6qaXN/EN5QCMyJPUmQRCOoy1L2uO0q42BTkIqjNuuiatso1/VXy/gRDuSxN26foVLESCj/iGisOBecWiXyDjDuO1siRiBs0nCZrah6nf55YTWFHVRfVEu9432JUnw5X6FVojuUqmwn9cMsrDGBiQiOX1/A98HzF0qJkckrHNGFuNqBWU0zt10mnNexGQGHhMqemJsjGUyzOmyyvOswFV1mWs9Q76puhKJd513+FIW0f8chiwFXNTYt6K3KOwAsY1iaxRAxX2cwGlynKlchErBN5KBaCa/i4DGjjmFj4eC1Jdh0+HjP+RUpylUFtgkSkZqmrMvPBVcpyFb/s4pQ39KPVhc0LVF5ze/twLW5hjvThF6sElQYibmGN9OGXagTl3VndqMDHXl3EzPbhFBZRgU+sb4jsA6dxCkv49SpCN4mPTJLaf4T6jSvUZi5jZvvCxSHPxSsX72qxOVz48tCEQSY2TCBddGFgaPGWva2lJ9CEQRDpyTXNZLjvGNnk2LZ1FyrXWFg735F0A89hbfatDRWi0HDqdybEO0HXTMYHHubQ2CcxjTDOiePVuL74DVzvzv9PzMwwNfw4Y/2nsMxUJP1vqOp0oaFbJnErSy41wXD+GDeXv83C2lvhouP7iL2pF+aK3PqNFzqmpbLjpDKj9A0dxbXLpHP7qJVLkEqFAWF22ZYkYEne3OTUsI6Kv1mReT14p2MdLk3e9l7cdM7D4bK/7Wazt8Ew4ehJk8HhMm+8vEjskMXsDY/4oMaZIyYLsxZCQCqtcfWiy9GPNZm5/C61asCBoxb2YkBch4eOW5x/zeT65fAmXred7aRfLW+5xu/+dIyhIR0hoFaTfPUrDpOTOhMTOidOKl78psvnv5CgWpUkkoLf/LVZaqPL5CY0Ro/D079x5wVNhWRVzrMqN29wbBpJhvMnqDdXqdTnWJGzrHSIfnbR/3br2DLTeH4TFZkcaWacilFnodn5/llHgE9BLlCQC5hGinRymFL1ZqueO0KAMI3Q/CfysRemEZr6eGHkquSZQ7izK9RfuxL+Pn0Qd6FA/ZXLHavMZTS+57sTJOLhw/vG2w7vvOuhdAthGBReex4VhG2VL56jZZqkFF65QOHV50LJW2gEThP3VgFnZQGA+uw1rPwg9urCrlRw6/ACm4ZXxJcOq/VrgCBQAU2vhEKhlKTplzdrW5TCDxz8wAFEi5RCgtpMUt2hWqoeAN2wSGRH8FZqdyjXHbpmsW/wLAfGPoFlJFEqtF2+Ov8M84W37uiNmLDyHN33WYZyR9A0I9z6HEkQBEjpIVWArpmhC7XQ0TSDVHyAIxOfImZmmFl64X0l3r3PXbugUV3CtFKsLryF3SziNMv4qopa9VD+d06krU4wTMHBoxaxuCCe0EhlNDJZjeExg33TJrWKJJ7UKCz7uI6iUVPYtmRsn8nkARO7oUhlNZoNxV53YbZtuHjBI5PV0DSYnNSZPqATiwn6+gWBVJgW1OuKl150yeU0DhzQMS1BJrO7hUdNM0jE+hAIGk4RP7CRKiBuZanU59CEQSIeuos27SJS+SRifei6heNWUEoyMXSWcm2WamOBIPCImWlkNBNplUej6ZbQhE7MyiAQ2G6FIHBIxAdIxPIkE4PUGkvEzHTYnlNCCB2lAgIZ1rvh3grW5DCpUwdBQO1bFxGWQfKhg+iZBJXnz+MtFfEWNuKDyFoTd2H7eCGPno3x8z/TRz4bLhT+q18qc21ZQ+TGsPID+LUKZjYPSuGWClj5AYRp4a6toDyX2Mg40raxl2ZRSmJm8ziri8QGR4kNjiKEhlNY2pWL8zoC5VFxQk8sX24sbJadxdaxG2yefbh+g3dm/hjLTGLoCUw9gWkkSCeGGe07iWnc2aZYN2IMTp3deKnqFoaV2BR7YTfQNYuJiHBNPYFSiqZb4ur8MywVL95xbAw9xsGxTzKUP4omdJRSNJw1VsqXKJSv0nTLKCS6MMkkRxjOH2MgexBds9A1i6nhx3C8CrOrr71vqoa9eaQdGCI2nKX06vVWRDGha2RPT+GV6jRnivi+jQxcatV5yCSwhodDP2j3OyuOajtcW/HqC03SGY1qOWB5wcdz4d23HIqFgOV5H9MS9A3qIODapVDfcOlth0o5YHXJJxbXGBk3KK7u7QXkeQrHBctRmIYgm9MYGNBYWZHUGxCLCUxToOuCoSGNREIxMKixvCwJgt0QvSCf2U8qMRSSYWOJQvkqvm+jRxGrcul9ZFLh1LRhreF6Nfqy0zSdtSjoSkAy3o/jVWnYBaQMyKbGQEC9uUI2PUE2NY4fOKSCIRCQTo7gOGUyKqDeXKU/dwDXa2DoMTShk4gPEI/lSPtNGnax9QIYyB9hfuVc+KBoguSJ/TQvz2JfDiXx2NQwQa1JbGoYYyCLt7T7KfDHHo8xOmwQj4Uvr0TKJDm+j2bdRWg6iZGJcOSsGHo8idU3gFcpER+ZwC0sEesfwczkQ9L1XIjF0eNJEmOTBM0GWvwuo69F+uP2EKObJNtNx6EE3vRKNN3NFja51D4Gs4d3RLoIDRl41IrrOt04ydz26opu0DWLyaHHODD6MUwj0SLMK3NfZ7l08Y7lBRpj/acY6TveItxyfY7Lc1+jVLt9plyzl1kpXWZq5AmmRz6GoVsYeozJ4ccp1W5RbS52aOXusSfSjY3kSB8dofT6TMvgGk2QPjqCu1pDLgaksxPYzSKZ/CTF+kxoVrGHadOHCVLC4uyGvvjKOxsvkHJx43hlMRyU+Zud8spNdewWf/G0g5ShKS4iNMc995qLDKBpK06fNpm9GVApS6YPGPzpn9j8xq81kBJsZ+ekqwmNVGKQRKwP1wuniu3TTSE0kolBEvE+HLeKQBC3cjSdEstrGw9IwymyVrnesgetNVdIxvsRaMSsDNXGIo5bYTB/FNerU63NU7dX6c8eIm5lqTdXqTdXGOo7RszKkoz3oUVSSaH0HsnEAOnkKLXGEjJyY0UqgkoTaziP8nz8YpXEg5OoQLZUDXomgTmUQ3kBWiqOEAJzKA9SoqXi4U4FbUgmBKdPxtgagnVdGlIyaM1eRJiAV6vgVcuYmTyx4Qmk64SLa7qBkcpiJDMIw2x5EN6NTtfMxYkPZ/CrDplDAzSXqtRvFklO5PGbLpquYaRjNJeqmCkLqy9J/WaR2GCKoOnRXKzsycgj8GwKs2+2dLpCaNjV3dsiG1qMfUOPcGD0Yxh6HKUUdXuFK3NfZ7WyM0uIeCzHxODD6Fr4J9luhffmn6FUu9W1jC8dZlfOkY4PM9J3HCEESSvPaP8JanPLe5p13Am7Il1haJh9Kaz+FEYmTnws33ID1tNxElODuItVvCjgxfD4GRZnXyVIgm4Y3U1hBGimTnwsT/rgIPGRLEY6hrAMlB8QND3ctTr2YoX6jQJ+pRkGJ9nlFF3oGpoVtpOc6iM+msNIx9BMHeUF+A0XZ6VG42aBxq0ige3tKDylZumhcXQkXCjPb0Wi2l0HRViXJsK6lIosQ6LkmEnNXmdc0BIxgkYT3xhAqQCvssbl+RixLJijw3zrfJlixUXELKTvgy/R+xKhgbfthuTSsMP6ZOjNZe0fxV8pEZRqVGpzoamYdGk6ReJWjnx2P5rQqTVXqdYX0DWTQLo07FUQgqG+B5gYfpRS9Sb15jKe12C47xgrxUsE0qU/dxDLTFFrLNG0i/TnDiDlKA1n3VQq1BFK5dN0ywzljxK3cuF03EhiGkk8v0EgPVy/gec3SMUHubW4WQ9bP3eZxNFJzOE8fqlG48INzNF+/Dev4c6tosUt/EIVpUCLmaBrBGuhNYUWM28j3QP7DQ5Nb47ypXwfe+EWKj1Ec/FWKM0OjhHYTdy1ZbRYHOk6BM0GwjAwM3nc4ipoGoFrh0b4ro29PIeRTOMWV/dMvNkjQ3hVJ3yWLIPUVB/Sl+SOjSDdABVIpBcgdI3EaAYrlwAFuWMjOCs17JVaK17H7hDa4af795MbPoxmmNTWblGc77y+AgopN9vth1YUT7B/5ElMIyTcanORK7NPU6he23FP+tL7ySRGW//RauUKa9Xr3Olt4vo1looXGMgewjTiaJpBX3qamJXFdre3s98LdkW6RjrOyOdOkT87jZlPkpgc2OTn765UMQtZ9h38JJrQ0XWLwZETrHo3uirltbhJ/qEJRr/3OJmjIxiZOFrMRDM00EToche5HQeOj1+1qb23zNrLMxTP3cAt3tnERk+YJPb10Xd2ir6zUyT39aEnLLT4lnYCSeD4BA2Xxs01Fr92geIrM3iV7kp1Yers/y+eZODJg6FxeyC58aWXWXn+yq7jCWePj3Hwb30cMxsGz6hfX+G9f/cs7lp4jeZQHi2dRAgQlomwDLyFAuboAEGxipgYpLFvghfnG+jVBMrIYAwtIuJWa9uTxIkDeIuRzi+QBA07jNvgePjFCta+IYJyKNlW6vPYThlN6Lh+A4FgcfnN0F7Zq2OrEq5bRQgd37dBSRZW3kQIHS/SHy4W3sLQ43h+A6UkCytvIITA85sE0sdxKyAEnt9oOVVIFeAW3iGQbpgOSOkRSJ96cxmFIghcLDNFzExTrS/cFhtANl3qb27oFYNiDXd2Y6EyALyVzRY11ZXuFjaHD5jsG9/6uCi8SglndUNV0ZjdIImgWY/aCsfTLWwEtG/ObsQmcJbnuxjZ7RxOoUFyPIdXdWjMl0GBbhkoX+JVmvhNH7fUQDNDAcFvuHiVJs5qneZy9a43Hkj378NzqghXx4ylu+ZTKPw20jX0OJNDj7J/5EkMPRaqBBpzXJl9uqNKoDsEg9nDm+ICr5avsFPxvWYv4/q1lkolFR8gbmY+eNL1Sg1mv/QSzdki6aOjrD57ERnFy1Wuj7taC/dNA9ZXboXQMCdGiZnmbbEPjEycfT90lvHvP4WV39j+ZtMikybQNA3N1DFSMWL9KVL7Bxj6xBHe+af/kcK37vwmnPjCGcY/f5rYcGazpLKlHaHpmJaBmYkTH8mSPTHO0tcuMPPrL+GVOq/6Ky+gcWOV8b92CjMT/mFD33WU0rlbeJWduz4LXdD/+DTZY2Oh5B1ICi9dDaWXCO7iGtnvmsKZWUC3jHAmQLgIJAwdNIGeiuOvVZFNB3+lDIaGno4jYiZBqY5s2Mi6jZ5Pgy4RTYGeSaKNxvELZVTTCVf5CW9c16uT1vLEyIW2kh4YIkFapJGEElSAj1IaKZHH9W0aqtpydthYHQ+x1e10azStdfhBOHbrpNupfMoawnErlGvv7/5xibjgzEMx0qnOnnZaIoExOIBsNJGNRuh+uv7wuy7CstDTaYJaLdRRKYWeTocSb72O0DSUlAghkJ7XcuHeDWrXC9Suh5Yu7Xdd5fJml1w9YWLl4oDAWWtQv3Vl1211gu828Jw66b7JO3qgBlFMB0OLsX/4I+wf+UhLJVCq3eTy3NOU67v7T8MFwKHWb8erhbOnHcLxapssFgwjTszKsIeY+XfErnW6ypfU3p3HLdSoX1nq+IZMpAaxYhmseI54aoCivhSSmq5t6PF1jbHvO8nkDz6CHjejqXSAs1LFKzXw6w4qUOgJEyMVw0jHsPqSaHETIQT1G2vUb+5sS4/A9jDziRbhBraHW2zgVZoEDZfA9tAsAyufIDYcqTaEwEhajH7vCZyVKrN/eA7ZZZ+z4huzNG6tkTseGoXnTk6Q3N9P+fzcjsfV6k/Td3oyjJMLuMUGa6/e2BwE3g+ovXwBZbuga2hxKwwiv1JCGDrSdvGLNZTrh7vfRdNFf6UMughfjPOrocmUpiEsA9l0EaaBMHWCaoPG+Wu3/acCgUkcHy8k2lb4doUVOXFIJAKBpSVoBF1cLe8x6s0V6s33P5ZBOi14/GznWMsAwrIQVgwjkUTfvz9UkyiFt7qKOz8PQmAODxM/eADphAFbtHQK5fl4i4tY42NI10NogublK51dWO8RgqZH6cISQkBg37vtaooLF9F0E5TE3sZOVymFH7jomsnE4FmmRp6ICFexVp3h8tzTVBu7X8BKWH3o+sZ/FEiPdGK4ZeN7J2hCR2sLNyAQWEYSgXbP9bp7s9OdL2HPl8IVg60xc6VC12Mk08OAwHfqBNVV/NIK0mnbtWEiz9jnHkJPhITrlZvM/dEbrH7zPZpzpVCXud7JbJz4aJb09CCZ42PkH9rH2svXcJZ39nAXXr7OyGePY/UlKb1+i9L5OerXVmgulPGrdmuVNzaQJnd6H5M/9Ajpw0MITUOPm4x89gSrL12jMdOZ5L1Sg9Vvvkf22Fi4IJOJM/jxw7si3dT0AOmjw6GKQinq11epXFy4LZ+sRXKMHxBEu6MqP2jtlCqrtz+w7XtBredTAM3w/2iPObpVl6mQVGQBdrBn1ZpyMTBv27bog4TQQjWXlP6eY4CMjxgcf6B7WFBZr+NLiZZKIqOAKwDe2lpLsg2qFWSjjvIDjHwe5bg4c3Moz8OdD/9na3wM9L1HaNsMEd1LtxOGdO79/5PIDJMZPIAAAt/FqXe7XxRKBYz1PxRaKejh7ND1G8wsvkC1cfs9vxPEzBS62KCzVHyA0wd/eE91rUPTzPfFi3wHpLsenKTtzxOCxL4+0g+OY+aTm7QGxVev483XMIw4xcJ7xOJ5pAzCIOdtElT2xDhWf/QWkorFr7zD7O+/Fi5ebYFfsalVbGqXl1n5xhWs/gwEAuVvGGUrGSD0MPg0SiF0vWWo7qxUee9/fwbp+jQXygQN9/aBVOCs1lh+5hJuocbRf/A9JCdDN+PYQIrc8bGupKt8SemNW9iLFRJjOYShkT+1j/hoFnvxzjtnCF2j/4kD6DGzNR6rL169Jw+HEJBJC/ryOpmUIB4XGEYk8Qdg24pqXVJYC6jV1V3FWZEEuNsEg78TEnHBQL9GJq2RTLT3U9G0FdWaYq0YUG/srJ9CaGT7D6AbFo3qMnZ99xHVdB0+8miMTLp7EB/l+wTVKkG1sxCgHAd3duMF7C0vg6ahIiEkKJVA0/CLxU2Cyd2gL3+AXHY/cwuv4Hl7d1TYKVJ9E1G8BUVmYDrcpLIDBIK+zHS0aLUhhRpajJH+k1QaC3jB7iMS6pqF2GMM5254vwJqbUu6Ao28PowmdKrBWstdMzaaY/rvfArp+jgrm01NtJhJw17h1rXnAKiV5zDHRkkePELttXOt2AuxgTRaLGw+aHrUrix3JNytCJoeXsEhMTCObtWJ942iGSbN4iLx/AhOpYBuWMTyQzRWZ/FqJZQvqVzY4RtUKioXFll57hJTf/MJhBBoMYPUwaFti9VnCpTPzxEfziB0jeRkP7mH9mEvXbyjlYU1kKLvzMZGhPZShdIb3c1cdoJYTHDsqMmnP5Hg9EmLQ9MmoyM6+axGLCYQAlxXUapIFpcCrlzzWFgKkLtY/Lt42eO3/qBGEESeV+tSwS6MynUdJicMvvvjcR59OMaDRyzGR3UG+nTicYEmwHEUxbJkftHnves+r77u8PXnm1y74XGnMKbre4HdybtKi9ZTTVMwNqpzaNrk0AGDg/tNPv3JDS+0dnzPU0n68vou7Z9D075f/c0qc+23pJTI5t7CnxpGnL78QXQ9huOUKZauUastkkqNokWxhfO5AyQSA9h2kWptjnzuAIYRx/dtCmuX9uwIoOkmuZGjxNNDoWSIQm5jgaFpJuP9pyKCXHcSEmiawVjfCRyvysziC53jPGwDsX7/RQikH+lo9y5FBLKDcHYPsC3pamgYwsRV9qbV4fhYHq/S5NavfxOvWN/UMdnB60w2wtiU7Tpd6Qet1X3N0sNFLl3syNRK6CZ6LIluJTDiKUCgGTHMVBi4w4insdJ9OOVV9jKhlK5P9coy0vHR4yZC11oWBV3LOD6Fl67S/8QBrFwCLW4w8MQBCt+6FqowtkH+zCSxoXDFV0nF2qs3cFb2phe1TDh1IsZ/+WNpPv3JBKPDekSyt5NGIiFIJDTGRgwePtVdZ9kNf/61Br/zH+oESpCZPIoeT9JYvIlbvrNEqWlw5KDJj3whxee/L8X+fQbplEDrsMWTYQhSKY194waPPaz4wueS/N2fyvJnX23wG79X5dJ7XseNWJWSuM0Smm52JZXRYZ2zp2M8cMjk2AMmRw6aDA/qpFOCdFojEe/cJ4CPPh7no4/vfieIckXyp19pMLdwbzw0DT1GNjPJ/MIrjAyfolZf6hD5z8FxygwOPEDTXiOf28/C4jkGB49Rrc3jOHuLq6uUwnfqlBbfZZ31PLv77C68D3Vcv8Zy6V1st8rU0GPErAy6bjE59CiuV2V29dyuXgRSBZuuudKY58rc07je3vXjnt+4/3a6kgBXNrFEnGZbbFXlB8imS9Bw7jwFFgIjl7tNN9K8tYbfcLEsA80yGPsrJ6nPFFh7dQbuRLxK4dsNpO/iVApohoVv17CLS+G58gpevYRX37u5h1+1CRpuSLpCoMX0O74USm/OUp9ZxTo9iRCC/P/P3ZtHSXaeZZ6/766xR0bknlmZlbWodkklS7Iky5JsS/IibAOmwUCzNjs0zDDQnJ450PQZuqGHGY9pelgH0xhMA16gLS9Y2JZs2Vqs0i7VvuVSuca+3v1+88eNiqyoXKukMqfnOUdHVVERcSNu3Pt+7/e+z/s8R3eQmMhtmmWrCYP87TtRkxFdxm/YlI9dJLCufbkwDfjhD6f5lZ/PMjWpfzuMMroI7HbUhd9G3TQWE3zn+xL8Tz+d5bZbzGv6nEIIkgnBTbsVfumnM7zvoTgf+cMaf/v3Ta4edhSKipnII4BYsh/7qjqjosC/+GCS3/33+Q0D6/8oCKWP7VSRMkRRNMLQ72R/Al1LkM/txbLL6HqUpDhuE8sqE/guinL9agAy9GmUprf/fClpWMucX3iCUv0y80iye+R+FEVDV+PsGrkP261TrJ3dtuRiZCm0GosECrZT29DB+J8TW5YXYkoSXZhood6VLnSWamiZOGPf81ZqL89EZYHOKuMs1fCqvatL0GrBSqFbYwWovb5A60IB4y07AYhP5Nj3Pz9I6ZkLrDxxmtZ0KXKlWOece+0aXjtamX17ldNhFVdpJl7rjSniy0D2cJBXfZU2vgj8pkPxG2fpOzKOUBW0VIyBe/dEDbENXhYfzZI9MtZtoLXnytReW1j/yZsgkRD8zI9m+OWfyzLYr6w1UZTg+5JmO8R1o69iGoJkUqAq4poCn5QS14VWuzOgIgW+Ey2Cobf5tjCXVfipH0nziz+dZSC/9nNeCxRFsH+vwX/43/JMjmv8wZ/VqdRWMxMZBrhWjXhqELu1vpuBqm6cyf6PBF1LMD52F47bwPNaDPQfJB7Pk8/fRLlyllBIdCNF2yoihcTzWqAIXL+1Osn3bYAkpNqco1BbHWa5VHgBU093psl0TD3N3vF34QX2trm6llslCFwu2yDGjGykrnZ9vdMbii2XuED6xJRkz4qj9yXQUibZWyfIHp3sef783z5L+ZkrxvaEQMvlMcZG8VYKSCe6Kb26xezfPU98PNflz8aGMox98FaG3nWA6itzlI9NUzk2g11oXPOgwaZQIoZBbDSLkYu+ixrTUXQNoUec4NhYH2pideZzO8FBBiHVly9hLdaIj/dFDbW3TGIOpNcvFyiCzOExzOFM9PdQUn5uGr9hIzQdLduHX62gZfsI2k3CTbSIP/CeBL/2S1ly2d5mgpSSZkvyxDctnn3eYXrOo9GQKApk0go37dZ5+90x7rnDJBZbW4aQMmpgXVrwWVwOmF+M/r+w5HP8pIfng6JpJIYn0FN9NGZOEzjr1ybTKcEv/lSGX/qZ7BrO62XOdBhCpRbVmRvNECEgm1EYHVZJp5TOMF7vIjE0oPKrvxCZVf7uf6n2ZLyOXSUIHBR1fTt1KeWWNVmhRBu1q89NGF5f4zF4M69lovW8bZe4NP9M97Fi9TRNs0UYuGhDQ5RZAhW8Whl9oJ8GdWKjk5TKFwicG99ou/LDXq0U5wUWF5a+gaElGM4dRghBKjbE3rF3cWLm0W3xbW03ymoTscjI1dRTJGIDNO0bQCm8nHttpGuxwawBUQAAIABJREFUBbas6QYENIMawRVd6fZ0kXMf+dK6r/GbNvHkILqRpF6Zpn/wAI6iRPJqV5K+JVRfnuP0R77MxIfvIHtoDCWmrVKu7t1L7i07cb6nSfWlWQpfP0PzQnHL+uhGELpKYryP7C07yN2xk8R4FFQVQ+tO6aBEQUcoovvna4U1X6H68hyx0SyKqpDYkSd7yzgrj59a86OoptadZANwq23Kz00DoPXlSB04hFtcQevL0zp1fMOgOzWh8a9/MttVv7qMUEpeeMXl//wvVZ561qZSC9eoBmoqfOyvVR55KM4v/1wfN+3uHXV1XfjUZ5v83h/XqDckbSvEsmVPDVUGPm6jilA0fGv9G9g04Ae+J8Uv/GSWZKL3cwaBZGkl4MlnbL7wWJtT51waTYnvRydM1wWZtMLttxh88H1J7r7DJNfXu7jEYoKf/tEMc/M+f/3pJr4f2cik+nagmykC31nDXghD+IcvtHjtxObZ+c/9qwyPPJRAu+pu+cSnmnz60RZeEC0CW051KdG15bkhF1dURGf8/I3CdRssL7/S85hQNfRUH16tjBZL4NvRRKGiGaixJKFro6eyeLXyG+CbvHlwvSbnF5/E0FPkUpMIodCX2sGesXdydv4rW3q5SRlSql8gl5qK7mEhGMkdolg721N2uB7sPJggllSYO2ORGzLYfUuS40/X2XUkwczJNrkhg6EJk7kzbeZOW1suxJsGXVXoJJQMofSJiWSXvSB0FT23PulYE3H6M4eIxfOk+yYw432stM8QttvRFvqK50o/pPLCDM3zKwy9Yz9DDx4ks3+4G/C0hIE2mSc5mWf4oYOUvnWRxS+8Ru31+WuaE9ezcUYfuZnR9x0hNpqNgipXTaStAynlFWOF2ztW6AYUnzrHwL17oyw6YdB/1+5uBnslknsGSe0e6B6r+tIc1mJUhw7bLdxigaDVwi2s4NfXv+g0Fb7rOxLcfMhYM2137oLPr/5GiededDb8/H4AyysBf/l3TQrFkN//T/2MDqvd9zJNwX33xPjjv2hw9sL6wUmLJdBTfYS+i6Kv35C7+44Yv/xzWbJXLQxBKHn6mM1vfzRaGNxNtoOvHnf55GdbfPi7k/z6r+R6PidEGe+v/EKWF191eO2EF9UbyzNAh3O5DubmA+bmNw87H3hPct3zd3HG48lnHZQdwygJk/aZBaQfoKbiCAF+KyqPic6YuTnRj5aJY11YIXV4B8kBC2umQHCdicRlSBlG5YIrENhtaqc6etFXtRTs4iJC1TByg4Tem0NRezNwWeDm0OT7ScUj1tRw7hCu1+TcwuM9TsfroVg7y8TgnZh6tHPOZ3Yz1LefpcoJ3ggNwYgrxFMqibTK7puTjO2Ocfr5Bq1aQLXgMbY7Tm7YYPZ0e1tH2TToOtKiGZQxlDiOXP1R4xN5Jn743u7fhSLQUjEQgrm/eJrGxTkcq0qrsUQQeoQZE6Xm99R0r4RXtZh/9BWKT58ne2SMwQf2k75pCCOfRNGjjEZLmgy9Yz99t+xg8R9fZ/4fXsKrbU2xMfqT7P7p+xi89ybUeHTjhX6IV4kEdNqXKthLNby6jd+0CWyfwPZITvUz9SP3dEd7ryXprZ9aonF2mf63RpbUfbdNkNjRR/3k6qSNUAT5O6bQs9HiFbRdysem8Vsd7qZtYc/NdCT/4t1R0asx0K/y3nclunKDl9FsSf7043VeeGXjgHslggD+6WttPv1ojJ/9sQzGFbvxPVM673swzplzHo679s1kxyYbQNHWBrfBAYVf+MkMk+O9l5sfSB77qsWv/3aZk2e2V3xrtSWf+GSTSlXyu/8+z46x3sC7d5fOj/9Amn/7W2U8T8WM96HqJoqi0ahcyyz/NqEqxHb0I1QF68ISsckBkgfGaR6fRe9PgwJqzMCvtomN96PEdOy5EmoyhpqOY473U378tTcl470WyMDHKV7fIMKNRK01z7mFxzkw+QgxPXJ1Ges/iuM1mC0cWyOWcyVadon54otMjdyLKnR0Nc7e8QcBQbF+tmccfT2oio6hJUnE8rTsUje7nj7eIpnVaDcCXv56ldefFjTKPrWih+9JTh1rMHOqTbvubyu2b1HTlTTDKkbHnLD75c6vcO4j/whEdS6hKSR2DZG7azfWQhGn3Ii80TrFMOmraAMDuAuLPdNPPQglzkqDlcdPU3jyLKm9Q/S/dRf99+4hOTWAoikIRWAOpJj4vjtQ4zrTH38mGnTYAIqpMf6dRxl6YH83ePttl+Uvn2D58VM0zyxvONpLKK9TdSlqqBWePEv+9p0dulmM/nv39gRdoz9F39GJSGdBSqyFGpWX5ro/mmIYxHZOYV04T3znFM7SIn5lbW1rdDiiPF2NSws+n/3H1rpUqo3guvCZz7X4/g+lGBpY3b6rquC9Dyb4879u4JTXXlWBY+FUltEzebxmL2NEUeD+e+I89EAcVe3NxM+c8/j3v1vZdsDtfk4P/vsXW9x8UOd//eW+q44neOTdCf7yk01ePSHRjASKpq+x436zIF0ft9QgdDyCho1QFOyZAta5ZZKHdiDUSPvCr7VxS42IeVNt4zdtnMUK8akhFFMn6ARdIVQ0Re+Kaqtq9OdUfCi6nzrQ1Rj92T34gUMQuAShRxCu/n87dCshVFTFWD1e51iZxCjqFTsDXUuQS+/E8RoEgXddx7qGM0qxfo4Li19n3/hDHWW5GFMj9+J4TRbLr274ylD6zBWeJ50YZTC7L5JpNHMcnHyEQu0Mxdq5jrBNGykDFKGjqSYxI0PCzJFOjJBN7EDXYrw+/Wg36HqOpLoSXaOutfpdfS+6F1w7xLW3fw42Ly+g06cOYijxznBEtA2SXoBX7t3OuJU2qf0jpPaNIKoaQ+O3EfgOYehTbl2IxMu3+eNIP6RxaonmuRUWHztO/s4pxh65meSeARRNRTU1Rt93hOpLc5sK3sTH+hh68EBPwJ375PNc+syLmwZriARohHqdXe1QUj++QGumRGr3IEJVyB2dYH4ghVuMap6JqTzpvUPd51dfmo085y6/RWdxSh48jBKLY19af1hi3x6dRGLt5zx+ymO5cO2BZmbO5/xFryfoAuyc0Bgb0SiW1543oemYueHuVOCVyKQVfuBDSRLx3rKC7Uj+8u8avH7y+kTtpYRPPdriwx9KsWeqN7seHdZ45OEEr52o0qrNIxQ10gW4QfArTVK37MQrNQgtt+uQ4hUbZN66F0VXsWeK+JUm6dt24VfbBE27E6itbpN4cvCtjPXfilA0FKFcYZujoChaTyCMmVkO7/xgZILZ+S+UIZIAKUOml59hqfzaup8XYHLoLkbzt3Qsa3qPpSp611cNIBUb5ODEI4QyuOJYQccCx2Wu8PymwfBaIWXIUvl1TC3FrpG3o6oGuhpn9+h9uF6T0iZyja7f4tz84yhCJZ+eQggVXYszmr+Fwex+vMDqsDUih2uhqN3vq4ion+Hc4Am+TYNugEclWEYNtC3n6fVMJBbTni6iagaN2iXKK5GYtRKPocx6kdjHNUD6Ic5yncXPv0rl+Wn2/ut30n931HhSEwYD999E6bmLGzIb0vuHMfOravztmRILn391y4AL0WTd5Yk52H5N9zKshSrVl+dITOZRNJXEZJ7s4TEKXz+D0BT679yFEove37dcik+f772OwoDWyeNomWwkfr3BtNLkhLbusOLpc5Gw+bXCsiXnpz3uubOX9B+PCaYmNV7doOkk5WX6WO+JuvmQwe1H12bi5y/6fO5L7Tc0djw95/PVr1vs3tnb/DN0eNudJsNDcVreGKoWAxlSXt7afeB6YM+VsOfLEEq84ipLxV2pUfzCC9FfOt/TLbYixbH5Jn65iru4ujMwjSzpxEhPRrsRFKFi6htLKJraxv8GEaUqnVidWNv0WIqKoazvahEE7qaf43oRhB6zhWOYRobx/qMoikbC7OemHQ/hTj+6qatD017hxOznmBp+O2P9t6CpkYCVrsW25YYRZfBrd19CqMTNHAiB71tdcf9rxRbsBZW8NoqGTiMs0wqjdDt1YJSJH7q357lCVfCqbbRCktTQDnQzTTI1TBA4rCy8jF/cniLYRrCX6sx98gX6btmBloxOYnJnftWaZB3ERjI9gjzN84VtBVyhKcTH+1CM1dNzrUQG6YcUnzrP4P03YQ6kURMGudsnKT8/jRrT6bt1R5ebW3ttgfZcb+lAqBrJQ0eITewkdB3qx57Fr60d9ujLrs91rdbC66MzBZJ6Y+2ORFXFmibY6nf1sZbnUGOJnqArBLzrvvgapoGUkieftq8rE78SliV59biLZcmebF8IweEDBoP9ksLZZRRF21Yge0PYiAZ21cNCVdH7+/Hra0n7jlePFLbehKkW199ck9DxGjTaS1sydHJZFU2DQmn93yoM/S2P5QU29Y6QjZThhnKeQkAmI2g2JUEAfmBzYfHJyOE4MUznDRjOHaJlFzdlJUSuEV+lWDvNSP4WsslxdC0eGVIKFRAdw87IYy8IXGyv3uEQn94wqBt6MqLBquaNCbq6MDEwO0J+qz+Os1xn+Ysvrz5Rgt9yaF8sgC3QjRSKouJ7Nqqqo+kJfO+NzUEDOMUGbqWNlowyJ6WjIbsRhNJ7o/ltd1t8Xy0Vi+qxb/Dib5xZpnF6GXMg3ZlQm8DIJUnuGsAcTAMR26H83MU1QulC15C+j7O4gGKaG6pPaRuUQILg+s62lKybIQsBhr7+saLywhB6ug+nvNKljWUzCrceMdZY3Fi25IVXHJqtN85XPXfRo9YISVxFQxseVBkbFrx+0gYthroBT/fNhlDUjg6FQAZBdA1dvkZldK5Q1XU1c2dXvsXsynNrHr8+bH5uZ5afYWb52TWPaxqMj2g0WyHlashdt5vcut/gc99sYBowOqJRq4fU6iGD/SqeJ6nUNl88G/Z5Xp25gK6D70dDOqOjCtWqxLYlmawgERe025IH3mny3LMuzaak1YpcgI/PfJYeYYVtXtl+4FCsn6dUv4hpZEjHh4mbWTQ1hkAhlD5+4OJ4dSynQsspbTooImVAtTGzrWNvhk2Dri9dWrKGit6liwF4lRblp8+hJk20Tnc/aDndCbJYPM/IxJ2Ulk+gG0nMWJZq+QKNaqd7LASKrhJ62+v2XYaZT2JcQVVzK+1NR4a9arsn84oNZyK92k06xUJTGHrnfjIHr89c70oEthc11N66C0VXMftTZA6OkNozhJaKxn7txRrVl+bWLAbS97FmLqLGYhjDoxtqrDZb4bqnMJO5vmkvTRNk1slowzCaZlsXYRDpXDSrBO5qh3hkSGVqQluzeFWqIbNvwCfuSpQqAba99gwoimDnZIxMfgRVT+P7Dnb7yt3E5c8kiVyFw6ieqRqRC8b1LFmKQmJ8d+QM3GogAx/FMBFK9P6KbtKcPY2iaaipFF5xPY2KG6CwsiHWHiuVULjtVp2bdun84Z/XmZ7zOLhPRwjJ7UdNDuw1ePYFm9FhhZsPGei64Ctfs1ha2fiemphU2H9Apz8vWFgIUVQwDIGqwje+7vD9P5jgtVc9Tp30icUEd91jcPKEz5nTV14j2z8vly2J3IqF33aRhNhu9Ya4QFwPtpxIM0Uk5BsTSTy5ekOlDowy8h1HSewZAgH2XIXlL71K7cVpAOrVWWKJ/khwvBnZsl+Gno4x8t7DuNU21VcuRdNaW2Sg5kCK8e++rWdKrPb6/IalBYDmxSKhH3K5/5A5OEp63zDVV9ZXpVdMjaF37mfyw3egJnszo+uqPYaS2olFWheLpPcNo+gqA2/bE9HrlMiNtX5ykfaltawEc3ScoNXEq1XxyhuXZi4trL9w7ZrUUJRrNyEwTcHOibWXhefB4tL6bybDAL+5lkfcn1cYG1mbodcbISvX6YZ8NVptieev/+MM9YPdKiA0m9BfLSspik48lotcNwKXWCxHGHp4vkUuu4vlwmsEW9CL1ocg9Bzs4iKh66CnMt3gG3rR7HUY+KAoKKk3vw76ZmB0RO1OAV6W1oTo+p9fCNg5ISMRJUNw6ozH6IjK8JC6adAF2LNHpVyWjI5G2fEzz7jccotOX06h3ZY89U2XWEzQ1ydIpVSe+Or184eNvji3/tq7OP+3L7HyzPR1v8+NwqZBVxISSB9DxEgoGTzpYMsW5kiWse+5k9b5FZa+GE3CpPePMvqB2/DKTfzlNrF4DikliqIihEKrsVojUQyV/rt3k9o7GBlBzpWon1qiPVPGKTS6ppNCUzBySTKHRqPn74nkFaWUtGfLFJ++sGk0bM+WqZ9YJPeWSYQQmP1J9vz8O7j06RdpnFokcHwQAtXUSEzkGHrnfvpum0TPxLDmq6gJo9uIu95Kg7Ncp/z8DMmpfoSukr15R/TdhMC3PArfPLs2aApBfGo3SiKJVyoifY/W6ZOE1tps9/gpj7YlSad6P+CtRwz6c1vfDFdjclxl3561nf5COeDi7PapXUJAPqeSSa/Nmid3aHz8DwbX5fxeKwxDMDG+/mWcSmskswOEUqfdWNVekDJE1xPEY/20rRXSqTFsp4rjNggCd82Y6rYRBlhLnd2couDWyki/t4cgdB17ZgaxQbmob3AvifQISzPf2lB0XTOSTOx7kKXpZ7CavWOuAoFBDIlEEQrhZWlLouabQNCWGyvYJRMKY8MapXKIrsNtNxtM7lCZmoxU4Ewj6iO8+KrLA2+LdrnHXto8QJaKIctLIcViiKpBYSXkbW8zaDQlxUJIqRj1H8JQcuFCQLvtc/CQxvPHPFQS6Foc22tsW+5RiIgH/+0UfLoWbJHpChphucvRvZzpxsdzBJbL8hde7k5ZtS8WMAbTJHYPUZw+wdz5rwESVYthxjJY6yjJa0kTNWGQ2Jln4G17owmxUBI4PjIIOz5garcudnmb6hQazHziWdozm0sIupU2C599heTuAYy+BEJVSO0ZZP+vPIxbaeFWLIQqMPoS6Nl4NAosoHW+wIU/+ybD7z7E0Dv2dyfYrgcyCCk+fY7hhw5gDqbR+1YlIpvnV2ieW2c2XEpqL3wLc2gEr1pB+j6hs/7U0ty8z0uvOdx/T6/05K5JnUceivMXf9PctmyFpsGHPpAkn1sbEJ455lCpblBeUJTuJJr0XGQYoCgw2K+uKyaTTCgcvfnapSSvFaYh0PQ4WmywZwxYdqhPll2m1S4ACp7XxvcsHLeOIjTCN+p+EYbIdYKE9DyC2sYjrZ7TwlbLm3JfFUUlkR5C0dbWqTV0EkoaDZ2kyFKTRZBRf0YS4kmXNk022q6/8IrDK8cdwjDaJT32uMWXn7AIwiiYnT0f6W2EIVxaaEb9nC3WqGpV8ulP9Y7HHn/dIwii9/niFzpCWg589curAVxKmBy4lR35o5xceIxic/vOwGz4Df/5sWnQNUWcfm2cAJ9aUOgGXRnKaCXRV29OoSkomoKChqLoqFp0U/X176FaOt8zSRJ6QTQFtmsg8iNTBKirOu1XsgYuQ0qJ33RoXigw98kXKB+7uLX2bigpPXcR/b8+zcT33k5srA+hRPXk2FCG2FDmigNE1K36iUWm/+oZ6icWSUzkGLhnT8dSaPNDbYZ2R+B8+MGDPeeg/OzFNYps3X9vt7Fmp1kjlnAVCqWAz32pzR1HzR4ubCYt+PmfyHLyrMdzLzhsJQtgGPC+BxN87wdTGFcluqVywOe/1KLVXv8kaPEUmV2HkEGAVVzALsx3BXX+eRFSLZxDMttp5EYQQsUPHOyOGE6tvtocqdam39ARFVXHMDMdFxMfz2kS+E7n3wx0M9mxDwpw7TphEAVmoaiY8RwSSbM6v2b6UFF0jHi2K9d4NdS+FMI0CMoNal4JFY06ZcLLygoS6DrAbHwxS0mPMHwY0lWUlRKuZH1ey+DN1Zfx1ce48vhXQgiBwvq7guy+QcYf3oeZT+K3XRaeOEf55VWHjsyeAQZu34GZS1A/X2LuiydwKxZaQmfs4X1k9w2hxXXcisWlx05Rv1Bix8P70NIxUpN92IUW7fkaA3dMUHxhjoXHzxIbSDL+ngMkx7MomkLrUo3Zzx3HLm7fwXLLMeB6UMSVNo5cDQ7WXBklpjP+fXfROLkAUpLcM4wxlKHxtRVS2THygwfw3BbJ9CiN6lyPwppXszj/J09SPjZN5vAYiYkc5kAaPRtHjesILaorhW6A33Y6JYgy1ZfnKD83jXuVcPpmkF7A0mPHaZ4vMPzQQTIHRoiNZiPamSIIbB+30qI9V6Hy/DSFb57rDjCUj02jZeKopkZrurhp/XgzBLZH4RvnGLjvJtTOguIUm1Remt186q3nSl1t/FwJ349EW77nA0nuut3s7gYi2pTOR36rn//8JzW+9FWLWn39Yw30K3zvB5P84k9l14zVer7kS49bPH3M3nDhCRwLt1ZCMWLdcWAhWDOa/G2HlPheG99vX/VwQKu1vMGLrh+KqjMydTeZ/l3dxlzx0ssUF6LBgf7Rw+RHDkVTZ7pJZeUMixefiZTAtDiDO26lb/AmrGaRi69/nsCPFgohFEZ23U3/6GE8p4XnttCM3p2NmssQu2mC1reOE9Saq8H2/6eID6fZ9xN30ZqtMP+VMyR3ZNn3Y3dy8o887EJ0/+ZvHWPuCycQmsLUdx4BKTn/Ny9FCaKiUHx+Dr/lMvbgTez6F7dy4g++SWwoTe7ICEtPXmDqu2+m/NoijekyYw/to/TyPGpMx286LD55HtXU2Pn+w7h1m5nPvr7tCdYta7qaMMhroxT8uS5P1y01uPSJpxl86DBDDx8BAdZsKbJnv1jB0JOUC6ewWkX6+uuRziWgCTOaegG8mkPl6zPUnr6EktLQkzEwFTTdQCoSVdGRQYhjN1EtDafWxGk1ty1q3PM9OhNurQtFjHwCLR2LptSEgEAS2j5OpYnfdDtKUZF1fHuuyvRfPNPl00ZjzRJF0ZFh0OEJRs+N6oBX/rnnROLVLaQbgKEhQ0njzBLNC1eUR4RAj2VAhiiqjlBVfLsNAhRVQzOTyDAk8B18u9XNkAAuLQR89I9q/P7vDDA0qPQE3qM3G3z0P/Tzr/6ly4uvuJw971GpBQgBuT6VQ/t03nKryeEDBulUr7JaGEqee9Hh9/64Rq2+SXbk+3itOloY4lurK/5GNTXXkxsuAG8mmq3w27rH1PQYueEDLM88R710EUXVu1kuQK10gUZljsB3SOcmGb/pAYoLr+G0y3huk/mzT+K7Nqncjp73TWbHyI8cZO7ME7QbS+RHDpPO9Uqqhi0LJR7blEK55vMqJoaWwPYaCKFgaAkUoRLKENdvbVhD1dQYhhqN+YdhgBu0N3yuIjQMLRHdz1LiBzZuYLHeDxM9N4mqaJ33bW240PffvgNFVznz8WP4TRehCtJTeUbu283Mo8cBWH7qIotPnAMBetJg5L7dXPz0K3gNh0v/dBo9Y6LqKpXjS4w9eBN6Otqd184UWHj8LEN3TVI9sUTl+BKDb51Ejeu05qq4NRstYaDGNeoXSqQmcyiaQvBmBF2BgiFiBNLvsetBRvKOs//1G3CZJxrIrrSd7bvYVmTDXFo5RRh4CAQD8Z0gJboap+YsMpzYiyo0VprTmFYcy69jqAah9MmaQwgULjSeZyp1GwXvIp6wCN6ATFvo+pFRZMcsUgiFRGIIVY2B1SaT3EEoI26laaSxOt9B1xM4To1kchjPa2EaGTzfwrJKKIqGYWRotZeIx/L4gbMmixKaQu7WiS4jIvQCik9d6BE5UfUY+YkjBJ6D3SigmUnUfIeO5znIjpttLD2I0yrTrvQ6Df/TExa//dEKv/GrOfrzq4FXEYJ8TuX+e2Lc+9bYZXPazvePKKOKslYrNggkL7/u8uv/sczxU1uMTGs6Zn6Y4IqAG21F179jzp73+K2PVPBvsMD09Jy3ZVnlzUTgu1iNFQbGbkFRdaorZ/Gu0KoNfZdE3xBmIoeZyKEbyR7XhjD01+WJJrOjhIFHtXAWZEitcJaBsSNXHTwkqDauSS+kP7WbPYNv40LhabKJcQbTezG0BF5gUWicY6b4HJbXW3/OJSfZkTtKLjGBppo4fpNS8yIzpeex3F7rdVNLsSP/FobSe4kbfYShT81a4FLlFYqNcz0JlKbE2JE/yljfEWJ6Gte3WKmfjpKYdSxzYvkEgeXhN6NrUwYSa6VJcjyLakTliO6WX4JTaqOaGqqpER9KMfWhW9BSJqHrE+tPoupaN0sILI/QCwi8AK/lRjtcKVE0hdyRUSYeORh99kCS2pWncbF8TZ32Le16SsEiCkoPXUxNGOh9SZzlWpS9ASiC+EQ/Qd1DWqvBJJvfRa08je+2CKVP26+RFgPoSgw7aNL2qjS9EqoYJm0MUXOWiKlJWl4Fy68Thj5eaFNz3/ztICgYehLbqWHoKTKZHThOPZpH79wMppmNIoiAeDxPKINo2CN0icfzxGI5FEXD9RpoWpy2tZb+ZeST9L9tVTe3PVOifqLXHSL0XSqXTnSDq1A0FDWanAkDP9JrlRIjke1xy7gMy5Z8/O+aVGshv/xzWQ7s0zEN0ZP1Xq0Hux7CUFKthfzjV9v8/p/Uee2ku1VZGWRI6DposSSKphNED9Forv/CeiPkq1+3aDT/+VodWjJL30230l6apb00jZbM0Lf3KO2VOdqLF6/rPQPfYe70V8gO7qVvaB/9o0dYvPg01ZUzaEaSnQfeDULQKM/gOc1tsySEohGGYVe7JAz9tbKkigKIa+IICiEw9TR7hu6j5RS5UHgKKUP6U7sZ67sZKSVnl5/oJlx9iR0cGHmIMPSZLn4Lx2+SiY0w0neImJ7h5MJjOH60yOhqgr3DD5BPTrJSP021sICuJhjtO8T+kQeRMqDYjDQUBAoT+dvYOfBWyq0ZLhSeRgiFwfRe+uLj604TOlULNaahxvXI2koRmP0JnKpF2ElmYvlOCUZENLLQCwgcn/GH9xMfTnPqT5/BLrUYuXc3E+8/1H3vjbJrPWWy63tvpXGhxOwXTiD9kN0fPooF/KOBAAAgAElEQVSWuram8Ja3oSfXds0TuwYZevfNzHzs6/j1aGhCMTSG3nszZqGf9iuFriNoKjNKs7aAR4u2X8MLLFpUsIKoS6wpBpKAmrsUuQ57RZygSc7cEW11CKh7N0D9HYAQy650tUgbjYWo/icUBIIg9LDtGqpqRP5TYYDrRo0RRdGw7AqO28DQk7hugzD08f21Ggn9d+0mPhapYckgpPTshW7d+DJkGOC2K2teezV8p81Ge2bblnz6cy1ePeHyf/xmnoceiG97AQ5DSakc8vQxm//+xRb/+BWLam17WZMMQwLHQtGN1WwhhJVC0KNJfBnxmCCbVmg0b0zdUTFi6Kk+hKIQeg6KbuK16sQHdwCS9tI0fquO16iimNFuwm81cBsVVOPajSavhOe2KM6/SmX5FBP73sXA2C1UV84QS+RI5XZw9sVP0W4uk87tZLuW4a5dR9UMdCOJ57YiucqrJuxkEOAtFZHete0EFaHhhTZnlp+g3RkeKLdmSZr99CXGMbQUtldDU0zGc7eiKjonFh6jZi0AkmLjPH7oMDVwNwOp3cxXo/r1QGqKwdRupkvHmCu/0NEyEDTsJW6Z+C7G+m6mZi3gBTZxI8tI9iBtp8KZpa9iew1AUG8vcPOODxIzsms+d+mleUYf2MOu77mF4ouXSIxlyezu58xfHCP0AiQwdM8UrUs1hCIYfWAPleNLhJ2dgFAFWlwnvaufgTsnti1uJUS0c9XTJsmxLH0Hh2nOXtvQxTYd6Xq9KNRklJbLK7giMggJLQ8Z9ymtnOzWsnzvcm1I0vQi2pgTRo2Ngt2bUSxb57r/vmSteihVnN6t9PVBoCczBK7dFW6WMsS2VzPTam3zDMe2o6DYtnqpapfzTm8d59H4jj6GHz6ImjAiCcfFGsWnz3dX42vH5tlhNq3w8Dvi3LR71ZjSDyQzsz6zl3z6spEFexiCZYcUiiGzS5FA+GuvW0wvKZSrksAGhEDoOrLTplZisUg713FAURCqivQ8FE1HiyUwc0N49TJeo4KUEeuh3pBkM70XdCajMDSkcmk7brgCFFVBhp3NaCcNudzFl2GInjTwLa/LZlHNOMmRnZFzQquK0Ayk54JQUM04SElz7mzEEOieTrklU2QrxBL9DE7c1sliJbFkP41qNIjju208p0X/2BGS7TEy+ame1+pmikR6mGRmGDOWJTuwG6tZwGqVqJemGRw/yuTB99CqLZDIjK5RTQvqrQ5N7dq+gySg0prtBNzoZDh+A9urkzIH0DqTRboWJ5+YpO2WkYQkzXz3PbzAigYqEmOdoCvIJ6eQRBoPMT3Tc0zPb5OJj6IqJl5gE9MzJIwcc5WXOgE3+mRtt0rVWmDkqtcDtBfqnPvrlxh/+Cb2/tAdBF7IxU+/SvnVJYx8iuZ0JWqAPbgvYi9cKDHzuRMQSua/cgYjl2DPv7wDp9Km9NI8vhVEOttNt3tvOmWbwA6QocCpBzhVm+l/OM7EIwfZ+0N3YC01WPrmNIqhROp6SrCta2iLmq4greRR0WiGFTyi+kno+OjZRKQD2hGQUTQVvT9J5ZUzWK3VzLSw9DryeuSu3mQITWf0be+nevZl6tPHvy3H1LNxxr/zKOkDIwghCP2A0rMXaJ2/MZl7MiH4pZ/J8As/kSWVjEoLrif52jctfuv/qnLidIeedFkKQAKxBMauPTiVGvZ8m8TNh0hMqLSOH0eoKuaOHYRtC69UInX0VrxiEev8eYzRMdRkAndxEb9Sob0yR+i5PY20YjlkftEnm+nNyvpzKpM7NF58ZWuye2qij/RUDmuliQwlTsWKRs3zcQI3oL3cYOwdu1l+dq5bwwtdpyMxKdGSWbxGBT2dQyDw2g0C+/ptuTdDgIfnt4hlB5FhSKlwksryaYSh43gNLp37GtmB3ZjxPooLr9KozOJ7FiCIJfLkhvcjpaTVWCLTvwtVj+FYVXy3xeypf6J/9Ai6maJw6SVatQU854oykx8QVK9dgCUMg44AzVVj6DLoEGaii0VX42hqjHxqijsTEz3PF0Lp7FqjbbbaaYiZWorD449cxTkWqIqGG1jdskGkAqauEcKRhBvboCsK7arOiT98BnNsrCN/WkIfGkEfGeXMfzuBkkix/HyJ0HNREmncjpBTa67KmU+8jt7fT9huE1gO5fM+bgsWny0gPQ8RTzPz5SUCCwJfZ/orFayCjWfrnPjYKxD4qH15QtclbDUxxqcQpSJecet7e0uVsbiSIq6kcX0br9OhtOZKyDBk4kffTu2FiC+bObIDvS9J83SvGr38NjqNbgYBCFWHG6A2ZQ5n0BJGlP1LiaJrJKfyDD14kPxbdkaZmpQ0z66w+MXXrpt6thkMA37kwyl+9scy3YArpeQrX7P41d8scWF6/d9BVRS0dD+ybiGDgNCyCG0badtoA4NofTmU0VHcpSWCVhuvUEBNJEgc2N+J2oKw2SIxEjVJr7zBFpcDzl+8PLu/mu2mU4Lbbjb50letdXUTrkRiOEViJIX0Q8y+OO1CE7dqYeTiGBmT5lyV0AsjR+oOAtdGhgFWYR4j049TK2KXlkiMTkWsEKdNLD9MYmQnUoa49SJCUUmMToEMcWtFnOq1LYxqLot5aB/VSgm8IkosBjmJ3rcTQwiEruFUalya/gbSXjvB1ajMbupsYTULXDr7xOrzy9PX9Pk2hlw3qK35VaQkKidcYKH62roN7ctBM9qTSGyvzsXiM1jr2KBLGazq1naLqOtt8Tfe9qvpDF6xiIhnCGwLrS+PEo8jwxA1248+MIhXLCCdMub4JO7y6lSikkgj0RCJDHpajXoS6QxoGkIz0eOS0Jco8RR+vYnftJCej9ANhGEifQ8ZghJLIEMwRsfw1jEZWA9b6On6tMMGISFuuFrb9cot5j/1HEMPH2HoPbeAEo27Ln7mGPbSjROV0NM5jHQO1YgRuA6B3SI2ME576SJuvYxQNeIDY5jZQYSq4lQLtJZn15XNUs0EybE92KUF3HpU9jBzw8QHxwGBVbiEU9le827k4YOMf9fRaAschKgdLV7FiGhpUkrcUovZv3kOa27ruu31YNekzs/+WORBdjnAlSoh/8+f1bg4s4lyUhDgLi1iTu7Enp0ltCy0XB6vUMAYG4vKCZ4XXWS+h5bPd7PboNHEXZiPzA7NOH6r0bO9qtVDXnzN5aF3xInHVm8eRRG86744H/tEY0vhm/ZSA9/2ces2bt0mNpikvVAHLGQQIgNJe7GOljTwOp1sZEjl5LGonty1OZLYpaXIySTw8RCsHPtyJO8XRNS/5WNf7pyT3s8k5fpERaU7JSmRros7vxQFEFVBegGhbaOYBjIIEaqCMPTrFPF4cyDMiIa55QjZOvACCy+wQECpNY0fbOzrFkof22uQiY/QckqUW5src3mBTRj6xPXe2q1AwdDiCNYmSlFpy0WYJl6xEAlCCYEWBtHCubSI9H38agWkxJ6+uObcC03DK5eQroOe68dvNlFTKRTTJGg0ooxX1wlti6DZRBg6MgxRFIXAtgiajY5rdzb6d7VDQ93iN96yvCAQqGiYShwvXF2h2+dXmJn7OnomHon6NixC+8ZmtfH+MYZuf5DW4kUSw5M41QJC0UgMTbLw9GdRzTjZvUcjgr5Q6Nt7G4XXvkFj+kTP+2jxNAO33Iei6VgrUXaRHN3FwC1vx61XEIpC355bWXnxq7SXt5ZyE5qKljTXTNJdHmu2FqrM/s0xSt+62M1yNdVkpO8wjtegUD8LRBQbQ09tKtC8Ed5+V4zdu3oVvV497nLitLfpNSADH79cwSsWo8CxsEDYtggdB+v8OZR4Auk6SN/HvngRxYwRNJtYp88gYrHIoTgEp7wc7SKuktP8ytcsfuz7U+yc6H380H6d97wrzp/9VWPTz9ecq8Hc5k6wpVfXnq/LJa0rHXplj5aB7BHBAblGJ+EyHHd9q/VVS/iIJ+u21nf3AEBVovq4u0FJRVVQ04loYKe1GtBkEKJ0gqUSNyNqedOKlPoSse7fpR+gZpLR65vRgqQm46AIpOcjXZ/UHfvxS3Wc2WXC9rUJyrhBm5X6WUb7DjOc2c9y/XSX3qYoUWnB9duEMjrHhcY5BtN7Ges7gu3VO7Va2bEjMgllgBdEZR7Lq9N0CuSSE6RjQzSdKAlKm4P0JXasS8eSrkv71FpR+istrfzqaoLjlXr7MH6tiru81DUH8ArRziZYR7MawDof3aPuemp/Mkq2wnZ7W4vqlo20kJAQf92hBOkGa7rwNxq+1aJy6hhGOo+1ModdWWbwlvsRQsFvN1n+1pe6W9zRu7+D5MhUT9BVdYP+m+9FNeOsvPAVvFYNxYjRt+92WovTlE9+C6GoDN3+ENndN2OVFrpTVhvBLbewl+uRkWbHbSKwPJyVBvXTSyw/dpz66eUeDqUfOLSdMqYW6eoqQiMZG+ia5ylCIxMfQVVNmtZyl4qzEQ4f0FGvSgiazXBLURnpungrqxl9aNu4S1GJKHCcHp2AoF4nINoqXnlBC1VHiyXREuk1Fuwnz7h84xmbnRO9jZ9EXPDjP5Dm60/ZnDl/gwm7bxDVWkgY9GpKQyTcYxiCtrWN7DUIkZsol+nDObLvOIq3UqV9Yhp9sA8UgbdUJnFkF0GjTWzXGEGzjbdSRXo+8QOTBPU2XrGGu1Akdcd+pO3hFas4sytkH3wL7mIJb6GEV6qRODJFUG0h/QD73LU1p4PQY77yCqnYIHuH7qc/OUXbrSKEQlzPRJSxxcdo2NEWvtKe5VLlZSbztxM38tTa84TSx9ASpMwhluonmSu/EDWzvToL1dfYO3Q/B0ffQ6l5EQRk42NR8eMGlCj9ypu34/RKxUgJcJtNzC2Dri9d2mEDJ9zaeXc9KAroemR6KBTQtYhK6PvR44dv1qlWQ6YvbG/LEzhtAsci9By8Vp3Qdbqi0aoZIz25n1j/KIpmkhjZibW8mn0IVSW791a0WJL5Jz+D14oCiqIZxPtHMbMDxAfGQQiMVA6nVoi0UNk8KKx87TS11+ej0WItogGFro9XsyLVNGf9i+ayVxNEdTBNMUkYeerWIun4MPnUFI7fJGH0MV9+uXdA5SoY64zcHtxvcNNunWMvbc8R+HqhGAYo0XTS1QtUqy35xKeaPHh/nJFhrZu0CCG4+ZDBv/s3ffy736lwYZMSyLVA10DTBdZ2AuE2MXPJx/MhftXjtx422DGqbptatxnCpoW7WEJoGoQyEl9SI4aIYhqEloMzt4J9YYHUnfvxizXc+SLW6TlSdx9EGBrO7ArO9BLptx/BK9YILYfmMycIrSjY2xcWcS4udQNuELq03SreOouB6zex3FjPNddyS5xcfIyh9D4GUrsZygxGmtBejaX6ydXarYhKk7PVF2h5pej5mV0IoeJ6LerWAuXmzBX1f8li9Th+6DDWd4Th7AEcv8Vy7SSO32Iif9uW9usAajIV1dI78Os1pLf9BV1JplB0Db/RuDZN1HVsqjbDpkFXw8AUCUwlDgia25RWuwxFgUNHdAYGFeZmfBJJhcEhBduWvPKix+13Guw/qPHCMXfbQRcpV7Puq2ptfTe9hczUIYqvP4VbK0XNpCu7rIqKbzWRYUB65yHsaqEbJKQMacyd6Skn+FYr0kHdAn7dxq9vXOPa3tcKsL06uhrd2qaWIm704QUW9jqNiKuxuBSs+d33TGn85q/l+NgnGrxy3GGlENBqyzfKjFqDyxqyMgzWUJakjKT/PvP5Fj/5w5kePQZdF3zgPUkUIfhP/7nK8dPuNev/XoZpCo4c0Ln3rhjlSsh/+0yz+z11HcZGVZaWA5xNdtWKEunJNhoh9cbqyTx91qPVDtcI+IyNqHzvd6U499EqtvPGg3xou+iDCczJIYJ6m/jhKbRsCmFoCEXBGB9A6BpB3UIGEnNqMNLorVv4yxVi+ydQEiZhw0K6UUnhShfk0HKJ7RkjaNl4iyWKzYtUWnPr2t6cW3kSgYJ/1T1vuVVmS89zqfJyt9YqCaOBDcJoSGo0S+gF6Nk4llJj2vkWqjAQqkLjwhJhEKw5ZiA9lmqnKDTOI1C67wlQac1uK+imb7+D1NG3oMTjKLEYSx//86iWu030PfAOYpM7KXz67zYQmH9zsEUjLcCVFmEYrDsksRVyOYV3PRTVofr7FXxfUlgJ2TGhEviQSAqKxfWdD4RhoMTjBM3mtlcdMzeEU1mhNX8eRTc7PM3V7XHoudQvvIZTKzHy1vfQt+dWqmdfIvQcrJVLqGYcp1aIKCaqRuh73SmgNxumlqY/tQtNjdFySjheg/70bkwtRdYZp2EvEzOyCKHg+damWS7AU8/ZNJohfdlVwn3UsIpx+1GDuUsBlVqA42wcdMMwql9WayHzCz6nz3u8etxlcTmg3tjEc00omLkhpJQEVpPgqom5VlvyBx+rs3+vwYP3x3rkHk1T8MH3JTi4X+fTj7Z49EttFpZ8Wm2J6/Z+VkUBTY1eEzMFiYTCnimNu26PccdRk4P7dSbHNf7fv2rwN3+/WuZIxAX33m0ydyngzDmPWi1k/z4dy5acO+93hLgVZucCDh/SWFkJKZZCFpcCgiASin/pVZfRh3tvl1hM4cd/MM1KIeDTj7YolIJNF7TI8ijym7Ps3jqx9AL8Yg1vqYK3XI5qhJ4PUhI2LYydw/jVJs7MEt5KlfhN4wTVJs7sCt5KBWm7BJYTNYdWKkg/oPVSR6tZi7aXrZfPYYzmowEKESmOBWoQlb2uagBtFuQk4YZaC0IRGLkEMpTEhjMElhepEmoKQduJymcbrk9y3ff1w+3VnxsvvUj73FmSBw6Re/d7N9Qs3giKbqDE4jeE4XQlthC8CVCFjib0HpWx7cLzJQsLAZVyyMULPrt2axRWQvoHFDxPkkoLkkmxLikkddttZO6+m9LnPod9YXs6mq2F8/QfuoeRt76327VeL/Oyy4sUX/smg7c+gNes0pw/R/nUcwze9k5G7/6OaIpJM6icfp7m/HluhGqK6zeZKUZ+WIH0kTJktngMgSCUPqEMmS+/BCjIbehNvPK6y99/vs0PfV+qx8tMCEFfRqXv0NYXoJRRIAglBD54nqRYDvjGszaf+VyLbz5rr+trJj0Xp1pAS6Rx6+vTZqZnff7j/11hZGiAwwf0nsCraYIDN+n8m1/s48d/MM3LrzkcP+WxsOTTaEaLsqkL4nGFTFowPqqxc0JjalJjZEjDNAWGzrravZeRTivk85J33h/jiSdt9u3VOHLY4A//tMHD74px6owXBUVDcN/bTJ56xuHSfLTQVeshn/+nNu98e4z4FfKZQsBgv8Jv/GqO++6J8djjbaZnfRotSRhKNPXy4iBIJRX6sgpDAypBIPnzT1pUm+C3PdSYFrFIphdJjqTxAx/N0NDtBq3FJoqhImsJ7FIdf6EQCUaV6vi1Fs6FVYqmO3sFJSoZR+kfQPVBzWeRtoO3sILf9FBSafRYHKFpqH0Z/HIt4quWqm+YXSH9kPqpqLHZPLcSNY4v15RCeUMFiIJGnaBRxxgcunEHeROwadBVO4LIIDCEi3uN2W69JnnqSZdcTlCrRiUFy5LUaiH1mqRRl+gGzF9am8VJzyOwLMIrajLt5RncRoXAblN45Un8dp0w8Fl+/svIwKc+fQK3XkaLJfHtJr7V6oprh4HXbZwhJc35893aMIBdWmTxmc9jZgdRVI3AtbArK2x2laiaID9mUphd/7wIXcXoT+E3nUhcJmniVVqoSROhKfg1C70vAXZnm+YFoICeTCIDid+wyNw8RvP0ImFj80y3Ug35nd+rouvwofcnSSaufbUWIlLbV1jNKFMphZ0TOu9+R4JHv9Tio39U4+Ks33NvClUj1j+KmR8i9F2c0jpsAgnPvejwv/xGif/93+a48zazxw5GCEHMhB1jGjvGNN7/nmgRuCzQo6pvzAmg2QxZKQRMTmjs36cThOA4kYuwbUtm5wLa/x95bx5k13Xfd37Ouetb+m29oxsbsREgKW7gJpKiZEm2FmuzLY+8x+U4k0w2TzlOjWcqnoqd1FRc9kxlpuKM7alEcXmVY8UjiZYskaJEigtAEiBAEAuxdaP3fv327a7nzB+38RqN7sZmypOa+aFQ3f3ufffed989v/M7v9/39/32EkdZyEuCcC34iyL4+nNdPvuJNB/+QArDuG5Syws++4kMn/homlpd0e0plEruoW0JHFeQdgWOk0DMzlyMeKU7zsx8TPNygpZJj2ZYemOekYfHmX3hMunRLLldBaJeROnQMNIyqJ4pM/7YJJXTy/SucbBbfJsY+QFUu4MxkEVn0sS1JsZABplJCP21UkjXxXIdonKVuNq4facrJeZAjrjXQ6/q4wknhbRsolYz6QeXEiOTTQjur0LNpEzSAJYNaJQfoLzeuvML08QYyBF3Ov1jA2AYmNkBVOD30Qe3bVIiU2mkba12035/GmY2sxs73VWp4usJb27HFuZjFtZzu9BZjZbOv7t1BNc5eZLumTOoa5JwUa/dr457lbWD9spJq6WOo/7vG0zr9du02rBv1GkSdZL8qWkLtu9LkxrI02tFzJ7rUhyzGdnh0qpFLF3usf1ghkc+NsSb36ww+26H4qhNecanOGrTbUWoUhErl6I7tUJ23ygYkiDr4I4X6V5ZQQhB9u5xmidmyB4YI6h2MFI21qr4ZuOtK9hDWfSZm6c4jNUi5evHfR572GX/nvduiSQEjAxLfu4nBigVJf/if6mta7YQUhJ7bVSQZ/N1S2JKwStHPX75X1T41V8qrOJ3t75OIcRWIsi3ZUEI5RXFtjGD6emIejP5/eLliKWlpBX68UdsXj8WcPFyxPkLIfm87BeAARaXEvrMbWMm9x7cXFnYtgSjwwZsQbp9zQfDdC2ido/0aBankDR6zL00TewlE1rYCVChwnAMBiZzBK0Awzbwmx69ys0djer26B55Cx0rwpmFxJfFMXGrnbR39+lKBSCSotYdJPuNdIaRL/wkrTffoHXsDaRlM/ixT+Du3kP5P/8Z3tRlrNIgQ5/+LK23jtM+9gbCssnccy/ZBx/CHh5FK0UwP0vzjaP0zr/bd7z2tglGf/JnqfzVV+mcXFMft4olRn7ip+m88zb1bz9329csTJP0gYPkHn8Ca2gE1e3QeedUorr9t4CjvqHTDbRPrEOEsBIH/LeI69ZR1O/5/3/D3IzB4Y8Ncea1Ovc+VUQIwZ4HB1i81OPAI0kvuIo1dlri92IsR3Lv00Ve+2qZ/YdzzF/qMb8SYbg20rFQQYxVcFBhTFBp4S81MbMu3lw94dqNFdl9o3iLTcy0TdwNiXshcTfASDvEnc1zaIYBd+2y+MkfzfCxH0izb49FJr3R8Sm1OdYUALHasSc2Ujz2dxHJEv5TP5Sh3tD86m9U+sUmFQaErQYgCLs3LvrFMRw7GfBPfrXCj/xwhp//qQHu2mmScu9MvXjtuJpGS3FpKuJ7r3nr/Eevp/nm8966tOW5d9ei9ee/s7ZtZpNVFySP/kuvevzyr1X4tV8p8tD77BtOGDcyFSkWXp1h5ZKPMASGm6A6VKQon1xCxypxrOcreDWPmRemAI1f9/Br3k1FXJML1uggWSU6liCdFdSrehX6tjaU/6ZDWscRcbuNPb4NYZoI18EaHUPaNs7EJN7UZYyBAczSIFG1CoZB9oEHKf7AR+ldeJfK0SNgmmTvex+Dn/w0lWe/Su/8OdAaYRgYmUyC6LjWpMRIpxMneQfm7r6LwU99hmBxgerXvwYC0ncfInXXXmLvDiPn27CbtAEnUS5I0iJHiN/nXwAw8nns8XFkKoW4BhSv4xh/enoNCycEZrGINTqKdByU7xMuLBA1GhuWE86uXZj5pDNFhyHe5cvErfU92dJ1SR04gDc1BXGMPTGRtP/5PsHSUnLedevf5Pz22FgCKblmdOs4xrtwISnYXWehp7hyusPYrhSFURvDEEydapMftkjnDOYv9GiUQ66c7pAaMFY7lMBOS6QB3nydYKWNDmOCShthSpQfIaRAR4ogiAhrnUQ1+MQM0jIw0g6q59O5XEYHEbUjF9cB/NfdBwkf+3CKf/6PC9x/j4PjrDnNKNbMzUdcno4oV2O6XU0Ybj7EhEjyqo6T5B5LBcmOSZPxUWPdUhoSJMCnP57ma3/d4RvfTnSvhGnhjk4iTQsVRet4dbeyhaWY3/+DJt9+qcenP57mIx9IcfBAIqZ5O8xo1bri4uWQs+dDnn+xx0uveVQqG5EcsP6RuH77rQQ4UZw43n/wz1b46c9n+eRH0+zdbeHcokKG1pp2RzM9HVKbaeHVNjr4zvzas96eTSawrrf2bMb++vdYFuzcYzF3JaK3hZzSg4/ZfPJHMvzmr9VpvgfwtmtNhyHB0iLO9p0Iw8QaHEL7AcHCAvbIaDL2cjlQmqhRw8hmyT32BN7MFarf+gZxswlC4M9cYeS/+Qlyjz6GPzuD6t66/M3tWvaBh0Apas99C38ugZT6c3PJxHFdEU24NsIyUa33Lv1wEz5dRVs1MERCHnIt/MrdvZvChz+Mkc+jfR+ZzWIVi8S9Ht6lS0SVSuL8TJPMffeRf/JJZDabOGeliJpNmt/7Hp1Tp/rLGmGauLt3k9qzB2toCAyD5T/+4w1O18jnGfrRH6V19Cjm4CD28DDCthFSEtXrVJ59Fv/yGlQkffAg+Q9+EOm6qCDAzOcx83miZhPv0iWCublNnW5qwOCJz4wgJUydamM5kic+O4xWsPhyndBXxKHiyc+NcPz5Cu16yKOfHCZbNIneaoPSqKucADF90vI+94Jau6d6lTQ59kKiZhe1ylO8FcbXNOGzn8jwr/7HIju3r3WixbHm0lTEF/+kxXde7jEzF1Otx7ekZyVlUunP5yST20yeetzlF392gB0TZr9IJYRgeNDgUx9L870jHq22xnDSGHYKtLqu6+vGFoRw5t2Qcxca/MGfttl3l8XB/RaH7rbZtd1kdMwim7ewbInXjd0vTIQAACAASURBVOgFglYzZqkhmZoOuXCmzeV5mLrQY2FFE8Wg/DvEnF01IbnKn5z0DKvVanaCxdTC5Pwlxb/67QZ/8hddDt1tcviBFPv3uowNa3I5A9PQxMrA86FS8VleUczORVy4HDA9EzE9E1KpvjckUIPDBj//D3P8n7/dYGYLfo13T4c0G2263fceiaOjiHB5mex9D/SjW+X18Geu4O7egzGQwxoeJSgvoXwfZ3IHZj5P8+hra+Naa6JGHe/SJQYOP4qRyXzfnK50XOzRMYLFhaRLbXW2DVfKhOVlrOHR/r7CMnF2j6N7Pn7XwxzMIV2HaKWOcOxErSPjErd6t9VafUOna4sUWaOA0jE93SZabRIQjkPu/e/HLBRY+fKXCZaWMPN5Sp/6FEY6Te0b3+jj3NydOyl+9KOE5TLVr3+dcGUFa2iI/DPPUPjoR4nqdfwrSSuu8jwa3/0uzZdfJvfkk+Qee+yGF599+GE6J06w/NxzqF6P9KFDFD/6UXJPPMHKzAw6ipDpNPmnn0YYBuUvfYmoVsMaGWHoM58h7napPvtsMttuYo1yyPHnKrRrEV435q3nK1iuQRwq/F7yAD/3BwtICb12zNFnVzCdhIYw6N3hA6503+HeyO45YPOrv1RY53ABTrwT8Ku/UeW11711IoK3dGoF7Y6m3YmZW4h5622fWj3mX/9PJYqF9XnKxx52yecSTtyo26J58e3kGHcgB6FUEvkuLMW88rqHYwssK5FfGdibCIh2plcwBwdonJojdWAbsjDA3MunSe8fp+5Nk7q7hI4VzRM3aMW9iZluFidbwmuWkaaNkynid2rY6QJKhUReh0xpgtDvEHltLi3A+ak2L76VJVso4FUuk8oNEvbqmG4Ow8nSWChjpEpEkUV9qUa02pU4PGqw/5DFsdf8fiOHlPDQYw7lpZjpS4kDndxhcNcBG8OAmamIqQshUZQQHO07aPPAIzbve8jmmY+mKC/FtJqKV7+b6NllsoKHHnNIZySd6wjlLQsO3W+zshRTKBmMbjPotBRnT4U0rlF9Hpsw2LPfIpNdS/8szEacPLb2cEXNBjqOMEsl7OGRJJiZuUL60L1Yg0NYw8OEy0voMMRIpxMWzeuKZmhN3GkjHfuO0wa3YsKxEaZJ3PM2NPIkRbm1a9Iq4cwwhgvEXY/U3TuTdut8JmmiaHawRkt0jp65rTTNTZUjlI4JdbCO8MbIZjGHhgiWlvAuJ0QSgefhnT9P/kMf6kezwjTJPvggQkrqzz2HP5MMiLjRQPk+oz/7s2QfeIBgYaHfOaKDoP//ZhZVKtS+9S1UJ5kV28ePk9q/H2fbtj4PrFkoYOTz9M6dI5hNCmfB3Bze1BTpgwev6ya5Gs1Jgp7krW83qS9FRGGimxb6EF4XeXqdNQdpR0VKxjbK3hRxvD46fy9NyoRR7NCB9exdjWbMv/m3dV585RoRyX4LGMnzZBlrs/L1T8p1a2w/gGe/2eNHf9jnw8+k120bGTLYNmYyO58QjFxFgdzMdkwaLK+oLdnFogiGhyT3HbJ49d2AYMLEDW0CN0+04OFHNn41pLg7hzYM/OUWmZ1DmPk0zbeT50tYFtnHH8Hdv2/9xwtC2kffwDv37manJpUfQak4kXEqjJMZ3kFr6SJCWlhGFieTcMgKaWJYLobtEhkWoUrTaAn8oIiWO+j0IG56mI6P76UpDO1G+11ivchVbd2hEck/+OU8v/3rNY4fTZ718UmT//a/z/GH/1eLK5cjHn7c4Sd/YYAw1ESBpjhk8LX/3OEb/3cXyxbs3mey94BNLi/ZtdekNCQpL8W8+iKwivgYHjV47GmXyR0mJ97waa2mmNIZyRf+zgCuK/B9Ta+nmdhu8O7pkN/5rQbdjuaufSa/+E/z1GtJG/QHPpJi6mLIn/zH9avCqFEn7nZxd+zEGBjAe+cUYWUFISXW8DBWaZDu2TPoMFztIAVhXVeMFAJpOwnB0lVnqK9uWp++EYaBsK6TrL5F01Hc903X84RsqNrGiqjSxJ4YQqYcVNdDdb3Eabe6uPsmCReqt00cf0OnG+kQhcKVaTzd7t8EHQRo38fIZJCpFKrbRZgmZqGQ0AJeJb1OpbCGhog7HYKl9Yxd4dIScauFNT6OdF3iTdr1bjZ7BIuL6yAjOoqSYw4P94HRV6/HGBhAOE6SCnEczHwe1ev1O3YM08Wys0hpoFWMYbm0KxrTtHBTqUQJQ4DXqayTk7/WvLiF0gpTOEALR2ZIm3kiHazqv6XxohaWTFoVFTFZs0isY9ph5ZYVXLeNGTzz5PVNqfDK6/4G1V5zdDBpTdYaHcWYo4NEyxUQEmEa6DBCZlJoLyBcXNnQiNJqK06eDvnwM+vPZZiJinD/bwmTkwb1egKXGh4yqNYU+/cmqYmz74Y4Nvz8T2d562TAkTd96g3NoQMWriM4ez7E8zT3HrLYMWkyMmzw6lsedimDN1cnNVnEn6+T3T9G1PETKkcNrTPzjH/qAbzFBkFldUkqJfbkJJn771t3zcrz8C9eZCvgo9+pk8qPEJoWSsd4zTKh10bHEdKwQYA7MIxalXOXhkUcekn7c+Al7FadBqHXIQ49DDtNHPTo1ZdQobeuO2xmOmLqYshjT7t9p/vw4za+B6eOBeSLkh/7mSxXLod88XdaKKX50MfSfOrzGU4eD5ibjvjmV7sszMY88n6HP/tim5nLEUrrfj9Ps6H4yp936HY0P/5z2U0/c74k+df/Q5XFhZiHH3P4+X+YY+ddJmffCbn/sEMmK/hff6OJ11M06ortO01OHls/wcatNnGnjTO5Hem4BAvzCadHrYozMQlS9rk6wvIyyvNwtm2j87bdD66kZWNPTBIsLxN3kzGtVYyOY4yBgXXns0qDGOnMFt/ijU31ukTNBtbgIEYmS+Rfhbk5WMXSxkLyarASLtcxB/OYg3l652ZQ7R7p+/fSuXL7MmI35V6whE1a5mireh+nG7fbtN96i8IHP8jQ5z9PuLSEkcvh7tpF68gRoqvLdcMA0+zTA15rWqlEdcCyNs44t2iq19sQna1TXQSiep3OiRMMPP44Qz/2Y0QrK5hDQ9hjY9RffDFhySJRcjUMi3RmGN9rIA0TEEjDwk2ViGMfw7DxezU241QGUDruO05DWIym7qIXt8kZwzSERd4eodybpuRM0I6qZM0SGoVrJA9VM7wZ9jKxew/ajA5fJ5Ueao6d8KlU11+cURhI2KhMM6lmGxKZTmMUBzCyaXSkkGmXcKFMVG2s07eDpCBXq2+cDASsw9ki4J4DFmEE3a5m+6RBvaEZHZF0u5rxUYPXj/sU8jLB3CJ44D6T9z/q0mwpdu00mZ2PuP8em5VqTDYjiL2QyvfOEza62MUMfrlF90oFq5ihd6WCt9hI8Kaxpnulsk7J5E4saFcJ2klzR9DZgm2qtrDhtWv37aysceKG3aQbsjl/bsN72k3N66/4fPjjKUqDEj/QHH7C5Z0TPpUVxb67LQ7cY/FnX2xTryXf6YvP9fjsFzIcus9mdioi8BOssdbge3rT1UMUQhRu3ZRw4g2fK5cjwhCmLkb4nqY0ZCBImkXieBUvrROGVKU2MqXqKCSq1cjcex9RrUrUaKDCkGBhnoGHHyHudIjqyT2KajXabx1j4OFHiKpVOmfeQcgE0eBs20b9uy8Qd5JIOu60CVfKZO65D296iqhWxRoaJvf4+zcKI0iJtJ3k5yr/gnRTyHQGVIwKAq6Cvjtvn2Twk58i9/gTtI4eAa3J3Hsf1uBQkvZYNWusROrunQQzy2gvoHtsdYVkGqTu3oF3YZaoefu555s63UAnM3mkr1nua033zBlS+/ZhFgpo30d5HrVvfpPu2bP92UsHQcLPOjCATKf7aQAAaVnIVIqoXt8SGvY3QBCtmVJ03n47udZcLsEqNptUvvpVehcu9CM732sQ0KTXXUnwi/2za9rNeUzTJZUevGWMjSFMpDBpBIsYjokpLNphjUF3OwKBH3cYc/cQaI9YB5uz429h46MG6etgYX6gWVja2IbqX7iSOGcp+xOSjhVyycaaHCOuNYibneQ72AQlIYXYtDqvNOtIZeIY3r0Q8dEPuWjgyJsB+/eYnDsf0mhqnnzMoV5XzC/EHD8ZsFSOefIJh507DM6dVyyVFYMlg8tXIi5PRYyOJBDF7lRSGwhrSfU4anl488kAlo5J4fBuvOUGvdlbI5D+r8nefNXjE59Lc/B9NuWlmN17Lb78x8kYMa2kOeXaXGy3k/yezrwnIwOAelX1EWhXG1Gkkfx+4o2AD/5Qir/3S3lqlZh9d9v85Zfa+Js497C8jJHJ0rt0MeEriWOC5SXMfIGwskLcSgIxHUU0jx5BuikGDj9K7v1PAxrt+7TefJ32W8f7YzKq1Wm+9gr5J59m9As/jQoDVLeLd2VqQ97X2TZB/v1PY+QGMAtFAIo/+DFyzSZxs0n95RcJ5hOin87Z01gjowlW+L77Ub5PsLhA5+xp3O1r0vbhSoP4yGmUH/ZJta5inIPZ8h1zE9+kDVhRjzePvtzdu7EGB6l87Wt4Fy9uirlRvR7+5cs4738/6f376bz9djK4TZPUwYMY2Szt48e37Cp5r2DBqf37ka5L+c//nGBuM0o7gWWm+ueUQqJ08gRKaWBaaeI4oNcpI6WBkGm0Vqg4XKfomrfHyFsjWMJlqXeBXtRiPLUfISSLwSKRChhyt1P15/HiNiv+DGkzn7Dox7c+Y2bSEstcP/CUgmAzSFgUb3ofVaeHf+7mZCCplGD/3o35szDULJXXP3BTMxEaSKck75wJiCPNh552URqOvRXQ7Wlm5yM+/YkUX/9Wj+MnA0aHJWEIM7MRfgCf/niK3TtN2p0b4IqvfgY/ovry+Zt+hjs118rjh60bTohpp4RtZmh05m5r4gRYXox590zIo086LM7HLC1ETK/SXHbamlpFMbHd5N3TyWtj25LhurJ8jTah2pTG+JZtld99U6usxLTqivJSzLnTAd96tsflC+GmVCjdd8+h/vOfEqzyMgN401Ms/ekfIZSi8MDjNN85TtzrELeaVL/5dewTxzHzBezBUaQ0qb/y3fWdZyqmdewN/NkZzNIgiGTlGlZWcMbG+6tUgKjZpPPO2wm5+XXXdjXt2D9sp0PtuW/SeecUZm6g73SFaWINDRM1V/laohgVxQjHwr1nF0gBShO3upilHOFSFdXeZLV9E7tFYcqNJh0HmU6T2r8fI5NJOA7imLjVIlhaSm681rTfegtn1y4KH/kIZqnURy9kH36YYGGBzsmTfciYMTCAOTiYfPjhYaRl4UxOJoJ7q9CUO2n7k66LzGRI33031uBgQi4ex0T1OsHyMoY2GBw+SBwHBEGbTGYU328S+A3c1CCWlUIIidaKMOwShYm+U7dT7otVQpIe6ERVNJpIBZS9yxjSQmtNtNrRd6n5RsK1gKLiz9AIk5zQrZJ6QMJpodR6flfbFgwW34P2rets9w6TRx7YWE1eWo4pr1yXiojgD7/UQYqkk+udsyGz8zGCRHZdKfjLZ3tk0oJWWxGG8Edf6uC4kp4PnVbMf/oS5EYc5s93+sxd0hTkRlxaKz5x8P0hILreHGuA7UOHqbYv0+jOo7Um6w6h0XS8MiDIusNk3CG01nT9Kik7ibA6/gqmtIliH43GNtN4QXODUw5DeO1Fj1/4Rzn2HdR855s9Gqs42uXFmFdf9Pj4Z9NUKzFhoPnYZzKsLMe8c422XLejiCPNfQ85/XTAlcvJytFxBZmsIF+UWHZSVDMMRbt1a/cwl5cUhwzOvhNiGoLxiYQ3YvpStCHFELeadN45te411enQPX0KK18i/8yDtN49BavDV4ch/uwM/uwM0fAKdmlo8xWvSlQgrnI8X7Xr2cPiZoNoqUzYqqP8m9MV6DDAvzLF9aNuU57dq1GuSoruqtlBp120H95RB9sdOV1hWasSLhG5xx5LnBgkF+R5dE6coP788+gwJKpWqXzlK+QefZTMvfdiZLPEnQ6dU6dov/FG0qWyaqn9+xN4l2UhbBsdx+Qefxz18MMQRVS+8pWE/EYp4lZr3Ux37flVu90nuhG2newnBPkPfGDtWpUi7nZpHT1K98gbeF6dXrdCFPXwulWuTv9R5CckNKvUdXo1+rWsTJ+JSUhIF2yiQOG3ewgJwhSoSKF1jDAEQgm00jhDYLsO1ZkuSIUyA9AwfneOTi2gueyhlSadX1UPboYbIpHlcsITcA11KI4Nh++3KRYktfp745hKRcnf//kc28Y3KmK8ctSn1d54nnZ7fcqhWlu/T6+n16UlGi3N2HiabWMpLr9eITBMRh8cZuayj5VWBJ0YO2WQLlh0qj5Kgp02kUZyf/1OjJUyMO0k1PPb0XuwQhKrUjEZhDCQwiSfmcAyHBCrryNwrCxSmMQ6RgoT20yTcYexDDdBtWtFpHxSdj6h59yk/HD+bEh1JSZXkLz5mt93Zl5P86dfbPOFX8jzD36lSBQolhci/tPvdahVVp9tCYvzMX/yH9p87DNpfvAzGc6c8Pn3v9UAAU9+0OUjn0wxNGyQyQr+u1/JUykr/uKP2izMRjQbit412F0Vaxp1ReAnOPDd+yziSLP/kMXOPSaZrCSTFXzxd1ocO7J5kCAMM1n6i0SxQgXXjVEhkLabyD/FEdJ2iLptwkZ1LVksJdK0QYq+4rQKbhCUCImRSlM8/BSNd44RVJZR/mqRXK4qQEuBCvyktiRkwgFN0hugo2jNUQuJdNyEMCsMUGGA9gJ6b61HvEQrdy5LdvtOV0rS995L/plnaB05gnfpUkJKIwSG65J76iky991H59SpPkQrWlmh9q1v0XjppaS4Fif6UdcW10xs1Okpli5dwhUZIh1gChtXZgh0j65q4XYNCsYoYS1g8fd+n1TkkhUJ/WFb1dBBQP3b30aYZh9RMfDYY2QffDCBrM3OJrOpEBiZDIUPf5jsgw/SO32GRmWqnyq4Kh9/I/O9Zp+EOZW3OfiRcVrLHlOvr1DaniVVsKhMd3BzFtlBh7AbMXuqzuCODH47QkgY3Z8jP56i1wjJj6UY3iOoz3VZPNfk7h8Yxe/GnH9xiaC7Pqw4ez6kVlcUC2vdW0IIPvh0ig895fKXf9X9G3PmTo4b/OLP5fjsJzJY1z0l9abi+Rd7dLbogLodc7MWe98/xMhdSXV9+VKb0vY0939iHNMxeO2Pp5m4J8/uwyVeXfJwBwSP/8ROmsseTtrglT+a5qHPTCClYGh3hu/83kXaN6cfvoklxNxe2KTRmSVWEbaVptmdRwiDXGoMgLZXTuBl9iBpp0TKLmCbaUzDod6ZpZjdia3TtHpL6C0oQitlxW/+ehM7bbI4nTwXTtYm9CIqyzHPv5Hi6HnJ1EvzRMIgPZEHPOyMSXF3jvZSl//yJ22OviXJlGxmjifLaCdnc/x4xJm3G+t8vVLQqCmCQPPvf7tBGOh+umBpIeY3f61Gp60pDhp8/mcyfPmPO7z+sofSSeH0n//LAg895mzqdIVhkL/3YZzRpLPLX1mifuLI2nYhcEcnyR28n8apN/HLC2R272fg7vsJKstUXnsBHYU4Q2MUH36S2OtiuGlir0f16Hf6BbbrzUhnKD70JOmde5G2Q9is0Tj5OmGjSnbvPWR27QMpCWsV6ieOIG2HwSc/QtxtY7hpdBRRfeMlwkaN9I67yO67B2mYRJ0W9ZOvEzXfW13D284ECcsivX8/Oo7pnDyJPzNDuLhIuLCAPzubNEUIsa4tGFZZw1ot4nq9L/q27kKEZCgaQdUalFoDiHqX4XYRo+4z1Coial3MUOKIFNuM3USNBiPhKFlZQF5DMKK63aTZQWuk65I+cADV6dA5cYJgdnbtWq9cIaolDE9IsS43eyuW7J88znGoiPyY+kIPN2czvDeLVrD9/iLbDuaRUjCyL4flGqhYU9iWxjAlk/cWyBQcsoMOWmm69YDhvQOYjiQKFI35LpG/cbCevxTy5gmf60OnwaLBr/1KkS98LkuxIG8rzydIouWRYcnnPpnmd35riH/y93LkBuQ6lEQUab79oscrRwPQSRQoxdWf5qYigjeyXjNk+liN6WM1zn5nGTQ0lzxe//MZpAFuzmThbJNeK0RKgTQEcaQ48mdXkp75gkW6YBEGivnTDdord0bMtJkFUZfx4n3YZpqOt0Ipu5tidiet3hJtr0wxs4NCejtKR5iGg2EkKYUw9vDC5iphj03X37rIZ2Usig9sI71nmJFDgwztL7L98TF2PbUNYUiWLrSoVDXLizGhNhkYTyan7Eiane8fJz85gO+DJxyankWjrtj20AiTh0cR6RQLczGL1/xfXojxvQRWVq8qOtevTFYUvqcxLcgXDAJfE0VJEJrLSbIDGxstrpp0XNI79tCZvsDKq9+mdf7UNSIBGnd8O/n7DtO+cBq/nKQLWufepnHqTQw33UccCcPEHZ2gc+kcKy/9NYbtkprYteU9jDstKq8+h19epPLK85S/+w2CahkzVyR3z0M0Tr1B+cVvYKQzDNz9PoSUuCPb6M1dofzdr6NVTGbXPsx0hvx9j9Cbm6b65ssYqTS5u++/rWfmVuz2I904JlxZIXXwILknn8S7fDnp/HIc7IkJsvffjzc93Rd6u1ULtIenOwyZk3i6g0aTkQUiHRLpAEvY5IwSkQ5JiSwCQaxj6nEZT29ehFJhSLiyQvree8k99RTB3Bw6jpGpFO6OHaT27qVz6lQfznKnFvkKvxORyll4jUSa2U4ZxJGCHjQWe7h5C8s1SOUsBkZcpClQCtJFm5mTVUrbMzQWeri5pGjlNSNSBRthCIjXO9cogj/+izYfeSZF6bo87v49Fr/16yU+9uEUX3+uy7sXQyo1RaulCMIkqhEiUW1wHUE2k7T9jo+ZvO+QzYeecjm4P0lTXM+7AEmU/Tv/oUHsDVFKO9hGmlhFGMIkVB6h8mj55U3VCLayONQMjLgM35UhChV+NyGjUbFGGoLSZJrCmMvQrgz1xR5BNwKdtDwLKVBKE3kxjUUPacqblrOE6yItk7jbRRhmovpqGiAkOgiS9JmKWaidwHSTlVbgdenRRgchkYxAKbp+FWHbxJGH1pp6dzaRqol8UnYBU7rUOlduWGCTlkRKQW2mTW5bFiGhfLbG0P4CqdL6XHqn4mEPrD4frYDmfIfa5SSs79U8cttKGK5BdizN4skVWot3zhfQqClefqHH538my2NPu0iRdKdVVxQvPrd5XSXudWmdP0V294HEaV48S9RKilKG41B44HG6Vy7RW7h512BQW8FfWSLudYg6TQzbveH+ehV6kfxM7reZGUDHIf5Kkm7wlxdwRrfRvXKJqNXALy8kx281kE4K6bikxiYRhkFm1z4M2yWoVW7zzt3cbs3pCtHX/tJa03rzTYRpkrr7brKHD0McJ/mrlQrNo0fpnDyZ4OLMBEuqle5HvlrFSb5E6w1wi3ZcZ7u9n+ngLL7uUYsXkUgC7RMR4cg0SnfxdC9xuiSpAoG8Zvl2tfVKIIKIxosvocOQzKFD5B59FBCoICCsrFB/4QV6p07DNR0lQhirUewaZOxmFkdJlDUw5NKp+SycbpDKW9TmukhTEHQigl5E6MW0Vny8dkS6kORsW2WPoV1Zls436dYCgl6E145YONOgtCONlGLTlomXXvX43f/U4h/93RzZjOhHo1IKSkWDH/9sls99MsPCUsxyOabeVPi+Joo1cpWsO+UKcgOSwZLByJCBbW/NMhYrzYVLIf/zv6ly5E0fqZsYwsITrdW7pIlUkv/eaim9lZWn2jgZA9MxaCx6nH95BaU0514q02tGDAwpLh6pEHoxXiPk3EvJhP7uS2XcrEnkK6JQsfuREs1lj+rS1qsWo1DA3XsXMuUSrVQwCgWilRXsiW0oPyBuNJI8XxwnEMd8HuYDrMFBMA3C2Xns8TGEaxOVK5iDJfTScgLgl5KoUkEFEVIYNLqztL2bY6/TQylM16Q51yHsRYzdlxTnVKgYPlAkN5FhoeSSGXbJjWco7c7RLveQhmT4YJG5N5cZ3FugsDOHM2DjtwK2PTDMyrs1yu/W7wgG1O1o/uB3Wxy8z2ZoxEBrzQt/rXj3TEjzBjWD9oXT9GYuk969n+LDTxJeXZprTfOdY6Qmd5PeuZfOpbPXkVKtP46+XrXlZig5rQGddJqtUsbpKEQYVkKyFQYY6Qxxr5twhFwN36++VySOO2hUqb3+Ev7KUpKXvr5i+B7YLTldo5jH3bcbHcVElRoy5dC9fIGw18TM5/AvTWOODBFMz6LDAHO4iMxlEsloKVGenxAnS0HcbGMODxJVavgXLvdp6kws0jJHRzUJdA+NYjG8TNouYdlpzChFxaiQSQ0z27xAJj1Miw6uUyIlhvCCBlHk4dg5ul4F18mTTY3Sai8Qvfw2zddPIw0T00zR7VXwe3VUr0fGGcIZGMQLGvhBi4H0OK3uAtnUCAhB11shjG6CmNDQmO/RmE/2W5namHvyWoljXzidzPx22qC94mOnDBqLPWqzXbRa269V9miVt67Cdrqa/+P3G+Rzgp/58QEGshufSssS7Jg02TF5xyAVIMEAv/aGx2//uwYvvNRbJc9571iXgk7MpaNrS/DypWTlUr6Y/Jx7Z32StteM+ttTeQu/HZHOWVRnurRWfG70WBvZTDIoY4U5NIiRyxG3Wsh0mrjTwRwaRLU7iVyUbSf1Cq3BNIhrdWTKxZ7clgxIz0+QNasUivbkBOFyQnzf8W9dY6u10GXx7RW6Kx5KaWrTTeJAEXZDpl5eQJoCvxkQ9ELe/OIZgk5I5MdcfGEmCT5CxcyRReaPlfGaPrNHl7DSFlEv+hvhLrsdzZuv3Xq6xkhnye47hNBJY0LsddFxjJAGKgzpXrlIUK+Qv+cwUbtJWFvBGd1GattOrMIgmd378eav3PxEm5nWhM06uUMP4s0P0p29TFCvENbKFO5/jLjbxh4coXbs3KT+MAAAIABJREFUlbVi+nUWdVp0r1xk4OD9OJUlEJLe/BX8pdtTTr6Z3dpoVBrhOCivSVSpkX7gHvyLU6h2mghFWC6jfI9gdg6EILN7O/7FaXBthOsgghDVaidFNCGRrpPkdNexL0piQsrRTL+rKyZCWjbZgXHcqESru0CgeiihyLnDfXlmjSabGqHVXSTtlIhij4w7jCFNMqkh0s4gXtBERgZR18MMFb1VFiMpDAxpk3VHiOMA03AwDYfiwE68oIVWijBaf9OLh3cy/PQ+lp4/S+PkFqTpN7GgG3P+pVvrQNvKqjXFv/zNOufOh/z9n89x1y4L29o6Wr0dU0rj+5pL0xF/8WyXP/tKl5l5RSwk8uo5BMRBjDRkQlsZxkhrteU41qjo2khFYjguhptOBD/9XrK0j9dy43divUbIG3+x/jsQztaPdVheIa5WiWKF6naRAwPE9Qaq3Ub1vKRy7jhJRbvbxSgWQSnCxWW074Nh4F28nKBf6g2MwVLilOOYaGkJfZssQ34rYP54mc5KIjYJ0KuuOTq/uXY8FcVE13QM+s3wmuOEsEpIpaKYyNsiQpNi7XbLqwTD4o4IzK835XsE1RWsgTxxrUvr3Enibhtl2dSOv0rU7RA26wnyKV4taDspwnqFsF5BWjbCMAnrFeonjiYCBkrRevfULXF7VI++SGpiJ1onMl3K61F57QVSE7uQtkP19RcJVpaQtkPt+CtEq2oR7cvvJnSrYUDt2Cuktu3AzOaIvV4/PfJemtjK6wMIIfTqLwjX6c/qwnWS3NfVNl/PT9oxwwiZSWPvnMA7dwlhyCQ/1ievSZaewraT6OAW+AbTziApt4TWMe3uEvnsdjq9MsXcLoKwjRc0cO0CYdTFD1vkM5PU2zOknAKONZA4YncQP2wiMAiiDqDp+cmyJ+UUyaZGiWKfnl+lkN1BozNHNjWMH7SIYp+uf01eRwj2/eMPMf7J93H+3z7HwtdPvXddHHdotgV377P58c9meObJFHt2mRTy8oaaYZuZ1ppON+lsO38x5Duv9Pjr53u00nmciQKZbQPUL1SRpkFqKIVWmulvXaJ0cIjBQ8PMvXiFofeN4FV7BK2A5WMLCY2llGR33U3pgfdjZQusHH2e5sV3KN77KK3LZwjr723eTDgOpR/5DANPPLrudeV51P7yq7ReObLFO+/QpMQcLCUFYu+9K+S9VyYzDjLlIG0LYRkJbCuVkOsHizXMXJqw2sLIpggrTVTr+0/k/f9101pvOfhuLdLVGt1bW+pe/V3HKiFFhT4uViuVRLlxQlZxdem17nA3W65fY12/ss7pletnMQ2XenuGTi+JFFvdNU2uq87UC67ph/e2Lur1/Fr/PQCL1bdXX9+84mwX02R2DyHN904OR5oC05ZEvuoz+0sziUhUfHOPHoRw8nTAydMB2ydaHDpgceiAzYE9FtsnTUaGJPmcgeMIDJl0+wahpttR1JuJ8u3cYsTUlYhLUyGXpiMuXArpruJph+6T6JqHmbYwXRM77+I3fHrlDpmxLHbGRpoS6Rj4NY/OYhu3lEqiB6Wx84MMPvgU7alzSdRh2aAUdmmEVLe96nQFhjCTTr9bJP75r8aUIip//yS7AZAyCXKMVc4NsVq70KtjT8UJ98QmQZSRdkntn0C6duJki1mMjAumRHkh9lgxkXc3DVTHuz2nKwDDRBhJW9xV+R+0TpbxSiWrmVtU9P7/g/3Nkn2b2LXO+ftlUewR926TLPY9MncsR2o8/54eszSZ4r4fGufsd5dZOJsUpoZ2pAl9RW3u9qKOmbmImbmIb7/YI52SOG4is2OaAinXyoxXiUuiWBOESSrB8zSbsdQFrUSvSxiCsB3QmmuR35lHK03kRVgZC6/SI2wF9Fb3uarBBWBm88R+j/qZN7EGCslBtUb5PQw3ab/OWcMMOpM0g2Wqwfxtt9T+rZkQWONjGLlEsHW9aeJmi3Bh8YadStb4GEb+mmdIJ+mLuLFxKStTKazRYeyJCezJbZiDg8hMBmGZq/fQR7XahOUywew8wfwCUXllHSQzrDSJj/WSPHQUrzLwaTAkOozxZ5a5mmJQ/q3xIQvLwiwVsUZHsCe2YY2OYORzSNdFGAYqilC9HlG1RriwSDA7R7hcJm40b7uLS2Yz2JOT61/UCn/qSpLyuRUzDZzt2xGbcPXG9Trh0vItX5dZKmIOD3GtVLvqdgnm5m9pcrktpysMiZGyEriOH6Kj6waGFBiuhVjNG8Ve0M9TrdsnlfDAxn7UV1NYdx4zoR0UUiTRkma1GqlQUbyquLD1oDRSFsKQqCBaIwSXAmkZScpDiKRjKFKoML6p5pQwJcKQSNtk4MAYdimhlZOOhZl1N/2ytvpsm1nlSpfqbBfTSb7EwrYU2+8vMP1WHTtlsOPBArNvN8iPuhi2xGuG7Hq4hNKad18s061vHChhBI2Wgi1pfa+635tbc6q++o5VEUMU1XfKq8RAktb0WqMI05scYJWjVhpmX5FBWDZmJodXXiBl5JhMHyRSCW+zKW2GnZ1IYVLz5/BUm0FnO45M0wxX6EQ1hpztGNKhESzSjv6WyG4Mibt/H6Uf+QzWyPC63LnWmqhSpf7sN4hWVhKO1esko5IquSD3oQ8w8Pha6kNHEbWvfYPmt7+ztrtt4+7fR/aRh3D33IUcyN40V6+VIqpU8c5fpH30dfzpmcQJKI26JkjRrJ9Zb0dzVtgW9uQk2cMP4dy1K7kP12uYbWKq5xHMzdE9dZruW29vlNS6gVljo4z84t9JGAmvXnMUsfjvfhf/4s35QwCsoSGGfvoLWMNDG7Z1336H8hf/cEPvwFaWfeIx8h/+YP9za63pvXOG8h/80Tr6zq3stpxu7t5t7PvHP4AALv3+S1SOrP/AmV2D7P+nH8YqpFFeyIXf+Q71E+uLHLmDY+z5ex/AzLpc/g/fY+Xli/1tdjFNekeJ3MFxsntHcEcHMNIJsXHY9OnOVGm8PUfj7Vn88ubdKWbW4cA/+0Eyu4eY/9pJ5r58HCvnUnhgO8XDu0hPFjAyDsoL6c7WaJycpfr6FP5Ke4MPEqYkd3CM3KFtpHeUyOwcxB3PJ0spYOdPPcrEZx/YcA0qiJj7L8dZ+KtTG7bdinWqPhrIDTtUr3QpTabx2xHbDuZZONvk4A+MEvZiBoYdxu/OcfG1rXOirsyidNIaawmbWEcEukdG5lEouipBBgiSxhO9+s8WDjExGp2IkyJIyQEMYdGOEycnhCRrFIl0SE+1kBjY0qUT1xM436oF9RVUFDB4+IPYxWGEYWKXhpGOS2/hCnHcoRPV6MVtunEDgSRQPdJGjqIzQSNYIm0WWOxdIFI+A9YQA9Yw3bjJkLODbty8LVzwHZkQpA7sp/jpT27pcGtf/Su8c+dwD+wkXKomJMNhBIZE2BZxrYXqbLJykRJrdLj/p5HPk3vmKbKPHkZmMxsajba8RCmxhocwS0XcvXfRfOll2kfeQF/fLn8nJgRmqcjAk0+QefB+jEI+ua5bLNrKlIuz5y7s7ZOk33cvzRdepHf23C0VHlWrnSBHRtbuEasE6bfsdEdHkJn0ptvM4SFkOkXcuAWnKwTWyHBCxda/wGSyu9V8/m05XdULMVM27miO9K5Bqm9Or0W7AlITRXIHx/tR5sDB8Q1ON7NriMxdwwgBQXWtqWHg7jF2/tRjFN43iZFZZZVXur9EFVJSeGCS8U/cS+3NK1z+jy/TPr9J9V8K3JEB0pNFBg6Mktk1yI6ffJTBJ/YgLWONk0FKcgfHGfnQAWpvTnP5P76y4XhG2mb75w9TPLyr/9pVhwtgDrgY6Y3LFRVEGOnNZbo3M8OSmLbEtA2ETPK4cagw7ATPPPVmjXt/cIxeM6S20GPPE8lsXZ7qUJ+/cfrBFg5Zc5x6tIwlbExhg4KckTiObnDV6SZONWUM0I2bCTUlEkNY9FQL0H0MriPSmMLCEBamsJEYmIZFSmYxMAlUj/gaJxh1WpSPPE/x3keRtoM7MoFXnqdy9AXCZuLAQxUQxj1iHVKyJxgwE1YpqROEidaKUHkoHWMIE1PYxCqko2q3jQu+bVuNcIuf+/TmDne5TO2rf0X37XcQpkHc9TByGczREsHUAtb4EKrTRff8zZ2uEFhDQwjLRGazlD79SdL333dLEeRmJgwDa2SY4g9/HCOdpvHcC/0ozhzMJZIzWZdgsbYpnedm1+fs3kXh4z+Iu3fPujFwW9clBMJxcO/ajTUyQuvlV2l+50VU58bww7jbJVqpJM7ummuyxke3ftO1JiXW+FifZ3fD5lQKa2w0SX3cxIxMBiOfT1bzq6bDkHBx8QbvWm+39a0GtQ7+SivJa04UkbZJHCUzlTANsnuHQQriXoB0TLJ7htevYoUgNVHAcEy6szWC+toDqPyI9I4S0jHxFhp4S028hQZhs4cwDVLbCmT3jeAMZSkdTrSKzv1vz/V5Vjez3P5Rdv7sE5Qe2UWw0qZ1YRl/OVEfTU0WyR0YwyqkKD68i7gXcv5//zZhY+2a4l7I/FdP9iN6YUiKD+9k+Km9ACy/cI7GqfkNyyStNK1zt/gliITspjiZJpW3qFzpMjDkMLQzQzSmKF/qsDLVodcKWbncoV32Of38IrseLpEt2YQ3EWLsqTYpmSPUPrZwk2hXhGgUoboGjkRMTESkAjzVJmsUiYkIlEdaDtBRDULt4cosofJJySwIQaA9LGHjqTYqjrGFu557edWidoP66TdoXT4DCFToowIP6aSSbiHVIVxd54bKQwhJpEO6UYNOVCdrlphMH6IZLtMMyzgyg2Ok8OL29zf/KwSpAwcofvoTmzrccGmZ2leepXcmkQ3XYUR4ZRGkIJhbRvshUbUBsepP+BtPIZCZNNboKNnHH93U4WqdcM4q308cpSERlo10nS0jYek4DDz5BGGlSueNY6AU7q5RjFyauN1LnO7NTAqc3bspfe5T2JMTW55LxzHK8/vsghgJ9E44zqZpEZlJk/vg0wjbpvncCxvEZ6811e0RViq4Wq871jonfKOP4DrYY6Nb3yfXwRobxTt3c5pQo5BLsN7XmA5CgsVbV5C4TafbxVtqkbtHk95exHBM4u5VuQ2Dgf2jqCCi+vo0Q0/tJTWex8ql+o7MzNikVpfn3ekKUWctHO9MV5j98nGEFDROzdGdrqKCazrFTEnp0d3s/fvP4G7LU3pkF9m7hqi9uTWYOjVRxB0vsPLyBa78yVHaF5b7OWZpmww/s4+7/u7TiSN/ZBcD+0epvj7Vf78O43V/C8vAzDp9p9s4OXtnkDEhGR57H4HfolG9yMyJOjMn1tAWzSWPuXfWF1Ve/cO1ZOnS+TZL5zdPr1x7DrQi1D5LYTJp1OO1ZWZHrT9+wpmgqcfLBLqHF62tQhrxGvrjajri2u03MzObY+iRH8AZGtuwrXr8e7QunKIazCOERAhJK6rQWVVduKpZtuAnA0JrjdYxC9751drP97EqLiXugX0UP/epzR3u/ALVrzyLd2a9MsT1mlm6d82yc4vluJHPkf/YR0nt27MuV6h9H//KLN75C4RLy6hOp89JLd0U9sgw7t37cHbuTEQXrzu+kRsg/6EP4E9NEy2X6Zyawkg7SS3jFqJce2KC0md/GGf75Lpr16vagnG7jX/hEt6ly0TVWkK9Gqskas9ksEZHcPfuwdm5fZ0Dvhr15p56ArSm/ld/vbUuYhwTLpWTdu2r7GCAmc8n4gjdG0fKMp3Gnti2/tpZw7ML28YeG+2Tcd3IjEIBI7te+iiJxG+9rnBbTldHit5sFRXEuGP5ZGm9GmlahRTpiSLKi6gdmyZ/3wRWMY37/3D35jGWXFea3+/G9uLtS77ca69iFauK+ypKLYraSXWrW42W7R4v8BhjjxsNGAYGtsewAQMDb/DAxtge22g0BrbbY2im1Z5uSZRIaiGpheK+s5asLSsr98y37y+2e/1HvHyZL/NlVmaRYvfMByRZmREv4ka8iHPPPec735lKbxrdpE1kPBXqj86X+wYbAKlYff4jVCB3Jt965y6/eZP1uyc48oePolkG8WN5Ku/O7270FLTmitz6f9+geX0wdCBdn+Kvb5A8M8H0tx5Aj1nET45SeW9+Z4LwE4ZAkMmdoN1ap1a+cfsP3AHy4+eplq7je/tjPygkzaD6G6FrWekRrNwohdd/ir9NAsxvN7HMBGOj96AJnWp9Ht9vkx85G5ZJN5eJ2lmidhYpfTy/S6U2Sy5zCk1oNNtrlCs3+MTJ0kIQvXsXD1fK0OB+/4d0r30y358WixE7f7avRq6CAHdpmfovXqF75SpBqz3UIHQ0jeZbbxO79zzpr3wJPZfdYXjNyQli589SLxQxR1LYp6ZwV8t0ry/vmUTWEgnSX/0S1qHpHZOFcj06M1do/OIV3JUVZHsXMW9dp/n6m9in7yL1+c9iHTk86HEaBonHH8FbW6P55ju7Gj1vfT2MmfaMLkIgolGMkSzubYyunkphZLNbxu4SNJqY+ZHeoQTGaB49HiOo79FQVgiMbCasWdg6trX1QfH12+DAwZnWXAnpeBgxE3sLdSpxYhQjEaG7WqM1F/avsjIxolOZ/j5m0saeSPWTWNuNm3SDoQZ3A8qXobHu0VrMTGxXzwHCypzSa7M0Z4fzdIO2S/3yCsqToezcWLKvMfFpQdMMNN1CbA3MCw1NM3bst0FR0jQjZANoJppuIrZQV4TQiUSzjE0+gBVJ7Tw2IHrn3HpMAIkM9xVi6LHvFG6tjFsuYI9NE8mNYWXy/R89YhOL5ZHSZ37pVeqNRRLxSZqtNYqly6SS05hGlFZ7HddrAZJs+jiJ+AQIQTIxha7dWWfYXaFp2GdOhx7u5MQOD9ddWKT8lz8Il6OfQCUX9Dw/fUOrRNK9eo3iP/1ntN56JzQEu3lgUhLUGzRee5PSX36foFbbUeYqNI3YffeEzRfH0iDCuO+eSTBDJ/7wg0TP3d1v8grh9UvXpfHKryl991/QvX4jjMnuxkQIAoJandZb71D8znfpzFwdyPCLnsxq+ktPDXij2+Gtre/Qz9aiNsbIyO7X0IN1eBq2vNd+sUT7o4sD98nM59ESw5t39sdqmZhjYzvCFN7KyoE6Ah84Ut+eLxN0PMx0lNjhLJW35wBInBxFj5q0FyphPHa1TvLuCeJHcmimjvQDrNEEZjKKU2jQWd69vE7oGkYigpGIoFlGn+qFJrCn0v2HRTP3Ngh+26VxdW3P2Txou6FAiaWHdLdPrv3UnlAo4slJjp1+GstO4TkNlm79mm67TCZ3kvzEea5f/B4AppXg2OmvsTj3CoHX4cipr9BpFYgnJ9B0i1ZjlZX51/C9DrnRM4xO3k8yc4Tjp58mCBwqxWusLb0DQDw1xfjUQ1h2Chl4FFY+oFK6DkqSTB9iZPw8rcYqmZETGIbN8vwb1MqzDPMkBRqGYaNpBp7f2bVLsmZa2GNT2GPTBJ3WwMNevfAGQaGOYdgkE5M4bgPP72DbmVBXIHAQaEjpI4SOrpu4XgvHrdPulOh2q30x+f3d99tACKJ378JSkBJ3fpHy95/FuTm373MeBEop3OUVKj98IeSO7hdS0rk0Q/Otd0l/5Ys7Nhu5HNbEGN56Ddn1UH6ANZ7FXRueTDNGRkg8/mhf7LuPQNL+4AK1l34RlvYfAN7qGtVnn0OP/gGR40cHz5cfIfnEY5SWl4f2HZONJn61OhDH1SIRjFzutufdHhpxV9fwVlbDqtoeb1dPpzAyabzlnY1HNyBMa0ccWUkZeroHKP44sNF1yi2cQgNrJEHsSHjBum0SOzaCMHVat0q45RbtpdCTjR/Po8csVLNL/EiYkfbqHTrLO+UU9ZhF6vwU2YeOED86QiSfCA2vbaKZOtoW/m7vNuw5Vul4OOt7LBdgs3IG6Lfl+BQggERqkvnrL9HtVpk++jnGpx5m/sZLGEaEiL25QhBCI2Jn0DQDKXQSqSlMK87SzV+hGzZHT32ZVn2FcuEytfJNpPSx7BQLsy/T7VQJgjCMY5pxDh9/kmZ9hbWld0mmDzF97PM43Srt5jqabpHNn0IpyerCmyC0sPvxEFMVj44xMXIPyfgkhm4xu/QLyrVZMskj6FqEcv1Gn1VgxBIEnTbrrzyHWysPHE9JiSD02m07SyA96o3FsHeDZlAozWAadi+2qyOEhus28LwWphkb0DW+LZTam5Otadh3ndqVpeDMzlH5wY9w5oaRkT8BKIXsdKn//Fe79PK7DYKA9nvvk3jsEYx0amCTFrUxJyfx1+eJ3X0YBHTn1ghaHYLazuV5/P77sMZ3Jqq8QoHaSz/vG1xN6AgRepGGHunF5UWoW6J2NmBzl1eo/fyX5Ce+jRaN9v8udJ3o2buJHD48dEILQzqrRE/fNfAZIz+CsMyhla8AopckG7iG5RW89QKy1e43uBS6jjU1SWfm6q6rFy1ihUURWyCbTfxy9UAFHwc2usoNaM2VSJ2fIjqRQrN0rFyc6GSaoOvRWaqifElrrkTQcYkdy2PEI/htl/jR0Eg76w286pYvukc3O/pvPkbu0WMY6WioGlTr4re6ePUuyg9QgcRMR4lOZfZFW1FSEXT37wV9uhBUitcpF2aQMqBaniWTO4Gm336pLISgsPIB9eqtvqdrx3KAwPc7eG4TGfi4TgPX2YyhxlOT2LEchdWPMIwInttENywSqWnazdCrUkqxvvwendbupdPJ2CRnjj1DNJLFly6xSBbLCDO6yfgkY9lzdJwK7W6vk2+zDihyDz2JVy8PZPFb89fori9Rqw8asnL1ev/frrtz4qzW5m57n7ZDSYXydknW7OXhBgHOzTnK3/sh7sKdCRzta3yAt7gUyo3epmBnN/jVGu7CIkb63MDfhWVh5LK0P/oAYfbKdtVg0cQGtESc6N2nw549W8cnJa33PsDf4oEnomMopYjb+Z5YuyJiJomaGWrtZQK57fhK0b12ne7sXBi62HKf9Uya6NkzuIuLO5frvRXAdpj5EUQksqvRNcfG0BPx/nmUUnira/iFIkGrhZ7N9LdZh6ZDDZldjK6ezaDFBrm+frW2dxx4CA5sdKUX0LoZvkxmJoaZjWPl4tgTabxyu+/BtmaLBG2XSC6OPZ7CrbaJHsmBVDRvFgcmQCuX4NQfP0XukaMgBK3ZAmsvX6F5dQ2n0MBrOkjHR7o+Y1+6m7v+oy9h7IcHu1GX/jcUrtNAyvDhUoGH0PSh9Jp+PfsWON1eeEaFSmuadvtYtGFGMc0E+fF7+p0yWvWVzWMRCnB77u7MBF2zmB57GMOIcvXWC7SdCg+c/lv97a12gch4kpid6xtdIQROOXxRNWswCSH0T7wSfXdIiRxGxr+Nh9u9dp3KD36Eu7j8mx1fENC+dPm22fi9IDtdvLU11PmzA9chNA0tHkf5EnepiJFP49waHnqzDk1jjo/teBaDeoPuzNWBBpJhw02FEBpe0IWelrWm6bvyp2WzRefSZeyTxxFbuLNC04ieu5vGa28QVLathJUKG9N63kBlmpEfQbPtXUMd1sQ4WmyT4iXbbfxKtd/FhkPT/W3mxHhowHeJz5rjYwM5H6UUQbW2J91tGA7u6QaSzlIV2fVDNsJIHHsihRGP0LpZpLsavsBOsUG30MTKxYmfyNMtNDCTdjhbzm4RBxGQfegw2YeOIDSNxrU1rv4vL9K8th4K6mzDRhnvvxoY7s2E8UuB0AyU9DGtBPo2D3ivgoDNcMm2gL/bxnFqLMz+Ameji7Gib/j3GtMGdN0ikzzCavFD1iszmEZ0YCyu10QIDUPfNK5utUjh1Z8MHkiAHo1zuxDRJwkl5U5a0m083O7V65S//8NQT2EbIkYCQ4/Q9Ro7Pbo7gHTdUDby4yAICJqtfsnxVmiWSWRyBCOfRI/boebCdqOra1hTUzuqt0IPcRVvm7CP26MOev7mRNGlihD6nlWC3euzBK32joIFc3QUM5/faXSBoNnCL1ewxsc2h5tOo8diDD2TrvcM6aaD5hfLIdMCcJdXiZ47G1LFCBkk5ugoTnO402GOjw8kFQkCvEJxd6rbLrij1LRTauKUmpgpm8hYivjxPChFZ6mC3wipE9INaF5fR+gaiZOjRCfT6LZJ0PFoz29y2oQmSJ+fDpNtQcD6L67SuLo21OAiQmqaZn26DINPG512EU0zmZh+mMzIKcanH8Ywo7f/YA+e2wQUI6N3k0wfxo6GYZ1mfYluu8zUkcdJpg6RTB1iZOzsDoO+FwRh3y/Xax2oEkzJYNuPJH7kNKlT9+z7GB8b2z1dTcM+3fNwt7IUem1fOpdmKP/l98PkypCYXSo6wXTmfmLW7ZM5+4FfLO2rKup2UI47nO2g67iFGu1L87Q+vDk0gaZZEazpyZ2FBL2EkWxtMUhbbHriWLb/XirUoMEdMq/6lSp+cWf5ujANIkcPD70u2W7jFweNvjB0jF2KJLRoFGtqciC04JdK/ZWEuzRY2KTZEaypyaHHQtdD3YYt9+WglWgbuKO1nVtp46w3iE5liOQTxI+NIL2AxpYyWhVImtcLIeNgMk3sSBYtYtCeL+M3t1I/RL/sV/ky5PTuEs8y01HS56ZC8eW/DvREdzagRYxwLPuQX9x2IDqdEm538wXzvBadVhGlJJ12meX51xgZO0siPU21NIsQgogex4yEWsQ6OoZuY0cyaErDdztEzAS6HhLQ1xffITN6mnhqitL6JbqdMoHfZf7GS4xN3s/4oYfDVUdzDbXRYNPv0mqu72lMpQroOBVS8SnWy5e3bRWkEoeQ0sc3JEY8hd+qh1nfZGZwT6FhZUYIOvsvsvi42O7pRo4fI/t7vzPUw+1cmqHy7HN7MgjqnfCF84JPpouGXyyFMWdN6+tTYxhsNLYThh7qWUessMJtFz1qJYOeGNEgBAJrLIN1eARvtYJfbe6YTIRlYY7uNGLK8/DWe3F+IUidGMHOx2ivNnCrHWJTaTqrDeyxBEHXR48YqEBixC0gGvj4AAAgAElEQVQimSjNhepABWpY8LBG9MxdgyfStB2Jrw3ITie8R9sr07Z4vgOHikUHj6UUXrEUVvUB/noB6bjovdi1sKxwf03bkUzTU0n0ZHLgvNJ1D1SJtoE7MrperUN3LTQY0ekMsaO50LPdql0gFZ3FsADCTNkk754Mje6tMv6WogilFF7vy9BMHXs8hTC0HRxeoWuMPXWG7MNHempXnz5UoHCr7V7PN0Hi1BhG1MJvHky4WinJ4uwvBv5WKV6jUtwsQyyufkRx9aP+7/XiLNOjD2HoERor14jqKTyjScIeRTVbtCpLTOYfQEof04hSrF7l6upf7Dh3t11i/sZLQ8fVrC/RrO+dNfcDh0J5huPTTyKVT625iKYZ2JEMEyP3cnjicVqdAvqxw+TMo6y/8hzRsUNMff1f74U9+jXhGHaMwus/3d9N+ySwxeiaE+Nkf/cbWNNTgx6uUrQ+vED12efwS3tXGdlmioOotd0OQT18pyJ3HUN1nZDAPzVOUGsiG00iJw7TvXITcyJP0Gjhrx9c/F1tMDh2CdEJ00DP7pQuVb6PXw5DUrptMPLgNDKQBG5Ae7VOfDpN9eIqRtQkc/cYum3Smq8w9rnj6BGD6LUCyy9f77/XSsr+8QYHIMJChmHVYYHEWy8MUL2AXY20kcuiZzavRTlOOHH0Jpqg5znr8SPhqTWtL34jt4UYjEx6R/mvbDSHX8NtcGdZDBlWlEnXJ33PFFYmRnuxGip1bYFbadNZqRHJJ0ieCZdv7YWQ59uHUtQuLjH1O/eiWQbjXzlL+1aJ+uUVpBsgDA0rF2f0ybsY/8pZgrYX1qpbn2ICZstYOwsVnGITeyxJ/omTdJdrFF+9QeCE4xKGjmZqOMVmfzL5pOB4DTpulSDwMA2bqJ3D0CPYVgpDjyClT6l2g5idxTD2H47YDYJe0082pTSVClgtfUTUzjGWPcvEyL1YZpyjk59FSo9mp8Dc8iu0V5r9JapmmrSX56h88Cqy15JbCI3U6U++vfWe6IUXjNE82W9+g8iRwzvyA+7CIrUXfnpbgwubRSpRM4Prd5Hq4zFlgl6sUYuYOCvrmJNjWNPjuFKhZ5JYp47iLqwgIhbaEC7rvs7R7OJXm7smmLVoFGHuTFKrQPaFaQLHD1d4vqK1VEPTNXTLwEzZOKU2o48dwa128ZoO3WKLzkqd1kJ1MGQoJcGQ5FdYHmyhRe0dhg/AW10Pey5uMbpGNhMmwLZp627n58qug7++ycqRjoO3uk7k6JH+38z8CHoisePceiaDFh80uu7q+r7lILfiji1Xa65I0PWIH8uDgNZsYbCsl55Ww1KVxMlRzHQUv+nQXqoMhg8UVN9fpPTGTfKfPYk9keLMf/K1MAzRctBtk+hUBiNl05orMf+dNzn6bz/e5wh/2mjOFii+co3J374PI2Vz7G8/wdTv3o/X6CI0gR6z0Eyd2T/9Jas/vvSJnlsp2f8RQiduh9U4vt/t/T1AIQd5xwcUjN5AhCg5bRwFdFSTmtqMpXl+m+sLP6NYvUomcRjLjBNIn1ZnnWL1Oq43mM31Oy1at67RWVva7MAqBHZ+Ei2yd2vtTxJKSrSIRebprxI9f3Zn3FII9EwGY2w09Ihuw3xx/Ta95g0YmoUbfDyjqzwvFE9ZWg9FXhbCSqegUkOLRelevIbsdDdLbu/g+zUycYx0nO6t9aEOumbbAwpafUjZrwiLZKKh4ZWKxOEM7eU67ZU6RtSks9qg/MEygePTWqpjJNaJ5GLIQA6eT6kdFWYbEIYRdvAdZnTX10Opyh4PWQiBFo1i5LI7kp3WkcHYsOx28bbEhJXj4K6thVzx3rNgZLPo6RTe1rCBHrZj0qwter5KhZVo+2g5th13bHTbCxWCtouViaGkonmjMOjBElZ7tRYqjPgSzdTx6h26QyrRvGqb2T/9JV69Q+6RY5jpKIlTY4BCugFevUv1less/PnbNK6tMf7Vc39tRlc6PvN//hZB12P0ydNY2Vj4k4uH3oDr45ZbAyEUIBRvHwk5gUG9hZFLEzTbm7E7KdFSCWSjFVYLHRoH08C5dgsCiRu0KeurCEPHr9QxU1lkq4th2pSa87iiiWNIvIiH5y5jTo1hHzpJ98rNgQofI5vDr1V3NShGJoORzWLOVZFIWqqGN8SDk9KjXLtBpTaL0PS+EM0wOIWVkDK2VZxGKZq3rg5mg3/DEIZB6stfDFW8dlleG6kk2We+hmy2cGaHMwnMZBYrk6e9NEvLLYVl27pOKFsh0EwTGYSdeDeuT/n7MMhSojyPoBQuWaXfwZ0NBZ2CSg1vKTQqbvPjUMpchKlj5lO4S8UdhndXOUml+lVXSiq8ehfN1HGqHdqrodHdQO3qpjdZm9mjqs73h7Is0LQ+o2A7glbIYNgax9VsG2MkN2B0tUSir62wAa9QCAV5NiAVfqGA7HbRe/xbYRqYExOhpsaGDKzZi3NvHWcvsTiseu52uGOj65Za3PrOm0RG4qhAUX771sDyYexcjiNPTJA5b5O16yy+U2Dx5VtDK9EAOss1rv2vL5E4OUr85ChWKooC/EaX1q0SzavrfdWxpe+9R/3iEvXLO7PK0vFZevZDIq/ewG86ePW9BZzbCxXmv/MmmqUjKw3u+b3jLL61SulGbddQnVtscfP/epW1n82QODVKZCSB0AVB18drdOkuV2ndGoy3ack41qFx3PlVIsenQdPQR9LgS/SRNN3LN9CzqXDmTsXR7AiYRv+LFlETcSiLN7+KPp1FWiYia+O7Hnouh7rmEMQEQo8hTB2VMMNjbHugM1/8Iu3Ll2nPXN5x74yRPCPPfAPZ7dCce56IiKGh0aGFo4a96CGtTSDC04jNF0Uqv5+QUzIYNLg9+K2Pn6k/CIxcjuQTj20awi3X3zfCvXY82W8+Q/E7f45fGJJhNwzMRIrY9HG6xVXs0Sl0O4ZTDMn7kfwE0unit+pEcuMgoDl3hcC5TbjpDlclB4FyfYJaG+Xu4qGJ/n+GfDgcn1vtsPLy9eH7HGQse13ubmkbKXFXVoiePdP/k2ZHMEYGnTBzYixsa7S1/Hd+aUeS3iuUkM1W3+gCWIemEPqm9rZmWQMi8xBylv3qnXUKvmOjK12f1ed374wgNIGdtogZHnmzwexHN1h5bibsDrsLlC9pXFmjcWXvjGD5jZuU3xjuhUhncFxWwsROW3Rrw7l0naUqC999G4Cph0Z56I8/g0BRmWsMthDfcSJF+1aJ9q39JTOU46ICiZ5JEdSaGJN5gkod2WihZ5Iox0OzLMRIhqBcRc+kkB2n/2Qqzw9ZC9kUQbWBOT2GbHXwixX0kQyy08XIZxCJGN5qEWMku7NlDKBcj9zXnw7Hf+1KOJtrGvaxY+S+8jWEbVP5yY9xVZeGrGAIA4dBY2EZcabHHmYkcwpDt4cmZW4svEihMhPun8lj5cZozV0JDTCAEMSmTxB0mjilg2eA7wTbqxhV18GvVDBGcqFk4MYEp2lEjh0l8/TXqPzgRztoXEJoxCaPUZ+9iG5FiB860X9BjWgcI5ogcLu9XmEewjAxk5nbG91PAXo85MrrcRuhaZvfRw/hcnnIOyrEHYuq7wZh7CK6I+XuHqRUuMurgx6yrmOO5EKmR2+5b42PocU28xpKStzFxR3vg18sETSaGKP5zcq0yYmwi3kvXivsnRoPfrV64KKIDfzGslFrF0qsXSiRP53hd/7R539Tp9kTQhec/voRrLjJ+9+5urcRBcqzdd78JxdZfr94230PCtV16X54tf+7t7y57PIL4XKy88HMlu2DZbjK9eh+tMlu2Jq5br3yLgDdS5syg/7q8O601Z+/jGbb5J55BiUDurM3iN19ltwz3yBotSl+769wFuZJiBS2iOKqLmkxQkdt1NobHBp/lGPTn6fdKdHuloZSzHx/c4VhZUZIHDtDe+F6/yUXQiN++BR+s/apGd0NKKXwiyXqP/8l7QuXyXztyyQ+8+hAqEPoOrH77yNoNKg+/5OBtjJKKZxKAT0aRzSqeLUygdOhU1gmkskjPQenvB4K/YyGFU/dtYVP9Rp3g1eqIywNr1gfKtIiHWe4C6qJvpbtJwIhBpJhW6GCYM8Elb9RmbahrStE2LAzEkH6Pughd3dr5VpQqw9lGijXxVtfDwV4ekZXTyYwcpuSkUYuOzBWpRRBpXpgwZ8N/DVQAD49RBImhx8dp77c2peOTbfqcPGvZn/zA/trRNBsUP7x8+S+/jS5rz+Ns7hI7K7TdJcWqbz4U7z1sCuqFJse6VaNXUOPkM+cplC5wuziyzi9Qowd55FeGEKJRNEiUTTDRI8lEV6YYdYMCyORwmsMp9yISASkDBMb8TjO0lJI9xJi0wvZSIRtGInbJL5UT1Cm/eEFGr/6dVjLHwTUfvYSWiJO7J5zA4ZXMw2STzyOX6rQfP3NviHw6mUql95CCIH0PYJOE82MELQbtNsNjFiSwHOJZPO4tRLd9SW81p0tRT9pCMsgaDtYYxnclfKOAgnZ7iBdb0CMBkBoOvouPcbubCBiBxsAwu9IuR5yj67iQauNXypjTW6K4oeG0UK2WqG4z9jYgBcdFnYMj4W7C0vw6MP950lEIpiTE7iLIX3SHM2H/e424Pt4hcIdJdHgDoyuYetMPTDKyKmQ/1a+WWf5vQJe+2M0BhSQPZpk6oFR7EyETrnL4jvr1JdbQ1c6ifEoUw+MkhgPH4J2uUvhcoXyXB0VKOxMhOkHRxm9O8PUQ6PER6M88nfO9bV6r/74FtX5zVlq4t4RDj0yhmaEN3bulWXWZypDz62bGuP3jDB6JoNu6TRWWiy8tU63uklX0SM65755nNULJToVh8OPjxPL2XRrDsvvFajM7VyWpKfjTD00RmzERvqSVqHD+uUytcXWniGZO0HQaFB+4QVyX3+a5COP0J6ZofiD7yGbm/ekrcJ7b4oINbU5XiE0LDPG4vpbtDpF9uKoGvE0I498gfj0CfR4EjOd6xtGzQyX8+X3f73zc5kM9okTeIWQUxk5dgxjZITujRuhEU4k8MtlhG2jRSJhZ1khbqvMpXyf5quvU33hpwNFEhtdfI1MOhTZ3hLfFbZN+qtfImg0aL//YXicwEcFW3rA+R6wee/cWrgKcTUNamX85gENroCJe/OkDydprDRZfq/A6JksuZMZqrfq4Qry7iwjJzPUFhqsfjh8VTMMetxGT+yM9W9AeR5Bvb5DpUwY+mDb+I8JoWkYQ/jAAEGruaenK9tt/EJx0Ohms33u7kbb+g2EbZXWCHbRtHCXlsPW9L3wiRYZVCYz8/mByVi6Ht7Kna/ODmR046NRHv+755m4L4/vBAgBhm2w8n6B1//kAu3SwbuOCl1w11cP89C/czcIQeAGmLbOPd8+xRt/coFbr24qC2mG4OhnJ3n4b5/FzkQI3AAUaLrgxkuLvPV/XiIIFInRKMd+a5LEeAwrbhLL24ydDcV20ATz75RgKRReFlqo3Rsfj5E9kmT6oVE6FYfC1eoOQXU7bfHAv3WGk08dwu8Jrhu2zpnfPsabf3qR9Usht9OI6Nz/h6eZnCkTSZjYmQiaLrDTEdq/2+VX/+g9Vj/cDA9MPzzGE398L2bcIHBlSD0zNFY/KvLq//bhrvHo/cDMjw5dximlaH7wPnoqhRaJYI2No9K9qrG2g6zUwk7ABGS0PEUZir1IJXG8JroWtobZLpi9FUGnSeWD1/BbDeKHTlK/9mE/iy99D7dS6AvhDEDX0VMp/EoF6Xn4lQoqCDDHx9ETCdyVFazx8dAISIkej+Ou7eMlCIKwPfqQWnlvdY3KD19g5NvfwthSoSaEQE+nyDz9VYJ6I5Qd3GfC68DGtgdN15h6aIz6YpNOxSGatTnx1GHWLpU4+rkp3JbHyacOs3axyNHPTlJbaNCp7K9Ax680cZbW0SLW0JWBcl28tfWQ47oFwjR3yBp+LOh66I1uh5R4q3vrCMtOSP3aWpkmbBs9lcRbWUVPJDAymxWQynVxV9Z2FYIP6nX8ShVro9Glpm1RL3PRR7KD5b+uO0gpOyD2bXR1S+Pc7x7n6Ocmee3/+IjrP1sAAWeePsbjf3QPlVsNPvjza0jvYLHQ/F0ZHv075ynMVHj9Tz6isdIidzLDF/7Th3js756ncKXSN+bZoyk+//cepF3u8st/+C5rF8sopUgfSuB3AwI3PHfxWpUX/5u3SB9K8K3//SluvLTI63/yUWjQDJ3o6UNET0+HGVxNUCwolv7H9xk9neb3/vEXdh3rXV87wtlvHufDf36Vi9+bxW35TNw7wuf/3gM89u+f52f/4I0BA3niC9O882czfPjda/hdn+Ofn+IL/9nDnP7aEYrXqvidAN3UOP31w0TSFs///VepzNXRTY3s8RSaJnCaH4/7OfoH38Y+fmLnBqVQvt/jrkaI3nW6v8l77xKdv/gxUeJ9BakNBEGX9fJl8tnTVOpzNDu7P3wqCHArBZpzM0jXoXbl/d2lFbd+rmdo7RMnaF+5EtJ8enoIKIU1MYHy/X4Flzk2RuvixQPcleHoXrtO9fmfkPuD30NPJvt/Fz1GQ+YbX6f83b8MQzC/QUhfcv1n80w9OMqhR8e59esVEuNR6ksW5RtVdFPr/R6hdKOGPEAZeuzeY6DCTimNN2ZQ2xJW0nHxlld2lNqi61jjY0OLEO4Eejw2tLHkRpuiPSF7lWmOu9k6R9BnMJgb/c42dm938FZ2FyeXnS7e6mrf6IpeWx49HkPq2s6eaPUGfu3Ow0X7Nrp2JsKprxxm4Y01Zn+xhN8Nv6wbLy9w+ukjHP3cJFeev3Ugb1cIOP7kFLql8eFfXKO+FJKhS9erXPvpPI/8e+eYvD/PjZdCDdMjT0wQH43yy//pPeZf3+TkFWaGxAUV9PM7CtjQUxYCzbbQohZ+rRXGLNtOuEjew4OJpCyOf36K2kKDSz+42Teuy+8XuPrCPPf/4V2Mnc0NjKt4rcrMD2/i1MN9519fpXKrQfpQAtM28DsBSircpo9uaqQPxWmXunRrDoXLBy8vHIbKz19Gf/vtA33GLxVx5Rr0atEsBj3lrlMjYiW5565vU28u4vQFqzexXpmh2e7xSsvrePXqvqt3ZLeLs7hI9+ZNZLeLr2nh9yMlrmmGUn7tXosYTUO7ceNjySFunljS/vACRi5H6itPDdCIhBDYJ46TeearlP/q2b7B/01AMwTTD41hp8NQW3O9za1XV0iMRQk8RWWu3jfEMnBwDzAxu4slVOAhW90dBheAntGTzRZ6ctPYCCEwJ8Yx8iN4tzOK+0Dk2NGh7XFko7ln94YNbLTv0bb0KzNyYR+0rWEHANlq7amhIR0Hd2WV2AP39ScaPZsNKWeGEVbpbaWeraweqD3Pduzb6CbHY8RGbOJjUe7/N+7qz666qWGnLMyYgR45GNHdiBpkjiQxowbHn5xm+uHN5cbIyTRW3CA5sfngj5xK01htU7115w+8cn2a7/ZYAKH057aw5HDDG8tFiOejFK9VB5dyCorXa+i2TnIqNsAvrMw1BjxVGSichosZNfpVPzJQzDw3R/Z4iq/8V4+x8mGJuV+vMP/aSn8S+jjoXJm5/U5DYGH3wgseujD6t8U0Ypw49BS2lQplEa3hcbl2t9w3uioIUMH+6VLKdfFLm+EXtW3bgNIVfDIGd+P4vk/916+ip5Ikf+uJQZqUrhG7/178ai2MC38CHt8wSF9x+dnZzYIzBdd/Nj/4+4uDv+/72B2HoN1Gj9thKe+QfIGzuIRXKKIlBnmuxsgI9rGjYRHCx9CpFqZJ9OwZtOjOasTujZv7EgX31gq9irbN52+j+eR2ARxvbX3PxBxBgL+2jnKcvr6vnkigJxNIy9oxTm9l5UDtebZj30ZXM0Md28yRJFbcHPAK/a5PfamJdA82kI3YpRULWQbb68GL16q0y1sSVJZO4AUHWk4Nhdrl3+Godh0rGmE54zZslDhqujaQnwjcYHgSbNspStdrvPzfvc3Ug3lOfvEQD/yt05z75jHe/2fXuP7iwkDIRug69qFjpM7eh5WfABRuuUTj8gd05mcHEjx3Cg2dnDaOhoaLg4lFu0cZc/0WH1377o5ml9vRdYYXwfzLANXpUnvxZfR0Kmzo2FuqCiHC7rWfeZSg0aD+y1/3eaGf/CC2Lbxu9/s+ocVtrIk0mm3hlepDja5sNOlcnsE6PI3YQrsSukb80YfoXL6CX95/y/HtsI4cxj59144ybNnp0rk8s2t58MC+7R6DYUvCy8hm0RJx9G1JQGd+Jz93O7xiiaDZ6uv7Cl3DGB0lqNcHNH/lhtrap2F0O2UH3w2Y+eEc7/7TmTCJtRWKAxvDwAnoVB3qyy1e+C9epbm202PZmsxqF7scfnQsNPr7woYGwYGGNRROw8Nr+diZCLql9ePHAPF8FBR0aw5bV9l7JZm2o1XocO2nC8z+fInRM1ke/6N7ePw/vIe1CyVqi5uZ8fjJuxl/5tsYyWT/wmLH7iJx11nWf/J9GjMf3v5t1HXMkTx6IrFTfwAIGk266y1MYeGqLjW1xetUkmbnNxvT/JuAoFan+vxP0JNJ7FODMXEtFiP1xSfDrrIXLn1iHYE/DfjFOpqVCRtS7sFFb3/wEfGHH8CaGFyqW4emSTzxGLUf/+yOKFNaIk7yt54YUP+C8F1xl5bpXL22v9lEqdDjPn9289ixaCjAbtubGrpBgLu8jBHRMKM6KgDf3ShnDh0mJXtFEs1mWCjT+6w5Phq2TdzCTw7q9Y+tebxvo9sqdlh+r8CRJya4+aslStdrSF+hmRpW3EB6EqdxsKRP4EkW317nyGcmOP7kNDM/vInb8hCawLANzKgRxoh7X8LCW2uc/9YJzjxzlFax04+rmnbYLdhpuAOeq5IKr+MTH41i2DqBJ/ehxDd8Y7vUZfmDIie+MM3UQ2Msvb2ODCSxnM3xz0/RLHQoXjt4cF3oYeVe4EjctkfgSgpXq6x8WGLyvjyGvZWwb5B55HMYydQO7QAjmSbz0GfoLNzEb+7+UOjpNNkvfpnoqVOhZsKQ621duED1uefRlIaGPvSe6JpJPDpGJnmEiJUgCDwa7VVqzUVc73akcYEpIvjKGTi/LkyUkgO84L9OeGvrVH70AiP/2u8PiJwLITBSKTLf+DpBu41z/V8ebreRSxA5MoqejuMV60OFzCH0/JqvvkHmd54Z6AgsTJPkZx/HL5Vpvfv+gbomaPEY6S9/kdj5czueX9np0nz9zf0XHPSM9EbCT/T425HjRzeTa4BfqaA5TY48mEP6ikjcQDc1WhUHpaBTdSncbIZavYUikSOH+3S6jYTtVsckqNx5JdoG9m103ZbHB//8Kk/954/wpf/yURbeXMNtedgpi+yJNLM/X+TS92aRvsKI6oyeyRLNRMgeS2FGDUbPZDj15UO4bZ/KXL0fr7z16gqT9+d5+N+9m/FzuTB7H9FJjscw4wYv/tdv4fSM68r7BS4/e5Nzv3uc7LEUhZkKSimSE3Gaa23e/X9m8Dqbs6/T8Fj5sMjJpw7x2H9wD/WVFrqlcf1nC/3zmzGD0TNZ7LRF7kQa3dIZO5/jVM3BaXpU5uo0VkIN3Uvfn2X0TIbf+o/v5+avlnEbLuP3jDB+foR3/uzyHcWao5kIj//RPeiGRuVWg8ANiOejnPzSIRbfWqNd3lxqmeksVnZk6HGEEETGJtHjiT2NbuL+B0jcdz/ta1dxFheHJre8wjoxkSQiosRJ4eGyIjfLrnU9wpGJxzk0/himHiWQHpqmIxCUarPcWHypH88dBkMYTEVOserO4sjN1Y0tYvjCx5GfnrD5nlAK58Ys1Z+8SO5b38TY6p31EkvZ336a4ne+i1/YP1d2NwhdMH42i6aD2/aJZSN06x7VxSZe5xOaiKRC6Dpa5DarRd+n9c772KfvChtIbhQOCIGWSJD5nWfQ0ykar76+L0Npjo+R/MJvkXjskYGQBYQluu2PLoSrhgPA69H/Nvi5wrKwjx8boEh6q+t41SYtC7yuJJo20Q1BdaVDLG0hdNH3KdzFZeIP3t+nhxmjeaS7Gd5USuGXK2E7pI+B/fN0FaxfqvDiP3iTU185zNQDeay4SbfmUrxWZfm9Yj+8EM1EOP97J8gcShG3EnQKDpkjSe7/w9P4TsDlH97sGz2v7fPWP7lI4UqF45+f4uQXQw5sY6XFjRcX8VqbRsFpeLzxpxdYn6lw/Mkpjn1+CiUVjZUWpevV0JPdArfp8e6fzeC1fSbvyzP5QJ52qcvcK5vZ0VjO5vy3TpCaimNYBu6KYuKuPNkjSdyWx+Vnb9JYCQ1Ddb7BL/6Hdzj32yc5/uARpO1RXajzi3/4DgtvrfVDC0oqagtNgqLAkCZer4OTkorGahvD1vuxYbfpsfJ+kRNPTXPyS4fQdIHT8Jh5bo6ZH80NJO20SAShD29eCWGrldupdtlHj9Gdv0Xp2e8TNHd/WeIiRVJkqcoCBlviekJjLHeO6bGHKZQvs16+jB90EUInFZ9ievwRTkw/yeWbz+L5m8mzpD5C1pwEFEV3EUuLMhU5DSiWu9fQhclo5ChFdxEPh8nICQwtgo7BsnMVU0TImVNoQmepexVXfXo6Bp2PLlKLx8n89tcHMtl9jYZnvkblB8+FjQ4/DoQglo0gpSSej6IZIuSu3/zkwhd+pUnjnWthjuI2YZGg0aD2kxfR06mwU+5WTz+ZIP3VLxE9c5r2hUt0r99ANpshDbHHfxeGgZHNYp+5i9g958Pl+pAOw87NOeo/e/nACVHZbOMXS1jTUwDoiTjayeP9CUJJibu6hldvsVbdea2dmjdQ7u8uLaGCzSIJPZnAPrEZWlKevzOeK0CPhKtyr+XtDLsOwYGKI5RUNJY13v2/Z3kruLhFz9PAMBMIdBQBjdU2L//3bxMXSc6Kh7ms3qVN6JIr2KFr0K25XPnRHFd/PB8+DEqhFKhevGX7vjM/usmVF25haAYWETqyjYNVW6EAACAASURBVAyCHfsC1BabvPqPPwiPK8JsbbDl/LWlJi/9t2FJp4nF3eJBVtQ8RVZ2jlVBdb7JhT+dp2FY3OASXdkJ99myAnebHj/++29wWt5Hxh+jQ9hePHAlr/zP7wP0k2O+E3Dl+Vtc+8n8ZhsipZCB2lGcIV13eO+4je2OM5wGtAVC00Kxjj0MLkBLNfCCWQJ8tgbFDT3CxMg9lGo3uL7wEv4WVkKtuYjntzk+/QWSsQnK9dA7Fgjy1mEK7jytoIouDBSKdXeOrDFBVE9S8ws4so0hLASCqJ5i1blB2hgjqqVwVYeObDBqHSWiRXEPwIb4uFCeR/PNtzFyWVJPfg4Gkks6sfvuIahvaDTcOaNBBZK5N9Y2nyVBj5/88ca/FdahPEJTmONZ6qXGbZ8XZ36B6rPPkfv27w+IwiAEmmUROXmcyPGjSMclqFZDLzAIwg4UqSR6Oh0aMW14Q1l3eYXqcz/ek9K1G2SnjVco9o2u0PUBp0M5btjDbJfJZfvqwS9XCBrNvqcsNG0gVDGsKMKMmYzeN07meIbl1xepzt5+4j1wGfDY8UepF25SW9sUb7FiMSZPP8nyzMu4nVASMXAlPhKJwifAv02cTilQ+yysUDKcxWzijDLFTS7vGaaV/h68mt5YAQTBvsbbChrcCGbo0Ol3VNiOwA16jLRtsash16ikIthHqa9Xq+DVKpjZkR0PsFKK7uoifmvveJOztEhk+hB6MkXQ2D0MYRNlSj9BRa4jEJRV+LBpwiAWybFaujBgcHujoNqYR9N0LDOx5a9hbzVLs/FVDKl8fOniKw9JgEBgCBNDWFiajS70ze3KxxAmKWMEV3Z79/PTb9ekHIf6z3+JloiTeOShbRoNJsknHiOoVGm89jrKvcOCFsWOifaThl9uoFRA0HZ27R4xACnpXL1O+V98j8wzX8M6fGhQGEiIsIIwFkWP7b9bifIDnFu3qDz7PM7crTu5lLCoYb0wIEI+uL29L85vf/9uF29ldYcOb3+74+wwun7Xp1vusFru0Cnvr0bh4NoLZgxtW/dYIXQi8RxCG344mxgpsggEdcq0el5vllF8PBqEs0OUBDESVFjHwiZGAheHFFkCAioUcOmioZNllDGmiZPEw0UhWWcJh/DCLWyy5DEwadGgRqmftEmRRSIRaCRJ4+NRJpxpFWARYYxpdEwaVGgSJsgEGjlG++Ny6BBsMbo6BhnyRIji0N5hcKPESTOChkaTGnUq/eu2ieLjkSSDj0+VAi6DXpPyPWrvv4E9PoUeGxQL8aplqu++RnAbo9v84APsY8fJfPFLtC9dxK/Xd2jdyq6D2dIRhHHW/o2BXqfXAF0zGJaV1DQjnEAHJiPFunuTrDlFVEtQ8pao+0UC5dEKqnjSIaol0dCwhI0urC3ba70p0CeqJah6awNx4E8TQa1O7ScvhkvmUyc2X/SeRkPqS1/Ar9dpv/fBX8v49gWlsKZyBLX2/vm9UtKZuUpQb4QhhfPnwjLiOzy/dBxa731I/eVf3lE33a3H8nst0IW9k/MbNFt46/uPtatur0jinnNDtSmCam1oiyElFdF8lG71Eza6sfQkidwRYplJhKYTifVqm4UgmhwFJZHBzkymQOMQJ2hSwyTCBIeZ4X26tBjnEG1afaObIsM4h6lTJkGaE5yjQYUuHRKkyDLKVd7v+zp6b/ihPy37z5BNjBOcw8fHxyHPBAWSrDCHQpFnkgQpWjQJ8LGJUWFTSnGcw9QoYWIxzjQ3uESDCqDwcDGxGWWaGuXe8nvjOk+SJU+dCknSJMn0jXmCNMe4G4cOkoBRplhiliKrpMhwlDPUqeDQYYQ0WfJc5cMdnnRj5iOk65A89wCR/DhKSZzVZWofvU1nce6232PqsccxR8eInjhJ4t77Qk6kkgPvX/vSRWov/IyGqqJjDFDGpPSot5YZy56j2pin3SkhlY9AwzBsJvP3E0iXdjfkceoYGMLCkR2Wnc3VUbeXLKv5m/e9HhR7rAaPjgy9cC8IJ55W8DeD9+sXilR/9Dy5b//+jjinnkmHGg2NBs6Nm3dGpP0NQ0/FiEyPovIuzmJx/50PemyB0v/3PaIXLpH4zGNYkxNo8Vh4D24j46f8ANlu4dxaoPnWO3SvXN27YGGf2Ch80IYYXXd55UDsCuX74fG2yEYOHG9Yex4FfsdDNxJYCYv22u2TbAfydE07iWHFiKbG0I1I75wK6XVZufYrPGf4CUusscwcFhHu5kGSZOiy9+A2vsIl5mhQIUGaU9yLTYw2TYqsEiNJQIoVbhEQsDF15wl719/kEj4e4xxmgsOUWMWhQ0iEMlhitifQLVBITMJ4YpUi81xDQ+Mk58kzTosaEkmdCho6KQZbitvEGGGcea5SYo0YSZK9fQQaYxyiQ5M5rqBQHOIk4xymSql/H5e4SZMqKbKc4Bw2UTrb7pPyPZrXLtGavQIbmghS7rsowiuXaF38aM993LU1dDbpZOE9Dz1oP3BZKX7A2ePf5NyJb1GqXcfzWuiaRTI+SSZ5hKX1d2m2w8kmqiWYsE6w5s3RCG5PqJ80T1H0F2jLj8GFDAK6166jRzT8ltP36KyMjfB7Gqkpm+TpCdqLZZzVOkbSJnlmgs5She7K3tQ/59YCle//kNi95ze/gz4URjqNq+vDeaxK0b16fWgIYkNzwE5b2EmT6uKdZcm9lTUav359R1LVXVjEXS2j2QZ+vX1HBH/ZbNJ65z06M1ewTxzHvutUWB6cC8tmtUioHqeCIOxo3Aw75nrLy3Su3cC9NY/s7qLZeyfXur5O47U3dugjpFKCR9LXWbnf5NJF77Y1LImEID+qsbK4gPPaK9z/eBLXVVz8yOvfpvaFizsq0YQmiKQjeG2P9vr+vq99G912bYV2bRUEdGpr1NY3BbVl4KPk8KtSSOqUUUh8vJ6nuL/iBg+XJlUUCpcuCjWQSd84Q2gcNr/EJBmSZDjN/b2LNNHQ0em1aQHaNOnS7h9jK1rUCfCRCJrUe6ERDfpe584HxsJCR6dFA4XCoUO313FBIEiSxiSCTay3f4SAAA2td60OLWq9z4YegI4ZJte2JlZ6HYfDNtoybGm9vc+UCtXUhGH0EiWqf4z6G69tXsJGcnGIt6Nj0lEtLCJhiKF/yYpKfY6rt17g0PijTOUfCKvTlML1mswtv8JK8YN+v7S2rA8Y0JwxQVIbwcej4M0T1zIk9RwBPuvePJrQMEWEcfMYNb9IV+2Pt6lZBkYq9Ha8aofu5YukD0Ptteu4pfBliE5n+s1TpRcQmUiBJnBW6+HvY0mEod3W6CIl3avX6V69jtAE0ayFYWp0ai4yUEQzERI5k05NYdo6RkRHSkXgSaQvEfOXcOcv0am6KAWxbKjY5lcdhC6IpsyQykSowxDNRNBNDd8J8J0AK24ghKDb8IZKqjo350I1tF3QvnjwGKpm6dijSdpL1TBE0GzR/vAC7YuX0OPxTYNrhGNTUqI8H9ntIlvtTa2MXU+gkTv/OOnTD+w5DiUDln/+PdxKOKnLdofaCz8d2CcSgc89YzM5qbPgK3QdciMaugbVmsI0IRoVSBnm2GpVSSotsCyBd2ud4vefo7VucfyEwWvfa6PpYaDQ7SpSCUGrtTlfaVbI/6/Pl/Da+4vlHzCmqygvXSDwugTe/pcGu5HdFWog6qmxne6kkButvw80SkWTGotsktYlQd8Ihr/vnkTYGosVt6+m2DK+wSWWGNiuqFKkwKZYSDgJef3t28dkZBKYCQsjk0B5Acp1UUph5DN4y0VExIRAhoyGQKKPpJDNDu7iOuZkHnM8F1J4XD9s9+P7KC8IPRHPx8gmkR0Hd2F9R4bXQCcqEujoeNtjyyqgULlCpT5HzB7BNGNI6dHulnG95tBOEhBq8yb1POveLTL6GFljAltL9H4fJannEAgmzBOU/RUctX9PL/3AYRKnx3HW6lTeniMyliLzwBECx6P82g2EEIw8cYrWjXWc9Qay4+GVW31DILsebqU9tCx2L+SOJTj5+Uncts/C2wWclsexz4yRHI+yerFC9miC1HiUwJO0Sl3cdkB6KobXDShcrbF+tcqxx8dIjEVZvVRh+cMyxz47TuBKynNNMofi3P3VQ+iWhtPyqdxqcOjBPK1Sl2ahy+UXFlFSkTgxQmwyhVNu07xZIjadoXGjSPLECG61Q3Q6jRmP4JTbtBYqJE/lMeIR3Eqb+rUC8cMZYtMZuoVm+LmTeXTLQLcNalfWyJybYPSJ46z94gbViytE8nESR3O49Q71qwVioxF0G/QYVC+s7GhSezsIoWFl8iSO3LXnfjLw0a3hHSc2kM1pPPiQieuAaQpsW/CZJyzGxzWWlyXRmODoMR2loNlQ/OjZDo8+ZpHNatyc9fE8WFuVTE+Hz8KpUwaHj+i8/prD15+2+dGzXer13nPjBcTycexslPKVEs4+4roHTqR1aqvoZpRILBsurTYsi1K47erQF27YY6wAF4cocSxsQJFheNZwN/h4GFhYRPBwkQQoFDVKjDGNj4dDp29E91fppEgzQpUSGhopsjSo9g2iQCDQev8N/7/hiXu4ZMjj4RInRZQEsIZCUqNMnGR/Pw2tH4veDVo8CrjoqThaMoZstkPqjWkQVJsIy0CPR5Guj2YZ6JkE7mIBYegYmQR6Iop0PIiqkLYjA4JGB9nuoqUTaMlouATeMjvYx0+gx2J4F6+HqxMVrlDipGj3vPiN++QHXeqtvYXD/3/y3iNKkixLz/ueaTfXImRGalmVpWXLqa4W6FGcHs6ZBXBILsAFsCC54IorrrghV1xwRyxAHhAcgD1npjENTAM9raplaZVZqWVkZEjXyrQ9LszDIzw8hEdmVk8P+Z+T3eXh5qbffffd+9//asJAFwaGsPDoAxJNGKhCI4x9QKILA0Vow3enH7exFBtNGARyMgqWu9LCrGSTvl9BTPfGKv37Ndb/7koy0QC9u9UDY4+HgoDyiSz1+x1uvZVkyafO5FANBTOjk5tLEXoRa9dbqLqCZqkYtsbqZw06Gy5HnivRq3tJ9aWlUjiS5t5v1lm53KByOpGWFEKg2xrt1T4rlxvYJZO1a01WLjc488YsqqEQuhFTXziBt9HFXe+iZUzKLx+lc7dG+eWjtK6tM/3lk6y+dYvK68cByfSXTrH+y9tUXjuOjGLKLy3QvLJG6YUjRH2fyivH6C+36C02iIOYyE08ar/ZRzFVZt84g7vRJXdhmrDrU3pxAb/p0L1T3ZfWuCekJOx38ZrVhKqlqEmiUlFRDfNArY/tWF2J+eC9gGYj5pOPA+aPKGSyAlUTnD6jsrQUcfVySDaXeLuqIvj0k4DXv2DsSox58CDk7HmNp57W2diIhwYXEkrp+ier6LY+EUcXHsHoWpkK8xe+RrqwMPL+hoHLrXf+Db4zacJDUmOVMzzLWZ4lJh4s/yf3NNrUmWGBU1wkxGeRm7j0qbJKmhyneHpo4Do0WOLOrmWv2xESYpLiJE+hkwTTE85ujEmKaY6QpUCKDAucpkuLDZZxcVhniVmOUaBCTDQw8kn4Y40HnOQCp3mGEB8FlSorrLO3TF6wXCWgSbBaQ6vkCautrZryTc90e5cDKROBnTjGuXpvwKNO7vXO9tHDCVPKkVLQ1KnTqLks4soStkiYITlRohGvD0RvDucJ2koegYIl0nRoUA9XyKkVAunRiFZxZY+cWiGSAZ2ohoJKN2qQVnMYwprY6CIg7HtkzkyjF23CrouUktKrJ2l++gAtZZA+nei3dm+tI1RB+vQ0cRjRu7MBikLm9BQylnRvrw9DEvtCQrfqMv98mTO/N0f1TpvSiSxmRsfvhwlVUQhkKIlFjEBFUQXTFwpkZz2cpk9+ziaVN4bbWzmduWeK5OdtisczgwIJ8NoDIr+UhF40aHm/9VjXf3mHwjNz5J+aYf1XdxFaIr6kppJwXOQGdG5VKT53BNU2iL2A9q0qhYtzSUfvGNo318mcLKNnLWI/onunRu9BwrBx1zv4DYfuvTp63iI1nycOY7xqjziIkGFM926Nzp3JGrWO3co4ovbJL2leex/VSKFaSZsnLZVh+rVvJF2VHxELCyqzsyobGzEgkBKCUBKEAlUBOy248JTGmbMa585pPHgQ8ezzGmfOaZw9p3H9WsjqcsSLL+v8278YpUlqpkb+ZIH0bIbm7Qatu58DT7d89HlU3eL+J98njgI2B6GMYwJvNP7m0uc6nww8HIiIuM8NwsGSukOTa3yISYqIEA8HHZOQgCb1kXBAgM9tLtPf1halLzpckx9jkUIOmAXJth53uYpNFg2NmAiH/tDgLnNvV6ZnSMBdrhERYmKhoOLSH8Z+I0K6tOjRYZUHw79tGtdVFmnTQMcYJuiiwbW69LnFZVIkS/aIaHAtkgYbI9fl43KLy1tGLowIV5Mk1H4mb+S7TUOqKIn3t5lJUJSt5I+EpKRmG+fUTg00GWJCGRDgIWVMQz6ayE0zWqMZbXEb/cgZSah1ovrI51qYeM5eeDhaWNTzcVda9O/VcFeTmOzaDy6h5SxkGBN5Ic337yWcYT9EKILGe3eRkkQdT8Q03r+37bNAsW3MuXncxftJAkWS0Os272kUsXatmSxjDYHfDVl8b4PcoMVSr+oiVJFwb4VAMxTmny+j9ULWrjVoLfUQiqC91keGkl7NJQpiVi7VWbvaJHQjyieztJb7KLrCwosVbv18hdCN8PshN368TOgl3lX+/DRa2iAOIvy2SxzEHP2TZxGKQhzF6PkUC3/yDLEX4m100fMpjv7xReIgonV1DT1ncfRPniXyQnpLTdLHi0R+sgpSDRVFgJ5SOf6dZ6i+c4/Gxw8wiulE36TtEnQ9Iu/xFNdkGBCGwQjXXKgaxYuvHdrovvO2jx6mmVJnuP7ZKrGjEIYqtZaLjFTCUGJoKprQaDkRlqKzvqjRa+loocWdq7B4J6DRkJimwEoJ7t2NaDZGvXgpJV7LI3TCiUILAGI/JSwhxNiXJ174E5zOBmu3f3Oom/B54Pz5hBN6/37I5yRt+g8e6eeex5iaov3uO0SdDpkXXhxrJz2y/VMX8dfXqH73u9gig4ZBX7aHE9r/LyAE+tQ05tw8ai5H78pl9MoURBHBxgbG7ByoKv7aCkgwpqcT4ZsHi/smi4QC0+cKBF5E/e5koimVU1mOvFQhDmM2brZZu9rYtUJNtTRQBDKMif0IxdSG1Z16zmL26+d4+B8+I/JC9JzF3DfP8/AHV4icgNiPELqKoitbv9dV4jBG0QSV8yXsioXfC8kdyaAZCssfrtNe6aMoEHkhqqGiKiCExOmET6yKTqgaJ/7zf0bm6Bkgiene+bf/G84E3ZVtkSWnVGjEa+REki9oyzplZY6ebGGIFAKBJx3sAU+8F7fIKiUC6VGLVvBxOXdB45lndX71C4+11dELE5pC/ngeoQg6S23CgfaLlHLPONZEnq4QynAd43ZrmHYRzbCJwiQmlxyFYcb6t4V0RnDxaY1jx1Tef9+n2fzd40X+fSN19hypEyfoXb1K1OmQffU1zIWjxO7uZbSqbRPW1lG0GJc2cXTYgMIBUBQU3UBoelK2qSjDcnIpZdL0MQyRgf9YQtGPA6HrmHPzRN0OajqNOXeE2OmjpDMYczpqNkvUbmPMzKHl8qi5LNLz8ddW9y0DljGsXTsc37h6p0P1zsEGejPmuol4m9cpI4e1n90k6Cb0uaDlJp/b7tbwDSKiYOt+x4P/jkNJ836bzkqXsB/SXe4S+RFuyyNX1jn6dDaRB6j7GJZK4MU8uNIl9J+Q1X0MhDLAlV0iGSBFjBAaQioDJhREMsSTPRRUYpmEA0MZ4EsXEIQDR+PGtZAb1/bw4mOJoilUnq4QOiGdpYOpjgcaXUUzmD7xCqqW0HE00yY/cx4zXcLprA/5oXEUsH7vfaLgydXEa9k86VPnx/4e+y69OzfotAP+5m8Sl17XD2YZKIZJ+vQFlD2yn35tHWd58dD6qEZlBmtuYSzYH7TqOA/uHcihVQyT9KnzKOY4wfsw8DZWcJdHPYDG3/2Qtm3jV5MiBBmGtN/+De1339l1H6Vvfot8UeHIl7LEMSzecKivb52/UDXs46fRsqN6qLHv0b11dc8eaIqVwpyex5o9gjk9h14so2WyKLqJ0FRkFBMHHlGvS9Bq4G+s4q2v4K4+JOy0ecKmf38M+rGp2RwoKrHvo6QzKIZJ2O8RDf6p6QxRt5OUm9Z2b3j5u4A4iHBWO3t+Fqo2iD3vwjiQ4He2rit4uBUGk7EkcCOEIogCiRdHuP3oUDrSTxpCqGiKgaoYKEKhG7aJRUxTVpOSfxngRtvj9cm5dqMmm3z9XpSEp/ZjOA2PpyTho8WfL+I1nlBFmhAKRiqPZmzVVXdriZCJYW0RkqPQ37X++XGgZXPM/MGfJe26tyFoN1n+q3/F6TPLfP0bGv/uew6rqwffIKMyw+wf/vlYCe0mujevsPy9f72nF7gXcs+8SOmLb6Jooxzixge/xl1+cKDRVe00U9/4I4zSeKO+w6D+m5+OGd2o3SJqb/FOY8ch2FgnrO1eHhm2mlAskMqqCRUpr40YXcUwKH7ha2ROj06GYbeN/6/X8dZHa91VO0PmzAWyF1/EnJlHS2f3f0/KSasVKSWx6+DX1uneukr78kcErfpvRTBcBgHu3duouTzy4RJhu4VeKCLjmLDdRtH1pHqpXgMp0QrFYfPMf2jQ8yVSs0cJOi2clUUOExdorvk01/6eJhpFGXKZN6HGOkX7KH7UR1MMhFBQFQNDs5EyRtds1ts3dl2Rb0+wH5Rs3w6hCjLzGeZen2fpF4u0F5+ApxsFLktXfsQml0K3skSBQzxmSDaTBTah7/AkPJOo3ydo1jGnRtXrFcNEL5ZZXVlibk4yqa03Z+bHtDy3wyhPoVqpQxldoaqJAI26Q7JOxvjVNeIJmzH+ttB+713C5t5NL4N6nVoYEq965EoatbXJzl9oOnqxsmV0hcCaPULpS18nffIcijXa3O/A/QmBmrKxjhzHnDlC5uxF6r/5CZ0bnz1WqxQAxbQwC1PomTxCN0BKIs/Bb9UIWnVkHBF1uyNKbP7alkZA5I16NP4ekoSancXIl9HsTHIcIA48wl4Hv1kjcp+MbrDQdIx8GT1bQDUshKISRyGR28Nv1Qk6jT0nBCGUJKwTbiXFD4SiYOTLGLkSqmWDEMSBT9hr49XXif3HL+89CKmT01gnZ1AsPSkAEoLer28TRyF+0MUjuTZNNYllSCwjAq++J4d8853Q0oPVlxDEYUDodPGbtUFyb/z+CFXBbbikpuyJ6YgTxXQTlkKCyrEX6DWWCPw+UeDi9RrIOCSVm2Xu3FfRDJvW+k3W77yDjB9vcERe4unsZnSNYgXZEqytRbjuZC9Lau7ovnqzipnCqMwQNCfv/6TaGfRsYVwJ33UJGrVDeQ6/Dbi3b+37fe/Spxi2zunndSxbwUwp9NoHP0ehapiVabrXSehXZy9S+eq3MGePHMrYju130BHAmj/KzLf/DC1XpPXR2yPi0juhZwrMfvWPUXQTGUes/foHePU1VDNF5sQFChdexirPoNqZRLxJSqLAI+g0cVYXqX/2Du76w0frN6comMVp8mefJ3PsLHq2iJayEQORqDgMiJwufqtO595VWjc/3dco7gc1lSZ74inyZ57DKE2h2QODoSjIKCTyXMJuk/7aA5pXP8BZXxoLIYROD7XXGYTGtkJ0U698HXv+BACd+9eof/JrUBRSU0coPv0q9pFT6Jk8qmElRjcMiJwebn2V9s1Pad++TOR+fsJEQaOHXnHQixm6l+6TfuYY0hLU1u4e/ONNCIGeyZM78xzZk09h5suoqQyKrg/CLSGR18dvN+g/vEvj6vv4jY0RuxY6m+W/CYthEjwST7dy7EVCr4eUMRv3P6C6+BGl+acBaCxfobTwLN3aIr3G0mF3P4LYc/Fr1WFLjiGEQC+UODmdolN3J3J8FMPEqEzvUiu/bRvdwJyeo3fr6sTnqNrpsfgmQNTvErR+N0RaDoPYcZC4CCVLcUZHNyYzmEJV0UsVEAr28TNMvfmHGJXpxzK4I/sXAjWTpfLVbxH7Lq2Pdo9JQ/KsMycuoFk2UkpaNz8hdHpMvfImxYuvoVr2eLsj1UY1U1jlGez5k2y8/xNa1z86lOEVmk7u9DNMvfImZmkGoWpjx1ENE9Uw0XMl7LljZE8+xfrbP6S3fPdQoRNreoHpV79O5tg5FHN8FSE0HUXT0ewMVmWe7LHz1C79msbld0aMoZbOkl44mUiDVpeTsnIgNbNA9tRFIAn1tK5/QvrIKWa+9PuYxamxld3WdRWx506Qml5g4/2fJhPK54Cw3sF9oGMerSA09RFCm4LM0TNUXv0G6bnjCM0Yv4e6gaIbaOk8qZmjZI6fo/rBz2jdvIQcOKKapVG5OJUY3AknzkMbXRlHNJY/Y+P+B5h2kfLR52mt3UC3snRri1QXP8LOTZPKTj220SWO8WvrIy05YFClUyiR0myyqQDj04OTaEniZry32HYIXcecngVFHZM73AtaJouWGe0+KqUk7HaSGOQEiPpdNn72H9FzBdSUnRDDrcH/pxKiuFA1hKKimBZC1x/bmAnDGHj94/sJ4pCbn/RZue/Rqk1odIRI4oMLx5l649uYU6O8SillUrPve8S+T+y5yTJUShTTQjFMFMNAMaw9Ba+FEChWivIX38RbX8V9eLCGgBCC9JHT2LPHKT37BYSmEftuch5B0idLNUwUM5WwKhQVszTN7Ff+GOKY5o2PJ3oXhKZTfu5LVF7+GnommYRlHBG6LnHgJZ65oqDqW8dSdJP0whnmv5Fn9Zffp3PnysEDVwjsuRPM/d5/Rmr2+PA+xYFPNLguGYUouomiG6hmKgkHFMrMfOHb6JkC6+/8kMhJQhtRv0vv/k20TH7XYwshMHIlihdfofTclzHyZWQUEvbaxIGPDIPkWgwLh1F+JwAAIABJREFU1UwhFAXNsik+8wUU3WTlF38zPNaTRlBt49xYxpwr0ru2RFCfsHeZopA7+TSzX/ljjOJUohUhYyLPJfK9wXuZTN6qMXg3VQ1r6ghzb3wHxTBpXHkfGQaJFrYfYeRNFG0yw39ooxuFLt36A5z2OoHXo3L8JTTDTgj1MgYkYeCOae5Ogs3s/3b33a9vEHnOSN8jSPqF/eB74FedicILerGCms7uf3yRJBa0dHqQMT8AioJeKI3HiaUkaDWIvcliW7Hv07ny8YA+pSRiNSL576TjhYJimqhWitLrb5C9+OJE+90Vqkrq5Cns8xcSVf9dPAS9cR9r8V1K0zpRCMt3D142CSEwylNMff2PMOeOjnwn45igWcNZvEvv/i28tWXCbicJvQyEdxQzhTUzj33iDOlT59CLlT0Nr16sUHjpi6xX1ya6x/mzzyK0RP2qu3iT1vWP6K/cI3L7SEC3c2SOn6P49KuY5ZkkFmhnqLz0Bs7GQ7zaAZqvQiF74ikqL38NLZ1LkoC+S+fuFVq3LuGsPSQOvKTNjZ0hffQshfMvkpo9ihAKZnGK6de+hd+o4tXX9j2Uka8w++U/JDVzbCgs4zU3aF37kM796wSdJjKOUXQdszRL7vQz5M48g25nUXSD4tOvEnl9Nt77KTJMtDy8Zo3QSQo6dj1mocLUK99ATaXxGxu0bn5C5+5V/HZ9eCx75hj5Cy+SPX4hmbxUldzZ5+iv3KPx2btIKUmV5rCyZSSS3voiQf/wjVzHoCqgJ41pJxW3T00fZeZLfzA0uHEY0Fu6TevGR/RXFgndHkhQrRT27HEKF14iffQMiqqhpjJMvfJ1gnaTzr2rxGGMU3Mw8+ae928nDm10+61VygvPo5sZDLuAaRc5cuEbmOkCgddFNzPoVga3M54dF6qKlsmjGCZBp5kE/AMf1bCIA5/0sTPIKKT34PYw9uTX1olcBy2bHxmEWjaHr6Tp9w++UKGqmJWZHYZb0n9wD2tuYRvrQKDni2jZ/ERGV6gaRmV2zDjIKMJbS8p7hamjlQsJcd3xifoO+kyZuOsQ9xzUQhZ0lajaROh6og1qm0Tt3shyM+p1CEiYG48D++w5Kt/5U4SqETYbuyb6ooZGtxUl3VK7k8fl9VwBPbclebnJQOhc/YTmh7/BW1/dc7ke9boE9Q26Ny5jzS1Q+sLXSJ95Oomv7YBQFLIXnqX1yTs4iwfH8DQ7i4xjGp+9y/rbPyTojt7DqN/Fra3grC4y98Z3sKYXkkaflVlKz36R1V98f98wg1moMPXKm0MPN+x3WX/3RzQ+e3csqRT2O7jVFbqLN5h74ztkjp5FKAqp6SOUn/8yq7/6D3vGq4WqUn7hK9hHTg28M0l/+S4rP/8bnPWHI/mDyIGg3aC3dIv+w9vMfOkP0XNFVNOi/PxXcNaW6Ny5gman0TJ5VDNF2G3vqpugGiZSN/Dqa6z87Ht0l26NvpsOtNoNeku3mf7itylefA1F01ENk9LzX6b38A5Bp0V+4SlU3SQOfSLPeWyjq5ezWEcrdC/fJ/P8SfzlOv7a/uNDMVNJ+Kc8OzC4IbWPf0n1w7cIe6NjPnK6+I0Neku3mHrtm5Quvp6E0XJFyi9+FWfjIUgHq2Ah1ETi0Z2ANnZoo9tYvoJmpslNnyEKPZau/ChZMika+ZlznHrlz5EyZvXmr3e94Mypp4h6XczCVJJpbFaxKnM4K4todnaQVNh24f0eYbsxlkwTioperMC9/RNDAMIwMWfmRoyjjCK61y+h54so2wyFlsmi5YqwfHDFi1BVzAHFaTtkHOFtJN6RVi5gnj+BViniL64QPFjFODaHWsrjfHId8/RRZBThaxpapUBYbaIVc/Q/vnbg8R8F6YvPEHsete9/F291ZfclpYxQ4gC3H+N7j5YIlFISOX3qv/oxrU/em3iJKaMIZ+k+az/8d0zHMbmnX9g1K6yYFunTT01kdAG8xjq1j38xZnC3nTC95XtUP/w582/+WRLSUVQyx85iFMp4tT08UCHIn3sBq5JoOMs4pnntQ5pX3ts3i+/VVqm+/1Os8mzColBUsqefofHZuzjru4flrOkFcqe22peH/S7r7/xw3+osGQY0b3yCls4z86U/QGgaqmVTeub1oTFUTRu/39s38R0HHvVL79B9cGvP5HDodKl+8BapqSPDJJxZKJM5fp7G5XcJ3S5+tzEIQ0wuYLMvFJEI5DAZ9yJz7CzphdNA8o72Htyk9tEvxgzudgSdJrWPfoE9dxyrMo8QCvb8Sey543TufEa/2sfMmzi1yVhPhza6od9n5fpbKJqRaGZu09Ht1BaxC7N43QZud2OXXycE7LDXSTxXJKplo6Zs4igg7LYJe90xbV53bYX06afG9mZOzQ6rmfaDaqYwZ+ZHr6PbwdtYw69tjHhnQtUwp2fp3bxyML/WtNB3aYkeOX38xsDTVxSiZidZ/kiJfmQa6QUIkRCrg9VqEkKIYqTnY548gndveUSE5klCzeXwHizi3L2zZ+KmOK1x/HyG4pTGrUt9+p3D11jHvkfj7bdovP+r3Un3ByBsN6n9+ieY03MYlZnxJIcQpE+coaYbexZkbELGEZ1713DrB+hHyJjOvas4Gw9JL5xOvN3CFKnpo3j19V3fMz1TIHviAmKwWgo6DZrXPiTyDh6A/ZX79Ffukz/7XLIvO0P62Lldja5QVHKnn0HPJu+qlDGtm5/QW55AGzeOaN38mNyZZ0nPn0AIgT17nPTcCboPbxP0O8l+9xlLQbtB+/anB7Jx/E6dxrUPSM0eG3QENsgcP0fzyvu0lq6hqDp2eR6nuX8YZRIEtQ7u/Q3sc/M4d9YID4jpKmaK3Olnh4nU0OnRuPLe3hPxNnj1NbqLN4cJUtUwyRw/j7txhziMqV6pjlUF7nkeE221C+LQ3zKOQiFdOELgdWg8/Ix+a2/lLNVKYxQr+I0qfmMDszRNHAQJ2bzTwJqaRdFH47ebS/WdMMpTE3Hj9HxxLNkVtJsErTpedfzhW9PzY62id91vobxrdVtQ2xjGGmPHJWq0CdZqRPUWseMhTD353HWImh2iZpe47+AvrqLmMgSLkzfTOyyibnfPRNUm2rWQax/0+PCtDtXlwxtMKSW929dpffLuIxncTXgbq3SuXdqTl6tmchjFyoH7iTwXZ/X+RAmxyO3jrN4fen1CVUlNL4xl6zdhlqYxStPD5b5bW8OrT9b3K46CUS9VUbFnj+66rZpKY88eS2L9QOQ69BZvIsPJihPCXofu4o2hOp1q2aQXTqNnC1il6cTb3uedcNYeTLZaiWPc9aWh57iZiNucLCK/T+B2iYMnIJYi2ZokJgjnanaW9PzJrZVCr03v4eQUM2f1wUhpemp6AYRG9kiWqYtTGLnJ+sYd2tPdDapmMHv2yzy8+mPc7i7SbkIk6lZIvNoq/Yd3hxlCr1kdDiq3sYZbS0REts+6fm0DGfgIYzyZplopov7+L4M1P9rBVEpJ2GoSDspNd3YTNadmUEzrwCSNUZnetdjC21ghHpSERrUWUe3g2JUwdayLZ3Cu3EYGj6fWNAJVHQ5UgP61qxTe+Bqpc+dx791N1Md2ODeqIXn2CzYzRw3e/2kbp3eIqiMpCbttmh/+mrD7GC13AKKI7o3PyD/3Cnq+OPa1apoYpQre+t6TPCRLY6+x28prdzjrS8kqZ2BozfIMiqoR7ZxAFBWzNJMwBBiElRprxAd43lsnJvHbWwwXQeI5K4Y1FprQUhms8mBlhyRyeklMcULIKMTdWCb23cTTU1XMyiyx59C9X0U1rX11cN3a2sRaGEGnid9pDA2tZmcxi1PoFZvIdzEyybPsrtye+Px3g17JYh2bonflAZnnjuOtNAj2iemahXLC0hjAa1aJ3Mk6k0DixW+fuLV0FkVN0bzTxMgaRN4T0tMVQiGVm8Z3WoS+g5WpoJmjZbSankK3suw13SjZNMaRGcJ6i4AeSjaFauQG9e1pYifhuGnlAsHKBoo9IFz33aShndvHb9SwdoQIFMNEL5QPNLrmzJGRGJKMIvxmjdj3CZp1YtcZKQ1WUjZGaYrwgKSVUZ4aK/2VcZxMEof08KQf4l69g/SfoMEFsi+/gl7cUhUTmgpCUPnTP8NbvE/YqI8NNrW9Qr9zM6FTqZNlhDchpcR9uIj7cPGJnL9fXSPqd3c1ukIz0LK5XX6145zC8MDW9CPHbDdGQi9GtrhrUY2iqpiF8paHKBPjmDvz7IRHEliVbe+0EAhNTxLLO4yumrJRU8k7KiVEXp+we7hEVNBtEg2MLiQG3ppeIOy1MQplOrfHe4BtIuy19qzmGtvWdUa4wKppoVppNDU5f6e+8kjspjGIRGtYxnGiLXzA5kZhasSbVwa86ola0QNGrpTQSTcPr6gYhSy5WR1FVwjdkPb9g5/JgUZX1U3mzn6V9Xvv06neZfbsl8lNnSGOt4yKIlRUfe+e90JV0GYrqMU8wtCIb3qY504Q9x2k6w/+rqMYOoptoWTSxH0XkImhvreCX1vf1egapQru8t4DXLUzGKXKSBhCBj7+IKwQtBqEvc6o0dUNrJl5+vduju1veE26gV4os7MGOXL6+M3a4SuMpEQ6T16f0j53HuvYsV2/s44fh+PHx/7e/+wSd3/yKbW1gE7zcFWFMgzo3ro6loFXdAXdTgaaUBVkFCM0BRkl6mJEEr877iHGgY9fq2LNjS+7haahpuwDzymOI6JDLGcjtz8yEIWmDWO2oxelotlbRl/RNIpPv0rx6VcnPtZOCCF2NfCanR1ZsUSuc+iKz8h1RtgqmmUjhJKUu/Y6ewrVSCmJBpzqSSBDP+ELD4qaki4QgvbStYS5cIhWX/shqHXwluqkTs/h3l0j7OwfR9fToxN09sQFsicuPPLxEwOu4vcCzJzx5HqkhYHL4uX/NFQPE0Jh+fpPaW9s9R/TDJuFp7+15z6iVpfe258kH6REBgFhvTWayBm2cR482M3uBlGEjAbeo4xHXrxNDYb9YBTLicjKNqObDOQkqRJ2WoTdzkiyRmg6xgFJOi2d2bXYIup3E9GY3xFUv/dXE8WntyNlRiyctgjDxH2YpAx4E5Hr7Fq0UDhVxCxaWMUUQoDX9tBsfdi8onW3iX9r92KSsL/7ElCI5FkdmEyNo0P1PpNxPOLxCaGgaOPxOiHEmBjTY2OPduY7V1TxhLHc7ZBRANvEXoSmEQc+im4mMda9bpGMJ/YGt441umJTDAs9nUNRdYSi0V27s8cvJ4dqG6gZi/7VBwhNJfvSKfrXlgiqu69qFMOclMp7CAg6D9t0V8RQS/cgHDwapSRwt2Jzneo9uvUH+P0twxL6Dr6zz/IjjpE7etzv/HwQ/No6se+jbpM/FKqCXigjdB25h7CMXp4eK4pIhHSS8sTYcwkaVTh+CkTiYQhFwSiWUe0M0R7LUjWdHVvaykE882Au7Va7S0UoxDKCiUkvh8N20ZZJ0ddAOWJTrGjUVw8XJvFrG7su5XurXby2R2+5SxREyEiiWRqRH1I8U0rsjGBA+B+9D3vH1gde4YEMFnH4wTbp9ttXUHFM2GuPCeIcBn6r+rnqCMsdH/RcAcV3UTQdr7axR1z38JZq7GnImNDpIRSVVGEGM1tOYqSPAS1nkzo7h5IykpWTIrAvHKX1yyu7/2DzJRsg7HcInd4jD7vI7SNESOFkARlL+tU+wS6rtbHzPuyBakuXxqTR4tBn5cbP8Z3HTJzsA79eTWKvI5qzSTGDaqYIdzG6QtMxKzNjnl5Q3xjxFLyNVWQUjcR9tVwBLZvb0+hqmSxqOjP6RxkTNGq7UphUoWPpOSQxmmImZYexj20U6XpVVMXcKkeMfdxw8hjkk0YUwq1LDqom8N3DeTh+fWPXCdBreXsKgrhNN2lpI6F0rkhnubul4SrZ3wjtGEi7bjJga0w6toSi7ki8xrsnx+SoRxeHAdUP30pKhx8VcUTojAvF7Dz+bp73QRCqjhDbcxshqplCxvGgJHqPZz0IERwGyo4xFwU+cegjFDWRgX0CmhyxHxLUOujFDCgC72F9UJm2O7aU1JJjN699SPXDn08cqx7foSTy+sggnfB0NyYT+Hkk9kIiB7d9IEi83uislSrOka4cI44Cumt3sfLT9DbuY+YqiZRcHJKZOo6MI9rLN7ErR9FTidZq496nhDuyin5tg8jpo+VGFb30QgnFSsEumXLFtLBm58cesLuxMkJD8tZWkiz+tqWilsuj54t4q+MZYqGqGKUplJ1yjmGEu7Y73UtXU5TsY8REBJGHlMlgVYWOodmkjTKxjFEVja5X+9yMbuGNrxHUqvQuXx7/UlHIfeGLhLUa/evXCIPDuwBhq7Env9mupDjy+jypkkXrQZvatTrzr8wiVMHqR+tIKXnmnzxF816LB79con5rc8XweCsAoWooZmpiVoGaskfCWDIMdk2MShkR9reJgW9SkQ6Z4JoEYb8zwrJRrUHp/SHiuqqVGqnwC90+zsp9hG4Mrnn3Oy2EGEo4ThLX3dSV2GqiGiGDADNbSpTfwoB+7fFpkUG9i3t7NfHONQW9mMW9tzf/N+x3tttchKIS9NoT66zshUmSZ9tx+MaUC88SxxH1pU/33EbRDArHLuL321iFGfxeEzNXJvR6ZKZP0F2/T/7YM4RuFzM/je31SVeO0np4Dbs0j5WborvD6MZun6DVGCty0LK5PUXJVSuFURmtZEt0btdHYlRBq07k9EaSMkLVMMrTCFUd87SS8t9dKtGiEH9jd46mF3ZZaSfLnljGI76ZJKbnJ/J+WWuaKP78hKGtU6cRur670ZWS1OkzhKUy/euPUBE3qELbK/5nT9nYlRShGyIElM+X8PsB/arD3Msz3P3JIt2VHqsfrtFeenKTjlA19HRuYmNoZEez1H6nuatxk1GI36qzOZKFqmIUDuYNPwpCp0fY7wz5tKqZQsvkCdqTL9H1TKK3O9xnr03o9FDieMCM2Nv7TKrmlIlCH6plo1pbYzLyHIJei96DpHo0ET96/OIf6QV0P703/CxMDblPG3SvUWX7tGIUp5Jrekyje1gcujgiUzqKbmYO2Eok3oWi0q8/xOvW6a7fo3DsGWQcEfSbKIqGUDSc+jJet0HkJ7XYodffcymzW5GEoukDgvz4C2OUp1Dt0ex25CTGe/uMHXveWJGEGIhw75a1FpqGWRnvTprIOe4+CCQxYewRxh6xDIi2/YtlRBT7RDKk6Tyk529xncU+UpSfC4SCYj1a2yAp44S1sIc3FAURiq7Qedhh/XIVISAOYuIgQlEVIj8i6AdEQZy0L39CUA0TszQ+Se6F1PSRkfCCV1vdRbQ/CXu41RWiTaaGULAqc2j2/sJKj4LI6eFWV4YMAzWVJjV1ZOLfC1XFmppLVNxI4s9efY3I99Ds9JhO7E5Y5dk9C0R2Qs8UMLJbVZ5hvzsQ4omSY3xOGtPSC/f1xL3G+khS1ixUMPL7J+IPgmqqpGfS2DPppDnoBDj0iPad9tiyeifi0KN5/zIIgW6mkVGA21on9Ho4zVUCp0tj8RLIGM1KE4c+XqdOHAYE/Taht3tsZGcrmE0YlWlQxo3ubn3LwlZzjNcbBz7++riHalRmd604U1O7a+j6tY19xbUPgmlmMc384F+BlF0hXziJldq7e+8kELqOViyilcqJfGLKRiuVR/+VK6TOnEUvFg/kPe+FnVn/kXNQwMpbKKqgeKbI3Euz1G81SU/ZTD8zxerHa4RuyMaVKrMvTJM/fjD/dlIohpWUpU5gNFTLHpSwDhTvoghnH0Fzr76G11gf0qPM0myiO/CEdIQ3ETo9+st3h4ZRtVKkj51N1NMmgGbnhuI6kPB8ew/vDmUZzdL0vnFba/rIZJPJQLxHG9CzpJRJ94oJSm0/b4S9Nr3lu8OJS7Oz5E4/M0b7PAysgsX8lxaYe3We7MJkk+2hwwvN1etMn3qd8tHn6bfWhi+BlDF+vzmM9fZrS/RrozXk61d+Mfxvp76MU9/yXP1u4iHuDCtsh19PymtVa5QTbA7oXiNznFCwZhdGbqiUEr9RJdoR/5VhkCTTwnAk6aZlMujFMmFndFlqlKfGqEJSSrz1pBJtdkblD75lceK4RiadHP/9jzz+4rt9DAO+/AWTL75moqqCt9/z+NkvXIIgCTuUymeJ4wjPbVAsnUVRNDy/w+rD94jjRyucMGbnKHztTdRMBmN2Fr1cSTi6I/dLoKYzyMCnf3VyEfcdN2HvpoRCoKc0/F5AHMREXkh3rcfVv7oxstnKB2usfPD4dfkjh1YUsicu0PjsPdw9xGQGG5I5foHU1JFhWa/X3EhKdfe4Lr9dp3P3GlZpFqEbaCmb8gtfwa2t4TcO0HrY5fh7eoEypnXrEoWnXsYsTiOEQv7s87RvXaL3YG8++eZ+c2eeJTWzyXWWuBvL9B7eHtCoBIpukpo9Oih1HX/PjGyB/Nnn2fjgp/uKreuZIvkLLw2Nuwx9uos3HssZeVKI3D7tm5+SOXoW1bITqcuLr9Fbuk1/5d7huPWDZ+XUHdr3W5g5k6AzWVjw0EY3N32GbOkY2fIJ4sgbnmcUuNz98K/wnSefRNhE5DoEjRrq3MLI37V8EaEbyGiLHK1lc2jbhGxgUIlWW981oRK0GoT97qj4jaZjTs/hLI5yCo3y9HjYIY7xahvoSsh//V9lKRYU/tOPXb79DYszp3T+5f/VRVHga1+1+Of/NMPPfuERBDH/9L/MYKcE//4/OgR+D4HAMNJ0Wg/w/S6+18FxasSPEXfy11Zp/fLnWMdPouXzSSuhjR0GQYLbu0Pv2hXcxQlEVA4JGUmW31+hei0Jnfhdnzj47bUyMnJlpl55k9Vf/S1Ba5dSdQT27FEqL3112JVZxhHdxZv4u24/gJS0rn9I9vg57LkTANjzJ5n98h+x8d6PcDeW948ZKgq6ncMsTYGi0Vu6tWc1o9dYp3n9I6Zf+yZCUdHsDNOvf4sVt49bXdnVYAtVI3vqacovfHno6Ue+R+Ozd5OqMSmTsJhXxWtW9zxXoRkUn3kdt7ZK9/71XQ2zatlUXvrqSNjDa9bo3r++9/X/ltG5f53eg1vkBiJDRr7M7Ff+mPV3fkjv4Z39K0mFQE2lk15qdpbug5vEnkPjZh0zbz654oidaCxfoVsbH5RSxgRej7RRomAdoda/jxtueZR5a44gcukHDayCiZbScBsuWippaeK2PHJHMqiGSmelh5HWceoucbj1IsWug1ddw5o7wvYYrmpa6IXSCNPAKE2NF0X4Ht7q7nX6QatB2GmP6PYqujGWuIPE091JQ0uKIuqkbcHZ0zp/87d9fvKWSxhK/pt/plGtRhiG4M+/Y/N3P3X5F/9HlziGZjvmn/x5ml+97VGrg+s20Y0MrtukWb+NaeUHPeoePcYpfR/37l3cu3fRKxXCRp3Gj3/0yPt7VIRuRHjITstPAnEQIJTE29PSOZpX3qP38M6AoynR7AyZY+coPftFrKm5LS+3tkr90m8OVJvzGlXW3/0R82/+GXq2iKJq5M48g1WepnP/Bv2Hd/HbNSLfTQotdAMtncXIlTHLM1jlWazyLO07V+iv3Nt74Mcx9Utvk5peIHviKYSikD5yiqO//1/QuPo+3fvXtkTMNQ2jOE3+9LPkz78w1PqNw4Dm1Q9o3026VKh2BrMyiwyDgeEef89kFCFljJEvM/+1P6V5/WM6dy4nRjqKEJqGPX2UwlMvkzt9EUU3Ei3lwKf+6a/xm7t3nh5CKANmhTHoeGGiGgZCN9Gs1EglmRCC7MmnMXIl4sBLqGiBP+jO4SddQfapPow9h433f4yRLw11k+35Eyz8o39M5941+g/v4jXWibw+SAarlzRGrohZmsUsz5CqzOM1qzhri6hqSPmpCvZ0mtrV6ki7+r1woNHVFJOUnjywnl9Hug5KqBEOuKSGauOFPQwthYqKF/ZQhIahpnDDNoaawtRyFFMLtN1VnLjJyTePgoDm/Q4ykhRP5ti4Wiczm0bVFXJHs9iVFEu/Waa9tBVuiH0v4YHGMpFD3HwQhjlmdPVCCSU1GoaIPXeoc7sTSVFDA2t+W7mpEBiFEoplEw9qyRXTSuK5O2J2Ya9L0G7id2Mufebzh99OYZmC5581ePd9n3ZXoqowPaWyuBQOGWv37ofMzqrYtqDTzaBpFo36LUDiODUcZx8v6xHg3rt7+BLlf+DoLd8lcnuJtOGRU6Qq80ReP0mASTnUBlAMc2hww36X6oc/x29sgKoOKiTZ4gUPa1kS5kJ38Sbrb/+QqVe/gVFIul4YxWnK+QqFCy8ltLOBJyqEkrRfGvQxO0x/r7DbYu1Xf4ui6aQXziAUBasyy8zr36L8wpeTNkRhkGhcGyaqlR4mBeMopH3rEhvv/WRYcBJ22wStGqHT39PLDfptOneuJNKSuRKVl36P4tOvEPkuclDRplqpIY0NEkPdvnWJ1s1PD2QHpGYWWPhH/zipuhPKoINK0j1FKMpoXkUoVF5+I8kdDCrlpJRJAZaM6d6/zuqv/pZ4H2lNZ32ZlV/8e2a/8kekZhYQQkHP5Ck+/Sr5M88NFBSj4fEURU2elT44PyGGq58oiGjeadK613xynu5M5jySGD/s4Sk9ZjPn8GMXS8vQdB5SSB1huX2Fin2Snl+j5a4RDXQZhFCZyZwnigNMNTMkZkd+jNNwMdI6dtnCzJkgk0xgkskWqLpCtHP5KeUwWbU9rqsYxojEn9D0XcVogmZtLD47RBzjr68izz49DB0IIdCyBfRsDm9gdNV0Bi0z6kFvVqKFnRYyhE8uB7z0goFlCX7ylsvb73n0+xLbFrieJGVtDbJUSuB5kigEz2vhbTyZ8IwiVDTVwg97aIpBPjVPvbdI95OPJ0ryVLJnaPTuD5/lP2TI0GftVz8gDnzy515AMa2xvAAw4I/H+O0aG+/9hNaNjxGZFHo+D6qCYpkJhTAMiVodtEppoNLSvKV/AAAgAElEQVQm8e49oHH1fYJOk8orb2LPnRi0rVHQUrtTGrcfMw59wn57ouaUbnWF5Z/8FVOvfZPcqYvDHnPGLknfzd50Qb9L8+r7VD96a0T9TdENnLWHSRXhHpOxomq0bn6Ks7bE9OvfRM8W0TN5dMaTyYmH69G6/hHr7/6YyDm4IlLRDIxC5cAEPQw4w8be7Bqvtnow40fG9B7cZPnHf0nlpTfInnwKxbAQikgmD3bXkRn2+Rsk/GUUImNJeiaNntGpXTnAox9g36sUKFhaloftS7hhG1PNoCgatc5dKulTmFoGMShpVYTGTtqWKjQ0xWSjdxtV0ZMXLJLce+sBcSQTxUd9MHN0A7rrvYRCFEk0U8XbxVUP6htJZdq2QSNULYnfKgrEcWKEd+no4K4+3Jdn6K4tJ4Nom7HWsjm0bH7oIaupNKq9gzI3VBZLlqHHj6q4rmRxKcT34ci8hnsnIPAl737g8bWvmHx21ScI4Pe/meLd9z0arScX3xQo5FLzFNNHWW9fJ4w8CvYCYewnIR6/gaXnMFSbIHbxgg6WnkNVDGIZ0vfq5FNztJ0VDDWNF3aJ5ZNVP/ttQjVTBN0ma7/6D/RX7lO48BJGoZIIvmhaMpACn7DXpr/2gPqlt3FWF5MGj0IQOw7adAWhKsSunyypDX2ok6Dms8N3r/vgJm59neyJ8+ROP4tZqKCm0kmxgKomycY4SpbEnkM4aBXUuX+d3tKdiXVmvcZ60jrn/jXy51/ELE6j2ZkkwSsS7mnsu4S9Du7GMo1rH9BfvjuW0BKaRmruGH6zhrs22vJnE5vVb81rH+C3akkL9rnjSct3Y3C8MCBy+3iNDVo3PqF165OJu4VEXp/eg1uP0NF3HAfG0LfBWV9i+Wd/Tfr2ZfJnnh3Q/TKJAVbVpOJQxsjAJ/I9wkH7nu7963SXbidlzQPaY6qUwsgajx9eSEj7VWYyZwkil1r/Hn7kMJd7GlVorHVvYqppZrIXSGk5Ot46eWuOvDWLoaXwOj38qMdM5hymlqXrbyZRdvee3MbWC7GXeIRfryaVafnilkCNEGjZpPda7DoopoVRGe9G664s7ftA/OoqsTvKjhh2hxgMKtXOjBVjyCgc0tmKRYWFIxqqKvijb9voOsxMq/zLf9XlB3/n8n//Pz3+u3+e43/8H/IIAcsrEf/i/+xO1OttUgghMPUMtlHC0NKEkY+hp7H0HFO5syzW3sfUsqSMPFlrmqXGxxwtv0yr/5CMNc1S/SOEUCimjyFQqHZuHdLojpZ+bH1OrtHQMxh6hp6zjm2Vcb3WoCBkdLsnBTFgmoT9Lo3Lb9O+9SlmaRYjV0gqpGQ8NBg7+apRKynSCKu78a8fIkwz6XMXDn4jJWG3SePyOzSvfYiRL2PkSonhVbWB4xFCEOB16vjt+iN3y428Ps1rH9K6dQmzOI2RLw26IijEUUDk9PCa1YHq3R4tdroteouDFjx7bJN0olaS/oVLt+gv38UoTmEWpobVe3HgEXSaCW95sLQ3M2VKx59j/ebbRP7ey313Y5l7f/2/P9I9eFwkjIZPaN++hJErYeTLieHVDCTJs4o9l7DfxW/XB7oi295PRSCRbFzeoLc22XM80J9f791GV1OAxI8c1jo30FWTWEb4kcNy5wqakgTOg9hFESr3Gh1iYvzIYXWw/eb3B0EoSWIteXHCcZ0HzyVo1jBnR4nhejaXCI+7DlomN9YpIup3CQ6QXIwcB7++jl7Ypt0qREJJUzUkIXouP8b3jMNw6Ak/f1Hn1AmN/+l/abG2EaGp8N//tzlee9nkB3/nsroW8z//ry0qpcTDrzcimq0na2RiGdF117GNAo3eIoaWxg06bHRuYepZTC1DxkrCMSmjgCJU/LBPtXMbQ7XRFBNNMSnaR1ltXSE8RIWcIhRymXlUxUDKmJ6zTsoqIYRKt79GyiiQtpNOC57fwU5V8PwuIMim59FUk3Z36YlW5SVrsa2JIHL79Jfv0N9f+3wiSM8jXNvdO5VhgFdbHesmnLJKnDjyVe61HhC5j9+eXIYB7sZD3EOImg9/G0VEe6i4DSGG/5P8Jo52va6d0FM5iseeo3bvo32N7u8E4hi/WR1L+imagaLqhN7uz0nRFOyKjWpqCAHtxYP1Zw40urEM8bbpAETSJ9omFrNZZbV9++2fI6KR7Q9COjeLnZ8FKWms3SDwxstB3bVlMuefHYlNatk8qmkSAub03JgmadCoJW2/97vWwMddWyZ98tzIvo2pWRRNJ5Yyabm+M4nWbRN2EvK3H0jyOcHTF3RyOYXjCyoXn9L5y+9tFXx0OpJO5/MtPYziAEO1mc6do9VfHhgxSSxDFKFi6TncoI0f9hO91NhHwkDxTOKFXaqd2xTTC7hBBzeYLNasKDrp9Dy6miII+6SsIhl7BoGCphiYRo4gctCUFLEMMI0cmmoQxwEpM0+5cAbPb9F3n2wC8XcJitAwjTyKeELNGYcQfy9lrf9fRmbqBKncFOs33xnr3QiJDxe6IYqmED6pzhG/bbj9OlGUGPbQ368ybdQ7VO30sMTRqMyMxIeklElYorf/jC7DAH9jbaDbuzUg9GIJxbKQcYSeH68O86trxH6AJgwuX1b4i+/2+eqXTGxb0OtJ/s1f9vnhj3+7M70XdLhXfRdJTBA6rDQ/I5YRa62rRHGAE7QQQqHauY0fJd9HccBq+xpxHLBU/5gwcnCD9qGTaXEc4EYecRyiqiZ9p0bPreL7XQwjM4iVRShCQ1V0DD1hbWhaCtfvID8Hicu/b2iqhZ0qAwJFqCMBGE21sMw8mmoSxSGO2yCMHIRQSKemcL0WYZSsEhVFJ52aoudsJBOooiGEQhR6mKk8pdmnWbmbdOJWNZM4jpBxiKIaCCGIQg+EgqaZRFGAjENUNVmVPGrxzXbodh7DLiT7NSy2j1OhaFjZMqqRIg593G6NOPDQrAyqllDFZBwlmiyZEoHbxe8lEqyaaWOkSyiqRuB28br1beEQgZ7KYNgFhKol7Zm6NaLAQzWSrjZ+v4WVLaNoBr7TToqxhMBI5dBTOYSqEfkOXqdGHAUomoGVm6Kw8DS6mcZpJ8qEbqc6IsYlY0n7QRtFFRPJOsITMrqWKYhiyU5FP8NIFiXejnNRFNA1gaIkjySOJWGYJG7jKMS0S8g4IgoconB89ggatST2uqPbg5pJEho7G1bKwfL/wBY6UuI3akS93ohW7mZboNjzRkMPbFairSIDn4wxjSltvv/9db77121URUdXLLzQQxEGKc1Mklmxi6HaKKh4UfdzMTISObJCCQaFI8Fg8Prh6HJp8/vNwR1H/ZG/T4o4Dml1lxLDSjzwYJOWNl7Qodtfw9DSOF4dIVT6ThUJ+EEX12vi+W2CYDKJvEeBik7ZOIImDCLps+4vklWLZLUyXtzHix0kMSklSz9qoSsWzXCfCrlhq569n6GmmpxceINMehbP7yAGRm8T5eJZKsXzCRdWz9Dtr3Fv6S2kjDmx8Aa1xg1WNhK5yHzmCKeOfoPP/l/23jTYsiw9y3vW2uOZz53nHCuzMquyxq6u6urquUUDagmwMNjIDjtwOPhDBA5wGNsR4Ah+GCKMHRgrMDImDAgCQiE6hCQk0ZJa6u6q7q4ha66c5zvfe+6Zpz2ttfxjnzyZN++YWdkNAt6Mirr3nn32sPbe7/rWN7zftW+BLSmMHAEpaVauE/YbaaUU4GVHKI4ew2hFbeMShfI8mfwE9c2ruH4RL1Oi01zF6Jji6HHAUFu/hFKP7tbxS1PMP//HsRyPJOikUpIypRhh2UycfJny/FPDlLZubZm1C9+lNH2a8Sc+SxJ28XKjdLbu4BfHUVHAnXf+FdJ2mXvuGzh+EaNihOVQufE2jcVPMEaRnzjC7Lmvg5BDydb1i9+ls7VIbnSemXNfo7V2lezILNJ2aW/cZP3y69heltln/gi2nwdjcDJ5qrfep3LtTWw/T3n+aQoTxxFSMnbsBVQcUL39/jbStT2L8rEyWmmCekDz1sHlzp+adEtFyT/6hQl+dD7g7/3DFr1++vD5vuBv/rURPFfwv/ytOtW6Hm7/J386y5/4ozlmpizixLC0nPCbv9vj1/9Nj0jZWJZNELb31LlUQZ+otkXmgYCWUxzBzhWwc/ltloSOgj11Gx5E0qqTdFrbSFdYNu7oOHF9C/sBQXSjkjR3WCUIW5Kxirh+hka0Tt4eRaNRdhlbuggElnSoBytMZE8gsdnoX6cbfzox53+XoI2i19/uFwujNiCwbY9Ob2Pbfa21biKQJCqg1vx0jQoPA0vYlO0pVsIrTDrHyFstJtxjdFSNsj1FoLvEJmLMmaEW2wgkDe6RrnBt7PER7jZbFYMeXbobIDNemh0jBKrWHPa7KxWPks9Nc/3O7xKEDWYmniPr31sx1Ro3aLRuo1RCITfNE0f/CKtOjl5Qo9a4zmjpJJX6FZIkYKR0kkb7DmHcJpuZGooMeZky0X3NBgrleTK5cZSKcP0ilpMhk5+g00j9vrabRUobvzBFpjCJSiJsN4vqPxrpCsth7OhzGK248/avopOQ8ZMvkx2ZASA3Os/IwtOsXfguvfoaXn6EI5/5E3SnlkCkxsv6he8yc+5rCGmx+O5vcOSlP4lfGKc4cxrL9lh899dJwi7F6VPMPPVl+vVVom6D6bNfIWhX2bj8OioOU6v1Pv0WN1NEJRGL7/5Gmo2g06ChivqsXfzuMC939OhzlObOUF/6mKjbYPPKD7AcL+2W88l30Emcdt+4D3Evpl/t4ZV9ej9OPd37YVlw8rjNrUVrm26EFLAwZ+N7AtsWw7/9/J/O89//xRLf/v0e3/9RH98TnD3t8tw5l9/+Tp9+GBL06thOZs98Ox0GRFsb+HNHtmvrlso45dF72p93tw/6hyfddpO4VcebnrtXmWY7uGOThJtrO8p/VbdD0kyXQAZNK65gCRvfyqNRdOMGBWcUhKQb18g7Y+ScMTCGblK9lxVwV6v0kJqlf9jguQWy2XHanVWUivG8EknSI5sZx/OKtNorhGGLx525sBsi3aOv2ignwhYurvSwtUNLVYl0nxF7hkB1yVklqvH2aJtVyuOfOQ6WJNmopiSrDTLr485PoXsBwrbpvXdpSLr57BRx3KXdTfdVby0yNnJ6uE8hJKXCUTL+CL5bwHVySGkDhkbrDjMTz5P1R+kHdfLZKRbXfjAMMPvZUbRWBP062cIUmdwYmcIkYb+B7ebod7YQCDL5cZIkwGDQSdq/LF+eo9taw/ULBN0q0S7xk8NCWg7ZsXmaq1cI2ltgNO3KLUaPpuW2ufGjuNky5bmzFGdOI6WF7efxi+PE/TZxr0HQqRL3W4SdOlGnjlEK6fgUJo5RX75A0Ewnv07lNubJL+AXJ0FIHD/P+uXXU5fDLkjCHs3Vy8S7NFmwHI/SzClsL4dXSJvuSssF00YlYZppIq20EGQ394uBxs2HE/P5ifp0XVfw3DmXal3xt/7PBsurCimhWJAIoNXWCOmgVYKVcQdtr3dGhnUUEtYq90hqALtQwimPpKLm9yGqpQLoh4FRirCyQe7k2XulvlLilEZwRsZ3BOiSbnvYnifRYZp1IRxiHZC1y5S8KbpxjaxdZsSbI1Rd6uEqtnSRwiIhwR4fwx4bJd6sYJUKmCAiqdUx0Y9PV/cnDSEtPK9EEDTI50sDYnEIwxa+P4Lj5KjXb9APfrxWv0ET6G7qftE9At1lM1okZ6WaG3W1RsmeoBavMubM0VMPiCPFimSrTrS8ie70tqnbxSsVZMbDmZ3Yfu2IB1xImnvpczmeOPpHSFRIvXmTMGoxUjwx3DIIm7Q6K4yWn6Db20TpkFZn5e7F0O9W6dQXiaIOUtqs3Pg+SdQj0AlR0EZrRRL1qCynRTFJ2EVYFmozIIkDtIqJw+49C/ARkRbr3Q3iDa7VmOGqRso0Gyns1oeBvuDy63SrS/jFycF2hlRq965L0QyrALcJKQ30eNOGlxYGdifE4eZqV72V0uwZps58gebqVfqNdYwx+IUfjx7y/fiJkm4YGT66EPH1L2X4q3+pzK/8Wpd3PwhptvTQuLMsB9vx7pX37QZjiKsDxbH7hMed4ghOeWyHHONB+bk7znNjJfX/DkhXDBS4/KnZbelixhiSVnOYFdGJt0fcU7dBWj5iC5d6uEJ/oEex2r0ECGQuQ+6ZswjHAaOxyiWk79P74GPUv0ekG4YtkiRAShvXydHurFHIz2LbPr3eFgiw7b07Sj8uxCZkPUoFjDai2wD0dZtqfE997E6QCry31c4JQDXa9Bu7W4QG0N0+ydZ2yycIG5QLR3CdPFHcJZsZx5Jp7rDnFslnp7lw/Vt0exVKhQUs656CnTYJW/WrHJl9jaw/Sr15exjwCoMmcdwlCtLMEqXVNiMl7N87j7vbpDuNt3Xk3fbZI0LrhKC9RXZkNg3gqRgvP4blpMHtfmMdNRvQWr9Ov5mmmt1dyfqFiT33i9H0mxvkRmepuRlUHOLm0xZdYaeaBtq0Ij9+lF5tdbACuLtavJ8/dnJJcfoJwk6dzSs/QOuYqdx2gSyMQasEx82kWSHm8axCf6yk+2CxqTHwz7/VwbLgP/0TOb7wis/SSsKv/FqX3/69HtW6RsUBcdRDq2TfjqdRfQsV9LaRrl0qp8Lj92cuaJ2K3DxEs7+wsrGj1NjO5VN93vt7Zw1Uy/Zv92EwGOrh6iAda/tnuh/QPZ8GSUycIFbW0gkn+LcvhffYIQY+394W5dJxlAroBzWKhXkQgnb7MSTOPibIbAZsCxOkqXYym0nPvxcg8zlQCh1Gh2qwWm/dZnzkSU4e+Sn6QZVsZnxIOFHcpR/WmZ18kX5Qo5Cd2ZEt0uqsYExCLjPF7ZU3hn9Pok+f4/u4oJOI+uLHzD37DRZe/BnioEOmPJW2bgfaldvkK3eYe+6PEjQ3MMYgbZetG+/su1+jNVs3zjP//B9j4cVvEgcdsuVZarc/JBhkFFRuvMPk6VfxC+PEYQfLdqkvXaBTub3vvrvVJSZOvcr0018GBH5hfJtxZrSiV19lZuYrzD7zUyRBh8bqZYLmQ0p2PoDHRroPEqwQaVbDg/NCo6n5hf+3xb/89S5ffs3nZ76R5W/+9VFefNblb/ztOt3AJ1uYSlNDws624MD9iOpVVK+HKZuh79XOFcgeO7Vtu6TTGrRUOTxUr0Ncr26TebSLZex8abs+732VaAfu0+ytHKXbaXEAD/aoEjL9s2Wn4uODXlZ7QWZyOCNjqdpSHA50AWBXP+lP0G8spUOxuIDr5GnEt+knfTrde4n1nc6n75d1P5Jem803v420Uv973Gs/dO6qe2weZ2GG/geXsMoFZMZH5jKoZhtnehLVbKF7fYKL1zn5UwuMPzkKAnqbPa7+9m3C1n2NT6MW1xd/h9HSKYQQ3Fn5AdnMGFHcJU563Fj8DiPFowgkyxvvsFm/NAg+plA6ptPbJIzSDI/dYPsWJ762wMjJ9JntbvT45Feu7uMi37/7dOPye/Q2lgdbGcJDaAO3N26y9N6/Jjd2BJ2E1Bc/xCtMoKI+Kg5Z/eT3KEyewC9OYLQmaG8SdtJquSTqopOExvIFkqiP0Yqtm+cJ21WiXoM77/wqhamTWG6GzSs/oLV5Y6j+Vr39PokJmXj+y+j1Vbprt1K/MhC0t9KKuF3Kq2uLH2Pl8/jFSbqrt6jdeg+/NLUtVbW5egWtYjKlKXQS7TAEBRLLckhUtO943o9PTbrGQBAayiVrEDBLDzw6IpmbtVle3d3Xsrah+OVf7fK73+3zP/13Zf7zn8vzD36pzZXr4aCYwuzqzx0eNwqJ61vbVMGEZWFZD/pzD87PfRB3iySyR08O//ageA6AiWPCyqMJbgvHJbtwDLtQRnoe0vUH//dSAZO7P7seYqBEJaTcqftwHwpnniF75Hjqm9N6UMIYpspTg/9UGGKiMA0MhCFJv0ewcpuk/ePr5Kx1Qre7Qbe7QZL8+POVVdCj+v7rB2+4D+K1TWQug3tiHlVvYuIYk7ipX7RaRzVbaVsjITj6xTlO//HjCCmoXKpx543VbaQLqW92dfP88Pdu/x6J9foVev3KvY2HBqzAtn1cJ08uM87K5nvoPXKmLc/i+FcXOPG1IwBsXqxy4VvXEEoy6R+nEW8QqJTIfZlnJnOKerROI959wmvd2KWHHinJjLsL9HWbTvKgMWPoVpfoVpfuXVv93v51EtFcvUxz9TJ5e5S+aqFNQr+5QX8QJGut3xNkry9+PPw57NT2DJRhNK2Vq4hygXBrg/7yTYSTimAZY6gtfox0HNzSCMZoklY91SMulIgI6G1conv7Enl/CrsV4Qof47r4TpFI9elu3EY0u2lT2X6bnD+Oa+foBBV8p0gpN0e9s0g3qOx+fg/gU5NuHBuuXo955SWPz77g8d0f9BkpSf7Cf1VkbmY76eZzgpdf9NmoJNy4lRAnBtcRFPKSODapep4QwxQSDlALCjbXKDz1/J6qWcYY4lplW1+kw8DEMeHm2qBIYu9ziFuNQS32w8POF5j42jfxZxYO3viQSOX1Hs4vmvS6bPzmr9C+vHej0UeFsATZo2OoboQKE1Q/Qvr24B4L7LxH0g5QQfyTSFo4PIRAOA46CFGNFslWDe/EUVSzTby6gcz4mDBC2Z0f64phmN+bnaTRukO9eeuh9yGEwJc5przjhLrHVrhIoDt0kwaWsBEIivY4fd3BFi4CSWwCRt1ZQFANl4nNPReKAFyZIe+MUrQnqEXLKJMw6s4hhKQWrSAQ6e8IqtEyAknBGcMWDu2kijaaY9nnaSdbVMI79NSn9ymbgYBQepKS/PEz2Lk8ViZP5+ZFdBjgT8/jT8zQvPge7ugkTj4Vs9LrS1iWy2jh2EBRsYfvFrGkTb/doJSbY7x4CiEkS5V38N0SY4UTCCFIVIg2apjffhh8atLt9Q2/9Msdzp52+Tv/6xj1hkIpWF1XvPFmwH0dn8n4gj/7J3O8+JxLnIBSBs8VWJbg//oHLZZWE6TlY1lpvbOXKdNX0Z5Lw3Dzrlr+7sRo4ijVRHgIf276RUPcSN0Xdm5vyzKqpEUR/xG7Q9gWTimDkILSuVl0ogk3WiS9iNyJcaRtYbSmfn6RuPWTr8138mUyUwt0lq5huWlFlOp3U/7vhMStJexMASKFXqmnQdR+jGXnEFaeoLr+UKTrZCyKM1namwHSEhSns7TWe0grNTSSQFGYztJY7JCfyuDlXLZqH9DzMjDSQ6xrHkXsTQqbVlzBt/IUnQlq0T2NBoGk6IyjYoUnczjCwRJpZo0lbMruFJVwkftnRSksAtUBDGVnOtUnkVkMhlF3lnq0TqT7FOxxis7E4OcxNoJbhDo142Md0IorhPrgrCLpSLJjPjox9GsBRu8/5tK2ycwspAU6WiFdP1UHHGhh29kCTr5I585VvLEpMAalYoSQJCokiBq4TpZ+0Ejzob0xOsEmvbCKgGEJvTZptafWyUMVOH1q0tUavvO9Pn9+s8LnXvIo5iXLawnf+2HAyaM283M2nW6aNlKta/7OLzb5zHMe01MWtpV2Tnj/o4j3PgpRCoSMiIIWlu2mpYHSRu0lrlyvooL+joKF4bmFwa4dhA+DuNkgaTf3JF1jDGFlHf1gGd5/xBA6VvRuV5G+Q3+1iVGa/noTy7UJ1lsIa9AkMfoxjuF+ec9CDDVc/fE5km4LbUdkB+1muut38MppZVR2Yh5EmvrkTyyg45Cg+nDPll90mTk3ivm4hpd3OP7aNFd/b5nCVAav4HLj+2uMLOTpbgXMnBsFbWhYXUrHXWwvy8onj2ZVGzSh7mIJG0vcfeXvXx2K1DcpHKRwcKWPFDZ91RqQ63YoE9NTTSRpMZAtPVyZoZvU6as2JWeSjJXHlR6hdokI6Ks2PdXEkHJBYiIC3U1Lca0siYqwLDcVZ4oa24KJE2dHee2vfIbWSoc3/vfz29QI78KbmCEzewy33yNu1uiv3kmt2DgmbtbIP/E0QojU3RaHJJ1Wag1ncvTXl/CcPEqHCARZf5ReUCNR6XFavTUsy0EKmyjpEcQtLOUQRM3UL696eHaOKD7civqxBNK0gY8vRnx8cbvVt76hgPvEcDRcuR5z5freL5nRik59ac/Ptx03DIiqlT1JV/V7O1qrHxZJu0HSamCmZncI3EDqgojqWwdkLvwHDm2I6qklE6zdW0ImQLj1cC6fh4XwXPJffIHsC0/S/I3vk1SbODPj9D+5MRQK11EwaG3jEHfTohgV9snNHidsbCEtG8tPLaS400grl4xOdXX70VDu87DQiUZFGidj4xddok5MrxZSmsvRqwVYtsArOOTGfLrVgH49HIh2S6JegmXvIux/AIwxBKrDmLuAEIL14AYFe4yyM4lG01ctOqrBhHcEEDTjdVpJhTF3DldmUCbhfivXAJEOUDpGC0moezTjTca8eVyZoZ1skZgQS4wQm5hAdVAmHigM3ttPJ6ky7Z+kY5okUuHZeXphjXxmAtfKUO8upvfREsy9NMXUM+PYvoVl776qjdsN6u//ILVa+106t66k7bqkJOm2aV18b9j7Lum0CCprKW8Ygwp6OMYhTgKEsIiTPr3wXvqn0iFbzXu+5mpre+VktbW9h+JBeCjSTYWbwYT/blh3qt+l9tb36d5KO8oK28adGyfebKD7QWoJH7IoAtLquieftKlVNesbMY333iTTX+a113xu3ky4cOnedeswJFjbp7Psgefeo/7uj7ALFx55H48DJo4PNTFZjkBagiSOaX18nv7yLv5FpYi21kEKLM/e1VcrLEFuvkznTj0VAPdtVJikVV2eDdqg4/snMkPvzg0q3/s3u55XsHxnVx1Y/+kTOFOjJLUmMutDpU7mhScJLt/GROn2KuzTvP4hkGY8QNquPeo0CKprGJVQv/Q2cM90cPJlokYlFTIXcmi5HQbdasjl31kanu6tH6xj+xZhO2bzapOoE3PhN9L+g3m9JEsAACAASURBVNXBe+yXXLJrHkYbpC1QD/nqaRRrwc5uwe3OPVLpqSb1aLvVvjNIlsKg2YoWd15bb3tWRe3B/bF9fxvhLQjBkg4ZdxDg0hGdYGsQbE2D8tISzDy/syHBg9BBn+iB/nvxfVlLKol36BbHjXtjEBKwXt89ePi4cWjSFY5N5unjSN+lf+k2qtnFKuUQvovu9NHdPlYpj/Q9dD9EtTpYIwWk56K6fUwYIxwb3ekjC1lMGCE8FyuXQfcCVKubbu+76F6AjhKsXGa4CkrqbaTvYuWz6f6bHWTep795m2BrkaTaxCpksW+XiFa3MEGE8F3syTIoPfxcZnx0GKEa7R2EYFvwzNMOjYbGu5KwvniBxTsXeNbKUAwNtdcDikVBHKcr1rwHPTfte2ZZsFXVZHxBkhgsW2AMRJFhbDSt0d+saFwXwhBU0sdZfJt2RzM+JrEdQaWiP7WYucylJdC6M3jA9rDGxqw5jrhnSEzC7SjiQc+040lmzhSJA8XWnS6nXh3HzVlc+8EWevVj8laW9lZIHGpKkx4G2LjWIYk1+SMjjD4/R+PiOnEnHKwUREqOQuCP5+jcqZObKzL+0gKNS5t0l+pMfeEESS9i690l3GIGfzxHb61FXF1GF3uoMKF1fetQQTd7vEx4YxlrLG0pY7RBOAc/7iaJ6W/s3oYcIO40ByWh6sCGlbvu/4FboWLN+sU6SbD7iiloRax9XMNoQ9z/929VpXRMJ9g7Ha0wk6N0ZPeV7B9WHJ50fRfv+AwynyFpdMCSFL7wHKrTR1iS7lsXKH3jFZJ6m2hpA9Xp4S5M4UyMIHMZum9dIPv8KVrfOU/xKy/QPX+Z7Aun0f0Qq5Cl+Ttv485PYk+UsUeKBNeX8E8fQWY9dC+k/9F13OOzmCDCKuVp/e7bFL/+EqrRRrV6JI029tQo+Veeovntt0mCiMJrz4IlUa0uqtnBmRnHmR7Dnhyh8euvDxLftyObFczPOZw96/DOOxE/fDOiVtf4vsC24Stf8llbV8Sx4YmTNkvLiheeT6OF6+uKyUmLdtswMiJpNjUffBhz+pTFwoLNpcup5ac0rK4qPveKy4cfxXz+VZcohPVNxW/9dkCSAFIis1mEbaEaTWQ2g3AcVKuNsG2sUhHV6QxSwzTCtjFxjDM3gz0+RveHb2NPjGGPjxGtrKJb95bzAsFR9wzj1nyag2n6tMPz28YhU3I48+UJrv0wzXe0XEHUTV/64y+NEnYTJk/k6DVj/IKN7VqEXcXW7S7CSv2ocTtk5OlpvHIGFSZE7YD6J+sUToyx9e7ysIQ27gQYkwZM4laA5TnM/dEnsbMuQaVD/ZM1xl9aYPU71w6d5ZCsbZF54UmEk6bbucdnUbXWgaWuB+fzGnR0+Ej1QTDKkOwX6DUQdR+F3P9dSgd5dEw8NYZf2tn77Q8zDk26ut0jvLmKcGyCS7fxTx9Btbp0fvQJxa9+Bpnx6V++gzs3jnBtpO/ijJdBCpyZdJlnlCL7mSdR7XT54J2YI7y1BpaFVcpjT6SJ3c7MGOGNZaLlTaxCFtXu4c5P4j+Rbi9sici4mCih9/FNks1UcCZeqZBUGmlBgWNjjZVo/usfpCIkjp3uXwqc6VGEbe36/sYxvPGDCGnB7IzFtk7rgy9IkVrFvp+KlR8/ZrO+kbZY7/cNk5MSzxM0GlAoCEZHLQp5wdSkxQ9/FPLlL3lMTkiuXUs4smBz6gmHW7cSspmU2JME7PFRsi8+R3D1OkYl5D7zPMJx6F+4jD2WdjoOb9zCGimj2m2ciXHCO0skWzXs0RGQAntsFGdynKSytW0RPAibDH4GuUv2R9BOuP6jKnNPFVm93KZbjXB8a9DVxRB2EpJyqtEa9RVa3cvci1shSS/GqJR4/fEctu3SWWqkPkrPxso4JJ0o3S4x6DAhagQYlXZ6jlsBrasVemstEILuUoPu0uGFRYLLtxFZn9zLT+OfOUp0Z532H5y/11bnAEhHUj5aZO6zqT8xN5lFAL1awOaFKitvr1O/2SQJFTp5NIKzfYvSQoGZFyeZODtGYTqL5VnEvYT2aof1D7dY/6hCe7Xz0MfQiR5eR2k+z9xnp5l+dnAdtqRfC6herbP0ozVqNxrEvcMTu5ACv+yRn8oycXaU0ZNlCrM53LyLEBD3E7qbPWo3m6x/WKG52N6Rt7wXpC1xsjZuzsEtuBz5/CxOLjVqLM9i9GSZzOjejSl71YDe1uEyYYSEzGiGiadGmXl+kpHjRbxi2uUmqAVsXa2z9t4m1euNQ5//YfBQPl3dj8gMyDbZauCfOUr+1XOASS3bI1OYWOEdmyXeqGNPjxItbabN/GJFdGeD8s++Ru2Xv4NqtAlvraLaPXS7B0qlZHtrDd0feND0wCw0BtXuEd5YRXX7xOs1VKMzaBp37yXyTs3jHptB9QJ65y+j6m1yr55DN7sEt1ZxZ8eJVqv39r8LhICXX04fnvfej5iesnj5sy6uI7hyJabd0bz6ORdtoN7QXLoS4/mpa+Gjj2KePG1TLku2thSeB2OjknJZ0OkYosiwtJyS89EjNv/q1wNOHNMsLEjW1zUXL8WEd09NSpJqjejmHdwj8wjXJak1MFqTVLZwj8wji3mE6yAcJ3Ur3C/zpg2q1U6T+WvbyUqj2UpWKchRIhOwleyMwtuexPElG9c7qEhTudPj6HNlLEeyeqnFzJMFtu508bIWU0/kqS71aawPtHqbAVGzjz+Ro3V9i7gTYnk23eUG3miWYKuLN5Ih2OoSbnXxJ3L0N9p0FusUjo/SvlWlfmGD7EwBYyBu9mnferiqQpMoem9foPf2J9ytxReee+D3AJyczZmfPcmzf+4M+dncjjTwE19doFcNuPitq3zyL68R99KusELuni++G3JTWZ75s6d54o8dIz+Z3VnSCZz5U0/QuNXkwreucfU3b+3ZW3A3qFBhexanv3mcZ3/+LKX5/I5jHP/KPOf+zGmu/tYtPvhnl+hVDiar/FSWI1+Y4/hX5pl8egyv4O567gAYCNsRd95Y4YN/cpHazeaeKxVpC45/dYGJs2MU5/OUFgoUZ/M4uXsUVVoo8M1f+Oq+5/f+P77A23//owMtfWEJjn1pjmf+syeZenYCy91peBz/2gJxN+HW95b4+F9coXLp8YgxiT1FZQAhxPaqVNfBHisNksY7WOU8MuOlxNntY0+MICyJGvh47fFyekMSRbLVHHy/SFxpQKKQxRxWIYOJEpJqc7C9SEtju0GapG7JQYWVAUtiFbOp0lOljlXKp1bzYHlmjRZTH3EYkdRaSNfBKucxA5+uXS6kFq7WqSjJAzdGiLSxZLmUPkWrawrbFszOWCBS94FSMDNtoY2h0TB0O5qpaQvPFWxW0vPwPEGSpBwYBoapaQulDPW6ptEwTIxLXE+wsqKw7XR/vi+oVhW1enpO1tgIztQkwcUrCM8j+8IzoDXB5WtYoyO4czMktTqq3cF/4jhISe+Dj3GPzOPOzdB54y2M1mSfO0d4e5F4+QGJQhwyMo82ir7p7B4Q2r9SFICZJwsYbVi/9piyEe4/5iGOvxf8Z57ABBHh9cU0LWlhOs1k+M03MPHeVp2TsXnxvz3H0z93Cq+YkrRRhqgXk4QKKQVO1sbyLJJ+wrVv38Fow5M/cwLbs6hcqvE7/+PrtFb2GA8BY6dGeOUvPsf8y9NYrpW2SwoVUS/BKI3lWDg5B8sZ6DN0Yq7+1i3e/sUPCZu7W1xeyeUrf+2VYUXa8jvrrLy9znP/xVm8kouKNHEvRiuDZUvcvIOwRNpNIlLc/P0lfvR336e7uX/g+diX5/nS//wyuYm0CMcYg04McT9GRxoD2K7EyTnIQUqgVpq1Dyq88b+dp3Z999WKk7X5mb/3NaaeGd81W+iweO8fXeDt//vDfUnXydk89XOneO7nz5CdyKSpZEoTd2OSSCNE+hzYGXv4We1Gkx/93fdZeXv9UK4bY8yeF/FQpPtwEJSdSWzhUotW0ShKziSO8Ae//+Ft6f1vBUKkfg2lhwLaw6IPKQdydwMpvKHK0gOf/QcE99gsha++RPsPzmMVsuS/9ALdNz+h997lPcdCWIInvnGUL/7Vz+IWHIQQBM2Qm7+/xOq7G/S2+liuRflYkSOvzjD7mal0KdqMyI76SFseSLq5yQxf+eufY/6VaaQlSYKElfMbLL+1RuNOmyRUeEWXiTOjnPz6AqUjxWHhxPv/5CIf/NJF1C69uB4k3X4tQNoSO2Oz8ckWd15foX6rSdJPcAsu089OcPKnjlCYSRsBJGHCR//iCuf/n49R0d4umNxUlm/8rS8w9cw4nY0eG59sUb1Wp7nUIWyGGG3IjPrMvDDJ8a/Mk5tItUJUrLn8a9d542+f39VVYrkWz//XZynM3MuLFxKmn52gfDRtKBA0Q5bfXCPeI+gIsPjGCjf/YGnfyfrsnzrJq3/5Rdxceo8biy3ufH+FzQtV+vV03AozOY58fpb5z83gZNIuzpsXa3z3b/yI2o2DK+j2I91HztOVWLjST1X/TURsQlyZwRIOsQ5JTIgyCSPODM24gjYqLRd0xmjGmw/Z0vs/AmNAmft+vu/Buz849KDAzafQSP3DjOj2Kp0ffEDpj38e4To0f/uHhNeX9p18CtM5zv3Z08Mlc2ezx/lf/Ijrv3tnm89z8YerXP/2bZ77L8/y9J8+lboHDgHLszj7nzzB3EtTSEsStiM+/GeXuPita/Qb4bbbduf7y9z67hKf+0svMPfSFE7G5tyfOc36hxVW3t6/Cy9AZtRHJ5pLv3aD9/6/T+hs9Lbtf/GNFZbeXOPzf/lFRk+WsD2b0z99nMXXV1j7YG8NgX414P1fukh+Msvqe5t0Nrqp2+OBYb39/RVWzm/w2l/5DLnJDNIWzLwwSWmhQP3WTp0PFSk++CeXtlX+W67F5//Ki0PS7VX6vP33P6R71w0iUr0VIQVGpQFlneh9CXf8zAjP/LkzuDkHow1rH27y1i98wOalGvqBHOhb313myZ89wYt//mncvMP46RGe+fkz/PD/ePehfOAP4pFJN2eXmfWfpKvSGu7N4BbT/kliEyEQrPSvpEnU95FrrINPRba+yJOTRXyRwxYOYEhICHWPjm4QmL2l7jKiwIQ9T2xCNpMlDIqyNUVelkmIaSYVuiadwQSSohyjaI0Bhrau0VTVQ+VkOngUrFEyMoeNg0an2QGqTt8cTqchIwqM23NYWKwnt4fXJbEoyBFyVhkHF4MhNiE93aKjG6g9Vg8OLpPOURx2RoENirraoKUf3V8lscjLEllZxBEeEmtwbhGh6dLTLQJziHJPLHKyRFYWcIU/2I8mNhF93aat63teI4DMZ8m9/PR9wuICHUbYhSzesRns0SLddy6kq4VdMP/KNGOnRobaH1d/8xbXvn17ZzqXgd5WwIf/9BLlYyWOfXHuUOM0crzIkz99PHUpaMP137nDx798hai901erE0PlUo33//FFxk+P4Jc8MiMeT37zOKvvbmDUwSuXrSt1Pvili3TWd469ijTLb67x0T+/zBf/6kvYvk1uIsOxL8+zebGKinYfI51obn9veTgOeyHpJ9z+3jKzL07y1M+dwnIkucksxfndSTc9p+3jrJXZdp3GGJJADe+HN1Wk8NQcRqWVj/3F/TtI237q4x49UUIIQXujy9t/70PWP9radft+LeDCv7zKxNlRTnxtAWkLjr42y5XfuMn6PhPTQfgUFWmCUHdZD66zkHmKgjNGbEJW+9dYyJ7FszLoPXqcPQwsbMbsWabsYxTkCK7wsYUziL4bNJrExESmz2ayxHJ8dVfyzVtlnvCeJ9A9urrFqDXNUfcsrshg0HScBtfCd6mrTRacJzninsEX6dIrMD2W46vciS6i2X1p4wiPafsYM/YJsrKALVwkEnPfxLClllmOrtEz+yt65WWJJ9zncYRHz7QJki5FOcYx9xxla2JISCllJsQmYiW+zo3oQ3Z7E1zpc8x5mrxV3vFZYmKuhe8+Euk6uIzb88zYx9OJQHhYWIjBdWsUiYmJTUBFrXAr/JhkR0YwZESeSfsoE/YcGVnAES4W9rb93J1cluNrbCZLu7unpED47tAnaIB4rUpSqSMcOy3u2QOWI1l4dQbbS7M62mtdbv3B0p75swD9esj1b99m/pVpbHf/dupCwvGvLpAdLLc7Gz2u/tatXQl3CAOVS1XW3t/k2JfTMuTJc2mgqXln/wlcxYo7b6zQXt1fc3f5rTUql2vMPD+JEIL5V2b46F9cpru5T1DtkJ4qHWtW393k1B87hlXycLJOuop4DJCuTebIGDpKCNYaJIfQ7ijM5Dj+pflhwPPO91fYuLA/UcfdhBu/e4cjn5/FzTlkxzPMvDDJ5oXqDsv4sHhk0hVAxioy6R0DoJPUmfLKTPnHAUh0zKg7S8Eeo+e0qMdrlJwp8vYYZafJVrR8KMsxJ8s86X2WjEh9PUIIEhMTmB6ClOw8mcHFJydL5GSRS+FbRGb3XEpPZpmw51lwTqetq4lxhDckNTe+wTH3aWzhEpkQV/hkRJ4jzhkaapO62lm95YksJ93nmHVODlOxEiIiEyCxcYWHI11yskRRjnM5fIuOPlz6kyt8SnKcs/7nKMqxoRWmUUgsbOEOzrXPXm9DZEJWkxtkdQEbF1s4gwns0Ts1+CLHcfcZZp2TKUHeJTqj0ahBLb+NLRw8MtTU+q73WyCYcU5w0n0+7bEhBMZoYiKUSVI3lvCwZA5f5ChaY1ihzUq8s8pKt7q0fvuH+0bT93Iv3LXC7n63frNJc2l/YjPaUL/ZpLPepXykuO+2Xslj6tw4lisxxtBcarN1pb7vdwDCVkTteoOjX5xDWpLMiM/YyfKBpBu2YzYvVA8M+vRrIWvvV4ZVX5kxn/LR4v6k+xDorHeHVrOQqbX5aQKkd+HPj+BPl5C+g3RtuloTN/ZfTY2fGSU/nRpSOtEsvbl2KOKsXmsQdaKhD3jyqVEs1/rJk64hreluxRUiExLpHmvBdWzpEYuIhIiOrhMkAZHqYISha5oE/U9IhBoGfIS0Bs3fJNJ20UnMMOnTGDq6TlNVMFJTV5vU1Bo93R4uM13hMWUfY8Y5gSNcJuwjVJIVVpMb7HZnHVzmnCfYSO6wGt/AEg6nvBcoyQlGrCkyIk9Pt7kVfUJk+kw7xzjqPIUnspTlJE1VQd9HHhbOkHwEkpbeYjm+RlvVUCSIwdJ7zjnFiDXFqDXFKfdFLoVvHmrJXbYmmbGPk5F5NpNFKmqZrmpiMNjCoWSNU5LjNNTeVT2xCbkVfYzESv8TkjPey0zbxx7yrg/GUHic9J5nxj6BRJIQU0822EpW6OgG2iikkPgiR8maIC/LVJLlXV0DBkNNrTGjTxCbkJpao6G2CE0fMyDvvCwz75ymbE1i43LEOUM1Wd3dnWQMGBCujf/UCZyZ8bSTiJQk1Qbdtz7Z1b2Qm8ril71htLq10iFsH5yb2av26W0FB5JudixDcTaf7j/RNBdbJP1DuNoMdLcCVKSRGYmbc4bEsR/iXrx3BsV9UJGiudhCRQrLtXAyNuWjRVbeeTTNkgeRhGrYdksI8VBpdfshWGug+jGoQVuvQwSKJ86MDo/frwV01g/XeSMJEnrVgPxUOu7F+QKWLXlUMYRHJt1I96nHq3TUvdk60B2kFeFPzuF0XCKVYAoO4VaANz6HimPCXhtvdAq356LCHv7EHEFlFacwgj82Rb+yiup3EbZN1KiijeJ69AEYCExnG+HdRUvVMGgWnDNYwmLMnmU9uYnehXSFECQ64nZ0gb5JH8rVuEjeG8HCxpUZrgbvsqVWAIOJDZPWEXJWibxVRsbWtnMYs2eYdU4gsWioTS6Fb9F+YKne1lVaeounvdcoW5OM2bNMqqMsxpcOHOcp+ygGzY3wI5bjKzuIq6bWEMhdx+VBaFTqHjGgdrQOOhwEgglrnmn7GBJJbEJuRh+zqq5RGFVkMoJWQxEm0Emgad3BkRZdo/B8gUpMWnF3H9qqzsfB6/R1h4Roh0xeW9do6SrP+V+hYI2QkXnK1gTryd4vjf/UCfKvPovwHFS9jcxnEY7FXmawX/JwB0n4KtJ0Hwg87YWwHRMdgpz9kkt2fJDUL6A4l+fZn3/y4AMAk+fGh2QhLJEWCxxgLapQ0a8drnKuXw+JegkZ18JyJNmJw6+AhCXIjPjkp7J4BXeYTmc5FtJJswCczOPvCqb7Md7JHHErQIcx2WMTRAeIKJWO3lvJCCk4+Y2jzH126sBjOTlnmD4I4GZtpLO/1vd+eOTRCHVvVy1MtzRGdvoYwdYaUXML6foI28H2c/Rby3ijU2Qm5wi21nDyJfLzTwwaTOYxWqHjkNzcCaJOg6ieOqt7en8faELEZrLIlH0MX2TJySICCXsQUUvXtrkfmmoLjcIWDj3Voq3q3H2iQ9MnMgE5Snhi0BZ+8LDbOMzYx3GER2wiFuPLtPXuS8aubrGa3CBvjWDjMGkfYSW+jtpnvhRCII3FUnxtV8IFBh3YfnI1+Y7wmHFOYgsHbTRL8RWW4yuMThm++HWfTFby4fmQfEGwsa6YmEwr/6oVwfwRj/M/CkkeSBlSJLT0/r61rm5SVasUrBEsHHyxt84xgDM1Ru+DK8hshni1QlKpU/j6y4NI987tbd8a5sUaZYi6h7NjVKRIotSa2y+/1PZsbD993aQlWXh1loVXZw91jPshhMDxLaQU6H2CaVoZkuBwQeu4nwxFhoQlcDJ7+74BLFdSWigw9/I085+dJjedEq7tWViuhbTS/HppDSzbx2Pc7kDSjSg9t4AOY8LNA4LUArz8PeLMjmf4zH9z7pGOKyyZukkeEY99CtIqIaxtEHcapDkd6YiH9Qoq6BHWNtLS0WYNK5Olt7FI3K6nN0Yrkk4TphkS7mERmN4gSJMdBGH2vtN9vd1ijk2YtoAWEJlgm1K+NsmQ7B7cryfT5XO6zzZNVWE/86OptkhMhCNdfJkjKws7rOIHEZk+68ntfaP2P0lkRJ6CHAHSlcdmsjg4N0k2J6lsKPo9zdSsQ7Zlhn8797xLHEGv++jOvLuTb9pdeX9iUN1+WrjTaOOdPgK2hVXK7dllRNoy1Ywg9ZkfWkLRcLBvT4CdsR8f+Qz0g/Y9LW32JeVt2yo9zBIQUmDvUp11F17R5fTPHOfpP32K4mweaQ/6+N316StDEibEvRg1KDTIjPnDsX2cCNYGJf+WpL+8/3tkuemE8LjwaQo4Dke6chDg2CPV5n5E9co2wowa6c93ZdWSXpvOQEaPVpX+RqqdG3fSdC07m6e/sUzS3d26tbAHbUbkQDMgbXHui9zAumXwUD7Y5fEeErO9iVzarHkg92fibQRn7lvsPjjQBVkevvzKJNjCISdKew0Ntrg309o4BwayDIZYRPT8LvT2EOMetDcaVtdJsaPS7nGiYI1iibSKqqfbdO8SoRSMT6Ztl+I4FQ565gWX1eWE1aWE177q893fOTg4IxBYOFgizYIQg38gcESa8jZULdtnjR1cvIXMpqTrnpij8NWX6L17KW3WuQuM0mnQyRrUnjwMSRxi0/uLkHSiWXprjc0LtbRFjpCDW6jTbA2j9l29rH9QObjMVaZSnOoQxCut+yYcbUj2KI5w8w4v/YVnOPOzJ3DzLkYbgkZI7UaDtQ8qNO606G31B3oUGp0YRo4X+cL/8BKZkb31Eh4V7miO7LFxnGIGp5Shcf723hsbs+0etNe73PzO4qFXNPcjascEzUfv1n0g6QrHonhmChNr2jcq27QOHtgSy/ZQyadTYEp6XZLeg746QU4WKVuTFOQoWZnHET6OcAfBIYkUNjb7Wz93sTPt6x6x6m00e+/zu+dxPzyRGRJ9yZrg5exPH3hsazDkUkhsccDwGzA5Sf7MUXo31gnXGhhtsMtZdBCnvqyT0wgBvdsVnJEc3mSR3u0KqhNiFzPoOEH3ImTGRfoOuheiw0e3ml3hD685NP2he2R23uLSxzGuB7YN/+bXe1hSoLXh6EmHlcWEteW9j2thp0FBa4KcLJEROWzhYQt7GAC0Dhqv+6BqTdTA+Gn86h+k5eSx2nNCSkKFjtMS3HSJfbhjCUscmC6GgaSXpGI+djom6x9U+PCfXmHSPz4k3kRHRLpPX7XoJ3tXPaWiQ/sfUloC27dR0cH+Zsu3UouVgaW6i4SkkIIjr81y+pvHcQaFBbUbDd79h5+w/NYacV+h1c7CBMuRjywIdBDiVp/uzU28sQLyAJeIivQ2d0vQCLnwrWu0DxFsfBDGcKg86b1wMOlaEqfg019t7hMhFLh+kfHpc2ytfUwUdZDSwnYyafpP2B203/HT36MOluUhpYWQNnHUuU9Sb/sxHOFxxDnD9CCCL7FQJCiTYAYUqVBoowfW0cFLiP37GR1+MC3hIBDsV0r9IBTJMJB1mL5KWmjcyRLCknizI8S1Dvmz82AMtdcvkz89TdIJCFZrZObH8OZGCCst3NE8hWfTktDq719g7OvPEFWadC4ufyrStRhcM2ZbW/mrF2O2NhXGwOaaIo4hxiAE1KuKH35P0Wnvdr2CkhzjmPs0ZWsKT2QG9zRGG4Ue/kv9poclXvfEPLrZJqk2U2UxIfCemCe8vrxrlV7Yioh7CU7WwXKsYT7tQXCyNs4glWg/hO2Ifj0kN5FBSkFuIouKE7aSpfu+K9AmGVi7n46oLM8iM+ofSh0rM+IPlbxUrOlVd65I3LzDia8t4BfT1UavllaH3Xl9Zd8JwPKsg/rLPjLsgk/+1DQY6Fw7uEqvvdqFFwEBmREP27V+bBPCfjj4CdZpaZ0/U6K/1sQku+RaCkmuOENp/CRx1KVeuUpp/ASuV8C2fSqrH2HZHvnyPH52hLXbb1IaO4Hj5lFJn9rmZeJw54xj43DSfY455xQWdlpNphZpqi16ukVkAhJilFFkRJanM18gJ/ZP3YG77oRPD30fcbZ1jdX4+uG/i96W+bEf+nmE0gAAHcJJREFUkmaP/p0KmaMTOOUcwrZIWj2MMUTVNuFaA9UJiRvpCiGqtBl59RTStYmbPYRrI6Sg/eEiah+FtcPAoDCkrUDlfRNcr2vodXdaSMbA1ubeb2VBjnDGf4WSHAcMHd2gqlZpqxqB6RKbNF9Xo5ixT3Da/8yhztM/c5To9mpKuqRSn7lXzhHdXht2jrgf3UqfoBmSHc8gLEFhNoedsQ9M68qM+Pjlg/Vee9U+7bUOuYl0/yPHizhZm7gXPJ6H8QE4WYfiXJ7G7f2D0JabSj/eDSImQUJjcWdQysnYabXeANXrDdY/3DrQ4s6NZz5VpH+/ucfy0olCR8mhXGpbV+qc/qZJ+6CNZyjM5qleO7xc6OPCwaQ7aL0SN/t7joAxinb9DsWRI1RWP8CyfUqjJ0jiHkLauH4By/LAGPzsGLbtIYSk116jsbU3UZWsCeacU9jCIdR9roTvsJks7Rrxt0RaofWTRGSCIelGJmApvnIo6/VhYJRJW9ycniGqtIi22uSfnidp9VGdgLjZJ39ugajaIa53yT05S2ZhjP7tCnYxS9IKUO0+cbOHjpNPPUSRCQf+bzttYIi1Z5XeQZBYzDun06IPYEutcjU8T1c3dx3HQwUTpcR7Yh73yDQy4yMLaW6lPVpMO0fs8Qx31ru013uMDEpER0+UKM0f/FKmEoQH5832tvpULtaYfGoMaUvKx0pMPzfB0ptrP5bH1is4TJ0bY+nNtX2Xwn7ZZ3pQjQZpsUTj1k7XhnRSZbK76G709hXGgdTFMfXs+COnjKXBQD3MDBFSbCNwu+CTPTJGuNlC+ge7Ftc/rBA0QrJjGaQlOf6VeZbfXj9cvvRjxKFGwxiDjtS+s44xBiFtyhOnaDeW6bbXUlXHOCTsN5mYfY446qKTcPhC6QMaPo1Yk9jCwWCoJMv3Rcp34m7N/08SXd1EmQRHungigy9yw9zfxwXVDdj6vY+HPkmjDeFG2lkXbeheXaN/u4IOYlQ3oPr7n2CSVPwj2mpjjMEkmvrrlzGHFPDeD13dHKbX+SKPL3MHpvTtBU9kKFnjSCFRRrEUXd63Us8VhwvGWMU8VrmA9D1kLv2OiRM6r3+w5xgkgWL1/AZzL01he6m4+PznZqjfbu2ZneBkbY68Not7iNJWnRhuf2+Z419bID+ZJTvmc/ZPPUH1euNQOrYPC2lLjrw2x5XfvEVree9ncub5CSbOjg5/X31vIxXfefD8ldmmbubmnQMLHcZOj7DwuZmhv/hhYZQh7g4MBZFa79mxzLC02SSKYK1BVO2gegev4JrLbZZ+tMbpnz6OkIKFz8+y8OoMt7+7/BPttHEg6ZpY071dRTr7E5pKQjYW30Fa9v/f3pnFxpXl5/13z7lr7VUsksVNJEUtpNQtdbd6mXZmxsnEiZfM2Mi8xICdlwSwjQABkpe85ilvQfIWJPBkgiBwNhh2BrYxhjHj8YzbremWehOllpotieK+FVl71d1vHk6J2kiJ6m53MA6/pyKr6i5Vdf7nv34fceSzu3Edy1HhSOB32Nm4hq7bdJub+L0mjfA2UeSrooymxkcfx/4iS6CXtA81uBoaOTFw5EX5RaEbt2jHNWyRIiVylPQK68GdL9bbTRJlbB8qYMa9h/J0UfzI37EbHPj4izC4AO24Ti9uY0qbtMhRlqOsxgcPrTwLQpP73R8xEd2nbFg6BgX5bIFC4pju1Y8Vefv2Hv5yP9d3hBzp0ltrnPkH0wycKiB0wflvn6a52mL5r9afIIAxUjqnf3mKmV84ceT2oc35Krf/bIkLv34WoQsmvzZG6IZ88F8/prXWJjyAslHoGkbKwM5bDF8o01husTV/MEHLw9A0jfLZIhd/c473vjNPd/fRNIbQNcqzJS78xqzyRBOVp1380cqBHmzkhtSXmmRHVX90ebZE6VSBzY92nvDUha5ROlXg1d96kcJk7nO1V9WXmgTdADNjkhqwmfr5cWqLSsSzu7ynptI0iDrPzl37rYCP//A2wy+WyZ/Ikhqw+co/fwlpCFbf2cRr+k8aX031cJspg8JkjvRwiqW31o80EHMYjlBI05C2gZl38LZbhId2LyT0Og9axcK4Rxg82MG97h4ekHNGyFlDgEaYuGDnSJKItvfkDyl4iBzF0pxDQ9msGHiE9+DLQojPRnCXghxCojNhzNKNWwfyMzwMS0vtk7j8rCFIXLbCJbKihNAkE8YsvaRDNVx96mYjkUSPfXdxEu+z0AkElubQ4UlPVyAZNWbIydITzx2G7ge3VMHsOQpSzZUWt753h9f/2UUMRyc3nuHn/uUlSicLbF7bwW34CKmRHnIYfbXC6V+cxEwbtLe7+3y6T0PYC7nx+wsMnC4w9uow0hSc/qUpymeLLL21zu5CDa+lFr4wJGbGIDOcYuBUgcG5EunBFG//+/eOZHR7ey5G2uDsN0+SGUpx5wfLNFZahK4qFpZni8x+a4aBM0U00Scy/+EK2zd2D0x3+J2AtStbjL5aQRqCzFCK13/nAtf++y327jTwuwHSlKQHHYbODzD7rRlKpwt0dnqqqHeEvPdB2Lq2Q2enh5kxEYbg3D88BcDK5XXchg9JiLQkxkAWM12ivtR8ah5761qV6/97gdd++wJWziQ/keVr/+o1Vq9ssn51m852h9CNVFeKreMULQqTObXJzOTZXaixfnUL/2iEgQfi2Z5uAnraIg6jx6SxPxuEJkiZRUVmoinqvjA+eNdoRNX+HL9kSD9BPdpmN9rY76m97/2cMOdIiawigUm+PMN7P+1RDO4xYkyTEwOct36OjXCRvWgDN+70IyMNU9iktTx5WSYri9z15qlGR5dw12xbqSl3OocXDQ5R/oX73cxiv+dVPFRSFpqOxOjf0f2muYPPEROzEdylJCsMyBHSIs+c9RW25D12w3W6cYuYWN2zZpMRBfKyTC9ps+zffCRa8ROXdlxXBhzJpHmOwPMUJ0USo2nKEFeMk4zpp9RVPWPyax+HePa6kcJJDeCkBvA9JflkGA5B0KXT3mThTxYpnSpw6u9PKsM7luHV33qRXt0j6ARKHyxvYmZMQi/izg+W2b1d59I/feFIDFrNlTZv/7v3ef13LjDx5gjSkgycLlI6VSDyIgJXtZYJQ6DbOtIQ+/cb3yewfwaSJGH+f36CXbCY/bUZJr8+xtjrFdyaSxTESEuqTaJ/7DiMWbm8wUe/d/NQntjIj7nzg2XG3xxh9JUhhC4YfXWY0qkCne0eoRcidIFdsFTxTBc0lltc/c48Y69VmPu1mWde90GoL6uN8NXfvoBuS+yCxUv/eI6z35xWgp1JgjQksj9RePV3559qdJM44db37oAGF39jjkyfc+PU35vk5DcmCLohkR+hCW2fi2I/jZLwmVMlD+MI6YWIxo111TN6yHCEMITaLQ8Ij8y0QXrI2efQbHQ3aPa2oL+A7hPbHIR6tMNOuMaQPo6lOczZb9CK6/hxF6FJHC2LIzIkxNzzP6YgBynLo3GbflEI8LjrX0NoGsP6FCmRY9p8kUnm+m1hUZ9kRvYHOtRG88we3cdgTY5jnz1F80dvEbeeDMNFOoU+VMZfXH70fVqKQTmOrpn7rF+qJ3YQ+gKVw/okKS1DmIREBERJqLTTorUD2drcpMNt70M0S6MkK9gixQljljHjtCKrJ1J3+tA9b4R3Hwyv9BERsN6PFBwtQ1mOkXEK/Vy5Yn9LizyGZtGLW6z4C8yYFz9XGimOApIkxu3VSJIIIUyiOCSOAzRN4DZ6XPlP1/BaHmd+5SRO0ULognTZgfIDiRqv4bPw/UWu/d4t7ILFxd+cwzqiUvjenTpv/durnPv2aU5+Y0KF4EJ5VvdHhR/G/fPt3Nqjfu/ZqgVe02f58jqt9Q5BN2D2V2dIDTr7qYFHXtvyWfrJKu9/98YzCXKa623e/Q8f8co/eYHx1yvolsQp2k8MPkRBxMYH23z4326y+s4GQhec/eb0vnzP8yCJEm790V3MjMHsr86QHkqp72MwRXrw0ddGQXSkDTnohdz4/U9prXc49+1TjLw0hJHWEVIcunFGgSIpWru69bkIzOEIRtdMG0RhjG7J/VyPbikyi6AbEnkRw+cHsHImGx/s4LV8DEfHSOvEQYxdsJn66hhe0yfyYxU+PZy/fUr05yc9FvyrRPgMyDF0zaAohkDQ7+UM6cVtloObbASLJOYsA/L559k/L3pJi0+8qzSiKiPGSVIih0Tv5yvV1NT9XlM3calH27SOSO14H/7aBub4qPpRaRr23BmMoTJRo0nvk9ukXr6AfWaGbnGe7ofX9z3ejCgwa79+aH+rhkZBDlKQj/6CvbhLt9c8lCKzEe9ww73MmDHDkD6JLVII9P5m0vfMFNsxbuLSieoHpob2og0WvPeYNl8g1Seod2Rm//1RElAN11j0r+MlXSr6FKZ4PqOrCbCyFnEc47cC2s21p76+s9Xlyn+cZ+XyBif/zgmK07l9BdrensvOrRrbN6psXavS2uzQ3XP58399GTtn0qn2HvS5amr93Beu3EcC7c0uV393ntt/tkTlQpmhF8oUJrPYeQtNaIRehFv3aK232f20zu6nNWqLTXq1h74PKdCkTuL7BJ2A9797g1t/fI8oFNQWFYvZe9+9wfLlDU68OcrQCwPKaBkSrxOzc32T5b9aZ/PDnSf6eTXLRDNM4m53/7ekGSbbN2r8+N+8w8QbFUYvDVOczmNmDUVI1fSp32uy+eE2a1e2aG12IFG58u//ix+jCdi9fbg45WFw6x7v/5cbrLyzycQbI5Rni6SHUuiWJHQjvLZPZ0upD6/8dOPQ45gpNYrtd0LiIObeT1bZmq8yOFeicnGQ8tki6UEH3dGV7lsnoLPdpbPdZeOjHfbuNGiutg4leD8qnqmRNvutadrbPcZeHmRzvkocJuiOTm40rcKrH67wwrdPkR5OsfgXq6y9v83ZX55CtyTt7S71pRYXfv0M1YUaRsrgo//xyXPxUAoJjmHjBCVmxkbAdWg3FXF5YDYJMzusbNRJiMmLMuPGGfzE5a5/7ZFQNitKjBtnkJrOWnCbWvSgmdrQLE6aFzA0i3q0zWqw8OD8SE4Ys2RkkV7cYsn/mPChljWRskFoxO0eGkLx38oyGVHE0h60VPmJSzdu0Yr36MWdpxLdqGs9jdQM2nGde/51RCZN5o1LdK58QJIk5H/p7+LdvYd5YozWTy4j02ns82dp/flfkngPFlBGFJg0ziminudAmPgs+zfpPkPtQiCxtFT/nvMYD93zfeLxVrxHJ24+YXQ1Q/YVnxMcmaFSOY1RVcW1JEnwkh7NqEo93iFIPASSMfssBTHIdrDEdqzSM0nSFy49ZDXrtuTUL04hdI1b37tz5IZ4u2hx5lemWfzRyr53k0Qq9TLx5ijpQYebf3CboBdipHXyJ3LU7tT3F6Uqtk1z94fLuAd0BOx/DlKFsrIfMap7SlTLVJgQBwfLvItMBj2fw197IDoqCwVS5+doX3mP5KFpNGkKpCURUiBzWZwXztP4yU8JOt6BH5s1PYkzN0v7p+8S7tVASszKMGG9TtzpqvDbeuyaY8VZEfnR55rYgn4B0dbRBLitAKkL7JyB34swHB0raxCHMUEvQjfVGHNnxyVJEpy8idsK0IDAi5CGIIkTJl4ZIFO2+fhPV5GmJPKifWHQzKBNHEIUxhiORDcl3ZqHJjQqs3mWr+4QBTGpokXoRXjtp3u7n0sjzW8FFCaymBmD9GCK+kqLVFon8iPKpwrc+IPbbM5XsVcsli9v4JQUzduV71wniRKKUzla6x1u/tEiF/6R0p96nHJuYEhSGJB4bsLGckBlwsC0NNaXA6bPWoxMGFx7dwtxpoGUGs1PPVYXA6anLVIWiJ2YwYpONt9iq/ou7WbE9HkT09K594lHt6Mkd256P31wUg2M4RJ6MUNQbfBp7zpGOU/Y6KB1dIzhIkkYEWzVWSutI+wqYa1FYurYI0MkcYy/WsWcGCRqdonbPWQ5C/k01Y1ttrvLfFaoa33nkf8Jx0azLDTHIekqyXriGPeTO8TtLpqhpqI0XX/E6LbjOje8txWBtCWJ/Jg4ShBS2/e+pCn6KqiKx+GgBaNJgT2SQ5o63dUaRiGFkbXorTcIpE97qEXDr+FuNkmNFUAKvJ0WwtCJvADdUqQnRs4mbPsEzR6l1yaJOj71+TWifEJtuEptbRnpGDjjBYKmS9TxMAcyWFYeb9ulOZlmr7MF2OTSL+PvbgMaYbdFUDuYqSx0I7Zv7DJ0XhXijLTO1NfHsQsW1Vt7uHWf8a9UIIbGWouta1Wmfn4cO2diFyySOOHEV5VyQPWTGpsf7rDz8S7WGxXQVHpt9OUh7KJN/V4T3VE9oHbBIjOUwnB0KhcHyVRSNFfbrFxef2SoIIkSYgyss3OIVApvcYmo2cS5MKfEMe/ew7AsYt8najaxpibx7i3jzJ0h8Xz8tXVEJkP6pRfRLJX7fzz3G/nx/mYgEwvLzmO/8ipGu0Nv/gbW9CRGZYio3aE3/zH+xhbm2KiqEwDm6AjWySmij+bV39NTmKMjxD0Xb/EeRmUY79M7WJOTaJ0uwjIxx0eJPZ/e/A3i3vPRAwxMZxk5p9TEV97fZehMnlTepLHZQ0iwcyZCamx+XGfsYokkTli6WqV0IoOVNVi7tkeqaOF3Q/IjKTau71Ff65KrKA95/KUB6msdihMZthcaVGYLZIcd7r69xeSrZaIgZuNGnc6uS66iFIPNlM7YhRL50RQf/eES/mdMMzzT/WludMiPZ9i6sUd+PEMSJRQmcyq10C+shV5EfiLLwOkCoRcSehEn3hxh6FwJNAhclfBO4uSJOoAm4Pwlh8nTFpe+mmLqrMXcSzYz52xOzJhk86LPwwqmqZHOCi59NYWTFhgmjE+b2I7gK99IMzpl8MrfcpiYMTl/yeHiG86hvYSaZZJ+6RTW9AhmZYD0y6dJv3aW3NdexD49jjkxhDM7iVHOkXl9Fr2cByGwT45gTVVwZichjhFpG31ATcHpg3nSF2ewZydA01SIZpn981kgpZKNsSw020IW82qBcD+ce2wPlBLNtlTD//QkSIE5PoImNHrXb6GXS8hMmiQISHyfuOfivHgOfXhwf7Hch50zOP31YcYuFEkVTSpzeQZnslgZg7EXi1hpnZG5HKPnCujWkz8LayhL8eUJNCkwCymKF8dxxorkZitkZgYpXBxHEwJpGwz97TNIS8fI2uTmKjiVHLnZYYovTZAaL1J6bRKZNjHzKeXR9esF6SmljJE7N4I9nKN06QSZU0OUXjlB7sww9nAGv1YlbDdJwhB/r0rYbpFEIUJ/DhmYRHHIBt2Q0UvD2EUL3ZKsvrtB6WSe8pkiRkpn6e11kkTxBzhFm96uq3h2H0McxNSXW5hpA6FrZEfSpEo29368ShTGZEfTjL06jNfyqVwcxMo9Wck3KsNopkXn6vsEm5uYE+PErTbe0jL2zDQIDWtyAnN4GOE4xJ0O4XYVkVaDGWZlmCSKcBc+VaTtz0AcBHQ/nEcvFTBGR3DOzRJ3XazxcfRS8YnXh7t76vduWQjHIXV+jsT3MUcriHQavTyAPjiAOTEGSYLzwjmSMMKsDKMPDh5wBU+HYUt6dZ9ezWdgMkt5OovQBdIQGLbO7mKLJEpIly06ux6tbRc7Y9Cte+imwEobuA2f8YslMoP2I55p6EXolmD4bB7dEphpHaFr5CoOZkqn1/BpbHSxcwZuM9j35p2ihTQF2WEHYfw1sozVl5rc+pNFUlbMxl4br9qmfksSeDGrb7UZmXYIGh12buwQ+TFBN2Th+/fIVlJEfkRnu8e9v1wnDmIW/2L1QDb+wE9YuetzctaiPKzT2FPKEqalsbcToesa3XaM24tZWfSZOm3hpAW+l5DKCAxTo9mIWb6tjhGFCYWSZGHexesdnMpIghB0Sdx1CXab6IN5/LUq4W4TmXGIai3IpdFsk9gL6C2sEre6JCMlRNrGXVhVoWYQIRwTmU9jlPNEHRdNCmQxT+rCOcJ6k2h3D1nI469tYowMI2yTqN5EL5eIO12CrR308gDB+iZRo8/aZRhYZ2aI223ibo8kDHBvLhD3epiTE7gLd4kDn7jTRTg25miF3vzHICX2zBSdWv2RcVdNKM82XTIxrDxDp7M0Nnv0Gj52VsfK6JQmM8RhQmOzS3vn0XBYOgZB06W30cAoOES9AL/eVZ0tXkDn3i7d5T30tIlf79G8uYlZSiFtHSOfQqZMNbZc6yJtgyRK8PY6BA1Xqbj6EcKQSNtAd0x6mw31OG3SXav1vfKQ3so6jyNyu4eyh4EK3xXBtoGRMsifyDJ0rkSv5qFbkiRK6O66+32amtAQQkPqSlrH74SsvrNJ+WyRiTdHuPl/7uwfz0wbhK4KU3Vbx0ybivhNaKrSnaiwO4mVqOLalU2CAyeg+lNXUifpRzEYBkjVQxtWd7HGx7HPnKL9zhV1X7qOMNXGnoDaaIU4UpucBqBLFd3EUZ/FJca9fYeo1UbYFpplIRwbhEDTdXU+xyFqt/fTH+6dRcK9PTShkXntEt7KKlGrrdI9cYx79x5h7Wjj7o9cnwblmRxBL2Tx8jbSEhi2pL7aIT1g4bVDvHZI6MeEvq9IgJIELdGQhiBXcVi6skO6ZLH60R5C16jMFihOpEmVLKp3W8z+wijX/3iFwnia9IBNr+ETBTFuI8DvhAgpKE5myI84DJ1Rxc7MgI3b8J85/vw0PNPohm7E7qd1Bl7Lks3HTJ/Mk8Qxblf1QDalSa5ssHm3Q31JTYq0t7pK8rmP+/RpjYMmYxLY2QxpNWI2VwM2lgPOvWxjOYLbNxTh9dRpk6ExnepWRKses7UaEEcJgyO66pvMCLZWA9pNdYxURhJFCeVhnWxBUqse0HTuWGgayLSNzDh4dzcwJ9SO7N5ZxzmnPNlgp4HM7yojLYUK84XAKOcJCjVkLoWmKw82iWKSICTcbSppaMMgCQI03UAW8ujdngq3m200w4A4Vh5vTpW9k4fbvYTq6gh3a+iDA+gDJRWidRKMyhD+yroaczVN/HYHkc2ghxHBxpYyQI+tu8iL2VvqKHJrL0LoGu2q2iCELjAdHcOStNueasV5DO5Wk/SJEoUL4zSurxG5AfZQluaC6kkWUrXqxWFMd0UtsqDlEfUCrMEMXrWNPZglPT2Au9UiaPTwtlukJ0u4W02c0TxJFGMNZmkvVsnNVgg7Ht2VGsJS47uRe3AePPaeHrpaGUNxHTg6pZk8nZ0eQUe1Zm3NV+nVXDSpile1xSZ7dxukyjZDL5TZu11HkxqDs0WkKdm9XUd3JMWTeXRTUjpVIHCr/YEKjdKpvCqwbXUYvTRMbbFB/V6TlZ9ukBvLEIfxgcMHwcYWerFI6sILeMvL+CurOHOzmKMjuJ/eJmq28Nc3kIU84V4NkXL63rGBOVIh3N7GGCpjjo3gr66p9NMhSPwAf3MbZ/YswXYVf2ML7doNzNEKaBpJFGKNTKDpOsbwMGF1D2NEnUsfHiSoVuleu445NopIp1TUsbmNuVcjWNsgbrfV8xNjyP7zz4s4Stj6pM7atRp+J6Cx0UO3BIEbsbfcJo4SmpvdR9M0SYLQBbWVDoEbkR206ex67C21Cf2Yu5e3WLqyg9cJ6e55vPe/FvG7Id2az87tJkmcqOMvtftRufJm3/7PC0p2KGb/dd4Ba+SoeGYh7f7jTEHHSksMU8N3Y3RDI4nVh2PYguZuQLf5BSoYPESV+pSusgPx4mtO3yDD1Z90qe8+eV36QE4ZViHwV3bwFjfUCZ9yHs2QpF6cQaQskiim8/4CiXewIdBME5nPErsuoCFSDnG3pzzOoK8Jl04pY1JrIEsF4naHuNPfrIR48B5dIgt59VySqIW3u4fIpNGEIGo0kfk8ie8TdbroA0WivZo6zxFh2JL8qEP17nOMMT+HwKAmBYULY3TX6o+y/H8BIoXH+JsHK2sgBPQan1WJDLJDqhe5ud59LvvxReBphbQjG92fJegGmJaqWPZ6hxhSTUOzVPEp9oMDxQoPgmboaLokieNDDe7PKjTxbJ7Wz3V8Q+5zRhzjGH+T8f+d0T3GMY5xjP+X+MxG9xjHOMYxjvHF4q+J0/0YxzjGMY5xEI6N7jGOcYxjfIk4NrrHOMYxjvEl4tjoHuMYxzjGl4hjo3uMYxzjGF8ijo3uMY5xjGN8ifi/EBlyAcLoZekAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "text = ' '.join(df[df.data=='biden']['tweet'].to_list())\n",
-    "wordcloud=WordCloud(width=500,height=300,random_state=21,max_font_size=119, stopwords=stopwords).generate(text)\n",
-    "plt.imshow(wordcloud,interpolation='bilinear')\n",
-    "plt.title(\"Biden Wordcloud\", size=25)\n",
-    "plt.axis('off')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 166,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-07T17:54:54.689797Z",
-     "start_time": "2020-11-07T17:54:38.141476Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "text = ' '.join(df[df.data=='trump']['tweet'].to_list())\n",
-    "wordcloud=WordCloud(width=500,height=300,random_state=21,max_font_size=119,stopwords=stopwords).generate(text)\n",
-    "plt.imshow(wordcloud,interpolation='bilinear')\n",
-    "plt.title(\"Trump Wordcloud\", size = 25)\n",
-    "plt.axis('off')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Sentiment Analysis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 167,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-07T17:57:39.336962Z",
-     "start_time": "2020-11-07T17:57:38.766146Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[nltk_data] Downloading package vader_lexicon to\n",
-      "[nltk_data]     /home/mahendra/nltk_data...\n"
-     ]
-    }
-   ],
-   "source": [
-    "nltk.download('vader_lexicon')\n",
-    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
-    "sid = SentimentIntensityAnalyzer()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 188,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-07T18:09:19.231813Z",
-     "start_time": "2020-11-07T18:09:19.221589Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def sent(text):\n",
-    "    dict_ = sid.polarity_scores(text)\n",
-    "    return max(dict_, key=dict_.get)\n",
-    "\n",
-    "sent_dict = {'pos':1, 'neg':-1, 'neu':0, 'compound':0}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 191,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-07T18:14:43.901045Z",
-     "start_time": "2020-11-07T18:13:19.258960Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "df['sent'] = df['tweet'].apply(sent)\n",
-    "df['sent'] = df['sent'].apply(lambda x: sent_dict[x])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 215,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-07T18:27:31.818212Z",
-     "start_time": "2020-11-07T18:27:31.641057Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Biden Tweet Sentiment Score : -266\n",
-      "Trump Tweet Sentiment Score : -959\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Biden Tweet Sentiment Score : {}\".format(df[df.data=='biden']['sent'].sum()))\n",
-    "print(\"Trump Tweet Sentiment Score : {}\".format(df[df.data=='trump']['sent'].sum()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 216,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-07T18:27:36.887508Z",
-     "start_time": "2020-11-07T18:27:36.128160Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'Trump Score per States')"
-      ]
-     },
-     "execution_count": 216,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x1080 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(20,15))\n",
-    "df[df.data=='trump'].groupby('state').sum()['sent'].plot.barh()\n",
-    "plt.title(\"Trump Score per States\", size = 20)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 217,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-07T18:27:39.483529Z",
-     "start_time": "2020-11-07T18:27:38.739051Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'Biden Score per States')"
-      ]
-     },
-     "execution_count": 217,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x1080 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(20,15))\n",
-    "df[df.data=='biden'].groupby('state').sum()['sent'].plot.barh()\n",
-    "plt.title(\"Biden Score per States\", size = 20)"
-   ]
-  },
-  {
-   "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",
-   "version": "3.7.6"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/EksplorasiData_Jevon/task_event_2_jevonvalerian.ipynb b/EksplorasiData_Jevon/task_event_2_jevonvalerian.ipynb
deleted file mode 100644
index fb2ff56..0000000
--- a/EksplorasiData_Jevon/task_event_2_jevonvalerian.ipynb
+++ /dev/null
@@ -1,1485 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tugas Event #2 - Solving Real-World Problems with Data Science\n",
-    "\n",
-    "- Nama           : Hieronimus Jevon Valerian\n",
-    "- NIM            : 10819017\n",
-    "- Universitas    : Institut Teknologi Bandung\n",
-    "- Tanggal Mulai  : 07 November 2020\n",
-    "- Sumber Dataset : https://www.kaggle.com/c/titanic/data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Titanic train.csv\n",
-    "- Sebenarnya data yang disediakan ada dua, yaitu train.csv dan test.csv\n",
-    "- Namun, untuk mempermudah analisis dan demi pemenuhan spesifikasi tugas (satu file dataset), maka akan digunakan data dari train.csv saja"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. Data Preparation & Observation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "- Pada analisis kali ini, saya menggunakan dataset titanic. Data ini memberikan informasi tentang beberapa hal terkait  masing-masing penumpang beserta status \"survived\" (1) dan \"died\" (0).\n",
-    "- Nantinya dataset akan dibagi dengan proporsi 80% untuk training dan 20% untuk testing.\n",
-    "- Model akan dikembangkan menggunakan data train untuk belajar pola-pola yang muncul dalam memprediksi peluang seseorang akan \"survived\" atau \"died\"\n",
-    "- Setelah itu, model akan di deploy menggunakan data test untuk melihat seberapa akurat prediksi yang ditampilkan oleh model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Import library\n",
-    "import pandas as pd\n",
-    "\n",
-    "#Load dataset train.csv\n",
-    "df = pd.read_csv(\"train.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "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>PassengerId</th>\n",
-       "      <th>Survived</th>\n",
-       "      <th>Pclass</th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Sex</th>\n",
-       "      <th>Age</th>\n",
-       "      <th>SibSp</th>\n",
-       "      <th>Parch</th>\n",
-       "      <th>Ticket</th>\n",
-       "      <th>Fare</th>\n",
-       "      <th>Cabin</th>\n",
-       "      <th>Embarked</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Braund, Mr. Owen Harris</td>\n",
-       "      <td>male</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>A/5 21171</td>\n",
-       "      <td>7.2500</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
-       "      <td>female</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>PC 17599</td>\n",
-       "      <td>71.2833</td>\n",
-       "      <td>C85</td>\n",
-       "      <td>C</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Heikkinen, Miss. Laina</td>\n",
-       "      <td>female</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>STON/O2. 3101282</td>\n",
-       "      <td>7.9250</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
-       "      <td>female</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>113803</td>\n",
-       "      <td>53.1000</td>\n",
-       "      <td>C123</td>\n",
-       "      <td>S</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Allen, Mr. William Henry</td>\n",
-       "      <td>male</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>373450</td>\n",
-       "      <td>8.0500</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   PassengerId  Survived  Pclass  \\\n",
-       "0            1         0       3   \n",
-       "1            2         1       1   \n",
-       "2            3         1       3   \n",
-       "3            4         1       1   \n",
-       "4            5         0       3   \n",
-       "\n",
-       "                                                Name     Sex   Age  SibSp  \\\n",
-       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
-       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
-       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
-       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
-       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
-       "\n",
-       "   Parch            Ticket     Fare Cabin Embarked  \n",
-       "0      0         A/5 21171   7.2500   NaN        S  \n",
-       "1      0          PC 17599  71.2833   C85        C  \n",
-       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
-       "3      0            113803  53.1000  C123        S  \n",
-       "4      0            373450   8.0500   NaN        S  "
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Melihat bentuk dataset\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 891 entries, 0 to 890\n",
-      "Data columns (total 12 columns):\n",
-      " #   Column       Non-Null Count  Dtype  \n",
-      "---  ------       --------------  -----  \n",
-      " 0   PassengerId  891 non-null    int64  \n",
-      " 1   Survived     891 non-null    int64  \n",
-      " 2   Pclass       891 non-null    int64  \n",
-      " 3   Name         891 non-null    object \n",
-      " 4   Sex          891 non-null    object \n",
-      " 5   Age          714 non-null    float64\n",
-      " 6   SibSp        891 non-null    int64  \n",
-      " 7   Parch        891 non-null    int64  \n",
-      " 8   Ticket       891 non-null    object \n",
-      " 9   Fare         891 non-null    float64\n",
-      " 10  Cabin        204 non-null    object \n",
-      " 11  Embarked     889 non-null    object \n",
-      "dtypes: float64(2), int64(5), object(5)\n",
-      "memory usage: 66.2+ KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Melihat info pada masing-masing kolom\n",
-    "df.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "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>PassengerId</th>\n",
-       "      <th>Survived</th>\n",
-       "      <th>Pclass</th>\n",
-       "      <th>Age</th>\n",
-       "      <th>SibSp</th>\n",
-       "      <th>Parch</th>\n",
-       "      <th>Fare</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>891.000000</td>\n",
-       "      <td>891.000000</td>\n",
-       "      <td>891.000000</td>\n",
-       "      <td>714.000000</td>\n",
-       "      <td>891.000000</td>\n",
-       "      <td>891.000000</td>\n",
-       "      <td>891.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>446.000000</td>\n",
-       "      <td>0.383838</td>\n",
-       "      <td>2.308642</td>\n",
-       "      <td>29.699118</td>\n",
-       "      <td>0.523008</td>\n",
-       "      <td>0.381594</td>\n",
-       "      <td>32.204208</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>257.353842</td>\n",
-       "      <td>0.486592</td>\n",
-       "      <td>0.836071</td>\n",
-       "      <td>14.526497</td>\n",
-       "      <td>1.102743</td>\n",
-       "      <td>0.806057</td>\n",
-       "      <td>49.693429</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.420000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>223.500000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>20.125000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>7.910400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>446.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>28.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>14.454200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>668.500000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>38.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>31.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>891.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>80.000000</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>6.000000</td>\n",
-       "      <td>512.329200</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
-       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
-       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
-       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
-       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
-       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
-       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
-       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
-       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
-       "\n",
-       "            Parch        Fare  \n",
-       "count  891.000000  891.000000  \n",
-       "mean     0.381594   32.204208  \n",
-       "std      0.806057   49.693429  \n",
-       "min      0.000000    0.000000  \n",
-       "25%      0.000000    7.910400  \n",
-       "50%      0.000000   14.454200  \n",
-       "75%      0.000000   31.000000  \n",
-       "max      6.000000  512.329200  "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Statisitka Deskriptif dataset train.csv\n",
-    "df.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "male      577\n",
-       "female    314\n",
-       "Name: Sex, dtype: int64"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Melihat jumlah penumpang pria dan wanita\n",
-    "df[\"Sex\"].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Rata-rata usia pria meninggal = 31.62 tahun\n",
-      "Rata-rata usia wanita meninggal = 25.05 tahun\n",
-      "Rata-rata usia pria selamat = 27.28 tahun\n",
-      "Rata-rata usia wanita selamat = 28.85 tahun\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Filtering rata-rata usia pada kolom \"Survived\" dan dikelompokkan berdasarkan jenis kelamin\n",
-    "for i in range(2):\n",
-    "    if i == 0:\n",
-    "        a = \"meninggal\"\n",
-    "    else:\n",
-    "        a = \"selamat\"\n",
-    "    \n",
-    "    b = round(df[(df[\"Sex\"] == \"male\") & (df[\"Survived\"] == i)][\"Age\"].mean(), 2)\n",
-    "    c = round(df[(df[\"Sex\"] == \"female\") & (df[\"Survived\"] == i)][\"Age\"].mean(), 2)\n",
-    "    \n",
-    "    print(\"Rata-rata usia pria {} = {} tahun\".format(a, b))\n",
-    "    print(\"Rata-rata usia wanita {} = {} tahun\".format(a, c))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "1. Dataset terdiri dari 12 kolom dengan rincian 5 kolom bertipe int, 2 kolom bertipe float, dan 5 kolom bertipe object/string.\n",
-    "2. Terdapat null values pada kolom \"Age\", \"Cabin\", dan \"Embarked\" yang akan mengganggu proses perhitungan ke depannya.\n",
-    "3. Jumlah penumpang pria lebih banyak dari pada perempuan.\n",
-    "4. Usia pria yang meninggal berpusat di nilai yang lebih tinggi daripada yang selamat dan berlaku sebaliknya pada perempuan.\n",
-    "5. Ada kemungkinan usia penumpang laki-laki memang berdistribusi di nilai yang lebih tinggi daripada perempuan yang kemungkinan berdistribusi di nilai yang lebih rendah.\n",
-    "6. Data tidak berdistribusi pada rentang nilai yang sama dan akan berpengaruh pada penurunan akurasi model Machine Learning nantinya.\n",
-    "7. Sehingga perlu dilakukan beberapa proses pada dataset."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. Data Visualization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Import library\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "import altair as alt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\users\\asus\\appdata\\local\\programs\\python\\python38-32\\lib\\site-packages\\numpy\\lib\\histograms.py:839: RuntimeWarning: invalid value encountered in greater_equal\n",
-      "  keep = (tmp_a >= first_edge)\n",
-      "c:\\users\\asus\\appdata\\local\\programs\\python\\python38-32\\lib\\site-packages\\numpy\\lib\\histograms.py:840: RuntimeWarning: invalid value encountered in less_equal\n",
-      "  keep &= (tmp_a <= last_edge)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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B2npykoe24W2rgF8G/nV925UkaW204XevAj60vgVPG1K5cxu6+Ey6np0ZjxHSwrPokbTe0l0jaTXdOOxZx7KvhV3phnD8kO7A2t9p490lSVoU7Rjjm+mGGB65AE0+lO54rtvoLqfxR1X1tQVoV0NweJskSZKkXrOnR5IkSVKvWfRIkiRJ6rUlcXHSrbbaqlauXDnuMCRpWfvKV75yQ1WtmH/O5cc8JUnjN1eeWhJFz8qVKznvvNlO4S9JWgxJvjvuGCaVeUqSxm+uPOXwNkmSJEm9ZtEjSZIkqdcseiRJkiT1mkWPJEmSpF6z6JEkSZLUaxY9kiRJknrNokeSJElSr1n0SJIkSeo1ix5JkiRJvWbRI0mSJKnXLHokSZIk9ZpFjyRJkqRes+iRJEmS1GsWPZIkSZJ6bcNxByCtq5WHnTb0vFccsf8II5EkSdIks6dHkiRJUq9Z9EiSJEnqNYseSZIkSb1m0SNJkiSp10Ze9CTZIMnXkpzaHm+Z5Iwkl7a/W4w6BkmSJEnL12L09LwKuGTg8WHAmVW1C3BmeyxJkiRJIzHSoifJ9sD+wIcGJj8XOLrdPxp43ihjkCRJkrS8jbqn50jgNcDdA9MeUlXXALS/W8/0wiSHJDkvyXmrV68ecZiSJK0d85QkLR0jK3qSPAu4vqq+si6vr6oPVNWeVbXnihUrFjg6SZLWj3lKkpaODUfY9hOB5yTZD9gYeGCSY4HrkmxTVdck2Qa4foQxSJIkSVrmRtbTU1Wvq6rtq2ol8ELg36vqYOAUYFWbbRVw8qhikCRJkqRxXKfnCODpSS4Fnt4eS5IkSdJIjHJ42z2q6izgrHb/RuCpi7FcSZIkSRpHT48kSZIkLRqLHkmSJEm9ZtEjSZIkqdcseiRJkiT1mkWPJEmSpF6z6JEkSZLUaxY9kiRJknrNokeSJElSr1n0SJIkSeo1ix5JkiRJvbbhuAPQ8rDysNOGnveKI/YfYSSSJElabuzpkSRJktRrFj2SJEmSes2iR5IkSVKvWfRIkiRJ6jWLHkmSJEm9ZtEjSZIkqdcseiRJkiT1mkWPJEmSpF6z6JEkSZLUaxY9kiRJknrNokeSJElSr1n0SJIkSeo1ix5JkiRJvWbRI0mSJKnXLHokSZIk9ZpFjyRJkqRes+iRJEmS1GsWPZIkSZJ6zaJHkiRJUq9Z9EiSJEnqtZEVPUk2TnJukguSXJzkzW364Um+n+T8dttvVDFIkiRJ0oYjbPsOYJ+qui3JRsCXkvxLe+5dVfV3I1y2JEmSJAEjLHqqqoDb2sON2q1GtTxJkiRJmslIj+lJskGS84HrgTOq6pz21CuSXJjkqCRbzPLaQ5Kcl+S81atXjzJMSZLWmnlKkpaOkRY9VXVXVe0BbA/snWQ34B+BnYE9gGuAd8zy2g9U1Z5VteeKFStGGaYkSWvNPCVJS8einL2tqm4GzgKeWVXXtWLobuCDwN6LEYMkSZKk5WmUZ29bkeTB7f4mwNOAbybZZmC25wMXjSoGSZIkSRrl2du2AY5OsgFdcXViVZ2a5Jgke9Cd1OAK4A9HGIMkSZKkZW6UZ2+7EHjMDNNfNKplSpIkSdJ0i3JMjyRJkiSNi0WPJEmSpF6z6JEkSZLUaxY9kiRJknrNokeSJElSr1n0SJIkSeo1ix5JkiRJvWbRI0mSJKnXLHokSZIk9ZpFjyRJkqRes+iRJEmS1GsWPZIkSZJ6zaJHkiRJUq9Z9EiSJEnqNYseSZIkSb1m0SNJkiSp1yx6JEmSJPWaRY8kSZKkXrPokSRJktRrFj2SJEmSes2iR5IkSVKvWfRIkiRJ6jWLHkmSJEm9ZtEjSZIkqdcseiRJkiT1mkWPJEmSpF6z6JEkSZLUaxuOOwAtXSsPO23cIUiSJEnzsqdHkiRJUq9Z9EiSJEnqNYseSZIkSb02sqInycZJzk1yQZKLk7y5Td8yyRlJLm1/txhVDJIkSZI0yp6eO4B9qmp3YA/gmUkeDxwGnFlVuwBntseSJEmSNBIjK3qqc1t7uFG7FfBc4Og2/WjgeaOKQZIkSZJGekxPkg2SnA9cD5xRVecAD6mqawDa361HGYMkSZKk5W2kRU9V3VVVewDbA3sn2W3Y1yY5JMl5Sc5bvXr16IKUJGkdmKckaelYlLO3VdXNwFnAM4HrkmwD0P5eP8trPlBVe1bVnitWrFiMMCVJGpp5SpKWjlGevW1Fkge3+5sATwO+CZwCrGqzrQJOHlUMkiRJkrThCNveBjg6yQZ0xdWJVXVqkv8GTkzyUuB7wO+OMAZJkiRJy9zIip6quhB4zAzTbwSeOqrlSpIkSdKgRTmmR5IkSZLGxaJHkiRJUq9Z9EiSJEnqNYseSZIkSb1m0SNJkiSp1yx6JEmSJPWaRY8kSZKkXrPokSRJktRrFj2SJEmSes2iR5IkSVKvWfRIkiRJ6rUNxx2AFsfKw04bar4rjth/xJFIkiRJi8ueHkmSJEm9ZtEjSZIkqdcseiRJkiT1mkWPJEmSpF6z6JEkSZLUaxY9kiRJknrNokeSJElSr1n0SJIkSeo1ix5JkiRJvWbRI0mSJKnXLHokSZIk9ZpFjyRJkqRes+iRJEmS1GsWPZIkSZJ6zaJHkiRJUq9tOO4ANFlWHnbauEMYu7V5D644Yv8RRiJJkqSFYE+PJEmSpF6z6JEkSZLUaxY9kiRJknptZEVPkh2SfCHJJUkuTvKqNv3wJN9Pcn677TeqGCRJkiRplCcyuBM4tKq+mmRz4CtJzmjPvauq/m6Ey5YkSZIkYIRFT1VdA1zT7t+a5BJgu1EtT5IkSZJmsijH9CRZCTwGOKdNekWSC5MclWSLxYhBkiRJ0vI08qInyWbAJ4FXV9UtwD8COwN70PUEvWOW1x2S5Lwk561evXrUYUqStFbMU5K0dIy06EmyEV3B8/Gq+hRAVV1XVXdV1d3AB4G9Z3ptVX2gqvasqj1XrFgxyjAlSVpr5ilJWjpGefa2AB8GLqmqdw5M32ZgtucDF40qBkmSJEka5dnbngi8CPh6kvPbtNcDBybZAyjgCuAPRxiDJEmSpGVuqKInyW5VtVY9MlX1JSAzPHX62rQjSdJiW5e8J0maXMMOb/unJOcm+eMkDx5pRJIkjZ95T5J6ZKiip6qeBBwE7ACcl+S4JE8faWSSJI2JeU+S+mXoExlU1aXAG4DXAk8G3p3km0l+a1TBSZI0LuY9SeqPoYqeJL+c5F3AJcA+wLOr6pHt/rtGGJ8kSYvOvCdJ/TLs2dveS3dNnddX1Y+nJlbV1UneMJLIJEkaH/OeJPXIsEXPfsCPq+ougCT3Azauqh9V1TEji06SpPEw70lSjwx7TM/ngU0GHm/apkmS1EfmPUnqkWGLno2r6rapB+3+pqMJSZKksTPvSVKPDFv03J7ksVMPkjwO+PEc80uStJSZ9ySpR4Y9pufVwElJrm6PtwFeMJqQJEkaO/OeJPXIUEVPVX05yS8CuwIBvllVPxtpZJIkjYl5T5L6ZdieHoC9gJXtNY9JQlV9bCRRSZI0fuY9SeqJoYqeJMcAOwPnA3e1yQW48Zck9Y55T5L6Zdienj2BR1VVjTIYSZImhHlPknpk2LO3XQQ8dJSBSJI0Qcx7ktQjw/b0bAV8I8m5wB1TE6vqOSOJSpKk8TLvSVKPDFv0HD7KICRJmjCHjzsASdLCGfaU1V9MshOwS1V9PsmmwAajDU2SpPEw70lSvwx1TE+SlwGfAN7fJm0HfGZUQUmSNE7mPUnql2FPZPAnwBOBWwCq6lJg61EFJUnSmJn3JKlHhi167qiqn049SLIh3fUKJEnqI/OeJPXIsEXPF5O8HtgkydOBk4DPji4sSZLGyrwnST0ybNFzGLAa+Drwh8DpwBtGFZQkSWNm3pOkHhn27G13Ax9sN0mSes28J0n9MlTRk+RyZhjLXFUPX/CIJEkaM/OeJPXLsBcn3XPg/sbA7wJbLnw4kiRNBPOeJPXIUMf0VNWNA7fvV9WRwD4jjk2SpLEw70lSvww7vO2xAw/vR7cHbPORRCRJ0piZ9ySpX4Yd3vaOgft3AlcAByx4NJIkTQbzniT1yLBnb/uNUQciSdKkMO9JUr8MO7zt/5vr+ap658KEI0nS+Jn3JKlfhr046Z7AHwHbtdvLgUfRjW+ecYxzkh2SfCHJJUkuTvKqNn3LJGckubT93WL9V0OSpAW11nlPkjS5hj2mZyvgsVV1K0CSw4GTquoP5njNncChVfXVJJsDX0lyBvAS4MyqOiLJYXRXvX7tuq6AJEkjsC55T5I0oYbt6dkR+OnA458CK+d6QVVdU1VfbfdvBS6h21v2XODoNtvRwPPWIl5JkhbDWuc9SdLkGran5xjg3CSfprtC9fOBjw27kCQrgccA5wAPqaproCuMkmy9NgFLkrQI1ivvSZImy7Bnb3t7kn8Bfq1N+r2q+towr02yGfBJ4NVVdUuSoQJLcghwCMCOO+441GskSVoIw+Q985QkLR3DDm8D2BS4par+HrgqycPme0GSjegKno9X1afa5OuSbNOe3wa4fqbXVtUHqmrPqtpzxYoVaxGmJEkLYs68Z56SpKVjqKInyZvoTjbwujZpI+DYeV4T4MPAJdNO7XkKsKrdXwWcvDYBS5I0auuS9yRJk2vYnp7nA88BbgeoqquZ/5SdTwReBOyT5Px22w84Anh6kkuBp7fHkiRNknXJe5KkCTXsiQx+WlWVpACSPGC+F1TVl4DZDuB56pDLlSRpHNY670mSJtewPT0nJnk/8OAkLwM+D3xwdGFJkjRW5j1J6pF5e3rasTknAL8I3ALsCryxqs4YcWySJC06854k9c+8RU/r3v9MVT0OcIMvSeo1854k9c+ww9v+J8leI41EkqTJYd6TpB4Z9kQGvwG8PMkVdGeyCd3OsF8eVWCSJI2ReU+SemTOoifJjlX1PWDfRYpHkqSxMe9JUj/N19PzGeCxVfXdJJ+sqt9ejKAkSRoT854k9dB8x/QMXmfn4aMMRJKkCWDek6Qemq/oqVnuS5LUR+Y9Seqh+Ya37Z7kFro9X5u0+7DmgM4HjjQ6SZIWl3lPknpozqKnqjZYrEAkSRo3854k9dOw1+mRJEmSpCXJokeSJElSr1n0SJIkSeo1ix5JkiRJvWbRI0mSJKnXLHokSZIk9ZpFjyRJkqRes+iRJEmS1GtzXpxU6ouVh5027hCGtjaxXnHE/iOMRJIkqR/s6ZEkSZLUaxY9kiRJknrNokeSJElSr1n0SJIkSeo1ix5JkiRJvWbRI0mSJKnXLHokSZIk9ZrX6dHE6eM1dSbhejpe/0eSJC1X9vRIkiRJ6jWLHkmSJEm9ZtEjSZIkqdcseiRJkiT12siKniRHJbk+yUUD0w5P8v0k57fbfqNaviRJkiTBaHt6Pgo8c4bp76qqPdrt9BEuX5IkSZJGV/RU1dnATaNqX5IkSZKGMY5jel6R5MI2/G2L2WZKckiS85Kct3r16sWMT5KkeZmnJGnpWOyi5x+BnYE9gGuAd8w2Y1V9oKr2rKo9V6xYsVjxSZI0FPOUJC0di1r0VNV1VXVXVd0NfBDYezGXL0mSJGn5WdSiJ8k2Aw+fD1w027ySJEmStBA2HFXDSY4HngJsleQq4E3AU5LsARRwBfCHo1q+JEmSJMEIi56qOnCGyR8e1fIkSZIkaSbjOHubJEmSJC0aix5JkiRJvWbRI0mSJKnXLHokSZIk9ZpFjyRJkqRes+iRJEmS1GsWPZIkSZJ6zaJHkiRJUq9Z9EiSJEnqNYseSZIkSb224bgDkCRpuVt52GlDz3vFEfuPMBJJ6id7eiRJkiT1mkWPJEmSpF6z6JEkSZLUaxY9kiRJknrNokeSJElSr1n0SJIkSeo1ix5JkiRJveZ1eqRFsDbX4FhKvLaIJElaCuzpkSRJktRrFj2SJEmSes2iR5IkSVKvWfRIkiRJ6jWLHkmSJEm9ZtEjSZIkqdcseiRJkiT1mtfpkSRJWgujuPaa1zKTRsueHkmSJEm9ZtEjSZIkqdcseiRJkiT1mkWPJEmSpF4bWdGT5Kgk1ye5aGDalknOSHJp+7vFqJYvSZIkSTDanp6PAs+cNu0w4Myq2gU4sz2WJEmSpJEZWdFTVWcDN02b/Fzg6JtDWPQAACAASURBVHb/aOB5o1q+JEmSJMHiH9PzkKq6BqD93Xq2GZMckuS8JOetXr160QKUJGkY5ilJWjom9kQGVfWBqtqzqvZcsWLFuMORJOlezFOStHQsdtFzXZJtANrf6xd5+ZIkSZKWmcUuek4BVrX7q4CTF3n5kiRJkpaZUZ6y+njgv4Fdk1yV5KXAEcDTk1wKPL09liRJkqSR2XBUDVfVgbM89dRRLVOSJEmSppvYExlIkiRJ0kKw6JEkSZLUaxY9kiRJknrNokeSJElSr1n0SJIkSeo1ix5JkiRJvWbRI0mSJKnXLHokSZIk9ZpFjyRJkqRes+iRJEmS1GsbjjsASetu5WGnjTsESdIiW5tt/xVH7D/CSKSlw54eSZIkSb1m0SNJkiSp1yx6JEmSJPWaRY8kSZKkXrPokSRJktRrFj2SJEmSes2iR5IkSVKveZ0eSZK07HndM6nf7OmRJEmS1GsWPZIkSZJ6zaJHkiRJUq9Z9EiSJEnqNYseSZIkSb1m0SNJkiSp1yx6JEmSJPWa1+mRJElLyrDX1LniiP1HHImkpcKeHkmSJEm9ZtEjSZIkqdcseiRJkiT1mkWPJEmSpF4by4kMklwB3ArcBdxZVXuOIw5JkiRJ/TfOs7f9RlXdMMblS5IkSVoGHN4mSZIkqdfGVfQU8LkkX0lyyJhikCRJkrQMjGt42xOr6uokWwNnJPlmVZ09OEMrhg4B2HHHHddrYcNexAy8kJk0KqP6P1ybdkex/FFwm7U0LGSekiSN1lh6eqrq6vb3euDTwN4zzPOBqtqzqvZcsWLFYocoSdKczFOStHQsetGT5AFJNp+6D/wmcNFixyFJkiRpeRjH8LaHAJ9OMrX846rqX8cQhyRJkqRlYNGLnqq6DNh9sZcrSZIkaXnylNWSJEmSes2iR5IkSVKvWfRIkiRJ6rVxXadH0gQbxbVvXL7X1JEkaVzs6ZEkSZLUaxY9kiRJknrNokeSJElSr1n0SJIkSeo1ix5JkiRJvWbRI0mSJKnXLHokSZIk9ZrX6Zlm3NcHWZvreIw7VklSP6xNPllK15syT/b3PVhK30NNBnt6JEmSJPWaRY8kSZKkXrPokSRJktRrFj2SJEmSes2iR5IkSVKvWfRIkiRJ6jWLHkmSJEm95nV6Jkxfz6cvSVoYfc0TfV2vYS339V9bw75fXs9HU+zpkSRJktRrFj2SJEmSes2iR5IkSVKvWfRIkiRJ6jWLHkmSJEm9ZtEjSZIkqdcseiRJkiT1mtfpkaQlbG2u7eH1KrQQvJ6MlpJRbSNH8X/Q1230pOQpe3okSZIk9ZpFjyRJkqRes+iRJEmS1GtjKXqSPDPJt5J8J8lh44hBkiRJ0vKw6EVPkg2A9wH7Ao8CDkzyqMWOQ5IkSdLyMI6enr2B71TVZVX1U+CfgeeOIQ5JkiRJy8A4ip7tgCsHHl/VpkmSJEnSghvHdXoyw7S6z0zJIcAh7eFtSb61FsvYCrhhHWIbF+MdraUWLyy9mI13CPmbdX7pgsS7HsufstN6t9Aj65mnpiyF/x1jXH+THh9Mfowjj28BtpHrFeMCLH8+k/4Zk79Z7xhnzVOpuk+9MVJJngAcXlXPaI9fB1BVf72AyzivqvZcqPZGzXhHa6nFC0svZuMdraUWr4a3FD5bY1x/kx4fTH6Mkx4fTH6Mkx4fjDbGcQxv+zKwS5KHJbk/8ELglDHEIUmSJGkZWPThbVV1Z5JXAP8GbAAcVVUXL3YckiRJkpaHcRzTQ1WdDpw+wkV8YIRtj4LxjtZSixeWXszGO1pLLV4Nbyl8tsa4/iY9Ppj8GCc9Ppj8GCc9PhhhjIt+TI8kSZIkLaZxHNMjSZIkSYumV0VPkmcm+VaS7yQ5bNzxzCTJUUmuT3LRwLQtk5yR5NL2d4txxjglyQ5JvpDkkiQXJ3lVmz6R8QIk2TjJuUkuaDG/uU2f2JgBkmyQ5GtJTm2PJzbeJFck+XqS85Oc16ZNcrwPTvKJJN9s3+UnTHi8u7b3dup2S5JXT3LMWjeTmLMmPUcthby0VPLQpOedSc81k55blkIuSfJn7X/koiTHt/+dkcXXm6InyQbA+4B9gUcBByZ51HijmtFHgWdOm3YYcGZV7QKc2R5PgjuBQ6vqkcDjgT9p7+mkxgtwB7BPVe0O7AE8M8njmeyYAV4FXDLweNLj/Y2q2mPgtJKTHO/fA/9aVb8I7E73Pk9svFX1rfbe7gE8DvgR8GkmOGatvQnOWR9lsnPUUshLSyUPLYW8M8m5ZqJzy6TnkiTbAa8E9qyq3ehObvbCkcZXVb24AU8A/m3g8euA1407rlliXQlcNPD4W8A27f42wLfGHeMscZ8MPH0Jxbsp8FXgVyY5ZmD79o+9D3DqpH8ngCuAraZNm8h4gQcCl9OOX5z0eGeI/zeB/1xKMXsb+rOd2Jy1lHLUpOelSc1DSyHvTHKuWWq5ZRJzCbAdcCWwJd2J1U5tcY4svt709LDmzZtyVZu2FDykqq4BaH+3HnM895FkJfAY4BwmPN7WZX8+cD1wRlVNesxHAq8B7h6YNsnxFvC5JF9Jd0V6mNx4Hw6sBj7ShnF8KMkDmNx4p3shcHy7v1Ri1nCWUs6ayO/eJOelJZCHlkLemeRcs9Ryy8Tlkqr6PvB3wPeAa4AfVtXnRhlfn4qezDDNU9MtgCSbAZ8EXl1Vt4w7nvlU1V3VdeduD+ydZLdxxzSbJM8Crq+qr4w7lrXwxKp6LN2wnD9J8uvjDmgOGwKPBf6xqh4D3M5kDNmYV7qLNz8HOGncsWgkzFnrYdLz0iTnoSWUdyY51yyZ3DKpuaQdq/Nc4GHAtsADkhw8ymX2qei5Cthh4PH2wNVjimVtXZdkG4D29/oxx3OPJBvRJZaPV9Wn2uSJjXdQVd0MnEU3Pn1SY34i8JwkVwD/DOyT5FgmN16q6ur293q68cF7M7nxXgVc1fayAnyCLlFNaryD9gW+WlXXtcdLIWYNbynlrIn67i2lvDSheWhJ5J0JzzVLKbdMai55GnB5Va2uqp8BnwJ+dZTx9ano+TKwS5KHtar2hcApY45pWKcAq9r9VXRjlMcuSYAPA5dU1TsHnprIeAGSrEjy4HZ/E7p/qm8yoTFX1euqavuqWkn3nf33qjqYCY03yQOSbD51n2787UVMaLxVdS1wZZJd26SnAt9gQuOd5kDWDEeApRGzhreUctbEfPeWQl6a9Dy0FPLOpOeaJZZbJjWXfA94fJJN2//1U+lOBjG6+MZx8NKobsB+wLeB/wX+YtzxzBLj8XRjF39Gt6fgpcDP0x1QeGn7u+W442yxPoluuMWFwPnttt+kxtti/mXgay3mi4A3tukTG/NA7E9hzQGlExkv3TjmC9rt4qn/s0mNt8W2B3Be+058BthikuNtMW8K3Ag8aGDaRMfsbZ0+54nLWZOeo5ZCXlpKeWhS885SyDVLIbdMei4B3ky3Q+Ai4Bjg50YZX9pCJUmSJKmX+jS8TZIkSZLuw6JHkiRJUq9Z9EiSJEnqNYseSZIkSb1m0SNJkiSp1yx6pAWS5PlJKskvjjsWSZKmM09pObPokRbOgcCX6C72JknSpDFPadmy6JEWQJLNgCfSXcjvhW3a/ZL8Q5KLk5ya5PQkv9Oee1ySLyb5SpJ/S7LNGMOXJPWceUrLnUWPtDCeB/xrVX0buCnJY4HfAlYCvwT8AfAEgCQbAe8BfqeqHgccBbx9HEFLkpYN85SWtQ3HHYDUEwcCR7b7/9webwScVFV3A9cm+UJ7fldgN+CMJAAbANcsbriSpGXGPKVlzaJHWk9Jfh7YB9gtSdElhwI+PdtLgIur6gmLFKIkaRkzT0kOb5MWwu8AH6uqnapqZVXtAFwO3AD8dhsz/RDgKW3+bwErktwzjCDJo8cRuCRpWTBPadmz6JHW34Hcd2/ZJ4FtgauAi4D3A+cAP6yqn9IloL9JcgFwPvCrixeuJGmZMU9p2UtVjTsGqbeSbFZVt7WhBecCT6yqa8cdlyRJYJ7S8uExPdJonZrkwcD9gbeaSCRJE8Y8pWXBnh5JkiRJveYxPZIkSZJ6zaJHkiRJUq9Z9EiSJEnqNYseSZIkSb1m0SNJkiSp1yx6JEmSJPWaRY8kSZKkXrPokSRJktRrFj2SJEmSes2iR5IkSVKvWfRIkiRJ6jWLHkmSJEm9ZtEjSZIkqdcseiRJkiT1mkWPJEmSpF6z6JEkSZLUaxY9kiRJknrNokeSJElSr1n0SJIkSeo1ix5JkiRJvWbRI0mSJKnXLHq0KJKsTFJJNhxR+7+W5Fvr+NqnJLlqPZf/L0lWrU8bA23da12SXJHkaQvRdmvv4iRPWaj2Bto9K8kfLHS7krQuRp13Fsr65K952l3v9U/yT0n+coHi2THJbUk2aI8XNGcsZB6e1u5Hk7xtodvV4rPoWSbaD+cftw3O1G3bccc1jCSHJ/lZklvb7dtJ3ptkm6l5quo/qmrXES2/ktze3rMbk5yZ5AWD81TVvlV19JBtPWKueRZyXWbaWFfVo6vqrIVof5SSPKC956ePOxZJa2+J550HJzkqybUDeee1o1jWKPPXXAY+n1uT3Jzkv5K8PMk9vw2r6uVV9dYh25pz51xVfa+qNququxYg9sOTHDut/aHy8Lilc1mSb4w7luXGomd5eXbb4Ezdrh53QGvhhKraHNgSeD7wUOArg4XPiO1eVZsBuwIfBd6b5E0LvZBJ3yO5yH4HuAP4zUX8nCUtrKWad94FbAY8EngQ8Bzgf9eloQnfrj+75dadgCOA1wIfXuiFTPh7sNh+HdgaeHiSvcYdzHJi0bPMJXlQkg8nuSbJ95O8baDr+SVJ/jPJu9peoMuS/GqbfmWS6we7kpPsn+RrSW5pzx++LsudS1X9rKouBl4ArAYObe3da4hakm2TfDLJ6iSXJ3nlwHObtB6QH7Q9LUNvdKrqhqo6Bvgj4HVJfr61eU83fZJHJPlikh8muSHJCW362a2ZC9oezxdMxZ3ktUmuBT4yfV2avZJ8o8X8kSQbtzZfkuRL097bajEcAhwEvKYt77Pt+Xv2yCX5uSRHJrm63Y5M8nOD72mSQ9tnfU2S35vnLdo5yblt3U9OsmVr67QkfzotzguTPG+OtlYB/wRc2NZj8LWPbd+1W5OclOSEDPRoJXlWkvMH9l7+8jxxS1okSyTv7AUcV1U/qKq7q+qbVfWJ1s59ho1NywGD63AT8Na2LrsNzL8iXS/L1oPb/CSHJfnEtLj/Psm7h3jvNkjydy3vXAbsP+xnUlU/rKpT6HLrqqlYMzBaIMlWSU5t63JTkv9Icr8kxwA7Ap9tueY1A+/RS5N8D/j3md43Zs8Z98mDU7kryTOB1wMvaMu7YIbP4H5J3pDku+0787EkD5r2+a1K8r32fv3FPG/RVknOaDnni0l2am29L8k7psX52SSvnqOtVcDJwOnt/uBrH5bk7Lacz7f2jx14/vEtp92c5IKMYKh6n1n06GjgTuARwGOA3wQGx9j+Ct2Pzp8HjgP+mS4ZPAI4mK7HY7M27+3Ai4EH021s/yiz/6idb7lzat3jJwO/Nv25dF3znwUuALYDngq8Oskz2ixvAnZut2cwbaMzpJOBDYG9Z3jurcDngC2A7YH3tJh/vT2/e9vjeUJ7/FC6HqydgENmWd5BLdadgV8A3jBfgFX1AeDjwN+25T17htn+Ang8sAewe1ufwbYfSreXczvgpcD7kmwxx2JfDPw+sC3d5/vuNv1ouu8LAEl2b23OOHQtyY7AU1r8H2/tTj13f+DTdD1uWwLH0/X+TT3/WOAo4A/pvrfvB05JK+Ykjd1SyDv/A7w9ye8l2WUd1vFXgMvo9ui/BfgUcODA8wcAX6yq66e97nhgvyQPhK6QafMeN8Q6vAx4Vpu+J11v+VqpqnOBq5ght9LtZLwKWAE8hK7wqKp6EfA91vTq/e3Aa55M11v2jOmNNbPljLli/Ffgr+hGgGxWVbvPMNtL2u03gIfT9dq9d9o8T6IbvfFU4I1JHjnHYg+iy+1bAefT5SXoPo8D2+8OkmzV2jt+pkaSbEr3uUzlthe2nDblOOBcuu/+4cCLBl67HXAa8Da63PfnwCeTrJgjbg2w6FlePtP2Dtyc5DNJHgLsC7y6qm5vG993AS8ceM3lVfWRVmScAOwAvKWq7qiqzwE/pdv4UlVnVdXX216xC+n+6Z88PYghlzuMq+n+8afbC1hRVW+pqp9W1WXABwfaPwB4e1XdVFVXMsRGdrqq+hlwwyzL/xldAbNtVf2kqr40wzyD7gbe1N7TH88yz3ur6sqqugl4O/dOnuvjILrP8/qqWg28mYGNLN26vKX1sJ0O3EaXJGZzTFVdVFW3A38JHNCS9snALgM/Hl5El7B+Oks7LwYurKpv0H2PHp3kMe25x9MVnO9ucX2KLklMeRnw/qo6p6ruamO872ivk7S4lmre+VO6H6WvAL6R5DtJ9l2L9b66qt5TVXe27fpx3Hu7/X9YU8jco6q+C3wVmCrc9gF+VFX/M8Q6HAAcOZAr/not4r1X7Mye27YBdmrb3v+oqpqnrcNbrLPlttlyxvo6CHhnVV1WVbcBr6MrMAZ7md5cVT+uqgvodpLOVDxNOa2qzq6qO+h2Fj4hyQ6tSPwhXaED3WdxVlVdN0s7v0WXjz4HnEqXy/aHe3b27QW8sf12+RJwysBrDwZOr6rT2/f9DOA8YL/h3hJZ9Cwvz6uqB7fb8+h+mG8EXDOVlOj2im898JrBf9wfA0z7Z/4x3R4UkvxKki+kG1L2Q+DldHtFphtmucPYDrhplva3HUi0N9PtkXpIe35b4MqB+b+7lsslyUZ0e7tmWv5rgADnpjtT2u/P09zqqvrJPPNMj3ehDgbelnuv//S2b6yqOwce/4j2ec9iepwbAVu1RHEicHDbI3YgcMwc7byYtietHQPwRdb0yG0LfH9ash1c7k7AodM+/x1YuPdM0vCWZN5pP4b/qqoeR7fX/UTgpKnhV0O4ctrjfwc2afHuRNe7/ulZXjtYIA0WR/Otw3rntma23Pr/gO8An0s37PCwIdqa/j7M9fw9OWOoKOc2U27bkDW/AwCuHbg/dG5rRdRNrMkpgyMZDmbu3LYKOLEVw3fQ9QAO5rabqupHMy2X7vP/3Wm57Ul0haiG4IFly9uVdHsctpr2w3ZdHUfXfbxvVf0kyZHMvPFa7+W2H87PBj4/S/uXV9VsQxKuofsRfHF7vOM6hPBcuq74c6c/UVXX0vU2kORJwOeTnF1V35mlrfn2lEEX75Qd6fbEQTe0Y9OpJ5I8dC3bvppuQzr4XqzPgcbT45zqEYMuMRwDfIluz+V/z9RAkl8FdqE7ZurQNnlzut6eP6f7/LZLkoHCZwfWHGR8JV1P3tvXYz0kjcaSyztVdUuSv6LrLXgY3VAu6La9t7T7c257q+ruJCfSFTPXAadW1a2zLPIk4B1JtqcbuvuEIddhKrdNWevclu7A+u3ottP30uI9lG6n0qOBLyT5clWdyey5Zr4cNFvOmJ7bNqDb0Thsu1O5bbDtO+ne++3nee2ccbahlVuyJlceC1zUhm0/EvjMTA20z3MfYO8kv90mbwps3IbFXQNsmWTTgcJn8P25kq5n7GXrEL+wp2dZq6pr6LpY35HkgekO/Ns5yX2GBgxpc7q9FD9JsjfdHqoFXW6Sjdq42+Ppksw7Z5jtXOCWdCcH2CTdwZ27Zc1ZUk6k+0G9RdsI/ekMbcy2/C2THAS8D/ibqrpxhnl+t7UL8AO6jfPUKTqvoxtfvLb+JMn2bS/j6+mGfEDXJf/oJHukO7nB4dNeN9/yjgfekO6g2q2AN9JtwNfVwUke1cYtvwX4RBuiQity7gbewfx7ws4AHkW3N3QPYDe65LAv8N907+crkmyY5Lnc+9iqDwIvb3tUk+7U1/sn2Xw91kvSAlgqeSfJXybZK8n927b1VcDNwLfaUODv023vNmi9+TsPEetxdCcKOIgZhrYNxLoaOAv4CN0OvEuGXIcTgVe2XLEFMExPzNT6PjDJs+iOnzq2qr4+wzzPSneSnNAVe3ex/rlttpzxbbpiYP82suINwOBxmdcBKzNweu1pjgf+LN2JATZjzTFA61po75fkSemOv3krcE4bHk9VXQV8mS6vfXKOoXwvauu1K2ty2y/QHSd1YBvaeB5wePvePYFu5+6UY4FnJ3lG+95tnO6ED+tSxC1LFj16MXB/4Bt0P9A/wbp3lf4x8JYkt9L9eD5xAZf7giS30SWdU4AbgcfVDKc/bRvMZ9NtUC6n22v0IboD8qE7buW77bnPMfcP8CkXtOV/h+6g0T+rqjfOMu9ewDlt/lOAV1XV5e25w4GjW9f0AUMsd8pxLdbL2u1tAFX1bbpE8XngUu67d+7DwKPa8mba+/Q2uo3shcDX6caSr89F2I6hO8HAtcDGwCunPf8x4JeYpbBqPy4OAN5TVdcO3C5vba9qxwH9Ft2JFW6mG05wKt0eUKrqPLqetvfSfbe+Q3dAq6TJsBTyTtEVHTfQ7dF/OrB/G9oE3Tbm/9LlokcD/zVfoFV1Dl0PxrbAv8wz+3HA07hvcTTXOnwQ+De6nWFfpRs6NZ/PtvfuSrpjVd4JzHaWzl3ocs1tdDuf/qHWXPPtr+l2oN3ceuSHNWPOqKof0n22H6IrMG+nKw6mnNT+3pjkqzO0e1Rr+2y6XP8T1mIH5wyOozsJ0k3A45h2RlG6kQy/xPw79P5hWm67lu4spVND3A6i69m7kS4Xn8Ca3HYl3SiT19OdvfZKuu+gv+WHlPmPQZOkhZHkxcAhVfWk/7+9+w/W7K7rA/7+uAkTfjmAuQlbwrrCZIKUQhLWDDRWIYhGUAIKrZlKMxZdWqEDUzoaGKfGdpymMyJoa9EAkYg/gwikiGiMImXGJmwkQGJCw2CEkDW7iBhiHVKST/+4J7pNd5PnJvc8z73f+3rN3LnnnOfH+Xyzz72fvO/5nnM2+X2vTvLz3f2Lm/m+APBAqupbsv7HvL3dfc8mvu9vJLmpuzf9voA7kXQILMU0feGHk1yyCe/1rVX1+Gl62wVJnp7kgw/1fQFgI6bpd69J8raHGnimKZVPnqYunpv1IztHPUeIjRN6gNnV+j2SDmd9HvYx57JvwGlZn8Lx11k/sfal03x3AFiK6RzjL2V9iuGbN+EtH5/187nuzPrtNP51d39sE96XmN4GAAAMzpEeAABgaEIPAAAwtG1xc9ITTzyx9+7du+oyAHa0a6+99gvdvfbAz9x59CmA1bu/PrUtQs/evXtz4MCBVZcBsKNV1Z+vuoatSp8CWL3761OmtwEAAEMTegAAgKEJPQAAwNCEHgAAYGhCDwAAMDShBwAAGJrQAwAADE3oAQAAhib0AAAAQxN6AACAoQk9AADA0IQeAABgaEIPAAAwNKEHAAAYmtADAAAMTegBAACGJvQAAABDE3oAAIChCT0AAMDQZg89VbWrqj5WVe+f1h9XVVdW1c3T98fOXQMAALBzLeNIz2uS3HjE+oVJruruU5NcNa0DAADMYtbQU1WnJHlhkrcdsfm8JJdNy5clefGcNQAAADvb3Ed63pzkR5Lcc8S2k7v7YJJM30862guran9VHaiqA4cPH565TADYGH0KYPuYLfRU1XclOdTd1z6Y13f3Jd29r7v3ra2tbXJ1APDQ6FMA28dxM7732UleVFUvSHJCkq+tql9OcntV7e7ug1W1O8mhGWsAAAB2uNmO9HT367v7lO7em+T7kvxBd39/kiuSXDA97YIk75urBgAAgFXcp+fiJM+vqpuTPH9aBwAAmMWc09v+Tnd/KMmHpuW/TPK8ZewXAABgFUd6AAAAlkboAQAAhib0AAAAQxN6AACAoQk9AADA0IQeAABgaEIPAAAwNKEHAAAYmtADAAAMTegBAACGJvQAAABDE3oAAIChCT0AAMDQhB4AAGBoQg8AADA0oQcAABia0AMAAAxN6AEAAIYm9AAAAEMTegAAgKEJPQAAwNCEHgAAYGhCDwAAMDShBwAAGJrQAwAADE3oAQAAhib0AAAAQxN6AACAoQk9AADA0IQeAABgaEIPAAAwtNlCT1WdUFXXVNXHq+qGqvqJaftFVfX5qrpu+nrBXDUAAAAcN+N7fyXJOd19Z1Udn+QjVfU702Nv6u6fmnHfAAAASWYMPd3dSe6cVo+fvnqu/QEAABzNrOf0VNWuqrouyaEkV3b31dNDr66qT1TVpVX12DlrAAAAdrZZQ093393dpyc5JclZVfW0JG9J8uQkpyc5mOSNR3ttVe2vqgNVdeDw4cNzlgkAG6ZPAWwfS7l6W3d/KcmHkpzb3bdPYeieJG9NctYxXnNJd+/r7n1ra2vLKBMAFqZPAWwfc169ba2qHjMtPzzJtyW5qap2H/G0lyS5fq4aAAAA5rx62+4kl1XVrqyHq8u7+/1V9c6qOj3rFzW4JckrZ6wBAADY4ea8etsnkpxxlO0vn2ufAAAA97WUc3oAAABWRegBAACGJvQAAABDE3oAAIChCT0AAMDQhB4AAGBoQg8AADA0oQcAABia0AMAAAxN6AEAAIYm9AAAAEMTegAAgKEJPQAAwNCEHgAAYGhCDwAAMDShBwAAGJrQAwAADE3oAQAAhib0AAAAQxN6AACAoQk9AADA0IQeAABgaEIPAAAwNKEHAAAYmtADAAAMTegBAACGJvQAAABDE3oAAIChCT0AAMDQhB4AAGBoQg8AADC02UJPVZ1QVddU1cer6oaq+olp++Oq6sqqunn6/ti5agAAAJjzSM9XkpzT3c9IcnqSc6vqWUkuTHJVd5+a5KppHQAAYBazhZ5ed+e0evz01UnOS3LZtP2yJC+eqwYAAIBZz+mpql1VdV2SQ0mu7O6rk5zc3QeTZPp+0pw1AAAAO9usoae77+7u05OckuSsqnraoq+tqv1VdaCqDhw+fHi+IgHgQdCnALaPlFge0gAAFeBJREFUpVy9rbu/lORDSc5NcntV7U6S6fuhY7zmku7e19371tbWllEmACxMnwLYPua8ettaVT1mWn54km9LclOSK5JcMD3tgiTvm6sGAACA42Z8791JLquqXVkPV5d39/ur6o+TXF5Vr0jy2SQvm7EGAABgh5st9HT3J5KccZTtf5nkeXPtFwAA4EhLOacHAABgVYQeAABgaEIPAAAwNKEHAAAYmtADAAAMTegBAACGJvQAAABDE3oAAIChCT0AAMDQhB4AAGBoQg8AADA0oQcAABia0AMAAAxN6AEAAIYm9AAAAEMTegAAgKEJPQAAwNCEHgAAYGhCDwAAMDShBwAAGJrQAwAADE3oAQAAhib0AAAAQxN6AACAoQk9AADA0IQeAABgaEIPAAAwNKEHAAAYmtADAAAMTegBAACGJvQAAABDmy30VNUTq+oPq+rGqrqhql4zbb+oqj5fVddNXy+YqwYAAIDjZnzvryZ5XXf/SVU9Osm1VXXl9NibuvunZtw3AABAkhlDT3cfTHJwWv5yVd2Y5Alz7Q8AAOBolnJOT1XtTXJGkqunTa+uqk9U1aVV9dhl1AAAAOxMs4eeqnpUkncneW1335HkLUmenOT0rB8JeuMxXre/qg5U1YHDhw/PXSYAbIg+BbB9zBp6qur4rAeeX+nu30qS7r69u+/u7nuSvDXJWUd7bXdf0t37unvf2tranGUCwIbpUwDbx5xXb6skb09yY3f/9BHbdx/xtJckuX6uGgAAAOa8etvZSV6e5JNVdd207Q1Jzq+q05N0kluSvHLGGgAAgB1uodBTVU/r7g0dkenujySpozz0gY28DwAs24PpewBsXYtOb/v5qrqmqn64qh4za0UAsHr6HsBAFgo93f3NSf55kicmOVBVv1pVz5+1MgBYEX0PYCwLX8igu29O8mNJfjTJtyb52aq6qaq+Z67iAGBV9D2AcSwUeqrq6VX1piQ3JjknyXd39zdOy2+asT4AWDp9D2Asi1697b9m/Z46b+juv713Y3ffVlU/NktlALA6+h7AQBYNPS9I8rfdfXeSVNXXJDmhu/93d79ztuoAYDX0PYCBLHpOz+8nefgR64+YtgHAiPQ9gIEsGnpO6O47712Zlh8xT0kAsHL6HsBAFg09f1NVZ967UlXPTPK39/N8ANjO9D2AgSx6Ts9rk7yrqm6b1ncn+WfzlAQAK6fvAQxkodDT3R+tqqckOS1JJbmpu//PrJUBwIroewBjWfRIT5J8U5K902vOqKp09y/NUhUArJ6+BzCIhUJPVb0zyZOTXJfk7mlzJ/HLH4Dh6HsAY1n0SM++JE/t7p6zGADYIvQ9gIEsevW265M8fs5CAGAL0fcABrLokZ4Tk/xpVV2T5Cv3buzuF81SFQCslr4HMJBFQ89FcxYBAFvMRasuAIDNs+glq/+oqr4+yand/ftV9Ygku+YtDQBWQ98DGMtC5/RU1Q8l+c0kvzBtekKS985VFACskr4HMJZFL2TwqiRnJ7kjSbr75iQnzVUUAKyYvgcwkEVDz1e6+657V6rquKzfrwAARqTvAQxk0dDzR1X1hiQPr6rnJ3lXkv8+X1kAsFL6HsBAFg09FyY5nOSTSV6Z5ANJfmyuogBgxfQ9gIEsevW2e5K8dfoCgKHpewBjWSj0VNWf5Shzmbv7SZteEQCsmL4HMJZFb06674jlE5K8LMnjNr8cANgS9D2AgSx0Tk93/+URX5/v7jcnOWfm2gBgJfQ9gLEsOr3tzCNWvybrfwF79CwVAcCK6XsAY1l0etsbj1j+apJbkvzTTa8GALYGfQ9gIIteve25cxcCAFuFvgcwlkWnt/3b+3u8u396c8oBgNXT9wDGspGrt31Tkium9e9O8uEkn5ujKABYMX0PYCCLhp4Tk5zZ3V9Okqq6KMm7uvsHj/WCqnpikl9K8vgk9yS5pLt/pqoel+Q3kuzNNEe6u//qwQ4AAGaw4b4HwNa10CWrk+xJctcR63dlPbTcn68meV13f2OSZyV5VVU9NcmFSa7q7lOTXDWtA8BW8mD6HgBb1KJHet6Z5Jqqek/W71D9kqwfxTmm7j6Y5OC0/OWqujHJE5Kcl+Q509MuS/KhJD+60cIBYEYb7nsAbF2LXr3tJ6vqd5L8k2nTD3T3xxbdSVXtTXJGkquTnDwFonT3wao66Riv2Z9kf5Ls2bNn0V0BwEO2SN/TpwC2j0WntyXJI5Lc0d0/k+TWqvqGRV5UVY9K8u4kr+3uOxbdWXdf0t37unvf2traBsoEgE1xv31PnwLYPhYKPVX141mfgvb6adPxSX55gdcdn/XA8yvd/VvT5turavf0+O4khzZaNADM6cH2PQC2pkWP9LwkyYuS/E2SdPdtSR59fy+oqkry9iQ33ud+BlckuWBaviDJ+zZSMAAswYb7HgBb16IXMriru7uqOkmq6pELvObsJC9P8smqum7a9oYkFye5vKpekeSzSV62wZoBYG4Ppu8BsEUtGnour6pfSPKYqvqhJP8yyVvv7wXd/ZEkdYyHn7d4iQCwdBvuewBsXQ8YeqZpar+R5ClJ7khyWpJ/391XzlwbACydvgcwngcMPdPh/fd29zOT+IUPwND0PYDxLDq97X9W1Td190dnrQYAtgZ9D45h74W/vfBzb7n4hTNWAotbNPQ8N8m/qqpbsn4lm8r6H8OePldhALBC+h7AQO439FTVnu7+bJLvXFI9ALAy+h7AmB7oSM97k5zZ3X9eVe/u7u9dRlEAsCL6HsCAHujmpEdecvpJcxYCAFuAvgcwoAcKPX2MZQAYkb4HMKAHmt72jKq6I+t/+Xr4tJz8/QmdXztrdQCwXPoewIDuN/R0965lFQIAq6bvAYzpgaa3AQAAbGtCDwAAMDShBwAAGJrQAwAADE3oAQAAhib0AAAAQxN6AACAoQk9AADA0IQeAABgaEIPAAAwNKEHAAAYmtADAAAMTegBAACGJvQAAABDE3oAAIChCT0AAMDQjlt1AQAAo9p74W8v9LxbLn7hzJXAzuZIDwAAMDShBwAAGJrQAwAADE3oAQAAhjZb6KmqS6vqUFVdf8S2i6rq81V13fT1grn2DwAAkMx7pOcdSc49yvY3dffp09cHZtw/AADAfKGnuz+c5ItzvT8AAMAiVnGfnldX1b9IciDJ67r7r472pKran2R/kuzZs2eJ5QHAA9Ontr5F75GTuE8OjG7ZFzJ4S5InJzk9ycEkbzzWE7v7ku7e19371tbWllUfACxEnwLYPpYaerr79u6+u7vvSfLWJGctc/8AAMDOs9TQU1W7j1h9SZLrj/VcAACAzTDbOT1V9WtJnpPkxKq6NcmPJ3lOVZ2epJPckuSVc+0fAAAgmTH0dPf5R9n89rn2BwAAcDTLvpABAADAUgk9AADA0FZxnx4AgG1rI/f/WTX3KoJ1jvQAAABDE3oAAIChCT0AAMDQhB4AAGBoQg8AADA0oQcAABia0AMAAAxN6AEAAIYm9AAAAEMTegAAgKEJPQAAwNCEHgAAYGhCDwAAMDShBwAAGJrQAwAADE3oAQAAhib0AAAAQxN6AACAoQk9AADA0IQeAABgaEIPAAAwNKEHAAAYmtADAAAMTegBAACGJvQAAABDE3oAAIChCT0AAMDQhB4AAGBos4Weqrq0qg5V1fVHbHtcVV1ZVTdP3x871/4BAACSeY/0vCPJuffZdmGSq7r71CRXTesAAACzmS30dPeHk3zxPpvPS3LZtHxZkhfPtX8AAIAkOW7J+zu5uw8mSXcfrKqTjvXEqtqfZH+S7NmzZ0nlAcBi9KnV2Xvhb6+6hJXa6eOHB2PLXsiguy/p7n3dvW9tbW3V5QDA/0OfAtg+lh16bq+q3UkyfT+05P0DAAA7zLJDzxVJLpiWL0jyviXvHwAA2GHmvGT1ryX54ySnVdWtVfWKJBcneX5V3Zzk+dM6AADAbGa7kEF3n3+Mh5431z4BAADua8teyAAAAGAzCD0AAMDQln2fHgAAtqBV3/9nI/u/5eIXzlgJI3KkBwAAGJrQAwAADE3oAQAAhib0AAAAQxN6AACAoQk9AADA0IQeAABgaEIPAAAwNDcnBQBYsVXfGBRG50gPAAAwNKEHAAAYmtADAAAMTegBAACGJvQAAABDE3oAAIChCT0AAMDQ3KcHAFg596kB5uRIDwAAMDShBwAAGJrQAwAADE3oAQAAhib0AAAAQxN6AACAoQk9AADA0NynhwdtI/dUuOXiF85YCcD2Ntc9avzuhdVb9Ofbz+u8HOkBAACGJvQAAABDE3oAAIChreScnqq6JcmXk9yd5KvdvW8VdQAAAONb5YUMntvdX1jh/gEAgB3A9DYAAGBoqwo9neT3quraqtq/ohoAAIAdYFXT287u7tuq6qQkV1bVTd394SOfMIWh/UmyZ8+eh7SzVd9PZtX7327muJ79qv8NVr1/YPNtZp+ai/uDMCqfbTZqJUd6uvu26fuhJO9JctZRnnNJd+/r7n1ra2vLLhEA7pc+BbB9LD30VNUjq+rR9y4n+fYk1y+7DgAAYGdYxfS2k5O8p6ru3f+vdvcHV1AHAACwAyw99HT3Z5I8Y9n7BQAAdiaXrAYAAIYm9AAAAEMTegAAgKGt6j49MISN3H8HgK3L7/N5jPrfddXjcv+/jXOkBwAAGJrQAwAADE3oAQAAhib0AAAAQxN6AACAoQk9AADA0IQeAABgaO7TA/ex6mvvbydb4T4Bi9bgPgUAO8926ulbodY5eupW+H+FxJEeAABgcEIPAAAwNKEHAAAYmtADAAAMTegBAACGJvQAAABDE3oAAIChuU/PFjPXtcxXfS+TrXDteRY3x7+XzwBsXavuPcB8/Byuc6QHAAAYmtADAAAMTegBAACGJvQAAABDE3oAAIChCT0AAMDQhB4AAGBo7tPzEIx43fPtNKbtVOtGzHW/jJ1uK3xe5vj38nkBYNm2Qk/dKEd6AACAoQk9AADA0IQeAABgaCsJPVV1blV9qqo+XVUXrqIGAABgZ1h66KmqXUl+Lsl3JnlqkvOr6qnLrgMAANgZVnGk56wkn+7uz3T3XUl+Pcl5K6gDAADYAVYRep6Q5HNHrN86bQMAANh01d3L3WHVy5J8R3f/4LT+8iRndfe/uc/z9ifZP62eluRTRzx8YpIvLKHcrcBYx7NTxpkY62i+vrvXVl3EVvEAfeqBjPZ5MZ6tzXi2tpHGs+qxHLNPrSL0PDvJRd39HdP665Oku//TBt7jQHfvm6nELcVYx7NTxpkYKxzLaJ8X49najGdrG2k8W3ksq5je9tEkp1bVN1TVw5J8X5IrVlAHAACwAxy37B1291er6tVJfjfJriSXdvcNy64DAADYGZYeepKkuz+Q5AMP4S0u2axatgFjHc9OGWdirHAso31ejGdrM56tbaTxbNmxLP2cHgAAgGVaxTk9AAAAS7PtQk9VnVtVn6qqT1fVhauuZzNV1aVVdaiqrj9i2+Oq6sqqunn6/thV1rgZquqJVfWHVXVjVd1QVa+Zto841hOq6pqq+vg01p+Ytg831iSpql1V9bGqev+0Puo4b6mqT1bVdVV1YNo25FjZfNu9j43Uq0brR6P2nJF6y2j9o6oeU1W/WVU3TT9Hz96q49lWoaeqdiX5uSTfmeSpSc6vqqeutqpN9Y4k595n24VJruruU5NcNa1vd19N8rru/sYkz0ryqunfccSxfiXJOd39jCSnJzm3qp6VMceaJK9JcuMR66OOM0me292nH3FpzpHHyiYZpI+9I+P0qtH60ag9Z7TeMlL/+JkkH+zupyR5Rtb/nbbkeLZV6ElyVpJPd/dnuvuuJL+e5LwV17RpuvvDSb54n83nJblsWr4syYuXWtQMuvtgd//JtPzlrP+APCFjjrW7+85p9fjpqzPgWKvqlCQvTPK2IzYPN877sZPGyoO37fvYSL1qtH40Ys/ZIb1lW46nqr42ybckeXuSdPdd3f2lbNHxbLfQ84Qknzti/dZp28hO7u6Dyfov5yQnrbieTVVVe5OckeTqDDrW6bD8dUkOJbmyu0cd65uT/EiSe47YNuI4k/X/ifi9qrq2qvZP20YdK5tr1D627T//o/SjAXvOaL1lpP7xpCSHk/ziNP3wbVX1yGzR8Wy30FNH2ebyc9tUVT0qybuTvLa771h1PXPp7ru7+/QkpyQ5q6qetuqaNltVfVeSQ9197aprWZKzu/vMrE9RelVVfcuqC2Lb0Me2oJH60Ug9Z9DeMlL/OC7JmUne0t1nJPmbbJGpbEez3ULPrUmeeMT6KUluW1Ety3J7Ve1Okun7oRXXsymq6visN5hf6e7fmjYPOdZ7TYd8P5T1ufCjjfXsJC+qqluyPl3nnKr65Yw3ziRJd982fT+U5D1Zn7I05FjZdKP2sW37+R+1Hw3Sc4brLYP1j1uT3DodTUyS38x6CNqS49luoeejSU6tqm+oqocl+b4kV6y4prldkeSCafmCJO9bYS2boqoq6/M/b+zunz7ioRHHulZVj5mWH57k25LclMHG2t2v7+5Tuntv1n8u/6C7vz+DjTNJquqRVfXoe5eTfHuS6zPgWJnFqH1sW37+R+tHo/Wc0XrLaP2ju/8iyeeq6rRp0/OS/Gm26Hi23c1Jq+oFWZ/fuSvJpd39kysuadNU1a8leU6SE5PcnuTHk7w3yeVJ9iT5bJKXdfd9TyDdVqrqm5P8jySfzN/P0X1D1udRjzbWp2f9JL5dWf8jw+Xd/R+q6usy2FjvVVXPSfLvuvu7RhxnVT0p63+dS9YP7f9qd//kiGNlHtu9j43Uq0brRyP3nBF6y4j9o6pOz/pFJh6W5DNJfiDTZy9bbDzbLvQAAABsxHab3gYAALAhQg8AADA0oQcAABia0AMAAAxN6AEAAIYm9MAmqaqXVFVX1VNWXQsA3Jc+xU4m9MDmOT/JR7J+AzUA2Gr0KXYsoQc2QVU9KsnZSV6RqZlU1ddU1X+rqhuq6v1V9YGqeun02DOr6o+q6tqq+t2q2r3C8gEYnD7FTif0wOZ4cZIPdvf/SvLFqjozyfck2ZvkHyX5wSTPTpKqOj7Jf0ny0u5+ZpJLk2yrO7IDsO3oU+xox626ABjE+UnePC3/+rR+fJJ3dfc9Sf6iqv5wevy0JE9LcmVVJcmuJAeXWy4AO4w+xY4m9MBDVFVfl+ScJE+rqs56c+gk7znWS5Lc0N3PXlKJAOxg+hSY3gab4aVJfqm7v76793b3E5P8WZIvJPneac70yUmeMz3/U0nWqurvphFU1T9cReEA7Aj6FDue0AMP3fn5//9a9u4k/yDJrUmuT/ILSa5O8tfdfVfWG9B/rqqPJ7kuyT9eXrkA7DD6FDtedfeqa4BhVdWjuvvOaWrBNUnO7u6/WHVdAJDoU+wczumBeb2/qh6T5GFJ/qNGAsAWo0+xIzjSAwAADM05PQAAwNCEHgAAYGhCDwAAMDShBwAAGJrQAwAADE3oAQAAhvZ/AeTjD+pAcU17AAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 1008x1008 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Visualisasi frekuensi penumpang berdasarkan \"Survived\" dan \"Sex\"\n",
-    "fig, ax = plt.subplots(figsize=(14, 14), nrows=2, ncols=2, sharey=True)\n",
-    "gender = [\"male\", \"female\"]\n",
-    "\n",
-    "for i in range(2):\n",
-    "    for j in range(2):\n",
-    "        ax[i][j].hist(df[(df[\"Sex\"] == gender[i]) & (df[\"Survived\"] == j)][\"Age\"], bins=30)\n",
-    "        \n",
-    "        if j == 0:\n",
-    "            a = \"Died\"\n",
-    "        else:\n",
-    "            a = \"Survived\"\n",
-    "            \n",
-    "        ax[i][j].set_title(\"{} {} Distribution by Age\".format(gender[i].title(), a))\n",
-    "        ax[i][j].set_xlabel(\"Age\")\n",
-    "        ax[i][j].set_ylabel(\"Frequency\")\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "- Terlihat bahwa distribusi usia untuk male dan female berdasarkan status \"Survived\" tidak berdistribusi normal\n",
-    "- Jumlah pria yang meninggal lebih banyak dibandingkan yang selamat dan mayoritas pria meninggal berusia di rentang 10 - 50 tahun-an\n",
-    "- Sedangkan pada wanita berlaku kebalikannya dengan mayoritas wanita selamat berusia rentang 10 - 45 tahun-an."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 2160x1080 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Melihat hubungan antara usia, fare, dan survived\n",
-    "fig, ax = plt.subplots(figsize=(30, 15))\n",
-    "mapbar = ax.scatter(df[\"Age\"], df[\"Fare\"],c=df[\"Survived\"], s=200*df[\"Pclass\"], alpha=0.8)\n",
-    "\n",
-    "ax.set_title(\"Hubungan Usia Penumpang Dengan Ongkos yang Dikeluarkan\")\n",
-    "ax.set_xlabel(\"Usia\")\n",
-    "ax.set_ylabel(\"Fare ($)\")\n",
-    "fig.colorbar(mapbar)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "- Dari grafik dapat disimpulkan bahwa mayoritas penumpang yang meninggal adalah mereka yang membayar biaya perjalanan rendah.\n",
-    "- Dalam sistem kapal titanic, semakin rendah biaya perjalanan, semakin rendah lantai kamar yang mereka dapatkan.\n",
-    "- Ada kemungkinan alasan mereka meninggal adalah keterbatasan waktu untuk menyelamatkan diri sebelum air menyentuh lantai tempat mereka tinggal."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "<div id=\"altair-viz-daeb50125f0f48fa8aea5fd0cf443de5\"></div>\n",
-       "<script type=\"text/javascript\">\n",
-       "  (function(spec, embedOpt){\n",
-       "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-daeb50125f0f48fa8aea5fd0cf443de5\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-daeb50125f0f48fa8aea5fd0cf443de5\");\n",
-       "    }\n",
-       "    const paths = {\n",
-       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
-       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
-       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
-       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
-       "    };\n",
-       "\n",
-       "    function loadScript(lib) {\n",
-       "      return new Promise(function(resolve, reject) {\n",
-       "        var s = document.createElement('script');\n",
-       "        s.src = paths[lib];\n",
-       "        s.async = true;\n",
-       "        s.onload = () => resolve(paths[lib]);\n",
-       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
-       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
-       "      });\n",
-       "    }\n",
-       "\n",
-       "    function showError(err) {\n",
-       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
-       "      throw err;\n",
-       "    }\n",
-       "\n",
-       "    function displayChart(vegaEmbed) {\n",
-       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
-       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
-       "    }\n",
-       "\n",
-       "    if(typeof define === \"function\" && define.amd) {\n",
-       "      requirejs.config({paths});\n",
-       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
-       "    } else if (typeof vegaEmbed === \"function\") {\n",
-       "      displayChart(vegaEmbed);\n",
-       "    } else {\n",
-       "      loadScript(\"vega\")\n",
-       "        .then(() => loadScript(\"vega-lite\"))\n",
-       "        .then(() => loadScript(\"vega-embed\"))\n",
-       "        .catch(showError)\n",
-       "        .then(() => displayChart(vegaEmbed));\n",
-       "    }\n",
-       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-93290830beea3a875eeab316df346c54\"}, \"facet\": {\"column\": {\"type\": \"quantitative\", \"field\": \"Survived\"}}, \"spec\": {\"mark\": {\"type\": \"bar\", \"opacity\": 0.8}, \"encoding\": {\"color\": {\"type\": \"quantitative\", \"field\": \"Pclass\"}, \"x\": {\"type\": \"quantitative\", \"axis\": {\"labelAngle\": 45}, \"field\": \"Age\", \"title\": \"Usia\"}, \"y\": {\"type\": \"quantitative\", \"aggregate\": \"count\", \"title\": \"Frekuensi Berdasarkan Usia\"}}, \"height\": 300, \"title\": \"Distribusi Usia Penumpang\", \"width\": 600}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-93290830beea3a875eeab316df346c54\": [{\"PassengerId\": 1, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Braund, Mr. Owen Harris\", \"Sex\": \"male\", \"Age\": 22.0, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"A/5 21171\", \"Fare\": 7.25, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 2, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Cumings, Mrs. John Bradley (Florence Briggs Thayer)\", \"Sex\": \"female\", \"Age\": 38.0, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"PC 17599\", \"Fare\": 71.2833, \"Cabin\": \"C85\", \"Embarked\": \"C\"}, {\"PassengerId\": 3, \"Survived\": 1, \"Pclass\": 3, \"Name\": \"Heikkinen, Miss. Laina\", \"Sex\": \"female\", \"Age\": 26.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"STON/O2. 3101282\", \"Fare\": 7.925, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 4, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Futrelle, Mrs. Jacques Heath (Lily May Peel)\", \"Sex\": \"female\", \"Age\": 35.0, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"113803\", \"Fare\": 53.1, \"Cabin\": \"C123\", \"Embarked\": \"S\"}, {\"PassengerId\": 5, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Allen, Mr. William Henry\", \"Sex\": \"male\", \"Age\": 35.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"373450\", \"Fare\": 8.05, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 6, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Moran, Mr. James\", \"Sex\": \"male\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"330877\", \"Fare\": 8.4583, \"Cabin\": null, \"Embarked\": \"Q\"}, {\"PassengerId\": 7, \"Survived\": 0, \"Pclass\": 1, \"Name\": \"McCarthy, Mr. Timothy J\", \"Sex\": \"male\", \"Age\": 54.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"17463\", \"Fare\": 51.8625, \"Cabin\": \"E46\", \"Embarked\": \"S\"}, {\"PassengerId\": 8, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Palsson, Master. Gosta Leonard\", \"Sex\": \"male\", \"Age\": 2.0, \"SibSp\": 3, \"Parch\": 1, \"Ticket\": \"349909\", \"Fare\": 21.075, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 9, \"Survived\": 1, \"Pclass\": 3, \"Name\": \"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)\", \"Sex\": \"female\", \"Age\": 27.0, \"SibSp\": 0, \"Parch\": 2, \"Ticket\": \"347742\", \"Fare\": 11.1333, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 10, \"Survived\": 1, \"Pclass\": 2, \"Name\": \"Nasser, Mrs. Nicholas (Adele Achem)\", \"Sex\": \"female\", \"Age\": 14.0, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"237736\", \"Fare\": 30.0708, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 11, \"Survived\": 1, \"Pclass\": 3, \"Name\": \"Sandstrom, Miss. Marguerite Rut\", \"Sex\": \"female\", \"Age\": 4.0, \"SibSp\": 1, \"Parch\": 1, \"Ticket\": \"PP 9549\", \"Fare\": 16.7, \"Cabin\": \"G6\", \"Embarked\": \"S\"}, {\"PassengerId\": 12, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Bonnell, Miss. Elizabeth\", \"Sex\": \"female\", \"Age\": 58.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"113783\", \"Fare\": 26.55, \"Cabin\": \"C103\", \"Embarked\": \"S\"}, {\"PassengerId\": 13, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Saundercock, Mr. William Henry\", \"Sex\": \"male\", \"Age\": 20.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"A/5. 2151\", \"Fare\": 8.05, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 14, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Andersson, Mr. Anders Johan\", \"Sex\": \"male\", \"Age\": 39.0, \"SibSp\": 1, \"Parch\": 5, \"Ticket\": \"347082\", \"Fare\": 31.275, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 15, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Vestrom, Miss. Hulda Amanda Adolfina\", \"Sex\": \"female\", \"Age\": 14.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"350406\", \"Fare\": 7.8542, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 16, \"Survived\": 1, \"Pclass\": 2, \"Name\": \"Hewlett, Mrs. (Mary D Kingcome) \", \"Sex\": \"female\", \"Age\": 55.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"248706\", \"Fare\": 16.0, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 17, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Rice, Master. Eugene\", \"Sex\": \"male\", \"Age\": 2.0, \"SibSp\": 4, \"Parch\": 1, \"Ticket\": \"382652\", \"Fare\": 29.125, \"Cabin\": null, \"Embarked\": \"Q\"}, {\"PassengerId\": 18, \"Survived\": 1, \"Pclass\": 2, \"Name\": \"Williams, Mr. Charles Eugene\", \"Sex\": \"male\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"244373\", \"Fare\": 13.0, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 19, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)\", \"Sex\": \"female\", \"Age\": 31.0, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"345763\", \"Fare\": 18.0, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 20, \"Survived\": 1, \"Pclass\": 3, \"Name\": \"Masselmani, Mrs. Fatima\", \"Sex\": \"female\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"2649\", \"Fare\": 7.225, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 21, \"Survived\": 0, \"Pclass\": 2, \"Name\": \"Fynney, Mr. Joseph J\", \"Sex\": \"male\", \"Age\": 35.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"239865\", \"Fare\": 26.0, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 22, \"Survived\": 1, \"Pclass\": 2, \"Name\": \"Beesley, Mr. Lawrence\", \"Sex\": \"male\", \"Age\": 34.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"248698\", \"Fare\": 13.0, \"Cabin\": \"D56\", \"Embarked\": \"S\"}, {\"PassengerId\": 23, \"Survived\": 1, \"Pclass\": 3, \"Name\": \"McGowan, Miss. Anna \\\"Annie\\\"\", \"Sex\": \"female\", \"Age\": 15.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"330923\", \"Fare\": 8.0292, \"Cabin\": null, \"Embarked\": \"Q\"}, {\"PassengerId\": 24, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Sloper, Mr. William Thompson\", \"Sex\": \"male\", \"Age\": 28.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"113788\", \"Fare\": 35.5, \"Cabin\": \"A6\", \"Embarked\": \"S\"}, {\"PassengerId\": 25, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Palsson, Miss. Torborg Danira\", \"Sex\": \"female\", \"Age\": 8.0, \"SibSp\": 3, \"Parch\": 1, \"Ticket\": \"349909\", \"Fare\": 21.075, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 26, \"Survived\": 1, \"Pclass\": 3, \"Name\": \"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)\", \"Sex\": \"female\", \"Age\": 38.0, \"SibSp\": 1, \"Parch\": 5, \"Ticket\": \"347077\", \"Fare\": 31.3875, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 27, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Emir, Mr. Farred Chehab\", \"Sex\": \"male\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"2631\", \"Fare\": 7.225, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 28, \"Survived\": 0, \"Pclass\": 1, \"Name\": \"Fortune, Mr. Charles Alexander\", \"Sex\": \"male\", \"Age\": 19.0, \"SibSp\": 3, \"Parch\": 2, \"Ticket\": \"19950\", \"Fare\": 263.0, \"Cabin\": \"C23 C25 C27\", \"Embarked\": \"S\"}, {\"PassengerId\": 29, \"Survived\": 1, \"Pclass\": 3, \"Name\": \"O'Dwyer, Miss. Ellen \\\"Nellie\\\"\", \"Sex\": \"female\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"330959\", \"Fare\": 7.8792, \"Cabin\": null, \"Embarked\": \"Q\"}, {\"PassengerId\": 30, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Todoroff, Mr. Lalio\", \"Sex\": \"male\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"349216\", \"Fare\": 7.8958, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 31, \"Survived\": 0, \"Pclass\": 1, \"Name\": \"Uruchurtu, Don. Manuel E\", \"Sex\": \"male\", \"Age\": 40.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"PC 17601\", \"Fare\": 27.7208, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 32, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Spencer, Mrs. William Augustus (Marie Eugenie)\", \"Sex\": \"female\", \"Age\": null, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"PC 17569\", \"Fare\": 146.5208, \"Cabin\": \"B78\", \"Embarked\": \"C\"}, {\"PassengerId\": 33, \"Survived\": 1, \"Pclass\": 3, \"Name\": \"Glynn, Miss. Mary Agatha\", \"Sex\": \"female\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"335677\", \"Fare\": 7.75, \"Cabin\": null, \"Embarked\": \"Q\"}, {\"PassengerId\": 34, \"Survived\": 0, \"Pclass\": 2, \"Name\": \"Wheadon, Mr. Edward H\", \"Sex\": \"male\", \"Age\": 66.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"C.A. 24579\", \"Fare\": 10.5, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 35, \"Survived\": 0, \"Pclass\": 1, \"Name\": \"Meyer, Mr. Edgar Joseph\", \"Sex\": \"male\", \"Age\": 28.0, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"PC 17604\", \"Fare\": 82.1708, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 36, \"Survived\": 0, \"Pclass\": 1, \"Name\": \"Holverson, Mr. Alexander Oskar\", \"Sex\": \"male\", \"Age\": 42.0, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"113789\", \"Fare\": 52.0, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 37, \"Survived\": 1, \"Pclass\": 3, \"Name\": \"Mamee, Mr. Hanna\", \"Sex\": \"male\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"2677\", \"Fare\": 7.2292, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 38, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Cann, Mr. Ernest Charles\", \"Sex\": \"male\", \"Age\": 21.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"A./5. 2152\", \"Fare\": 8.05, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 39, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Vander Planke, Miss. Augusta Maria\", \"Sex\": \"female\", \"Age\": 18.0, \"SibSp\": 2, \"Parch\": 0, \"Ticket\": \"345764\", \"Fare\": 18.0, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 40, \"Survived\": 1, \"Pclass\": 3, \"Name\": \"Nicola-Yarred, Miss. Jamila\", \"Sex\": \"female\", \"Age\": 14.0, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"2651\", \"Fare\": 11.2417, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 41, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)\", \"Sex\": \"female\", \"Age\": 40.0, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"7546\", \"Fare\": 9.475, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 42, \"Survived\": 0, \"Pclass\": 2, \"Name\": \"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)\", \"Sex\": \"female\", \"Age\": 27.0, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"11668\", \"Fare\": 21.0, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 43, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Kraeff, Mr. Theodor\", \"Sex\": \"male\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"349253\", \"Fare\": 7.8958, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 44, \"Survived\": 1, \"Pclass\": 2, \"Name\": \"Laroche, Miss. Simonne Marie Anne Andree\", \"Sex\": \"female\", \"Age\": 3.0, \"SibSp\": 1, \"Parch\": 2, \"Ticket\": \"SC/Paris 2123\", \"Fare\": 41.5792, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 45, \"Survived\": 1, \"Pclass\": 3, \"Name\": \"Devaney, Miss. Margaret Delia\", \"Sex\": \"female\", \"Age\": 19.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"330958\", \"Fare\": 7.8792, \"Cabin\": null, \"Embarked\": \"Q\"}, {\"PassengerId\": 46, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Rogers, Mr. William John\", \"Sex\": \"male\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"S.C./A.4. 23567\", \"Fare\": 8.05, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 47, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Lennon, Mr. Denis\", \"Sex\": \"male\", \"Age\": null, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"370371\", \"Fare\": 15.5, \"Cabin\": null, \"Embarked\": \"Q\"}, {\"PassengerId\": 48, \"Survived\": 1, \"Pclass\": 3, \"Name\": \"O'Driscoll, Miss. Bridget\", \"Sex\": \"female\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"14311\", \"Fare\": 7.75, \"Cabin\": null, \"Embarked\": \"Q\"}, {\"PassengerId\": 49, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Samaan, Mr. Youssef\", \"Sex\": \"male\", \"Age\": null, \"SibSp\": 2, \"Parch\": 0, \"Ticket\": \"2662\", \"Fare\": 21.6792, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 50, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Arnold-Franchi, Mrs. Josef (Josefine Franchi)\", \"Sex\": \"female\", \"Age\": 18.0, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"349237\", \"Fare\": 17.8, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 51, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Panula, Master. Juha Niilo\", \"Sex\": \"male\", \"Age\": 7.0, \"SibSp\": 4, \"Parch\": 1, \"Ticket\": \"3101295\", \"Fare\": 39.6875, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 52, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Nosworthy, Mr. Richard Cater\", \"Sex\": \"male\", \"Age\": 21.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"A/4. 39886\", \"Fare\": 7.8, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 53, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Harper, Mrs. Henry Sleeper (Myna Haxtun)\", \"Sex\": \"female\", \"Age\": 49.0, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"PC 17572\", \"Fare\": 76.7292, \"Cabin\": \"D33\", \"Embarked\": \"C\"}, {\"PassengerId\": 54, \"Survived\": 1, \"Pclass\": 2, \"Name\": \"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)\", \"Sex\": \"female\", \"Age\": 29.0, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"2926\", \"Fare\": 26.0, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 55, \"Survived\": 0, \"Pclass\": 1, \"Name\": \"Ostby, Mr. Engelhart Cornelius\", \"Sex\": \"male\", \"Age\": 65.0, \"SibSp\": 0, \"Parch\": 1, \"Ticket\": \"113509\", \"Fare\": 61.9792, \"Cabin\": \"B30\", \"Embarked\": \"C\"}, {\"PassengerId\": 56, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Woolner, Mr. Hugh\", \"Sex\": \"male\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"19947\", \"Fare\": 35.5, \"Cabin\": \"C52\", \"Embarked\": \"S\"}, {\"PassengerId\": 57, \"Survived\": 1, \"Pclass\": 2, \"Name\": \"Rugg, Miss. Emily\", \"Sex\": \"female\", \"Age\": 21.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"C.A. 31026\", \"Fare\": 10.5, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 58, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Novel, Mr. Mansouer\", \"Sex\": \"male\", \"Age\": 28.5, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"2697\", \"Fare\": 7.2292, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 59, \"Survived\": 1, \"Pclass\": 2, \"Name\": \"West, Miss. Constance Mirium\", \"Sex\": \"female\", \"Age\": 5.0, \"SibSp\": 1, \"Parch\": 2, \"Ticket\": \"C.A. 34651\", \"Fare\": 27.75, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 60, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Goodwin, Master. William Frederick\", \"Sex\": \"male\", \"Age\": 11.0, \"SibSp\": 5, \"Parch\": 2, \"Ticket\": \"CA 2144\", \"Fare\": 46.9, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 61, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Sirayanian, Mr. Orsen\", \"Sex\": \"male\", \"Age\": 22.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"2669\", \"Fare\": 7.2292, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 62, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Icard, Miss. Amelie\", \"Sex\": \"female\", \"Age\": 38.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"113572\", \"Fare\": 80.0, \"Cabin\": \"B28\", \"Embarked\": null}, {\"PassengerId\": 63, \"Survived\": 0, \"Pclass\": 1, \"Name\": \"Harris, Mr. Henry Birkhardt\", \"Sex\": \"male\", \"Age\": 45.0, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"36973\", \"Fare\": 83.475, \"Cabin\": \"C83\", \"Embarked\": \"S\"}, {\"PassengerId\": 64, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Skoog, Master. Harald\", \"Sex\": \"male\", \"Age\": 4.0, \"SibSp\": 3, \"Parch\": 2, \"Ticket\": \"347088\", \"Fare\": 27.9, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 65, \"Survived\": 0, \"Pclass\": 1, \"Name\": \"Stewart, Mr. Albert A\", \"Sex\": \"male\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"PC 17605\", \"Fare\": 27.7208, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 66, \"Survived\": 1, \"Pclass\": 3, \"Name\": \"Moubarek, Master. Gerios\", \"Sex\": \"male\", \"Age\": null, \"SibSp\": 1, \"Parch\": 1, \"Ticket\": \"2661\", \"Fare\": 15.2458, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 67, \"Survived\": 1, \"Pclass\": 2, \"Name\": \"Nye, Mrs. (Elizabeth Ramell)\", \"Sex\": \"female\", \"Age\": 29.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"C.A. 29395\", \"Fare\": 10.5, \"Cabin\": \"F33\", \"Embarked\": \"S\"}, {\"PassengerId\": 68, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Crease, Mr. Ernest James\", \"Sex\": \"male\", \"Age\": 19.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"S.P. 3464\", \"Fare\": 8.1583, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 69, \"Survived\": 1, \"Pclass\": 3, \"Name\": \"Andersson, Miss. Erna Alexandra\", \"Sex\": \"female\", \"Age\": 17.0, \"SibSp\": 4, \"Parch\": 2, \"Ticket\": \"3101281\", \"Fare\": 7.925, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 70, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Kink, Mr. Vincenz\", \"Sex\": \"male\", \"Age\": 26.0, \"SibSp\": 2, \"Parch\": 0, \"Ticket\": \"315151\", \"Fare\": 8.6625, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 71, \"Survived\": 0, \"Pclass\": 2, \"Name\": \"Jenkin, Mr. Stephen Curnow\", \"Sex\": \"male\", \"Age\": 32.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"C.A. 33111\", \"Fare\": 10.5, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 72, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Goodwin, Miss. 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Nora A\", \"Sex\": \"female\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"226593\", \"Fare\": 12.35, \"Cabin\": \"E101\", \"Embarked\": \"Q\"}, {\"PassengerId\": 305, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Williams, Mr. Howard Hugh \\\"Harry\\\"\", \"Sex\": \"male\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"A/5 2466\", \"Fare\": 8.05, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 306, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Allison, Master. Hudson Trevor\", \"Sex\": \"male\", \"Age\": 0.92, \"SibSp\": 1, \"Parch\": 2, \"Ticket\": \"113781\", \"Fare\": 151.55, \"Cabin\": \"C22 C26\", \"Embarked\": \"S\"}, {\"PassengerId\": 307, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Fleming, Miss. Margaret\", \"Sex\": \"female\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"17421\", \"Fare\": 110.8833, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 308, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)\", \"Sex\": \"female\", \"Age\": 17.0, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"PC 17758\", \"Fare\": 108.9, \"Cabin\": \"C65\", \"Embarked\": \"C\"}, {\"PassengerId\": 309, \"Survived\": 0, \"Pclass\": 2, \"Name\": \"Abelson, Mr. Samuel\", \"Sex\": \"male\", \"Age\": 30.0, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"P/PP 3381\", \"Fare\": 24.0, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 310, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Francatelli, Miss. Laura Mabel\", \"Sex\": \"female\", \"Age\": 30.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"PC 17485\", \"Fare\": 56.9292, \"Cabin\": \"E36\", \"Embarked\": \"C\"}, {\"PassengerId\": 311, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Hays, Miss. Margaret Bechstein\", \"Sex\": \"female\", \"Age\": 24.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"11767\", \"Fare\": 83.1583, \"Cabin\": \"C54\", \"Embarked\": \"C\"}, {\"PassengerId\": 312, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Ryerson, Miss. Emily Borie\", \"Sex\": \"female\", \"Age\": 18.0, \"SibSp\": 2, \"Parch\": 2, \"Ticket\": \"PC 17608\", \"Fare\": 262.375, \"Cabin\": \"B57 B59 B63 B66\", \"Embarked\": \"C\"}, {\"PassengerId\": 313, \"Survived\": 0, \"Pclass\": 2, \"Name\": \"Lahtinen, Mrs. William (Anna Sylfven)\", \"Sex\": \"female\", \"Age\": 26.0, \"SibSp\": 1, \"Parch\": 1, \"Ticket\": \"250651\", \"Fare\": 26.0, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 314, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Hendekovic, Mr. Ignjac\", \"Sex\": \"male\", \"Age\": 28.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"349243\", \"Fare\": 7.8958, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 315, \"Survived\": 0, \"Pclass\": 2, \"Name\": \"Hart, Mr. Benjamin\", \"Sex\": \"male\", \"Age\": 43.0, \"SibSp\": 1, \"Parch\": 1, \"Ticket\": \"F.C.C. 13529\", \"Fare\": 26.25, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 316, \"Survived\": 1, \"Pclass\": 3, \"Name\": \"Nilsson, Miss. 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Jean Gertrude\", \"Sex\": \"female\", \"Age\": 16.0, \"SibSp\": 0, \"Parch\": 1, \"Ticket\": \"111361\", \"Fare\": 57.9792, \"Cabin\": \"B18\", \"Embarked\": \"C\"}, {\"PassengerId\": 331, \"Survived\": 1, \"Pclass\": 3, \"Name\": \"McCoy, Miss. 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William Loch \\\"William\\\"\", \"Sex\": \"male\", \"Age\": 3.0, \"SibSp\": 1, \"Parch\": 1, \"Ticket\": \"C.A. 37671\", \"Fare\": 15.9, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 350, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Dimic, Mr. Jovan\", \"Sex\": \"male\", \"Age\": 42.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"315088\", \"Fare\": 8.6625, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 351, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Odahl, Mr. Nils Martin\", \"Sex\": \"male\", \"Age\": 23.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"7267\", \"Fare\": 9.225, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 352, \"Survived\": 0, \"Pclass\": 1, \"Name\": \"Williams-Lambert, Mr. Fletcher Fellows\", \"Sex\": \"male\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"113510\", \"Fare\": 35.0, \"Cabin\": \"C128\", \"Embarked\": \"S\"}, {\"PassengerId\": 353, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Elias, Mr. Tannous\", \"Sex\": \"male\", \"Age\": 15.0, \"SibSp\": 1, \"Parch\": 1, \"Ticket\": \"2695\", \"Fare\": 7.2292, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 354, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Arnold-Franchi, Mr. Josef\", \"Sex\": \"male\", \"Age\": 25.0, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"349237\", \"Fare\": 17.8, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 355, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Yousif, Mr. Wazli\", \"Sex\": \"male\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"2647\", \"Fare\": 7.225, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 356, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Vanden Steen, Mr. Leo Peter\", \"Sex\": \"male\", \"Age\": 28.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"345783\", \"Fare\": 9.5, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 357, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Bowerman, Miss. Elsie Edith\", \"Sex\": \"female\", \"Age\": 22.0, \"SibSp\": 0, \"Parch\": 1, \"Ticket\": \"113505\", \"Fare\": 55.0, \"Cabin\": \"E33\", \"Embarked\": \"S\"}, {\"PassengerId\": 358, \"Survived\": 0, \"Pclass\": 2, \"Name\": \"Funk, Miss. Annie Clemmer\", \"Sex\": \"female\", \"Age\": 38.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"237671\", \"Fare\": 13.0, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 359, \"Survived\": 1, \"Pclass\": 3, \"Name\": \"McGovern, Miss. Mary\", \"Sex\": \"female\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"330931\", \"Fare\": 7.8792, \"Cabin\": null, \"Embarked\": \"Q\"}, {\"PassengerId\": 360, \"Survived\": 1, \"Pclass\": 3, \"Name\": \"Mockler, Miss. Helen Mary \\\"Ellie\\\"\", \"Sex\": \"female\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"330980\", \"Fare\": 7.8792, \"Cabin\": null, \"Embarked\": \"Q\"}, {\"PassengerId\": 361, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Skoog, Mr. Wilhelm\", \"Sex\": \"male\", \"Age\": 40.0, \"SibSp\": 1, \"Parch\": 4, \"Ticket\": \"347088\", \"Fare\": 27.9, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 362, \"Survived\": 0, \"Pclass\": 2, \"Name\": \"del Carlo, Mr. Sebastiano\", \"Sex\": \"male\", \"Age\": 29.0, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"SC/PARIS 2167\", \"Fare\": 27.7208, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 363, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Barbara, Mrs. (Catherine David)\", \"Sex\": \"female\", \"Age\": 45.0, \"SibSp\": 0, \"Parch\": 1, \"Ticket\": \"2691\", \"Fare\": 14.4542, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 364, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Asim, Mr. Adola\", \"Sex\": \"male\", \"Age\": 35.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"SOTON/O.Q. 3101310\", \"Fare\": 7.05, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 365, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"O'Brien, Mr. Thomas\", \"Sex\": \"male\", \"Age\": null, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"370365\", \"Fare\": 15.5, \"Cabin\": null, \"Embarked\": \"Q\"}, {\"PassengerId\": 366, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Adahl, Mr. Mauritz Nils Martin\", \"Sex\": \"male\", \"Age\": 30.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"C 7076\", \"Fare\": 7.25, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 367, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Warren, Mrs. Frank Manley (Anna Sophia Atkinson)\", \"Sex\": \"female\", \"Age\": 60.0, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"110813\", \"Fare\": 75.25, \"Cabin\": \"D37\", \"Embarked\": \"C\"}, {\"PassengerId\": 368, \"Survived\": 1, \"Pclass\": 3, \"Name\": \"Moussa, Mrs. (Mantoura Boulos)\", \"Sex\": \"female\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"2626\", \"Fare\": 7.2292, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 369, \"Survived\": 1, \"Pclass\": 3, \"Name\": \"Jermyn, Miss. Annie\", \"Sex\": \"female\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"14313\", \"Fare\": 7.75, \"Cabin\": null, \"Embarked\": \"Q\"}, {\"PassengerId\": 370, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Aubart, Mme. Leontine Pauline\", \"Sex\": \"female\", \"Age\": 24.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"PC 17477\", \"Fare\": 69.3, \"Cabin\": \"B35\", \"Embarked\": \"C\"}, {\"PassengerId\": 371, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Harder, Mr. George Achilles\", \"Sex\": \"male\", \"Age\": 25.0, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"11765\", \"Fare\": 55.4417, \"Cabin\": \"E50\", \"Embarked\": \"C\"}, {\"PassengerId\": 372, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Wiklund, Mr. Jakob Alfred\", \"Sex\": \"male\", \"Age\": 18.0, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"3101267\", \"Fare\": 6.4958, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 373, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Beavan, Mr. William Thomas\", \"Sex\": \"male\", \"Age\": 19.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"323951\", \"Fare\": 8.05, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 374, \"Survived\": 0, \"Pclass\": 1, \"Name\": \"Ringhini, Mr. Sante\", \"Sex\": \"male\", \"Age\": 22.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"PC 17760\", \"Fare\": 135.6333, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 375, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Palsson, Miss. Stina Viola\", \"Sex\": \"female\", \"Age\": 3.0, \"SibSp\": 3, \"Parch\": 1, \"Ticket\": \"349909\", \"Fare\": 21.075, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 376, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Meyer, Mrs. Edgar Joseph (Leila Saks)\", \"Sex\": \"female\", \"Age\": null, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"PC 17604\", \"Fare\": 82.1708, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 377, \"Survived\": 1, \"Pclass\": 3, \"Name\": \"Landergren, Miss. 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\"male\", \"Age\": 23.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"29751\", \"Fare\": 13.0, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 660, \"Survived\": 0, \"Pclass\": 1, \"Name\": \"Newell, Mr. Arthur Webster\", \"Sex\": \"male\", \"Age\": 58.0, \"SibSp\": 0, \"Parch\": 2, \"Ticket\": \"35273\", \"Fare\": 113.275, \"Cabin\": \"D48\", \"Embarked\": \"C\"}, {\"PassengerId\": 661, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Frauenthal, Dr. Henry William\", \"Sex\": \"male\", \"Age\": 50.0, \"SibSp\": 2, \"Parch\": 0, \"Ticket\": \"PC 17611\", \"Fare\": 133.65, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 662, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Badt, Mr. Mohamed\", \"Sex\": \"male\", \"Age\": 40.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"2623\", \"Fare\": 7.225, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 663, \"Survived\": 0, \"Pclass\": 1, \"Name\": \"Colley, Mr. Edward Pomeroy\", \"Sex\": \"male\", \"Age\": 47.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"5727\", \"Fare\": 25.5875, \"Cabin\": \"E58\", \"Embarked\": \"S\"}, {\"PassengerId\": 664, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Coleff, Mr. Peju\", \"Sex\": \"male\", \"Age\": 36.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"349210\", \"Fare\": 7.4958, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 665, \"Survived\": 1, \"Pclass\": 3, \"Name\": \"Lindqvist, Mr. Eino William\", \"Sex\": \"male\", \"Age\": 20.0, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"STON/O 2. 3101285\", \"Fare\": 7.925, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 666, \"Survived\": 0, \"Pclass\": 2, \"Name\": \"Hickman, Mr. Lewis\", \"Sex\": \"male\", \"Age\": 32.0, \"SibSp\": 2, \"Parch\": 0, \"Ticket\": \"S.O.C. 14879\", \"Fare\": 73.5, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 667, \"Survived\": 0, \"Pclass\": 2, \"Name\": \"Butler, Mr. Reginald Fenton\", \"Sex\": \"male\", \"Age\": 25.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"234686\", \"Fare\": 13.0, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 668, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Rommetvedt, Mr. Knud Paust\", \"Sex\": \"male\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"312993\", \"Fare\": 7.775, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 669, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Cook, Mr. Jacob\", \"Sex\": \"male\", \"Age\": 43.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"A/5 3536\", \"Fare\": 8.05, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 670, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright)\", \"Sex\": \"female\", \"Age\": null, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"19996\", \"Fare\": 52.0, \"Cabin\": \"C126\", \"Embarked\": \"S\"}, {\"PassengerId\": 671, \"Survived\": 1, \"Pclass\": 2, \"Name\": \"Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford)\", \"Sex\": \"female\", \"Age\": 40.0, \"SibSp\": 1, \"Parch\": 1, \"Ticket\": 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\"Embarked\": \"S\"}, {\"PassengerId\": 676, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Edvardsson, Mr. Gustaf Hjalmar\", \"Sex\": \"male\", \"Age\": 18.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"349912\", \"Fare\": 7.775, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 677, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Sawyer, Mr. Frederick Charles\", \"Sex\": \"male\", \"Age\": 24.5, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"342826\", \"Fare\": 8.05, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 678, \"Survived\": 1, \"Pclass\": 3, \"Name\": \"Turja, Miss. 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Urho Abraham\", \"Sex\": \"male\", \"Age\": 2.0, \"SibSp\": 4, \"Parch\": 1, \"Ticket\": \"3101295\", \"Fare\": 39.6875, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 826, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Flynn, Mr. John\", \"Sex\": \"male\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"368323\", \"Fare\": 6.95, \"Cabin\": null, \"Embarked\": \"Q\"}, {\"PassengerId\": 827, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Lam, Mr. Len\", \"Sex\": \"male\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"1601\", \"Fare\": 56.4958, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 828, \"Survived\": 1, \"Pclass\": 2, \"Name\": \"Mallet, Master. Andre\", \"Sex\": \"male\", \"Age\": 1.0, \"SibSp\": 0, \"Parch\": 2, \"Ticket\": \"S.C./PARIS 2079\", \"Fare\": 37.0042, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 829, \"Survived\": 1, \"Pclass\": 3, \"Name\": \"McCormack, Mr. Thomas Joseph\", \"Sex\": \"male\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"367228\", \"Fare\": 7.75, \"Cabin\": null, \"Embarked\": \"Q\"}, {\"PassengerId\": 830, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Stone, Mrs. George Nelson (Martha Evelyn)\", \"Sex\": \"female\", \"Age\": 62.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"113572\", \"Fare\": 80.0, \"Cabin\": \"B28\", \"Embarked\": null}, {\"PassengerId\": 831, \"Survived\": 1, \"Pclass\": 3, \"Name\": \"Yasbeck, Mrs. Antoni (Selini Alexander)\", \"Sex\": \"female\", \"Age\": 15.0, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"2659\", \"Fare\": 14.4542, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 832, \"Survived\": 1, \"Pclass\": 2, \"Name\": \"Richards, Master. George Sibley\", \"Sex\": \"male\", \"Age\": 0.83, \"SibSp\": 1, \"Parch\": 1, \"Ticket\": \"29106\", \"Fare\": 18.75, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 833, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Saad, Mr. Amin\", \"Sex\": \"male\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"2671\", \"Fare\": 7.2292, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 834, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Augustsson, Mr. Albert\", \"Sex\": \"male\", \"Age\": 23.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"347468\", \"Fare\": 7.8542, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 835, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Allum, Mr. Owen George\", \"Sex\": \"male\", \"Age\": 18.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"2223\", \"Fare\": 8.3, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 836, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Compton, Miss. Sara Rebecca\", \"Sex\": \"female\", \"Age\": 39.0, \"SibSp\": 1, \"Parch\": 1, \"Ticket\": \"PC 17756\", \"Fare\": 83.1583, \"Cabin\": \"E49\", \"Embarked\": \"C\"}, {\"PassengerId\": 837, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Pasic, Mr. Jakob\", \"Sex\": \"male\", \"Age\": 21.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"315097\", \"Fare\": 8.6625, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 838, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Sirota, Mr. Maurice\", \"Sex\": \"male\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"392092\", \"Fare\": 8.05, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 839, \"Survived\": 1, \"Pclass\": 3, \"Name\": \"Chip, Mr. Chang\", \"Sex\": \"male\", \"Age\": 32.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"1601\", \"Fare\": 56.4958, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 840, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Marechal, Mr. Pierre\", \"Sex\": \"male\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"11774\", \"Fare\": 29.7, \"Cabin\": \"C47\", \"Embarked\": \"C\"}, {\"PassengerId\": 841, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Alhomaki, Mr. Ilmari Rudolf\", \"Sex\": \"male\", \"Age\": 20.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"SOTON/O2 3101287\", \"Fare\": 7.925, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 842, \"Survived\": 0, \"Pclass\": 2, \"Name\": \"Mudd, Mr. Thomas Charles\", \"Sex\": \"male\", \"Age\": 16.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"S.O./P.P. 3\", \"Fare\": 10.5, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 843, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Serepeca, Miss. Augusta\", \"Sex\": \"female\", \"Age\": 30.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"113798\", \"Fare\": 31.0, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 844, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Lemberopolous, Mr. Peter L\", \"Sex\": \"male\", \"Age\": 34.5, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"2683\", \"Fare\": 6.4375, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 845, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Culumovic, Mr. Jeso\", \"Sex\": \"male\", \"Age\": 17.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"315090\", \"Fare\": 8.6625, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 846, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Abbing, Mr. Anthony\", \"Sex\": \"male\", \"Age\": 42.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"C.A. 5547\", \"Fare\": 7.55, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 847, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Sage, Mr. Douglas Bullen\", \"Sex\": \"male\", \"Age\": null, \"SibSp\": 8, \"Parch\": 2, \"Ticket\": \"CA. 2343\", \"Fare\": 69.55, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 848, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Markoff, Mr. Marin\", \"Sex\": \"male\", \"Age\": 35.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"349213\", \"Fare\": 7.8958, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 849, \"Survived\": 0, \"Pclass\": 2, \"Name\": \"Harper, Rev. John\", \"Sex\": \"male\", \"Age\": 28.0, \"SibSp\": 0, \"Parch\": 1, \"Ticket\": \"248727\", \"Fare\": 33.0, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 850, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)\", \"Sex\": \"female\", \"Age\": null, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"17453\", \"Fare\": 89.1042, \"Cabin\": \"C92\", \"Embarked\": \"C\"}, {\"PassengerId\": 851, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Andersson, Master. Sigvard Harald Elias\", \"Sex\": \"male\", \"Age\": 4.0, \"SibSp\": 4, \"Parch\": 2, \"Ticket\": \"347082\", \"Fare\": 31.275, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 852, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Svensson, Mr. Johan\", \"Sex\": \"male\", \"Age\": 74.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"347060\", \"Fare\": 7.775, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 853, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Boulos, Miss. Nourelain\", \"Sex\": \"female\", \"Age\": 9.0, \"SibSp\": 1, \"Parch\": 1, \"Ticket\": \"2678\", \"Fare\": 15.2458, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 854, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Lines, Miss. Mary Conover\", \"Sex\": \"female\", \"Age\": 16.0, \"SibSp\": 0, \"Parch\": 1, \"Ticket\": \"PC 17592\", \"Fare\": 39.4, \"Cabin\": \"D28\", \"Embarked\": \"S\"}, {\"PassengerId\": 855, \"Survived\": 0, \"Pclass\": 2, \"Name\": \"Carter, Mrs. Ernest Courtenay (Lilian Hughes)\", \"Sex\": \"female\", \"Age\": 44.0, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"244252\", \"Fare\": 26.0, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 856, \"Survived\": 1, \"Pclass\": 3, \"Name\": \"Aks, Mrs. Sam (Leah Rosen)\", \"Sex\": \"female\", \"Age\": 18.0, \"SibSp\": 0, \"Parch\": 1, \"Ticket\": \"392091\", \"Fare\": 9.35, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 857, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Wick, Mrs. George Dennick (Mary Hitchcock)\", \"Sex\": \"female\", \"Age\": 45.0, \"SibSp\": 1, \"Parch\": 1, \"Ticket\": \"36928\", \"Fare\": 164.8667, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 858, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Daly, Mr. Peter Denis \", \"Sex\": \"male\", \"Age\": 51.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"113055\", \"Fare\": 26.55, \"Cabin\": \"E17\", \"Embarked\": \"S\"}, {\"PassengerId\": 859, \"Survived\": 1, \"Pclass\": 3, \"Name\": \"Baclini, Mrs. Solomon (Latifa Qurban)\", \"Sex\": \"female\", \"Age\": 24.0, \"SibSp\": 0, \"Parch\": 3, \"Ticket\": \"2666\", \"Fare\": 19.2583, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 860, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Razi, Mr. Raihed\", \"Sex\": \"male\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"2629\", \"Fare\": 7.2292, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 861, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Hansen, Mr. Claus Peter\", \"Sex\": \"male\", \"Age\": 41.0, \"SibSp\": 2, \"Parch\": 0, \"Ticket\": \"350026\", \"Fare\": 14.1083, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 862, \"Survived\": 0, \"Pclass\": 2, \"Name\": \"Giles, Mr. Frederick Edward\", \"Sex\": \"male\", \"Age\": 21.0, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"28134\", \"Fare\": 11.5, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 863, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Swift, Mrs. Frederick Joel (Margaret Welles Barron)\", \"Sex\": \"female\", \"Age\": 48.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"17466\", \"Fare\": 25.9292, \"Cabin\": \"D17\", \"Embarked\": \"S\"}, {\"PassengerId\": 864, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Sage, Miss. Dorothy Edith \\\"Dolly\\\"\", \"Sex\": \"female\", \"Age\": null, \"SibSp\": 8, \"Parch\": 2, \"Ticket\": \"CA. 2343\", \"Fare\": 69.55, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 865, \"Survived\": 0, \"Pclass\": 2, \"Name\": \"Gill, Mr. John William\", \"Sex\": \"male\", \"Age\": 24.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"233866\", \"Fare\": 13.0, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 866, \"Survived\": 1, \"Pclass\": 2, \"Name\": \"Bystrom, Mrs. (Karolina)\", \"Sex\": \"female\", \"Age\": 42.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"236852\", \"Fare\": 13.0, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 867, \"Survived\": 1, \"Pclass\": 2, \"Name\": \"Duran y More, Miss. Asuncion\", \"Sex\": \"female\", \"Age\": 27.0, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"SC/PARIS 2149\", \"Fare\": 13.8583, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 868, \"Survived\": 0, \"Pclass\": 1, \"Name\": \"Roebling, Mr. Washington Augustus II\", \"Sex\": \"male\", \"Age\": 31.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"PC 17590\", \"Fare\": 50.4958, \"Cabin\": \"A24\", \"Embarked\": \"S\"}, {\"PassengerId\": 869, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"van Melkebeke, Mr. Philemon\", \"Sex\": \"male\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"345777\", \"Fare\": 9.5, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 870, \"Survived\": 1, \"Pclass\": 3, \"Name\": \"Johnson, Master. Harold Theodor\", \"Sex\": \"male\", \"Age\": 4.0, \"SibSp\": 1, \"Parch\": 1, \"Ticket\": \"347742\", \"Fare\": 11.1333, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 871, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Balkic, Mr. Cerin\", \"Sex\": \"male\", \"Age\": 26.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"349248\", \"Fare\": 7.8958, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 872, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)\", \"Sex\": \"female\", \"Age\": 47.0, \"SibSp\": 1, \"Parch\": 1, \"Ticket\": \"11751\", \"Fare\": 52.5542, \"Cabin\": \"D35\", \"Embarked\": \"S\"}, {\"PassengerId\": 873, \"Survived\": 0, \"Pclass\": 1, \"Name\": \"Carlsson, Mr. Frans Olof\", \"Sex\": \"male\", \"Age\": 33.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"695\", \"Fare\": 5.0, \"Cabin\": \"B51 B53 B55\", \"Embarked\": \"S\"}, {\"PassengerId\": 874, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Vander Cruyssen, Mr. Victor\", \"Sex\": \"male\", \"Age\": 47.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"345765\", \"Fare\": 9.0, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 875, \"Survived\": 1, \"Pclass\": 2, \"Name\": \"Abelson, Mrs. Samuel (Hannah Wizosky)\", \"Sex\": \"female\", \"Age\": 28.0, \"SibSp\": 1, \"Parch\": 0, \"Ticket\": \"P/PP 3381\", \"Fare\": 24.0, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 876, \"Survived\": 1, \"Pclass\": 3, \"Name\": \"Najib, Miss. Adele Kiamie \\\"Jane\\\"\", \"Sex\": \"female\", \"Age\": 15.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"2667\", \"Fare\": 7.225, \"Cabin\": null, \"Embarked\": \"C\"}, {\"PassengerId\": 877, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Gustafsson, Mr. Alfred Ossian\", \"Sex\": \"male\", \"Age\": 20.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"7534\", \"Fare\": 9.8458, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 878, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Petroff, Mr. Nedelio\", \"Sex\": \"male\", \"Age\": 19.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"349212\", \"Fare\": 7.8958, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 879, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Laleff, Mr. Kristo\", \"Sex\": \"male\", \"Age\": null, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"349217\", \"Fare\": 7.8958, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 880, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)\", \"Sex\": \"female\", \"Age\": 56.0, \"SibSp\": 0, \"Parch\": 1, \"Ticket\": \"11767\", \"Fare\": 83.1583, \"Cabin\": \"C50\", \"Embarked\": \"C\"}, {\"PassengerId\": 881, \"Survived\": 1, \"Pclass\": 2, \"Name\": \"Shelley, Mrs. William (Imanita Parrish Hall)\", \"Sex\": \"female\", \"Age\": 25.0, \"SibSp\": 0, \"Parch\": 1, \"Ticket\": \"230433\", \"Fare\": 26.0, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 882, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Markun, Mr. Johann\", \"Sex\": \"male\", \"Age\": 33.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"349257\", \"Fare\": 7.8958, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 883, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Dahlberg, Miss. Gerda Ulrika\", \"Sex\": \"female\", \"Age\": 22.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"7552\", \"Fare\": 10.5167, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 884, \"Survived\": 0, \"Pclass\": 2, \"Name\": \"Banfield, Mr. Frederick James\", \"Sex\": \"male\", \"Age\": 28.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"C.A./SOTON 34068\", \"Fare\": 10.5, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 885, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Sutehall, Mr. Henry Jr\", \"Sex\": \"male\", \"Age\": 25.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"SOTON/OQ 392076\", \"Fare\": 7.05, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 886, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Rice, Mrs. William (Margaret Norton)\", \"Sex\": \"female\", \"Age\": 39.0, \"SibSp\": 0, \"Parch\": 5, \"Ticket\": \"382652\", \"Fare\": 29.125, \"Cabin\": null, \"Embarked\": \"Q\"}, {\"PassengerId\": 887, \"Survived\": 0, \"Pclass\": 2, \"Name\": \"Montvila, Rev. Juozas\", \"Sex\": \"male\", \"Age\": 27.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"211536\", \"Fare\": 13.0, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 888, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Graham, Miss. Margaret Edith\", \"Sex\": \"female\", \"Age\": 19.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"112053\", \"Fare\": 30.0, \"Cabin\": \"B42\", \"Embarked\": \"S\"}, {\"PassengerId\": 889, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Johnston, Miss. Catherine Helen \\\"Carrie\\\"\", \"Sex\": \"female\", \"Age\": null, \"SibSp\": 1, \"Parch\": 2, \"Ticket\": \"W./C. 6607\", \"Fare\": 23.45, \"Cabin\": null, \"Embarked\": \"S\"}, {\"PassengerId\": 890, \"Survived\": 1, \"Pclass\": 1, \"Name\": \"Behr, Mr. Karl Howell\", \"Sex\": \"male\", \"Age\": 26.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"111369\", \"Fare\": 30.0, \"Cabin\": \"C148\", \"Embarked\": \"C\"}, {\"PassengerId\": 891, \"Survived\": 0, \"Pclass\": 3, \"Name\": \"Dooley, Mr. Patrick\", \"Sex\": \"male\", \"Age\": 32.0, \"SibSp\": 0, \"Parch\": 0, \"Ticket\": \"370376\", \"Fare\": 7.75, \"Cabin\": null, \"Embarked\": \"Q\"}]}}, {\"mode\": \"vega-lite\"});\n",
-       "</script>"
-      ],
-      "text/plain": [
-       "alt.FacetChart(...)"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Distribusi usia penumpang berdasarkan pclass\n",
-    "alt.Chart(df,\n",
-    "         width=600,\n",
-    "         height=300,\n",
-    "         title=\"Distribusi Usia Penumpang\"\n",
-    "         ).mark_bar(opacity=0.8).encode(x = alt.X(\"Age\",\n",
-    "                                         title=\"Usia\",\n",
-    "                                         axis=alt.AxisConfig(labelAngle=45)),\n",
-    "                               y = alt.Y(\"count()\",\n",
-    "                                        title=\"Frekuensi Berdasarkan Usia\"),\n",
-    "                               color = alt.Color(\"Pclass\")\n",
-    "                               ).facet(column = alt.Column(\"Survived\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "- Ternyata mayoritas penumpang pada perjalanan tersebut berusia 15 - 45 tahun.\n",
-    "- Terlihat pula bahwa mayoritas penumpang yang meninggal (Survived = 0) mengambil Pclass = 3 (3rd class) yang merupakan class terendah.\n",
-    "- Hal ini sebanding dengan pernyataan kita di bagian sebelumnya bahwa penumpang yang meninggal mayoritas adalah mereka dengan biaya perjalanan rendah.\n",
-    "- Sehingga kemungkinan lantai tempat mereka tinggal berpotensi tenggelam lebih cepat dibandingkan 2nd class dan 1st class yang berada di tingkatan yang lebih tinggi."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3. Data Preprocessing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 3.1 Handle Missing Value"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 891 entries, 0 to 890\n",
-      "Data columns (total 12 columns):\n",
-      " #   Column       Non-Null Count  Dtype  \n",
-      "---  ------       --------------  -----  \n",
-      " 0   PassengerId  891 non-null    int64  \n",
-      " 1   Survived     891 non-null    int64  \n",
-      " 2   Pclass       891 non-null    int64  \n",
-      " 3   Name         891 non-null    object \n",
-      " 4   Sex          891 non-null    object \n",
-      " 5   Age          714 non-null    float64\n",
-      " 6   SibSp        891 non-null    int64  \n",
-      " 7   Parch        891 non-null    int64  \n",
-      " 8   Ticket       891 non-null    object \n",
-      " 9   Fare         891 non-null    float64\n",
-      " 10  Cabin        204 non-null    object \n",
-      " 11  Embarked     889 non-null    object \n",
-      "dtypes: float64(2), int64(5), object(5)\n",
-      "memory usage: 66.2+ KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Melihat info pada masing-masing kolom\n",
-    "df.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Karena kolom cabin terlalu banyak memuat null value, maka akan kita drop\n",
-    "df = df.drop(\"Cabin\", axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Fill nilai nan pada column \"Age\" dengan nilai rata-rata column tersebut\n",
-    "df[\"Age\"] = df[\"Age\"].fillna(df[\"Age\"].mean())\n",
-    "\n",
-    "#Fill nilai nan pada column \"Embarked\" dengan metode ffill\n",
-    "df[\"Embarked\"] = df[\"Embarked\"].fillna(method=\"ffill\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 891 entries, 0 to 890\n",
-      "Data columns (total 11 columns):\n",
-      " #   Column       Non-Null Count  Dtype  \n",
-      "---  ------       --------------  -----  \n",
-      " 0   PassengerId  891 non-null    int64  \n",
-      " 1   Survived     891 non-null    int64  \n",
-      " 2   Pclass       891 non-null    int64  \n",
-      " 3   Name         891 non-null    object \n",
-      " 4   Sex          891 non-null    object \n",
-      " 5   Age          891 non-null    float64\n",
-      " 6   SibSp        891 non-null    int64  \n",
-      " 7   Parch        891 non-null    int64  \n",
-      " 8   Ticket       891 non-null    object \n",
-      " 9   Fare         891 non-null    float64\n",
-      " 10  Embarked     891 non-null    object \n",
-      "dtypes: float64(2), int64(5), object(4)\n",
-      "memory usage: 62.7+ KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Mengecek keberadaan null values\n",
-    "df.info()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 3.2 Encoding Categorical Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
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-       "<div>\n",
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-       "\n",
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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>PassengerId</th>\n",
-       "      <th>Survived</th>\n",
-       "      <th>Pclass</th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Age</th>\n",
-       "      <th>SibSp</th>\n",
-       "      <th>Parch</th>\n",
-       "      <th>Ticket</th>\n",
-       "      <th>Fare</th>\n",
-       "      <th>Sex_female</th>\n",
-       "      <th>Sex_male</th>\n",
-       "      <th>Embarked_C</th>\n",
-       "      <th>Embarked_Q</th>\n",
-       "      <th>Embarked_S</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Braund, Mr. Owen Harris</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>A/5 21171</td>\n",
-       "      <td>7.2500</td>\n",
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-       "      <td>0</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>PC 17599</td>\n",
-       "      <td>71.2833</td>\n",
-       "      <td>1</td>\n",
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-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
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-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Heikkinen, Miss. Laina</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>STON/O2. 3101282</td>\n",
-       "      <td>7.9250</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>113803</td>\n",
-       "      <td>53.1000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Allen, Mr. William Henry</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>373450</td>\n",
-       "      <td>8.0500</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   PassengerId  Survived  Pclass  \\\n",
-       "0            1         0       3   \n",
-       "1            2         1       1   \n",
-       "2            3         1       3   \n",
-       "3            4         1       1   \n",
-       "4            5         0       3   \n",
-       "\n",
-       "                                                Name   Age  SibSp  Parch  \\\n",
-       "0                            Braund, Mr. Owen Harris  22.0      1      0   \n",
-       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  38.0      1      0   \n",
-       "2                             Heikkinen, Miss. Laina  26.0      0      0   \n",
-       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  35.0      1      0   \n",
-       "4                           Allen, Mr. William Henry  35.0      0      0   \n",
-       "\n",
-       "             Ticket     Fare  Sex_female  Sex_male  Embarked_C  Embarked_Q  \\\n",
-       "0         A/5 21171   7.2500           0         1           0           0   \n",
-       "1          PC 17599  71.2833           1         0           1           0   \n",
-       "2  STON/O2. 3101282   7.9250           1         0           0           0   \n",
-       "3            113803  53.1000           1         0           0           0   \n",
-       "4            373450   8.0500           0         1           0           0   \n",
-       "\n",
-       "   Embarked_S  \n",
-       "0           1  \n",
-       "1           0  \n",
-       "2           1  \n",
-       "3           1  \n",
-       "4           1  "
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Mendummies kolom \"Sex\" dan \"Embarked\"\n",
-    "df = pd.get_dummies(df, columns=[\"Sex\", \"Embarked\"])\n",
-    "\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 3.3 Feature Centering and Scaling"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Karena data tidak berdistribusi pada rentang nilai yang sama, akan kita lakukan Normalisasi\n",
-    "#Import library\n",
-    "from sklearn.preprocessing import Normalizer\n",
-    "\n",
-    "#Membuat objek model\n",
-    "norm_scaler = Normalizer()\n",
-    "\n",
-    "#Memfitting model\n",
-    "col = [\"SibSp\", \"Parch\", \"Pclass\", \"Age\", \"Fare\"]\n",
-    "df[col] = norm_scaler.fit_transform(df[col])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
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-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>PassengerId</th>\n",
-       "      <th>Survived</th>\n",
-       "      <th>Pclass</th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Age</th>\n",
-       "      <th>SibSp</th>\n",
-       "      <th>Parch</th>\n",
-       "      <th>Ticket</th>\n",
-       "      <th>Fare</th>\n",
-       "      <th>Sex_female</th>\n",
-       "      <th>Sex_male</th>\n",
-       "      <th>Embarked_C</th>\n",
-       "      <th>Embarked_Q</th>\n",
-       "      <th>Embarked_S</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
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-       "      <td>Braund, Mr. Owen Harris</td>\n",
-       "      <td>0.941028</td>\n",
-       "      <td>0.042774</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>A/5 21171</td>\n",
-       "      <td>0.310112</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.012377</td>\n",
-       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
-       "      <td>0.470345</td>\n",
-       "      <td>0.012377</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>PC 17599</td>\n",
-       "      <td>0.882309</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.109705</td>\n",
-       "      <td>Heikkinen, Miss. Laina</td>\n",
-       "      <td>0.950778</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>STON/O2. 3101282</td>\n",
-       "      <td>0.289804</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.015720</td>\n",
-       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
-       "      <td>0.550202</td>\n",
-       "      <td>0.015720</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>113803</td>\n",
-       "      <td>0.834735</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.083243</td>\n",
-       "      <td>Allen, Mr. William Henry</td>\n",
-       "      <td>0.971173</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>373450</td>\n",
-       "      <td>0.223370</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   PassengerId  Survived    Pclass  \\\n",
-       "0            1         0  0.128322   \n",
-       "1            2         1  0.012377   \n",
-       "2            3         1  0.109705   \n",
-       "3            4         1  0.015720   \n",
-       "4            5         0  0.083243   \n",
-       "\n",
-       "                                                Name       Age     SibSp  \\\n",
-       "0                            Braund, Mr. Owen Harris  0.941028  0.042774   \n",
-       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  0.470345  0.012377   \n",
-       "2                             Heikkinen, Miss. Laina  0.950778  0.000000   \n",
-       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  0.550202  0.015720   \n",
-       "4                           Allen, Mr. William Henry  0.971173  0.000000   \n",
-       "\n",
-       "   Parch            Ticket      Fare  Sex_female  Sex_male  Embarked_C  \\\n",
-       "0    0.0         A/5 21171  0.310112           0         1           0   \n",
-       "1    0.0          PC 17599  0.882309           1         0           1   \n",
-       "2    0.0  STON/O2. 3101282  0.289804           1         0           0   \n",
-       "3    0.0            113803  0.834735           1         0           0   \n",
-       "4    0.0            373450  0.223370           0         1           0   \n",
-       "\n",
-       "   Embarked_Q  Embarked_S  \n",
-       "0           0           1  \n",
-       "1           0           0  \n",
-       "2           0           1  \n",
-       "3           0           1  \n",
-       "4           0           1  "
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 4. Model Development"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 4.1 Create Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
-       "             param_grid={'n_neighbors': array([  5,   6,   7,   8,   9,  10,  11,  12,  13,  14,  15,  16,  17,\n",
-       "        18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,  29,  30,\n",
-       "        31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,  42,  43,\n",
-       "        44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,  56,\n",
-       "        57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
-       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,\n",
-       "        83,  84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,\n",
-       "        96,  97,  98,  99, 100]),\n",
-       "                         'weights': ['uniform', 'distance']},\n",
-       "             scoring='roc_auc')"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Import library\n",
-    "import pickle\n",
-    "import numpy as np\n",
-    "from sklearn.neighbors import KNeighborsClassifier\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.model_selection import GridSearchCV\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "\n",
-    "#Memisahkan antara data feature dan data target (label)\n",
-    "x = df.drop([\"Name\", \"Survived\", \"Ticket\", \"PassengerId\"], axis=1)\n",
-    "y = df[\"Survived\"]\n",
-    "\n",
-    "#Mensplit antara data training dan data test\n",
-    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, \n",
-    "                                                   random_state = 21)\n",
-    "\n",
-    "#Mengecek kombinasi terbaik untuk model\n",
-    "model = KNeighborsClassifier()\n",
-    "param_grid = {\"n_neighbors\" : np.arange(5, 101),\n",
-    "             \"weights\" : [\"uniform\", \"distance\"]}\n",
-    "\n",
-    "gscv = GridSearchCV(model, param_grid=param_grid, scoring=\"roc_auc\", cv=10)\n",
-    "gscv.fit(x_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'n_neighbors': 20, 'weights': 'uniform'}"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Melihat hasil kombinasi terbaik yang disarankan\n",
-    "gscv.best_params_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.8448047425825204"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Melihat skor tertinggi dari kombinasi yang disarankan\n",
-    "gscv.best_score_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Membuat objek model\n",
-    "model = KNeighborsClassifier(n_neighbors=20, weights=\"uniform\")\n",
-    "\n",
-    "#Fitting model\n",
-    "m = model.fit(x_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 4.2 Model Test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Melakukan prediksi dengan model yang telah kita develop\n",
-    "y_predict = model.predict(x_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\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>PassengerId</th>\n",
-       "      <th>Survived</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>617</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>379</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>725</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>826</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>450</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   PassengerId  Survived\n",
-       "0          617         1\n",
-       "1          379         0\n",
-       "2          725         0\n",
-       "3          826         0\n",
-       "4          450         0"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Membuat dataframe baru dari hasil prediksi\n",
-    "result = pd.DataFrame(x_test.index)\n",
-    "result[\"Survived\"] = y_predict\n",
-    "result.columns = [\"PassengerId\", \"Survived\"]\n",
-    "\n",
-    "result.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    112\n",
-       "1     67\n",
-       "Name: Survived, dtype: int64"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Melihat jumlah penumpang selama dan meninggal\n",
-    "result[\"Survived\"].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "- Model memprediksi jika ada perjalanan kapal titanic dengan rincian informasi seperti pada dataset x_test, akan ada 112 orang yang meninggal dan 67 orang yang selamat"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.770949720670391"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Menglihat seberapa akurat prediksi model kita menggunakan auc scoring\n",
-    "accuracy_score(y_test, y_predict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Menyimpang model dalam format .pickle\n",
-    "pickle.dump(m, open('m_model.pickle', 'wb'))\n",
-    "\n",
-    "#Menyimpan data hasil prediksi dalam format csv\n",
-    "df.to_csv(\"result.csv\", index=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 5. Presentation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "- Dengan menggunakan library KNeighborsClassifier, model kami diset untuk bisa memprediksi status seorang penumpang kapal titanic berdasarkan beberapa informasi yang tersedia pada dataset.\n",
-    "- Setelah melalui beberapa tahap pengembangan dan Manual Hyperparameter Tuning, model kami memiliki akurasi prediksi di angka 0.7709 atau sekitar 77.09%\n",
-    "- Menurut kami, angka ini sudah cukup baik dan mungkin dengan beberapa penyesuaian pada dataset dan Hyperparameter Tuning yang lebih detail bisa meningkatkan akurasi dari model kami."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/EksplorasiData_Jevon/train.csv b/EksplorasiData_Jevon/train.csv
deleted file mode 100644
index 5cc466e..0000000
--- a/EksplorasiData_Jevon/train.csv
+++ /dev/null
@@ -1,892 +0,0 @@
-PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
-1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
-2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
-3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
-4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
-5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
-6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
-7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
-8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
-9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
-10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
-11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
-12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
-13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
-14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
-15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
-16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
-17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
-18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
-19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S
-20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
-21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S
-22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S
-23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q
-24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S
-25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S
-26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S
-27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C
-28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S
-29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q
-30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S
-31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C
-32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C
-33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q
-34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S
-35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C
-36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S
-37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C
-38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S
-39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S
-40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C
-41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S
-42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S
-43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C
-44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C
-45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q
-46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S
-47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q
-48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q
-49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C
-50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S
-51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S
-52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S
-53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C
-54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S
-55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C
-56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S
-57,1,2,"Rugg, Miss. Emily",female,21,0,0,C.A. 31026,10.5,,S
-58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C
-59,1,2,"West, Miss. Constance Mirium",female,5,1,2,C.A. 34651,27.75,,S
-60,0,3,"Goodwin, Master. William Frederick",male,11,5,2,CA 2144,46.9,,S
-61,0,3,"Sirayanian, Mr. Orsen",male,22,0,0,2669,7.2292,,C
-62,1,1,"Icard, Miss. Amelie",female,38,0,0,113572,80,B28,
-63,0,1,"Harris, Mr. Henry Birkhardt",male,45,1,0,36973,83.475,C83,S
-64,0,3,"Skoog, Master. Harald",male,4,3,2,347088,27.9,,S
-65,0,1,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C
-66,1,3,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C
-67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29,0,0,C.A. 29395,10.5,F33,S
-68,0,3,"Crease, Mr. Ernest James",male,19,0,0,S.P. 3464,8.1583,,S
-69,1,3,"Andersson, Miss. Erna Alexandra",female,17,4,2,3101281,7.925,,S
-70,0,3,"Kink, Mr. Vincenz",male,26,2,0,315151,8.6625,,S
-71,0,2,"Jenkin, Mr. Stephen Curnow",male,32,0,0,C.A. 33111,10.5,,S
-72,0,3,"Goodwin, Miss. Lillian Amy",female,16,5,2,CA 2144,46.9,,S
-73,0,2,"Hood, Mr. Ambrose Jr",male,21,0,0,S.O.C. 14879,73.5,,S
-74,0,3,"Chronopoulos, Mr. Apostolos",male,26,1,0,2680,14.4542,,C
-75,1,3,"Bing, Mr. Lee",male,32,0,0,1601,56.4958,,S
-76,0,3,"Moen, Mr. Sigurd Hansen",male,25,0,0,348123,7.65,F G73,S
-77,0,3,"Staneff, Mr. Ivan",male,,0,0,349208,7.8958,,S
-78,0,3,"Moutal, Mr. Rahamin Haim",male,,0,0,374746,8.05,,S
-79,1,2,"Caldwell, Master. Alden Gates",male,0.83,0,2,248738,29,,S
-80,1,3,"Dowdell, Miss. Elizabeth",female,30,0,0,364516,12.475,,S
-81,0,3,"Waelens, Mr. Achille",male,22,0,0,345767,9,,S
-82,1,3,"Sheerlinck, Mr. Jan Baptist",male,29,0,0,345779,9.5,,S
-83,1,3,"McDermott, Miss. Brigdet Delia",female,,0,0,330932,7.7875,,Q
-84,0,1,"Carrau, Mr. Francisco M",male,28,0,0,113059,47.1,,S
-85,1,2,"Ilett, Miss. Bertha",female,17,0,0,SO/C 14885,10.5,,S
-86,1,3,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33,3,0,3101278,15.85,,S
-87,0,3,"Ford, Mr. William Neal",male,16,1,3,W./C. 6608,34.375,,S
-88,0,3,"Slocovski, Mr. Selman Francis",male,,0,0,SOTON/OQ 392086,8.05,,S
-89,1,1,"Fortune, Miss. Mabel Helen",female,23,3,2,19950,263,C23 C25 C27,S
-90,0,3,"Celotti, Mr. Francesco",male,24,0,0,343275,8.05,,S
-91,0,3,"Christmann, Mr. Emil",male,29,0,0,343276,8.05,,S
-92,0,3,"Andreasson, Mr. Paul Edvin",male,20,0,0,347466,7.8542,,S
-93,0,1,"Chaffee, Mr. Herbert Fuller",male,46,1,0,W.E.P. 5734,61.175,E31,S
-94,0,3,"Dean, Mr. Bertram Frank",male,26,1,2,C.A. 2315,20.575,,S
-95,0,3,"Coxon, Mr. Daniel",male,59,0,0,364500,7.25,,S
-96,0,3,"Shorney, Mr. Charles Joseph",male,,0,0,374910,8.05,,S
-97,0,1,"Goldschmidt, Mr. George B",male,71,0,0,PC 17754,34.6542,A5,C
-98,1,1,"Greenfield, Mr. William Bertram",male,23,0,1,PC 17759,63.3583,D10 D12,C
-99,1,2,"Doling, Mrs. John T (Ada Julia Bone)",female,34,0,1,231919,23,,S
-100,0,2,"Kantor, Mr. Sinai",male,34,1,0,244367,26,,S
-101,0,3,"Petranec, Miss. Matilda",female,28,0,0,349245,7.8958,,S
-102,0,3,"Petroff, Mr. Pastcho (""Pentcho"")",male,,0,0,349215,7.8958,,S
-103,0,1,"White, Mr. Richard Frasar",male,21,0,1,35281,77.2875,D26,S
-104,0,3,"Johansson, Mr. Gustaf Joel",male,33,0,0,7540,8.6542,,S
-105,0,3,"Gustafsson, Mr. Anders Vilhelm",male,37,2,0,3101276,7.925,,S
-106,0,3,"Mionoff, Mr. Stoytcho",male,28,0,0,349207,7.8958,,S
-107,1,3,"Salkjelsvik, Miss. Anna Kristine",female,21,0,0,343120,7.65,,S
-108,1,3,"Moss, Mr. Albert Johan",male,,0,0,312991,7.775,,S
-109,0,3,"Rekic, Mr. Tido",male,38,0,0,349249,7.8958,,S
-110,1,3,"Moran, Miss. Bertha",female,,1,0,371110,24.15,,Q
-111,0,1,"Porter, Mr. Walter Chamberlain",male,47,0,0,110465,52,C110,S
-112,0,3,"Zabour, Miss. Hileni",female,14.5,1,0,2665,14.4542,,C
-113,0,3,"Barton, Mr. David John",male,22,0,0,324669,8.05,,S
-114,0,3,"Jussila, Miss. Katriina",female,20,1,0,4136,9.825,,S
-115,0,3,"Attalah, Miss. Malake",female,17,0,0,2627,14.4583,,C
-116,0,3,"Pekoniemi, Mr. Edvard",male,21,0,0,STON/O 2. 3101294,7.925,,S
-117,0,3,"Connors, Mr. Patrick",male,70.5,0,0,370369,7.75,,Q
-118,0,2,"Turpin, Mr. William John Robert",male,29,1,0,11668,21,,S
-119,0,1,"Baxter, Mr. Quigg Edmond",male,24,0,1,PC 17558,247.5208,B58 B60,C
-120,0,3,"Andersson, Miss. Ellis Anna Maria",female,2,4,2,347082,31.275,,S
-121,0,2,"Hickman, Mr. Stanley George",male,21,2,0,S.O.C. 14879,73.5,,S
-122,0,3,"Moore, Mr. Leonard Charles",male,,0,0,A4. 54510,8.05,,S
-123,0,2,"Nasser, Mr. Nicholas",male,32.5,1,0,237736,30.0708,,C
-124,1,2,"Webber, Miss. Susan",female,32.5,0,0,27267,13,E101,S
-125,0,1,"White, Mr. Percival Wayland",male,54,0,1,35281,77.2875,D26,S
-126,1,3,"Nicola-Yarred, Master. Elias",male,12,1,0,2651,11.2417,,C
-127,0,3,"McMahon, Mr. Martin",male,,0,0,370372,7.75,,Q
-128,1,3,"Madsen, Mr. Fridtjof Arne",male,24,0,0,C 17369,7.1417,,S
-129,1,3,"Peter, Miss. Anna",female,,1,1,2668,22.3583,F E69,C
-130,0,3,"Ekstrom, Mr. Johan",male,45,0,0,347061,6.975,,S
-131,0,3,"Drazenoic, Mr. Jozef",male,33,0,0,349241,7.8958,,C
-132,0,3,"Coelho, Mr. Domingos Fernandeo",male,20,0,0,SOTON/O.Q. 3101307,7.05,,S
-133,0,3,"Robins, Mrs. Alexander A (Grace Charity Laury)",female,47,1,0,A/5. 3337,14.5,,S
-134,1,2,"Weisz, Mrs. Leopold (Mathilde Francoise Pede)",female,29,1,0,228414,26,,S
-135,0,2,"Sobey, Mr. Samuel James Hayden",male,25,0,0,C.A. 29178,13,,S
-136,0,2,"Richard, Mr. Emile",male,23,0,0,SC/PARIS 2133,15.0458,,C
-137,1,1,"Newsom, Miss. Helen Monypeny",female,19,0,2,11752,26.2833,D47,S
-138,0,1,"Futrelle, Mr. Jacques Heath",male,37,1,0,113803,53.1,C123,S
-139,0,3,"Osen, Mr. Olaf Elon",male,16,0,0,7534,9.2167,,S
-140,0,1,"Giglio, Mr. Victor",male,24,0,0,PC 17593,79.2,B86,C
-141,0,3,"Boulos, Mrs. Joseph (Sultana)",female,,0,2,2678,15.2458,,C
-142,1,3,"Nysten, Miss. Anna Sofia",female,22,0,0,347081,7.75,,S
-143,1,3,"Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)",female,24,1,0,STON/O2. 3101279,15.85,,S
-144,0,3,"Burke, Mr. Jeremiah",male,19,0,0,365222,6.75,,Q
-145,0,2,"Andrew, Mr. Edgardo Samuel",male,18,0,0,231945,11.5,,S
-146,0,2,"Nicholls, Mr. Joseph Charles",male,19,1,1,C.A. 33112,36.75,,S
-147,1,3,"Andersson, Mr. August Edvard (""Wennerstrom"")",male,27,0,0,350043,7.7958,,S
-148,0,3,"Ford, Miss. Robina Maggie ""Ruby""",female,9,2,2,W./C. 6608,34.375,,S
-149,0,2,"Navratil, Mr. Michel (""Louis M Hoffman"")",male,36.5,0,2,230080,26,F2,S
-150,0,2,"Byles, Rev. Thomas Roussel Davids",male,42,0,0,244310,13,,S
-151,0,2,"Bateman, Rev. Robert James",male,51,0,0,S.O.P. 1166,12.525,,S
-152,1,1,"Pears, Mrs. Thomas (Edith Wearne)",female,22,1,0,113776,66.6,C2,S
-153,0,3,"Meo, Mr. Alfonzo",male,55.5,0,0,A.5. 11206,8.05,,S
-154,0,3,"van Billiard, Mr. Austin Blyler",male,40.5,0,2,A/5. 851,14.5,,S
-155,0,3,"Olsen, Mr. Ole Martin",male,,0,0,Fa 265302,7.3125,,S
-156,0,1,"Williams, Mr. Charles Duane",male,51,0,1,PC 17597,61.3792,,C
-157,1,3,"Gilnagh, Miss. Katherine ""Katie""",female,16,0,0,35851,7.7333,,Q
-158,0,3,"Corn, Mr. Harry",male,30,0,0,SOTON/OQ 392090,8.05,,S
-159,0,3,"Smiljanic, Mr. Mile",male,,0,0,315037,8.6625,,S
-160,0,3,"Sage, Master. Thomas Henry",male,,8,2,CA. 2343,69.55,,S
-161,0,3,"Cribb, Mr. John Hatfield",male,44,0,1,371362,16.1,,S
-162,1,2,"Watt, Mrs. James (Elizabeth ""Bessie"" Inglis Milne)",female,40,0,0,C.A. 33595,15.75,,S
-163,0,3,"Bengtsson, Mr. John Viktor",male,26,0,0,347068,7.775,,S
-164,0,3,"Calic, Mr. Jovo",male,17,0,0,315093,8.6625,,S
-165,0,3,"Panula, Master. Eino Viljami",male,1,4,1,3101295,39.6875,,S
-166,1,3,"Goldsmith, Master. Frank John William ""Frankie""",male,9,0,2,363291,20.525,,S
-167,1,1,"Chibnall, Mrs. (Edith Martha Bowerman)",female,,0,1,113505,55,E33,S
-168,0,3,"Skoog, Mrs. William (Anna Bernhardina Karlsson)",female,45,1,4,347088,27.9,,S
-169,0,1,"Baumann, Mr. John D",male,,0,0,PC 17318,25.925,,S
-170,0,3,"Ling, Mr. Lee",male,28,0,0,1601,56.4958,,S
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-836,1,1,"Compton, Miss. Sara Rebecca",female,39,1,1,PC 17756,83.1583,E49,C
-837,0,3,"Pasic, Mr. Jakob",male,21,0,0,315097,8.6625,,S
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-839,1,3,"Chip, Mr. Chang",male,32,0,0,1601,56.4958,,S
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-841,0,3,"Alhomaki, Mr. Ilmari Rudolf",male,20,0,0,SOTON/O2 3101287,7.925,,S
-842,0,2,"Mudd, Mr. Thomas Charles",male,16,0,0,S.O./P.P. 3,10.5,,S
-843,1,1,"Serepeca, Miss. Augusta",female,30,0,0,113798,31,,C
-844,0,3,"Lemberopolous, Mr. Peter L",male,34.5,0,0,2683,6.4375,,C
-845,0,3,"Culumovic, Mr. Jeso",male,17,0,0,315090,8.6625,,S
-846,0,3,"Abbing, Mr. Anthony",male,42,0,0,C.A. 5547,7.55,,S
-847,0,3,"Sage, Mr. Douglas Bullen",male,,8,2,CA. 2343,69.55,,S
-848,0,3,"Markoff, Mr. Marin",male,35,0,0,349213,7.8958,,C
-849,0,2,"Harper, Rev. John",male,28,0,1,248727,33,,S
-850,1,1,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",female,,1,0,17453,89.1042,C92,C
-851,0,3,"Andersson, Master. Sigvard Harald Elias",male,4,4,2,347082,31.275,,S
-852,0,3,"Svensson, Mr. Johan",male,74,0,0,347060,7.775,,S
-853,0,3,"Boulos, Miss. Nourelain",female,9,1,1,2678,15.2458,,C
-854,1,1,"Lines, Miss. Mary Conover",female,16,0,1,PC 17592,39.4,D28,S
-855,0,2,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44,1,0,244252,26,,S
-856,1,3,"Aks, Mrs. Sam (Leah Rosen)",female,18,0,1,392091,9.35,,S
-857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45,1,1,36928,164.8667,,S
-858,1,1,"Daly, Mr. Peter Denis ",male,51,0,0,113055,26.55,E17,S
-859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24,0,3,2666,19.2583,,C
-860,0,3,"Razi, Mr. Raihed",male,,0,0,2629,7.2292,,C
-861,0,3,"Hansen, Mr. Claus Peter",male,41,2,0,350026,14.1083,,S
-862,0,2,"Giles, Mr. Frederick Edward",male,21,1,0,28134,11.5,,S
-863,1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48,0,0,17466,25.9292,D17,S
-864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S
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-866,1,2,"Bystrom, Mrs. (Karolina)",female,42,0,0,236852,13,,S
-867,1,2,"Duran y More, Miss. Asuncion",female,27,1,0,SC/PARIS 2149,13.8583,,C
-868,0,1,"Roebling, Mr. Washington Augustus II",male,31,0,0,PC 17590,50.4958,A24,S
-869,0,3,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,,S
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-871,0,3,"Balkic, Mr. Cerin",male,26,0,0,349248,7.8958,,S
-872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S
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-875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C
-876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C
-877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S
-878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S
-879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S
-880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C
-881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S
-882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S
-883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S
-884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S
-885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S
-886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q
-887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S
-888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S
-889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S
-890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C
-891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q
diff --git a/EksplorasiData_Wilson Wiranda/DSC ITB - Wilson Wiranda - Exercise 1.ipynb b/EksplorasiData_Wilson Wiranda/DSC ITB - Wilson Wiranda - Exercise 1.ipynb
new file mode 100644
index 0000000..16c3ab7
--- /dev/null
+++ b/EksplorasiData_Wilson Wiranda/DSC ITB - Wilson Wiranda - Exercise 1.ipynb	
@@ -0,0 +1,1108 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Take Me Out DataSet Analysis\n",
+    "### Wilson Wiranda\n",
+    "### Norges teknisk-naturvitenskaplige universitet\n",
+    "\n",
+    "### Start Date : 15 November 2020 22:21\n",
+    "### Dataset : TakeMeOut"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. Import Library"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2. Read CSV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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>Timestamp</th>\n",
+       "      <th>Siapa nama kamu?</th>\n",
+       "      <th>Cewek atau cowok nih?</th>\n",
+       "      <th>Seberapa penting quality time bareng calon pacar untuk kamu?</th>\n",
+       "      <th>Seberapa penting physical touch sama calon pacar untuk kamu?</th>\n",
+       "      <th>Seberapa penting word of affirmation dari calon pacar untuk kamu?</th>\n",
+       "      <th>Seberapa penting dapet kado dari calon pacar untuk kamu?</th>\n",
+       "      <th>Seberapa penting bantuan dari calon pacar untuk kamu?</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2020/10/31 3:39:25 PM GMT+7</td>\n",
+       "      <td>A**************</td>\n",
+       "      <td>Cowok</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2020/10/31 3:39:36 PM GMT+7</td>\n",
+       "      <td>L****</td>\n",
+       "      <td>Cewek</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2020/10/31 3:39:38 PM GMT+7</td>\n",
+       "      <td>Y***********</td>\n",
+       "      <td>Cowok</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2020/10/31 3:39:42 PM GMT+7</td>\n",
+       "      <td>a***</td>\n",
+       "      <td>Cowok</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2020/10/31 3:39:43 PM GMT+7</td>\n",
+       "      <td>B****</td>\n",
+       "      <td>Cowok</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     Timestamp Siapa nama kamu? Cewek atau cowok nih?  \\\n",
+       "0  2020/10/31 3:39:25 PM GMT+7  A**************                 Cowok   \n",
+       "1  2020/10/31 3:39:36 PM GMT+7            L****                 Cewek   \n",
+       "2  2020/10/31 3:39:38 PM GMT+7     Y***********                 Cowok   \n",
+       "3  2020/10/31 3:39:42 PM GMT+7             a***                 Cowok   \n",
+       "4  2020/10/31 3:39:43 PM GMT+7            B****                 Cowok   \n",
+       "\n",
+       "   Seberapa penting quality time bareng calon pacar untuk kamu?  \\\n",
+       "0                                                  5              \n",
+       "1                                                  5              \n",
+       "2                                                  4              \n",
+       "3                                                  5              \n",
+       "4                                                  5              \n",
+       "\n",
+       "   Seberapa penting physical touch sama calon pacar untuk kamu?  \\\n",
+       "0                                                  5              \n",
+       "1                                                  5              \n",
+       "2                                                  3              \n",
+       "3                                                  5              \n",
+       "4                                                  5              \n",
+       "\n",
+       "   Seberapa penting word of affirmation dari calon pacar untuk kamu?  \\\n",
+       "0                                                  4                   \n",
+       "1                                                  3                   \n",
+       "2                                                  4                   \n",
+       "3                                                  5                   \n",
+       "4                                                  5                   \n",
+       "\n",
+       "   Seberapa penting dapet kado dari calon pacar untuk kamu?  \\\n",
+       "0                                                  1          \n",
+       "1                                                  2          \n",
+       "2                                                  4          \n",
+       "3                                                  2          \n",
+       "4                                                  2          \n",
+       "\n",
+       "   Seberapa penting bantuan dari calon pacar untuk kamu?  \n",
+       "0                                                  3      \n",
+       "1                                                  2      \n",
+       "2                                                  4      \n",
+       "3                                                  3      \n",
+       "4                                                  4      "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('takemeout.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3. Data Cleansing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>Siapa nama kamu?</th>\n",
+       "      <th>Cewek atau cowok nih?</th>\n",
+       "      <th>Seberapa penting quality time bareng calon pacar untuk kamu?</th>\n",
+       "      <th>Seberapa penting physical touch sama calon pacar untuk kamu?</th>\n",
+       "      <th>Seberapa penting word of affirmation dari calon pacar untuk kamu?</th>\n",
+       "      <th>Seberapa penting dapet kado dari calon pacar untuk kamu?</th>\n",
+       "      <th>Seberapa penting bantuan dari calon pacar untuk kamu?</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A**************</td>\n",
+       "      <td>Cowok</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>L****</td>\n",
+       "      <td>Cewek</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Y***********</td>\n",
+       "      <td>Cowok</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>a***</td>\n",
+       "      <td>Cowok</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>B****</td>\n",
+       "      <td>Cowok</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Siapa nama kamu? Cewek atau cowok nih?  \\\n",
+       "0  A**************                 Cowok   \n",
+       "1            L****                 Cewek   \n",
+       "2     Y***********                 Cowok   \n",
+       "3             a***                 Cowok   \n",
+       "4            B****                 Cowok   \n",
+       "\n",
+       "   Seberapa penting quality time bareng calon pacar untuk kamu?  \\\n",
+       "0                                                  5              \n",
+       "1                                                  5              \n",
+       "2                                                  4              \n",
+       "3                                                  5              \n",
+       "4                                                  5              \n",
+       "\n",
+       "   Seberapa penting physical touch sama calon pacar untuk kamu?  \\\n",
+       "0                                                  5              \n",
+       "1                                                  5              \n",
+       "2                                                  3              \n",
+       "3                                                  5              \n",
+       "4                                                  5              \n",
+       "\n",
+       "   Seberapa penting word of affirmation dari calon pacar untuk kamu?  \\\n",
+       "0                                                  4                   \n",
+       "1                                                  3                   \n",
+       "2                                                  4                   \n",
+       "3                                                  5                   \n",
+       "4                                                  5                   \n",
+       "\n",
+       "   Seberapa penting dapet kado dari calon pacar untuk kamu?  \\\n",
+       "0                                                  1          \n",
+       "1                                                  2          \n",
+       "2                                                  4          \n",
+       "3                                                  2          \n",
+       "4                                                  2          \n",
+       "\n",
+       "   Seberapa penting bantuan dari calon pacar untuk kamu?  \n",
+       "0                                                  3      \n",
+       "1                                                  2      \n",
+       "2                                                  4      \n",
+       "3                                                  3      \n",
+       "4                                                  4      "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df.iloc[:,1:]\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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>Initial</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Qt</th>\n",
+       "      <th>Phys</th>\n",
+       "      <th>Aff</th>\n",
+       "      <th>Gift</th>\n",
+       "      <th>Help</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A</td>\n",
+       "      <td>Cowok</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>L</td>\n",
+       "      <td>Cewek</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Y</td>\n",
+       "      <td>Cowok</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>a</td>\n",
+       "      <td>Cowok</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>B</td>\n",
+       "      <td>Cowok</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Initial Gender  Qt  Phys  Aff  Gift  Help\n",
+       "0       A  Cowok   5     5    4     1     3\n",
+       "1       L  Cewek   5     5    3     2     2\n",
+       "2       Y  Cowok   4     3    4     4     4\n",
+       "3       a  Cowok   5     5    5     2     3\n",
+       "4       B  Cowok   5     5    5     2     4"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns = ['Initial','Gender','Qt','Phys','Aff','Gift','Help']\n",
+    "df['Initial']=df['Initial'].str[0]\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 4. Data Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.1 Data splitting between Gender"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "male_df = df[df['Gender']=='Cowok']\n",
+    "female_df = df[df['Gender']=='Cewek']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.2 Mean Score by Gender\n",
+    "\n",
+    "### 4.2.1 Quality Time Mean Score by Gender"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Quality Time Mean Score')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xaxes = ['Cowok','Cewek']\n",
+    "yaxes = [male_df['Qt'].mean(),female_df['Qt'].mean()]\n",
+    "plt.bar(xaxes,yaxes)\n",
+    "plt.title('Quality Time')\n",
+    "plt.ylabel('Quality Time Mean Score')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.2.2 Physical Touch Mean Score by Gender"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Physical Touch Mean Score')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xaxes = ['Cowok','Cewek']\n",
+    "yaxes = [male_df['Phys'].mean(),female_df['Phys'].mean()]\n",
+    "plt.bar(xaxes,yaxes)\n",
+    "plt.title('Physical Touch')\n",
+    "plt.ylabel('Physical Touch Mean Score')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.2.3 Affirmation Wording Mean Score by Gender"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Affirmation Word Mean Score')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xaxes = ['Cowok','Cewek']\n",
+    "yaxes = [male_df['Aff'].mean(),female_df['Aff'].mean()]\n",
+    "plt.bar(xaxes,yaxes)\n",
+    "plt.title('Affirmation Word')\n",
+    "plt.ylabel('Affirmation Word Mean Score')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.2.4 Gift from Couple Mean Score by Gender"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Gift from Couple Mean Score')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xaxes = ['Cowok','Cewek']\n",
+    "yaxes = [male_df['Gift'].mean(),female_df['Gift'].mean()]\n",
+    "plt.bar(xaxes,yaxes)\n",
+    "plt.title('Gift from Couple')\n",
+    "plt.ylabel('Gift from Couple Mean Score')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.2.5 Service Help from Couple Mean Score by Gender"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Service Help from Couple')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEICAYAAABS0fM3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAbs0lEQVR4nO3de5wddX3/8debECTc5JJFYhISxSgFKrcl4SYERAWKgohc6qVcJEKxSKFYSy2UVn+WUqmViCHK9VEIBRFNNSlSEQy3QBLCJSAaKDSRCBuQ3EjBxM/vj/kemJycc3ayu3OW3Xk/H4957Fy+853P7M6ez5nvzHxHEYGZmVXXRv0dgJmZ9S8nAjOzinMiMDOrOCcCM7OKcyIwM6s4JwIzs4pzIrC3DEkLJE3s7zjyJJ0s6Z4eritJ10j6naQH+zq2tzJJd0n6XH/HYcU4EVhLkg6UdJ+kZZJelnSvpH3K2FZE7BoRd/VlnY0+kCRNlLS4L7fTxIHAh4BRETG+Ddtbj6TxkmZIeiX9/R6UdEp/xGJvXU4E1pSkrYAfA5cD2wIjgYuB13pQ18Z9G92AMAZ4NiJWNVpY9u9E0n7AncDdwHuA7YAzgSPK3K4NPE4E1sp7ASJiWkSsjYjVEfHTiHi0VkDSqZKeTM0ft0sak1sWks6S9Gvg15KmSPqX/AYk/UjSuWn8WUmHpfEhki6Q9LSkFZLmShqdlu0s6Y70DfcpScf3ZiclvV3SVZKWSPqNpK9KGtKkbEg6W9IzkpZKulTSev9Hkk4DvgfsJ2mlpItrZyKS/lrSb4FrJL1N0jclPZ+Gb0p6W6qjVv5Lkl5M8R0j6UhJv0r7f0GLXbsUuC4iLomIpZGZGxFv/L4knS5pYapruqR3pvlj075unCv7xtlVajK7V9Ll6Wzxl5I+2OJ33PQ4sf7nRGCt/ApYK+k6SUdI2ia/UNIxwAXAsUAHMAuYVlfHMcAEYBfgRuAESUrrbwN8GLipwbbPBU4CjgS2Ak4FXpW0OXBHqmv7VOYKSbv2Yj+vA9aQfWveM8XUqn3740AnsBdwdIptHRFxFXAGcH9EbBERF6VFO5CdXY0BJgF/C+wL7AHsDowHvpKragdgU7KzsQuB7wKfBvYGPgBcKOnd9duXtBmwH/D9Zjsh6VDg68DxwAjgORr/LZqZADwDDAcuAn4gadsG2ylynFh/iggPHpoOwB8B1wKLyT4spwPvSMtmAqflym4EvAqMSdMBHJpbLuB/gYPS9OnAnbnlzwKHpfGngKMbxHMCMKtu3pXARU3ivyvF9EpuWAksTsvfQdbUNSy3zknAz9P4ycA9uWUBHJ6b/nPgZ022Xb/uROB1YNPcvKeBI3PTHyFrTqqVXw0MSdNbpu1PyJWfCxzTYNsjU9mdW/xtrwL+OTe9BfB7YGwaAti47nf5udy+PQ8ot/xB4DMNyrY8Tjz0/+AzAmspIp6MiJMjYhSwG/BO4Jtp8Rjg39KFyFeAl8k+7EfmqliUqyvIvnGelGb9KXBDk02PJvuQrDcGmFDbZtrup8i+OTdzdkRsXRuAo+rqGwosydV3JdnZRjOLcuPPkf1OiuqKiP/LTb8z1dGsvpciYm0aX51+vpBbvprsA7ze74A/kH3Tb2adbUfESuAl1v37tfKb9Detafa7KHKcWD9yIrDCIuKXZGcHu6VZi4DP5z9kI2JYRNyXX62ummnAcamNeAJwa5PNLQJ2ajL/7rptbhERZ/ZwtxaRnREMz9W3VUS0amoanRvfkeybcVH1v4/nyT4oe1pf441EvArcD3yiRbF1tp2a3bYDfgPULnBvlitfn2xH1pr5kmaxFzlOrB85EVhT6aLseZJGpenRZN/mH0hFpgB/U2ufTxddP9mqzoh4GOgiu5B6e0S80qTo94B/lDROmfdL2o7sLqb3SvqMpKFp2EfSH/VkHyNiCfBT4BuStpK0kaSdJB3cYrXzJW2Tfh9fBP6jJ9tOpgFfkdQhaTjZdYB/70V9eV8CTpZ0fvrdIWl3SbXrADcCp0jaI12g/n/A7Ih4NiK6yBLCp5VduD+V9RPz9sDZ6W/wSbJmxBkN4tjg48Tay4nAWllB9q19tqRVZAngceA8gIi4DbgEuEnS8rSsyK2J04DDyD6ImrkMuJnsQ3o5WXv2sIhYQXYx90Syb5+/TTG8bUN3LuezwCbAE2RNKt+ndZPKj8ja5ucDP0mx9dRXgTnAo8BjwLw0r9fSN+5D0/CMpJeBqaQP64j4GfB3ZGdlS8g+6E/MVXE6cD5Zc9GuQP03+NnAOGAp8DXguIh4qUEcPT1OrE20bhOfmbUiKYBxEbGwv2PpT5JOJrsYfGB/x2K95zMCM7OKcyIwM6s4Nw2ZmVWczwjMzCpuwHUENnz48Bg7dmx/h2FmNqDMnTt3aUR0NFo24BLB2LFjmTNnTn+HYWY2oEh6rtkyNw2ZmVWcE4GZWcU5EZiZVZwTgZlZxTkRmJlVnBOBmVnFORGYmVWcE4GZWcU5EZiZVdyAe7K4N8Z++Sf9HYK9hT37T3/S3yGY9QufEZiZVZwTgZlZxVWqachsIHATpjVTVvOlzwjMzCrOicDMrOKcCMzMKs6JwMys4pwIzMwqzonAzKzinAjMzCqutEQgaVNJD0p6RNICSRc3KDNR0jJJ89NwYVnxmJlZY2U+UPYacGhErJQ0FLhH0syIeKCu3KyIOKrEOMzMrIXSEkFEBLAyTQ5NQ5S1PTMz65lSrxFIGiJpPvAicEdEzG5QbL/UfDRT0q5N6pkkaY6kOV1dXWWGbGZWOaUmgohYGxF7AKOA8ZJ2qysyDxgTEbsDlwM/bFLP1IjojIjOjo6OMkM2M6ucttw1FBGvAHcBh9fNXx4RK9P4DGCopOHtiMnMzDJl3jXUIWnrND4MOAz4ZV2ZHSQpjY9P8bxUVkxmZra+Mu8aGgFcJ2kI2Qf8zRHxY0lnAETEFOA44ExJa4DVwInpIrOZmbVJmXcNPQrs2WD+lNz4ZGByWTGYmVn3/GSxmVnFORGYmVWcE4GZWcU5EZiZVZwTgZlZxTkRmJlVnBOBmVnFORGYmVWcE4GZWcU5EZiZVZwTgZlZxTkRmJlVnBOBmVnFORGYmVWcE4GZWcU5EZiZVVzhRCBp8zIDMTOz/tFtIpC0v6QngCfT9O6Srig9MjMza4siZwT/CnyE9FL5iHgEOKjMoMzMrH0KNQ1FxKK6WWu7W0fSppIelPSIpAWSLm5QRpK+JWmhpEcl7VUwbjMz6yNFXl6/SNL+QEjaBDib1EzUjdeAQyNipaShwD2SZkbEA7kyRwDj0jAB+E76aWZmbVLkjOAM4CxgJLAY2CNNtxSZlWlyaBqirtjRwPWp7APA1pJGFA3ezMx6r9szgohYCnyqJ5VLGgLMBd4DfDsiZtcVGQnkm50Wp3lL6uqZBEwC2HHHHXsSipmZNdE0EUi6nPW/wb8hIs7urvKIWAvsIWlr4DZJu0XE4/nNNFqtQT1TgakAnZ2dTWMyM7MN1+qMYE5fbSQiXpF0F3A4kE8Ei4HRuelRwPN9tV0zM+te00QQEdflpyVtlc2OFUUqltQB/D4lgWHAYcAldcWmA1+QdBPZReJlEbEEMzNrm26vEUjqBK4Btswm9QpwakTM7WbVEcB16TrBRsDNEfFjSWcARMQUYAZwJLAQeBU4pcd7YmZmPVLk9tGrgT+PiFkAkg4kSwzvb7VSRDwK7Nlg/pTceFDgDiQzMytPkdtHV9SSAEBE3AMUah4yM7O3viJnBA9KuhKYRnZHzwnAXbWngCNiXonxmZlZyYokgj3Sz4vq5u9PlhgO7dOIzMysrYo8UHZIOwIxM7P+UeSuoQsbzY+If+j7cMzMrN2KNA2tyo1vChxFsU7nzMxsACjSNPSN/LSkfyF7EMzMzAaBnryzeDPg3X0diJmZ9Y8i1wge482O4IYAHYCvD5iZDRJFrhEclRtfA7wQEWtKisfMzNqs26ahiHgO2Br4KPBxYJeygzIzs/bpNhFI+iJwA7B9Gm6Q9BdlB2ZmZu1RpGnoNGBCRKwCkHQJcD9weZmBmZlZexS5a0jA2tz0Whq/WczMzAagImcE1wCzJd2Wpo8BriovJDMza6ciD5Rdll4zeSDZmcApEfFw2YGZmVl7tHp5/T7A8IiYmbqanpfmf0zSRgXeUGZmZgNAq2sEl9K4T6En0jIzMxsEWiWC7SLi2fqZEbEQ2K67iiWNlvRzSU9KWpBuQ60vM1HSMknz09Cwp1MzMytPq2sEw1os27xA3WuA8yJinqQtgbmS7oiIJ+rKzYqIoxqsb2ZmbdDqjOC/JX1N0jq3ikq6GLizu4ojYkntNZYRsYKsmWlkb4I1M7O+1yoRnEfWy+hCSbemYSHwPuDcDdmIpLHAnsDsBov3k/SIpJmSdm2y/iRJcyTN6erq2pBNm5lZN5o2DaUniU+S9G6g9gG9ICKe2ZANSNoCuBU4JyKW1y2eB4yJiJWSjgR+CIxrEMtUYCpAZ2dn1C83M7OeK/IcwTPABn3410gaSpYEboiIHzSoe3lufIakKyQNj4ilPdmemZltuJ68mKaQdG3hKuDJiLisSZkdatcgJI1P8bxUVkxmZra+Il1M9NQBwGeAxyTNT/MuAHYEiIgpwHHAmZLWAKuBEyPCTT9mZm1UKBFI2gYYnS9fuyOomYi4h246p4uIycDkIjGYmVk5iryq8h+Bk4GnefOVlQEcWl5YZmbWLkXOCI4HdoqI18sOxszM2q/IxeLHyV5VaWZmg1CRM4KvAw9Lehx4rTYzIj5WWlRmZtY2RRLBdcAlwGPAH8oNx8zM2q1IIlgaEd8qPRIzM+sXRRLBXElfB6azbtNQy9tHzcxsYCiSCPZMP/fNzfPto2Zmg0SRvoYOaUcgZmbWP7q9fVTS2yVdVusGWtI3JL29HcGZmVn5ijxHcDWwguzBsuOB5cA1ZQZlZmbtU+QawU4R8Ync9MW5TuTMzGyAK3JGsFrSgbUJSQeQ9RRqZmaDQJEzgjOA63PXBX4H/Fl5IZmZWTu1TASShgCfjojdJW0F675VzMzMBr6WiSAi1kraO407AZiZDUJFmoYeljQduAVYVZvZ6B3EZmY28BRJBNuSvUc4/yRxAE4EZmaDQNNEIOmSiPhrYEZE3NLGmMzMrI1a3T56pKShwN/0pGJJoyX9XNKTkhZI+mKDMpL0LUkLJT0qaa+ebMvMzHquVdPQfwFLgc0l5S8UC4iI2KqbutcA50XEPElbkvViekdEPJErcwQwLg0TgO+kn2Zm1iZNzwgi4vyIeDvwk4jYKjdsWSAJEBFLal1VR8QK4ElgZF2xo4HrI/MAsLWkET3fHTMz21DdPlkcEUf3diOSxpJ1Zz27btFIYFFuejHrJwskTap1etfV1dXbcMzMLKdIFxO9ImkL4FbgnAbPIqjBKrHejIipEdEZEZ0dHR1lhGlmVlmlJoJ0sflW4IYmzx0sBkbnpkcBz5cZk5mZratQIpC0iaT3S/pjSZsUXEfAVcCTEXFZk2LTgc+mu4f2BZZFxJJCkZuZWZ/o9oEySX8CTAGeJmvKeZekz0fEzG5WPQD4DPBYrtvqC4AdASJiCjADOBJYCLwKnNKTnTAzs54r8mTxN4BDImIhgKSdgJ8ALRNBRNxD42sA+TIBnFUsVDMzK0ORpqEXa0kgeQZ4saR4zMyszYqcESyQNAO4meyOnk8CD0k6Ftz5nJnZQFckEWwKvAAcnKa7yDqi+yjufM7MbMDrNhFEhC/gmpkNYq16H72cBg931UTE2aVEZGZmbdXqjGBO26IwM7N+0zQRRMR1+WlJm0fEqmblzcxsYOr29lFJ+0l6gqz3UCTtLumK0iMzM7O2KPIcwTeBj5C9rpKIeAQ4qMygzMysfQr1NRQRi+pmrS0hFjMz6wdFniNYJGl/IFKHc2eTmonMzGzgK3JGcAZZf0AjybqN3gP3D2RmNmgUeaBsKfCpNsRiZmb9wA+UmZlVXNEHyi4GLio5FjMz6weFHiiTdE79A2ZmZjY4FH1ncdMmIjMzG9hKfXm9mZm99TVNBJJWSFouaTnw/tp4bX53FUu6WtKLkh5vsnyipGWS5qfhwl7sh5mZ9VCrawRb9rLua4HJwPUtysyKiKN6uR0zM+uF0pqGIuIXwMtl1W9mZn2jv68R7CfpEUkzJe3arJCkSZLmSJrT1dXVzvjMzAa9/kwE84AxEbE7cDnww2YFI2JqRHRGRGdHR0fbAjQzq4JCiUDSGEmHpfFhknp7/YCIWB4RK9P4DGCopOG9rdfMzDZMkRfTnA58H7gyzRpFi2/vRUnaQZLS+PgUy0u9rdfMzDZMkW6ozwLGA7MBIuLXkrbvbiVJ04CJwHBJi8m6qBia6pgCHAecKWkNsBo4MSL84JqZWZsVSQSvRcTr6cs7kjamwJPGEXFSN8snk91eamZm/ajINYK7JV0ADJP0IeAW4D/LDcvMzNqlSCL4MtAFPAZ8HpgBfKXMoMzMrH2KNA0NA66OiO8CSBqS5r1aZmBmZtYeRc4Ifkb2wV8zDPjvcsIxM7N2K5IINq3d7w+QxjcrLyQzM2unIolglaS9ahOS9ia73dPMzAaBItcIzgFukfR8mh4BnFBeSGZm1k7dJoKIeEjSzsD7AAG/jIjflx6ZmZm1RdNEIOnQiLhT0rF1i8ZJIiJ+UHJsZmbWBq3OCA4G7gQ+2mBZAE4EZmaDQKs3lF2URj8XEWvbFI+ZmbVZkbuG/kfSVEkfrPUWamZmg0eRRPA+sgfIziJLCpMlHVhuWGZm1i7dJoKIWB0RN0fEscCewFbA3aVHZmZmbVH0DWUHS7qC7PWSmwLHlxqVmZm1TbfPEUj6H2A+cDNwfkSsKj0qMzNrm5aJIPU0ek1E/EOb4jEzszZr2TSUbhs9pE2xmJlZPyjS19B9kiYD/wG80SwUEfNKi8rMzNqmSCLYP/3MNw8FcGirlSRdDRwFvBgRuzVYLuDfgCPJXnJzspOLmVn7Fel0rqdNQ9eSvZz++ibLjwDGpWEC8J3008zM2qjb20clvUPSVZJmpuldJJ3W3XoR8Qvg5RZFjgauj8wDwNaSRhQN3MzM+kaR5wiuBW4H3pmmf0X2joLeGgksyk0vTvPWI2mSpDmS5nR1dfXBps3MrKZIIhgeETcDfwCIiDVAX3RC16jfomhUMCKmRkRnRHR2dHT0wabNzKym6KsqtyN9SEvaF1jWB9teDIzOTY8Cnm9S1szMSlLkrqFzgenATpLuBTqA4/pg29OBL0i6iewi8bKIWNIH9ZqZ2QYoctfQPEkH8+arKp8q8qpKSdOAicBwSYuBi4Chqc4pwAyyW0cXkt0+ekoP98HMzHqh1asq9wEWRcRvI2KNpL2BTwDPSfr7iGh1RxARcVI3y4Osa2szM+tHra4RXAm8DiDpIOCfyJ4JWAZMLT80MzNrh1ZNQ0Ny3/pPAKZGxK3ArZLmlx+amZm1Q6szgiGSaonig2Qvsq8pcpHZzMwGgFYf6NOAuyUtBVYDswAkvYe+uX3UzMzeApomgoj4mqSfASOAn6aLu5CdRfxFO4IzM7PytWziSX0A1c/7VXnhmJlZuxV6Z7GZmQ1eTgRmZhXnRGBmVnFOBGZmFedEYGZWcU4EZmYV50RgZlZxTgRmZhXnRGBmVnFOBGZmFedEYGZWcU4EZmYVV2oikHS4pKckLZT05QbLJ0paJml+Gi4sMx4zM1tfaS+YkTQE+DbwIWAx8JCk6RHxRF3RWRFxVFlxmJlZa2WeEYwHFkbEMxHxOnATcHSJ2zMzsx4oMxGMBBblphenefX2k/SIpJmSdm1UkaRJkuZImtPV1VVGrGZmlVVmIlCDeVE3PQ8YExG7A5cDP2xUUURMjYjOiOjs6Ojo4zDNzKqtzESwGBidmx4FPJ8vEBHLI2JlGp8BDJU0vMSYzMysTpmJ4CFgnKR3SdoEOBGYni8gaQdJSuPjUzwvlRiTmZnVKe2uoYhYI+kLwO3AEODqiFgg6Yy0fApwHHCmpDXAauDEiKhvPjIzsxKVlgjgjeaeGXXzpuTGJwOTy4zBzMxa85PFZmYV50RgZlZxTgRmZhXnRGBmVnFOBGZmFedEYGZWcU4EZmYV50RgZlZxTgRmZhXnRGBmVnFOBGZmFedEYGZWcU4EZmYV50RgZlZxTgRmZhXnRGBmVnFOBGZmFedEYGZWcU4EZmYVV2oikHS4pKckLZT05QbLJelbafmjkvYqMx4zM1tfaYlA0hDg28ARwC7ASZJ2qSt2BDAuDZOA75QVj5mZNVbmGcF4YGFEPBMRrwM3AUfXlTkauD4yDwBbSxpRYkxmZlZn4xLrHgksyk0vBiYUKDMSWJIvJGkS2RkDwEpJT/VtqJU1HFja30G8VeiS/o7AGvAxmtPLY3RMswVlJgI1mBc9KENETAWm9kVQ9iZJcyKis7/jMGvGx2h7lNk0tBgYnZseBTzfgzJmZlaiMhPBQ8A4Se+StAlwIjC9rsx04LPp7qF9gWURsaS+IjMzK09pTUMRsUbSF4DbgSHA1RGxQNIZafkUYAZwJLAQeBU4pax4rCE3t9lbnY/RNlDEek3yZmZWIX6y2Mys4pwIzMwqzolggJO0g6SbJD0t6QlJMyS9t4+3MVbS431Zp1VHO47RtJ1nJQ3v63qrwIlgAJMk4DbgrojYKSJ2AS4A3tG/kZllfIwODE4EA9shwO/THVgARMR84B5Jl0p6XNJjkk4AkHSFpI+l8dskXZ3GT5P01TR+blrvcUnn1G9Q0rslPSxpn3bsoA14DY/RiJgl6XxJD6UOJy8GkPQlSWen8X+VdGca/6Ckf0/jH5Z0v6R5km6RtEV+g5KGSfovSae3bS8HOCeCgW03YG6D+ccCewC7A4cBl6Y+nH4BfCCVGUnWGSDAgcAsSXuT3cI7AdgXOF3SnrVKJb0PuBU4JSIe6vvdsUGo4TEq6cNknU2OJztW95Z0EOseo53AFpKG8uYxOhz4CnBYROwFzAHOzVW9BfCfwI0R8d1ydmnwcSIYnA4EpkXE2oh4Abgb2AeYBXwg9QL7BPBCShD7Afel9W6LiFURsRL4AW/+U3YAPwI+nc46zHrjw2l4GJgH7EyWGOaSJYUtgdeA+8kSwgfIjt99yb7A3CtpPvBnrNuHzo+AayLi+jbtx6BQZl9DVr4FwHEN5jfqw4mI+I2kbYDDyb55bQscD6yMiBWpPbeZZWQdBB6QtmtWRKtj9OsRceV6C6Rnyc5M7wMeJWte2gl4Mv28IyJOarK9e4EjJN0YfkiqMJ8RDGx3Am/Lt4WmtvvfASdIGiKpAzgIeDAVuR84hywRzAL+Kv0kzTtG0maSNgc+nlv2OnAMWZcgf1rubtkg0uwYXQ6cWmvflzRS0vapyC/IjsvaMXoGMD99sD8AHCDpPWm9zeruQLoQeAm4otzdGlycCAaw9I/xceBD6da8BcDfAzeSfZN6hOwf8UsR8du02ixg44hYSHZKvm2aR0TMA64lSxqzge9FxMO57a0CjgL+UlL9uyXM1tPNMXojcL+kx4DvA1um1WYBI4D7U9Pm//HmMdoFnAxMk/QoWWLYuW6z5wCbSvrnEndtUHEXE2ZmFeczAjOzinMiMDOrOCcCM7OKcyIwM6s4JwIzs4pzIjAzqzgnAjOzivv/ZOTBqSznWe0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xaxes = ['Cowok','Cewek']\n",
+    "yaxes = [male_df['Help'].mean(),female_df['Help'].mean()]\n",
+    "plt.bar(xaxes,yaxes)\n",
+    "plt.title('Service Help from Couple')\n",
+    "plt.ylabel('Service Help from Couple')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.3 Statistical Analysis for Each Gender DataSet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 4.3.1 Male DataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "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>Qt</th>\n",
+       "      <th>Phys</th>\n",
+       "      <th>Aff</th>\n",
+       "      <th>Gift</th>\n",
+       "      <th>Help</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>81.000000</td>\n",
+       "      <td>81.000000</td>\n",
+       "      <td>81.000000</td>\n",
+       "      <td>81.000000</td>\n",
+       "      <td>81.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>4.185185</td>\n",
+       "      <td>3.296296</td>\n",
+       "      <td>3.827160</td>\n",
+       "      <td>2.740741</td>\n",
+       "      <td>3.765432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.163090</td>\n",
+       "      <td>1.269296</td>\n",
+       "      <td>1.148805</td>\n",
+       "      <td>1.301708</td>\n",
+       "      <td>1.087102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Qt       Phys        Aff       Gift       Help\n",
+       "count  81.000000  81.000000  81.000000  81.000000  81.000000\n",
+       "mean    4.185185   3.296296   3.827160   2.740741   3.765432\n",
+       "std     1.163090   1.269296   1.148805   1.301708   1.087102\n",
+       "min     1.000000   1.000000   1.000000   1.000000   1.000000\n",
+       "25%     4.000000   3.000000   3.000000   2.000000   3.000000\n",
+       "50%     5.000000   3.000000   4.000000   3.000000   4.000000\n",
+       "75%     5.000000   4.000000   5.000000   4.000000   5.000000\n",
+       "max     5.000000   5.000000   5.000000   5.000000   5.000000"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "male_df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 4.3.1 Female DataSet"
+   ]
+  },
+  {
+   "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>Qt</th>\n",
+       "      <th>Phys</th>\n",
+       "      <th>Aff</th>\n",
+       "      <th>Gift</th>\n",
+       "      <th>Help</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.750000</td>\n",
+       "      <td>2.650000</td>\n",
+       "      <td>3.300000</td>\n",
+       "      <td>2.700000</td>\n",
+       "      <td>3.700000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.371707</td>\n",
+       "      <td>1.496487</td>\n",
+       "      <td>1.380313</td>\n",
+       "      <td>1.454575</td>\n",
+       "      <td>1.380313</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.750000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>4.250000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Qt       Phys        Aff       Gift       Help\n",
+       "count  20.000000  20.000000  20.000000  20.000000  20.000000\n",
+       "mean    3.750000   2.650000   3.300000   2.700000   3.700000\n",
+       "std     1.371707   1.496487   1.380313   1.454575   1.380313\n",
+       "min     1.000000   1.000000   1.000000   1.000000   1.000000\n",
+       "25%     3.000000   1.000000   2.750000   1.000000   3.000000\n",
+       "50%     4.000000   2.000000   3.000000   3.000000   4.000000\n",
+       "75%     5.000000   4.000000   4.250000   4.000000   5.000000\n",
+       "max     5.000000   5.000000   5.000000   5.000000   5.000000"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "female_df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.4 Correlation Analysis for each parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.4.1 Correlation between parameter of Male Dataset "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1ad96dd9548>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(male_df.corr(), vmin = 0, vmax = 1, annot = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.4.2 Correlation between parameter of Feale Dataset "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1ad96d391c8>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(female_df.corr(), vmin = 0, vmax = 1, annot = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 5. Conclusion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Several insight that can be concluded from the TakeMeOut with respondent of 81 Males and 20 Females dataset are:\n",
+    "\n",
+    "1. Most of the mean parameters from male are higher than female such as:\n",
+    "\n",
+    "    a. Quality Time (4.2 vs 3.75)\n",
+    "    \n",
+    "    b. Physical Touch (3.3 vs 2.65)\n",
+    "    \n",
+    "    c. Word of Affirmation (3.82 vs 3.3)\n",
+    "    \n",
+    "    d. Gift from Couple (2.74 vs 2.7)\n",
+    "    \n",
+    "    e. Service Help from Couple (3.77 vs 3.7)\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "2. Parameters that are correlatable from both Male and Female dataset are:\n",
+    "\n",
+    "    a. Most correlatable from Male Dataset was between Quality Time and Word of Affirmation, but this correlation still low in R^2 -value (0.59)\n",
+    "    \n",
+    "    b. Least correlatable from Male Dataset was between Physical Touch and Service Help from Couple.\n",
+    "    \n",
+    "    c. Most correlatable from Female Dataset was also between Quality Time and Word of Affirmation with higher correlation than Male Dataset with R^2-value (0.85). Means the more Female demand quality time, the more she would like to have Word of Affirmation and vice versa\n",
+    "    \n",
+    "    d. Least correlatable from Female Dataset was also between Physical Touch and Service Help from Couple.\n",
+    "    \n",
+    "3. Correlation was still based on linear correlation, not from causation of each parameter. Further investigation was recommended\n",
+    "    "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:root] *",
+   "language": "python",
+   "name": "conda-root-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/takemeout.csv b/EksplorasiData_Wilson Wiranda/takemeout.csv
similarity index 98%
rename from takemeout.csv
rename to EksplorasiData_Wilson Wiranda/takemeout.csv
index cf476a7..df46942 100644
--- a/takemeout.csv
+++ b/EksplorasiData_Wilson Wiranda/takemeout.csv	
@@ -1,102 +1,102 @@
-Timestamp,Siapa nama kamu?,Cewek atau cowok nih?,Seberapa penting quality time bareng calon pacar untuk kamu?,Seberapa penting physical touch sama calon pacar untuk kamu?,Seberapa penting word of affirmation dari calon pacar untuk kamu?,Seberapa penting dapet kado dari calon pacar untuk kamu?,Seberapa penting bantuan dari calon pacar untuk kamu?
-2020/10/31 3:39:25 PM GMT+7,A**************,Cowok,5,5,4,1,3
-2020/10/31 3:39:36 PM GMT+7,L****,Cewek,5,5,3,2,2
-2020/10/31 3:39:38 PM GMT+7,Y***********,Cowok,4,3,4,4,4
-2020/10/31 3:39:42 PM GMT+7,a***,Cowok,5,5,5,2,3
-2020/10/31 3:39:43 PM GMT+7,B****,Cowok,5,5,5,2,4
-2020/10/31 3:39:44 PM GMT+7,M*********,Cowok,5,5,4,2,2
-2020/10/31 3:39:45 PM GMT+7,F***********************************,Cowok,4,2,5,3,4
-2020/10/31 3:39:47 PM GMT+7,F***,Cowok,3,3,4,2,4
-2020/10/31 3:39:50 PM GMT+7,R******************,Cowok,5,2,4,1,4
-2020/10/31 3:40:05 PM GMT+7,s******,Cowok,4,1,1,5,5
-2020/10/31 3:40:07 PM GMT+7,A,Cowok,5,4,5,2,3
-2020/10/31 3:40:08 PM GMT+7,R**,Cowok,3,4,3,3,5
-2020/10/31 3:40:12 PM GMT+7,D***,Cowok,5,4,3,3,4
-2020/10/31 3:40:14 PM GMT+7,F***,Cowok,1,3,5,1,5
-2020/10/31 3:40:20 PM GMT+7,P****,Cowok,4,3,4,5,5
-2020/10/31 3:40:21 PM GMT+7,A*,Cowok,4,3,5,4,4
-2020/10/31 3:40:22 PM GMT+7,M***********************,Cowok,1,1,1,1,1
-2020/10/31 3:40:25 PM GMT+7,J****************,Cowok,5,4,5,3,5
-2020/10/31 3:40:27 PM GMT+7,J***,Cowok,5,3,4,3,4
-2020/10/31 3:40:28 PM GMT+7,M**************,Cewek,5,5,5,5,5
-2020/10/31 3:40:31 PM GMT+7,F*************,Cewek,4,2,3,1,3
-2020/10/31 3:40:31 PM GMT+7,b***********,Cowok,5,4,3,2,3
-2020/10/31 3:40:31 PM GMT+7,u***,Cowok,5,3,3,5,5
-2020/10/31 3:40:34 PM GMT+7,B***,Cewek,5,2,4,5,4
-2020/10/31 3:40:35 PM GMT+7,h***,Cowok,5,4,4,4,3
-2020/10/31 3:40:36 PM GMT+7,E***,Cewek,3,2,3,1,5
-2020/10/31 3:40:36 PM GMT+7,q*,Cowok,5,5,5,5,5
-2020/10/31 3:40:37 PM GMT+7,v****,Cewek,3,3,3,3,3
-2020/10/31 3:40:40 PM GMT+7,R****,Cowok,5,3,3,2,3
-2020/10/31 3:40:41 PM GMT+7,D****,Cowok,4,5,4,2,4
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-2020/10/31 3:40:42 PM GMT+7,B**************,Cowok,3,2,4,1,5
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-2020/10/31 3:40:44 PM GMT+7,R****,Cowok,5,3,5,5,5
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-2020/10/31 3:40:49 PM GMT+7,L*********,Cowok,4,3,3,5,3
-2020/10/31 3:40:51 PM GMT+7,I***,Cowok,4,5,5,3,5
-2020/10/31 3:40:51 PM GMT+7,P******,Cowok,3,3,3,2,4
-2020/10/31 3:40:52 PM GMT+7,G**,Cowok,4,3,3,4,4
-2020/10/31 3:40:54 PM GMT+7,S**,Cowok,5,3,5,4,5
-2020/10/31 3:40:56 PM GMT+7,V***,Cowok,5,2,3,2,4
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-2020/10/31 3:40:58 PM GMT+7,A********************,Cewek,1,1,1,1,1
-2020/10/31 3:41:00 PM GMT+7,H***********,Cewek,4,1,3,1,5
-2020/10/31 3:41:02 PM GMT+7,R*************,Cowok,4,3,4,3,4
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-2020/10/31 3:41:04 PM GMT+7,I*,Cowok,3,1,1,1,4
-2020/10/31 3:41:05 PM GMT+7,i****,Cowok,5,1,5,1,3
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-2020/10/31 3:41:08 PM GMT+7,r******,Cowok,5,5,5,5,5
-2020/10/31 3:41:09 PM GMT+7,H****,Cewek,1,1,1,1,1
-2020/10/31 3:41:09 PM GMT+7,A*************,Cowok,4,4,5,3,5
-2020/10/31 3:41:09 PM GMT+7,K**********,Cowok,4,3,4,2,3
-2020/10/31 3:41:10 PM GMT+7,P********************,Cowok,3,3,3,3,3
-2020/10/31 3:41:11 PM GMT+7,T***************************,Cowok,5,3,4,3,5
-2020/10/31 3:41:11 PM GMT+7,A***************************,Cowok,1,1,2,1,4
-2020/10/31 3:41:12 PM GMT+7,M************,Cowok,5,3,4,2,4
-2020/10/31 3:41:12 PM GMT+7,a*****,Cowok,4,3,3,2,4
-2020/10/31 3:41:14 PM GMT+7,S,Cewek,3,2,2,3,4
-2020/10/31 3:41:14 PM GMT+7,A******,Cowok,5,4,4,4,3
-2020/10/31 3:41:15 PM GMT+7,M*,Cewek,5,1,5,3,5
-2020/10/31 3:41:16 PM GMT+7,w****,Cewek,1,1,1,1,3
-2020/10/31 3:41:19 PM GMT+7,a***,Cowok,4,3,5,3,4
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-2020/10/31 3:41:21 PM GMT+7,f*****,Cowok,5,5,5,3,4
-2020/10/31 3:41:22 PM GMT+7,N****,Cowok,5,4,4,3,4
-2020/10/31 3:41:22 PM GMT+7,a******,Cewek,5,4,5,5,5
-2020/10/31 3:41:23 PM GMT+7,A*************,Cowok,3,1,4,2,5
-2020/10/31 3:41:26 PM GMT+7,N*****,Cewek,4,1,3,2,4
-2020/10/31 3:41:28 PM GMT+7,R***,Cewek,4,4,4,3,5
-2020/10/31 3:41:28 PM GMT+7,R****,Cowok,4,3,5,4,4
-2020/10/31 3:41:28 PM GMT+7,A********,Cowok,5,5,5,5,5
-2020/10/31 3:41:29 PM GMT+7,m*,Cowok,1,1,1,1,1
-2020/10/31 3:41:34 PM GMT+7,g*****,Cowok,4,3,4,1,4
-2020/10/31 3:41:40 PM GMT+7,A********,Cowok,4,4,4,4,4
-2020/10/31 3:41:40 PM GMT+7,a********************************************************,Cewek,4,4,5,3,2
-2020/10/31 3:41:40 PM GMT+7,R****,Cowok,5,5,4,3,3
-2020/10/31 3:41:49 PM GMT+7,t*****,Cowok,5,2,4,3,4
-2020/10/31 3:41:52 PM GMT+7,d****,Cowok,5,5,5,1,5
-2020/10/31 3:41:59 PM GMT+7,j****,Cowok,4,3,3,2,3
-2020/10/31 3:42:02 PM GMT+7,W*,Cowok,5,3,4,3,3
-2020/10/31 3:42:04 PM GMT+7,a**,Cowok,4,3,3,3,4
-2020/10/31 3:42:05 PM GMT+7,I*********,Cowok,4,5,5,4,4
-2020/10/31 3:42:08 PM GMT+7,A**********,Cowok,5,3,4,3,5
-2020/10/31 3:42:16 PM GMT+7,u*********************,Cowok,5,5,5,3,3
-2020/10/31 3:42:21 PM GMT+7,D***,Cowok,5,3,4,2,3
-2020/10/31 3:42:22 PM GMT+7,a*************,Cowok,4,3,4,3,4
-2020/10/31 3:42:27 PM GMT+7,M**,Cowok,5,4,4,2,5
-2020/10/31 3:42:37 PM GMT+7,D********,Cowok,5,3,5,1,3
-2020/10/31 3:42:37 PM GMT+7,F******,Cewek,5,4,5,4,4
-2020/10/31 3:42:45 PM GMT+7,B***,Cowok,1,5,1,1,1
-2020/10/31 3:42:46 PM GMT+7,N****,Cowok,5,4,4,4,5
-2020/10/31 3:42:57 PM GMT+7,A********,Cowok,5,1,4,2,3
-2020/10/31 3:43:02 PM GMT+7,K************,Cewek,4,5,4,4,3
-2020/10/31 3:43:04 PM GMT+7,M*,Cowok,5,5,5,5,4
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-2020/10/31 3:43:22 PM GMT+7,B***********,Cowok,5,5,5,5,4
+Timestamp,Siapa nama kamu?,Cewek atau cowok nih?,Seberapa penting quality time bareng calon pacar untuk kamu?,Seberapa penting physical touch sama calon pacar untuk kamu?,Seberapa penting word of affirmation dari calon pacar untuk kamu?,Seberapa penting dapet kado dari calon pacar untuk kamu?,Seberapa penting bantuan dari calon pacar untuk kamu?
+2020/10/31 3:39:25 PM GMT+7,A**************,Cowok,5,5,4,1,3
+2020/10/31 3:39:36 PM GMT+7,L****,Cewek,5,5,3,2,2
+2020/10/31 3:39:38 PM GMT+7,Y***********,Cowok,4,3,4,4,4
+2020/10/31 3:39:42 PM GMT+7,a***,Cowok,5,5,5,2,3
+2020/10/31 3:39:43 PM GMT+7,B****,Cowok,5,5,5,2,4
+2020/10/31 3:39:44 PM GMT+7,M*********,Cowok,5,5,4,2,2
+2020/10/31 3:39:45 PM GMT+7,F***********************************,Cowok,4,2,5,3,4
+2020/10/31 3:39:47 PM GMT+7,F***,Cowok,3,3,4,2,4
+2020/10/31 3:39:50 PM GMT+7,R******************,Cowok,5,2,4,1,4
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+2020/10/31 3:40:08 PM GMT+7,R**,Cowok,3,4,3,3,5
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+2020/10/31 3:40:20 PM GMT+7,P****,Cowok,4,3,4,5,5
+2020/10/31 3:40:21 PM GMT+7,A*,Cowok,4,3,5,4,4
+2020/10/31 3:40:22 PM GMT+7,M***********************,Cowok,1,1,1,1,1
+2020/10/31 3:40:25 PM GMT+7,J****************,Cowok,5,4,5,3,5
+2020/10/31 3:40:27 PM GMT+7,J***,Cowok,5,3,4,3,4
+2020/10/31 3:40:28 PM GMT+7,M**************,Cewek,5,5,5,5,5
+2020/10/31 3:40:31 PM GMT+7,F*************,Cewek,4,2,3,1,3
+2020/10/31 3:40:31 PM GMT+7,b***********,Cowok,5,4,3,2,3
+2020/10/31 3:40:31 PM GMT+7,u***,Cowok,5,3,3,5,5
+2020/10/31 3:40:34 PM GMT+7,B***,Cewek,5,2,4,5,4
+2020/10/31 3:40:35 PM GMT+7,h***,Cowok,5,4,4,4,3
+2020/10/31 3:40:36 PM GMT+7,E***,Cewek,3,2,3,1,5
+2020/10/31 3:40:36 PM GMT+7,q*,Cowok,5,5,5,5,5
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+2020/10/31 3:41:05 PM GMT+7,i****,Cowok,5,1,5,1,3
+2020/10/31 3:41:06 PM GMT+7,C**********,Cowok,5,4,4,3,3
+2020/10/31 3:41:08 PM GMT+7,r******,Cowok,5,5,5,5,5
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+2020/10/31 3:41:09 PM GMT+7,K**********,Cowok,4,3,4,2,3
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+2020/10/31 3:41:12 PM GMT+7,a*****,Cowok,4,3,3,2,4
+2020/10/31 3:41:14 PM GMT+7,S,Cewek,3,2,2,3,4
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+2020/10/31 3:41:16 PM GMT+7,w****,Cewek,1,1,1,1,3
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+2020/10/31 3:41:49 PM GMT+7,t*****,Cowok,5,2,4,3,4
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+2020/10/31 3:41:59 PM GMT+7,j****,Cowok,4,3,3,2,3
+2020/10/31 3:42:02 PM GMT+7,W*,Cowok,5,3,4,3,3
+2020/10/31 3:42:04 PM GMT+7,a**,Cowok,4,3,3,3,4
+2020/10/31 3:42:05 PM GMT+7,I*********,Cowok,4,5,5,4,4
+2020/10/31 3:42:08 PM GMT+7,A**********,Cowok,5,3,4,3,5
+2020/10/31 3:42:16 PM GMT+7,u*********************,Cowok,5,5,5,3,3
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+2020/10/31 3:42:46 PM GMT+7,N****,Cowok,5,4,4,4,5
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diff --git a/README.md b/README.md
deleted file mode 100644
index 9c93eb4..0000000
--- a/README.md
+++ /dev/null
@@ -1 +0,0 @@
-# DSCDataScience
\ No newline at end of file
diff --git a/SpesifikasiTugas1DSC.txt b/SpesifikasiTugas1DSC.txt
deleted file mode 100644
index df2c69a..0000000
--- a/SpesifikasiTugas1DSC.txt
+++ /dev/null
@@ -1,20 +0,0 @@
-Tugas membuat analisis Data menggunakan Jupyter Notebook (Python)
-
-Spesifikasi tugas:
-1. Program memaparkan analisis data berupa tabel atau grafik, dan penjelasan lisan dari dataset terkait.
-2. Dataset yang akan digunakan untuk analisis dibebaskan. Jika menggunakan dataset dari sumber lain, cantumkan juga sumber dataset tersebut.
-3. Dataset harus dibersihkan terlebih dahulu sebelum melakukan analisis. Beri penjelasan lisan mengenai analisis yang dilakukan menggunakan markdown.
-4. Notebook tersebut harus memuat:
-	a. Nama lengkap, NIM, dan asal universitas
-	b. Tanggal mulai pengerjaan tugas
-	c. Sumber dataset (jika menggunakan dataset TakeMeOut, maka tulis saja TakeMeOut)
-	d. Tahapan analisis data misalnya "Data Observation", kemudian "Data Cleaning", dst
-	e. Statistik dasar data (min, max, avg, dst) dan analisis dasar dari Youtube https://youtu.be/8J1tGYEvf68
-	f. Insight baru mengenai data tersebut berdasarkan eksplorasi sendiri (tidak ada format, dibebaskan) minimal 2. Ini misalnya "A memiliki korelasi positif dengan B", dst
-5. Penggunaan library dibebaskan.
-
-Prosedur pengerjaan:
-1. Tugas dikerjakan secara individu
-2. Tugas ini dikumpulkan paling lambat hari sabtu 15 November 2020 pukul 23.59 WIB dengan mengajukan pull request pada repo github DSC https://github.com/gdscitb/DSCDataScience
-3. File submisi berisikan 1 notebook .ipynb dan 1 file dataset .csv yang terpakai
-3. Dilarang keras menyalin program dari sumber lain (buku, internet, program kakak tingkat, program kelompok lain)
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