CS418_Sixth_Sense.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "be774c81",
   "metadata": {},
   "source": [
    "DOES THE PUBLIC SENTIMENT HAVE AN EFFECT ON CRYPTOCURRENCY PRICE CHANGE?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7a012f3",
   "metadata": {},
   "source": [
    "https://github.com/uic-cs418/cs418-spring22-sixth-sense/blob/main/CS418_Sixth_Sense.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66f60548",
   "metadata": {},
   "source": [
    "INTRODUCTION\n",
    "\n",
    "Cryptocurrency has been a fast-growing technology that has become more widely accepted. In recent years, prices for cryptocurrency have skyrocketed and much is still not known about the causes behind these price fluctuations. Due to cryptocurrency not being controlled by any authority or being backed by any real items, its main price influence is due to the public. Thus, our project wants to explore the relation between public sentiment of cryptocurrency and whether or not it affects the price of coins.\n",
    "\n",
    "CHANGES\n",
    "\n",
    "N/A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b8e35f95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: textblob in c:\\users\\vbatch2\\anaconda3\\envs\\cs418env\\lib\\site-packages (0.17.1)\n",
      "Requirement already satisfied: nltk>=3.1 in c:\\users\\vbatch2\\anaconda3\\envs\\cs418env\\lib\\site-packages (from textblob) (3.6.5)\n",
      "Requirement already satisfied: click in c:\\users\\vbatch2\\anaconda3\\envs\\cs418env\\lib\\site-packages (from nltk>=3.1->textblob) (8.0.3)\n",
      "Requirement already satisfied: joblib in c:\\users\\vbatch2\\anaconda3\\envs\\cs418env\\lib\\site-packages (from nltk>=3.1->textblob) (1.1.0)\n",
      "Requirement already satisfied: regex>=2021.8.3 in c:\\users\\vbatch2\\anaconda3\\envs\\cs418env\\lib\\site-packages (from nltk>=3.1->textblob) (2021.8.3)\n",
      "Requirement already satisfied: tqdm in c:\\users\\vbatch2\\anaconda3\\envs\\cs418env\\lib\\site-packages (from nltk>=3.1->textblob) (4.62.3)\n",
      "Requirement already satisfied: colorama in c:\\users\\vbatch2\\anaconda3\\envs\\cs418env\\lib\\site-packages (from click->nltk>=3.1->textblob) (0.4.4)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import dates\n",
    "from matplotlib.pyplot import figure\n",
    "import json\n",
    "import nltk\n",
    "import sklearn\n",
    "\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from nltk.corpus import stopwords\n",
    "from  nltk.stem import SnowballStemmer\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "import re\n",
    "\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.base import TransformerMixin, BaseEstimator\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "!pip3 install textblob\n",
    "import textblob\n",
    "from textblob import TextBlob\n",
    "# plt.style.use('fivethirtyeight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d6f57fd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     C:\\Users\\vbatch2\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\vbatch2\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\vbatch2\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\vbatch2\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('vader_lexicon')\n",
    "nltk.download('punkt')\n",
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c7f86dc",
   "metadata": {},
   "source": [
    "DATA CLEANING\n",
    "\n",
    "For our data cleaning, we first started with a dataset of hundreds of cryptocurrencies and their prices tracked by date through 2013-2018. The dates were converted from UNIX time to a universal datetime. In addition, only the price and dates columns were kept. Because there are hundreds of cryptocurrencies, we filtered out only the top 5-10 most popular, which capture almost all of the most valuable cryptocurrencies. eg. Bitcoin, Ethereum, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "263c1a02",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>currency</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>64769</th>\n",
       "      <td>bitcoin</td>\n",
       "      <td>2015-01-01 02:40:00</td>\n",
       "      <td>317.848</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64770</th>\n",
       "      <td>bitcoin</td>\n",
       "      <td>2015-01-02 00:53:20</td>\n",
       "      <td>313.639</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64771</th>\n",
       "      <td>bitcoin</td>\n",
       "      <td>2015-01-03 01:53:20</td>\n",
       "      <td>314.870</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64772</th>\n",
       "      <td>bitcoin</td>\n",
       "      <td>2015-01-04 00:06:40</td>\n",
       "      <td>281.246</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64773</th>\n",
       "      <td>bitcoin</td>\n",
       "      <td>2015-01-05 01:06:40</td>\n",
       "      <td>266.723</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64774</th>\n",
       "      <td>bitcoin</td>\n",
       "      <td>2015-01-06 02:06:40</td>\n",
       "      <td>274.168</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64775</th>\n",
       "      <td>bitcoin</td>\n",
       "      <td>2015-01-07 00:20:00</td>\n",
       "      <td>283.238</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64776</th>\n",
       "      <td>bitcoin</td>\n",
       "      <td>2015-01-08 01:20:00</td>\n",
       "      <td>293.754</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64777</th>\n",
       "      <td>bitcoin</td>\n",
       "      <td>2015-01-09 02:20:00</td>\n",
       "      <td>282.507</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64778</th>\n",
       "      <td>bitcoin</td>\n",
       "      <td>2015-01-10 08:53:20</td>\n",
       "      <td>277.861</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      currency                date    price  Year  Month  Day\n",
       "64769  bitcoin 2015-01-01 02:40:00  317.848  2015      1    1\n",
       "64770  bitcoin 2015-01-02 00:53:20  313.639  2015      1    2\n",
       "64771  bitcoin 2015-01-03 01:53:20  314.870  2015      1    3\n",
       "64772  bitcoin 2015-01-04 00:06:40  281.246  2015      1    4\n",
       "64773  bitcoin 2015-01-05 01:06:40  266.723  2015      1    5\n",
       "64774  bitcoin 2015-01-06 02:06:40  274.168  2015      1    6\n",
       "64775  bitcoin 2015-01-07 00:20:00  283.238  2015      1    7\n",
       "64776  bitcoin 2015-01-08 01:20:00  293.754  2015      1    8\n",
       "64777  bitcoin 2015-01-09 02:20:00  282.507  2015      1    9\n",
       "64778  bitcoin 2015-01-10 08:53:20  277.861  2015      1   10"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Source: https://data.world/chasewillden/cryptocurrency-price-by-date-2013-february-2018\n",
    "crypto_prices_df = pd.read_csv('chasewillden-cryptocurrency-price-by-date-2013-february-2018/chasewillden-cryptocurrency-price-by-date-2013-february-2018/data/cryptocurrency_prices_by_date.csv')\n",
    "\n",
    "# Extract prices for the timeframe 01/01/2015 - 12/31/2018\n",
    "# 2015-01-01 00:00:00  => 1420070400000\n",
    "# 2018-12-31 23:59:59 => 1546261199000\n",
    "crypto_prices_df = crypto_prices_df[(crypto_prices_df['date'] >= 1420070400000) & (crypto_prices_df['date'] <= 1546261199000)]\n",
    "\n",
    "# Convert Unix time to Date time\n",
    "crypto_prices_df['date'] = pd.to_datetime(crypto_prices_df['date'], unit='ms')\n",
    "crypto_prices_df['Year'] = crypto_prices_df['date'].dt.year\n",
    "crypto_prices_df['Month'] = crypto_prices_df['date'].dt.month\n",
    "crypto_prices_df['Day'] = crypto_prices_df['date'].dt.day\n",
    "crypto_prices_df[(crypto_prices_df['currency'] == 'bitcoin')].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "48ed04d5",
   "metadata": {},
   "outputs": [
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       "      <th>currency</th>\n",
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       "      <th>0</th>\n",
       "      <td>0x</td>\n",
       "      <td>2017-08-16 13:26:40</td>\n",
       "      <td>0.111725</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0x</td>\n",
       "      <td>2017-08-17 14:26:40</td>\n",
       "      <td>0.211486</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0x</td>\n",
       "      <td>2017-08-18 15:26:40</td>\n",
       "      <td>0.283789</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0x</td>\n",
       "      <td>2017-08-19 16:26:40</td>\n",
       "      <td>0.511434</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0x</td>\n",
       "      <td>2017-08-20 14:40:00</td>\n",
       "      <td>0.429522</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
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      "text/plain": [
       "  currency                date     price  Year  Month  Day\n",
       "0       0x 2017-08-16 13:26:40  0.111725  2017      8   16\n",
       "1       0x 2017-08-17 14:26:40  0.211486  2017      8   17\n",
       "2       0x 2017-08-18 15:26:40  0.283789  2017      8   18\n",
       "3       0x 2017-08-19 16:26:40  0.511434  2017      8   19\n",
       "4       0x 2017-08-20 14:40:00  0.429522  2017      8   20"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crypto_prices_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "431d838b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Source: https://edloginova.github.io/cryptodata/\n",
    "news = pd.read_json('cryptodata/news.json')\n",
    "reddit = pd.read_json('cryptodata/reddit.json')\n",
    "bitcoinTalk = pd.read_json('cryptodata/bitcointalk.json')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9632f915",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63252\n",
      "2752380\n",
      "1642540\n"
     ]
    }
   ],
   "source": [
    "news = news[(news['date'].dt.year >= 2015) & (news['date'].dt.year <= 2018)]\n",
    "reddit = reddit[(reddit['date'].dt.year >= 2015) & (reddit['date'].dt.year <= 2018)]\n",
    "bitcoinTalk = bitcoinTalk[(bitcoinTalk['date'].dt.year >= 2015) & (bitcoinTalk['date'].dt.year <= 2018)]\n",
    "\n",
    "print(len(news))\n",
    "print(len(reddit))\n",
    "print(len(bitcoinTalk))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "67187145",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>post_id</th>\n",
       "      <th>date</th>\n",
       "      <th>Year</th>\n",
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       "    <tr>\n",
       "      <th>9698</th>\n",
       "      <td>Venture capitalist Lou Kerner says bitcoin is ...</td>\n",
       "      <td>9783</td>\n",
       "      <td>2018-12-31 23:37:58</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9699</th>\n",
       "      <td>Charlie Lee, the creator of Litecoin and the m...</td>\n",
       "      <td>9784</td>\n",
       "      <td>2018-12-31 23:30:27</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9700</th>\n",
       "      <td>As crypto’s value has dropped across the board...</td>\n",
       "      <td>9785</td>\n",
       "      <td>2018-12-31 23:24:38</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9701</th>\n",
       "      <td>Hacks and heists have been a threat for as lon...</td>\n",
       "      <td>9786</td>\n",
       "      <td>2018-12-31 23:05:03</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9702</th>\n",
       "      <td>It’s been over a year ...</td>\n",
       "      <td>9787</td>\n",
       "      <td>2018-12-31 22:46:45</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   text  post_id  \\\n",
       "9698  Venture capitalist Lou Kerner says bitcoin is ...     9783   \n",
       "9699  Charlie Lee, the creator of Litecoin and the m...     9784   \n",
       "9700  As crypto’s value has dropped across the board...     9785   \n",
       "9701  Hacks and heists have been a threat for as lon...     9786   \n",
       "9702                          It’s been over a year ...     9787   \n",
       "\n",
       "                    date  Year  Month  \n",
       "9698 2018-12-31 23:37:58  2018     12  \n",
       "9699 2018-12-31 23:30:27  2018     12  \n",
       "9700 2018-12-31 23:24:38  2018     12  \n",
       "9701 2018-12-31 23:05:03  2018     12  \n",
       "9702 2018-12-31 22:46:45  2018     12  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "news['Year'] = news['date'].dt.year\n",
    "news['Month'] = news['date'].dt.month\n",
    "reddit['Year'] = reddit['date'].dt.year\n",
    "reddit['Month'] = reddit['date'].dt.month\n",
    "bitcoinTalk['Year'] = bitcoinTalk['date'].dt.year\n",
    "bitcoinTalk['Month'] = bitcoinTalk['date'].dt.month\n",
    "\n",
    "news.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4314435e",
   "metadata": {},
   "source": [
    "EDA:\n",
    "\n",
    "This dataframe is what our data looks like after we've cleaned the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aaa28e6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "popular_cryptos = ['bitcoin','ethereum', 'tether', 'binance-coin', 'cardano']\n",
    "prices_of_popular_cryptos = crypto_prices_df[crypto_prices_df['currency'].isin(popular_cryptos)]\n",
    "\n",
    "# This method takes year and returns plots of price per every year\n",
    "def price_of_popular_cryptos_during_a_particular_year(year):\n",
    "    price = prices_of_popular_cryptos[(prices_of_popular_cryptos['Year']==year)]\n",
    "    ax = sns.lineplot(data=price, x='Month', y='price', hue='currency',marker='o')\n",
    "    ax.set(xticks=prices_of_popular_cryptos.Month.values)\n",
    "    ax.set_title('Price of Coin by Month ' + str(year))\n",
    "    plt.show()\n",
    "    return price\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7504e926",
   "metadata": {},
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SAQk4hgxsXAwmJyeH5OTkKNQqsoiI7sxXlEhRsccOfW36BApWwu7v7Eqw5L5hVSiglkqn47777qOqKnyTah2FZ599lrS0tGhXQ1G6JiXbIDYRNn0Mqf2h58Cm72klaql0YPbt28e5555LWloav/3tb/F4PFx99dVMmDABaNx1/fr16xk5ciRJSUmkpaVx5plnUlhog2POnj0bEeGqq67iwAMPJDMzkxdeeAGAqqoqbrjhBgYNGkRiYiInnHACYDc9XnrppfTp04eMjAwmT55MeXl5yHpv3bqV8ePHk5mZSUpKCjNnzgTgiy++4JRTTiElJYVBgwbxxz/+sWZ58oUXXsh1113XZP0URWkhvmobP8UAW1bBoLHWaokQaqk0wpxXv2DDln1hfebI/qn84ZxRTV8IfPzxx9x+++3ExMSwaNEixowZE/K6t956i1mzZjFr1ixmzpzJlClTiIuLY8KECaSnp5Ofn8/tt9/O7NmzWbhwYc1977zzDldeeSUzZsxg5syZjBs3jjvuuIM77riDM844g5tvvpl169YBMH36dBYtWsQ111yDy+Vi7ty5pKam8uc//5ni4uKaZ2ZkZHDRRRfx/vvvM23aNEaOHElJSQnV1dWce+65bN++ndtuu423336bm2++maysLC699NKQ7QpVP0VRWojH+uyjcKVVMIOadtPUFlSpdGCOOeYYZsyYwbfffsvLL7/M8uXLQ14XynW9x+PhmWeeqfFEDNQoiADXXnstkydP5qGHHuLrr78G4NVXX0VEeO6550hJSam5dunSpXi9XubOnVuTt2zZMq666ioGDx5ck1dSUsLy5cvJzc1l/vz5Nfnr16/nu+++49e//jVTp07ljDPOYOnSpbzxxhsNKpVQ9VMUpYWU7rC+vjZ9ZN2y9DvUKpcIoUqlEZprUUSapnawh3Jdf+utt7J27VrmzJnD0Ucfzdlnn10nMFf9+4IjN0oDpnG/fv146qmnas7j4+Pp168fb7/9drPb0tCzQ9FQ/RRFaSbGQOl2iEmwk/QDjgZXTESVSiTDCd8rIttFxIjI0nplCSKy0Sm7Pyh/rIisFRGPiKwWkcPbWtaZ+eSTT5g7dy7XX389ACeddFKz7w0ootLSUl5++WWqq5v3JTrnnHPw+/1ccMEFPProo0yfPh2wLvS3bdvGP/7xDzZt2sRLL73Ec889R0JCAqeeemrNkZycTF5eHitXrmT69OksXLiQu+66ixEjRjB06FBeeeUV7rvvvpr5kzPPPLMF74iiKC2iqhR8VbBzo91RnzM24iIjPVG/uIH8m4Hs4AwRSQBeBFKAa4C+wBIRcbe2LALtaVeOO+44Pv74Y959910uuugiJk+e3Ox7b7rpJg466CAee+wx0tPT6dmzZ7PumzlzJjNnzmT9+vX87ne/q4lnP3/+fCZNmsTzzz/PtGnT+PTTTxk7NvQX9Omnn+ZXv/pVzRzMzp07iY2N5ZVXXmHMmDHceOONrFmzhltuuYWJEyc2u02KorSQir2A2KEvVwxkh56XDSvGmIgdQA52zcHSoLzRQAU2hr0B7nfyf+6cz3DOb3HOT2ltWVP1O+KII0x9NmzYsF+e0nHRz0tRGmHzZ8bkf2TM3aOMWXiKMYWr7fHtcmO2fdHqxwIrTQP9arsuKRYRF/Aw8ADw73rFgdneQictcNIhbSgLVYfJIrJSRFbu3LmzxW1QFEXpFHir7AbH0h2w9we7lLimzBM2r8T1ae99KpdgrZcngSwnr6eIhHI8E5jRDTVL3doyjDELjDG5xpjczMzw+btRFEXpUAS8Em/62KaBpcTGDy43JDRvSLyltPfqrwFAJvCfoLzfAB7gNec8MNcSUDrfA6mtLFMURemelO4Ad5ydT0kfDsl9bH5VmbVS3JHp/iOmVETkLCDgzGmAiEwCXgbWO3mjgNnAm8BDwBfADmCKiJQAlwH5wHIgtpVliqIo3Q+/3y4l9nth+xdwxITaMq8HUg6ImOhIDn/NAO5wXo8GFgKHGWOWGGOWAB84Zd8aY1YZYyqBcUApcA9WUYwzxvhaWxbBtimKonRcqkrB74OCfwOmdj7F+G30xwgNfUEELRVjTF4T5cupnf8I5H0IHNrA9a0qUxRF6XZU7Ha8En8EPTIhfZjNj/DQF6hDyQ5Jc13fb9iwgdmzZzfovqW5qNt5RelilGy1k/EFK+s6kIzw0Beom5ZOR7Dr+w0bNjBnzhwA8vLyWv1MdTuvKF0IrwcqS6D4axuYK6f9hr5ALZUOTWOu7/Pz82u89s6ZMwcRYfny5ep2XlG6O5X7rGWS/zHEJsEBP7L5VWXQo09Eh75ALZXGeWMmbFvX9HUtod+hcMYdTV9H467vMzMzmT59OvPnz+f888/nl7/8JSNHjmT8+PHqdl5RujOl263i2PQRDDjSLisGa8GkRnboC9RS6dAEXN8H3M0Hz5306NGjxvfWIYccwvjx40lKSqrjdn7y5Mn8/ve/Z+PGjXz33Xecd955TJ06lbvvvhuAN954o0HZ1157LdOmTWPIkCHk5+dHrI2KooQRv9/uT9lTYANzBW94RCAhLeJVUEulMZppUUSawDBVfVriRr6l16vbeUXphFSVgPHBD5/Y+ZMBRzv5ZXbzY4SHvkCVSocm4Pr+k08+Aazr+4DXYIBevXoBsGLFChYvXsx5551HXl4e77//PtOnT2fUqFHs27ePqVOn1nE7/8477wDqdl5Ruhzlu60y2fQx9BsNCY7DEa/HxqZvB3T4qwPTlOv74447jlNOOYUVK1Zw4YUXUlRUpG7nFaU7U7LFhg8u/q7uhkck4qu+AkhDQyvdgdzcXLNy5co6eV9++SUHH3xwlGqktBT9vBTFoboS8lfAd8vhk/th/NOQmmWVTEIaHDA6bKJEZJUxJjdUmVoqiqIoXYFgr8S9cqxCgXYd+gJVKoqiKF2Dkm3WgeTWz4OGvpyRqHYa+gJVKoqiKJ0fvw/Kdtp9dcZfu5S4usz6/nLHtltVVKkoiqJ0djz7rDLZ9DEk9oI+zjxjtad2GKydUKWiKIrS2SnfbZXKD/+yVoq4nKEvA4lp7VoVVSqKoiidnZKtdhlxdVntfEoUhr4ggkpFRO4Vke0iYkRkqZM3XETeF5EiESkRkbdFZGjQPWNFZK2IeERktYgc3tYyxXLIIYe0eAe+oiidgKpyqC63Voo7HrKc7q+qst2HviDylsrieudZjsw/AI8BpwIPA4hIAvAikAJcA/QFloiIu7VlEW5bh8Xr9Ua7CoqitBeVe+1Q16aPIDsXYhLsudCuq74CREypGGOmAn+pl/2xMeZEY8z9TnkxNlY9wBlYhfCgMeZB4BFgMJDXhrJOy1NPPcWoUaNITExk6NChrFu3jpEjR5KUlERaWhpnnnkmhYWFQK27+ksvvZQhQ4YwY8YM9uzZw1lnnUWvXr2YOHEi1dXVNc9ev359k88K5fre4/FwzTXX0L9/f9LS0jjvvPP44Ycf2v/NURSlltLtULrNpvWHvmLi2r067er7yxhTFXgtIrlAb6yVAVYRABQ6aYGTDsFaIa0pe7ct9b3zX3fy3+L/tuUR+3FQ74P43yP/t9FrPvjgAy6++GIOPPBA7r33XvLz84mLi2PChAmkp6eTn5/P7bffzuzZs1m4cGHNfcuWLWPWrFkMGzaMOXPm8Prrr3P55ZeTmZnJV199VXNdc54VyvX9rbfeyvz585kwYQIjRozgpptuYvfu3Xz44YdhfY8URWkmPi+U7YLC1YDAwIADyUrIGBGVKkXFoaSIjABeAfKBqxu6zElD+ZFpbRkiMhmYDDBw4MBm1Lb9Wbp0KQB33303Z511FgDr1q3jmWeeYe3atTXXrVtXN9bL9ddfzxVXXAHAddddh8vl4v777ycuLo4nn3ySggKrbz0eT5PPuvbaa5k8eTIPPfQQX3/9NQCvv/46LpeLv/3tb8THx7N06VJWrFhBaWlpTYhjRVHakcAu+s0fQ9+RkNQ7aOgrLSpVanelIiIjgfcAD3CyMWarU/S9k2Y7aVZQfmory/bDGLMAWADW91djdW3KomhPbr31VtauXcucOXM4+uijOfvss6msrKxzTf/+dV0xBE/MB/t4a86z1PW9onQCyoqgcg/s3AhHXm7zqsuhR0ZUhr4ggkpFRM4CDnFOB4jIJGAjdrirN3ATcJSIHGWMWQy8AewApohICXAZ1pJZDsS2sqxTcs455zBv3jyuvfZatmzZwqZNm2rmREpLS3n55ZfrzJGE4qSTTuLzzz/nqquuIjMzs2bOBGoVTHOfFeCss85i1apVTJkyhREjRvDpp59ywgknqJWiKNHAGDuXsvU/9nzQcTatroSMA6NWrUiu/poBBKJcjQYWAkOBTMAN3A486xwYYyqBcUApcA9WUYwzxvhaWxbBtkWUE044gSeffBK3283VV1/Ns88+y+zZsznooIN47LHHSE9Pp2fPxld1zJo1izPOOIPnnnuOwsJChg8fXlN20003tehZAW688UamTZvGG2+8we23387ZZ5/NokWL2tRWRVFaSXU5eCvhh8/s0uG0gbUbHqM09AXq+l5d33dy9PNSui37tkDhKnjhEhj1czjmdzbCY1wP6H9YREWr63tFUZSuxr6tsH0D+KuDHEhWQEr7ubkPhSoVRVGUzoavGiqKoXAlxKdCv0Nq3dy3s6+v+qhSURRF6Wx4Sqy7+82f2r0prhhrpST2hpj4qFZNlYqiKEpno2wnFH9r96nUDH2VQ8/29/VVH1UqiqIonQljbJTHLWvAFQvZRwYNffWKbt1QpaIoitK5qCqzcec3fQxZh0FcUocZ+gJVKp2KDRs2MHv2bJYvX16TN3HiRESE+kujFUXpolTssfFT9hUGOZDsGENfoEqlU7FhwwbmzJlTR6mEC3WXryidhNKtsPVz+3rgMR1q6AtUqXRYHn30UUaMGEGPHj049thjWb16NePGjQNgzpw5iEgd5fLyyy8zcOBABgwYwIoVKwDYu3cvl156KX369CEjI4PJkydTXl4OQF5eHiLCtGnTyMjIYOnSpXz55ZecdtpppKamMmjQIP7yFxu5ID8/HxHh7LPPBmDevHmICI8//jgAOTk5JCcn8/vf/56ePXvyi1/8gmXLljFgwAAOOOAA3nzzzXZ61xSli+OtspbKD59ZVyzJfTrU0BdEyUtxZ2Hbbbfh+TK8ru/jDz6Ifjfe2Og1y5cv57LLLuP0009n4sSJPP7445xyyilMnz6d+fPnc/755/PLX/6SkSNH1tzz/vvvM3nyZGbNmsXs2bN59913mT59OosWLeKaa67B5XIxd+5cUlNTmTdvXs19a9as4c4772To0KGcd955lJWVcf3117N27VquvfZahg0bxqGHHtpku8rKyqisrOSYY47h5ZdfZvXq1cyYMYNrrrmGmTNn8tOf/rT1b5qiKBZPiQ3KtX0DHDHR5lWXQ/rQRm9rT1SpdEBee+01wMZHWbZsWU3+AQccANjQwOPHj69zz+zZszn99NP505/+RH5+PmBd6Hu9XubOnVtzXfDzAO6//35Gjx7NF198UePiftasWTXlb7/9drOUisvl4i9/+QtPPPEEb731Fr/97W+ZOnUqc+fO5fvvQzqMVhSlpZTtcBxIGruU2Bgb5COKvr7qo0qlEZqyKCJFwB/bXXfdxejRowHw+/2UlJQ0eE+wq3qfr9aXZr9+/XjqqadqzuPj65rIAXf5AZk/+clPuO666+rc73bbyMyBeZc9e/bsJz8xMZG4uDhiY2MBapxUut3uOvVRFKWVGGOjOxaugh59IH0YeCsgKQ1iE6Jduxp0TqUDEpi7ePbZZ9m8eTOfffYZU6dOpVcvOxG3YsUKFi9eTEVFRZPP2bZtG//4xz/YtGkTL730Es8991zIaw866CCGDx/OP//5T9asWcPGjRt54IEHWL16NX379iUhIYFVq1bx/PPP88QTT4S3wYqiNI2nBDylVqkMOhZEoKocUrObvrcdUaXSAcnLy+Oxxx6jtLSUK6+8kgULFnDsscdy3HHHccopp7BixQouvPBCioqKGn3O/PnzmTRpEs8//zzTpk3j008/ZezYsSGvjYmJ4ZVXXmHs2LH86U9/YtasWZSUlHDooYcSFxfHHXfcQXV1NbfcckuDz1AUJYJU7oHtX1h39zlO7BRDh1n1FUBd36vr+06Nfl5Kt2HzZ/DPv8D3H8DFr1jvxO44yB7T7lVR1/eKoiidGa/HWio/fAYDjgJ3bIcc+oIIKhURuVdEtouIEZGlQfljRWStiHhEZLWIHB7JMkVRlE5P5T7Y9a11dx9wINkBh74g8pbK4uATEUnAxqhPAa4B+gJLRMQdibIIt01RFKV9KN0OW1eDuKyr++pySOzZoVZ9BYiYUjHGTAX+Ui/7DGyn/6Ax5kHgEWAwkBehMkVRlM6N3w+lO6BgJRzwI4hPsUNfPTve0Be0/5zKYCctdNICJx0SobL9EJHJIrJSRFbu3LmzxQ1QFEVpV6pKbDz63d/XDn1Bhxz6guhP1IuThlqCFokyjDELjDG5xpjczMzMZldUURQlKpTvhi2r7OtBY62vr/ieEJsY3Xo1QHsrlYC/joDdlhWUH4myTkd5eTmzZ8+ucdbYGK+//jqzZ8+uccsCtc4dFUXpIpRssRseew2G1P7O0FfHcHMfioi5aRGRs4BDnNMBIjIJ+AzYAUwRkRLgMiAfWA7ERqCs01FeXs6cOXM48cQTmThxYqPXvv766zzwwAPk5eWRk5MTtjr4/X5EBBFp+mJFUSJHdSWUbIdt6+BHv3YyDST1jmq1GiOSlsoM4A7n9WhgIXAEMA4oBe7BKoNxxhifMaYy3GURbFvEyM21+4k++OADRIQbb7yR6667jqysLNLS0hg3bhw7d+7k8ccf54EHHgDgpJNO2k8BzJo1i/T0dMaMGcO2bdsAGnRtDyAiDB8+nHHjxpGcnMzevXvbqcWKojSIZ58NG2z8kHNshx/6gghaKsaYvEaKQ7q9NcZ8GO6ytrDi+a/Y9UNpWJ+ZMSCZ4391YIPlt912GxdddBEHH3wwN998Mxs3buT222/niiuuoF+/fsybN48pU6Ywd+5cTj/9dJYtW8asWbPquMEvKyujoKCAM888k0WLFrFw4UJuuOGGBl3bn3POOQB88803nHjiidx11137OZ5UFCUKlGyDLattvJTMg6C8GPqMbPq+KKJeijsYp59+OgB9+vRh/PjxjBljXTD87W9/q7lm2bJlLFmyhOHDh7Ns2TJOPvlk8vLyaspdLhcPPfQQn3/+OYsWLSI/P5+NGzc26No+oFTS09NZsGABLle0128oioLfZ1d9Fa6GoSfZPSrQoYe+QJVKozRmUUSK+sNYxhhiYmJYunRpjQt6v98f8toAiYmJJCQkEBNjP16fz9eoa/vg16pQFKWD4CmB7euhuswuJfZW2j0qHXjoC6K/pFipR2pqKi6Xi2+++Yann36ac845B6/XyxNPPMHmzZt58803a6yWgCv8JUuW1AT2aojGXNsritIBKS+2q77c8ZB1BFSVdUhfX/VRpdLBiI2NZcaMGezZs4ff/OY3DB48mBkzZrBixQquuuoq3njjDU488UQALrroIg466CAefPBBpk2b1uhzG3NtryhKB2TfFruLPjvXxp83HXvVVwB1fa+u7zs1+nkpXZKqclizCN6YASdcD8NOBsT6/eoAqOt7RVGUzoRnHxSuBAQGHdNphr5AlYqiKErHo2SbnU/pO8r6+OokQ1+gSiUk3XlIsDOhn5PSJfF5YedGKPqm7qqvuKRo16xZqFKpR0JCAkVFRdphdXCMMRQVFZGQ0PHiSShKm/Dsg4J/29eDxnaqoS9o5j4VJxDWLcDpwJXABcBHxpjnIli3qJCdnU1BQQHqFr/jk5CQQHZ25/mxKUqzCCwl7pkNaQOhfBckdUw396Fo7ubHe7COGgWIB9xY315dTqnExsYyePDgpi9UFEWJBMXfWQeSh/wcfFUQlwxxPaJdq2bT3OGvXwBzg85XAe2/3VxRFKUrU1UGP3wG/uraoa+eA6JdqxbRXKXipzb4FcCPsF6BFUVRlHBRuRcK/gXxqXbll/F3mlVfAZo7/PUacK3z+imgH/BwRGqkKIrSXdlbaF3dDzzWOpSM69Gphr6g+UplOtZSOQsbFOsJ7JyKoiiKEg581bDpY+tIctCx4CmFzM43y9AspWKM2QdcEuG6KIqidF88JVD4b3DFQvYYqC6HpPRo16rFNGtORUSWi8jdQed/EZH32yJYRKaLSL6IeETkexG52skfKyJrnfzVInJ40D2tKlMURenwlO6wDiSzDgOXu1MOfUHzJ+qPBNYFna8FjmqtUBEZDvwFuwDgWuyQ2r0iMgB4EUgBrgH6AktExO3slWlxWWvrqCiK0m5UlUPBZ1Cy1a768pRCala0a9UqmqtUdgC/EJEkEekB/NLJa6vcQuAdYBvgAY7GKoQHjTEPAo8Ag4E84IxWlimKonRcfF7YthYKVtnzgccAplMOfUHzJ+qfBf4X2AcYrFK4o7VCjTEbRWQmcDvwX6zFcgkQWJBd6KQFTjoEa4W0puzdYNkiMhmYDDBw4MDWNkFRFKXtGAO7v7fLhg/7DYw8F3r0sfMr8cnRrl2raK6lcjMwG1jjHLOdo1WISCZwNfA58DPgP8D9QP13MbA3JpQjrlaVGWMWGGNyjTG5mZmZLau4oihKONlbaBXIovPhkdNg6TXg90KvzuvVo1lKxRhTbYy5xRgzxhhzpDHmj8aY6jbIPQnIAl4yxrwCvIS1Nr50ygMOnQKDit87R2vKFEVROh6lO2z8+RcmwJ7NNm/PZnsu0vi9HZhGh79EZB8wAbsvpT7GGNOzlXK/c9LfiMhW4CLn/CvsXM0UESnB+hvLB5ZjJ/NbU6YoitKxqNwHW/9jLZKAQgmwZ7O1VjopTVkqRUA1UOy8Dj6KWyvUGLMS+D3WOeUDTnqVMeY/wDisC5h7sIpinDHGZ4ypbE1Za+uoKIoSEbweq1Bik+xAfVq9ud20gRATF5WqhYMmY9SLiAs4BNhpjNnaLrVqJ0LFqFcURYkYfp91w+IptUG3Vj0Bo38F/7jKWihpA2H8M9BnFLg6brirxmLUN7n6yxjjF5EPsJbFo+GunKIoSrfAGNj1FVTstqu93r8NvnkHsnPh4les80gD9B7coRVKUzS35s8AZ4tI51zjpiiKEm32FlhrJCkdPvurVShHXg79f2y9E+/eBK4Yu5u+E9PcfSoTgERgr4iUOXltmahXFEXpPpQXw44N1kJZ9zysfR5G/QJ+9Gtbbvx2aKxH59zwGExzlcouQu8HURRFURqjqgy2fg4JPeHb9+DTh2DISXDsVXbpsDFQtgt6D4H4lCYf19FprlI5ELgCONE5fx9YEJEaKYqidBV81bDlczustfU/sPxO6H84nHQDiMsqlPJdkJYD6cOiXduw0FylshD4DbU71X+OdTI5IRKVUhRF6fT4/bB9A/g8ULId3r4ZeufA6X8Ed1ythZI2wMZN6cQbHoNp7kT9ucDLwDCs1fKKk6coiqKEovg7KN1uh7/enAmJaXDGn2vd2ZcXWU/EGSO6jEKB5lsq7wOfGGO+AxCRj9E5FkVRlNDs2wpF39qlwa/PAAycMbfW83DZLus4ss/BnXr5cCiaq1R6A7eLSMA6OQb4p4j8A7sK7LyI1E5RFKWzUbkXtq23u+Jfv87uSzl7vh3mArsSrEcm9DukyykUaL5SOcFJjw/Ky3NStVgURVEAqivtxHxMLLwzB4q/h5/eBn0OsuXlxZDYC/oe0un3ozREc5VK5/XDrCiK0h4Egm0ZH3x0L2xZDXk3wgAnSG7FbohPhX6Hgru5XW/no1ktM8ZsinRFFEVROi3GwM6NULEX1j4L370PR/0PHHi6La/YA3HJdve8OzaaNY04XVddKoqitBd7Nls3LN+8A+tfhEPHwegLbFnlXohNgAN+3OUVCjR/SbGiKIoSirJd1kopXAn/XgBDT4Gjp9hlwpX7wBVrNzx2Ynf2LUGViqIoSmvxlFoXLDv/CyvmQdYRkDfT7pb3lIC4bV5MfLRr2m6oUlEURWkN3iq70mt3Prx/q3Wzctof7RBXValdF5t1uB366kZETamISJqIPCkie0SkVEQ+dPLHishaEfGIyGoROTzonlaVKYqihBW/H7avtwrlnTnW+/BP77CBt6rKwOeD7CPseTcjmpbKo9jY9I8A04FvRCQBeBFIAa4B+gJLRMTd2rL2bZKiKN2Com9g51fw3i12v8mZc61iqS63TiSzc2vdsXQzorL6S0SGYJ1SPg3cAPiMMQ+LyM+xCuF6Y8yDItIPmIXdaJnayrJ327NtiqJ0cfZuge1fwId3gmef3S2fmgXVFTb+fFYuxHffeIbRslRGOukYoAwoE5E7qd1kWeikBU46pA1ldRCRySKyUkRW7ty5s02NUBSlm1Gx225q/OfddhnxaX+EzBHgrbRWStYRkJAa7VpGlWgplcBSiB7ABcBHwPXsbzkFXHeGcgXTqjJjzAJjTK4xJjczM7NFlVYUpRtTVQ4Fq+Gzh+zO+bwb7DCX12NXgWUdYQNxdXOitfkx30lXGGNeEpFM4GRqlUG2k2Y56ffYIa7WlCmKorQNn9cG2Vr5COT/E46+EoadAr4quxclO9f69FKiplRWA+uAU0TkcuASwAe8BlwLTBGREuAyrAJaDsQCO1pRpiiK0nqMgZ1fwuonYeNrdqf86HF2Qr5ir102nNQ72rXsMERl+MsYY4ALgW+B+7Cu9S82xqwHxgGlwD1YRTHOGOMzxlS2pqx9W6YoSpejOB/WLIL/PAPDToOjrgC/186v9P8x9MiIdg07FFHz/WWM+QIbl6V+/ofAoQ3c06oyRVGUVlG6wyqTfy2A7DGQ979g/FC+Gw74EST3iXYNOxzqUFJRFCUYvx/Kd9rYKJV74PsPIWM4nDbHlpcX23goKf2iWs2OiioVRVGUAH4/7PgCFv/aLhlOGwjnPWh9d4kLyoqg78HQM6vpZ3VT1PeXoigKgN8H+wpqFQrY9JXf2XmT8mIbwTFtYHTr2cFRS0VRlO6NMVBeBDv+Cz3SaxVKgD2b7ZLi9OHQa1B06tiJUEtFUZTui6fE7pD/5j3411+h6Nv9LZG0gXb4q3dOVKrY2VBLRVGU7ofXA8Xfw66N8NWb8MXLNq/3UBj3BLwwoXZO5VeLoGe2DbqlNIkqFUVRug9+H+wrtEG1vn4b1r1g95sMPgHGXA5pAyAmEX77sp20j4mDngOsJ2KlWahSURSl62OMnWjfvgG+ex/WLrbKpd9oOP1P0HeUva6qFEp3QmKaDbqV2EstlBaiSkVRlK6NpwR2fQPfLYf/PGuHvHrlwE9ug4HHWKXhKYFqDyT1sntQEtJUmbQSVSqKonRNvB7rYiX/Q2uZFKyEHplw4v/C8NNr48j7qiApw1otiWnRrnWnR5WKoihdC78P9m2FzR/D589YCyUuCY68Ag75BbjjbHAtbxWk9IVeg7t9DJRwokpFUZSuQWDepODfVpl89brNH/0r+PFFEJ8Cnr3WOknuZ5cIx6dEtcpdEVUqiqJ0fjylsG09rHkKNvwdqsrsEFfuJdbpY+U+u8ExNcsuE+7G4X4jjSoVRVE6L94qu2FxzVOw9jko3wUDjoIjJ0PvwTbeSVmxXSqcNgDiekS7xl0eVSqKonQ+/H7YWwjrFsPqp2DPJsg8CE7+PzvhXrnXDoWlDbLKJDYx2jXuNqhSURSl4xNwR++tsqu2ClbCB3daj8Kp/eGUP0DOcXa+pHIPpOXYXfCxCdGuebcjqr6/RCRBRDaKiBGR+528sSKyVkQ8IrJaRA4Pur5VZYqidDJ8XqiusEqiYg9sXw8PnwrzD4HHfmqX/vbIhLHT4PxH4IDRduNiryGQczxkDFOFEiWibancDGQHTkQkAXgRqACuAf4PWCIiw7Fx6FtcpiGFFaUDYIwNweurtmng8HrAW2mP6gprifg84K+2sUvKdkHOWFhySV139P+4Cn7zEuzeZINp9R5mLZaYuOi2U4meUhGR0VgFcDPwZyf7DKAvcL0x5kER6QfMAvKA1FaWvdtebVKUbkPNcJQHXLF2AjygMLyVNr+6wioIr3MAYACxq7PKdtiJ9fJiuzKrbBeU7bT5ZUXOtcDE10K7owfIPBBSDgB3bDs1XGmKqCgVEXEBDwMPAP8OKhrspIVOWuCkQ4CUVpbVUSoiMhmYDDBwoAbbUZQW4/fDjg2w+MJaT77jnrBKparEllfuhYoiqzDKdlp/WmU7oHS7jfteXV73ma5Yu/Q3uS9k5do0ua/NS8qwMoIVS9pAu8dEQ/p2OKJlqVwC5ACTgEOdvJ7YYaxgAs53TIhntKrMGLMAWACQm5sb6l5FURqjdHutQgGbvjABzp4PL0+x1ofx170noadVEj2zIeuIWoURSBN7AWJdpgQOA2DshsVfPQnPX1yrxMY/Cz36tG+7lWYRLaUyAMgE/hOU9xvgO+d1YJ4lEAj6e+wQV2vKFEUJB34f7C2ww1uhhqPiUyHrsHoKw3kdEzRpbsz+yqO82DpwjO0BielWCcUl2fsC9056x865xMRBUia4NMZgRyRaSuV5YL3zehQwG3gT+BPwEjBFREqAy4B8YDnWitnRijJFUdpK5V7Y/gX89w07cR5qOKpHOuTdUJtnjJ1w93rsHErA8mhMeTSmKJL7Rqp1ShiJilIxxmwANgCIyC4n+1tjzEciMg4713IP8AVwubOCy9fKMkVRWovPC7vz4dt3YNXjsONLG4fkl4/DkolB0RGftEt/K4rrKY+klisPpVMjxnTfaYXc3FyzcuXKaFdDUTom5cWw6ROrTL552yqGIyfDiJ9aS8MVAzhzJ9UVIDHW229cD1UeXRwRWWWMyQ1VFu19KoqidDS8VTaQ1crHrD+t6nI45Hw4YoJdcVVVbpf8JvW20RHjU1V5KDWoUlEUpZbSnbDuefjXAjvs1f9wOPZq65yxusKWJ/SE7FyrVBSlHqpUFEWxu9I3fQQr7rJpcl84dQ4MPsGu0CrdaYe1so6wykRD7SoNoEpFUbozxkDxJlgxF9YvAeODwyfAjy+0jhvLiqwPrQN+ZH1t6TCX0gSqVBSlu1JVDqseg4/uhdJt1svv0VdCcqZdyeWOg76jbMhdlzvatVU6CapUFKW74ffbIa63b4Ytq+2S4DPnQv/D7H4UTylkjoCU/uDWLkJpGfqNUZTuRMk2ePsPdqjLHQdHT4GDz7MrvCr3qrdfpc2oUlGU7oDPa1d0fTjXblA88CcwZpJ15FhVaoNa9RoIMfHRrqnSyVGloiiRIjhaYTT9VW36CF6fYd2sZBwIp90CadngrbZu43sN0nC7SthQpaIokSCUe/jxz0Kfke2nWEp3wJs3wBcvQlwKHH+dnYz3VVsF13uwXSasKGFElYqiRIKynfu7h198IVywCLZ8bt2ZJKTZ3eiB1wk9bbCp1uwBCbaK3HHw3XJ4/ffWkePIn8HoC0Dc1sV87yFWpqJEAFUqihIu/D747kNrGfz4otDu4T0l8OrUhp8Rk2CHouJ6WGeMcT0gLtke8SkQn2ytjoSezpECqQMgLrFuvJFz74cRZ8Hg423ckcRekDHcxnZXlAiiSkVR2oKvGr55B9a/BF8vg8o91lI45PzQ7uETe9tgVtXl1u1JdYW1JqrL7a52b1B+4JqyXXXP68eeu2AR/P3G/WO4X7TETspnjLBKRXfBK+2AKhVFaSnVlbDxDdjwMnz7nrU+YhIg63AYcLTd75HSDy54Gp67qO6cSuYIO1nu99rd636vtXD8Pnvu9Vi3KN6gIFa+aieYlc9GVAzEfw8omV6DQ1tFMfG2PqpMlHZElYqiNIfKfbDxdfji7/D9B7ZDj02EAUdB9pG1bkzSBlhrJDbBznM0FK2wNZsK/f7QyshbEdoqik1UhaK0O6pUFKUhynbCf1+DDX+3cUW8lXaOI2csZI2BvodCYk8bd71Hxv4rqVyu8EYrdLnAFWJTot9vraD6K82SMsMnW1GaSVSUiogMBxYAo4E44FPgf4wx34rIWOAhYAQ2guMkY8xq575WlSlKs/D7oWQrfPmqPQr+DT6PXaE19CRrkWSOsJPmPbOtZRKfEn1rwOWyS5U1hrvSAYiWpZIFuIA/AAcCVwMPi8gZwItABXAN8H/AEkcJxbamTEMKKzWE2ozo99rwuF/8Hb56A7assfMXib3srvMBR1rXJTHx1n1JSl+I79nxOuxwW0WK0kqipVQ+NsacGDgRkYuAUcAZQF/gemPMgyLSD5gF5AGprSx7t70apXRgQm1GPP8RGyp37XNWuSRlwEFnw8CjoecgcLttR52aZZfiqqdeRWmSqCgVY0xV4LWI5AK9sZbGYCe70EkLnHQIkNLKsjpKRUQmA5MBBg4c2JZmKJ2Jsh37b0Z88TLrnddXDYOOhtRswGWDUPXMtqk7NqrVVpTORlQn6kVkBPAKkI8dAruw/iVOWm9hfuvLjDELsPM55ObmhrpX6Sr4vLBtnQ2PO+pnoZfd9h4Co39l50Z6DoCkdLtyS1GUVhE1pSIiI4H3AA9wsjFmq4h87xRnO2mWk36PHeJqTZnSnfD5oPBfsO5F+OZtG2cdYOjJoZfdxiTAoGPVB5aihIlorf4aACzHDnvdBBwlIkcBfwd2AFNEpAS4DGvFLMdOxremTOnq+H3w3QfwxUtWkZRsA8RGLTzqf2xc9fgUGPcEvDCh7rLb1OyON+muKJ2YaFkqQ4HAIvrbA5nGGBGRccADwD3YpcGXOyu4fK0sU7oi3irrHuWLl60iqdgNrhi7m/3QcXDAYdZposTY8LjJfezSYF12qygRJVoT9cupnfeoX/YhcGg4y5QuQlUZbHyz1j1KVZnjHiUXBh4F/X4EcUl2yW9qP+v5Ny65ruLQ+RJFiSi6o17p2JQXO+5RXobvP7R7SOJTat2j9DvUnif3rd2MqKFwFSVqqFJRokdDkRH3Fjq72l+BzZ9ZX1dJ6TAkDwYea3ePp/SxiiQ+1U6yR3tXu6IogCoVpT7tFQI35GbER+HfD8PaxfaalP5w4E9ttMJ+oyH1ALt3JC6ldQ4ZFUWJOPrL7Oi0Z5zzUB39BU9b9yRVZY5Ldo/1h+X1BLlpr3RctXuc10HlgXOfx7qMDzzjqCvgld/V24x4KZx1t3XvnnOcdSWf3NcOaWkMdUXpFKhS6cg01Mmn9IOqUiemRqV1w+6trA3u5K2AqgqoLgsK+OS89lYGBYSqDMqrsB36GzPqdvTPXQQ/uQ2e+03r2iBua1W44pw01qbu2NCbEdOHwVnzrDWiK7MUpdOhSqWltMVy8FbZyIAVe2xaubf2dcUeuyy2otjJ2wsnXAdLp7e9k3fHBR2x9c7j7CqpHn2s+/ZQHX3PgXa/hzs+6P54iIm1q68CIXCDX8cmQkyifY/EZZf7utz2NWItllCbEeOSbJhcRVE6JapUWkIoy2HcE1C6A4q/c5TD7tqjcm/dw1vZ+PPd8U5Mcic+eXxq6E4+bSAcO816zg0csYkQ28OJb57sxDdPhJgk25m7YqzyE7fjGFFsBy9OCrZ+oTr6lL5w+q1B14dhUlxjgChKl0SVSkso37m/U8IXJljL4a0bbF5skqMUHOWQkGa93MYm2bmB+GSIS4WEFHuelGEthB597L90d1ztP/oGO/l+cPot4W9fQx19jz7hH4rSGCCK0iVRpdISvFWhLYeMA+GSN62CiE2oVQwud61l4Iqpfd3cf/rt/W++vTt6jQGiKO2O8fvxFRdjqqqQuDjcvXsjYfyNq1JpCTFxoS2HxDQbETDcROPfvHb0itJlMX4/nq++puDK31FduIXYrP5kP/Ag8QcOD5ti0bGGlpCUaS2FNCcOS3vMAwQ6+bQBNtXhIUVRWoCpqqJq82ZKP/kEz1df1SgUgOrCLRRc+Tt8xcVhk6eWSkvQeQBFUToYfo+H6i1b7PFDAVU/bKa6sJDqwi1Ub92Kr6gIjA0dNfCpJ2sUSoDqwi2YqqpQj24VqlRaig4PKc0k0mPX0aS929ae8jpa2/wVFVZhFBZSVVhI9Q8/UF1QSPWWQqq3bN3fynC5cKel4e7Vi7icHGJyjyAmM5OYzD64U3sSm9W/jmKJzeqPxIXPX54qFUWJAO0xdl1fXnt2uu3dtvaSF25ZxufDVFdjvF6bVldD8LnLhamooHDq1Bp5/efNo+Td9yj7+GO8W7bg27On7kPdbty9ehHTqxfxw4fhTs8gJiODmD59iM3qT0y/friTk3ElJuJKSEBiY5GYGCQ2FuN2k/3Ag/u1z927d3jeQECM6b4RdXNzc83KlSujXQ2lnYhEx+uvrMRXVIS3eDe+4to0MTeXLddeu98/wv7z5lHy9tu4EpNwJSYgCYn2x1/zOgFJSMCVlGQ7BCfPlZiIxMY22K7WdITG78dUVuL3eDCVlRiPp+a1v9KD8VTid/KNx1OTl3TUURROm7Z/2+66i32vvV4T1EJEsPuhpO7+JnHKgsvtDfZekdp7geST8iicPn0/eVnz51Py7rvgN3Z4x/gxxgSdG4zx26Difr8zBGQw/qA8TJ170n55Pluuv34/WQfcdhu7/vpXTFU1eL34q6vBW42pqqswjNeL8XrByaOJ/jX7vvvYfsft+8nrO2sWO+ffQ0zv3rgz0q2l0acPsQMGENuvH+6UlBqFQUwsEhdrlUczVpaG43cgIquMMbmhytRSUerQVYcZmtvx+quq8BUX4ysuxltUXEdReIuK8e7aVVu+Zw+mvDykvIbGrvH52L3o6daNYbvdjqJxlExCAq6EBPr8/lq23nTTfpOvB9x6Kzvn34PfU4mp9GCqAorBOaqrW16HRtpmvF72LFlSmxnoUJ0OPjjfYGzHXi+/zj0OScccHVKev7KSogUL6ygtCSgm6uftf9R0wIHP3+Ui7Zfnh5QlcXF2bsLtRtwxuGJjITERcbshJgZxuxB3jC2PcTvXOWWuoNcxgWtikBg3sQOyQ8qLHzqUgY89isTGIbHWygjXb0NcLmIyMsLyrFB0KaUiImOBh4AR2OiPk4wxq8Mpw+/1UbG3Ep/P4HYLiT0TcMW4wykiavKM349n02bKt+3G9EhByraT1K+U+EEDIzPMECZZxu93/knv/6868Dr2gH7svP8+0m6+DemVjtldxM7776P3xRezY95d+HbvxldcjL+sLLQQt9v+O0xJwZ2aSuyAAcSPGok7JRVXairunj1xp6XZf5N9++Lu3YvkU08hcdxva+RVvPAUsdnZDHv/PYzXi7+iAl9pKf6SUvxlZfjLy/CXlWPKy63FUFWvTVVVUFVdU2aqqjEeD5KQgDsjs07bSh78CxIfj7/Kg8TF40pOdjqoWCQuDldcrD2Pi3P+5cYh8XH22jjndcCKSkq0lpNzSFJSyLbFZWcz/P337GfS0D/0+kqkqdeA3+ttWN6HH9iLgq2d4BRqlUdTKYK/rDT055aVxeAlS+pZVfu/bo6lEIx3166Q8lyJicT06tWiZzVbptdLaUklxme3ziWnJBATEz5V0GWGv0QkARuXvgKYC/wf4AGGNxRWuKXDX36vj6LCEt5YsIGSokpS0hM4Y/JI0rNSItLRNySvd78kBAN+P8bnt159fT77Q/b5rHnvHIHXxuezP1afD+M34K93nc9PTL++7N7t561nNtfI+8mvB5LWE6q//dYZNjBBz3WGHAKvA3XyG2e4od51gWEGv5+k48aytyJuP1mpMWXsefoZZ+ildjimZpjG+dft91RiPFXN/sc96MUllPhTeGtxQa288dmkxpazdeYNuFNTcQUpDXfPnrh7pdlhh759icnMxBUfb/9xumPsv0e38480REcS6e9K8GfnLSmleFv5fu9l735JuJN72Pq5HBc7LleLO776eKu97C4s5Y2FQW27fCS9spKJiQ3//9T2lOet9rJ7S+l+n1uv/hFsW3vK83op2lLKW3+rlfeTK0aS3j+5RYqlseGvrqRUfg68BFxvjJkrIrcAs4BTjTHvhrqnpUqlrKiMF+/+DyVFtT68UtITOPGCYbx313tNP6CF7/XJ153CB899E1revJBNahMnzziVDxZ/vb+88cN5b+477SfrrvdqhyggxNAFBIY36gxt1Ls2uOzESw8LLe/CA1mxeGPIf7ht4fhfHcgHz24MIW8EK57/KiwyamUN54Nnv9pP1vHjD+S5BWvDKgvggsmjWbG4Y8hbHGZ5F14xmg9DvpfDeebh5WGVBfDrSXmsCPG9bG955143mrReyc1+TneZUxnspIVOWuCkQ4CaHlhEJgOTAQYOHNgiAT6fqfNhAJQUVZKQEkdqkq9mgrLlBN9oas4TUuIalpcq+3eEwZ1ws9Pa1wnJsaHlJceSNqBX7T0hhg1qJlhDTbxSv8OHhOQG2pYcR+9DhwTJqZ3cDchpzduckBLfoLzUzB6teGIT8no08F72iCU1PbyxYeIbeC8Tk2OpSGr9sGVD73NiA9+TxORYKtsgryEak+cJs7yGfgOJKbHsTtobVlkAiSkNy9vTUnnN+GE0JC/0WE7r6EpKpT6Bt7iOeWCMWQAsAGuptOSBbreQkp6wn5ZPTo3l/IcuamN196esqKxhefePD7+8vZ7Q8non8vNbf9pOshI47/djwiqrUXm94jl32o/bT17v8MvbW1wZUlZiz3jm3JkXVllNyZvdyeXt2V0aUlZCzxjm3T4prLKakje3HeVJGEfvu8ZOLMv3TprtpFn18ttMYk873pmSngBQM/6Z2DMhXCKiKi8pJY4zpxxaR96ZUw4lKSV8G6OiIaury0tOjeMnVxxSR9ZPrjiE5NTItK0ry0tOsXMMdWWNJDklMr+5riivK82pJACbgHLgz8BNQBUwLFwT9dC1V38BGL+hvKQKv9fgihGSUuIQV3jmGaIpq6vL83n9lO6rwu/z43K7SE6Nwx0Tuf+MXVlepFdHdQV53WKiHkBETgAeoHZJ8eXGmAa1hm5+VBRFaTndZaIeY8yHwKHRroeiKEp3pSvNqSiKoihRRpWKoiiKEjZUqSiKoihhQ5WKoiiKEja61OqvliIiO7HLkFvDEcCqMFanO8vrym1rb3lduW3tLa8rt62t8gYZY0LGUe/WSqUtiIgxxkRuk0M3kteV29be8rpy29pbXlduWyTl6fCXoiiKEjZUqSiKoihhQ5VK62nvccOuLK8rt6295XXltrW3vK7ctojJ0zkVRVEUJWyopaIoiqKEDVUqiqIoStjoUg4l2wMRqR/0K2JLAEWkCoitl11ljImPoMz646E+Y0zEvicisgmoCcEZ4fdzv7HeCMvbC6S2hzwR8RM69l9Evi8h5BljTMT+pIaQV2mMCVsIzYZ+1yLiBYJjTXiNMfV/k+GSFZG+JdRzI9m3qKXSsQl8mYO/FJGJhFQXEyQzcsFbLC2L6RweAu3zR1hOsEKJ9ORlNbXtCpa1PtyCnA6pfmRVEZHKBm5pqzxvCHmRiWJVV+4m9v8NxojIC5GW3Q5ErG9RpdJCnH8PVe0kbqMxRowxrnbcFOXCBjqLdIeLiFQ7L9t7tchu5z2NmMIUEU/Q6ZfAi5H8DI0x8U6bXAS9n8aYIyIl08HbwOtwEuin6lhDIlISLgEN/K6za4vrWGG/iICsiPUtDTw3Yn2LDn91YIwxIwOvnX9r7cEeoEfQeciomW1FROZQ+/0zhB66iRS9nSGBSA7ZBP+2DgYObscd04E2ReTPjzEmLmg4KrY22yRHQl4QgWGbAJG2Vup/VoHvaXt+VyNCJPsWtVQ6Ac4PKfCvuiLC4j7HWimRHv6a5aTBQxuISGmE5EHtkFfwkI2nkevDRY3VJyIRUdJBz69pT6Tm3pzPKPCZ+WuzJVLfze+CXrdpPkOpSyT6FlUqHZx6E2pVxpikSMozxpxojHHXG2aIREcf6JRiqPvPr0eIa8NCYMirnnUSKWu9RpE4w2w1iixC8gIExsUjOXwZ+A6aem2LyHyfMWYYcAv2D0jwv+rtkZAXLLreef15nU5NpPoWHf5qIc4/zeB/1n7sipCw/6Ccic+a4QUgVkT8kRqycdoWGJMP7vxSIiCumtp/SMFDChEZ5nPmb2LYv0OI1PxYHE7HXm/lUsQsFRHZEHT6p0jJwbbLbUXWaVtEFJkzMf5L6n0vjTHZDd7UchmhfteB30OgnQFeioCswCqzsPctDcgLfIYQ5r5FLZWW46JuhytETjkHm/oCER/PDfVv2mci4HbBGBPnWA3B/3Rp63LNRggsCgh+D/3hXJYajPOeBRSkBGVHcvjmoCA5f4iUEGeJef3vSiTbFlCWwd/Lb8MsI9Tv2k3tn4CaPwXGmHERkBXTSH5bCfVcV73zsPUt6qZFURRFCRtqqSiKoihhQ5WKoiiKEjZUqSiKoihhQ5WKoiiKEjZUqSiKoihhQ5WKokQAEckREeMcNwXlPxrIb8OzR4rIbBHJC8p73HlubttqrihtQ5WKokSeS8TSA2jrHgeAkcAfgLwwPEtRwooqFUWJLN8BQ7AK4ALshtZCsNu0ReT/RGSTiJSIyPsiMsopm+1YHveLyFcislNExolIDhBwvf4H55q8IHk/F5HNIvKDiBzfPk1UlFpUqShKZPkS+Ay41Dn+jvUEDXAJ1p3KWuD/gDHAKyISvDP9VOABoCdwB7ATmO+UvQhcSO2Oc4CTgAVYt+2zw9sURWkaVSqKEnkexQ57jQUeC8o/00mvNcbcC7wCDAUODLrmbmPMPViLJ8cYUwZ85JStN8YsNsbsCLp+tjHmT4AHyAl7SxSlCVSpKErkWYz1IVUAvB2ivLFJ+2In9RIUrKqZ10c6aqei7IcqFUWJMMaYfdihryuMMcHebl9z0rtF5GrgXKyjxK+aeORuJz1eRMaLSEScYipKa1DX94rSDhhjnguR/TiQBVwOnAz8G7jKGFMt0qjD2H8C7wInOPcNCGtlFaUNqJdiRVEUJWzo8JeiKIoSNlSpKIqiKGFDlYqiKIoSNlSpKIqiKGFDlYqiKIoSNlSpKIqiKGFDlYqiKIoSNv4f0ff1XHfsLOoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plots of price per year\n",
    "price_2015 = price_of_popular_cryptos_during_a_particular_year(2015)\n",
    "price_2016 = price_of_popular_cryptos_during_a_particular_year(2016)\n",
    "price_2017 = price_of_popular_cryptos_during_a_particular_year(2017)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9613207c",
   "metadata": {
    "scrolled": true
   },
   "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": [
    "price_2018 = prices_of_popular_cryptos[(prices_of_popular_cryptos['Year']==2018)]\n",
    "ax = sns.lineplot(data=price_2018, x='Month', y='price', hue='currency',marker='o')\n",
    "ax.set_title('Price of Coin by Month 2018')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8daaeea2",
   "metadata": {},
   "outputs": [],
   "source": [
    "news_per_year = news.groupby(['Year']).size().to_frame('size').reset_index()\n",
    "news_per_month = news.groupby(['Year','Month']).size().to_frame('size').reset_index()\n",
    "reddit_per_year = reddit.groupby(['Year']).size().to_frame('size').reset_index()\n",
    "reddit_per_month = reddit.groupby(['Year','Month']).size().to_frame('size').reset_index()\n",
    "bitcoinTalk_per_year = bitcoinTalk.groupby(['Year']).size().to_frame('size').reset_index()\n",
    "bitcoinTalk_per_month = bitcoinTalk.groupby(['Year','Month']).size().to_frame('size').reset_index()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ce11ca67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Year   size\n",
      "0  2015      0\n",
      "1  2016      1\n",
      "2  2017   8366\n",
      "3  2018  54885\n"
     ]
    }
   ],
   "source": [
    "news_per_year.loc[-1] = [2015,0]\n",
    "news_per_year.index = news_per_year.index + 1  # shifting index\n",
    "news_per_year.sort_index(inplace=True) \n",
    "print(news_per_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "27f2c58a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vbatch2\\AppData\\Local\\Temp/ipykernel_21432/1746479164.py:14: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(labels)\n"
     ]
    },
    {
     "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": [
    "fig,ax = plt.subplots()\n",
    "index = np.arange(max(len(reddit_per_year['Year']), len(news_per_year['Year']), len(bitcoinTalk_per_year['Year'])))\n",
    "bar_width = 0.2\n",
    "opacity = 0.8\n",
    "\n",
    "\n",
    "ax.bar(index-bar_width, news_per_year['size'], color='r', width=bar_width, label='News')\n",
    "ax.bar(index, reddit_per_year['size'], color='g', width=bar_width, label='Reddit')\n",
    "ax.bar(index+bar_width, bitcoinTalk_per_year['size'], color='b', width=bar_width, label='Bitcoin Talk')\n",
    "\n",
    "ax.set_yscale('log')\n",
    "ax.tick_params(axis=u'both', which=u'both',length=0)\n",
    "labels = ['','2015','','2016','','2017','','2018','']\n",
    "ax.set_xticklabels(labels)\n",
    "\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Amount of content')\n",
    "plt.title('Amount of content regarding cryptocurrency from 2015-2017')\n",
    "plt.legend(loc='best')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8055bbfb",
   "metadata": {},
   "source": [
    "EDA:\n",
    "\n",
    "From our visualization, we see that we don't have any news data for 2015 and 2016. We believe sentiment analysis on news to be a bit more credible than Reddit and Bitcoin Talk because on those online websites, anyone can post a comment. Thus, our data is making us look more closely at 2017 and 2018 which are interesting years for cryptocurrency (meteoric spike for Bitcoin, etc.). Our hypothesis was that positive public sentiment will cause increase in cryptocurrency prices. This is important because with cryptocurrency becoming more mainstream, knowing the mechanisms behind price fluctuations will be paramount for investing, predicting the future, and more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "25feb99d",
   "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>text</th>\n",
       "      <th>post_id</th>\n",
       "      <th>date</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>comp_score</th>\n",
       "      <th>Date_extracted</th>\n",
       "      <th>crypto</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>78901</th>\n",
       "      <td>SuperNET Weekly No. 13CORE and CORE Media were...</td>\n",
       "      <td>79011</td>\n",
       "      <td>2016-06-04</td>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-04</td>\n",
       "      <td>rest</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38830</th>\n",
       "      <td>This week we have several updates about Waves ...</td>\n",
       "      <td>38915</td>\n",
       "      <td>2017-02-13</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-02-13</td>\n",
       "      <td>bitcoin</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78900</th>\n",
       "      <td>WAVES Weekly No. 30This week we have several u...</td>\n",
       "      <td>79010</td>\n",
       "      <td>2017-02-13</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-02-13</td>\n",
       "      <td>bitcoin</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78899</th>\n",
       "      <td>WAVES Weekly No. 31Last week has seen a number...</td>\n",
       "      <td>79009</td>\n",
       "      <td>2017-02-20</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-02-20</td>\n",
       "      <td>bitcoin</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38829</th>\n",
       "      <td>Last week has seen a number of updates that sh...</td>\n",
       "      <td>38914</td>\n",
       "      <td>2017-02-20</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-02-20</td>\n",
       "      <td>bitcoin</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    text  post_id       date  \\\n",
       "78901  SuperNET Weekly No. 13CORE and CORE Media were...    79011 2016-06-04   \n",
       "38830  This week we have several updates about Waves ...    38915 2017-02-13   \n",
       "78900  WAVES Weekly No. 30This week we have several u...    79010 2017-02-13   \n",
       "78899  WAVES Weekly No. 31Last week has seen a number...    79009 2017-02-20   \n",
       "38829  Last week has seen a number of updates that sh...    38914 2017-02-20   \n",
       "\n",
       "       Year  Month  comp_score Date_extracted   crypto  Day  \n",
       "78901  2016      6           1     2016-06-04     rest    4  \n",
       "38830  2017      2           1     2017-02-13  bitcoin   13  \n",
       "78900  2017      2           1     2017-02-13  bitcoin   13  \n",
       "78899  2017      2           1     2017-02-20  bitcoin   20  \n",
       "38829  2017      2           1     2017-02-20  bitcoin   20  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sentiment analysis using vader\n",
    "sid = SentimentIntensityAnalyzer()\n",
    "news['scores'] = news['text'].apply(lambda text: sid.polarity_scores(text))\n",
    "news['compound']  = news['scores'].apply(lambda score_dict: score_dict['compound'])\n",
    "news['comp_score'] = news['compound'].apply(lambda c: 1 if c >=0 else -1)\n",
    "news = news.drop(['scores', 'compound'], axis = 1)\n",
    "news[\"Date_extracted\"] = news[\"date\"].dt.date\n",
    "def text_extracting(data):\n",
    "    data = word_tokenize(data)\n",
    "    for i in range(len(data)):\n",
    "        if data[i] == 'bitcoin':\n",
    "            return 'bitcoin'\n",
    "        elif data[i] == 'ethereum':\n",
    "            return 'ethereum'\n",
    "        elif data[i] == 'tether':\n",
    "            return 'tether'\n",
    "        elif data[i] == 'binance-coin':\n",
    "            return 'binance-coin'\n",
    "        elif data[i] == 'cardano':\n",
    "            return 'cardano'\n",
    "    return 'rest'\n",
    "news['crypto'] = news['text'].apply(text_extracting)\n",
    "news = news.sort_values('Date_extracted')\n",
    "news['Year'] = news['date'].dt.year\n",
    "news['Month'] = news['date'].dt.month\n",
    "news['Day'] = news['date'].dt.day\n",
    "news.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "269d9db4",
   "metadata": {},
   "source": [
    "ML:\n",
    "We used sentiment analysis from Vader on the news dataset, and obtained a \"comp_score\" which is 1 for positive, \n",
    "-1 for negative. By averaging the sentiment over each month per year, we can then compare the prices of coins per \n",
    "year with their public sentiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cd6bc26b",
   "metadata": {},
   "outputs": [],
   "source": [
    "popular_cryptos2 = ['bitcoin','ethereum', 'tether']\n",
    "news_of_popular_cryptos2 = news[news['crypto'].isin(popular_cryptos2)]\n",
    "# This method takes year and returns plots of news per every year\n",
    "def sentiment_of_popular_cryptos_during_a_particular_year(year):\n",
    "    news1 = news_of_popular_cryptos2[(news_of_popular_cryptos2['Year']==year)]\n",
    "    ax = sns.lineplot(data=news1, x='Month', y='comp_score', hue='crypto',marker='o',ci= None)\n",
    "    ax.set(xticks=news_of_popular_cryptos2.Month.values)\n",
    "    plt.title('news of Coin by Month ' + str(year))\n",
    "    plt.show()\n",
    "    return news1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d0c4c42a",
   "metadata": {},
   "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"
    },
    {
     "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": [
    "#plots of sentiment per year\n",
    "news_2017 = sentiment_of_popular_cryptos_during_a_particular_year(2017)\n",
    "news_2018 = sentiment_of_popular_cryptos_during_a_particular_year(2018)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c08ffcfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "popular_cryptos3 = ['binance-coin', 'cardano']\n",
    "news_of_popular_cryptos3 = news[news['crypto'].isin(popular_cryptos3)]\n",
    "# This method takes year and returns plots of news per every year\n",
    "def sentiment_of_popular_cryptos_during_a_particular_year(year):\n",
    "    news1 = news_of_popular_cryptos3[(news_of_popular_cryptos3['Year']==year)]\n",
    "    return news1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ac685d03",
   "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>text</th>\n",
       "      <th>post_id</th>\n",
       "      <th>date</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>comp_score</th>\n",
       "      <th>Date_extracted</th>\n",
       "      <th>crypto</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61623</th>\n",
       "      <td>A $3.3 Billion Claim: Has Cardano's Blockchain...</td>\n",
       "      <td>61733</td>\n",
       "      <td>2018-07-12 04:00:31</td>\n",
       "      <td>2018</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-07-12</td>\n",
       "      <td>cardano</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19831</th>\n",
       "      <td>US exchange Coinbase is reportedly exploring p...</td>\n",
       "      <td>19916</td>\n",
       "      <td>2018-09-08 14:01:38</td>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-09-08</td>\n",
       "      <td>cardano</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56500</th>\n",
       "      <td>The Daily: Coinbase Explores Crypto ETF, Chang...</td>\n",
       "      <td>56610</td>\n",
       "      <td>2018-09-08 14:01:38</td>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-09-08</td>\n",
       "      <td>cardano</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    text  post_id  \\\n",
       "61623  A $3.3 Billion Claim: Has Cardano's Blockchain...    61733   \n",
       "19831  US exchange Coinbase is reportedly exploring p...    19916   \n",
       "56500  The Daily: Coinbase Explores Crypto ETF, Chang...    56610   \n",
       "\n",
       "                     date  Year  Month  comp_score Date_extracted   crypto  \\\n",
       "61623 2018-07-12 04:00:31  2018      7           1     2018-07-12  cardano   \n",
       "19831 2018-09-08 14:01:38  2018      9           1     2018-09-08  cardano   \n",
       "56500 2018-09-08 14:01:38  2018      9           1     2018-09-08  cardano   \n",
       "\n",
       "       Day  \n",
       "61623   12  \n",
       "19831    8  \n",
       "56500    8  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentiment_of_popular_cryptos_during_a_particular_year(2018)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6f128a93",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reddit['scores'] = reddit['text'].apply(lambda text: sid.polarity_scores(text))\n",
    "# reddit['compound']  = reddit['scores'].apply(lambda score_dict: score_dict['compound'])\n",
    "# reddit['comp_score'] = reddit['compound'].apply(lambda c: 1 if c >=0 else -1)\n",
    "# reddit = reddit.drop(['scores', 'compound'], axis = 1)\n",
    "# reddit[\"Date_extracted\"] = reddit[\"date\"].dt.date\n",
    "# def text_extracting(data):\n",
    "#     data = word_tokenize(data)\n",
    "#     for i in range(len(data)):\n",
    "#         if data[i] == 'bitcoin':\n",
    "#             return 'bitcoin'\n",
    "#         elif data[i] == 'ethereum':\n",
    "#             return 'ethereum'\n",
    "#         elif data[i] == 'tether':\n",
    "#             return 'tether'\n",
    "#         elif data[i] == 'binance-coin':\n",
    "#             return 'binance-coin'\n",
    "#         elif data[i] == 'cardano':\n",
    "#             return 'cardano'\n",
    "#     return 'rest'\n",
    "# reddit['crypto'] = reddit['text'].apply(text_extracting)\n",
    "# reddit = reddit.sort_values('Date_extracted')\n",
    "# reddit['Year'] = reddit['date'].dt.year\n",
    "# reddit['Month'] = reddit['date'].dt.month\n",
    "# reddit['Day'] = reddit['date'].dt.day\n",
    "# reddit.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "56c2a7d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# popular_cryptos2 = ['bitcoin','ethereum', 'tether']\n",
    "# reddit_of_popular_cryptos2 = reddit[reddit['crypto'].isin(popular_cryptos2)]\n",
    "# # This method takes year and returns plots of news per every year\n",
    "# def sentiment_of_popular_cryptos_during_a_particular_year(year):\n",
    "#     reddit1 = reddit_of_popular_cryptos2[(reddit_of_popular_cryptos2['Year']==year)]\n",
    "#     ax = sns.lineplot(data=reddit1, x='Month', y='comp_score', hue='crypto',marker='o',ci= None)\n",
    "#     ax.set(xticks=news_of_popular_cryptos2.Month.values)\n",
    "#     plt.title('news of Coin by Month ' + str(year))\n",
    "#     plt.show()\n",
    "#     return reddit1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "41adbe09",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #plots of sentiment per year\n",
    "# news_2017 = sentiment_of_popular_cryptos_during_a_particular_year(2017)\n",
    "# news_2018 = sentiment_of_popular_cryptos_during_a_particular_year(2018)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2f4e9501",
   "metadata": {},
   "outputs": [],
   "source": [
    "# bitcoinTalk['scores'] = bitcoinTalk['text'].apply(lambda text: sid.polarity_scores(text))\n",
    "# bitcoinTalk['compound']  = bitcoinTalk['scores'].apply(lambda score_dict: score_dict['compound'])\n",
    "# bitcoinTalk['comp_score'] = bitcoinTalk['compound'].apply(lambda c: 1 if c >=0 else -1)\n",
    "# bitcoinTalk = bitcoinTalk.drop(['scores', 'compound'], axis = 1)\n",
    "# bitcoinTalk[\"Date_extracted\"] = bitcoinTalk[\"date\"].dt.date\n",
    "# def text_extracting(data):\n",
    "#     data = word_tokenize(data)\n",
    "#     for i in range(len(data)):\n",
    "#         if data[i] == 'bitcoin':\n",
    "#             return 'bitcoin'\n",
    "#         elif data[i] == 'ethereum':\n",
    "#             return 'ethereum'\n",
    "#         elif data[i] == 'tether':\n",
    "#             return 'tether'\n",
    "#         elif data[i] == 'binance-coin':\n",
    "#             return 'binance-coin'\n",
    "#         elif data[i] == 'cardano':\n",
    "#             return 'cardano'\n",
    "#     return 'rest'\n",
    "# bitcoinTalk['crypto'] = bitcoinTalk['text'].apply(text_extracting)\n",
    "# bitcoinTalk = bitcoinTalk.sort_values('Date_extracted')\n",
    "# bitcoinTalk['Year'] = bitcoinTalk['date'].dt.year\n",
    "# bitcoinTalk['Month'] = bitcoinTalk['date'].dt.month\n",
    "# bitcoinTalk['Day'] = bitcoinTalk['date'].dt.day\n",
    "# bitcoinTalk.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6b6a2a87",
   "metadata": {},
   "outputs": [],
   "source": [
    "# popular_cryptos2 = ['bitcoin','ethereum', 'tether']\n",
    "# bitcoinTalk_of_popular_cryptos2 = bitcoinTalk[bitcoinTalk['crypto'].isin(popular_cryptos2)]\n",
    "# # This method takes year and returns plots of news per every year\n",
    "# def sentiment_of_popular_cryptos_during_a_particular_year(year):\n",
    "#     bitcoinTalk1 = bitcoinTalk_of_popular_cryptos2[(bitcoinTalk_of_popular_cryptos2['Year']==year)]\n",
    "#     ax = sns.lineplot(data=bitcoinTalk1, x='Month', y='comp_score', hue='crypto',marker='o',ci= None)\n",
    "#     ax.set(xticks=news_of_popular_cryptos2.Month.values)\n",
    "#     plt.title('news of Coin by Month ' + str(year))\n",
    "#     plt.show()\n",
    "#     return bitcoinTalk1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6832135a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #plots of sentiment per year\n",
    "# news_2017 = sentiment_of_popular_cryptos_during_a_particular_year(2017)\n",
    "# news_2018 = sentiment_of_popular_cryptos_during_a_particular_year(2018)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff7d4318",
   "metadata": {},
   "source": [
    "ML:\n",
    "\n",
    "From our sentiment analysis done on 2017, we can see that public sentiment for Bitcoin has dropped sharply from overwhelmingly positive from August to September. However, from our price graphs, the price of bitcoin increased against our expectations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8896cab4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#plotting price and sentiment in a single plot for bitcoin\n",
    "def sentiment_price_of_popular_cryptos_during_a_particular_year(year,coin):\n",
    "    popular_cryptos2 = [coin.lower()]\n",
    "    news_of_popular_cryptos2 = news[news['crypto'] == 'bitcoin']\n",
    "    news_year = news_of_popular_cryptos2[(news_of_popular_cryptos2['Year']==year)]\n",
    "    prices_of_popular_cryptos = crypto_prices_df[crypto_prices_df['currency'].isin(popular_cryptos2)]\n",
    "    price_year = prices_of_popular_cryptos[(prices_of_popular_cryptos['Year']==year)]\n",
    "    fig, (ax1,ax2) = plt.subplots(nrows=2, sharex=True, subplot_kw=dict(frameon=False)) # frameon=False removes frames\n",
    "    \n",
    "    plt.subplots_adjust(hspace=.2)\n",
    "    plt.title('Fluctuations in price and sentiment of '+coin+ ' for year ' + str(year) , y= 2.2)\n",
    "    ax1.grid()\n",
    "    ax2.grid()\n",
    "    ax1.plot(news_year['Month'], news_year['comp_score'], color='r',label ='sentiment')\n",
    "    ax2.plot(price_year['Month'], price_year['price'], color='b', linestyle='--',label ='price')\n",
    "    ax1.legend(bbox_to_anchor =(1.1, 0.8))\n",
    "    ax2.legend(bbox_to_anchor =(1.32, 1.6))\n",
    "    plt.xticks([1,2,3,4,5,6,7,8,9,10,11,12])\n",
    "    plt.xlabel('Months')\n",
    "    ax1.set_ylabel('Sentiment')\n",
    "    ax2.set_ylabel('Price')\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "770a307d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "price_sentiment_2017 = sentiment_price_of_popular_cryptos_during_a_particular_year(2017, 'Bitcoin')\n",
    "price_sentiment_2017 = sentiment_price_of_popular_cryptos_during_a_particular_year(2017, 'Ethereum')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "18a687fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "price_sentiment_2017 = sentiment_price_of_popular_cryptos_during_a_particular_year(2017, 'Tether')\n",
    "price_sentiment_2017 = sentiment_price_of_popular_cryptos_during_a_particular_year(2018, 'Bitcoin')\n",
    "price_sentiment_2017 = sentiment_price_of_popular_cryptos_during_a_particular_year(2018, 'Ethereum')\n",
    "price_sentiment_2017 = sentiment_price_of_popular_cryptos_during_a_particular_year(2018, 'Tether')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3e1e8cf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MrsA7251xxjpG1MuDPEUq1GAKhJows7HEVsvGjRs3gPA89Jxo/6c/tep3330tNxYUWPO1a1vmKi6AYirm5ihYvrxltxkzOvz3sstmLJk0qTYtitbZDiefXDjg739v9dmee7ba2LJlTu6lbrNkSauu775b8O8rr5y6bNKkNbmI2eW3v2273wUX9O/13HMlk7bfPiffR60nTGhx2OjRW7nZ+kmTJtU65ogRI4ZUP5UIpN7edHbHprbcH6tu2okTJ05LI8c04iqmYm5WB1tsbNas1OH3OYx5joM7dMthzBEx5qAcxtzdwb/q3Hmdx+fK5CBmoYPPOOWUj3O2nOqaZNfQrkEQkfrmvmJ5v36rgGPY1Iy51KM2y5e3ooIG4EQaswZTIJjZ4Wxq17yXmY0xsx3SzEkkX3y2557Lge2AXdPOJY8dk3YCIvWpwRQIwHlsatp2F0LzzEPTS0ckf3y6//4rCM0aayeWgNLmzR0doZE802AKBHcf5u5Wprsr7bxE8sG6Ll1KgKmoQEjEsh13XAnsAAxMOxeR+tJgCgQRSVwx0B+z/mknkm8W7rHHCsIF1irAJG+oQBBpOh6J/aNTzSIPrevceQPwX7RuJY+oQBBpKtznAy+iX7lJKQZ2RhdXS55QgSDStBQDgzDbLu1E8tDDsa8CTPKCCgSRpiWzE9Oh8PrmPg94FRUIkidUIIg0Je5zgNfRTiwpxcAQzLZNOxGRzaUCQaTpKQb2wqxn2onkoeLY1xEaafRUIIg0PZmd2FGpZpGP3D8A3kZHaCQPqEAQaWrcZwMz0E4sKcXAPphtlXYiIptDBYJI01QM7IfZlmknkoeKAUNHaKSRU4Eg0jQVE7b/kSnnkY9mArPRERpp5FQgiDRN7wAfoJ1Y/XN3QgF2AGbd0k5HpK5qXSCY2f6W9aE3s9ZmtnX9piUiidq0E/sOZp3TTicPFQPNgRFpJyJSV3U5gvAccGDW8Ejgk3rJRkRyqRhoARyZdiJ56A1gLjpCI41Yi5pOaGb7A8MIF98cZ5tahNsf2FD/qYlIwqYB8wg7sbtTziW/uDtmxcCPMdsC9xVpp9QYvPbaa1u2aNHiDkKz2ToFnqxSYHpJScmYwYMHL6poghoXCISjBhcTmjQ9NnYZT9c5RRFJR9iJPQyciVkH3FelnVKeKQZ+DhwB3JNyLo1CixYt7ujRo0f/wsLC5c2aNfO088lnpaWltnjx4gELFy68g0qOItamQnsAOJ5wBOE64DhCkXAQcPjmpSoiKXkYaI224SS8DCxApxlqY2BhYeFKFQfJa9asmRcWFn5BOFpToRofQXD3WcAsM+sDLHL3NfWQo4ik6wXgc8JO7L6Uc8kv7qXxCM0YzNrjvjrtlBqBZioOcieu60oPFNTlHM8Q4G0z22BmG2NXUucMRSQ97huBR4DvYdYm7XTyUDFQAByWdiIitVWXAuEWYBvCPdQzYzerPpMSkZwqBtoCh6adSB76D7AEnWZocl577bWC8ePHb/3YY491yIw75phjepvZ4KlTp7bNRQ5//vOfu4wfP37rJUuWNK/L++tSICwHznX3/u6+c6arS3ARaRCeB5ahnVj9cy8BJgJHYFaQcjaSQ2+//Xaba6+9dqtnn3326wLh7LPPXnzrrbd+1L9//3W5yOH+++/vcu211261dOnSnBUITwJnmtkZZnZ07PTMcZHGyn0DMAn4Pmat0k4nDxUD7YGD005EqjZ//vwWe++9d9+2bdsOat++/aBddtllxwULFrT45S9/2aOoqGjndu3aDdp33313mDlzZiuA8ePHb21mg3/wgx9s07t374GdO3fedcKECZ1nz57davTo0dsBXHvttVuZ2eDHHnusw0033VQ4bty47WbNmtV69uzZrcxs8JAhQ/rtv//+O7Rr127QhRde2OPiiy/u3qFDh9123HHHAbNnz24FMGfOnJaHHnro9h07dtxtyy233OWss84q2rhxIwBFRUU7t2nTZtCZZ55Z1KlTp90GDhzYf968eS3Gjx+/9ZQpUzoB7LjjjjsXFRXV+od8bW5zzDg79m+LfSPc+linCkVEGoRi4HTCXUlPpJxLvnkW+IJwhOafKefSeIwe3Yvp0+v3UPzAgV8xYUKlD/a74447urz00ksdzjnnnIW9e/de99prr7UrLi7udNVVVxUdfvjhy3faaaevJkyYsOXxxx+//fTp078+tf7vf/+7wxlnnLHo0ksv7XnxxRcXzZgxY+bo0aMXTZgwYctDDz10+dFHH7180KBBa+68885yMd988812F1100fwPP/yw4Morryzaa6+9Vh177LFL77rrri1/97vfdZ8wYcInJ554Yp8ZM2a0Gzt27OcLFy5sccstt/To2bPn+gsuuGAxwNq1a5stXry45YEHHvjFpEmTutx4442Fo0aNWjZ58uROs2bNanv55Zd/0qdPn1oftajLEYRLgd/EfvbfItJ4PQ2sRKcZ6p/7euBR4EjMWqadjlSub9++6wBeeOGF9h9++GHrUaNGLfvXv/7VCeDxxx/v/Pvf/75oyZIlLWfMmNH2888///pH8dlnn/35r371q0U9e/Zct2DBgtYdO3Ys3XfffVcDDBgwYO3YsWOXFxUVVXgx/6677vrlJZdc8vngwYNXuzu/+MUvPjv33HMXAXz88cetvvjii2avvvpqhy+//LLZtddeu9U//vGPQoCnn366U2YezZo1469//evHP/rRj75+3x577LG2e/fuGwCOPfbYFSeddNIXtV0ftT6C4O6XAJjZFsBXHj78ItKYua/D7DFgJGb/G8+dS/0pBv6H8MC5J1POpXGo4pd+UkaNGvVF9+7d3508eXLHZ555puMtt9zSY4sttigBuOWWW+b06NFjA4SHDHXo0KE0876uXbtuBGjRogWlpWG0mdXods2OHTtuBGjZsqUDdO7ceWOLFi08EyczXb9+/dZcddVVX6+Tzp07b8z83bp169K2bdt6Zh4bN260mEOd1kNGXRpr6m1mrxCuzN3fzJ43Mx1BEGn8ioGuhMenS/16EvgSHaFp0O68887OkyZN6tSrV6/1/fr1Wwtw6aWXfgpwzz33dP34449bPffccx2uuOKKrdq2bVtlAdClS5eNAC+++GL72267rfPq1avrtLfu1KlT6R577LHq/fffb/P88893+OCDD1rffffdXZ944omONXhvCcBtt93WNftuipqqyzUIfwZ6Eq49KAWmAicCv67DvESk4ZgMfEXYiT2bci75xX0NZo8TjtCcFZ8/IQ1M27ZtS//5z392XrBgQetWrVqVHn744ctPO+205QsXLmx59913F55//vnbdu/eff2IESOWVzevQw45ZPXee++96tVXX23/0ksvdTjooIPermte991335yzzjqr15133rllSUmJ9e3bd82pp566tLr3nXnmmYtffvnlDtdcc83WL7744qojjjiiVo9Tr0uBsA9wFXBZHP6QUDCISGPm/hVmTwBHYfYj3EurfY/URjHhcfX7Em4tlQbmhBNO+OKEE04od67+iiuuWHjFFVcsLDv+mmuuWXDNNdcsyAy///77MzJ/FxQU+AsvvPBe9vTFxcVzCa18AuDur9XktT59+mx44oknPqoo5/nz57+T+Xv//ff/Kvt9hxxyyJcLFix4p6L31URdLlJcwqZnN29JOHqwoPLJRaQRKQa2AvZOO5E89C9gLTrNII1EXQqE2wlFgQF/J9zbe2t9JiUiqXkcWI92YvUvtMXw/4CjMVNTxtLg1fpD6u5XEu6XfojQEtzp7v6H+k5MRFLgvpJwQd3Rm30JtFSkGCgCvp12Ig1UafaV+5KsuK4rPZVYpyrW3e8GfgiMAx4zsy51S09EGqBiYFtgcNqJ5KF/AhvQEZrKTF+8eHEnFQnJKy0ttcWLF3cCplc2Ta0vUjSz04CrgS2yRntd5iUiDdKjQAlhJzYt5Vzyi/sKzJ4BjsHsfNzVtHGWkpKSMQsXLrxj4cKFA6njD1ipsVJgeklJyZjKJqjLTv1qwmOV/wvoVh2RfOO+DLPnCDuxC7QTq3fFhGu5dgPeSDeVhmXw4MGLgCPTzkOCulRoi4AL3H1/dz8w09V3YiKSqmJgBzbdsST1ZxLh15tOM0iDVpcjCKOBf5rZtwnPbgdwd/9J/aUlIimbCNxC2InV+T5qqYD7YsyeJ6zbi9JOR6QydTmC8DugC/AD4JysbrOY2VAze9vM1pnZ62a2++bOU0TqyP1z4D/A0WmnkqeKgR0x6592IiKVqUuBMAD4G3AIoeGRA4HvbE4SZlZA2GA6AD8DugMPmZmakBZJTzGwM2Y7pJ1IHnok9nWaQRqsuhQItwGtgY8Ihx4z3eYYTigKbnb3m4G/AH2AYZs5XxGpu4djXzux+ua+AHgRrVtpwKy2FyibWSnhtsZs7u51vs3RzMYT7o442d3vNbOxhKczjnX327OmGwuMBRg3btyA4cOHz6xrzM3QH5ilmIrZFGIO+8lPdjR3e+6GG2r13h0efLBwwN//vs3kO+98a13nzjlpOrrnlClbDL7uuu3/feWVM5f1778mFzG7zJzZdr8LLuj/8v/937qFe+1V6f3kFel3773dd3zggZ7P3HjjO6t79Vpf0/e1XrasxWGjR+/69pgx6+cccUStf5yNGDFiSG3fI02Uu9eqA6YAz5XtajufMvMcTyg6TorD4+LwmMreM3HixGmbE7OuXRpxFVMxU4sJ5zu4w7a1fN858X3dcracMCLGHJTDmLs7+LSf/ez9Ory3T8z3vFq+r9DBZ5xyyse5/hypa1pdXR61PMyzbm/0+rnNcU7sZ1qFLCozXkTSURz7ulixvrnPAV5HpxmkgarxaQEzuwGYQLjNsSz3zbvN8QnC8xXONLNVwBmEZi+nbMY8RWRzuX+I2VuEndi1aaeTh4qBKzDrifunaScjkq02RxDOAfryzVsb6+U2R3dfCxwHrAauJxQLx7m7ntQokr5iYB/Mtko7kTykIzTSYNXmwsIDgZmxX+/cfSqwcxLzFpHNUgxcChwF3JxyLvnFfTZmMwhHaG5IOx2RbDU+guDuz7v7YuBU4Ks4/Dzh1/7QpBIUkZS5zwTeRefKk1IM7IdZ97QTEclWl+cgnAb0zhreA7isPpIRkQarGDgAs25pJ5KHigEDRqach8g31LhAMLOfmNlHhNsP/2RmH8Xhm4AVCeUnIg1DMaEV1xFpJ5KH3gE+QEdopIGpzRGEtkBh/Ltj/LsbsAz4dT3nJSINy5uE2461E6tv7k4owA7ErEva6Yhk1OYahCvdvQMwFRju7h3cvaO7b+vuNyWXooikbtNO7LuYbZFyNvmomHDR+JFpJyKSUZcHJR0IbDCzk83sB5kugdxEpGEpBloCR6SdSB6aBsxDR2ikAal1+wlmdg8wKnsU4bqEv9ZXUiLSIL0CzCfsxO5JOZf84u6YPQychVlH3FemnZJIXRpY+j7wGuHXRE4aYRGRBsC9NO7EfohZe9xXp51SnikGfgocDvwj3VRE6lYgPAe86O6/q+9kRKTBKwZ+BBwGPJRyLvnmBWAh4amKKhAkdXUpELoCl5vZEcDyOM7dXbc/ieS//wCLCacZVCDUp3CE5hHgVMza4v5V2inl0muvvbZlixYt7gAGUrdn9DRWpcD0kpKSMYMHD16UdjLZ6lIgDC3Th3ANgojkO/eNmE0ERmFWQGhHRepPMXAmcCjwSMq55FSLFi3u6NGjR//CwsLlzZo1azL7lNLSUlu8ePGAhQsX3kEDu4ulLlVanwq67eozKRFp0IqB9sDBaSeSh54nPFumKd7NMLCwsHBlUyoOAJo1a+aFhYVfEI6cNCh1uc3xY6AToeGW5sA2hEMkItI0PEd4empT3Ikly70EmAR8H7PWaaeTY82aWnGQEZe7wZ1WqXVCZnYi4S6GqwltMvwatUIm0nS4rwceBY7ErGXa6eShYsLTag9KOxFp2upSsfwGeDZr+HFgn/pJR0QaiWKgMwk1/97EPQ2sREdoGryioqKd27ZtOyjtPJJSl4sUtwYmAN+NwxuANvWWkYg0Bk8Cqwk7sSdTziW/uK/D7DFgBGbj4mkHaWBKSkr4wx/+MG/9+vWWdi5JqcsRhHeAzKOV/we4CHir3jISkYYv3L3wODASs+Zpp5OHigm3lB+QdiJp+fa36Ve2u+qq0GDgqlU0q+j1G26gK8Bnn9Gi7Gs1iTl79uxWZjZ4jz326HfwwQdv36FDh91GjhzZZ82aNVZUVLRzmzZtBp1yyinbdOjQYbdXXnmlzXnnnbfNWWed1Qdg7dq1dvbZZxdtvfXWOxcUFOw+ZMiQfgBLly5tftxxx/Xu0qXLrp07d9511KhR265atarBXW9Qkbok+XOgB+ERy6cSns1+bn0mJSKNQjGwJbBv2onkocnAV+g0QyreeOON9nvttdfqvffee9WkSZO6XH311YUAa9eubbZw4cKWl1xyyadbb731N47sXHTRRT1uvvnmHjvssMOaK6+8ct4uu+zyFcC4ceN6PfLII12OP/74paNGjVrywAMPdPv5z3++dRrLVVu1OsVgZubuL5rZt4C9gd2BD9z95USyE5GG7AlgLWEn9nzKueQX968wewI4CrNzcG9yd4q98gqzK3utQwdKq3p9q60oqer16uy6666rL7vsss9nzJixYuDAgVtMnTq1Q+a1+++/f27Xrl03ln3P5MmTtzAzJk6c+FHnzp2//n8988wznTZu3Gi33npr98y4KVOmdKxrbrlU4wLBzJ4hPBDpu4RHgd6W9dq33P3y+k9PRBos99WYTQaOxuynTXEnlrBiQvG1D+EJlpJjoZXzTdq0aVNaUXGQYVbx5QjdunXbcPvtt8/JDBcUFDSK2zlrc4phIOGcI8D/xv5lhF8OP6zPpESk0SgGioBvp51IHnocWI9OM+TcW2+91f5Xv/pV9/Hjx/cEOOCAA1ZV957DDjtsRWlpKSNHjtzuuuuu6zp69OheAAcddNAXS5YsaTlx4sQt5syZ0/qhhx7qfO+993ZJehnqQ20KhE7AUjPrBAwC5rn7JcDdhPOQItL0PEa4k0k7sfoWmnx+knCEJm+vlG+Idt9999UvvfRS+xdffLHDkUceuWz8+PGLq3vP5ZdfvvCss85a+N5777X5xS9+se3bb7/dFuDWW2/95IQTTljy2GOPdbnwwgt7vf766+2GDh3aKFpCrc01CHMJFygeRygsJsfx2wBL6zctEWkU3Fdg9jRwDGbnlzsmK5urGDgCGAK8mnIuTUa7du02PvXUUx9mj5s/f/47ZafLHldQUOA33XTT/Jtuuml+9jRdu3bdeN99930MfJxYwgmpzRGEXwH9CG2VLyU8SRHgROCles5LRBqPYkKbLLulnEc+ehQoQUdoJAU1PoLg7g+a2bOEhplmuftqM2sBnERow1xEmqZJwK2EndgbKeeSX9yXYfYc4QjNL3WEJln9+vVb7+6vpZ1HQ1Gr5yC4+1J3f9XdV8fhEnd/y90/TyY9EWnw3JcQLlbWr9xkFAPfAnZOO5GElZaWljbJay3icje4u4AaxdOcRKTBKwZ2xKx/2onkoYmEW8yPTjmPpE1fvHhxp6ZWJJSWltrixYs7AdPTzqWsurTFICJS1iPAnwhHEfRMlPrk/jlm/yas20tSziYxJSUlYxYuXHjHwoULB9K0fryWAtNLSkrGpJ1IWSoQRGTzuX+G2QuoQEhKMXA9Zn2B5Wknk4TBgwcvAo5MOw/ZpClVaSKSrGJgN8y2SzuRPPRI7Os6D8kZFQgiUl+0E0uK+yfAK2jdSg6pQBCR+uE+F3gN7cSSUgwMBnqnnIc0ESoQRKQ+FQN7Aj3TTiQPFcd+vt/NIA2ECgQRqU/aiSXF/UPgLXSERnJEBYKI1B/39wj3c++Qdip5qhitW8kRFQgiUt+Kq59E6kjrVnIm9QLBzIab2TtmVmpmbmbd0s5JRDaLdmJJcZ8JvJt2GtI0NIQHJbUFpgIFhOeNi0jjNh14n/QOhbfFrH3OYuVeMXBhCnGliUm9QHD3YqDYzKagAkGk8XN3zB4CfkloqjhXMrH+k8OYAJQ2b57LcA8BF5a2aKGWHSVR1lBaD40FwgFAoYfW4SqaZiwwFmDcuHEDhg8fPjN3GX6tPzBLMRVTMSvXcvXqZt2mTx/w2V575awBmuZr19o2zzwzsPUXXyzKVUyAja1bl354xBHdSlu3ztn6LZo6tdOi3XffekP79rWOOWLEiCFJ5CR5yN0T74BPCa2Rle1Oy5pmShzXrSbznDhx4rRc5N4Q4iqmYipmw43Z1JZVXdPpcnWK4QCgZQXjP8tRfBEREamFnBQIHh7wUSEz24FQQGwVR51iZu+7++O5yE1ERETKS/0aBDM7DbizzOjn3X1YNe8b6+63JZVXQ4qrmIqpmA03Zlpx01pWaTpSLxDqysymuXvOL7ZJI65iKqZiNtyYacVNa1ml6Uj9QUkiIiLS8KhAEBERkXIac4GQ1rm3NOIqpmIqZsONmVZcXX8giWq01yCIiIhIchrzEQQRERFJiAoEERERKUcFgoiIiJSjAkFERETKUYEgIiIi5ahAEBERkXJUIIiIiEg5KhBERESkHBUIIiIiUo4KBBERESlHBYKIiIiUowJBREREylGBICIiIuWoQBAREZFyGm2BMGnSpMlNJa5iKqZiNtyYacVNa1ml6Wi0BQLQrQnFVUzFVMyGGzOtuGktqzQRjblAEBERkYSoQBAREZFyVCCIiDQiZrQ34w9vvFHYLu1cJL+pQBARaVzaAOd++OEWbdNORPKbCgQRkcZlJXD0nnt+9kXaiUh+U4EgItKIuLPOnUd69Vq9Pu1cJL+pQBARaUTMaGXGYQsWtGuVdi6S31QgiIg0Lp2AJ154YetOaSci+U0FgoiIiJSjAkFERETKUYEgIiIi5ahAEBERkXJUIIiINC5fAIcMHTp/RdqJSH5TgSAi0oi4s96dp7ba6qsNaeci+U0FgohII2JGazOOmzevQ+u0c5H8pgJBRKRx6Qg88MorPTqmnYjkNxUIIiIiUk6iBYKZDTWzt81snZm9bma7VzJdLzObZGZfmtkXZvb3JPMSERGRqiVWIJhZAVAMdAB+BnQHHjKz5mWmM+AR4GDgD8D5wOKk8hIREZHqtUhw3sMJRcH57n6zmfUAfgUMA57Jmu5AYDBwBXAVsM7dPcG8REREpBqW1L7YzMYDVwMnu/u9ZjYWuBUY6+63Z013DnAj8C7QD/gSuNDdb6hgnmOBsQDjxo0bMHz48JmJJF+1/sAsxVRMxVTMNOJu2GA2a1bXtj17rtq2S5d1tf4OHDFixJAk8pI85O6JdMB4wIGT4vC4ODymzHQ/j+PfBkbEfinQt6r5T5w4cVpSuTe0uIqpmIrZcGM2tWVV13S6JC9SnBP7PWO/KDPezArMLNOW+dzYf9zdJwGPAwb0STA3EZFGyYw2Zoz+6KNOBWnnIvktyWsQngAWAWea2SrgDEIxMAUoAWYAA4F/xemOMbMPgGOB1cAbCeYmItJYtQf+8vrrW85LOxHJb4kdQXD3tcBxhJ399YQi4Dh331hmujWEomAdcBPwFXC0uy9KKjcRERGpWpJHEHD3qcDOFYy3MsP/rmg6ERERSYeepCgi0ri0B3j88e16pJ2I5DcVCCIijUtXgOXLC1pVN6HI5lCBICLSuMwHGDnygwVpJyL5TQWCiEgjZOZ64qwkSgWCiEjjUgTwyCM7FFU3ocjmUIEgItK4dEk7AWkaVCCIiDQuHdNOQJoGFQgiIiJSjgoEEZHGZUXaCUjToAJBRKRxeQ9gv/0+XZJ2IpLfalQgxNYXf29mb5rZUDO7wcxOSDo5EREpZxFw0jHHvL8w7UQkv9X0CML1wHhCewmtgebAeUklJSIileoO3Pvgg331qGVJVE0LhKOBP2QNvwb0rf90RESkGlsC/Pe/Rd3STkTyW00LhFIguwXGXQnNOIuISG61TTsBaRpq2tzz44RTDAB/A3oAdySSkYiIVKVd2glI01DTAuGnhCMIhwMtgbvRNQgiImnQEQTJiRqdYnD3le5+urtvGbvR7r6yuvfFOx7eNrN1Zva6me1exbSFZrbEzNzMzq3NQoiINCF61LLkRE1vc5xiZtdkDV9rZs9V854CoBjoAPyMcOXtQ2bWvJK3XA+0qVHWIiJN17K0E5CmoaYXKX4beCdr+G1gz2reM5xQFNzs7jcDfwH6AMPKTmhmw4HvA7+rYT4iIk3VRwDNmpWquWdJlHkNmhQ3s7mEAuEEwrUIDwA7uXvvKt4zHrgaONnd7zWzscCtwFh3vz1ruvbADMJtlKuBO4Hz3P2PFcxzLDAWYNy4cQOGDx8+s2aLWa/6A7MUUzEVUzHTiDtt2pbtL798734AEydOeq227x8xYsSQ+s9K8pK7V9sBVxJudSwBNgAbgSuqec94wIGT4vC4ODymzHSXETasvsD5cZorgc5VzX/ixInTapJ7fXdpxFVMxVTMhhsz13HB+4D7kCGfLU1jWdU1na6mdzH8GlhDOA3gwD+Bq6p5z5zY7xn7RZnx8fqEUndfD/QCdgRmZ733/4AvgctrmJ+ISFOxGmDHHZetDneciySjRgWCu28ALo1dTT1BeGb4mWa2CjgDmAtMIRyJmAEMBP4EPBbfMww4G/gr8FAtYomIiEg9qvIiRTNbaWZHxX7Z7ouq3uvua4HjCNXu9YRi4Th331hmumnu/pC7PwRMi6Pfcfd367xUIiIislmqO4KwlHDNwTLCqYVacfephAaeyo63CibH3e8C7qptHBGRJuQL4JChQ+f/EQaknYvksSoLBHfvY2bNgCOBxe7+WW7SEhGRirizHnhq0qSvNqSdi+S3ap+D4O6lwPOE5xqIiEiKzGhtxrHz5nVonXYukt9q+qCke4Ej4jMLREQkPR2BB195pUfHtBOR/FbT2xxPJTwG+Qsz+zKOc3fvlExaIiIikqaaFgiLE81CREREGpQqCwQz6wz8HlgFzAMucPe3c5GYiIiIpKe6Iwg3E9pfgPBQo53N7FvxwUkiIiKSp6q7SPEQQpPNA4ALCY9N1o23IiLpWQHsc8ABny5POxHJb9UVCJ2Bh+JTDW8ntOTYOfGsRESkQu5scOfFwsI1JWnnIvmtJrc5nmdmjwJ/IzxN8bdm9qiZTUo2NRERKcuMAjNO/+ijTgVp5yL5rSZ3Meweu4y9Yr/Wj14WEZHN1gGY8PrrW85LOxHJb9UVCH1ykoWIiIg0KNW1xfBxrhIRERGRhqOmj1oWERGRJkQFgoiIiJSjAkFEpHFZDux80EHzlqWdiOS3RAsEMxtqZm+b2Toze93Mdq9gmr3N7AUzWxG7YjMrTDIvEZHGyp0SoPjzz9u2SjsXyW+JFQhmVkB4CmMH4GdAd+AhM2teZtK+wBLgF8C/gKMJ7T+IiEgZZhQBfZ94oo9+SEmikjyCMJxQFNzs7jcDfyHcNjmszHT/cPcj3f1WYFwct1OCeYmINGbrAXr1WrUm7UQkv5l7Ms87MrPxwNXAye5+r5mNBW4Fxrr77ZW851jgQeBqdz+3gtfHAmMBxo0bN2D48OEzE0m+av2BWYqpmIqpmGnEXbasdYvRow/bdcyYt9cfccScd2r7/hEjRgxJIi/JQ+6eSAeMJzxt8aQ4PC4Oj6lk+qHASmAa0L66+U+cOHFaUrk3tLiKqZiK2XBj5joueCG4n3LKjI/TWFZ1TadL8hTDnNjvGftFmfFmVmBmX19gY2b7A5OBD4FD3X11gnmJiIhINZIsEJ4AFgFnmtmZwBnAXGAKsAZ4HSDe2fAE0JzQYuTBZvb9BPMSEWnMVgPn7LLLYv2QkkQlViC4+1rgOMKH+XpCsXCcu28sM+kuQFugDXAT8A/gxqTyEhFpzNxZ485Nffuu0EWKkqhEn4Pg7lPdfWd3b+Xug9x9Whxv7j4w/n1XHM7ueieZl4hIY2VGCzN2Wb68dU1a4xWpMz1JUUSkcekMvDV5cu+uaSci+U0FgohI49ID4P77d+xZ3YQim0OHqEREUmBGS8LRgM7AFu68HMcfBOyd9VpnoKU7R8S3FqeQrjRBKhBERDaDGVtcc03HAjNaubPejF2A/fnmDr4z8AN3VprxS+BCoF2Z+bRxZy1wJPBj4CtCw0zLgaVmmDsO3Atc3L79+hJQcwySHBUIIiJ1ZMY2wAfjxx/YEvgWMBM4ELguTvIlm3by7QgPg3sLuC1rfKbL3OF1AXC+O+sqCfsgcHFBQclGFQiSJBUIIiJ11w1oecghcz9/8snen8VxdxJu117hHtpNyObOvwgN01XInS+riTkT2Hj22W/OhX3qlrVIDegiRRGRzbTTTktXubMcwJ2V7iyqqDioD/EpuC0GDdKDkiRZKhBERESkHBUIIiJ1twS4rUePLxM5WiCSJhUIIiJ15M48d8b167dcjz2WvKMCQUSkjsywePuhSN5RgSAiUneDgNKpU3t2SjsRkfqmAkFERETKUYEgInljwwaztHMQyRd6UJKI5AUzboYjdyfcWTAbWAesj/1X3bk8TncZ0CGOz3TT3ZkUXz8RsKz3rgM+cefd+PqArNd2A5g+vWuH3CylSO6oQBCRfHFm7HcltGPQmvB449bAgqzpRgLbxPGt47j7IRQIhMcgl93h/wUYY4YB0wkFxNeeeqp393pZApEGJNECwcyGArcA/YAZwBh3f72C6UYCfwR6Ai8Bp7v7nCRzE5G8cxdwGjDCnUcrm8idnTN/xx1+C755unUXoIDQ0EGmiFic9fqJWeN3B8486aRZn0D/elkIkYYisWsQzKyA0CxpB+BnQHfgITNrXma6HsB9hEZMzgMGA3cnlZeI5Cd3Tp84cdJrVRUHFbzH3dmQ3TCSO3Pdededt9151Z3/uDM7a/oH3PmbO3e4c5Y7dvzx7y1KYplE0pTkRYrDCUXBze5+M+EQXR9gWJnpRhEq8Svd/UbgEWA/M9s+wdxERESkCuYJPeHDzMYDVwMnu/u9ZjYWuBUY6+63Z013A/AjYKi7v2BmvwV+CRzi7k+VmedYYCzAuHHjBgwfPnxmIslXrT8wSzEVUzEVM+W4dYo5YsSIIQnkIvnI4zGz+u6A8YADJ8XhcXF4TJnpbojj94nDV8bh71Y1/4kTJ05LKveGFlcxFVMxG27Mpras6ppOl9yM4ai4oz8/Dl8ahw8iXgAUx/8sjj8+Dv81Dm9fzfzHprLCUoirmIqpmA03ZlNbVnVNp0vyFEMB8DHhdqPfAxcR7h3+FlACzHD3gWa2FTCXcOvQXcBvgTfdfb9q5j/N3XN+qCyNuIqpmIrZcGOmFTetZZWmI7GLFN19LXAcsBq4HlgEHOfuG8tM9xnhQsUtCLc6vkG4VUlERERSkuhzENx9Kmy65zhrvJUZfhh4OMlcREREpOYac1sMtzWhuIqpmIrZcGOmFTetZZUmIrFrEERERKTxasxHEERERCQhKhBERESknEZXIJjZDWb2uZm5mT2Wo5g7mNlzZrbUzFaZ2VO5eBS0mb0c431lZtPMbP+kY8a4BWY2O67jP+Ug3twYK9O9mYOYW5jZX81shZmtNrOpCcc7rcwyZrreCcf9aVy/68xsjpn9KMl4MeZoM/vQzNaY2f8zs6KE4lT4XWBmQ83s7bjMr5vZ7jmImdj3UkXzTus7SZqWRlcgRPflOF4RYV1dDNwJfBe4IwdxXwB+DFxGaHc+FzEBfk1oWTOXphJudx0F/CIH8SYAJxPaCPkp8EHC8Z5n0/L9D+GZIJ8D85MKaGY7ANcCpYQnm7YEbjCzXgnGHEL4nM4n/B+HEVp0Tco3vgtq2khcfcaswfgkYqb1nSRNSdpPaqpLB/QmPG3xsRzFa1VmeCmwKAdxDegGfBv4Eng3BzF3AdYQWtZ04E85iDmX8JCsDjn6f24Xl+0eQpO+zXMRNyv+sTH+bxOO0y/G+Xf8exqwFihMMObPY8yT4/CLhAKla0LxvvFdwKYnuJ4Xh79+gmtSMasbn9BypvKdpK5pdY31CEJOufv6zN/xF1IXwi/epHUitEP/MuEX55gkg5lZM8KvkJuAV5OMVYEfACvNbJGZnZFwrAGxvweh8PrSzH6XcMxs4wg7zURvU3P32cD/AUOBd4FBhMfzLk4wbKbZ433NbEdgB0Kh2zvBmNn6xH7myMynsb9djuLnRIrfSdKEqECoBTPrB0wi/OJN/Fwu4SmUhxBOMxQQfg0l6XTCF/lfCYcwATqZWWHCcW8HjmfTofdbzaxP1W/ZLK1jvx1wAvBf4Hwz+26CMQGI54kPAia7+9yEYxUSPqdvAiOBt4A/mVmSp48eIKzP/yW0NNgqjl+bYMyqZB7Klpf3c6fwnSRNiAqEGjKzAYTzyCXAdzw8IjpR7l7i7k+5+43AK8CBZtYtwZC9gELCjuSeOO4UQgubiXH3K9z9IXe/B7gfaA70TTDk3Nj/t4eneD4Qh3Nxkdc4wk4ryfPyGQcSCr2H3X0S4WmlHYC9kwro7uuA/QnXzAwkHP1aC3yUVMwy5sR+pggqKjM+b6TxnSRNS6KPWk6CmR1O+OIB6GVmY4Dn3f39BGP2AqYQDuNdBOxpZnu6e2IXJZnZoYRf1S8Qdtz7EC5qW5pUTMKOcnr8eyfgEmAyCe7MzGxnQgNdTxA+jz8gXAPxTlIxgdfj/A8ysx8SjpxsJPzyTYyZtSK0MzIP+FeSsaLMTvkUM/uMcFEmwHtJBYwXA15DaFNlD8LFc9e4+5oEYlX0XfAy4TTHmWa2CjiDUBBOSTDm84SCNpHvpUpiziZcjJmz7yRpgtK+CKK2HWFD9zLdaQnHHFZBTE845h6EnfUaYAXwHLBHDtdzZpkTvUgR2Iqws1xCaPlzGnBoDpZvJ8IFdGsJO8yTchDzxLhOL8rh/3E84ddz5lf82QnHa0Y4pbGWUMzeCLROKFaF3wWEIxjvEE5XvQEMyUHMxL6XqoiZ0+8kdU2v06OWRUREpBxdgyAiIiLlqEAQERGRclQgiIiISDkqEERERKQcFQgiIiJSjgoEEcDMeme1sHhR1vgJmfGbMe8BZnaJmQ3LGndXnO+QzctcRCQZKhBEyjvdgnbAcfUwvwGEVveG1cO8RERyQgWCyDd9RGjYZxihnYaWxIZ/YtFwoZl9bGarzOw5M9spvnZJPCLwJzN7z8wWm9lxZtYbeDDO++I4zbCseEeZ2Twz+8TM9ovzGmlm75vZOjNbYGZX52LBRUSyqUAQ+aZZhMf1jo7dRMKTLCE8kvly4G3gQsLTLieZWcus93+X0BpmJ+AqQmuc18XXioFRwMys6Q8ktOrYk/Boa4DfAG2As4A/ElqcFBHJKRUIIuVNIJxaGArcmTX+e7E/3t1vILSitz3fbFjqGne/nnAkore7f8mmNh6mu/t97r4oa/pL3P1yYB2bmkR+n/CM/YMI22iizUKLiFREBYJIefcRGm/6FHiqgterumBxWeyXsGn7qun0zePfJxMaGfoMOA94qfqURUTqlwoEkTLcfSXh9MI4dy/Neunx2L/GzH4EHAl8SPWtIy6P/f3M7EQza1PN9FcCWxKa3V4EFJpZo2t5VUQaN33piFTA3e+vYPRdQBHwQ+A7wKvAOe6+wcyqmt1/gGcIrQx+h9B8d1VaAb8knGZYEGOU1CZ/EZHNpdYcRUREpBydYhAREZFyVCCIiIhIOSoQREREpBwVCCIiIlKOCgQREREpRwWCiIiIlKMCQURERMr5/0zL2IkdF++3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "price_sentiment_2017 = sentiment_price_of_popular_cryptos_during_a_particular_year(2017, 'binance-coin')\n",
    "price_sentiment_2017 = sentiment_price_of_popular_cryptos_during_a_particular_year(2017, 'cardano')\n",
    "price_sentiment_2017 = sentiment_price_of_popular_cryptos_during_a_particular_year(2018, 'binance-coin')\n",
    "price_sentiment_2017 = sentiment_price_of_popular_cryptos_during_a_particular_year(2018, 'cardano')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d2f7ae15",
   "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>Date_extracted</th>\n",
       "      <th>price_raise/drop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1132</th>\n",
       "      <td>2018-02-06</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1133</th>\n",
       "      <td>2018-02-07</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1134</th>\n",
       "      <td>2018-02-08</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1135</th>\n",
       "      <td>2018-02-09</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136</th>\n",
       "      <td>2018-02-10</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1137 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Date_extracted  price_raise/drop\n",
       "0        2015-01-01                 1\n",
       "1        2015-01-02                -1\n",
       "2        2015-01-03                 1\n",
       "3        2015-01-04                 1\n",
       "4        2015-01-05                 1\n",
       "...             ...               ...\n",
       "1132     2018-02-06                -1\n",
       "1133     2018-02-07                 1\n",
       "1134     2018-02-08                -1\n",
       "1135     2018-02-09                -1\n",
       "1136     2018-02-10                -1\n",
       "\n",
       "[1137 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#label creation depending on price variation\n",
    "crypto_prices_df[\"Date_extracted\"] = crypto_prices_df[\"date\"].dt.date\n",
    "crypto_prices_df = crypto_prices_df.sort_values('Date_extracted')\n",
    "crypto_prices_df['price_variation'] = crypto_prices_df['price'].diff()\n",
    "crypto_prices_df['price_raise/drop'] = np.where(crypto_prices_df['price_variation']>0, 1, -1)\n",
    "crypto_prices_df = crypto_prices_df.groupby(['Date_extracted'])['price_raise/drop'].sum().reset_index()\n",
    "crypto_prices_df['price_raise/drop'] = np.where(crypto_prices_df['price_raise/drop']>0,1,-1)\n",
    "crypto_prices_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69f87a2a",
   "metadata": {},
   "source": [
    "ML:\n",
    "\n",
    "From the public sentiment, we can then add a label of whether or not the cryptocurrency price increased (+1) or decreased (-1) the next day. This will be our truth values for our machine learning model as our goal is to predict whether or not the price will increased based on public sentiment. We take a further at Bitcoin, which is by far the most valuable and most well-known coin. The ML model can be extended to the other coins fairly easily. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c95bcf3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date_extracted</th>\n",
       "      <th>comp_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-02-13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-02-20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-06-15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>2018-12-29</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>2018-12-30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>474 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Date_extracted  comp_score\n",
       "0       2017-02-13           1\n",
       "1       2017-02-20           1\n",
       "2       2017-06-15           1\n",
       "3       2017-07-26           1\n",
       "4       2017-08-15           1\n",
       "..             ...         ...\n",
       "469     2018-12-27          -1\n",
       "470     2018-12-28          -1\n",
       "471     2018-12-29           1\n",
       "472     2018-12-30           1\n",
       "473     2018-12-31           1\n",
       "\n",
       "[474 rows x 2 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Bitcoin \n",
    "news_bitcoin = news[news['crypto']=='bitcoin']\n",
    "news_bitcoin = news_bitcoin.groupby(['Date_extracted'])['comp_score'].sum().reset_index()\n",
    "news_bitcoin['comp_score'] = np.where(news_bitcoin['comp_score']>0,1,-1)\n",
    "news_bitcoin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5e5cefad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Date_extracted</th>\n",
       "      <th>comp_score</th>\n",
       "      <th>price_raise/drop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-02-13</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-02-20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-06-15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>2018-02-06</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>2018-02-07</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>2018-02-08</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>2018-02-09</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>2018-02-10</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Date_extracted  comp_score  price_raise/drop\n",
       "0       2017-02-13           1                -1\n",
       "1       2017-02-20           1                 1\n",
       "2       2017-06-15           1                 1\n",
       "3       2017-07-26           1                -1\n",
       "4       2017-08-15           1                 1\n",
       "..             ...         ...               ...\n",
       "145     2018-02-06           1                -1\n",
       "146     2018-02-07           1                 1\n",
       "147     2018-02-08           1                -1\n",
       "148     2018-02-09           1                -1\n",
       "149     2018-02-10           1                -1\n",
       "\n",
       "[150 rows x 3 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#combining price and sentiment for bitcoin\n",
    "bitcoin_price_sentiment = news_bitcoin.merge(crypto_prices_df, on = 'Date_extracted',how = 'inner')\n",
    "bitcoin_price_sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c366b7cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date_extracted</th>\n",
       "      <th>comp_score</th>\n",
       "      <th>price_raise/drop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-06-04</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-02-13</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-02-20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-06-15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>2018-02-06</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>2018-02-07</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>2018-02-08</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>2018-02-09</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>2018-02-10</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>164 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Date_extracted  comp_score  price_raise/drop\n",
       "0       2016-06-04           1                 1\n",
       "1       2017-02-13           1                -1\n",
       "2       2017-02-20           1                 1\n",
       "3       2017-06-15           1                 1\n",
       "4       2017-06-27           1                -1\n",
       "..             ...         ...               ...\n",
       "159     2018-02-06           1                -1\n",
       "160     2018-02-07           1                 1\n",
       "161     2018-02-08           1                -1\n",
       "162     2018-02-09           1                -1\n",
       "163     2018-02-10           1                -1\n",
       "\n",
       "[164 rows x 3 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#combining price and news for all coins\n",
    "news = news.groupby(['Date_extracted'])['comp_score'].sum().reset_index()\n",
    "news['comp_score'] = np.where(news['comp_score']>0,1,-1)\n",
    "price_sentiment = news.merge(crypto_prices_df, on = 'Date_extracted',how = 'inner')\n",
    "price_sentiment\n",
    "#news"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "adc31dc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #combining price and reddit for all coins\n",
    "# reddit = reddit.groupby(['Date_extracted'])['comp_score'].sum().reset_index()\n",
    "# reddit['comp_score'] = np.where(reddit['comp_score']>0,1,-1)\n",
    "# reddit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6a732e13",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #combining price and reddit for all coins\n",
    "# bitcoinTalk = bitcoinTalk.groupby(['Date_extracted'])['comp_score'].sum().reset_index()\n",
    "# bitcoinTalk['comp_score'] = np.where(bitcoinTalk['comp_score']>0,1,-1)\n",
    "# bitcoinTalk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "926a72c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# news_reddit = news.append(reddit, ignore_index=True)\n",
    "# news_reddit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "45da10e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#train and test split\n",
    "x_train, x_test, y_train, y_test= train_test_split(price_sentiment['comp_score'], price_sentiment['price_raise/drop'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f7fa0e82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train and validation split\n",
    "x_train, x_val, y_train, y_val  = train_test_split(x_train, y_train, test_size=0.25, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d341f9f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#baseline ML model \n",
    "class Bitcoin_label_baseline():\n",
    "    def _init_(self):\n",
    "        self.mode_value = 0 \n",
    "    def fit(self, X, y):\n",
    "        index = (crypto_prices_df['price_raise/drop']).max()\n",
    "        self.mode_value = index\n",
    "    def predict(self, X):\n",
    "        length = X.size\n",
    "        y_predict = np.empty(length,dtype=int)\n",
    "        for i in range(length):\n",
    "            y_predict[i] = self.mode_value\n",
    "        return y_predict\n",
    "baselineClf = Bitcoin_label_baseline()\n",
    "baselineClf.fit(x_train,y_train)\n",
    "y_predict = baselineClf.predict(x_train)\n",
    "sklearn.metrics.accuracy_score(y_train,y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5cf0278d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting data in a format compatible with svm\n",
    "def data_svmReliable_conversion(z):\n",
    "    z= z.values\n",
    "    return z.reshape(-1, 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "45a7257a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# learning the classifier\n",
    "def learn_classifier(X_train, y_train, kernel):\n",
    "    classifier = sklearn.svm.SVC(kernel = kernel)\n",
    "    return classifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1991c22a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#evaluate the classifier\n",
    "def evaluate_classifier(classifier, X_validation, y_validation):\n",
    "    return sklearn.metrics.accuracy_score(y_validation , classifier.predict(X_validation))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cd22f6d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vbatch2\\Anaconda3\\envs\\cs418env\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "#calling classifier\n",
    "classifier = learn_classifier(data_svmReliable_conversion(x_train), data_svmReliable_conversion(y_train), 'linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f59ff06b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5365853658536586"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculating accuracy of learned classifier with validation data\n",
    "accuracy = evaluate_classifier(classifier, data_svmReliable_conversion(x_test), data_svmReliable_conversion(y_test))\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "51cf746f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KFold(n_splits=4, random_state=1, shuffle=True)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kf = sklearn.model_selection.KFold(n_splits=4, random_state=1, shuffle=True)\n",
    "kf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8467f84d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vbatch2\\Anaconda3\\envs\\cs418env\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\vbatch2\\Anaconda3\\envs\\cs418env\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\vbatch2\\Anaconda3\\envs\\cs418env\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\vbatch2\\Anaconda3\\envs\\cs418env\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\vbatch2\\Anaconda3\\envs\\cs418env\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\vbatch2\\Anaconda3\\envs\\cs418env\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\vbatch2\\Anaconda3\\envs\\cs418env\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\vbatch2\\Anaconda3\\envs\\cs418env\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\vbatch2\\Anaconda3\\envs\\cs418env\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\vbatch2\\Anaconda3\\envs\\cs418env\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\vbatch2\\Anaconda3\\envs\\cs418env\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\vbatch2\\Anaconda3\\envs\\cs418env\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\vbatch2\\Anaconda3\\envs\\cs418env\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\vbatch2\\Anaconda3\\envs\\cs418env\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\vbatch2\\Anaconda3\\envs\\cs418env\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\vbatch2\\Anaconda3\\envs\\cs418env\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "#finding the best kernel for svm\n",
    "def best_model_selection(kf, X, y):\n",
    "    scores = []\n",
    "    dict = {}\n",
    "    for kernel in ['linear', 'rbf', 'poly', 'sigmoid']:\n",
    "        for train_index, test_index in kf.split(X):\n",
    "            X_train, X_test, y_train, y_test = X[train_index], X[test_index], y[train_index], y[test_index]\n",
    "            classifier = learn_classifier(X_train,y_train,kernel)\n",
    "            scores.append(evaluate_classifier(classifier, X_test, y_test))\n",
    "        dict[kernel] = np.mean(scores)\n",
    "        scores = []\n",
    "    return max(dict , key = dict.get)\n",
    "best_kernel = best_model_selection(kf, data_svmReliable_conversion(x_train), data_svmReliable_conversion(y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c666de3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#prediction on test data\n",
    "def classify(classifier, x_test):\n",
    "    return classifier.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "be3a4363",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vbatch2\\Anaconda3\\envs\\cs418env\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1, -1,  1,  1])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#predicting \n",
    "classifier = learn_classifier(data_svmReliable_conversion(x_train), data_svmReliable_conversion(y_train), best_kernel)\n",
    "y_pred = classify(classifier, data_svmReliable_conversion(x_test))\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8f485b42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy 0.4838709677419355\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vbatch2\\Anaconda3\\envs\\cs418env\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1, -1,  1,  1])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prediction using Gaussian Naive Bayes\n",
    "gnb = GaussianNB()\n",
    "\n",
    "#Train the model using the training sets\n",
    "classifier = gnb.fit(data_svmReliable_conversion(x_train), data_svmReliable_conversion(y_train))\n",
    "\n",
    "#Predict the response for test dataset\n",
    "\n",
    "accuracy = evaluate_classifier(classifier, data_svmReliable_conversion(x_val), data_svmReliable_conversion(y_val))\n",
    "print('accuracy' , accuracy)\n",
    "\n",
    "classify(classifier, data_svmReliable_conversion(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "8645c013",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy 0.4838709677419355\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1, -1,  1,  1])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prediction using Decision trees\n",
    "clf = DecisionTreeClassifier()\n",
    "\n",
    "#Train the model using the training sets\n",
    "classifier = clf.fit(data_svmReliable_conversion(x_train), data_svmReliable_conversion(y_train))\n",
    "\n",
    "#Predict the response for test dataset\n",
    "\n",
    "accuracy = evaluate_classifier(classifier, data_svmReliable_conversion(x_val), data_svmReliable_conversion(y_val))\n",
    "print('accuracy' , accuracy)\n",
    "\n",
    "classify(classifier, data_svmReliable_conversion(x_test))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a55a9092",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy 0.4838709677419355\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vbatch2\\Anaconda3\\envs\\cs418env\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1, -1,  1,  1])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#prediction using KNN\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "#Train the model using the training sets\n",
    "classifier = knn.fit(data_svmReliable_conversion(x_train), data_svmReliable_conversion(y_train))\n",
    "\n",
    "#Predict the response for test dataset\n",
    "\n",
    "accuracy = evaluate_classifier(classifier, data_svmReliable_conversion(x_val), data_svmReliable_conversion(y_val))\n",
    "print('accuracy' , accuracy)\n",
    "\n",
    "classify(classifier, data_svmReliable_conversion(x_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "3aee708c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy 0.4838709677419355\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vbatch2\\Anaconda3\\envs\\cs418env\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1, -1,  1,  1])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#prediction using logistic regression\n",
    "clf = LogisticRegression(random_state=0)\n",
    "\n",
    "#Train the model using the training sets\n",
    "classifier = clf.fit(data_svmReliable_conversion(x_train), data_svmReliable_conversion(y_train))\n",
    "\n",
    "#Predict the response for test dataset\n",
    "\n",
    "accuracy = evaluate_classifier(classifier, data_svmReliable_conversion(x_val), data_svmReliable_conversion(y_val))\n",
    "print('accuracy' , accuracy)\n",
    "\n",
    "classify(classifier, data_svmReliable_conversion(x_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "aa3fc741",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy 0.4838709677419355\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vbatch2\\Anaconda3\\envs\\cs418env\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1, -1,  1,  1])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prediction using Gaussian Naive Bayes\n",
    "gnb = GaussianNB()\n",
    "\n",
    "#Train the model using the training sets\n",
    "classifier = gnb.fit(data_svmReliable_conversion(x_train), data_svmReliable_conversion(y_train))\n",
    "\n",
    "#Predict the response for test dataset\n",
    "\n",
    "accuracy = evaluate_classifier(classifier, data_svmReliable_conversion(x_val), data_svmReliable_conversion(y_val))\n",
    "print('accuracy' , accuracy)\n",
    "\n",
    "classify(classifier, data_svmReliable_conversion(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1e10e1f7",
   "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>Number</th>\n",
       "      <th>Name</th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Market Cap</th>\n",
       "      <th>Price</th>\n",
       "      <th>Circulating Supply</th>\n",
       "      <th>Volume (24hr)</th>\n",
       "      <th>1h</th>\n",
       "      <th>24h</th>\n",
       "      <th>7d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>BTC</td>\n",
       "      <td>1.507030e+11</td>\n",
       "      <td>8940.740000</td>\n",
       "      <td>1.685576e+07</td>\n",
       "      <td>8.103300e+09</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>11.22</td>\n",
       "      <td>3.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Ethereum</td>\n",
       "      <td>ETH</td>\n",
       "      <td>8.754697e+10</td>\n",
       "      <td>897.729000</td>\n",
       "      <td>9.752049e+07</td>\n",
       "      <td>3.062570e+09</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>11.99</td>\n",
       "      <td>-0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Ripple</td>\n",
       "      <td>XRP</td>\n",
       "      <td>4.062771e+10</td>\n",
       "      <td>1.041490</td>\n",
       "      <td>3.900922e+10</td>\n",
       "      <td>2.504810e+09</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>32.86</td>\n",
       "      <td>23.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Bitcoin Cash</td>\n",
       "      <td>BCH</td>\n",
       "      <td>2.294639e+10</td>\n",
       "      <td>1353.040000</td>\n",
       "      <td>1.695914e+07</td>\n",
       "      <td>1.149550e+09</td>\n",
       "      <td>0.11</td>\n",
       "      <td>7.89</td>\n",
       "      <td>18.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Cardano</td>\n",
       "      <td>ADA</td>\n",
       "      <td>1.163729e+10</td>\n",
       "      <td>0.448847</td>\n",
       "      <td>2.592707e+10</td>\n",
       "      <td>7.603720e+08</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>27.62</td>\n",
       "      <td>23.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Number          Name Symbol    Market Cap        Price  Circulating Supply  \\\n",
       "0       1       Bitcoin    BTC  1.507030e+11  8940.740000        1.685576e+07   \n",
       "1       2      Ethereum    ETH  8.754697e+10   897.729000        9.752049e+07   \n",
       "2       3        Ripple    XRP  4.062771e+10     1.041490        3.900922e+10   \n",
       "3       4  Bitcoin Cash    BCH  2.294639e+10  1353.040000        1.695914e+07   \n",
       "4       5       Cardano    ADA  1.163729e+10     0.448847        2.592707e+10   \n",
       "\n",
       "   Volume (24hr)    1h    24h     7d  \n",
       "0   8.103300e+09 -0.63  11.22   3.58  \n",
       "1   3.062570e+09 -0.41  11.99  -0.25  \n",
       "2   2.504810e+09 -0.37  32.86  23.94  \n",
       "3   1.149550e+09  0.11   7.89  18.32  \n",
       "4   7.603720e+08 -0.71  27.62  23.61  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "currency = pd.read_csv(\"All Currencies Table.csv\")\n",
    "#currency.head\n",
    "currency = currency.dropna()\n",
    "\n",
    "#Market Cap is total values of coins mined.\n",
    "#Circulating Supply is total coins actively available for trading\n",
    "#Volume is how many times a coin changes its hand in given time frame.\n",
    "\n",
    "currency['Market Cap'] = currency['Market Cap'].astype(float)\n",
    "currency['Price'] = currency['Price'].astype(float)\n",
    "currency['Circulating Supply'] = currency['Circulating Supply'].astype(float)\n",
    "currency['Volume (24hr)'] = currency['Volume (24hr)'].astype(float)\n",
    "\n",
    "currency = currency.sort_values(by=['Market Cap'], ascending=False)\n",
    "currency.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f096644b",
   "metadata": {},
   "source": [
    "Taking News dataset and Selecting Bitcoin Mentions Since Bitcoin has Largest Market Cap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "28a381be",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                                                    text  post_id  \\\n",
       "0      The crypto rally has lots more runway ahead, e...       85   \n",
       "1      The crypto markets have continued to surge tod...       86   \n",
       "2      BTC Closing Above Its 200-Day MA Signals Bull ...       87   \n",
       "3      Bitcoin&#8217;s [BTC] surge over $5,000 gave m...       88   \n",
       "4      Litecoin has continued cryptocurrency’s price ...       89   \n",
       "...                                                  ...      ...   \n",
       "78895  Top Bitcoin Exchanges : Places to start invest...    79005   \n",
       "78898  BitcoinThe post Bitcoin appeared first on Cryp...    79008   \n",
       "78899  WAVES Weekly No. 31Last week has seen a number...    79009   \n",
       "78900  WAVES Weekly No. 30This week we have several u...    79010   \n",
       "78901  SuperNET Weekly No. 13CORE and CORE Media were...    79011   \n",
       "\n",
       "                     date  Month  \n",
       "0     2019-04-06 23:30:38      4  \n",
       "1     2019-04-06 22:30:24      4  \n",
       "2     2019-04-06 22:23:25      4  \n",
       "3     2019-04-06 22:03:21      4  \n",
       "4     2019-04-06 21:59:57      4  \n",
       "...                   ...    ...  \n",
       "78895 2017-06-15 22:28:11      6  \n",
       "78898 2017-06-15 19:26:43      6  \n",
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       "\n",
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     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btc = pd.read_json(\"cryptodata/news.json\")\n",
    "\n",
    "#First Data Set\n",
    "btc = btc[btc[\"text\"].str.contains(\"Bitcoin | BTC\") == True]\n",
    "btc['Month'] = btc['date'].dt.month\n",
    "btc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b76e10ab",
   "metadata": {},
   "source": [
    "DATA CLEANING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "245bc554",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "                                                    text  post_id  \\\n",
       "0      The crypto rally has lots more runway ahead, e...       85   \n",
       "1      The crypto markets have continued to surge tod...       86   \n",
       "2      BTC Closing Above Its 200-Day MA Signals Bull ...       87   \n",
       "3      Bitcoin&#8217;s [BTC] surge over $5,000 gave m...       88   \n",
       "4      Litecoin has continued cryptocurrency’s price ...       89   \n",
       "...                                                  ...      ...   \n",
       "78895  Top Bitcoin Exchanges : Places to start invest...    79005   \n",
       "78898  BitcoinThe post Bitcoin appeared first on Cryp...    79008   \n",
       "78899  WAVES Weekly No. 31Last week has seen a number...    79009   \n",
       "78900  WAVES Weekly No. 30This week we have several u...    79010   \n",
       "78901  SuperNET Weekly No. 13CORE and CORE Media were...    79011   \n",
       "\n",
       "                     date  Month  \n",
       "0     2019-04-06 23:30:38      4  \n",
       "1     2019-04-06 22:30:24      4  \n",
       "2     2019-04-06 22:23:25      4  \n",
       "3     2019-04-06 22:03:21      4  \n",
       "4     2019-04-06 21:59:57      4  \n",
       "...                   ...    ...  \n",
       "78895 2017-06-15 22:28:11      6  \n",
       "78898 2017-06-15 19:26:43      6  \n",
       "78899 2017-02-20 00:00:00      2  \n",
       "78900 2017-02-13 00:00:00      2  \n",
       "78901 2016-06-04 00:00:00      6  \n",
       "\n",
       "[60543 rows x 4 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Clean the text\n",
    "def cleanTxt(text):\n",
    "    text = re.sub(r'@[A-Za-z0-9]+', '', text) #Removes @mentions\n",
    "    text = re.sub(r'#', '', text) #Removes the hashtags\n",
    "    text = re.sub(r'RT[\\s]+','',text) #Removes RT \n",
    "    text = re.sub(r'https?:\\/\\/\\S+','', text) #Removes hyperlinks\n",
    "\n",
    "    return text\n",
    "\n",
    "#clean text\n",
    "#btc\n",
    "\n",
    "#Function to get subjectivity\n",
    "def getSubjectivity(text):\n",
    "    return TextBlob(text).sentiment.subjectivity\n",
    "\n",
    "#Function to get Polarity\n",
    "def getPolarity(text):\n",
    "    return TextBlob(text).sentiment.polarity\n",
    "\n",
    "\n",
    "btc\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "501f08fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to label the negative, neutral and positive analytics\n",
    "def getAnalysis(score):\n",
    "    if score < 0:\n",
    "        return 'Negative'\n",
    "    elif score == 0:\n",
    "        return 'Neutral'\n",
    "    else:\n",
    "        return 'Positive'\n",
    "\n",
    "#def plotSentiment(btc):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4cbc1b9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# All Functions \n",
    "def All_Functions(btc):\n",
    "    \n",
    "    #Cleaning the text\n",
    "    btc['text'] = btc['text'].apply(cleanTxt)\n",
    "    # Create Subjectivity and Polarity Columns\n",
    "    btc['Subjectivity'] = btc['text'].apply(getSubjectivity)\n",
    "    btc['Polarity'] = btc['text'].apply(getPolarity)\n",
    "    btc['Analysis'] = btc['Polarity'].apply(getAnalysis)\n",
    "    #plotSentiment(btc)\n",
    "    return btc\n",
    "    \n",
    "btc = All_Functions(btc)\n",
    "#btc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65b9f700",
   "metadata": {},
   "source": [
    "Plotting Sentiment Vs Polarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "21a43776",
   "metadata": {},
   "outputs": [
    {
     "data": {
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uV88PdTrmdXY6xWyzyzaUZRKPkX7f/Jr7/XKf1/Sc4VTn85Z5/PGcPXkAnFCGeJQjGxIFpqfNLXSdjq53WsRjV1bMgxEktd609CNaqjzvVVv5vua5OV2yLq7XA06f1v+XPdKcyzaUZRKPkbrOI0193jKPP56zJ49tZEMG0kSBPCfGvCdVnpT9TOL+8n3NU1Pm+SLA5cuD27Yvd0Vw3YYy8BgZNomBdJnH3yQeX5POFkgzR5ookKeCBatfVGsS97fva3atulHmSHN1Vv6YxGOEhrHyDFWBgTRRIE8Fi7zVL+bn/eZPurT9PY6dzHyPsSZU3ahzG3z31zgMh97v+80fd2Uefzxn0z8xJU63YWJnQyrD8vKgA1an49cxKc9jlRrtvMJOK8ls+3ucO5n5HmMbG0r1ekqJ6L9VdPJr0ja47q+kTmlN2Ic+4h0Oy+5oqFQ9He9M75mpg2OZ7x/P2ZMF7GxIRJNgEjuZUT62Tmn79gEXLgwPld7tZisDaFJmfnqV6sgXrrMjK00m5kgT0URo4sA31Gy2Yc/PnRsOogF9+8iR/M+ZNKokpeMQ9tQUDKSJaKywkxn58u18VkSwljSqJKVjR0JqCgbSRDRW6hj2fBw7Nxalyn2TdaQ5U6e0JEUEa1l/Odm/f/g17t+ff1uyiHbO3L3bvEzWjncux0wTOtMSAWBnQyIaP3k7ftqYOi6Nc+fGvKrcN3k7vG1sKLVnj3kdZYyMl2VUydlZ82NmZ/Nvj4+kjn55O975HDNt6whK7QZ2NiQiym5zU7dqxzuePfaYeXAHdm6stuNnER3ebNsbrv/AAd3iWVRHQ99RJZsyCEiZHf3YWZiaip0NiWoyDvVpSXcwM3U8s42QVlXnRtfUiTzLZT2Gy+r4mSddxPRawvUlbdflyzpILCKIBnSwvLw8yN3vdIobmr1seTr6pb137CxMrWNqpm7DxNQOaoOk+rTULiLpP/u7/kRfFNefwfMuF09DcD2Gs6QvZH3NSVPI9Hm0bWPV76ULl9dYhV7PvA29XvLjXI7DMo4ZoiLAktrBFmmiEtlaMYson0XVsnUwm5kxzy+zc2PItfJD3uXirYGux7Bvx0+XlmbfqhbRDm+mz6NLS2cV76WL2Vm/+WXJ2tHP5Tiso7MwUR4MpIlKxFqn48MWPNx5Z30/0bv+DJ53OROXY9gnfcG1rnLSNsarRMzPA8eO+W1zVLi9N93UjPSss2dHg+bZWT2/SouLOvjt9fSXnl7PbZAal+OwzSkvNJnY2ZCoRBx9a7xsbupWzTNniu14lpVrx6y8y5kUfQwX/VpMbJ9Hk3B9tk6mRY1uWIWmHLfsSEhtxs6GRDVgrdPxsriog8eiO55llfYzeFonuvjjbeuLD2ZTxjHs2mqe56d/0+fRNlBPuL62p2eFXwS2tnS28daWvl1HqzrTNmgsmRKn2zCxsyG1BWudUplsNbOTOuUl1dY2ra+KY9ink1meOuG2WuC29dk6mYrkfcXVyNoxsCxl1XgnKhssnQ1rD4izTgyky8cTXntM4nuVJ7hr6/7y2e6s1Q98nsP2HmR5b8oYvCXP+9zv27+IZAlEffZJkcdn278IEDUFA2nywtHa2mMS36s8ZQXbur98tztLqTTfUeVM78Hycvb3Jj46YdbR8XxfS1xaEO1bwtLneC36+Gxai/SkyvPFn79qNgMDafLCWp7tUcd7VXeLbp7goK3Htu92Z3mdPo+xvQdZW3CLrrme531Oex2+2+RzvBZ9fLKWff3yvAd8/5rDFkizagcZNWUoWkpX9XuVZWjjok1NmV+biH2kwegyNk0+tn23u+whqG3vgU3ae1N0hZss73NY3SKpskeWYyRpX8Xnl3F8LiwAx48PbsfLAlK58hzbrPzUHKzaQV5sPdlt82lyuA7uUSbb4Ci2+VFtPbZ9tztLPV6f53DZ1z7LF11z3Xd/RatbFM322kVGq2cUfXyurAwH0YC+7TOsOuWT59jmWATNx0CajFimiGxcy5SVKU9ZwbYe21m2e21N1+dVSv9N+8XA5zlM74GNy3uT58uRie/+MpW5i+v3s23L6qq5pVmp0TJ6RR+fTfjiO+nyHNtFfy6oBKZ8jzZMzJEuX915sOSm6pzfpuQYs2pHOdudpWpHETnFZeSC+rwWW3WLcOr3s2+HUvb1mqpnFPk+J70mqgZzpMcD2NmQaDxVXYWiyGoIe/cmBwxVBbxFPk/VPeyr2Efx1zQ/P/yctioXvlU3XPedrRa0KRh2fQ/Krm5RV/UM2xff+DQ7m+950o6RtnxZLQurdrQfA2miMVVHObcswVtaSbH4dlf1uop8nqytR1mD4Sr2kek12QKx+LwyWs5M2zM9nbxtLtvh+97F37P5+eRgp66WxaSBeYoKpl2PkUkPpqndGEgTjammpFqkcb2Yh9td1esq8nmytDrmCYar2Ee+Ze58XnuV29PrpX9hcW35cwlOTUFyXS2L8dedtN1ZuL4nTTsnEfmwBdIsf0fUcm0p55a0nXFKVfe6inyeLGX5pqfNHTU7Hd1BMEkV+8i3zF2cS0nCKrcnzlbJJCyFd+aM7th1yy3A3Xfr267P39QSZUUcN9H94/N+NOmcROSD5e+IxlSWMl9zczogmZsbLb9Vt2i5tqT7i34+0/yVFR3oiui/aSXDsvSwz1MFpYp9lLc6QNHVBYpen6l6RbQUnlL679Gjg9uu2liizKUsXnz/uGp6iUmiLBhIE7WcT7ksU4CwtFRNMO1aOizc7qrK1NnWd8MNOngKA9qdHX07KdDIUpYvTzBcxT5yLXM3P5+9JGEZ2xPfDpudndEvSS6l8Fw0tUTZ7Kz9PpeyeFn3T9NLTBJlYsr3aMPEHGmiAdfOanVVDgglVe0ABlUXwtdQZ9WOrPnHvnmweTsMlr2P9u4d3TZbRYaqcoA3NtxycsNje2PDrXpF+DrSSuG5TPEcadf3yWUfFrGf8+RJ++4fVu2gcQB2NiQi2wXQVMu2SnVUHklTdIesJE2ta20KosMvQHXz/aLj0kEwfKxvh8a0qh2ux7dLZY+iqn/k6ajqs3+IxgUDaWq1pgYabZOlRbqKVsYmVh5p4jZFFfmZsK2rzgAp6fVtbCg1M+MWnNrWmfS6XMq5+QSvtueMf4F1+XwW9atSni+vruXufOuIEzUZA2lqrSa2VraVb2tWVbVvm9ii1eTjrshtS1pXXe+L7bn7ffuvKlNT7q/f5UtS/Avk8nL2L5RJ+zG6zS6/GCWlVfi+/3m+jMX3Tzxti0E0jRsG0tRaTW8ZbBufFua01q+iWkWb+h439ZeQIvdX0rrqCqRdR+MzHZMuqv6SlPR6ou9ZnhZp39fA0fKI/DCQptaq62JOyS1kVbWK0qgiPxO+ASug1K5d5X7ByLJNvnn+pjSPsr4subbuZ82R9v0yVdcoi2TW1C/sNKyWQBrATQA+BOBxAPcBeJZhGQHwcwAeBPAYgP8L4DvT1s1AenI0tbVyEiS1kBX9vvBi4iYpKCuyRdp3KvL9KrtFOlTlFzjX98y1akeeL1N1V++hATYitEflgTSAKwF8CsADAFYAnAXw9wA6seW+HoAC8AEAPwDgCwAuANiVtH4G0pODJ5phVQacSS1XeS7k0fXz52Uzl45xPp+JeAWOsPKGSzWLLM8ff29tJfNsr93nebO2piZ9GTR9zlyP13hnyKmp0RzivOcx12Oj39f73mXZPNV76voi3PYv4Gwoao86AukXBQHyjwS3fya4PR9b7gXB/N8C8BUAPgngM/GAOz4xkJ4sbT9ZFqWOLxW24CHvBWB5eTR1hD8va1mDW5u0MnZZgvakYNq1qoNrhY1Oxx6I7tkzesy4ni98X9vUVPrxmlSzut8vttJKEe9XdMraIl1XY8c4NLJk+TxTPeoIpH8oCJC/J7i9FNz+vthyAuBXg/tU0Bq9kLZ+BtI0iZrUepG3fJYt/5o/L2cLapOOAdeLdRHBdKfjXmfY97h1CZBtx+WePe5fBvMEn0mvvejPaXx/5H0tWStt+JyXimwUqet8OA6vgfw1IZC+Nbj96thyXwHgEQB/ELRinw5apWcM61wCcALAiQMHDpS8y4iaxzUgqkrWC0pSsFH34DBNkCUIStr3rseNy/PYWobj76HrdhfNJaAMW5KLaNWNH69pr71MRb+WIp43qugW5Dr2c9GvYRxa1SeFLZCechpHPJsHgr/XBX/3h/NF5EoR2R3c/lYATwbwVqXU7wA4Fizbj69QKbWulDqklDp07bXXlrjpRM3U6fjNL9vaGnDpkj71X7qkb7s4c8Z+34EDxWxbm/m+n8vL7vs+6/N2Ovp5nve89HW4vodlHLc7O+nLbG8DR47ofba8nO/54q816bXX9Tl1pRSwsuL/ONfz0vq6eTnb/KKet0hFv4bwGAy3OfycFfF5pmqUGUi/FzrXeVlElgG8Crq1+R7o9I37guU+HvxdFpHvA/DNAC5iEIgTUWBpyW9+U9mCDRFgdbXabWki3/dzbU0HQNPTeh9OTwMLC8DcHDDlcZa3Pa8IsHcvcMcdwNGj6etYXdXbkPX54jY3B69lbk6/tuhrjQZ/rkFU+GVubU2vJ4tud/h4XVkBPvEJ+/Jlf077I81P/kwBYfzYigfbrucl25ccly8/LutPm1+Eol8DkL1BghrC1Exd1ATguQA+DB0YfxDAoWC+AvCR4H8B8J+hq3o8BuAUgJekrZs50jSpxqHjpakzWlibmjTXDoBJNb1dHxs9nvLk2obvX9r2+By3rhUnwvxen30R5ku75nSbHuvzPvm+dltH36TqIS6pN2nTzIxet20YdlPqgct5qYx84KrPh8xpnlzggCxE1CQsfZfOpfpFUk1vl4Apb9AVnUJJ2+Pzfvt+QQjXGQ2uRJSanrY/ptvVy5vKPO7bZ9/nWbYvKRB1ee+TttVUuaTI97aIwHEc8oHH4TVQNgykiai1JjXoTmspdanpnfTYeDm3PFO0o5rr8mnBh+8XBFvFl7RW52jLdPQYSxsBME/rfVogatte23PGX3tR5QyTJpukVuJx+EVtHF4D+WMgTUSFMQUmZV1Qklrmyg6u675gJlWAyFLGrYrgKstkK7vmux5Tqkr0PUsa8t4m6UtcWYFo2nvv8hrKfs9sXwSa0mJb9Ge37nMB1Y+BNBEVIi3doOgLjK1lLs9gLnnqEVcxyIRLwBtv5Utbvtt1zzcusqXadTIF076Bv235cF/ZUjX27cv2Xrlsky0gbnuLdDQf3rWWdVWBKEvUURkYSBN5YOuDXVq6QdGdbnxa5lwGc3G9KCYFBfH7+n3z8/geQ755rdFW0vjzzc+PtqS6BlfLy+Xn2Lq8B0VuQ6ej1O7d5vuyBNKuHSqzBmG2X2Lm592+RLp0OswabLt2KvV9v/NKS+HJem5iB0NSSikG0kSO2PqQzCWwLZJPRQWXASVcL4q+QUE0mM56DGUNbFxb410CK1s+azhlaa1OGsnStI+KHLK8qOMmLun1xGX9Yh5PKzF1NBQZHXrctXKHUv4VS6JfVvO+R1NT/vs9aV+5DEufRdHro3ZiIE3kqI2tD1W0oLsGN0XvJ1upvLSLvI3rRTHrMN1J+ylt3+QJSmZmiik/Fg/C4q3tWUrFKaW3xyWY9s0NLnJK++xEA1vf4Mr3M2paPsu+T9tO1wA0+v4UcbyGU1HnKpd9wxZpyoOBNJEj3wtk3apoQff5CbesDodpLXOurbKuF8WyUhuybFtRQUrasWJryYwG01kC3ej7WGegnOcY9gk443w/o1Wl1USrk9jyx5O223a8ivj9clHEOcPluGKONOXBQJrIUdtaH6rYXteW6CovLKZqCkV3Iiw6xSDtPSk6gDI9X/Q1TU0NBt9Ia9EL+baKlvn68uyXpMDLtN98Xnd8v9bRedN1in4BjX6m9u1LPvbT8uh9apQXca5Ky41m1Q7Ki4E0kaO2tT4kXaCa+hxl1IXOGiC7XBR9f/5OCj5cXkeRgVdRrynr47J0dix7CtMTfPZbG1rS0yZbcBv+yhPvrGrrnBkuEx6vReybvNJqfhPlxUCayEObWh/ytEi7vs4iW73LuuCV3TIfDf5dynzlOYZ8Wj+TWjzTWqSzBjrxL0Kzs+mPdw2+q2i1DgP7tCAx63vShCl6nIbHX9FfBkJ5vyCV8RmdpIGbqBoMpInGVNYWdN8W3CzPYWILSOIdBX0DUZcLftHK+vXCNeBJG6q6qHJypvJ+0XUW1dJsqk1cxpSWE2zLkS5re8qYdu8eDSSL/DIQDX5dH2P7wtfkhgqiEANpojGWpfXTtwW3qFZ6l9HlsgSodeW2l/HrhUvAE2/Fd9mOLMFpWhBdVGAWL3uXtxU16bWmBcX79o0GoU3J7/aZ4l9Obb8GZX2/0o6rqals/RiImoiBNNEYy/KTZtJFMu+6k7bTdtF1rU8bPn98lD5beoFrXrLrxT1t2fh22Ya/ju+XeFUSU+vdvn2j70NS58GwZFrWYDQt8CniJ32XtBCfaXZ2sE9ty/hUqHCZXOs2Vz2Z6mOnDVqSNk1NjX7psR1fDJLLMYlfRprwmhlIE42prDnHLi24ReYzJ3VSi68zLTiwBV+zs/4n2yJTXGxDcCcF06b9YntvikrV8AmakrYjz7r37UvuzJZnmp93Hw69iClLq67PkO159nF4jMW/qOX5ErR3r9tyu3alf/7IT9s6wxehKa/ZFkiLvq99Dh06pE6cOFH3ZhDVbm4O2Noand/rAadP2x+3sgIcPTo6f3kZWFvLt26f7QSA+Xng2LHB7elpYGfHb/0h31Oa7bk6HeDSJb9lRfy3K2m/pG1Tnv2Uh8vrJbuZGeDOO4E3vQk4fry859m3D7j9dmBpCdjeHswX8f+cZNXSEKOxfM5X46Ipr1lEPqCUOjQyn4E0UbtNTZkvViLA5cvJj11ZAdbX9Umq09EX3DCIzrtu1+0ERk+ItiDfxfIycPfdwJkzwIEDwOoqsLhoX94n+E1bNksgnbRf0tZTZyCb9nqLMjXlf6xRc7Q0xGisLOeYtmvKa7YF0lPVbQIRleHAAfv8lRX9bV5E/11ZGV5mbU0HsErpv9EgOmndT3mKbkmdmtJ/Nzezbycw2tqwtqYD4iyOHtUtvErpv0tLydvX6bjP91k2zrYNSfsl7Xlcnrcs09P2+/bt079aFIFBNNFAnnNQWzX9NTOQJmq51VWg2x2e1+0C11+vg8owSN3Z0bfjwbTvunfvBj7/eb9gNVyXjemEuLYGbGzkb/Xc3gaOHLHfv7TkPt+27M0367/z8/bnsW2DaR/bLhDx57dtTxWSUkrOnQM++Um9P+KvTQTYs8ftOfr97NvnqtPRqRZUvF27iltXWqPApPA5X42Lxr9mU+J0GyZ2NiQaMFXWKKocXHzdtooH8VJbJrbOVWlVMuJVAbpdv2oPpuoF8edw7aRoew1hNYMs22B6/3btcuu4VXbN5bxT/L2bnW3WkNk+JfbK6hyZNkXLyNW9v1ynIjsaZjlvjLMmVLCoWhNeM1i1g2iyJF3k8nCtA2076WU5IZoCTd/R/4qS9rxJwzC7uuoq8zr27h1dtowR69o0RQdxyfpY23sqMlxy0LfKRliZI+8XnaKqtbiMylnEVCTfESiJysJAmmjClDVASdrIhHlLFbnWrXYNHosYftzneaem8pUMTBssJKqNg4SUMfX7/gFi9ItfUmnG8P1OGoo9aQrLzWV9babSiUmf7bTPX9YRGqP1yYv8xSvNnj3J20VUFQbSRBOmrNqbabWl81xgfepWu7RI79lTbBCtlNtgHnkGsUl7XdH1Zg3OOOnjMfq+JAWHZW+HyzKutZvjx4XpV5+s2xofCMi0jG2EyqzS9gtRVRhIE02gsvLKkgLFpAtfmqTgMP48Sa2IZeXQbWyk58nmvbhPcppG1ZPI8HFSx74vM4DvdodHtww/Q3nXaRpVM/zMFf0FPmlbJiE3mJqDgTQRVSLtQpwU5KYFMvHW6SKHL4+yfQFxaQXOe3Evevjq6FTlaH9tmUQGx80VV1T//HlTP1xeX/R2llEYTdtsU3TKR9I5gahKtkCa5e+IqFJJZfjSairHS9ktLuoRFi9f1n+TBl5xFQ4GYyobeOZM8mOnp4GbbjKvs87SXZ2OrssdHT2SNKX0MbWwADz+ePXPf+aMLvWYVDoxD6WGb29v56+/m/Q5sJVF9B2Bc3NT16iPb38oa515oqIxkCZqoPAi4jPgSVO4XqTX10fnmWoqx6UFs0lc9qtpu8L5aYH+pUuj9aKTAnOTz30u+Tl8dLu6Fnc42E6bjqMqbW2VO1R3kvCYOnZMD2STlWttbkAfg2mfsyRJnwPb5z/8zLmc0zY3dY3grS3z+peXRwePIqqNqZm6DRNTO2hc+XS4K1oROdU+P1ObpJW2y1rKznW/Jm1vUl529Kf0KN+fun06EbqUVwv3l8u2c6p2iucb562vHU+DsKVFREtI+pb2SzsX2T7/8deWtJ60ykBEdQBzpInaoa6LSJGdhFwGCUnLmUwLfPv94fv6/eT1ue7XtMDXN9BP2gemQMI1WA+DoY0Nt7xqdmKsdwpzoU05/WXlSM/Pmz9D0S9f0S/MacF0uM1pX7jj9/vWVnepVZ+krL4TNNkYSBO1RN6LSFZl1YW1XZyvvDL9AhcNWsPt6/Xsoxr2+/aLqOt+df1C4drCnfSFwtYql1ZZIXx9+/Y1a5TASZlE3EfW3LXLHri6HCN5ppkZ95ZnU9Adn3w+H1G+57Q8jQl1/qJH442BNFFL1NUinXYBzSqp9dblAuebkmC7iPrsV9cUl2iQMjMzCGqjj0lrbTQ9f94SZb6Tz3DrdU51VNWwvWdF1PEOj5EytzX+hTJr0B5+oc7yRd/3nJYnGGZaCJWFgTRRS9TRolLmMLxpKQVpF7giApYwkChiv5pavNMGp0gLdMp4zZzKm0SKS5VxSYMqYgqP9TzbqVTyMkmfGd/PXtb0jLp+0aPxx0CaJkZZg5BUKX4RseVWFsH1Amrisq/TgsK0C1wRAUv4HKaW6fh2R/f9vn16ir4PpoDAll7R6aQHSdEvEqzz3I6pjhEQi5h6vWzbHh24xvUx8aHNkwLjInOai2iRHodrCBWPgTRNhLKGxa5TmS3ULhdEmzy5xD4XuKJapNO2u64pDDgYRHMqewoD4qyP931sPJg2Kfr8lnd943gNoWIwkKaJUFaHuTqVmfOXdiEsagQzW2WJrDnSvjnT0edoWmtiuL/q3g5O4z+Fn+es6SRh66zPY9KUcX7L08I9jtcQKoYtkOaALDRWihpVq0lsA5DkGZjERberB0ix8dnXi4vAZz+rBwfp9fQIf72eHuQkbTTCxUW9XPRxL3uZ/t9k377k5yjjWJiZyf7YIrYn70h1NP7Cz/PKiv5M7Oz4Hzc7O3oglOXl4o65Ms5veUY8HcdrCJWLgTSNFdvJvc2Bhm0UsbRR9vJKC3Kz7OusF7j44+6+W7cTxYkAt9+e/BxlHAtXXpl9iOcitmdpKf86aLxduAC8/vWjo2z6CI/VtTU9WqbvMN0rK8D0tP6cTk/r23Wd32zG8RpC5WIgTWPFFlC0OdAwDZud1lpchLQgN8u+PnhQX0TD6eDBbNtma61SSm+36YLtsn1ZnTsH3HOP/f5wWGOTcHuyBuIiwE03ZXsstdeU59VbKeDUqXzPGf/srK8nLx89pldWRoP4o0eB66+v5/xmM47XECqZKd+jDRNzpMlmHHtclzlSV5a8xpDPvo6PRBhOaSMSmiTlVbp0Fqqq5Fj8udP2V9Z6zrt3V/daONU/JQ0IU9YAPabPdtLy8Y6GSbnHTTtnN217qBlgyZEWfV/7HDp0SJ04caLuzSAiR7acZkBfUn1sbuoWou3twbzpad3KZVtXp6N/jo6K5ouWKdymeH7q0pL+mTwUvgaiJjF9dgC/z3TSst3u8Ge523XrP5FkcxM4ckT/enXggG7hzrM+IhH5gFLqUHw+UzuIJsDmJjA3p38OnpvTt9ss3gFxzx59oU8KyOMBavyn5rKEuZW2n7ajaScMoqmJosdlNG3KR1IqSjSIDm8fOeK3/qjwi/bWlj4nbG3p220/71EzsUWaaMyZWm+LaPHxVWSLdJxLS268Va3o1t9Ox7y+5WXd6mx7vk4HuOsuHThsbRW3PURFCnP8jx5NX9bUgr1nD3D+vPvziegOw2lMv/Lcfbf5s9Tr6Q7IRFmwRZpoQh05UnyLTxb9vt98Hy4B8ZVXDrfIuwbRYUfBaIvazMzodt9883BZsPBxYepGUlmtsPWMqCp79/otv76e3rkwtLQ02uHXJ4gG3Kp22H7lsX2Wyi4ZSpOJgTTRmKurDnXcyZOjwWe/r+fnEU2NSHL+/OBn3pe+1P2n6TCPOcy/Xl7W64pXQDh+XP+96y7d8nX5sm4Zc/k5Of5Fh6hsjzziV75uZ8ftV59oy7VLmb19+7JX7XAN7EN1ldSj8cZAmmjMudZpTSoZV5STJ4f77BcRRLv81BwXPr9JGGDHW5Rdnu+OO3SQbsrN9M0pLUO/71/7l8bXn/yJ+7KdTnItZRHguut0KUbXALfb1XXfTQMuHTky/AuSqZ9HUoDepJJ6NOZMpTzaMLH83XiLl0rLUiJtEpnKNtmG2I6W0HMpGeeqqPfOpeRfWgk7l/tFhueJ2MvVZS0d1rRhyTlxKmuKn2viU9pn2nS+2r1bqV27Rp/HVuqv0ym3ZChNJljK343MaMvEQHp8FVlveJIkBcNpF5WkGq8+inrvXIJ/pdIv6svL9prTaZPttYzbxCCfU5VTWkDr83mdmTHPZ91nKoMtkGbVDmqcMqs7jLOkqhCmGrBRRe3zotYzN+fW6z6t8kZYEeOlL+WxQ1SkXk/3s/D9XKVVzpiacl+nCHDbbcm12YmKwqodRGMuqSpEGlvuY1JOZJlcO0imDdsbvvZdu/JvExENbG3Z+1/0evbHpXVy9ukQeOCADprDGvKXLjGIpuoxkCYaE3mCYVtAmhaolsW1g+TaWnrnuaUl4OLFYraLiAY+8YnReWGnPlswfeBA8gBRq6ujHQV37x79MszOg9QUDKSpccqsNzzO8gTDYUBqqoHsMypiUe/d6uroF4BOZ/jCGW7XHXckr6uppeW6XWB+vr5Wf6K8lWRMA6Zsb+uqG9dfb66cccstyaMOxkct7fWAN74ReNObhudVPaAUkZUpcboNEzsbjjdW7cjGVLUjD9dOf1FFvHdpVURM29Wmad++0X1YVIUQTpyKmIo4BufnRzs52zoT9nrmzwE7DlJTgJ0NiciXa6e/oqV1nLRtV9zMjP+IanmI6LAgTdr+y1ofm8iHbVj78PhcWdG/+GQNE0wdnW2dCcOOg6bjPl7PnagO7GxINEF80jGS5BkVMc8AL2kdJ9OC6DA15c47R39ezmp+Xo/ClsQ14Ejbf74jthFlYfqciejP1549OqjN09a2szN67rH1f1DKnqbFzwM1GQNpojGzuZmcg+jy+DAIn7KcIdJ61octqtEhgo8etQfT8aDblrvpmk8clsBaXASe/Wy3xyRZXgaOHcu/npBSo19wFhb06xZxq7RCVIYwcC7ql5z4ucfUmTD+3HH8PFCTMbWDaMzkScdYWACOH09epttN7+jjU0/aJ40h/Ik3rZNU+JNyESkSMzO6ZXtxsfhhvjsdvT9MnbaIxkX83LO5CRw+7P54l1r4RGVjagfRhMiajrGyYg+iO53yessnBbqmKiIuwhasIn4SPn8eeMUrsqfHJNnZYRBN7SWSXn4SGD33+J4/6irDSeSCLdJEYyZri3TSKIEifgGfT4t0ltEQr74aeOQR++PCFqwiW5DD/Ohz54pbJ1Gbzc/rlKe0z9nMDPDoo8Pzks43YSdIjlRITcIWaaIJYcpBdBm8ICkP0We0McB+YS0qsH34YWDvXvv9YQtWkTWaz50Dbr+doyTSZAn7SZg+u8ePu3UiPn9+dDlbK/PyMkcqpHZhIE00ZkwDGrikYyQFnaurflU4brvNfb5P0B3tCPnkJwMbG/aBZADghhvs25jFvfcOBoYgmgS33jrI5TdxTZ8KlwvPI0ePDn/GfdO32q6oykrUAKbi0m2YOCALUbFsg6DMz6cPkGJbn0j68q7r9h0cxrbevFN0uzY2lNq9u5zn4cRp3KYs55FxlGWgK6ofOCALEaVZWdEtR/H8xLQBUmzrch1cwfa8UUmDsJgek5SDmUf8NV9zDfOmaXKFvwalfdaSlpu0qhx1DXRF+TBHmqgAeQYZaYO1tdH8xIWF9AFSTGw/+UbnR3/mBQb5kaafd5MGYQnrVE9NDd6bsmrPxtfLIJom2dKSW1WNpaVs5xFg/NIg8gx0Rc3DQJrIke8gI21julil1ZVOyqtOumhubqbvz+iXFtdOiuEPbGUO4BB9zZubfh0ou11d6YCo7aI5zR/7mNtytvNF0nkk7wBTTWTrvO3bqZsawpTv0YaJOdJUtU7HnN/X6dS9ZdrGhlK9ns5L7vX88u1sOXsuOY9KKdXvD8/v9+37K1z31JR9f5aV31zkND+v97Pr8tH3pA2vjxMn2xTPaU5aNirpuA8/9/Fzme080uv5niGbgznS7QTmSBPlk6XecVXCVpvt7cG8pBEINzeBI0f0T4kHDugar74pCmEr08GDwKlTo/fv3Ztc6zlJWEd2nExNcfAVar9wAJZon4akz2r83BjtD2EyPw+8//3D5zIT39r2TRM/B6+uFjvQFRXPliPNQJrIUZYOd1Xx6bxiCrqzCE8dSV8w5ufThxyPKzKIHseAnKgO4Rfze+9NHo00zhZi5O3HwI55VDV2NiTKydahpgnD1/p0XjlyxD2I3r3bPN81z/f4ceDKK833zcyY5y8tFTeQStF1pIkm1dycPnf4BNHh40z5zHm/4D76aHKe9MLCoH+FiL5NVAYG0kSO1taSB/+ok0/nFdee4d0u8MY3jgbN4bDArh57TLc+xdd95532/VlEALy8DHz0o/nXQ0Q6fSupco6NrXOg75flqVi0cu6cvdOhqZP08eMMpqkcTO0gGgM+OdK2NJB9+4A9e/xz9mw50lEiep0u67bVn/ZN01CquCHJba68EnjiCaaPEKWJp2LYPuemHOluF7jqKnM/DlOKR5P7s1B7MbWDaIz5DAu+uqovTFHdLnD77fqCdPmy/uva8eXkSaDfT15GKfd12+pPKwXs2uW2TYC+UBeVImLz+OPNSO0harr4L2G2X/iOHTOfyz73Obf1ElWt1BZpEbkJwFEANwA4CeDVSqn7DMs9DcCvAlgAcAnAe5RSiZdxtkgTZVdGj/HNTeDwYfN9Ph0yk1qTdu8GLl50W0842qFvTicRFS9v50CfDtVskaYyVN4iLSJXAng3gCcBeA2ALwbwLhHpxJYTAL8D4OsB/AKAHwXwUFnbReRjXEcyXFwctBDPzOgAOOyUc/Bg9nXaOiHaWm1Ng8AktSK7BtEA0y2Isir6l5xuV39Zt3EZudD2S5ppvbbzUNGDIe3fP9yhcf/+YtdPLWEqLl3EBOBFABSAHwlu/0xwez623POD+f8JwJUIWsnTJg7IQmWzDR4QH4ygzeIDqYRTv599ncvLg0EUooMsxNkGJZifL2bQCJHyBqTgxGncp6I+P2mDQ/kMTuIz6FT8PDI/73ESczA7a369s7PFPg81B6oekEVEfgjAGwAsKqXeJiJLAO4EsKSU+vXIcv8WwK8A+L/QKSDnARxRSv1y0vqZ2kFla3Ld6CLYOvuE8p4aogMvhGkW0QonST/V3nJL8qANRNQOaecRn5SNJmH6yORpQmfD8LCLH2JXBH+fgG7FfgDAL4nIl4+sQGRJRE6IyImHHmL2B5XLFsSNQ3CXFkQXtf5wX+3s6NvR1Jik2tdra/rLStYLUtmdDInIrSpOUq1nwK8GPlETlRlIPxD8vS74G2YPPSAiV4pIONTD6eDv7ymlfhfA70EH3U+Pr1Apta6UOqSUOnTttdeWtNlEmi0YG4cgzVYZo+z1R+e71r723d8czZCoGgcOABsbycvYaj1H1+Ezn6hpygyk3wvgMwCWRWQZwKugg+Z7AFwAEFbvuDtY7ttF5FUAvgPAowA+WOK2EaVq8kiGeaUFmmnl7LKuPzrftfNQ2v4OB2rodHTHSQbRRNUIB1tJOl9sb+sKQTY+nQibZHbWbz6Nr9ICaaXUYwBeAh0U3w4dLL9EKbUTW+4CdPD8OIBfA7AN4MVKqc+UtW1ELpo8kmGatOFxk1p5+31dG9qFraqJS2t+tPZ1eF940Y22YK2tJV+on/Y0nQJy6ZL70OdEVIztbeD8+eSKGElpGj418EPRKh979uhzR9WVlc6eHQ2aZ2f1fJowph6IbZhYtYPIzFb1ItprvYiKJEnr8Fl/Wq/9aBUQW3WBUK9XT2UCTpyaPO3dO1xJp+j1h59B2+ev1/M8iSUwnS/i0zhVVqLmQNVVO8rGqh1EZq69ydOqaqRJq2qStP7ofTZh9Y60TpHR3v2modKJaPiz79JJ0Eevp1MxXvnK0Vrv3W56C7MPW5WPqHGprETNYqvawUCaaMxUVZYp6/O4VgwR0T/duuQ8RwP1lRXgjjuKfa1EZNfvAx/9qPmzOj+vh/0uytSU22ebn38qWhPK3xHRGPGpahLNaXQtu3fggHvHwWh5vbvv5kWUqEqnTtk/q/fck/54nxFkXap5jENlJWoPp0BaRD4hIj8nIjn78hNR2aoaHte1qkmYbrG15R7ghr32fS+I6+usP0vUJGlfhl1qzkeZqnzEjUNlJWoP1xbphwG8FsCHReSvROTfisg1JW4XEWV07Nho0Ozy86pPqxDgXtXkyBH3nOV4r33fC+LODuvPEjVJ2pdhl5rzUfEqHzMzwyUw21JZicaHc460iDwDwLcD+E4A/w+ASwB+F8CPKaX+vqwNtGGONFFxbHnLRVyUXHMa9+wBvvCF0fkLC8Dx427P1ekAd93FDodETRE/h2xu6i/XZ87oL71JHQeZokVNUkSO9B4AswAOQI88uAXgedDBNBG1mG+rkA+XFuLpad1B0OTYMT162sxM+nqWlgYtVj6YU0lUvPn50SA6muaVFETzM0lt4ZojfR/0SITfD+BPAbxAKfXlAF4B4GB5m0dEZdvcdBuJMCtTTuOuXcC+fYNUjje/eVAeK+yYGB1Q5mUvA773e4erxSallSwuDgZ6STM/r1uxp1LOhnlHeyRqq6zH/v33D9/2SfNinjO1hVNqh4icBfAbANaVUmcj878IwFcrpf6kvE00Y2oHUX5pdZeLqsca/Tn3KU/R8z73Od1avbo6HEQnbY9rqkneEngzM8CFC8DlyzrAvuoqvU38qZnInYj+DIWS0rw6new17YmqkDe1400A/nsYRIvIARH5HqXU5+sIoomoGGktREW1Ci0u6kFT3vpWHaCeOzf4aXdpaTAkeNr22FI2oh0lwxJ78Qv29LT79p4/r4ceX14GrrxS32YQTWRmS7uKp3XZ0rx6Pf2FXSn9l0E0tYlrIP3jGE7heA6Atxa/OURUpaRScWX0fjcFytvben7a9gDmVJN4+SxbwKuU34huW1t6vey0SJNmYyP5/vjnyJZGdcstw7dNaV5hqUuitkoMpEXkZSLyR9CdC39aRP4ouP3zAM5XsYFEVJ6kFqK1teGBVObmBi3HWdkC5XB+WsdEUwck146FOzuDtBIisgu/2NrEv6yeOmVe7u67h2/HS9dFS10StVVai/QcgJsBKAD94P+boat3/Gp5m0VEVVhdHW1dEtHzTT3so2kYWdgCZaV06sX11ycPtrCzMwjoFxb0tvp0iPzHf9QdHfPat8+tighRGyVV0/Bh+uIcpnldvqz/Li7m+8Je9Jd9Il+JnQ1F5CoAMwD+CsBPAPhD6KD680qpArogZcfOhkT5HTxobk3q93VesOmC2uvpC6BNvE6sT2dCQFfRuP/+5Iu5SPac5SuuAB5/PNtjAWD3bv3cTzyRfR1EkyDtXAGYzwndrltLdZ7HEvmydTZMC6SfBeDjAJ5hul8pdV9hW+iJgTRRfkk5w7ZgNd4TP8olUN63T/89d858f7xSyNxccS1kee3ZowNx27YTjbOrrgIuXnT7Fcg1oLV9vl2C8DyPJfKVtWrHXwF4AYATwf/xiYjGlC0NIymP2aVO7LlzwMMP2+/f2RkO8NM6IFbpwgVdto9okvR6uvPxU59qD6Ln55Nzn6OVdaan9W0gvd9EkjyPJSpKWkGotwA4Hfxl8SeiCbK6av7ZNKmHvesFzNaiHRW2iKcNI+xj927d2u3y/CY7OzpIaEoLOVEVTOeCkEvd57CyTmhnZ3Db9vl2GRE1z2OJipLYIq2UeoVS6i8B/GLw/9BU0TYSUUlsI5b1+9l62JdxATOVzMriiiuAN74RuPXW7OvodFiqiyaP7ZemsP5zWplMW2Wd9fV8JfFYTo+awLWO9AdF5EMi8qMicl2pW0REiWw/kWZx8uRoMN3v6/mAuYd9ktVV3epbpGhAn9XyMvDKV+qhxqMtY744bDFNItsvMGm/QIXnKls6yM5Oti/s4XoPHwYee0xX0GE5PaqNUip1AvAL0J0OLwO4BOCPAbzS5bFlTTfeeKMimjTLy0rphIfhaXm52u3Y2FCq11NKRP/d2BjMFzFvY5YpLm35fr+45zZNs7Plrp8TpzZNvZ79HGE7V0WnTsf/3NOUcyBNHgAnlBqNRxOrdsQFVTyWAHyfjsGVx6C7xWLVDppEttadeKWLMiWVnLr1Vl02L8nMTPoyofjpKal1i4iqtbFhb/11+axmGT21CedAmkxZq3aED94nIq8G8HoAr4Ae6fATxW4iEaVJ+om0KknDfCcFyL2evvA++qi+gJpGKYwyfcdnagVRcySlUCSdkzqdbEF00nr5BZvq4poj/SkA6wAOAXgTgOcqpZ5e2lYRkZEt+EwLSrOwjRiWteRUdGTEtTXgrrvMHYU2NsxBNKAft7zs8yqIqAxp55ykc5VLB8Us6yWqg2sg/TsAXgjgnymlblNK/Vl5m0RENrYW2aJbapOGB0+qLz2VckYJW64Be8v24cPJHSiTgmleTIny6fV0Teg0aeecss5VVZ0DiVwlXvZE5Fki8mQAPw/gkwCeGcx7VpAvTUQVCoPIMGDM8xNpkqT0jaSSUy6l5cKW66QW7KNHgf37dU/8cFpYGNwf3w9TU3riz7tE2Smlq/McO6b7MtiEX2STqgeVda6q6hxI5CptiPAdAN8N4B3A6IAsSqna2n/Y2ZCoPFNT5vSKcHjwzU0dVJ85o1uiV1cH+ZIrK7rjoS2onZnRedJZh/7u9YafD2jWMOJEbTQ1BTztaYPP9C236PQrU6fie+81l5FkQEvjzNbZMC2QfhOAowBWYA6kaxuUhYE0UXlsgWmvp1usXHQ65hEEw5ZjU/UPV+EFPQymbYE/EbnpdIa//Ha7uu763XePfmFm5QyaRLZAOrF8XRgoi8iVAE4ppT4buf2UMjaUiOqXZXjwONsw3OH8MAg+fNh/+8I0k3AdT3kKcO6c/3qISIsHxtvbOog2fXEuunJG9FcslyHHiZrEtbPhHwP4usjtbwPL3xGNrSyjjcW59K5fXMxehePMmcEIZwyiiYoX9mOIV/ARMS+fpbPvyopOEwmD8J0dfTvPiK1EVUrrbPhcEfkp6LrRLxGRnwpufx+AJ6rYQCKqh+/w4HGuvevjnYdczcwMX4CJqFgHDpgr+Ng+q+Fn21Y602R93W8+UdOktUh/HYDXQedHf0fw/+sAPB/A+0rcLiJqkbBlONqD36d3/dqazq3s9dyer9t1Hx2RiPyFqVymCj6XLgF79pg/26bA+/Dh4ao7URxghdouLZD+LQD/H3SL9C8BeAl0QD0P4JtK3TKiGpgCwqpES72FUxXyvuakn2bDAFkp8yAM8RJ3LpU3Oh19YWfnQqLyhDXdbZ/J8+eHP9tHj+rP8OHD5g7Ex4+bzy1lD7BS5jm9zusFNYhSKnUC8JUAnhS5PQPgSpfHljXdeOONiqhIy8tK6cvC8LS8XP5zm543nMpUxGvudMzr6HSSHzc7m/y645Pv8pw4cSpv6vUGn2XXx5jOCWWed9u6bmomACeUGo1HE8vfhUTkwwCOKaVeE9z+RQALSqlnlhXgp2H5OypanSWdklqfHT6imRXxmrNuu2uLe7cL7N0LPPig2/JEVL75eT1wC+D365npnFBW1Y4yz+ksATh5bOXvXKt2XA/gQ5HbHwbwjCI2jKgpJjFXr4jXXOZPs70e8OxnM4gmahpbqkaSMAUingqxtqaD57CW9fo6cPBg/rSJMs/pk3i9IDPXQPqTAF4tIl8hIl8JXbXjbHmbRVS9snP1mqiI1+xanQMYzilMIwJ88pP6gk1EzeNbWUMpc18KUz+LU6fyl8Qr85w+idcLMnMNpO8A8GwAJwF8BMDXQI94SDQ2fALCcVHEa3atzhG/WKaJXnSJqHlcP59JweX6untA7hu4l3lOn8TrBZk55UgDgIh8L4BvDm7+L6XUW0vbKgfMkaYy1DnClqmVtsz86FBVr9mWUxgXH6qYiOolYj4XRfOBTeev6BfqoqoQ+Z4Tyzy/cUTGyWLLkfYJpL8YulX6L6GHFn9YKfWFQrfSAwNponbx6ZRYVek/IkomAtx2m/41Kc5WF94kqXMe4Ne6zcCV6pCrs6GIzAP4OwDvAvAVwd87Ct1CIhprPjmFdeYZ7t5d33MTNc2BA36DK9kkpUL4pENwKHFqGtcc6TcA+FvogVkA4J0AnlfKFhHRWPLJKcyTZ6iUvshndfGiHn6ciIBbbtF/0wZXShucJCkYN93X7w/ftv1KxaHEqW4+5e9+J3L7YQB7C98aIhorm5vA3BwwNQXcfbeuPZvUqhVejMNR0nyF6w4vzFmFw4/PzmZfB9E4+I3f0J/jJLbRTUWGA+p4MA4Mgu/1df0FOrzv5MnhZW1ZqOxPQXVzDaTvB/Ctwf9fD+BHAHy0lC0iorGwuakvjFtb+iK4tQW8//3AXXeNtmqtrOhgO3oxDi+cIjoojrZY2YLsaEv22tqgdToavPtg/WqadE88ARw5krxMUquwLf3CFnzbUjVYbo6ayjWQ/kkAz4JO7XgtgF4wj4jI6MgRYHt7eN729uhFObyg2lqclBp0dApbpi5fds/ZNLWCEZG7M2eS709rFTYF2rbg2zaf5eaoqaZdFlJKvUdEngndGi0A/lAp9XelbhkRtZrt4huf71ND9qabdCB+5ozuBHXXXcDiYr7tJKJkBw4k359WstJ0n+/IgOGXZJabo6ZJbJEWkReLyKyIvBjAMwF8CsA/AHimiHxbEFwTEY2wXXzj811zHHd2RlNFXvpSv177abmeRDRs1y5gdVUP2S0ymA4eHPRpSPsM+1TmSUrVSOvwmCatQyRRFmmpHe8E8BzocnfvjE2/DeCvReQNpW4hEbXS6irQ7Q7P63b1/CifHMd4qohSwB13uAfIabmeRDRsZwd4/ev1kN1Rp065j1TqU5mnrFQN35xsIleJA7KIyE9DB9HfYbobugTeVyul9paydQk4IAtR821uDqdirK6OpmKEF7g8ej3g9On05aamqhktkogGbP0XqhwZMGlAGPadIBe5RzYMVvIkAEop9Whwuw9gVSn1osK21BEDaaLxsbKiW5azBrkiugNi0oV5cxN42ctYLouoak0IVn1GViUyyRVIi8g/B/BmAF8dzPoAgFcqpT5S5Eb6YCBNNH7KaDFeXtadFF/6Ul4wiepS92ePLdKUV64hwgG8HcBXAbgXwJ9DB9RvK27ziIjKudiurwMvf3n9F3KiNtq1S48ymEeWWs9Fdwxk+Twqi2sgvQ/AjymlnquU+loAPw7gqeVtFhG1QdEXuzIGV9jZYYsTURa7dgEXL+pRBuPBtE9wHQ1WXc4ZZXQMTBqinCiPtM6GTwn+fQ2ArwTww9CdDH8BwF8rpf5T6VtowdQOonrZOgnmuTgV0fGQiAa6Xf2rzOHD7o9x/fUmKe84FD0fuJ4zmIZBTZQpR1pELgMwLSDQnQ5rG5yTgTRRvcq62MU7DN5ww2jpLSIqz8ZGerUdILmGdBjAh49L+pIcP2ewYyA1UdYc6fdZpj8J/hLRhPIdmSyr5z2vnJQPIhq1sTE68NHhw8DCwuiytvziTgd47DH9uOlpPXhL0i9N8XNGlsFa4jj4ClUlMZBWSt2slPo621TVRhJR8xRxsYuz5UbecEP2dRKR2fKyDpaj05EjowMfAcDx46PBqCnvuN/Xn9vLl/W8nZ30X5Ti54y8HQM5+ApVybX83U8ZZiul1M8Wv0lumNpBVK8ycqST0kWWlgYpH0SUn+nyn1SC0iVty2XI8DjTOSPPYC1V5VhXOaAM1S9vHenLhtnMkSaacEVfSFxzI106ORFRMtPlf25Op3P4PCbK97NZRuWMKnKsy2hIoGbLG0h/BwadDq8G8P0A/kopVVsFRgbSRONlc9NeWSDekpSl1YuIhpku/z6fQxOfz2ZZQWcVLdKsLDJ5cg3IopR6l1Lq3cH0GwD+C4DnF72RRDSZNjeT8x/j99mWXV5mazWRC1sb2uIiMD9vvs8lR9m2TL9fXQ3nKgZfqaqzNTWfUyAtIh+KTKcAvBHAk8rdNCKaFLYOToD5gps0uEK3W+62ErWNyGinwiTHjg1/vsJ1HD2aXgHD9tk8eVK31Cql/5aZ/lDF4CtldLamdsqSI30ZwGkAr1VKvbuk7UrF1A6i8WHr4CQy6P3vggO6EJllzQ22faZmZ4GzZ/NtU9JzNr0TH3OkJ0/m1A4REaXUlFJqCsAtAF4LYKXOIJqI2mFzU3demprSfzc3zcsdOJA833U96+v2bQlbpYgmVZbayrbP1IMPmmtL59WW0nUccpz+iVLKOgE4DuBY8P+rAOxEpp9MemzZ04033qiIqH7Ly0p1OvoH405H31ZKqY0Npbrd+A/KSs3Pj67DtGy3q+fb1jM1NXiu0OgP2IMp3NakZThxGscp/Fya7ot/huJc112U8Fxieh6iOgE4oZQhVjbN/Kc7gU8DeE3w/18FAfTrAPwRgK2kx5Y9MZAmqp/t4jwzk3zxNV14NzaU6vWUEtF/Nzb0/F7PfV22izCgVL9ff0DDiVMdU/TLrmmKf+aikh4Xf44iJD1HkWznGyIbWyCdmCMtIo8BWALwuwDOAfiEUurpIvIyAHcopa4qvIncEXOkieqXtQydT4mopAEi4utijjTRwNQUcOutOt3ApZpNt6tTORYXB/NcP1NFlX2roqxcWCUo2sHZ9NqJorLmSJ8G8B8AbATL/n4w/wB0YE1EEyxrqSefx9nyp03rCvMWiSbZzIz+8rmzM8jZdakmsb2tK+hEra3pjoVpdnb8869NqihdZ6oSZHrtRC7SAun/COAGAN8EHTi/IZj/XQD+osTtIqIWyFrqyedxq6vArl3u62JnH5p0plKSroHomTOj886edQ+mw79ZOwhW0YnP9BqT5hMlSQyklVLvBLAfwNcAeLpS6n4RmQbwPdCjGxLRBMvaSuTzuMVF4Iu+yG9drOVKk0yp0VbheIBqY/oFaGVFV+nwlVRFJ8naWrk1p9OqBBH5SC1/p5Q6p5T6K6XUo8HtS0qpv1FKfbr8zSOiJotfnKemRi/U4c+9QPbWpc99Lnkb4m64wW/9ROPG1CocDVA3NkYHL+p29S9AcVkD4qaO8re66v7aidI4jWyYlYjcFIyG+LiI3Cciz0pY9loR+ayIKBH54TK3i4iKE7047+wAd90F9Ho6eO71gDe/WQ+qkqd1ydZS1OuNzltZAU6d8n8OonFkC4IXF/V90c+qrbNd1oA4+qXaVgvetUZ8kXxeO1EqUymPIiYAVwL4FIAHAKwAOAvg7wF0LMu/DcB5AArAD6etn+XvaNzY6jGXLU+Zqbzb7PrcplrS09O6dFX8uesuNcaJU9OmvFxL4MWnpJry3a6+31Y/vmpVl8PL83zxUp79fllbSVHIUkc6zwTgRUFQ/CPB7Z8Jbs8blv1GAF8A8FMMpGkSZR0sIa88F9+82+z73NELz5499ueuO2jhxKlp065dzqcEr8+6bYp/qbbVgrcF6L1evu31lTQgVNOez1YPn8F0+WyBdGId6TxE5Iegq3wsKqXeJiJLAO4EsKSU+vXIcnsAnATwCwAeBfCmIPj+r0nrZx1pGidV1E41Saotm3ZqyLvNZT13U/Myierke6lfWdHpDjs7+nN1ww3ARz86fNuUQmXqA5FWCz5ORKeDVWVuDtjaGp3f6wGnTzfr+fKcNymfrHWkC92G4G/8rX4tgG0AfwjgqcG8fSJy9cgKRJZE5ISInHjooYfK21KiitmCvyYHhXVuc9Jzuww8QTRuijzuw0FYouXsTp3SFXKU0l+UT550L1Nn6+NgqyBSdfWMqsvhsfzeeCkzkH4g+Htd8Hd/OF9ErhSR3cHtpwH4CgAfBfCfg3k/BkN5PaXUulLqkFLq0LXXXlvSZhNVz3ZBaXIZN59tXlkpZrAGF7fdVt66iZpoebnYFlxbB8X4fNcydbYqGUtLzaieUXU5PJbfGy9lBtLvBfAZAMsisgzgVdAjJd4D4AKA+4LlfhXAS4Lp14J5bwHwrhK3jahRqhjNq2iu22xq3SpzGO+wJN9Ulb+3EdUo6+AnNkX/2mSrkrG25l89o4wv5VWXw8vzfP2+33yqgClxuqgJwHMBfBjARQAfBHAomK8AfMSw/MuD+9jZkCbOuFbtsHUoss13kVZFoNOxd8rhxGlSp/n5fJ+vTsf93FCGMjtls2oHpUHVnQ3Lxs6GRO1QRueYgwdZK5ooi/l54Nix5GXCX5HifAdTindYXFrKN0phXZ2yiYBmdDYkoglURv73Rz+a/bFEk+z48fRl4iOWZhmR1JbSlScVo42dsl3VMTANFYOBNBGVqoz873G4cBI1TTSYu/tuPUqpUtlGJHXtsJhk/379i1Y42dTdwTmvzU19Ptza0vt7a0vfZjDdDgykiahU8dat0Pp69otbk6uZEDWdqcWz6GAub+vx/v3Agw+6LevawXlqqpkB9ZEjwPb28LztbT2fmo850kRUiaLyLpPWFdfvDwaRIKKBbne4QkbRg5KUOWBT1MwM8Oijbs8dynLOKZNtwJqqB6ahZMyRJqJaFfFTb2htbbTc0969wxffPXuAn/iJQZ3b5WX/5yEaV/EWz6IHCamqpGe8JRdI/+Kc5ZxTJtaVbjcG0kRUKFtuYtafek2dcFZWRqt2PPLIcO3oRx8d/mm6aRdPorpFg+Sig7l4StfUlG49vuOOYjvTmbYvLfWrab9QlVnHuk254q1lqonXhol1pImaJ6nOa5batBsbSnW7w8t3u0pNTbnXzu319LrqruHLiVPTpl4v+bPZ7RZTT9n2OU5a9+xs+vbb1mE7D4VT3fWwTcqoY11m3e1JBNaRJqKyJeVFLi3550jb8jZ9hHmGSXmTnU7zWqmIfMSPYREdNiWZn7eXwxMBbrutmFzirPnX8Q6He/cCT36ybkk/cEC32NpGQUzqR9G0HOmysO52sZgjTUSlS0rfyFKbNmt+ZpRS+kJ+883m+5eX9UWFlUCozeKfvbQgemoKuOce+/1K6RJ4Rciaf3327HBb6sMP68D78mX9N2ko8bU1/Zi89bCj2pYmMc51t5uEgTQRFSZt8JW1tUHnP5fatLb8zJkZv+ff2gLe/37dAme7qBbdCYqoyW69NT2g2toaBI558prr7Ezne86xKWOAmbKVMRgWjWIgTUSFKbqnvq0Tzp13mluabK3OgO7df/w4cN11wMbG6EV1En7qJQIGXyBdAqowcMxTV7rMznRVKbLqUFWqqpwy8UyJ022Y2NmQqJminZc6nfwdW3w64dg6Tbl2UhKptrMXJ051TNHPqu9jw867ZX6Om8hlfzZR0efjSQZ2NiSicec6iAPRpIp3NFtZ0a2qPnmzLQ0bcmHHPWJnQyIae8z9I0q2tDTcaS6a9+tCpLga0G3CNAmyYSBNRI2TtXc8L2rUVvEc4jLMzAAf+5h/8Byl1PCIiL5MAyy1QZaqQzQZmNpBRLWL/rxsq3/retFiegdRucLa7L42N/WX3eiw3t2u/uwnlbIjagKmdhBRI8XLStm+27v2jt+3r5jtIiKzXbuy/WJ05MhwEA3o23lauInqxkCaiCpjStlwDZDjP0WvrOifV0X0tGdPe34mJvIlokcC3NgYTjGow8WL2eopZx2YhajJmNpBRJVIGrLXRbR3fN51ETXRVVcBFy6Mzt+9WwevTeZSvSLrUOFETcDUDiKqVd6BC6IdCfOuq9vVLXvMp6YmCYPosLU5TFNqehANuHVeHIeBWYjiGEgTUSWyVgkw9Y73Xdfu3TooCX8eX18H7r13MuvhUvPt7FRTxaNILqkmi4v6s9frDX8W2dGQ2oyBNBFVwnahDQNlW+vw0tJotQ7f/NCLF3UO9Vvfqn9CDi/oRE21vQ2cO1fd89k+f/Pz+otoGtfSk4uL+jN4+fLgs5hH1lKZREVhIE1ElUga0GBtTdeVNTEFvFnqRW9t6cdtbuqLbVKrNlM+aNLcdpu5TvIrXjH6y43I4PNaZz3leMUfn46PREVhZ0Miqky0XnSnM9zanBS8mk5TKyvAnXf617OdmQHOn8+/DFHZ9u0DHn44W81mX7ZQoMkdBDlsN1WJnQ2JqHZra/oCp5T+G23FSkrXMLUwra0NBnDx4RIgZwmibS3qRFl0u8DttwNveYv+YhdXVSm8Jpess/2qlLU/BlEWPPUTUSMkpWuYfq4Nhxpuyo9qVbQaUjt1Ovo4dT1Wp6b0F8SXvlQPVnLnnYPHh1OYXxx+OU0LpsOa67b7bA4c8JtfpaR+F6GDBwe15kX0baIiMZAmokZYW9OtazbRXOnNTeB7v9f8k3OSbpctx1S9nZ1BhzgXly/rX0WUGs7tT3uOJEtLOg/adp9Nk0vWJfW7AHTQfOrU8H2nTjGYpmIxR5qIGsUlV/qqq4DHHrM/3nRam5nRLXv33pttMJdOhz8ZU33ScpJt+cKhMG84Hlz2+8DJk8nPvbmpW8bPnNEt0aurzSlZV2S/C6IkthxpBtJE1CguHYjSWvbCdSg1enEF9MX3jjvMF1MGzNREIsnpQy6jfS4v25cxfU7ajoE0FYmdDYmoFdJ+rnVx6ZJuOYt3agzzJY8e1fft3Tta7uuuu3TrH1GTpOUkp6VGdTrJtdNZOo4oGwbSRNQoYUAQD3B9W8q2tgYDNKys6AA6ni/5yCPADTcMB9zhgBFEVVBKV99IG8nQJSc5KZheWnL7pWWcBirq9/3mE2XBQJqoIaI9y8NpUiWVyQP0aGsuwla2pJ+8T53S+zqsAjI1NfhLVAWXkTbDnOTocbpnz6AaRzjdccfoY8Mvoi5l8qLBdttHDTx5cjRodskJp+bZvXv4OHcZbbMqvFQQNYAtaJ7kYDrJsWPuwbSrw4d1K3ZYKYHl7Khs0SBvcTG9BXVzU7csh8fp+fOjx2lS7q9LelQYbI/LqIEnTw6XDWQQ3T67dwNPPDE874knmhNMM5AmolZ6xSt0LrOIHgFu3766t4jInall1DYQUDj/yBFge9v/ucLW7njalEkYbNtayMcp9YPaIR5Ep82vGgNpImqdeMvcuXPAhQtMx6B2WF42t4ymjSKYdTTBaLpGNG0qqS8CRw0kcsPLDhG1jqllbntb15cmaiqR5I6zaaMIZh1N0JYiltQXwWXUQCJiIE1ELWRrmdveNreypf2cTVS25WWdzxwGq9GOfOFkG6nz+uv1X9Mogy6y1Ez2LUO5f//wa9m/3/85iUx27fKbXzUOyELUEKZWo5Z+PEs3N2cOOtJGfwuZ9vXGhm7p9h12nMhF9LPsMnhKVHQwougog92uTmly6Rib5VySNGpg1P79wIMPjs6fnQXOnvV/XqK4eIfDXbuAixer3QYOyELUcNGe5eFEZquro/nQU1NutXYB875eXNSPb0orB42P+K8hphJ1SXZ2Bi29hw/r4e4vXwYefXQwgmc4iqfL84fSytullaEMmYLopPlEvi5eHD5fVx1EJ2EgTUStc++9o61wly/r+Xm88pXN6QlO4yOaDrG5mf9L8qlTepTOpOdJmz8u5e2I6sbUDiJqnelpc/WA6E/gvnx/bidKE02HiKZJFMV0+XZNxyjyM5RU776lIQbRCFtqBwNpImqdMi7cnQ4HYaHiRPP1Dx4cHZ6+CHku30V+hpqcI+36xYIoDXOkiWhs2OpFZ60jvbnJIJqKFVaWWVkpJ4jOq8jydt/2bX7zq8L0FaoCA2kiah1bvWjXOtLxTla33ur+3E0ZlpaaLaz5XNZIgLbhxF35lrdL0tRREJu6XTReGEgTUevYhkl2GT55YWG0lco2NHNcrwdcc43bsjTZwgoyeXOi5+dHg+a9e4GPftRebcNFfLjw+MiGPpo6CmJTt4vGCwNpImqdtBHgTFZWdLBw/Hj2573+epb0onT9vi6n6Co6cFA8Pemv/xr4iZ8YlP1aXgYeeSRbukL8lxjArbydy/b7zK9KU7eLxgsDaSJqHdMIb92uvY50mCuZNw86TxBOk6HfB06eHNxO69QXDWJvumkQ4IbOnQNe8Qqdxw9kT1coM1+4yDSRIjV1u2i8sGoHEbVSdIS3Awd0EG1rBbSV+oqammKHQ8ovPrqmT3UM24id0fVmrbZRRsnIqKZWx2jqdlH7sPwdEU2spOAjtLzMOtKUn8jwF7KkL3Gdjnu+brjerAExaz0T5cPyd0Q0sdJyIvt93UrF3EnKK56nf8MN9mV9Or2F682arsB8YaJyMJAmorG1ual/Lk8LWMKqHcydpDx27x7O0y+qhvSuXYP1Zqm2sbJiT1viMU+UDwNpIqrNwYP6J+dwOnjQ/bHRx4VT1OamDhJsOadRW1uDTl7RIMVFp6NHcCN61at0nn74Ba6IVKGZGeBNbxrO/19bc6+2EXYyNKVvZC13R0QDDKSJqBamYZNPnXILpm35ntH5R4641ZUOhVUMgEGQ4vq4T30K2LfP/bloPN19t98XOBd585dt1Tw6HQbRREVgZ0MiqkWezk9pj93cBA4fzrZd0U5bLp0UQ6z6QSI6l7moIDoUrwTig50MiYrBzoZENBHCFsGswnzqsG6vKwbRdOCALsfoyjWFKE9gzk6GROViIE1EYyUppSM+iIvN5ibwgz9Y3DbR+AsHBEoaXTMq/OUjHJRFqXKCXg5KQlQuBtJEVIt+32++q6QWQdec6SNH9IhyRDZTUzrlQkT/XV/XHQJvucXt8aZA1lZdxqdMXlyWKh9E5I450kRUm3iHw/jwyklMuZ9KJY8O50qE+aOUznSMpB1/SaPr2R6bJ0eaiIrBHGkiapyTJ4d/2nYNooHBYzY2Bi2Dc3O6RdA1hcPmwAFW4aBktnQL2y8i4ZezpHJ1q6ujx26YMkJEzcRAmohaK1pqTCn99667gJe9bBBcJ9nYsAcuX/zF5W03tZ8tx9iWI+2SO724qFNETCkjRNRMDKSJqLVMHQu3t3U939OndSWNpA5cSYFLESPSUbt1OvoLmk+O8S23mL/AhYP+rKwkP+fi4uDYPX2aQTRR0zGQJqLWsv2MHp2fVrWAgQvZhJ38XEcS3NzUv4jY8uvDQX/SgukirKzowF3ELYAnomwYSBNRa7n8jL62NloJpN9n1QJKF/81I21Ie9fRNG2jDRYlHBY8/CJQZQBPNGkYSBNRa7l0zlpZMQ9FzqCC0kR/zXAZ0t51MJY85exc2AL1sgN4oknEQJqIWsulc1bWoGJ5ubjtpPaJ5znbcuZPndLLdjrAU57itu6yRxUsox41EZmxjjQRjbWkyh1pp7+0qh80/sKOhUUeC2UPiDI9bQ6aw9EUicgf60gT0UTKM+xy2S2H1Hx50yGuuKL6UQU5LDhRdRhIE9FYcw0qNjf1gC5TU/pvWKOaJtvOTr4vVBcvulX8KFJ8WPDQ0aO6ZX1hYXg+K3wQZcdAmojGWjyoMLUKmgZ2WVoCbrpp9LHxCiA0/i5fzv5Yl4FYskoKgMOSffPzo487fnwQTLPCB1E+zJEmook3N6eD57heT9eWjltY0MEIUZJut7yRCcMAOC7+JTGtjwDzqYnc1JIjLSI3iciHRORxEblPRJ5lWObZIvLnIvJIML1bRK4tc7uIiKJcBnaJuv/+8raF2mtjo7rhvYsqcccKH0T5lBZIi8iVAN4N4EkAXgPgiwG8S0Ti2WZfDuCzAF4L4G4ALwbwX8raLiJqjoWF4QEu4rmbSfLkde7fP/y8NuHP8tH8aRFz6zVNtrCe+enTwPOfr4+Rw4f9j2tXRQXAeTrjjgPmh1NeZbZIfyN08LymlFoD8JsAng7g5thyb1dKfatS6k4AtwbzYuNFEdG4MaVHRHM3k+TJ69y/H3jwweF5tgy3668fzZ+myTUzM6gZHbe9rUc2zHNc+0gKgKNf/HbvNi8X5k5PcoUP5odTEUrLkRaRHwLwBgCLSqm3icgSgDsBLCmlft3ymO8A8E4Ab1BK/XDS+pkjTdRueeo758nr9KkH3OkA113HFuhJJaKPxU5HB5Zh7vHUVLYvVUVebm050vPzwPvfnzxU+fw8cOzY8LrW1wcVSqKvdZwxP5x8NKGOdHj5Mp5KROQmAG8E8AEAr7MssyQiJ0TkxEMPPVTKRhJR81WV17mz4z7ssw0HdWmfsLLL5cvmsnVlVuJwZatGc//95iC619OvRanhIDpcV9Ul+pqA+eFUhDID6QeCv9cFf/eH80XkShH5px+cROS5AH4fwMcBvEAp9ahphUqpdaXUIaXUoWuvZX9EoklVVV5np9OMoImqEQabacHk6uogJ7pOpgDYt+PsJJv0/HAqRpmB9HsBfAbAsogsA3gVgNMA7gFwAcB9ABBU8ngvgA6AXwfw9SLyLSVuFxE1gKm+bdL8qDx5nbOz6ctE17e66r68CfOq2+P6692WW1zUqRC9nvu6XY7rIti++PEL4ahJzg+n4pQWSCulHgPwEgCPArgdOqh+iVIq/qPJvwDQBXAVgF8D8HYAv1LWdhFRMxw7NhpcxHM3bVwGWbE5e3Y0mJ6dta8vrXzZVVeZR5Gj5rKl29xzj/s6Fhd1hQ6X9931uC6CqbW8283/hXAc5TmPEIU4IAsRkcXmpq7EYOtsGHZKinbWomYLOxDa2O4Lj4UzZ3Tr7uqqDqZtnf5c1lkW27YSUXZN6GxIRNQa0bJ3NktLoyW0qNluuy25FdlUT9g2hPzm5qBV06aOXyrC1vLLl/VfBtFE5ZmuewOIiJroyBF7CbFoibBpnkVbIV7WLakVOawnDOjlTcdCWDf63nuTRxNkvi3ReGNqBxGRga1WsIhu6Yvepmbr9cwpGWnpOGHqTpa60ZNUj5loEthSO9iWQkRkcOCAOa0jXv2g02FaR9OF72OYkgHoADcMcm1fhsL31XYs2HBAD6LJwRxpIiID1+oH/Om+XcKUjKi0esK2Y8GGX6yIJgcDaSIig8VFc15svONWvIQWNV98cJK0esLRutEi+u/6Ogf0ICIG0kRERraf+03zwxHmqDn6ffuAKfH0HJd6wqZKGBzQg4gYSBMRFWTv3rq3gAAdBJ886Tc4iWm47TQc0IOIWLWDiMggqRqHqQpE6OqrgUceKX3zKEE46EqnA9x8M3D//fr96naB8+eHl2XgS0QuOCALEVFBTANzhB5+WN+nFLCxUd82TrKwfWhnBzh+HLjlFj0QSzyIBnS96OjgK0REPtgiTURk4FMfet8+YM+e0VbquTm/smlUjjD1wlZNg+XqiCgN60gTEXlQyj2YPndOT8BwreJ4dQiqR1o5OparI6KsmNpBRI20ualbdKem9N9o+kRVwhSNcLJVgYjb3gYOH/YfDY/qYSpXt7Kih38X0X/D9A/bfCKaTAykiahxNjd1q25SLnIdbrml3uencsTL1a2s6NzpsKV6Z0ffPnjQPJ/BNNHkYo40ETWOLbe419M1fOvCnOfx0+/rUnlR09N+6R7MsSYaf6zaQUStYcstrjvnuO7np+KdOjXaouybM80ca6LJxUCaiBonPvJc2vyq2J5/3z73/GlqnvV1/TfMf/bFIcGJJhcDaSJqHJ8R6apk267bb9cpJ8vL5sctL2cL0MhsakrX6I6/F1nt7IzmRZv0++b5HBKcaHIxkCaixllc1K2EvZ6ujtDr6dvREQSbtF2vf72+ffTo8PLRIaP5839xLl/WVVF27y7ml4BOZ9Aqbbs/HHacQ4ITURQ7GxIR5XDwoM6zjdu7V49yGPIZ4IXczc7qvw8+aL7v7Fn9f9jibLK8bL8PYBlDImJnQyKiUpiCaAB45BGWRavCgw/qYDkMqEPRIBpIbnFeW7PnOTP/mYiSMJAmIirJ+rp/B7arripve8bZ2bO65ThMvXjwweEBU9JSa2x5znnznzmAC9F4YyBNRFSScMAOl/zoMN92e7v87WqTqSn3PGjbQCorK+ktzmtrxec/J20PEY0H5kgTEeVgy5F20e2aO1FefbVODSEd0CZ9EYmmcNgGUul0dMuyKQ+6zM6CSdvDAVyI2oU50kREJTh5Uncs9JVUieThh4Fdu3JvWuvNzLgH0YB92Z2dclqc0yRtDxGNBwbSREQ5PfywvYa0Saej606HQbQpj/bVry5lU1sjKc1FROdDR4NowC1949Il/dhLl8ovW8cOjETjj4E0EVEBfIKyaAc2Wx5tUjm2SXD0qH3AFdsIk2V1GMyqadtDRMVjIE1EVBCXihvz88NBd1JZtkl34cJo622nYx/hso70jaSqHHVsDxFVi50NiYgKlDbwSryDIQdq8deUYNQ2yEtTto+IisPOhkREFdjYsKckADrv98iR6rZnHEXrc7vUZy6rlrPt1wT+ykA0ORhIExEVaHFRB1JJtY+3tnRQl7U1etIHbYnX506qz1xmLWdW5SAiBtJEVJvduwcBpYi+XYWFheHnXVgANjeBuTk9AMjcnL7t+tiozU3d4nzmTHnVGS5cKGe9TRHmEvt+0Yi3BIvYO21Gl83aYp30/kaPkSLTd1yPUyKqBnOkiagWu3cDTzwxOn/XLuDixfKed2EBOH58dP7UFHD58uC2abAU22Pn54Fjx3RQs7TE0QnziA5WkiUADS9pLo9VKl+es+2xaduWlen4sg3qQ0TFsuVIM5AmolokBTplnpZ8grNeT9d7dnmsUrqFcGtr9L600flomE8wHOUThIfL5h19cGVFB7I7O+nvc97j2nZ8xY9TIioeOxsSEXk6c6aY5aMt3ZQsTJfIksPsU585XDZvnnN8kJcy2Y4v3+OUiIrDQJqIyMI28Ifv8r7rmQS2kSCXlvxTJkKuJeeiaRttGn2QxxdR8zCQJqJa7NrlN78o8/Pm+VOxs2G3Ozrwh+2x4fzV1dHSd6b12GTpYNdWSYOVZCkf5zNEezTgbtPog3mPLyIqHgNpIqrFxYujQXPZHQ0B3SkwHhDPzwNveYvONRXRf00duGyPPXZM/x8tfRdfj0t+7NGjwMxM9tfWJtPT+m80LSIMcJPSKpaX3UYLtO3v+PyiRx90fd4sko4vIqoHOxsSEeUULXl34IBuITQFN9HlWnrqzaTTAW6+2VzxxBS02joA2pYnIiobOxsSEZUgLEm2taWD460tfTte3ze+XJqi6w/XaWfHHEQD5jSOpLSKogZTISIqAlukiYhycC1JZlsuiVLJrbPjwnQZSupw6FqajoioKGyRJiIqgWtJsqwlyprY6a0KSekb4/7Fgojag4E0EVEOttJjYWtymIaQtURZ2Bkuq14v+2OrYkvVaFNpOiKaTAykiYhyMJUkC+3sDHJ6k5Yzueqqwf95gunTp4fX1US2cndtKk1HRJOJgTQRUQ7RkmQ2YYmyaOmyJFddBWxvD8+Ll2nzceGC/2OqZEvVKLo0HRFR0RhIExFZ7N8/qJ4hom+b3Hsv8MlP2tdjChRtAXGvB7z85cPPKwIcPDgYjnrcJH05iA/B7RNEr6zo9BqR4TQbIqKiMJAmIjLYvx948MHheQ8+OBpMh9UlkjrAdTqj5e9My3e7wPXXm6tVnDqlg2nAPe/ZNhJj06SlamQJiOPvSzTNhoioKCx/R0RkkJR+ET1tupSnW14G7r7bXP6u0wEuXx4M5PKylyWvT6lBUB5P/4iKjrhYdT3qTsf8GsI87/V1fX+no19HUiuzrQxeWoqH7X1h6TwiysJW/o6BNBGRgWsgnbRcNFCcmjLXSxbRgbTL+qLPvbkJHD6cfxuL1uvpLwT33usXMNtkDYhd3z8iIhcMpImIPORtkY4Heq4Dt6S1cGd57ioD6aIvKVkDYrZIE1GROCALEZGH2Vm3+a4l2kzl77pdPd9lfQDQ72d77qrEt68IWWtJN23fENF4YiBNRGRw9uxo0Dw7q+dHuZZoW1wEdu8enrd7t54fWlmx11QGdIfDaCWPo0fNJfGOHh10TKxKvw+cPDk6P2/ljCc9yTw/LSBm6TwiqgJTO4iIKmCqAgIMgnNbpzpbx72ofl8H2ab5J0+Wk9rhEpRm7SgYOnjQ/Lr27gUefthpM4mICsEcaSKiGqXl+rpU/8hCqXICaZdc47x5yuwwSERNwRxpIqIGKyOILpPL9tqWadtrJSKyYSBNRNQAWYb+rpPL9mbtKEhE1BYMpImIKpBWBcTWeS5v0Dk3l+/xNi7VL/JWzrBVASmjOggRURYMpImIKpBWBcRWZeKuu3St6ax5zqba1Xn4VL/IWznj5MnRoNlWHYSIqA7sbEhElFNYti7vKH6uiuw86FIVBGDnPiKabLbOhtN1bAwR0biIl3jb2RncbkPN4kuX0gPz+flqtoWIqG2Y2kFElINtAJWkgVXyqjqwPX682ucjImoLBtJERDlUWeItHCWw6MCWLc5ERNkwkCYiyqGsEm+bm7rixtSU/ruwoFNGygjQjx0bHb6ciIjSMZAmIsohb4k3k81N/fitLd3Jb2sreyu0a8fEJ56w38cWayIiMwbSREQ55C3xZnLkCLC9Xcz2uVbbOHDAPP+KK3SLNRERjWIgTUSU09qarn6hlP6bt1rHmTPuy3Y6us50XqurQLc7PK/bBX7zN/Ovm4hoXDGQJiJqGFvrsMnSkjkI9rW4qCuNhIO/9Hr69uJivvUSEY0zBtJERA1jax2enzenkJiC4H37/J93cRE4fRq4fFn/ZRBNRJSMgTQRUcPYWoePHbOnkMSD4Ntvz99KTUREyTiyIRFRAy0u5msRDh975Iiu+mHCahxERPmU2iItIjeJyIdE5HERuU9EnmVZ7oUicr+IPCYi94jI08vcLiKiSRC2Uis1GjTPz7MaBxFRXqUF0iJyJYB3A3gSgNcA+GIA7xKRTmy5LwHwDgCfB/AjAG4EcFdZ20VEFI4QKKL/rqxU87z79+vnDKf9++3bFZ8WFnSqRnRetzsYuCW+/MGDw+uO16H2qUu9sDC6La5M2+wjz3PXpY3bTETZlNki/Y3QwfOaUmoNwG8CeDqAm2PLfTeAKwD8nFLqVwD8DoCvFZFnlLhtRDShVlaGRwjc2dG3yw6m9+8HHnxweN6DDw6C6fh2xR0/Dly4MDzvwgXg8GFz6sapU4Ng2jYoi8tgLQsL5iDcJTjsds3b7BpM53nuurRxm4kouzID6TA942zw95PB3y/NuBwRUW7r637zixIPouPzy3j+U6fyr8PWcu3Soh0PotPmF/ncdWnjNhNRdlVW7QjbPtLG2bIuJyJLInJCRE489NBDhW4cEU0GW4uvbX5V6n5+IiLyV2Yg/UDw97rgb5gN+ICIXCkiu9OWi69QKbWulDqklDp07bXXFr7BRDT+Oh2/+VWp+/mJiMhfmYH0ewF8BsCyiCwDeBWA0wDuAXABwH3Bcu8AcBHAa0XkBwC8CMCfKaU+XuK2EdGEWlrym1+U2dnk+WU8f7+ffx22EnkupfOuuspvfpHPXZc2bjMRZVdaIK2UegzASwA8CuB26KD6JUqpndhy/wDd4XAvgP8K4IMAXl7WdhHRZFtb0yMCmkYILNPZs6PB9Oysnm/arrj5+dEA9KqrgI0NPWBLXL8PnDyp/1eWhDrb/Khjx7KXztveNm/z9nb6Y/M+d13auM1ElJ0olzNpAx06dEidOHGi7s0gIiIiojEnIh9QSh2Kz+cQ4UREREREGTCQJiIiIiLKgIE0EREREVEGDKSJiIiIiDJgIE1ERERElAEDaSIiIiKiDBhIExERERFlwECaiIiIiCgDBtJERERERBkwkCYiIiIiyoCBNBERERFRBgykiYiIiIgyYCBNRERERJQBA2kiIiIiogwYSBMRERERZSBKqbq3IRMReQjAVk1Pfw2Az9b03G3E/eWH+8sP95cf7i8/3F9+uL/8cH/5qXN/9ZRS18ZntjaQrpOInFBKHap7O9qC+8sP95cf7i8/3F9+uL/8cH/54f7y08T9xdQOIiIiIqIMGEgTEREREWXAQDqb9bo3oGW4v/xwf/nh/vLD/eWH+8sP95cf7i8/jdtfzJEmIiIiIsqALdJERERERBkwkI4RkW8UkQ+LyGURUSJyTcKysyLyXhF5TES2ROSwy33jRESeJCJvF5HzIvIpEflhy3Jzwf6MTy8P7n+z4b69Vb6WKrjur2BZ6z7h8WVc9rdF5KyIXBCRD4nIv4ncN9bHl4jcFLzmx0XkPhF5lmW5F4rI/cFxc4+IPN3lvnHjsr9E5Nki8uci8kgwvVtEro3cHz+e/kelL6JCHseXdZ/w+BpZ5nWma2Lk/kk6vn5ZRD4dvM73JCzXzPOXUopTZALw7QB+DcDfAVAArklY9ncBPA7ghwC8D8AOgOvT7hunCcDtwX56HYDfCf5/vmG5GQDfFZk+Eiz7VcH9bwbwUGyZXXW/vrr2V9o+4fFlXPbvAfx4sE+2AXwBwMy4H18ArgTwKQAPAFgBcDbYF53Ycl8C4DEA9wH4gWD/vC/tvnGbPPbXywD8TwC3AnhbcOy9KXK/AvCuyPH0nLpfW537K2mf8PgyHl//PLKfvj/Yd/dN2vEVvNZfjpzr32NZprHnr9p3YFMnAPcgIZAG8BQAlwH8XnD7+cHyP5t0X92vq4T99AiAk8H/Xxq8zremPOZLADwB4M8j894M4DSAPQhy98dx8tlftn3C48u6v3ZH/n93sGx/3I8vAC8KXuuPBLd/Jrg9H1vuNcH8lwS33xLcfkbSfXW/vhr3V/R4elKwzF9G5qngsTN1v6Ym7K+kfcLjy7y/Io/54WCZpUk7viKvdw7JgXRjz19M7chuDoBAf9sEgE8Gf7805b6xISJPAfBk+L/OVwGYBnA0Nv8A9DfJbRH5VREZq+Mz4/4y7ZM58PgaoZS6GDzuyQC+BsA/ALg/ssi4Hl/hT5hp+ylpOdd1jAOn1xoeT4EXBH/fF1vXTwJ4NEiv+uZCt7I5fI8N0z7h8WV5rSIiAJYAfB76l4+oSTi+XDX2/DUuFxIvIvJJU26SBPm6WVcb/FWe9zWebX8BeHl80eCv9XUGwcv3AfgcgHdG7vojAK8A8EIAJ6B/6los5hVUq8D95bpPeHwN1rUH+uf4awB8TyQYGpvjy4Hr8TC25yxPia9VRG4C8EYAH4BOMQr9ZwAvhg6CrgbwdhHplreZjZG0v1z3CY+vga8D8GUANpRSj0bmT+rx5aox56/pKp6kgZ4HYJdh/j8kPUhEdgHoQOemnoZ+k64L7t4f/H0g5b42StpfPwXL64zuLxX83gLgGwD0ALxBKfVYuCKl1FvC/0XkMoDnAOgX+BqqVMj+StgnvwceXyP7S0SeBOC9AA4BeLFS6p5wRWN2fMWF7/vIfhKRKwFcDr5QWJdLuW/cuO4viMhzoT9v9wN4QTTQUUr9WPi/iHwDdNDzNAAfLXfzK+e8vxL2CY8vw/4K3Bb8vSO6kgk6vqxac/6qOy+maRP0N8NXQx+sCsC/B/BNwX1vDuYdCm7/T4x2+PqytPvGaYLuJKAA/DQGncHmTfsrmPe70Pm9XxZbzx8D+FEAr4Ru+VEAXlj366tzfyXtEx5fxv31F8HtN2HQQeep4358QXdu+jT0RWMZ+ufNB6C/ZCgAHwmW+2fBMfMBDDrk/GnafeM2eeyvZwE4D91xdSU4nr4luO8W6J/hlwC8NljmM4jkVY/L5LG/rPuEx9fo/gqWfSqAiwD+LLaOiTm+gtf7TcHrVAD+BjoG+7K2nL9q34FNm6B/Tlax6Z7gvjdj+MK9H8AfQPcWPQPgeyPrsd43ThOALwLw34MP+qcBvDZyn2l/XQJwzLCedQCfCPbXxwG8pu7X1oD9Zd0nPL6M+yv+uVUAbp6E4wvAcwF8GPqi/MHYPoleuF8cvP7Hob+APcPlvnGbXPaX5VpwOrjvIPSXs0cQVAgA8C/rfl0176/EfcLjy/h5/LFg3uHY4yft+LrH8Fl7uWF/NfL8xZENiYiIiIgymMjOhkREREREeTGQJiIiIiLKgIE0EREREVEGDKSJiIiIiDJgIE1ERERElAEDaSKihhKRudiIj58TkXeIyD6Hx7wn43OeFpFHg//7IvI6Ebk52ysgIhpvkzqyIRFRm3wQwC8A+A4A3wk9UMirin4SEelAD2iwO5jVhx4MB9C1XomIKIIt0kREzfegUurtAH4yuP01IvIcEfk/IvKoiNwvIkumB4rIj4rIgyJyUUQ+KSI/HbnvtIicF5E1EflHAM8E8CsA7hKROQDvDBb96aCV+98Gf38pePw1InJJRN5R0usmImo0BtJERM23S0SuBfDC4PYnoIeJ7wH4Yejhg+8UkecbHvsJAD8L4N8D+BCA14nITZH7uwBmI+sJPQTgl4L/3w3guwH8FoA/BbAoIruC7ekAeEueF0dE1FZM7SAiar5/g0GQexbAvQC+AcDrlVJ3iMjHAfwhgG8E8Guxxz4VOj3j6si8ZwbrCL1MKfWPACAiAACl1HkRuRc6AP+IUuodwf1HAbwNwLcA+Hboodv/oJBXSUTUMmyRJiJqvv8DYAHAswA8A8BfB/NV0oNEZAbAfwPwKHRu9euDu66MLHY+DKINTOt/N3RQ/4MAng9gUym1k/4SiIjGD1ukiYia77NKqePhDRF5P4CHAbxKRD4B4KXBXXfHHifQwfAV0C3S3+z5vA8Hf79WRL4LwO8qpS6IyG8C+PHgPqZ1ENHEYos0EVHLKKXOAfhWAGegW5y/BMCtSqk/ji33KIAfhQ6k/x10+oePPwNwHMDXAng7gLDs3p0ALgP4kFLqbzK+DCKi1hOlEn8ZJCIi+icicgA6P/tOAP9OKfUrNW8SEVFt2CJNREQ+XgndofFuAL9e87YQEdWKLdJERERERBmwRZqIiIiIKAMG0kREREREGTCQJiIiIiLKgIE0EREREVEGDKSJiIiIiDJgIE1ERERElMH/D5Q2vjDisJpUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the polarity and subjectivity\n",
    "plt.figure(figsize=(12,8))\n",
    "#for i in range(0, btc.shape[0]):\n",
    "#plt.scatter(btc['Polarity'][i], btc['Subjectivity'][i], color='Blue')\n",
    "\n",
    "plt.scatter(btc['Polarity'], btc['Subjectivity'], color='Blue')\n",
    "\n",
    "plt.title('Sentiment Analysis')\n",
    "plt.xlabel('Polarity')\n",
    "plt.ylabel('Subjectivity')\n",
    "plt.show()\n",
    "\n",
    "#Percentage of positive tweets\n",
    "ptweets = btc[btc.Analysis == 'Positive']\n",
    "ptweets = ptweets['text']\n",
    "        \n",
    "ptext = round( (ptweets.shape[0] / btc.shape[0])*100, 1)\n",
    "#print(ptext)\n",
    "    \n",
    "#Percentage of negative tweets\n",
    "ntweets = btc[btc.Analysis == 'Negative']\n",
    "ntweets = ntweets['text']\n",
    "\n",
    "ntext = round((ntweets.shape[0] / btc.shape[0])*100, 1)\n",
    "#print(ntext)\n",
    "   \n",
    "#plotSentiment(btc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c879a407",
   "metadata": {},
   "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": [
    "# Show the value counts\n",
    "btc['Analysis'].value_counts()\n",
    "\n",
    "# Plot and visualize the counts \n",
    "plt.title('Sentiment Analysis')\n",
    "plt.xlabel('Sentiment')\n",
    "plt.ylabel('Counts')\n",
    "btc['Analysis'].value_counts().plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "188ce87c",
   "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>text</th>\n",
       "      <th>post_id</th>\n",
       "      <th>date</th>\n",
       "      <th>Month</th>\n",
       "      <th>Subjectivity</th>\n",
       "      <th>Polarity</th>\n",
       "      <th>Analysis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34954</th>\n",
       "      <td>As Bitcoin and other cryptocurrencies gain pop...</td>\n",
       "      <td>35039</td>\n",
       "      <td>2017-12-30 22:00:13</td>\n",
       "      <td>12</td>\n",
       "      <td>0.529960</td>\n",
       "      <td>0.048214</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34956</th>\n",
       "      <td>US dollar will end 2017 as worst year since 20...</td>\n",
       "      <td>35041</td>\n",
       "      <td>2017-12-30 19:20:21</td>\n",
       "      <td>12</td>\n",
       "      <td>0.911111</td>\n",
       "      <td>-0.522222</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34957</th>\n",
       "      <td>Storiqa launched its token sale on November 28...</td>\n",
       "      <td>35042</td>\n",
       "      <td>2017-12-30 18:30:07</td>\n",
       "      <td>12</td>\n",
       "      <td>0.173148</td>\n",
       "      <td>0.058333</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34958</th>\n",
       "      <td>The Poland central bank has appeared to sponso...</td>\n",
       "      <td>35043</td>\n",
       "      <td>2017-12-30 18:00:02</td>\n",
       "      <td>12</td>\n",
       "      <td>0.249053</td>\n",
       "      <td>0.065341</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34959</th>\n",
       "      <td>The ‘revival’ of the supposed SegWit2x Bitcoin...</td>\n",
       "      <td>35044</td>\n",
       "      <td>2017-12-30 17:54:15</td>\n",
       "      <td>12</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>-0.120000</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78893</th>\n",
       "      <td>2017’s Best Bitcoin Wallets for your Android M...</td>\n",
       "      <td>79003</td>\n",
       "      <td>2017-06-29 12:12:26</td>\n",
       "      <td>6</td>\n",
       "      <td>0.445833</td>\n",
       "      <td>0.525000</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78895</th>\n",
       "      <td>Top Bitcoin Exchanges : Places to start invest...</td>\n",
       "      <td>79005</td>\n",
       "      <td>2017-06-15 22:28:11</td>\n",
       "      <td>6</td>\n",
       "      <td>0.511019</td>\n",
       "      <td>0.377548</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78898</th>\n",
       "      <td>BitcoinThe post Bitcoin appeared first on Cryp...</td>\n",
       "      <td>79008</td>\n",
       "      <td>2017-06-15 19:26:43</td>\n",
       "      <td>6</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78899</th>\n",
       "      <td>WAVES Weekly No. 31Last week has seen a number...</td>\n",
       "      <td>79009</td>\n",
       "      <td>2017-02-20 00:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.325284</td>\n",
       "      <td>0.039512</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78900</th>\n",
       "      <td>WAVES Weekly No. 30This week we have several u...</td>\n",
       "      <td>79010</td>\n",
       "      <td>2017-02-13 00:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.411508</td>\n",
       "      <td>0.076587</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7052 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    text  post_id  \\\n",
       "34954  As Bitcoin and other cryptocurrencies gain pop...    35039   \n",
       "34956  US dollar will end 2017 as worst year since 20...    35041   \n",
       "34957  Storiqa launched its token sale on November 28...    35042   \n",
       "34958  The Poland central bank has appeared to sponso...    35043   \n",
       "34959  The ‘revival’ of the supposed SegWit2x Bitcoin...    35044   \n",
       "...                                                  ...      ...   \n",
       "78893  2017’s Best Bitcoin Wallets for your Android M...    79003   \n",
       "78895  Top Bitcoin Exchanges : Places to start invest...    79005   \n",
       "78898  BitcoinThe post Bitcoin appeared first on Cryp...    79008   \n",
       "78899  WAVES Weekly No. 31Last week has seen a number...    79009   \n",
       "78900  WAVES Weekly No. 30This week we have several u...    79010   \n",
       "\n",
       "                     date  Month  Subjectivity  Polarity  Analysis  \n",
       "34954 2017-12-30 22:00:13     12      0.529960  0.048214  Positive  \n",
       "34956 2017-12-30 19:20:21     12      0.911111 -0.522222  Negative  \n",
       "34957 2017-12-30 18:30:07     12      0.173148  0.058333  Positive  \n",
       "34958 2017-12-30 18:00:02     12      0.249053  0.065341  Positive  \n",
       "34959 2017-12-30 17:54:15     12      0.520000 -0.120000  Negative  \n",
       "...                   ...    ...           ...       ...       ...  \n",
       "78893 2017-06-29 12:12:26      6      0.445833  0.525000  Positive  \n",
       "78895 2017-06-15 22:28:11      6      0.511019  0.377548  Positive  \n",
       "78898 2017-06-15 19:26:43      6      0.333333  0.250000  Positive  \n",
       "78899 2017-02-20 00:00:00      2      0.325284  0.039512  Positive  \n",
       "78900 2017-02-13 00:00:00      2      0.411508  0.076587  Positive  \n",
       "\n",
       "[7052 rows x 7 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## For Only 2017\n",
    "\n",
    "start_date = '01-01-2017'\n",
    "end_date = '31-12-2017'\n",
    "btc_17 = (btc['date'] > start_date) & (btc['date'] <= end_date)\n",
    "btc_2017 = btc.loc[btc_17]\n",
    "btc_2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e50f3056",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentiment Count Positive    5525\n",
      "Negative    1038\n",
      "Neutral      489\n",
      "Name: Analysis, dtype: int64\n"
     ]
    },
    {
     "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": [
    "btc_2017['Analysis'].value_counts()\n",
    "# Count of Positive, Negative and Neutral Tweets\n",
    "print(\"Sentiment Count\", btc_2017['Analysis'].value_counts())\n",
    "# Plot and visualize the counts \n",
    "plt.title('Sentiment Analysis')\n",
    "plt.xlabel('Sentiment')\n",
    "plt.ylabel('Counts')\n",
    "btc_2017['Analysis'].value_counts().plot(kind='bar')\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "1b233777",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5525"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Pbtc = btc_2017['Analysis'].value_counts().Positive\n",
    "Nbtc = btc_2017['Analysis'].value_counts().Negative\n",
    "\n",
    "Pbtc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "fd8607eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Month  Analysis  counts\n",
      "0       2  Positive       4\n",
      "1       6  Positive       6\n",
      "2       7  Positive       9\n",
      "3       8  Positive       8\n",
      "4       9  Negative      51\n",
      "5       9   Neutral      33\n",
      "6       9  Positive     310\n",
      "7      10  Negative     338\n",
      "8      10   Neutral     127\n",
      "9      10  Positive    1523\n",
      "10     11  Negative     324\n",
      "11     11   Neutral     130\n",
      "12     11  Positive    1735\n",
      "13     12  Negative     325\n",
      "14     12   Neutral     199\n",
      "15     12  Positive    1930\n"
     ]
    }
   ],
   "source": [
    "#btc_2017['Month'] = btc['date'].dt.month\n",
    "btc_2017\n",
    "#btc_20177 = btc_2017.groupby(['Month'])['Analysis'].count()\n",
    "df2 = btc_2017.groupby(['Month','Analysis']).size().reset_index(name='counts')\n",
    "print(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a58aaf79",
   "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>Month</th>\n",
       "      <th>Analysis</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Positive</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>Positive</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>Positive</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>Positive</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9</td>\n",
       "      <td>Positive</td>\n",
       "      <td>310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Positive</td>\n",
       "      <td>1523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11</td>\n",
       "      <td>Positive</td>\n",
       "      <td>1735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>12</td>\n",
       "      <td>Positive</td>\n",
       "      <td>1930</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Month  Analysis  counts\n",
       "0       2  Positive       4\n",
       "1       6  Positive       6\n",
       "2       7  Positive       9\n",
       "3       8  Positive       8\n",
       "6       9  Positive     310\n",
       "9      10  Positive    1523\n",
       "12     11  Positive    1735\n",
       "15     12  Positive    1930"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dPosit = df2.loc[df2['Analysis'] == 'Positive']\n",
    "dNegat = df2.loc[df2['Analysis'] == 'Negative']\n",
    "#Pbtc = df2['Analysis'].value_counts().Positive\n",
    "#Pbtc\n",
    "dPosit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "bb8f0318",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(\"dPosit['counts'][0]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e41d2ac6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dPosit[\"counts\"]=dPosit.iloc[dPosit[\"counts\"].values.astype(int)]\n",
    "#type(\"df2['counts'][0]\")\n",
    "\n",
    "#dNegat[\"counts\"] = dNegat[\"counts\"].apply(lambda x: [int(el) for el in x.strip(\"[]\").split(\",\")])\n",
    "\n",
    "#type(\"dNegat['counts'][0]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "e3dfbbc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Nbtc = df2['Analysis'][1].value_counts().Negative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ecaea264",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\"\\nfinalSenti = [0] * 13\\n#def totalCounting():\\nfor i in range(13):        \\n    i += 1\\n    for j in range(13):\\n        j += 1\\n        if dPosit[\\'Month\\'][j] == i:\\n            Pbtc = btc_2017[\\'Analysis\\'].value_counts().Positive\\n            finalSenti[i] += dPosit[counts][j]\\n        if dNegat[\\'Month\\'][j] == i:\\n            Nbtc = btc_2017[\\'Analysis\\'].value_counts().Negative\\n            finalSenti[i] = finalSenti[i] - dNegat[counts][j]   \\n#df2[\\'Total\\'] = finalSenti\\n'"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\"\n",
    "finalSenti = [0] * 13\n",
    "#def totalCounting():\n",
    "for i in range(13):        \n",
    "    i += 1\n",
    "    for j in range(13):\n",
    "        j += 1\n",
    "        if dPosit['Month'][j] == i:\n",
    "            Pbtc = btc_2017['Analysis'].value_counts().Positive\n",
    "            finalSenti[i] += dPosit[counts][j]\n",
    "        if dNegat['Month'][j] == i:\n",
    "            Nbtc = btc_2017['Analysis'].value_counts().Negative\n",
    "            finalSenti[i] = finalSenti[i] - dNegat[counts][j]   \n",
    "#df2['Total'] = finalSenti\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "e1354b2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#x = df2['Month'][1]\n",
    "#x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "4ab2ff42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1ca43320430>"
      ]
     },
     "execution_count": 70,
     "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": [
    "#For 2017\n",
    "#df2.plot(x='Month', y = 'counts')\n",
    "from matplotlib import rcParams\n",
    "\n",
    "sns.lineplot(data = df2, x = \"Month\" , y = \"counts\" , hue = \"Analysis\").set(title = 'Sentiment Count for 2017 on News Data', ylabel = 'Count of Sentiment of Tweets')\n",
    "rcParams['axes.titlepad'] = 5\n",
    "plt.legend(labels=[\"Positive\",\"Negative\", \"Neutral\"], loc = 2, bbox_to_anchor = (1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "5151a7b4",
   "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>text</th>\n",
       "      <th>post_id</th>\n",
       "      <th>date</th>\n",
       "      <th>Month</th>\n",
       "      <th>Subjectivity</th>\n",
       "      <th>Polarity</th>\n",
       "      <th>Analysis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9698</th>\n",
       "      <td>Venture capitalist Lou Kerner says bitcoin is ...</td>\n",
       "      <td>9783</td>\n",
       "      <td>2018-12-31 23:37:58</td>\n",
       "      <td>12</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9699</th>\n",
       "      <td>Charlie Lee, the creator of Litecoin and the m...</td>\n",
       "      <td>9784</td>\n",
       "      <td>2018-12-31 23:30:27</td>\n",
       "      <td>12</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9700</th>\n",
       "      <td>As crypto’s value has dropped across the board...</td>\n",
       "      <td>9785</td>\n",
       "      <td>2018-12-31 23:24:38</td>\n",
       "      <td>12</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.216667</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9701</th>\n",
       "      <td>Hacks and heists have been a threat for as lon...</td>\n",
       "      <td>9786</td>\n",
       "      <td>2018-12-31 23:05:03</td>\n",
       "      <td>12</td>\n",
       "      <td>0.296212</td>\n",
       "      <td>0.134091</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9703</th>\n",
       "      <td>Even in the wake of a crushing bear market, cr...</td>\n",
       "      <td>9788</td>\n",
       "      <td>2018-12-31 22:46:00</td>\n",
       "      <td>12</td>\n",
       "      <td>0.516667</td>\n",
       "      <td>0.216667</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74432</th>\n",
       "      <td>Australian Banks Reportedly Freezing the Accou...</td>\n",
       "      <td>74542</td>\n",
       "      <td>2018-01-01 12:00:09</td>\n",
       "      <td>1</td>\n",
       "      <td>0.288889</td>\n",
       "      <td>0.225000</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74433</th>\n",
       "      <td>NiceHash CEO Steps Down After $63 Million in B...</td>\n",
       "      <td>74543</td>\n",
       "      <td>2018-01-01 08:00:16</td>\n",
       "      <td>1</td>\n",
       "      <td>0.364242</td>\n",
       "      <td>0.093333</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74434</th>\n",
       "      <td>Bitcoin Outperformed All Bonds, Stocks, Gold, ...</td>\n",
       "      <td>74544</td>\n",
       "      <td>2018-01-01 08:00:07</td>\n",
       "      <td>1</td>\n",
       "      <td>0.359470</td>\n",
       "      <td>0.021591</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74435</th>\n",
       "      <td>As We Say Goodbye to 2017, What Does 2018 Hold...</td>\n",
       "      <td>74545</td>\n",
       "      <td>2018-01-01 04:00:52</td>\n",
       "      <td>1</td>\n",
       "      <td>0.366631</td>\n",
       "      <td>0.086833</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74436</th>\n",
       "      <td>Pineapple Fund Donates $5 Million in Bitcoin a...</td>\n",
       "      <td>74546</td>\n",
       "      <td>2018-01-01 02:00:20</td>\n",
       "      <td>1</td>\n",
       "      <td>0.513068</td>\n",
       "      <td>0.321212</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>41284 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    text  post_id  \\\n",
       "9698   Venture capitalist Lou Kerner says bitcoin is ...     9783   \n",
       "9699   Charlie Lee, the creator of Litecoin and the m...     9784   \n",
       "9700   As crypto’s value has dropped across the board...     9785   \n",
       "9701   Hacks and heists have been a threat for as lon...     9786   \n",
       "9703   Even in the wake of a crushing bear market, cr...     9788   \n",
       "...                                                  ...      ...   \n",
       "74432  Australian Banks Reportedly Freezing the Accou...    74542   \n",
       "74433  NiceHash CEO Steps Down After $63 Million in B...    74543   \n",
       "74434  Bitcoin Outperformed All Bonds, Stocks, Gold, ...    74544   \n",
       "74435  As We Say Goodbye to 2017, What Does 2018 Hold...    74545   \n",
       "74436  Pineapple Fund Donates $5 Million in Bitcoin a...    74546   \n",
       "\n",
       "                     date  Month  Subjectivity  Polarity  Analysis  \n",
       "9698  2018-12-31 23:37:58     12      0.333333  0.208333  Positive  \n",
       "9699  2018-12-31 23:30:27     12      0.222222  0.266667  Positive  \n",
       "9700  2018-12-31 23:24:38     12      0.277778  0.216667  Positive  \n",
       "9701  2018-12-31 23:05:03     12      0.296212  0.134091  Positive  \n",
       "9703  2018-12-31 22:46:00     12      0.516667  0.216667  Positive  \n",
       "...                   ...    ...           ...       ...       ...  \n",
       "74432 2018-01-01 12:00:09      1      0.288889  0.225000  Positive  \n",
       "74433 2018-01-01 08:00:16      1      0.364242  0.093333  Positive  \n",
       "74434 2018-01-01 08:00:07      1      0.359470  0.021591  Positive  \n",
       "74435 2018-01-01 04:00:52      1      0.366631  0.086833  Positive  \n",
       "74436 2018-01-01 02:00:20      1      0.513068  0.321212  Positive  \n",
       "\n",
       "[41284 rows x 7 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## For Only 2018\n",
    "\n",
    "start_date = '01-01-2018'\n",
    "end_date = '01-01-2019'\n",
    "btc_18 = (btc['date'] > start_date) & (btc['date'] <= end_date)\n",
    "btc_2018 = btc.loc[btc_18]\n",
    "btc_2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "237dbdc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentiment Count Positive    5525\n",
      "Negative    1038\n",
      "Neutral      489\n",
      "Name: Analysis, dtype: int64\n"
     ]
    },
    {
     "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": [
    "btc_2018['Analysis'].value_counts()\n",
    "# Count of Positive, Negative and Neutral Tweets\n",
    "print(\"Sentiment Count\", btc_2017['Analysis'].value_counts())\n",
    "# Plot and visualize the counts \n",
    "plt.title('Sentiment Analysis')\n",
    "plt.xlabel('Sentiment')\n",
    "plt.ylabel('Counts')\n",
    "btc_2018['Analysis'].value_counts().plot(kind='bar')\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a1de8fb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Month  Analysis  counts\n",
      "0       1  Negative     269\n",
      "1       1   Neutral      92\n",
      "2       1  Positive    1455\n",
      "3       2  Negative     241\n",
      "4       2   Neutral      79\n",
      "5       2  Positive    1447\n",
      "6       3  Negative     443\n",
      "7       3   Neutral     164\n",
      "8       3  Positive    2334\n",
      "9       4  Negative     529\n",
      "10      4   Neutral     162\n",
      "11      4  Positive    2555\n",
      "12      5  Negative     528\n",
      "13      5   Neutral     163\n",
      "14      5  Positive    2695\n",
      "15      6  Negative     504\n",
      "16      6   Neutral     147\n",
      "17      6  Positive    2334\n",
      "18      7  Negative     521\n",
      "19      7   Neutral     162\n",
      "20      7  Positive    2486\n",
      "21      8  Negative     687\n",
      "22      8   Neutral     166\n",
      "23      8  Positive    3232\n",
      "24      9  Negative     603\n",
      "25      9   Neutral     134\n",
      "26      9  Positive    3354\n",
      "27     10  Negative     741\n",
      "28     10   Neutral     136\n",
      "29     10  Positive    3919\n",
      "30     11  Negative     963\n",
      "31     11   Neutral     172\n",
      "32     11  Positive    3972\n",
      "33     12  Negative     610\n",
      "34     12   Neutral     152\n",
      "35     12  Positive    3133\n"
     ]
    }
   ],
   "source": [
    "#btc_2017['Month'] = btc['date'].dt.month\n",
    "btc_2018\n",
    "#btc_20177 = btc_2017.groupby(['Month'])['Analysis'].count()\n",
    "df18 = btc_2018.groupby(['Month','Analysis']).size().reset_index(name='counts')\n",
    "print(df18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "02ed5a26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c934d0a340>"
      ]
     },
     "execution_count": 74,
     "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": [
    "#For 18\n",
    "sns.lineplot(data = df18, x = \"Month\" , y = \"counts\" , hue = \"Analysis\").set(title = 'Sentiment Count for 2018 on News Data', ylabel = 'Count of Sentiment of Tweets')\n",
    "rcParams['axes.titlepad'] = 10\n",
    "plt.legend(labels=[\"Positive\",\"Negative\", \"Neutral\"], loc = 2, bbox_to_anchor = (1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "98e3a4e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training-set accuracy score: 0.5366\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vbatch2\\Anaconda3\\envs\\cs418env\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn=KNeighborsClassifier(n_neighbors=3)   #,algorithm=\"kd_tree\",n_jobs=-1\n",
    "\n",
    "knn.fit(data_svmReliable_conversion(x_train), data_svmReliable_conversion(y_train))\n",
    "kn_predicted=knn.predict(data_svmReliable_conversion(x_test))\n",
    "#knn.fit(data_svmReliable_conversion(x_train), data_svmReliable_conversion(y_train))\n",
    "#kn_predicted=knn.predict(data_svmReliable_conversion(x_test))\n",
    "\n",
    "print('Training-set accuracy score: {0:0.4f}'. format(accuracy_score(y_test, kn_predicted)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33cd36a4",
   "metadata": {},
   "source": [
    "Our ML model with our current data has a final accuracy of 45.5%. This could be an indiction of many things such as our hypothesis being wrong, not having enough data, or training on the wrong metrics. We are looking more into this.\n",
    "\n",
    "REFLECTION:\n",
    "\n",
    "One of the hardest parts about the project has been wrangling the massive amounts of data into something usable. \n",
    "\n",
    "Our initial insight was that positive public sentiment would increased cryptocurrency price, but from our EDA we can see that this is not always the case. In fact, sometimes the price is the inverse of the public sentiment. \n",
    "\n",
    "Going forward, our biggest problem will be to train and tweak the machine learning model. If positive public sentiment is not directly correlated to price, then our machine learning model will have to train and capture the patterns that we humans can not see.\n",
    "\n",
    "We think we are on track with the project.\n",
    "\n",
    "Our belief is that it is worth proceeding with our project. Just because our initial insights were wrong, does not mean that it is not an interesting hypothesis to test.\n",
    "\n",
    "NEXT STEPS:\n",
    "\n",
    "Our next steps are to work more on the machine learning model and hopefully achieve a higher accuracy of predicting the price of cryptocurrency based on public sentiment. Our goal is to achieve 50%+ which means that it is better than randomly guessing whether or not the price will go up. If we require any additional EDA while working on the machine learning model, we will evaluate at the time and get it done."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d5fb13a",
   "metadata": {},
   "outputs": [],
   "source": [
    " "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}