FinalProjectGroup026-Fa21.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Can we Predict Bitcoin Value with Tweets that Mention Elon Musk?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Permissions\n",
    "\n",
    "Place an `X` in the appropriate bracket below to specify if you would like your group's project to be made available to the public. (Note that student names will be included (but PIDs will be scraped from any groups who include their PIDs).\n",
    "\n",
    "* [ X ] YES - make available\n",
    "* [  ] NO - keep private"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This project seeks to identify whether or not there is a correlation between tweets that mentions Elon Musk and Bitcoin values and its buying volume from 2021/1/1 to 2021/10/31. The data used for the project is collected from online datasets such as Kaggle and Investing.com. We analyzed our data using linear regression models and used the p-value and R-squared to deduce whether such correlations were possible.  The result of this analysis concludes that the counts of tweets that mentions Elon Musk and the user favourties gained by thoses tweets cannot be used to predict bitcoin prices and buying volumes from 2021/1/1 to 2021/10/31."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "- Jasmine Chen\n",
    "- Jingwen Liu\n",
    "- Hoang Nguyen\n",
    "- Ruoxuan Li"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='research_question'></a>\n",
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From 2021-01-01 to 2021-10-31, how will tweets that mentioned Elon Musk affect the change of Bitcoin values and its buying volume?\n",
    "\n",
    "- Does more tweets that mentioned Elon Musk increase the bitcoin value on a weekly basis?\n",
    "- Does the total of user favorites on tweets about Elon Musk of the day affects the buying volumes on a weekly basis?\n",
    "- Does tweets that mentioned Elon Musk increase the bitcoin value of that particular day? How about the user favorites and its impact on buying volumes of that particular day?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='background'></a>\n",
    "\n",
    "## Background & Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bitcoin(₿) is a type of cryptocurrency that was invented in 2008 and was first released in 2009. Bitcoin, as a decentralized digital currency, is easy to set up and trade. There does not exist an administrator for Bitcoin and the transactions are made by individuals without the need for intermediaries. Meanwhile, bitcoin is anonymous, fast, and transparent. There is also an important rewarding process known as mining. This is a procedure by which clients unravel scientific conditions utilizing programming and acquire remunerates consequently. Since its first implementation in the market, bitcoin gains lots of popularity for its distinguished characteristics stated as above. There are also a lot of discussions about Bitcoin going on Twitter.\n",
    "\n",
    "One interesting phenomenon on Twitter is that some Bitcoin-related tweets actually influence the Bitcoin value significantly. An example would be the Bitcoin tweets posted by Elon Musk. On 12 May 2021, after Elon Musk tweeted that Tesla will no longer accept Bitcoin during transactions, the price of Bitcoin dropped significantly around 15 percent.<sup>1</sup> On 19 January 2021, however, Elon Musk placed #Bitcoin in his Twitter profile tweeting “In retrospect, it was inevitable”, which caused the price to briefly rise from 5000 in an hour to about $37,299. <sup>2</sup>\n",
    "\n",
    "This interesting phenomenon triggers our interest in finding the correlations between Bitcoin tweets that mention Elon Musk and their impact on the Bitcoin value. Recent research has shown that the trading price is completely based on speculation, and its trading volume is highly impacted by news media. <sup>3</sup> The research done by Ashrit Deebadi furthermore presents us with a stronger predictive model for Bitcoin value based on a combination of Twitter feed sentiment and other metrics. From these previous works, we are motivated to explore how tweets that mention Elon Musk can influence the Bitcoin value as Elon Musk's tweets could promote great changes in Bitcoin value. Here we raise the question of \"From 2021-01-01 to 2021-10-31, how will tweets that mentioned Elon Musk affect the change of Bitcoin values and its buying volume?\" We hope to find out some interesting relationships between Bitcoin tweets that mention Elon Musk and Bitcoin values, and our research might provide some helpful insights to those who are interested in Bitcoin.\n",
    "\n",
    "\n",
    "References (include links):\n",
    "- 1) https://www.vox.com/recode/2021/5/18/22441831/elon-musk-bitcoin-dogecoin-crypto-prices-tesla\n",
    "- 2) https://www.kaggle.com/kaushiksuresh147/bitcoin-tweets\n",
    "- 3) https://scholarworks.sjsu.edu/cgi/viewcontent.cgi?article=1911&context=etd_projects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We hypothesize that the more tweets that mentioned Elon Musk and the more user favourites gained by those tweets will increase the Bitcoin value and buying volume, because Elon Musk promotes bitcoins, and more tweets mentioning Elon Musk means more people cared about Bitcoin and thus more people buying Bitcoin. Likewise, the more the total user favorites on tweets about Elon Musk implies more people cared about bitcoins and more buying volume.\n",
    "- More tweets that mentioned Elon Musk will increase the bitcoin value on a weekly basis.\n",
    "- More user favourites gained by the tweets will increase the bitcoin buying volume on a weekly basis.\n",
    "- More tweets that mentioned Elon Musk will increase the bitcoin value on a daily basis. More user favourites gained by the tweets will increase the bitcoin buying volume on a daily basis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Dataset Name: Bitcoin Tweets\n",
    "- Link to the dataset: https://www.kaggle.com/kaushiksuresh147/bitcoin-tweets\n",
    "- Number of observations: 1793124\n",
    "- This dataset has all bitcoin tweets with #Bitcoin and #btc hashtag. It contains user_name, user_location, user_description, user_created time, user_followers, user_friends, user_favourites, user_verified, date of tweet, tweet text, hashtags, source and is_retweet. This dataset is for giving us the tweets that mentioned elon musk in the time period we specified.\n",
    "\n",
    "\n",
    "- Dataset Name: Bitcoin Historical Data - Investing.com\n",
    "- Link to the dataset: https://www.investing.com/crypto/bitcoin/historical-data\n",
    "- Number of observations: 304\n",
    "- This dataset presents a contains stock open, price, high, and low values, buying volumes, and % change from 2021-01-01 to 2021-10-31. This dataset is for giving us the bitcoin prices in the time period we specified.\n",
    "\n",
    "We will combine these two datasets since we want to see if Elon Musk-related tweets will affect the Bitcoin prices. We plan to first filter out Elon Musk-related tweets in the first dataset and then count the number of Elon Musk-related tweets and the total of favorites gained by those posts in each day, then we will be able to merge the two datasets by date.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## import libraries\n",
    "import pandas as pd\n",
    "pd.options.mode.chained_assignment = None\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "matplotlib.rc('figure', figsize=(15, 8))\n",
    "import patsy\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we want the import the two datasets. We store the Bitcoin-tweets dataset in df, and the Bitcoin dataset in BTC_value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3173: DtypeWarning: Columns (5,6) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_name</th>\n",
       "      <th>user_location</th>\n",
       "      <th>user_description</th>\n",
       "      <th>user_created</th>\n",
       "      <th>user_followers</th>\n",
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       "      <th>user_verified</th>\n",
       "      <th>date</th>\n",
       "      <th>text</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>source</th>\n",
       "      <th>is_retweet</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DeSota Wilson</td>\n",
       "      <td>Atlanta, GA</td>\n",
       "      <td>Biz Consultant, real estate, fintech, startups...</td>\n",
       "      <td>2009-04-26 20:05:09</td>\n",
       "      <td>8534.0</td>\n",
       "      <td>7605</td>\n",
       "      <td>4838</td>\n",
       "      <td>False</td>\n",
       "      <td>2021-02-10 23:59:04</td>\n",
       "      <td>Blue Ridge Bank shares halted by NYSE after #b...</td>\n",
       "      <td>['bitcoin']</td>\n",
       "      <td>Twitter Web App</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CryptoND</td>\n",
       "      <td>NaN</td>\n",
       "      <td>😎 BITCOINLIVE is a Dutch platform aimed at inf...</td>\n",
       "      <td>2019-10-17 20:12:10</td>\n",
       "      <td>6769.0</td>\n",
       "      <td>1532</td>\n",
       "      <td>25483</td>\n",
       "      <td>False</td>\n",
       "      <td>2021-02-10 23:58:48</td>\n",
       "      <td>😎 Today, that's this #Thursday, we will do a \"...</td>\n",
       "      <td>['Thursday', 'Btc', 'wallet', 'security']</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tdlmatias</td>\n",
       "      <td>London, England</td>\n",
       "      <td>IM Academy : The best #forex, #SelfEducation, ...</td>\n",
       "      <td>2014-11-10 10:50:37</td>\n",
       "      <td>128.0</td>\n",
       "      <td>332</td>\n",
       "      <td>924</td>\n",
       "      <td>False</td>\n",
       "      <td>2021-02-10 23:54:48</td>\n",
       "      <td>Guys evening, I have read this article about B...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Twitter Web App</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Crypto is the future</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I will post a lot of buying signals for BTC tr...</td>\n",
       "      <td>2019-09-28 16:48:12</td>\n",
       "      <td>625.0</td>\n",
       "      <td>129</td>\n",
       "      <td>14</td>\n",
       "      <td>False</td>\n",
       "      <td>2021-02-10 23:54:33</td>\n",
       "      <td>$BTC A big chance in a billion! Price: \\487264...</td>\n",
       "      <td>['Bitcoin', 'FX', 'BTC', 'crypto']</td>\n",
       "      <td>dlvr.it</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alex Kirchmaier 🇦🇹🇸🇪 #FactsSuperspreader</td>\n",
       "      <td>Europa</td>\n",
       "      <td>Co-founder @RENJERJerky | Forbes 30Under30 | I...</td>\n",
       "      <td>2016-02-03 13:15:55</td>\n",
       "      <td>1249.0</td>\n",
       "      <td>1472</td>\n",
       "      <td>10482</td>\n",
       "      <td>False</td>\n",
       "      <td>2021-02-10 23:54:06</td>\n",
       "      <td>This network is secured by 9 508 nodes as of t...</td>\n",
       "      <td>['BTC']</td>\n",
       "      <td>Twitter Web App</td>\n",
       "      <td>False</td>\n",
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      "text/plain": [
       "                                  user_name    user_location  \\\n",
       "0                             DeSota Wilson      Atlanta, GA   \n",
       "1                                  CryptoND              NaN   \n",
       "2                                 Tdlmatias  London, England   \n",
       "3                      Crypto is the future              NaN   \n",
       "4  Alex Kirchmaier 🇦🇹🇸🇪 #FactsSuperspreader           Europa   \n",
       "\n",
       "                                    user_description         user_created  \\\n",
       "0  Biz Consultant, real estate, fintech, startups...  2009-04-26 20:05:09   \n",
       "1  😎 BITCOINLIVE is a Dutch platform aimed at inf...  2019-10-17 20:12:10   \n",
       "2  IM Academy : The best #forex, #SelfEducation, ...  2014-11-10 10:50:37   \n",
       "3  I will post a lot of buying signals for BTC tr...  2019-09-28 16:48:12   \n",
       "4  Co-founder @RENJERJerky | Forbes 30Under30 | I...  2016-02-03 13:15:55   \n",
       "\n",
       "  user_followers user_friends user_favourites user_verified  \\\n",
       "0         8534.0         7605            4838         False   \n",
       "1         6769.0         1532           25483         False   \n",
       "2          128.0          332             924         False   \n",
       "3          625.0          129              14         False   \n",
       "4         1249.0         1472           10482         False   \n",
       "\n",
       "                  date                                               text  \\\n",
       "0  2021-02-10 23:59:04  Blue Ridge Bank shares halted by NYSE after #b...   \n",
       "1  2021-02-10 23:58:48  😎 Today, that's this #Thursday, we will do a \"...   \n",
       "2  2021-02-10 23:54:48  Guys evening, I have read this article about B...   \n",
       "3  2021-02-10 23:54:33  $BTC A big chance in a billion! Price: \\487264...   \n",
       "4  2021-02-10 23:54:06  This network is secured by 9 508 nodes as of t...   \n",
       "\n",
       "                                    hashtags               source is_retweet  \n",
       "0                                ['bitcoin']      Twitter Web App      False  \n",
       "1  ['Thursday', 'Btc', 'wallet', 'security']  Twitter for Android      False  \n",
       "2                                        NaN      Twitter Web App      False  \n",
       "3         ['Bitcoin', 'FX', 'BTC', 'crypto']              dlvr.it      False  \n",
       "4                                    ['BTC']      Twitter Web App      False  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## read in the first dataset\n",
    "df = pd.read_csv('Bitcoin_tweets.csv', dtype={'user_location':'str', \\\n",
    "'user_description':'str', 'user_created':'str', 'user_followers':'str', \\\n",
    "    'user_freinds':'str', 'user_favourties':'str', 'user_verified':'str', \\\n",
    "        'date':'str','text':'str','hashtags':'str','source':'str','is_retweet':'str'})\n",
    "## display the original tweet dataset\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We import the second data that contains important information about Bitcoin value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Price</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Vol.</th>\n",
       "      <th>Change %</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Oct 31, 2021</td>\n",
       "      <td>61,309.6</td>\n",
       "      <td>61,842.2</td>\n",
       "      <td>62,393.7</td>\n",
       "      <td>60,005.7</td>\n",
       "      <td>50.06K</td>\n",
       "      <td>-0.86%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Oct 30, 2021</td>\n",
       "      <td>61,840.1</td>\n",
       "      <td>62,245.0</td>\n",
       "      <td>62,338.5</td>\n",
       "      <td>60,786.0</td>\n",
       "      <td>40.51K</td>\n",
       "      <td>-0.65%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Oct 29, 2021</td>\n",
       "      <td>62,242.8</td>\n",
       "      <td>60,579.9</td>\n",
       "      <td>62,952.2</td>\n",
       "      <td>60,322.2</td>\n",
       "      <td>63.39K</td>\n",
       "      <td>2.74%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Oct 28, 2021</td>\n",
       "      <td>60,582.5</td>\n",
       "      <td>58,437.2</td>\n",
       "      <td>62,251.3</td>\n",
       "      <td>58,128.1</td>\n",
       "      <td>88.47K</td>\n",
       "      <td>3.67%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Oct 27, 2021</td>\n",
       "      <td>58,438.1</td>\n",
       "      <td>60,312.5</td>\n",
       "      <td>61,452.9</td>\n",
       "      <td>58,112.6</td>\n",
       "      <td>88.11K</td>\n",
       "      <td>-3.11%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "           Date     Price      Open      High       Low    Vol. Change %\n",
       "0  Oct 31, 2021  61,309.6  61,842.2  62,393.7  60,005.7  50.06K   -0.86%\n",
       "1  Oct 30, 2021  61,840.1  62,245.0  62,338.5  60,786.0  40.51K   -0.65%\n",
       "2  Oct 29, 2021  62,242.8  60,579.9  62,952.2  60,322.2  63.39K    2.74%\n",
       "3  Oct 28, 2021  60,582.5  58,437.2  62,251.3  58,128.1  88.47K    3.67%\n",
       "4  Oct 27, 2021  58,438.1  60,312.5  61,452.9  58,112.6  88.11K   -3.11%"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## read in the second dataset\n",
    "BTC_value = pd.read_csv('Bitcoin Historical Data - Investing.com.csv')\n",
    "BTC_value.head()\n",
    "## display the original bitcoin value dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we want to process the first dataset, which is the tweet dataset first. We first get rid of unwanted columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <td>924</td>\n",
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       "      <td>Guys evening, I have read this article about B...</td>\n",
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       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>2021-02-10 23:54:33</td>\n",
       "      <td>$BTC A big chance in a billion! Price: \\487264...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10482</td>\n",
       "      <td>2021-02-10 23:54:06</td>\n",
       "      <td>This network is secured by 9 508 nodes as of t...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  user_favourites                 date  \\\n",
       "0            4838  2021-02-10 23:59:04   \n",
       "1           25483  2021-02-10 23:58:48   \n",
       "2             924  2021-02-10 23:54:48   \n",
       "3              14  2021-02-10 23:54:33   \n",
       "4           10482  2021-02-10 23:54:06   \n",
       "\n",
       "                                                text  \n",
       "0  Blue Ridge Bank shares halted by NYSE after #b...  \n",
       "1  😎 Today, that's this #Thursday, we will do a \"...  \n",
       "2  Guys evening, I have read this article about B...  \n",
       "3  $BTC A big chance in a billion! Price: \\487264...  \n",
       "4  This network is secured by 9 508 nodes as of t...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## delete unwanted columns\n",
    "df = df[['user_favourites', 'date', 'text']]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we define a function to clear up the text column for future process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "## standardize text\n",
    "def standardize_text (str_in):\n",
    "    try:\n",
    "        str_in = str_in.lower()\n",
    "        str_in = str_in.replace(' ', '')\n",
    "        return str_in\n",
    "    except:\n",
    "        return np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>user_favourites</th>\n",
       "      <th>date</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4838</td>\n",
       "      <td>2021-02-10 23:59:04</td>\n",
       "      <td>blueridgebankshareshaltedbynyseafter#bitcoinat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25483</td>\n",
       "      <td>2021-02-10 23:58:48</td>\n",
       "      <td>😎today,that'sthis#thursday,wewilldoa\"🎬take2\"wi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>924</td>\n",
       "      <td>2021-02-10 23:54:48</td>\n",
       "      <td>guysevening,ihavereadthisarticleaboutbtcandwou...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  user_favourites                 date  \\\n",
       "0            4838  2021-02-10 23:59:04   \n",
       "1           25483  2021-02-10 23:58:48   \n",
       "2             924  2021-02-10 23:54:48   \n",
       "\n",
       "                                                text  \n",
       "0  blueridgebankshareshaltedbynyseafter#bitcoinat...  \n",
       "1  😎today,that'sthis#thursday,wewilldoa\"🎬take2\"wi...  \n",
       "2  guysevening,ihavereadthisarticleaboutbtcandwou...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## apply our previous defined function to standarize the text column\n",
    "df['text'] = df['text'].apply(standardize_text)\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the dataset is in wanted format, we go on to filter out posts that mentioned elon musk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>user_favourites</th>\n",
       "      <th>date</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21368</th>\n",
       "      <td>52</td>\n",
       "      <td>2021-02-05 12:26:13</td>\n",
       "      <td>@elonmusk@zibininnovationoftechnologyshould,bu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21291</th>\n",
       "      <td>1670</td>\n",
       "      <td>2021-02-05 12:57:21</td>\n",
       "      <td>#bitcoinclosetomajorbreakoutover@elonmusk#btct...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21261</th>\n",
       "      <td>54714</td>\n",
       "      <td>2021-02-05 13:11:11</td>\n",
       "      <td>@dogecoinrise@elonmuskwhy$xrpisthebest#digital...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_favourites                 date  \\\n",
       "21368              52  2021-02-05 12:26:13   \n",
       "21291            1670  2021-02-05 12:57:21   \n",
       "21261           54714  2021-02-05 13:11:11   \n",
       "\n",
       "                                                    text  \n",
       "21368  @elonmusk@zibininnovationoftechnologyshould,bu...  \n",
       "21291  #bitcoinclosetomajorbreakoutover@elonmusk#btct...  \n",
       "21261  @dogecoinrise@elonmuskwhy$xrpisthebest#digital...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## filter out posts that mentioned elon musk\n",
    "df_elonmusk = df[df['text'].str.contains('elonmusk', na = False)].sort_values(by= 'date')\n",
    "df_elonmusk.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we need to look at our dataset in the correct timeframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>user_favourites</th>\n",
       "      <th>date</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21368</th>\n",
       "      <td>52</td>\n",
       "      <td>2021-02-05</td>\n",
       "      <td>@elonmusk@zibininnovationoftechnologyshould,bu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21291</th>\n",
       "      <td>1670</td>\n",
       "      <td>2021-02-05</td>\n",
       "      <td>#bitcoinclosetomajorbreakoutover@elonmusk#btct...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_favourites       date  \\\n",
       "21368              52 2021-02-05   \n",
       "21291            1670 2021-02-05   \n",
       "\n",
       "                                                    text  \n",
       "21368  @elonmusk@zibininnovationoftechnologyshould,bu...  \n",
       "21291  #bitcoinclosetomajorbreakoutover@elonmusk#btct...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## transfer object time to datetime \n",
    "df_elonmusk['date'] = pd.to_datetime(df_elonmusk['date']).dt.date\n",
    "df_elonmusk['date'] = pd.to_datetime(df_elonmusk['date'])\n",
    "## filter out correct dates from 2021/1/1 - 2021/10/31\n",
    "starttime = '2021-1-1'\n",
    "endtime = '2021-10-31'\n",
    "mask = (df_elonmusk['date'] >= starttime) & (df_elonmusk['date'] <= endtime)\n",
    "df_elonmusk = df_elonmusk.loc[mask]\n",
    "df_elonmusk.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we begin to process the second dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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</th>\n",
       "      <th>price</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>vol.</th>\n",
       "      <th>change %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-10-31</td>\n",
       "      <td>61,309.6</td>\n",
       "      <td>61,842.2</td>\n",
       "      <td>62,393.7</td>\n",
       "      <td>60,005.7</td>\n",
       "      <td>50.06K</td>\n",
       "      <td>-0.86%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-10-30</td>\n",
       "      <td>61,840.1</td>\n",
       "      <td>62,245.0</td>\n",
       "      <td>62,338.5</td>\n",
       "      <td>60,786.0</td>\n",
       "      <td>40.51K</td>\n",
       "      <td>-0.65%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-10-29</td>\n",
       "      <td>62,242.8</td>\n",
       "      <td>60,579.9</td>\n",
       "      <td>62,952.2</td>\n",
       "      <td>60,322.2</td>\n",
       "      <td>63.39K</td>\n",
       "      <td>2.74%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-10-28</td>\n",
       "      <td>60,582.5</td>\n",
       "      <td>58,437.2</td>\n",
       "      <td>62,251.3</td>\n",
       "      <td>58,128.1</td>\n",
       "      <td>88.47K</td>\n",
       "      <td>3.67%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-10-27</td>\n",
       "      <td>58,438.1</td>\n",
       "      <td>60,312.5</td>\n",
       "      <td>61,452.9</td>\n",
       "      <td>58,112.6</td>\n",
       "      <td>88.11K</td>\n",
       "      <td>-3.11%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date     price      open      high       low    vol. change %\n",
       "0 2021-10-31  61,309.6  61,842.2  62,393.7  60,005.7  50.06K   -0.86%\n",
       "1 2021-10-30  61,840.1  62,245.0  62,338.5  60,786.0  40.51K   -0.65%\n",
       "2 2021-10-29  62,242.8  60,579.9  62,952.2  60,322.2  63.39K    2.74%\n",
       "3 2021-10-28  60,582.5  58,437.2  62,251.3  58,128.1  88.47K    3.67%\n",
       "4 2021-10-27  58,438.1  60,312.5  61,452.9  58,112.6  88.11K   -3.11%"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## standardize the bitcoin value dataset\n",
    "BTC_value.columns = BTC_value.columns.str.lower()\n",
    "BTC_value['date'] = pd.to_datetime(BTC_value['date'])\n",
    "## find null data\n",
    "BTC_value = BTC_value.dropna()\n",
    "BTC_value.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After standardizing our bitcoin value dataset, we want to continue to merge the two datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_favourites</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-05</th>\n",
       "      <td>270156.0</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-06</th>\n",
       "      <td>690833.0</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-07</th>\n",
       "      <td>947126.0</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-08</th>\n",
       "      <td>5759872.0</td>\n",
       "      <td>882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-09</th>\n",
       "      <td>2050391.0</td>\n",
       "      <td>318</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            user_favourites  count\n",
       "date                              \n",
       "2021-02-05         270156.0     27\n",
       "2021-02-06         690833.0     98\n",
       "2021-02-07         947126.0    141\n",
       "2021-02-08        5759872.0    882\n",
       "2021-02-09        2050391.0    318"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## transform the valuecount table into dataframe for later merge\n",
    "number_post_per_day = df_elonmusk['date'].value_counts().rename_axis('date').to_frame('count').sort_values(by = 'date')\n",
    "## get a subset of the elon musk table, only including user_favourites and date\n",
    "favorite = df_elonmusk.drop('text', axis = 1)\n",
    "## calculate the total favourites in a day\n",
    "favorite['user_favourites'] = pd.to_numeric(favorite['user_favourites'])\n",
    "favorite = favorite.groupby('date').sum().sort_values(by='date')\n",
    "## merge the two little datasets\n",
    "tweet_data = pd.merge(favorite, number_post_per_day, on = 'date', how = 'outer')\n",
    "tweet_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>user_favourites</th>\n",
       "      <th>count</th>\n",
       "      <th>price</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>vol.</th>\n",
       "      <th>change %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29,359.9</td>\n",
       "      <td>28,951.7</td>\n",
       "      <td>29,627.1</td>\n",
       "      <td>28,712.4</td>\n",
       "      <td>100.90K</td>\n",
       "      <td>1.42%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32,193.3</td>\n",
       "      <td>29,359.7</td>\n",
       "      <td>33,233.5</td>\n",
       "      <td>29,008.0</td>\n",
       "      <td>240.87K</td>\n",
       "      <td>9.65%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32,958.9</td>\n",
       "      <td>32,192.9</td>\n",
       "      <td>34,755.9</td>\n",
       "      <td>32,029.6</td>\n",
       "      <td>155.21K</td>\n",
       "      <td>2.38%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32,022.6</td>\n",
       "      <td>33,016.6</td>\n",
       "      <td>33,587.5</td>\n",
       "      <td>28,204.5</td>\n",
       "      <td>255.27K</td>\n",
       "      <td>-2.84%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33,991.5</td>\n",
       "      <td>32,015.4</td>\n",
       "      <td>34,414.7</td>\n",
       "      <td>30,010.5</td>\n",
       "      <td>202.13K</td>\n",
       "      <td>6.15%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  user_favourites  count     price      open      high       low  \\\n",
       "0 2021-01-01              NaN    NaN  29,359.9  28,951.7  29,627.1  28,712.4   \n",
       "1 2021-01-02              NaN    NaN  32,193.3  29,359.7  33,233.5  29,008.0   \n",
       "2 2021-01-03              NaN    NaN  32,958.9  32,192.9  34,755.9  32,029.6   \n",
       "3 2021-01-04              NaN    NaN  32,022.6  33,016.6  33,587.5  28,204.5   \n",
       "4 2021-01-05              NaN    NaN  33,991.5  32,015.4  34,414.7  30,010.5   \n",
       "\n",
       "      vol. change %  \n",
       "0  100.90K    1.42%  \n",
       "1  240.87K    9.65%  \n",
       "2  155.21K    2.38%  \n",
       "3  255.27K   -2.84%  \n",
       "4  202.13K    6.15%  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## merge the two datasets\n",
    "data = pd.merge(tweet_data, BTC_value, on = 'date', how = 'right')\n",
    "data = data.sort_values(by = 'date')\n",
    "data = data.reset_index(drop = True)\n",
    "number_post_per_day.sort_values(by = 'date')\n",
    "## display data\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that there are NaN values in the above table, and we want to replace those values with a meaningful 0, which means that there is no elon musk tweeter on that day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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</th>\n",
       "      <th>user_favourites</th>\n",
       "      <th>count</th>\n",
       "      <th>price</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>vol.</th>\n",
       "      <th>change %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29,359.9</td>\n",
       "      <td>28,951.7</td>\n",
       "      <td>29,627.1</td>\n",
       "      <td>28,712.4</td>\n",
       "      <td>100.90K</td>\n",
       "      <td>1.42%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32,193.3</td>\n",
       "      <td>29,359.7</td>\n",
       "      <td>33,233.5</td>\n",
       "      <td>29,008.0</td>\n",
       "      <td>240.87K</td>\n",
       "      <td>9.65%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32,958.9</td>\n",
       "      <td>32,192.9</td>\n",
       "      <td>34,755.9</td>\n",
       "      <td>32,029.6</td>\n",
       "      <td>155.21K</td>\n",
       "      <td>2.38%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32,022.6</td>\n",
       "      <td>33,016.6</td>\n",
       "      <td>33,587.5</td>\n",
       "      <td>28,204.5</td>\n",
       "      <td>255.27K</td>\n",
       "      <td>-2.84%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33,991.5</td>\n",
       "      <td>32,015.4</td>\n",
       "      <td>34,414.7</td>\n",
       "      <td>30,010.5</td>\n",
       "      <td>202.13K</td>\n",
       "      <td>6.15%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  user_favourites  count     price      open      high       low  \\\n",
       "0 2021-01-01              0.0    0.0  29,359.9  28,951.7  29,627.1  28,712.4   \n",
       "1 2021-01-02              0.0    0.0  32,193.3  29,359.7  33,233.5  29,008.0   \n",
       "2 2021-01-03              0.0    0.0  32,958.9  32,192.9  34,755.9  32,029.6   \n",
       "3 2021-01-04              0.0    0.0  32,022.6  33,016.6  33,587.5  28,204.5   \n",
       "4 2021-01-05              0.0    0.0  33,991.5  32,015.4  34,414.7  30,010.5   \n",
       "\n",
       "      vol. change %  \n",
       "0  100.90K    1.42%  \n",
       "1  240.87K    9.65%  \n",
       "2  155.21K    2.38%  \n",
       "3  255.27K   -2.84%  \n",
       "4  202.13K    6.15%  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## replace nan with 0s\n",
    "data['count'] = data['count'].replace(np.nan, 0)\n",
    "data['user_favourites'] = data['user_favourites'].replace(np.nan, 0)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see that the columns in the data that come from the bitcoin dataset do not have numerical format that could be used for processing. Thus, we define a function to standardize these columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "## define a function to standardize the data for bitcoin prices\n",
    "def standardize_number(str_in):\n",
    "    try:\n",
    "        str_in = str_in.replace(',', '')\n",
    "        str_in = str_in.replace('K', '')\n",
    "        str_in = str_in.replace('%', '')\n",
    "        return str_in\n",
    "    except:\n",
    "        return np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        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</th>\n",
       "      <th>user_favourites</th>\n",
       "      <th>count</th>\n",
       "      <th>price</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volumeK</th>\n",
       "      <th>change%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29359.9</td>\n",
       "      <td>28951.7</td>\n",
       "      <td>29627.1</td>\n",
       "      <td>28712.4</td>\n",
       "      <td>100.90</td>\n",
       "      <td>1.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32193.3</td>\n",
       "      <td>29359.7</td>\n",
       "      <td>33233.5</td>\n",
       "      <td>29008.0</td>\n",
       "      <td>240.87</td>\n",
       "      <td>9.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32958.9</td>\n",
       "      <td>32192.9</td>\n",
       "      <td>34755.9</td>\n",
       "      <td>32029.6</td>\n",
       "      <td>155.21</td>\n",
       "      <td>2.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32022.6</td>\n",
       "      <td>33016.6</td>\n",
       "      <td>33587.5</td>\n",
       "      <td>28204.5</td>\n",
       "      <td>255.27</td>\n",
       "      <td>-2.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33991.5</td>\n",
       "      <td>32015.4</td>\n",
       "      <td>34414.7</td>\n",
       "      <td>30010.5</td>\n",
       "      <td>202.13</td>\n",
       "      <td>6.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  user_favourites  count    price     open     high      low  \\\n",
       "0 2021-01-01              0.0    0.0  29359.9  28951.7  29627.1  28712.4   \n",
       "1 2021-01-02              0.0    0.0  32193.3  29359.7  33233.5  29008.0   \n",
       "2 2021-01-03              0.0    0.0  32958.9  32192.9  34755.9  32029.6   \n",
       "3 2021-01-04              0.0    0.0  32022.6  33016.6  33587.5  28204.5   \n",
       "4 2021-01-05              0.0    0.0  33991.5  32015.4  34414.7  30010.5   \n",
       "\n",
       "  volumeK change%  \n",
       "0  100.90    1.42  \n",
       "1  240.87    9.65  \n",
       "2  155.21    2.38  \n",
       "3  255.27   -2.84  \n",
       "4  202.13    6.15  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## rename volume columns\n",
    "data.rename(columns = {'vol.': 'volumeK'}, inplace=True)\n",
    "data.rename(columns = {'change %': 'change%'}, inplace=True)\n",
    "data.head()\n",
    "## standardize our data \n",
    "data['price'] = data['price'].apply(standardize_number)\n",
    "data['open'] = data['open'].apply(standardize_number)\n",
    "data['high'] = data['high'].apply(standardize_number)\n",
    "data['low'] = data['low'].apply(standardize_number)\n",
    "data['volumeK'] = data['volumeK'].apply(standardize_number)\n",
    "data['change%'] = data['change%'].apply(standardize_number)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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</th>\n",
       "      <th>user_favourites</th>\n",
       "      <th>count</th>\n",
       "      <th>price</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volumeK</th>\n",
       "      <th>change%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29359.9</td>\n",
       "      <td>28951.7</td>\n",
       "      <td>29627.1</td>\n",
       "      <td>28712.4</td>\n",
       "      <td>100.90</td>\n",
       "      <td>1.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32193.3</td>\n",
       "      <td>29359.7</td>\n",
       "      <td>33233.5</td>\n",
       "      <td>29008.0</td>\n",
       "      <td>240.87</td>\n",
       "      <td>9.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32958.9</td>\n",
       "      <td>32192.9</td>\n",
       "      <td>34755.9</td>\n",
       "      <td>32029.6</td>\n",
       "      <td>155.21</td>\n",
       "      <td>2.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32022.6</td>\n",
       "      <td>33016.6</td>\n",
       "      <td>33587.5</td>\n",
       "      <td>28204.5</td>\n",
       "      <td>255.27</td>\n",
       "      <td>-2.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33991.5</td>\n",
       "      <td>32015.4</td>\n",
       "      <td>34414.7</td>\n",
       "      <td>30010.5</td>\n",
       "      <td>202.13</td>\n",
       "      <td>6.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  user_favourites  count    price     open     high      low  \\\n",
       "0 2021-01-01              0.0    0.0  29359.9  28951.7  29627.1  28712.4   \n",
       "1 2021-01-02              0.0    0.0  32193.3  29359.7  33233.5  29008.0   \n",
       "2 2021-01-03              0.0    0.0  32958.9  32192.9  34755.9  32029.6   \n",
       "3 2021-01-04              0.0    0.0  32022.6  33016.6  33587.5  28204.5   \n",
       "4 2021-01-05              0.0    0.0  33991.5  32015.4  34414.7  30010.5   \n",
       "\n",
       "   volumeK  change%  \n",
       "0   100.90     1.42  \n",
       "1   240.87     9.65  \n",
       "2   155.21     2.38  \n",
       "3   255.27    -2.84  \n",
       "4   202.13     6.15  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert strings to numbers\n",
    "data['price'] = data['price'].apply(pd.to_numeric)\n",
    "data['open'] = data['open'].apply(pd.to_numeric)\n",
    "data['high'] = data['high'].apply(pd.to_numeric)\n",
    "data['low'] = data['low'].apply(pd.to_numeric)\n",
    "data['volumeK'] = data['volumeK'].apply(pd.to_numeric)\n",
    "data['change%'] = data['change%'].apply(pd.to_numeric)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we want to explore the weekly relationship between the variables we chose, we group our data by week."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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</th>\n",
       "      <th>weekly_count</th>\n",
       "      <th>user_favourites</th>\n",
       "      <th>weekly_volume</th>\n",
       "      <th>weekly_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2021-10-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>403.55</td>\n",
       "      <td>1.658571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2021-10-10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>568.62</td>\n",
       "      <td>1.868571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2021-10-17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>506.95</td>\n",
       "      <td>1.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2021-10-24</td>\n",
       "      <td>9411.0</td>\n",
       "      <td>40576328.0</td>\n",
       "      <td>500.97</td>\n",
       "      <td>-0.108571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2021-10-31</td>\n",
       "      <td>3801.0</td>\n",
       "      <td>16328746.0</td>\n",
       "      <td>438.73</td>\n",
       "      <td>0.148571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  weekly_count  user_favourites  weekly_volume  weekly_change\n",
       "39 2021-10-03           0.0              0.0         403.55       1.658571\n",
       "40 2021-10-10           0.0              0.0         568.62       1.868571\n",
       "41 2021-10-17           0.0              0.0         506.95       1.750000\n",
       "42 2021-10-24        9411.0       40576328.0         500.97      -0.108571\n",
       "43 2021-10-31        3801.0       16328746.0         438.73       0.148571"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Group by week\n",
    "weekly_data = data.groupby(pd.Grouper(key = 'date', freq = '1W'))[['count','user_favourites', 'volumeK']].sum()\n",
    "wbitcoin_change = data.groupby(pd.Grouper(key = 'date', freq = '1W'))[['change%']].mean()\n",
    "weekly_data = pd.merge(weekly_data, wbitcoin_change, on = 'date', how = 'outer').sort_values(by = 'date').reset_index()\n",
    "weekly_data.columns = ['date','weekly_count','user_favourites', 'weekly_volume', 'weekly_change']\n",
    "weekly_data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we want to explore the relationships between the variables we defined in the research question. First, we look at the description of the daily data we have right now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>user_favourites</th>\n",
       "      <th>count</th>\n",
       "      <th>price</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.040000e+02</td>\n",
       "      <td>304.000000</td>\n",
       "      <td>304.000000</td>\n",
       "      <td>304.000000</td>\n",
       "      <td>304.000000</td>\n",
       "      <td>304.000000</td>\n",
       "      <td>304.000000</td>\n",
       "      <td>304.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.705574e+05</td>\n",
       "      <td>182.773026</td>\n",
       "      <td>45909.092434</td>\n",
       "      <td>45803.535855</td>\n",
       "      <td>47256.364474</td>\n",
       "      <td>44204.352303</td>\n",
       "      <td>111.863717</td>\n",
       "      <td>0.343553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.398165e+06</td>\n",
       "      <td>523.171281</td>\n",
       "      <td>9629.814353</td>\n",
       "      <td>9636.158639</td>\n",
       "      <td>9689.652071</td>\n",
       "      <td>9602.530561</td>\n",
       "      <td>65.268895</td>\n",
       "      <td>4.408186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29359.900000</td>\n",
       "      <td>28951.700000</td>\n",
       "      <td>29627.100000</td>\n",
       "      <td>28204.500000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>-14.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>36832.650000</td>\n",
       "      <td>36740.550000</td>\n",
       "      <td>38212.875000</td>\n",
       "      <td>34842.125000</td>\n",
       "      <td>69.822500</td>\n",
       "      <td>-2.187500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>46467.500000</td>\n",
       "      <td>46410.300000</td>\n",
       "      <td>47919.200000</td>\n",
       "      <td>44736.950000</td>\n",
       "      <td>99.495000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.538025e+05</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>54894.875000</td>\n",
       "      <td>54849.625000</td>\n",
       "      <td>56510.925000</td>\n",
       "      <td>53343.200000</td>\n",
       "      <td>132.680000</td>\n",
       "      <td>2.742500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.796461e+07</td>\n",
       "      <td>5547.000000</td>\n",
       "      <td>65979.100000</td>\n",
       "      <td>66004.600000</td>\n",
       "      <td>66967.100000</td>\n",
       "      <td>63543.700000</td>\n",
       "      <td>607.100000</td>\n",
       "      <td>19.410000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_favourites        count         price          open          high  \\\n",
       "count     3.040000e+02   304.000000    304.000000    304.000000    304.000000   \n",
       "mean      8.705574e+05   182.773026  45909.092434  45803.535855  47256.364474   \n",
       "std       3.398165e+06   523.171281   9629.814353   9636.158639   9689.652071   \n",
       "min       0.000000e+00     0.000000  29359.900000  28951.700000  29627.100000   \n",
       "25%       0.000000e+00     0.000000  36832.650000  36740.550000  38212.875000   \n",
       "50%       0.000000e+00     0.000000  46467.500000  46410.300000  47919.200000   \n",
       "75%       1.538025e+05    27.000000  54894.875000  54849.625000  56510.925000   \n",
       "max       4.796461e+07  5547.000000  65979.100000  66004.600000  66967.100000   \n",
       "\n",
       "                low      volume      change  \n",
       "count    304.000000  304.000000  304.000000  \n",
       "mean   44204.352303  111.863717    0.343553  \n",
       "std     9602.530561   65.268895    4.408186  \n",
       "min    28204.500000    0.260000  -14.400000  \n",
       "25%    34842.125000   69.822500   -2.187500  \n",
       "50%    44736.950000   99.495000    0.100000  \n",
       "75%    53343.200000  132.680000    2.742500  \n",
       "max    63543.700000  607.100000   19.410000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns = ['date', 'user_favourites', 'count', 'price', 'open', 'high', 'low', 'volume', 'change']\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are interested in the weekly data. First we want to take a look at how the count of elon musk posts and change of bitcoin vary on the weekly basis. Intuitively, we think that the number of tweets that mentioned Elon Musk will increase the Bitcoin price. And the user favourites gained by those tweets will increase the buying volume."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## display the weekly distribution \n",
    "## a scatter plot\n",
    "fig0,ax0 = plt.subplots()\n",
    "sns.scatterplot(x = 'date', y = 'weekly_count', data = weekly_data, color = '#FC766AFF', label = 'weekly tweet count')\n",
    "ax01 = ax0.twinx()\n",
    "sns.scatterplot(x = 'date', y = 'weekly_change', ax = ax01, data = weekly_data, color = '#5B84B1FF', label = 'weekly price change')\n",
    "plt.title('Weekly Distribution of the Number of Posts that mentioned Elon Musk and Bitcoin Change%')\n",
    "lines, labels = ax0.get_legend_handles_labels()\n",
    "lines2, labels2 = ax01.get_legend_handles_labels()\n",
    "ax01.legend(lines + lines2, labels + labels2, loc=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the scatterplot of weekly distribution that mentions Elon Musk and Bitcoin Change%, there is no notable upwards or downwards trend to demonstrate any correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## line graph num of tweets vs. bitcoin change% weekly\n",
    "ax1 = weekly_data.plot(x = 'date', y = 'weekly_count', legend = False, color = '#FC766AFF')\n",
    "ax11 = ax1.twinx()\n",
    "weekly_data.plot(x = 'date', y = 'weekly_change', ax = ax11, legend = False, color = '#5B84B1FF')\n",
    "ax11.figure.legend()\n",
    "plt.title('Weekly Trend of the Number of Posts that mentioned Elon Musk and Bitcoin Change%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To better visualize the scatterplot data, we made a line plot to observe any possible correlation. The results are still random, revealing no potential relation. For the visulization of the remaning variables, we decide to stick to lineplots. Here, we create a model using weekly count of tweets that mentioned Elon Musk to predict the weekly change of bitcoin value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:          weekly_change   R-squared:                       0.010\n",
      "Model:                            OLS   Adj. R-squared:                 -0.013\n",
      "Method:                 Least Squares   F-statistic:                    0.4282\n",
      "Date:                Tue, 07 Dec 2021   Prob (F-statistic):              0.516\n",
      "Time:                        20:15:20   Log-Likelihood:                -85.464\n",
      "No. Observations:                  44   AIC:                             174.9\n",
      "Df Residuals:                      42   BIC:                             178.5\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "================================================================================\n",
      "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------\n",
      "Intercept        0.3198      0.286      1.117      0.270      -0.258       0.897\n",
      "weekly_count  6.142e-05   9.39e-05      0.654      0.516      -0.000       0.000\n",
      "==============================================================================\n",
      "Omnibus:                        0.911   Durbin-Watson:                   1.507\n",
      "Prob(Omnibus):                  0.634   Jarque-Bera (JB):                0.349\n",
      "Skew:                          -0.188   Prob(JB):                        0.840\n",
      "Kurtosis:                       3.221   Cond. No.                     3.35e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 3.35e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "## test linear regression model count and change%\n",
    "outcome, predictors = patsy.dmatrices('weekly_change ~ weekly_count', weekly_data)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit() \n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## We want to visualize our model to have a better sense about it \n",
    "## Plot the orginal data (as before)\n",
    "sns.scatterplot(x='weekly_count', y='weekly_change', alpha=0.3, data=weekly_data, s=100)\n",
    "\n",
    "## Generate and plot the model fit line\n",
    "xs = np.arange(weekly_data['weekly_count'].min(), weekly_data['weekly_count'].max())\n",
    "ys = 6.142e-05 * xs + 0.3198 \n",
    "plt.plot(xs, ys, '--k', linewidth=4, label='Model')\n",
    "\n",
    "plt.xlabel('weekly_count')\n",
    "plt.ylabel('weekly_change')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the p-value we get in this case is 0.516 > 0.05. This indicates that there might not exists a correlation between the number of tweets and bitcoin price change. Also, R-squared is 0.010 is very small, which indicates that the linear correlation is very weak. From the visualization of the linear regression, we can see that there is not obvious relation, and the line is highly influenced by the outlier. Thus, we failed to reject our null hypothesis. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We move on to see if there is a correlation between the number of favourites gained by these tweets and the buying volume on the weekly basis. We analysis the data through a similar process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## number of favourites vs. bitcoin buying volume weekly\n",
    "ax1 = weekly_data.plot(x = 'date', y = 'user_favourites', legend = False, color = '#DAA03DFF')\n",
    "ax11 = ax1.twinx()\n",
    "weekly_data.plot(x = 'date', y = 'weekly_volume', ax = ax11, legend = False, color = '#616247FF')\n",
    "ax11.figure.legend()\n",
    "plt.title('Weekly Trend of the Number of Favourites and Bitcoin buying volume')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph comparing the number of total user favorites related to tweets mentioning Elon musk and bitcoin buying volume appears to indicate an inverse relationship. The more the user favorites the less the buying volume, and this is likely due to outliers. Here, we create a model using weekly count of user favourites gained by the tweets that mentioned Elon Musk to predict the weekly change of bicoin buying volume."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:          weekly_volume   R-squared:                       0.026\n",
      "Model:                            OLS   Adj. R-squared:                  0.003\n",
      "Method:                 Least Squares   F-statistic:                     1.122\n",
      "Date:                Tue, 07 Dec 2021   Prob (F-statistic):              0.296\n",
      "Time:                        20:15:20   Log-Likelihood:                -319.89\n",
      "No. Observations:                  44   AIC:                             643.8\n",
      "Df Residuals:                      42   BIC:                             647.3\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "Intercept         794.3726     57.356     13.850      0.000     678.623     910.122\n",
      "user_favourites -3.574e-06   3.37e-06     -1.059      0.296   -1.04e-05    3.24e-06\n",
      "==============================================================================\n",
      "Omnibus:                       22.966   Durbin-Watson:                   1.163\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               36.375\n",
      "Skew:                           1.576   Prob(JB):                     1.26e-08\n",
      "Kurtosis:                       6.148   Cond. No.                     1.82e+07\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.82e+07. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "## generate a model \n",
    "outcome, predictors = patsy.dmatrices('weekly_volume ~ user_favourites', weekly_data)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit() \n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the orginal data (as before)\n",
    "sns.scatterplot(x='user_favourites', y='weekly_volume', alpha=0.3, data=weekly_data, s=100)\n",
    "\n",
    "# Generate and plot the model fit line\n",
    "xs = np.arange(weekly_data['user_favourites'].min(), weekly_data['user_favourites'].max())\n",
    "ys = -3.574e-06 * xs + 794.3726 \n",
    "plt.plot(xs, ys, '--k', linewidth=4, label='Model')\n",
    "\n",
    "plt.xlabel('weekly count of user_favourites')\n",
    "plt.ylabel('weekly Bitcoin buying volume')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To confirm the previous graph of a potential inverse relationship we ran a linear regression test to observe the results. Similarly, like the regression test performed on the count of elon musk posts and change in Bitcoin value, we did not find a correlation between the number of user favourites and the buying volume of Bitcoin on a weekly basis because the p-value is strictly greater than 0.05. R-squared is still very small with a value of 0.026. We also visualize the linear regression, and the result shows clearly that there is no relation at all."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately, weekly data does not show that there is a correlation between the variables we chose. Let's then look at the daily trend of bitcoin price change the number of tweets mentioning Elon Musk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## count vs. bitcoin price change% daily\n",
    "ax = data.plot(x='date', y='change', legend=False, color = '#00239CFF')\n",
    "ax2 = ax.twinx()\n",
    "data.plot(x='date', y='count', ax=ax2, legend=False, color=\"#E10600FF\")\n",
    "plt.title('bitcoin price change and the number of tweets mentioning Elon Musk')\n",
    "ax.figure.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph of bitcoin price change and number of tweets mentioning Elon Musk reveals no correlation as there is inconsistency across the plot. Let's use linear regression to find the relation between bitcoin price change and the number of tweets mentioning Elon Musk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                 change   R-squared:                       0.012\n",
      "Model:                            OLS   Adj. R-squared:                  0.009\n",
      "Method:                 Least Squares   F-statistic:                     3.800\n",
      "Date:                Tue, 07 Dec 2021   Prob (F-statistic):             0.0522\n",
      "Time:                        20:28:41   Log-Likelihood:                -879.93\n",
      "No. Observations:                 304   AIC:                             1764.\n",
      "Df Residuals:                     302   BIC:                             1771.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.1719      0.267      0.645      0.520      -0.353       0.697\n",
      "count          0.0009      0.000      1.949      0.052   -8.96e-06       0.002\n",
      "==============================================================================\n",
      "Omnibus:                       13.880   Durbin-Watson:                   2.142\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               29.464\n",
      "Skew:                           0.160   Prob(JB):                     4.00e-07\n",
      "Kurtosis:                       4.491   Cond. No.                         586.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "## generate a model \n",
    "outcome, predictors = patsy.dmatrices('change ~ count', data)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit() \n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AANAFve7e+fo2d31DVwfSAQnX0VQuqesP+Dq/UNFC2ZO1jX36ssmoDg0nNTWcUDzSj8VVAAAAAIOqH9f07Unj2YTG0jFdlUsqFnFUGvIVWivXOErHXY0PxTWWjmt8ON7roQIAAADYRwh9HTI6FNe1k0OaL9UVWqvhZHSl0hcxRrlkRNdOpjWWIvQBAAAA6B5CX4dEXUdPm8yo7gV6+FJRC9VAYSg5jjScjOj6iSE9bTKjSJs1fwAAAACwGwh9HXQgl1QoaTwT19ySJz8MFXEcjQ5FdSCb1IFcstdDBAAAALDPEPo6bCqX1EQmrvmyJz8IFXEdjaSiVPgAAAAA9AShbxdEXEcTGdbuAQAAAOg9yk8AAAAAMMAIfQAAAAAwwAh9AAAAADDAWNO3x3hBqMKqJjG5VFRRmsQAAAAAaIPQtwt2K5hNFyo6ky+pXAtkJRlJqbirY2NpTbEdBAAAAIAWCH0dtlvBbLpQ0anpRYX2qWNWUqkW6NT0oiQR/AAAAACsw7zADloOZqVm4JPWBrPpQmVH5/WCUGfypTWBb7XQSmfyJflBuKPzAwAAABhchL4O2c1gVih7KteCDR9TrgWaL3vbPjcAAACAwUbo65DdDGZ+EKpNllxhm48DAAAAgNVY09chuxnMIq4j03x+O6b5OAAAAACdt5e76BP6OmQ5mElSJGLk+1ahtXKMWbm9/LjtyqWiSsVdlTaoJKbirkZS0Z0MHQAAAMAG9noXfUJfh+RSUWWSES1WPJ2fq6jihwpDK8cxSkYcTWQTyiajOwpmUdfRsbH0uu6dyxwjHRtLU+kDAAAAOmwQuugT+jok6joaTkR06vyCzhWqKlY9BaGV6xhlElHVg1BXj4ztOJgtv5H28m8YAAAAgL1kq80aJzLxvi7AEPo6xAtCzSzVdKlYlWOkyWxC1loZY1TzAl0qVjWzVNO1zTnAOzGVS2oiE9f8qrnEI6loX7/BAAAAgL1qO80aJzLxLo1q+wh9HTJfquuJfFnZZFxffHJepy8uyQ+sIq7RdQeGdNvVI3pirqwTB+uazCR2/DoR1+nrNxQAAAAwKAaliz4log6ZL9dV9QJ99MvTOn1xSZIUcRutXU5fXNJHvzytqhdovlTv5TABAAAAbNHqZo3t7IUu+v09uj3E9wN97vGCSjVfoZXqQaiaH6oehAqtVKr5+vyZgoJ2E4IBAAAA9JXlLvob2Qtd9Al9HVL2Qj2RX2qEvDBUPOIoEXEUjzgKw0b4e3yurFLN7/VQAQAAAGzBchd9p025b6900WdNX4c4xigVi8gPpEKlrvxSXaGsHBml4q5yyZiSUUeO2axADAAAAKBfDEIXfUJfh6RijnLJiM7OlVWqB/LDcOUNUaoHclTX0bGk0rH+/i0AAAAAgLX2ehd9Ql+HJCOuRobiikddhZK80MhayRijqGMUj7oaTceUiPMlBwAAAPaavdxFf29E0z1goeLpmrG0RtIxxSKO4q6rRMRVPOIoFnE0ko7p2Fia7p0AAAAAuorQ1yE1P9Qjs0t63rXjOjqWliOj0Fq5xuj4WFrPu3Zcj84uqe739x4eAAAAAAYLcw07JBF1dGGhqn88PacbD6b1jTdOyBhH1oY6O1/W3V88pxMTaSWjG7d8BQAAAIBOIvR1iGOMcsmYvGBJX3hiQV94YmHN/a7rKJuKym3X7xUAAAAAdgHTOzvED6WJTFzHx9LKJKIyza0ZjGOUSUR1fCytiaG46gGbswMAAADoHip9HZKIuSp7vq6fHFI2EdFcua4gtIq4jkZTUU1mEip7PtM7AQAAAHQVoa9DRtMxHcqm9PBMUdmEq/FsVs09G1T3fBVrnk5MZjQ6FOv1UAEAAADsI4S+DhlJx3Tr0ZyKNV9z5bpKVV/WWhljFI86OjgU09cdyWk0RegDAAAA0D2Evg6Juo5OTGZU90M9PlvSuYWK/MAq6hodyqV0dCylE5MZRVyWUQIAAADoHhJIBx3MJTWSiiq0VvGIq0TUVTziyoahRlJRHcwlez1EAAAAAPsMlb4OOnV+Qf/48KzyS3XVwlA2tDKO0VLVV6keKBWL6IZDw70eJgAAAIB9hEpfh1Tqvv71sbwemy2pGoSS1cqfahDqsdmSPvtYXrW63+uh7hleEGqmWNN0oaKZYk1eEPZ6SAAAAMCeQ6WvQ87NV/TIzJJiEUdP5MuaK9cUhpLjSKOpuI6MJvXIzJKeLFR03WSm18Pte9OFis7kSyrXAllJRlIq7urYWFpTTJMFAAAAtozQ1yGLFU91L9SXzy/K85+qSIWhNLtU00LV0y2Hslosez0c5d4wXajo1PSiwlX72FtJpVqgU9OLkjTQwc8LQhXKnvwgVMR1lEtFFaUBEAAAAHaI0NchEcfoyfnKmsC3mueHOleoKuKaLo9sb/GCUGfypTWBb7XQSmfyJU1k4gPZCZUKJwAAADqN0NchjiNFI0YRx2h8KKZcKiYjIyurQrmu2aW6Yq4j1yH0baRQ9lSuBRs+plwLNF/2NJGJd2lU3bGfKpxUMwEAALqH0NchjjG69fCIzg6V9ehMSWdmCwqslWuMJrJx3XRVVodHUjJkvg35Qag2Rb4Vtvm4frWTQLOfKpxUMwEAALqL0Nch8WhEo+moTl8KVfefCi7WSHU/VLUeajQVVTzCl3wjEdeRkTYMfqb5uE7pZNVpp4Fmv1Q491M1EwAAoF+QQDpkMhtTvlSXMdLhXFJTuaSstTLGyDWSMVK+XNdUNtbrofa1XCqqVNxVaYMAlIq7GklFO/J6naw6XUmgGYQK52b2UzUTAACgn3Bl1SFVz2o0FVPEMYrHXCUiT/2JR11FHKPRVEwlb7NL+/0t6jo6NpZWu6WPjpGOjaU7EgqWQ1qpGfiktSFtulDZ8rm2GmjahbblCudGOl3h7LbtVDMBAADQOVT6OsQPQh0eTcsY6VKxLj+0srZR4Ys4RpOZmK4aSe/pSk23LFfDdnPdV6erTlc6PbPbFc5e2A/VTAAAgH5E6OuQiOso4hgdyqVU9UKdLVTkBaGirqPDuaQO5VKKOGZPV2q6aSqX1EQmrvlVa+1GUtGOff06vYbuSgPNcoXz8umhyzpZ4eyVXqzXBAAAQB+HPmPMGUlFSYEk31p7srcj2lguFZXjSA8+uah6ECjqOnKNI8eRFqqeHjy/qGceze3pSk23RVxn15qWdLrq1IlA040KZy/th2omAADY26y1Wlpa0tLSkqampno9nI7p29DX9BJr7WyvB7FVpZqvxaqn+VJdXmBXLtqjrtFIOqZSze/1ENHU6apTpwLNblc4e2k/VDMBAEB/KRaLmp2dXfmTz+c3/djzPJ04cUJf/epXez38jun30LdnFMqeStVA6ZirqueoVvbkh1LEkZKxqNKxRiDY6y33B0Wnq06dDDS7WeHstUGvZgIAgN1hrdXi4mLLoDY1NaU777yz5fOOHz+ufD6/7debnd0zdact6efQZyX9rTHGSvrf1tp39XpAG6l6gWZLNdX9UPMlT6V6oNBaOcbIWikTj2p2qaaat/E6sn7Ryb3r+tFuVJ0INFszyNVMAACwM5VKRb/7u7+7YRXO91vPmnvJS17SNvSNj4/vKPTNz88rCAK5rrvt5/ajfg59L7DWnjPGTEq6xxjzFWvtp5fvNMbcJekuSTpy5Eivxrii7oeaL9f1j6dnFYRWxjFabt95KbR6Yq6sF54YV83v/86Endy7rl+0CrG7EdIINFszyNVMAAD2E2utFhYWtjx18nWve53e+ta3rjuPMUZvectbdjSGjULd2NjYts+XSCQ0Pj6uYrGoXC63ozH1m74Nfdbac82/Lxlj7pb0HEmfXnX/uyS9S5JOnjzZ883vYq5VfqkmSYpHHcUijmSNZKzqfig/sMov1RXv2694w5VsMN5pnao2bhZiOx3SCDQAAGAvCsNwTYA7fPiwDh8+vO5xp06d0mtf+1rNzs5qbm6ubQWulUcffbTl8UQioXQ6rVKptO1xbzQV8+qrr9bVV1+t8fFxjY2NaXx8fNOPU6nUtsfQ7/oyghhj0pIca22x+fEdkv5zj4e1oUtFTyOpmEbTcZ0rlLVQ9lamdw6noroql9JIMqqLi3UdHu31aFvr9N51V6JT1cathlhCGgAAGESFQkGnTp3aUhVubm5OQfDUUqR3vOMdLatvsVhMDz300I7Gs1FAGx8f3zD0pVKpdUFtbGxswy6bH/zgB3c0zkHTl6FP0gFJdxtjpMYY32+t/ZveDmljNrSq+aGGkxEtViKq1kMF1sp1jLKJiIaTEVX9UHbTjQJ6p9N71+1Up6qN/RRiAQAAdioMQxUKhQ1D24/+6I/qlltuWffcT3ziE3rta1+7o9dtF9DGx8d3dL6NzilJP/iDP6hqtdqyCjc2NqZkcm8uM+oHfRn6rLWPSvq6Xo9jO1zX6PG5ks4XakrFHF0zmZaRlZVRserpqxeXdDiXVMTp33Cxeu86IykSMfJ9u1KxXL691b3rdqKTQa1fQiwAAMCyIAhkjJHT4prwnnvu0Yc+9KF1gW5ubk5huPH11x133NEy9F1JQGu3Vi6bzSoSiaxM6xwaGtry1MnJycm2r/dTP/VTOx4rNtaXoW8vSkYduY7RQrmui4uhglXfmBHHUTzi6NhoUomoWTnebx0yl/eucxwjPwj18IUlLVT95X40Gk5EdGwsvatj7GRQ6/QG7AAAAKsFQaD5+fk1AW2zaZRzc3P6yle+ohMnTqw730MPPaTf//3f39FYOlWVy2QyK0Gt1Xo+qdF05b777lMul9PY2JgSicS2x4vuIvR1iJV0IJPQKbeoqHWawWh5NVqjEjiRia+EkH7skJlLRZWOuypWfT14flH+qnKbtVKh4uv0zJKePpXdtTF0Mqh1egN2AAAwuFoFuLm5OX3P93yPmkuO1viRH/kRvfOd75S121+6Mzs72zL07ca0ycnJSZ08eXJLVbixsTHF41ub/XTTTTfteKzoPkJfh3iBVaFU03OPjeprl5Z0br4iPwwVdR1dlUvq+skhzZc9eYHtqw6Zq0VdR0dGU/qbB6bXBL5ljpFG03E9OV/WweHEroSlTga1Tm/ADqzWb5V6AEB7n/rUp3T69OmWlbflv+fn51sGuNe97nVKp9PrjieTyR0FPmlnVblsNrthaHvuc5/b8nkTExP63Oc+t6NxYnAQ+jok7jrKpeN64Nyijo2ldPNVWYWh5DhSserrkZmSnnE4q6Tr9nVzkWjEVSYRU9kLVfPClSpkPOpoMpPQaDq2q+vgOhnUdmMDdkDqz0o9AAwq3/dXKnAbTaNMJBL64z/+45bn+LVf+zXdc889O3r9fD7fMvTtZP+3ZYVCoeXxm2++Wb/7u7+7LtCNjo4qFovt+PUAQl+HOK7RVcMJ1f1QZwtVPT5XVmgbwSKbiOqGqYyOjaa0WPW0WPHkbtDQpZfNRfwg1Eg6pmwyoqVaoCBsdCAdirsrY97NdXCdDmq7sQE79rd+rdQDwF7g+77m5uZUr9fbrhd7wxveoIcffnhNBW4rNtpE+0qnTR45cqTtOZfXtW11H7jR0VFFo61/eX3o0CG9+c1v3vFYgXYIfR2STUQ1nIpp0reqeKEc06jaRR2jTDKibCKqwFrNFGs6PVtaqZq10svmIsvTK13H0XCydbDa7XVwnQ5qu7EBO/YntgEBgKdYa3Xp0qUt7f+2/PFyhevFL36xPvnJT7Y877333qtTp05tezyFQkG+7ysSWX95u9XQ1yrADQ0NtXzsd3/3d+sNb3hD2wAH9BNCX4cEoZVvpbmlqkZSER3KJWStFIZWS7W68uWaRoZikmNU80OdnS9LUsvg18vmIv2yDq7TQS3iOmzLgC1rt16vU91lWQ8IoN94nqd8Pt8yqN1444169atf3fI5Bw8e3NHrbbZB907Nzc213BLg+c9/vhYXF1vu/ba6AtcqMLbDdEvsJYS+DinXA1XrvmIRR4/OljW3VJMfNLZiH0vHdHQspaWqJ9dIQ/GIilVfl4pVDScj66Z69rK5SD+tgyOooRc2Wq+3WZMhafNK/aCvByTQAv1pZmZG7373u9tW4RYWFto+97u+67tahr5YLKZMJqNisbjt8WwU+lqtlRsZGWkb1lZ/PDw83PKcd955p+68885tjxMYFFsKfcaYo5Kut9Z+3BiTlBSx1m7/O3yABWGoUj3Q1y6WdGmhorIXqOY3Lvwqnq+qF+jWq0dU8wIloo5mi4GMpKVasGYaZT80F2EdHParzdbrXTWSkiNpo8nXG1XqB3094KAHWqAX6vX6lvd/m52d1Vve8hb98A//8LrzLC0t6ed//ud3NIbNqnLbDX2jo6MaGxuTtbblVgi/9Eu/pLe85S0rQW5kZGRbFTgA6236HWSM+Q+S7pI0KulaSYcl/a6kb9jdoe0tnh/q8XxJj80syWv+lr9cDxRaq6War0LZ02QmrmhkVAsVX1PDCS1WfYXNq79+uzhiHRz2m62s17tUrCqbiqhQ9tuep12lftDXAw56oAU6oVarrQlwN9xwQ8vpkZ/85Cf1pje9SbOzs9sOVGfPnm15/Eo6Tebz+bb3XXfddYpGo1tuYjIyMiLXdTd8vdtuu23HYwXQ2lZ+bfJDkp4j6V8lyVr7sDFm/WTpfc4Lrc7MNi7orCTHMXIdI9caWVn5odUjsyVZa+T5oQpBqPGhmG47Mqx0PNqXoYrpldhPtrJer+6FOpCJa7Hib3v6c6fWA/ajQQ+0wEYuXLighx56aEtVuKWlpTXPff/736/Xv/71684ZiUT02GOP7Wg87QJaJpNRNBqV53kt7zfGaHR0tGVQu+6669q+3t/+7d/uaJwAumsroa9mra0vl9+NMRFtvqxl36n7oVzTuOgLrBT4oWQlPwzlOEYx15WRUdnzFXGNvMDKC6yyqZgmM4leDx/Y9/wg3NJ6vUwiqhumotuexrjV8/eqc++VGORAi/2hWq1uGtp+/dd/vWXb/g9/+MP6kR/5kR29bruAdiVVuXZTMY0x+sVf/EUlEomWVbhcLrdpBQ7A3rWV0Pf3xpifk5Q0xnyTpB+U9Be7O6y9JxF1NDoUV6HiK7BS1QsUcR2Fsoq5roYSrsaGYqp7gfzAKuIYHRlNKQgsjQ+APrC8XclGwWx5vd5EJr7t6c/bOf9eM8iBFntPtVpVPB5vuVbsAx/4gP7yL/9yXaArlUqbnvcHfuAHWoa+3Qhoq7tXOo6jsbGxLU2dHBsba9m5ctkv/uIv7nisAPa2rYS+n5H0JklflvRmSR+V9Hu7Oai9aCge1dWjKU0v1jSajikRdWUlucZoqVbXYjXQoVxC9SDUcCqqo6MpJaKuFqs+jQ+APrDd7Uq2O/25X7ZD2Q2DHGjRW5VKpW3lrV1zk1KppMXFRWUymXXn+9KXvqT3v//9OxrLVgLaZlzXXRPgrrrqqrbn/NrXvqaxsTHlcjk5Dt87AK7MpqHPWhtKenfzD9pIxV0dHUur5jc6eD4+V25U9FyjA9mEbj8+rOPjaU0OJZROROX5oaysnsgvyZin/jOn8QHQG7u9XUk/bYfSaYMcaNE5rQJcvV7XG97whpaPf+UrX6m/+IudTSzK5/MtQ9+V7P/WLvQdPnxYL3zhC7dUhRseHt5SgHMcR9dff/2OxwoAl9tK985XSPpVSUebjzeSrLU2u8tj22OMDueSemRmSVHHKOG6ChwrY6SI02hkMD4U00LdUyoRVTRitFTzFW0zf57GB0D37fZ2JYO6HcogB1pszUc+8hE98cQTGzYxqVQq656XzWbbhr5sdueXGbOzszp27Ni64+2mYrquu2loe/7zn9/yuTfccIM+/elP73isANANW5ne+VuSvk3Sl621NHBpwzHSVy4samaxpnTC1fjQkGSMZBv79E0XKvry+Yi+5eaDqvqhkpGIPN/KjRj5vm05LaofGh+w3hD7zW5vVzKo26EMaqAddNZalcvlTZuY5PN5HT16VL/3e61Xd/zcz/2cHnzwwW2//uLiour1umKx2Lr7dlqVc11Xi4uLLe970YtepPe+973rAl02m225BhAABsVWQt+Tkh4g8G1soeLpwfOLqvmhql6gQugptI2LHmOkVNTV2bmSlmq+al6oc5WqnpwvazgR0dHmb8CDy35F3uvGB/t1o+XtBN0rCcUE6v6129uVDOp2KIMaaPcKa61KpZLy+bxc19Xhw4dbPuaOO+7QzMzMSqCrVqtbOv+NN97Y9r4rmTY5NzfXcq+6sbExRaPRlpW3y/9e/fFGAe7aa6/Vtddeu+OxAsBetZXQ91OSPmqM+XtJteWD1tr/tmuj2oOKVV+uMTp9qSgvsHKMkZEkI1krJaOOjo6nVfdCOY4j12lUAQsVX0vnF3XToawcY9ZU/HrZ+GC/brS8naB7JaG4E4Ga0Ih+NKiBthd839fZs2e3tP/b8se1WuPH9Hd+53fqfe9737pzGmN07733qlAobHs87da0SVvrYNkuwLVb4/azP/uz+oVf+AUqcADQAVsJff9F0pKkhKT18y8gqfHb00tLVbmuo3LdU9kLZEPJONJQPCI3NMqX6nIcR1ZSOu4qHnVU9UL5odXjc2WdOJBR3X+qsterxgf7daPl7QTdKwnFnQjUy6FxseJpqRYoDK2GU1HdOJXV4dHUtj5vALvLWqulpaW2Qe2FL3yh7rjjjnXPO3funI4fP76j19wsoO0k9OXzeVlrW4awb/7mb9bk5OSGTUwymcy2AlwkspVLFADAVmzlf9RD1tpn7PpI9jjXSKlYRIuVJU1mEprIxOWYRkCaL9d1dq6i42MpOcYqsEYRx9FkJqGz82WFVlqseKp5gRarvoLQKuY6evrBTE9C1X7caHk7QddKOw7FnQjUy6FxdqmuS8Wqal5jjzQzJz06s6R/c2JCNxwa3tonDqAjHn30Uf3hH/5h2ypcvV5v+9yf/MmfbBn6dqPT5PJ5H3nkkZXbsVis7fTJyz9uF/ruuuuuHY8VALD7thL6PmqMucNa+7e7Ppo9zFqrpx/MyjVGT8xV9OD5ooLQynWMDmbjeubREV03MaRyPVA82viyj6YbhdNLxaryxbrOFyqaK9WVSUZ1bDSl8wsVuY7p+jTK/bjR8naC7vLHW3ns5aH4SgP1cmicXaqv/MJgmZW0VAv0mUfyGk5FdShHxQ/YiLVWxWJxy1MnZ2dn9Vu/9Vv6ju/4jnXnmp6e1tve9rYdjSOfz7c8nkqlFI/HV6ZsbkU8Htf4+LgOHDjQ9jHvfOc7Za1dCXLpdJoplAAw4LYS+n5A0k8YY2qSPLFlQ0v1wErN34DW/UBLNU9+KEUdqepH5Kixxs8LA8VXfdkbwc/KD0JdNZLS4ZGUohFHnh9qseLrVHX9dL/dXsu1Hzda3m7Q3WkovtJAXSh7Wqx4ulSstq0WFiqeHpstaTKTGKh/I2Aj1lotLi6u6TZ5++23a2RkZN1jP/CBD+gtb3mL8vm8PM/b1utcvHix5fGtrGlrp11Vzhijm2++WZ7nbbr/2/LHqVRq0wD3rGc9a8djBQDsTVvZnH397qZYJxZxNL1Y1XShrBMHhnTy2KjUbNFRrNV1fr6qZMzVM4+uvQDxw1CXijUNJaKKRRzV/XDNur7Lp/t1o6PmftxoebtBd6eh+EoDtR+EWqoFqnntq6yhHbzpt9i/Hn30UZ06dartNgKrj/m+v+a5f//3f68XvehF687pOI4uXLiwo/G0q8ptNhUzkUisBLPLg9rNN9/c9nmf+9zndjROAABW28rm7Ot/Ykqy1rIT6SrWWtnQ6sjokIq1upbqFVnb2K7BNUbHxlMrWzisVqoFCgKrowdS8vzWF/LLF/B+EHalo+Z+3Gh5O0HXNj/eSSi+0kAdcR2FYet9HZc5RnKMGajpt9i7rLVaWFjYNLC95z3vUSaz/neM7373u/X2t799R6/droK2G2vlRkZG9Eu/9EuamJhoWYVLpZhuDQDona1M7/zJVR8nJD1H0uclvXRXRrRHhVbKJmM6v1DRTLGu+XJdobVyTGOfqnjU0aFcUq5jVio9RlIq5ujGQ9mW+/Qts5I8P9Djc+WuddTcbxstbzfo7jQUX2mgzqWiGk5FZebaVwuzyUbVeJBCOfpDGIYqFosaHm7dKOi3f/u39clPfnJNsMvn8wqCjdexStJv/MZvtAx9VxLQ2lXlVk/FTKVSW546ufxxK67r6ld+5Vd2PFYAAHbTVqZ3fuvq28aYqyX91m4NaK+KRxxVPF+PzpRkjNZMqyvXAz1yqaSRZFQHsnFNjaRX1uNZWT3w5ELbwCc1wlbFC7veUXO/bbS8naB7JaH4Sp4bdR3dOJXVozNLWmrxfog4RkdHU4q6ZqCm36LzwjBsWYHbqBqXz+eVTCZVLBZbnvPee+/Vn/3Zn+1oPLOzs7rmmmvWHd9O6Ls8wE1OTrZ83I033qgnn3xSY2NjSiYH6xdYAAC0spNNcM5KuqHTA9nrQmtV8wINJSK6tFjTk/MVhbZRuRlNxTSZjavmN+Z3rg5lXhAquYXpfvGI05OOmvtto+XtBN0rCcVX8tzDoyn9mxMT+swjs5qv+LLN91k2GdXR0ZRiEUdHB2z6LTYWhqEKhcK6oBaPx/Wd3/mdLZ/zrGc9S/fdd9+2X2tpaUm1Wk3x+Pr/F3ajmcm1116rO+64Y9MtBbYT4GKxmA4fPrzjsQIAsNdsZU3f/9RTM8kcSbdK+sIujmlPKtd8Hcyl9M+P5lWuhYq5T63eW6x48sNAz79mTEuVtZ3itjPdb7911OyV7QTdKwnFV/LcGw4NazgV1WOzjWqhY4xiEUdR1+joAE6/3e/e+9736tKlS20rcnNzcwrD9b/wefrTn9429LXqarlV+Xxehw4dWne8XVVuaGho0z3gbrvttpbPfcELXqCPfexjOx4rAADYWqXv3lUf+5I+YK39p10az54VyOiLj8/plsM5PT5b1tmFqoLQKuIYHc4ldGQspS88Ma8XP21i3XO3Mt3PC8J911ETGzuUS2kyk9g302/3oiAIND8/v6X9326//Xa94x3vaHmet7zlLZqbm9v262+0QfdOq3JDQ0NaWFhoGfpe/epX68SJE+sCXquqIAAA6J6trOl7bzcGstc5xqpY9XXqQlEHskk96+qcjGNkQ6uLxZo++9icjoy23z9ps+l++6Gj5m7vPziI9tv0214KgkBzc3PK5/MaGhpqOT0wn8/rla985Uqgm5ubk7WbTcxu2Ki749jY2I5C33IF0HHWfx+Nj48rk8m0rby1O7ZRgLvpppt00003bXucAABgd7UNfcaYL6v1bMLlzdlv2bVR7UGOjI6OpXR+saon58t6Yq68cp8xUiLq6OrRpFzT/gJw+QJ+OfzMFGtrws8gd9Tsxv6DwGrValWPPfbYlqpw+Xxe8/PzKwHux3/8x/Wbv/mb686ZTCb1z//8zzsaz0ZVufHxcT388MMbPj+bzbYMap7ntQxq73znO/U7v/M7OxorAADYWzaq9L2ia6MYAK7jKJuM6OkHsjpXKKtQ9hRYKeJIuWRUh3Ip5RJRuS1+477aZuFnEDtqThcqXdl/cDuoOu4dvu9rbm6ubWj7tm/7Nj3/+c9f97wvfvGLLY9vRbuAlkqllEwmValUtn3OdtsLSNK3f/u36/bbb2+7lcDo6Khisdi2Xq9V9Q8AAAymtqHPWvv48sfGmAOSnt28+Vlr7aXdHtieY6yG4jFlk75CJTQ+1PjNupFRLCJlkxENxTdeb7fV8DNIU/q8INSZfKlr+w9uBVXH/vPFL35R73vf+1pW4QqFwobPPXz4cMtwtxv7vy2f98knn1y5PTw8vKX939ptLyBJP/ZjP7bjsQIAAGyle+frJP2GpE+pcf37P40xP2mt/fAuj21PCQIpGjHyw1DpqKtEKrI8EVZVP5QXhnIjjrw2Wyr0Y/jphkLZ6/r+gxvpx6rjXuV53koFrtXeb62qcnfffbde/OIXrzvX6dOn2zY52cxWNujeqlwup/HxcR08eLDtY+6++24lEomVClw0SnMlAADQW1vp3vnzkp69XN0zxkxI+rgkQt8qsajRbLGu6yYyOl8oa7ZYk2+lqGM0NhTT1HBK+WJVUiNYXB4c+i38dIsfhD3Zf7CV/Rq8t6JVgPu3//bfttwX7Td/8zf1tre9TQsLC9t+nXbTJq+kKtfunLlcTidOnNDo6OimVbjlABeJbP5f5rOe9awdjxUAAGA3bCX0OZdN58yrsV8fVql7oa6fTOnex+d1KJfU06eGZYxkrTRTrOix2aJOHhtRseq1DA79FH66qZ/2H9yPwfvLX/6yvva1r21agWsV4L72ta/p+uuvX3fcdd0dBT5p51W50dHRtqHt5MmTLZ/jOI6++tWv7micAAAAe8lWQt9fG2M+JukDzdvfIemjuzekvSkedbRYDXTzVSP6l8fy+tMvnlfdt4pFjG6+aljPPT6mYsVXxHVbBod+Cj/dlEtF+2b/wb0YvOv1ettpk5cuzWj60oyKxaL+3w/8n5bNaN7xjnfove/d2a4s+Xy+ZejbjarckSNH9Pa3v71lFW5kZGRLFTgAAID9aitXSpck/ZGkW5u332WtvXvXRrRHucZoKBHRB//1CVX8UJl4VGpmutOXlnR2rqw7n3NEjqRA64NDP4Wfbuqn/Qd7HbxrtZqWlpbaVrV+8Rd/Uffee++aSlyxWNzSuT/38LRGhofWNaPZ6Qbd0uZTMY0xbStw7Y6NjIy0PGcul9NP//RP73isAAAA+9lWQl9a0s9ImpP0IUk724RqwDlGuv/JgmqhVd0PVQ6eCm/RiJGR9OVzC3r20RFVfbsuOPRT+Om2re4/uJvbKHhBKGut6kGgcj1UOu4q0qKl/VaDd61W2/L+b8t/F4tFnThxou2Uw8985jP6xCc+saPPb6Ewr1giua4ZzVarcssBbnVIaxfQXvKSl2h2dla5XE6u6+5ovAAAAOicTUOftfZXJP2KMeYWNaZ2/r0x5qy19ht3fXR7yFLd6om5imxoFYs4SsTMyn1haGWt9MRcWVXftg0Og7z5+mY2239wN7dRWD53pRYo4jg6O7ck1zWazCQ0mo6pXqtqsTCvYmFOw05VT35+SbOzszpw4ID+3b/7dy3PedVVV23Y1r+dzbYC2KmF+TlNHDy0rhnNM57xDL3qVa9aCXLRVFY2PqTE0IgyuRHlRkZ18MCEnnH8kA6PDW3ptRKJhBKJxI7HCgAAgM7azkKYS5IuqNHIpf2GUvuUtVaOY5SKR1Su+6p5T4WTqOsonWh8qc0mFbtB3Hx9q9rtP7ib2yg8dmFev/W//rcKc3NaLMypuDCvwlxeM7OzKi7Mq7xYUK1Sbvncl770pW1D3/j4+I5C39zcnIIgaFkhazUV03EcjY2NrYS2oeERmURG2dyosrkRZUdGNZwb08Grrl55zuo1pd/6rd+qb/3Wb5XU+uu87GuXSnJdd8//4mE3q8UAAAD9aiv79P2gpNdJmpD0fyT9B2vtQ7s9sL0mFnE0PhTTxcWqql7QuHBuXjz7YWOq5/GxlNKxzS+cV4cfLwjXBMD9dpG60TYKtWpFC/NzevL0QzoY8zQ/P9d26uRrXvMa/cIv/MK6cz8+V9Jv/+rP7mhs7da0SY3Qt93OkK7ranR0VIuLiy2nTn7f932fXvayl61ZC5fL5eSsmoY6XajowfOLG75Oq2Y0+2G7it2sFgMAAPSzrVT6rpb0n6y19+3yWPa0dES6eiSls/MVBaG0VPNl1djbIh2LaDgZ1dWjKQ0nt15c3U8XqeVyeSWgHTx4UFNTU5LWbqPwxKMP69d/6ge1MD+n4sKcqpXKls9/0003rTtWKHsKFFUimVK1TTVvIxtV8g4ePKjJyckt7f+2/Pfw8PCaAHe52267TbfddtuGY9ppM5pB365iN6vFAFqjsg4A/WMra/p2VgbZZxZqocaH4kpGXWWTVsPJqCQrIyMrq1TM1fhQXAtlf0vn2+sXqYuLizp9+vSWm5lUVgW4d7zjHXrLW94iae02CpFIVKdPfXlH42kV0JbPnc2NbBj6XNdtGdQOHTrU9jkf/vCHdzTOK7XTLrB7cbuKrdoPVUyg3+ynX1oCwF7A5lYdUvVD/ePpGT3/ujGdvrSk84WqQmsVcRxN5RK6djytfzg9o687nNN0obLhD71+uUi11q6pwLX6+/u///v1jGc8Y91zP/7xj7dd77aZ1QFtdeUqm2vdLXIrWk3FXD73y1/7XarVqsoOj2h4ZFTZkVFlc6Mazo1qeGRUz7vhak1m90Zjkp12ge31dhW7adCrmEC/2eu/tASAQUTo65DlC+J/eSSvmw5ldNuRZkCx0vlCSf/yaF7pRFShtZv+0NuNi1RrGz99jTHr7vvbez6uD/7xhzWbn1VhLq+FwrzyzWBXrVY3PO+LXvSilqGvU/u/ra5cpTNZOa6rsLkdRiQSVTY3ouHRUWWHlxuXjGl4VROT3Miovu66w3rG9cfWvc7yuf/99/9Y27Gk465G07Edfy69sJMusIO8T+QgVzGBftMvv7QEAKxF6OuQ0WREJyaHtFj1dd/ZRd193wX5oVXUMTo2ntLTDmSUTUSUiUdU8sINf+htdpFqrVWlXNZjj83rCa+0pX3gZmdn9aUvfUlPe9rT1pxrulDRRz/9r/rD3/vfO/q8261r2+72AtFodGUK5YEDB546vrpyJaP/54N/raHMsLIjo0qlh5SIOlqq+YpusB9cOu7q0FWj619zgPdG3G4X2EH+WgxyFRPoN1TWAaA/Efo6xDXSNRNDevc/PKpSPVAm7jaulEOr6UJFxYqn//DCaxRpFtqWf+iND8W0tLS0JpydOXdR1z/vZS0vUn/3v75VH/nge+TVa9se4+UBbXkKTjLT2WmTkjQxMaGbb765bQOT3Mio4kM5ZXMjmpyY0FWTI4pFWge31ZWrEzfesqZyNZaOqT5X2fCCfqMLjEHeG7HdFhjtDOrXYpCrmEC/obIOAP2J0NchRS/QqQuLunZiSKG1ikYczTx8v5Zmz6u0OK9iaVG//fdLypmq8vk5lYvzqhQLmsvnVa/X153v7778uAJn/QW74zo7CnzS2oC2egpONre+CrYsGo0193p7aspkNjei8fFx3Xzt1XrhC76+5fMmJyd1//33t7xv9QL/oqSlQqALlfkNg0W7ytVMsSarjbt4bnaB0Ym9EQelS90g7hM5yFVMoN9QWQeA/tS3oc8Y8zJJ/0OSK+n3rLVv7/GQNlT1rB48V9Bzjo/r0ZmSpgtVfeFP36X5hz+/o/MN2aqKJr7uInWjgLaZ1ZW+1VNwjl//dP3gz75Nw7lRZXIjyo2M6mzFVTo7ohOHJ5RLrV/TZiTdcnVu29NzrmSBf6vKVacuMLZbFVtt0LrUXcnXol8NahUT6DdU1gGgP/Vl6DPGuJLeKembJJ2V9DljzEf6eVN4xxg9/WBOf/GlcxqKRzWcjCqZyWl+h+dzvSXdcPXV6y5SJ8YbDVLi8fiGe7+1+jidTq+cf/UUnPEDU3rNd33fyn3WWpXOLkhS28X4O5me06kF/qurasZI2VREi2W/bfDbzQsMutTtHYNYxQT6DZV1AOhPfRn6JD1H0mlr7aOSZIz5oKRXSerb0DcUd7VQqSmTiMkxUtkLZJKZLT03kUisC2rJZLLlRepzfvQu/cJ/vEupVKplJ86t2qhC5hgj07zTdVq/xk6m53RigX+rqlo9CBR1HbmOo+Cyq4zdvMCgS93eM4hVTKDfUFkHgP7Tr6HvKklPrrp9VtJzezSWLan5gdKxiIbirmaW6lqsevJHrlXmxhcpkRlWNjeqQ5MTuv3GoxoZG9fY6LhuO3FEN117WKlUqu1511+kduaCdaMpOJGI0XAioqofaijeurnKTqpnV7rAv11VLeq6Wqh4msjEZEMpVHcuMOhSBwCtUVkHgP7Sr6FvU8aYuyTdJUlHjhzp8WgkL7TKJqOqeIGSUUe5VErmpd8q+5JXqOYFch2j510zrpPHc5ov+TpxIKNjU1mlUrsbBto1GNloCo7vWx0bT2uh4st11v+A3mn17ErW321WVRtORuUYo2dcnVUQ2K5cYNClDgDao7IOAP2jX0PfOUlXr7p9uHlshbX2XZLeJUknT57c7Np710Uc6cm5sq4ZH9ITc2VdKFTlW6uIMTo4nNDVoymdmyvr668b1ULFk+No1xeyb9ZgZKMpOMfH0rJt7ttp9exKFvhvpapW80IZGU3lEtse207QpQ4AAAB7Qb+Gvs9Jut4Yc1yNsHenpO/s7ZA2FnGN0vGIPntmTkdGU3rh9WOScSQb6vxCVV94Yk63Hx9TorkX3WgqtqthYKsNRjabgtPJ6TlXssC/H6tqdKkDAADAXtCXoc9a6xtjfljSx9TYsuEPrLUP9nhYG/IDo/FMTM86OqKZYl2PzJQVGsmxUjLm6uSxUY1mYpIxuno0pfHM7lWjtttgZKMpOJ2enrPTBf79WFWjSx0AAAD2gr4MfZJkrf2opI/2ehxb1bjmN3KNo8Wap4uFqnzbmPZ5cDipg9mEXBm5Mjo8ktzV6k+/NxjZyQL/fq2q0aUOAAAA/a5vQ99ek4pIdT/UfU/O68BwQtdfP75y34XFqu57cl7/5mmTyiQjOjSSuuLqT7sGLVJ/ToW83HYriP1cVaNLHQAAAPoZoa9Dqr40X6rrxkNZnStU9fnH5xVYK9cYjQ/FdcOhrAqlurzAquoFqtR9JWNrv/ztgtzq41HXUdUL9OR8uW1lqR+nQnZCP1fV6FIHAACAfkXo65B6EGokHdXnHy9odqmuUs1XKMmR5AVWXhDqWUdGVPF83f3Fc5rIxHXy6IhuODQsqXWnzUwyokw8otlSXTOLNYXWKuY6erJQ1sRQQqPpmKT1DVrGM/G+nArZCVTVAAAAgO0h9HVI1DGaLdZ0YbGqMJTi0adCSN0PdWGxqnyprpjrKrTSxcWa7nnooiQpl4rpK9OLclwjSbLWyjFGSzVfnzh1UalYREFglU5EdL5Q0WLFU36prqcfzK4EP2ltg5Z+nQrZCVTVAAAAgK0j9HWI4zi6tFRXOhZR2fNV9UKFthGwElFHqWhEM0s1uY5ZeU49sHpoelEHswn5odXjM0taqPqyVsomIppeqKpc91Ws+jqQTSjiGM2V6gqtdKlYUzyypOFkbs0G6ssNWvp5KiQAAACA7iH0dYgXhMolo3rk0pKS8YhyqbgcSaHUDG6enj6VUc1b2zylWPUUWqtz81X5q8pyxkhfvbCowErHxlIKAitr7Urlzlrp4mJVxaqvXOqpat/qBi1MhQQAAABA6OuQILSKuEbPOjqix/JlXVioKLSNTdunsgk941BWrjEK7dr5lnHX0WMzJTnO2iBW90N5gZWVNFOsaWo4KWOMHKOV4Ff3rUr1QLnUU8+7vEELUyEBAACA/Y3Q1yHxmNFYOq5HZpY0mooql4zKysqoEfSKFU9PO5hVIro23Dmuo6War2wytua4VSPgNZq0+ApCqyC0Gs/ENbNYa3TmNI1HrrZXG7QAAAAA2B3M8+uQhOtqbCiupVqgcj2QMZLrODLGqOYHmivXNRR3FXEaYW7leRFHY+n1lbgwCDU2FFex6skLQllrNV+u6/BwQrmkq1TM1VDc1VD8qdy+1xu0AAAAAOg8Kn0dUvNCRVyjmw8N6ysXF/XkfFlBKEUc6dBwStcfGJIkPTJb1FA8osWqr5hrdGQspbpv9eR8eU2nzfmKp5F0VPlSVE87kNG5hYrOzVeUiLo6PJKUY6wODic0FI/QoAUAAABAW4S+Dom40lLFV8ULNJKMaTgRlTVGRlauY1TzQi1WG9M0YxGjA9nGPn3XHcio6oWyki4Vq6p5oQIbaqFSVzzi6JtvPKB/Op1XzQ8UWqniBZpZrOmGQxldOzGkI6MpjWcSNGgBAAAA0BKhr0OsjL56cVFfvbika8ZTmhhKNFpwWquZpaoePL+oo2MpveC6cU1kE3rm1TnFY40v/7GxtCr1QMPJiJZqgZZqviLG0XAqqkdml3RkLKWyF8iGVsYYucaoWAtU960KFU/XH8gQ+FbxglCFVR1Lc6moonx9AAAAsE8R+joksI1Alk1E9ehsWQ+cKyqUlSOjVNzVUCKqUI1unpV6oLmyp6lm6Fu9p17EcRSEVjJSOh7RaHM7BmOMwlBynMa+f45pPG55Xz46dDZMFyrsTQgAAACsQujrkFot0LGxtL52sailmi8jI8kolFWx6isIQz376Eij+pRwdCZf0kQmvlKhW72n3lyprvF0TDJG5+YrspJSsbX/VEaS65g1+/Ltd9OFik5NL65ZG9nofhro1PSiJBH8AAAAsO8w561DkvGIHptd0i2Hczo0nJRVY+8+I6NDuaRuPJTTuUJZY0NxVer+SoXOC0LNFGuaLlQ0X/aUS0V1zURaybgra21zW4b14lFHQ3F33b58+5UXhDqTL60JfKuFtlFJJSADAABgv6HS1yGOMTqQTeofHr6kqeGknn0sp8AahaHVTLGmhy8V9Y1Pn1QQWj18aUlX5ZKaLVZ1+lKx5VTE42NpPZZfUi4R0XzZa6zlc42CwMoYaTKTkOs4+2pfvo3W6hXKnsq1YMPnMxUWAAAA+xGhr0MqXqDJTFw3TA0rsFaJiCvHcSRZJSNGxkkqm4hqqepLkubLdX3+iXkdzD413XB5KuLXLhY1mYlroeIpGnF0rlCRF1qlo44OjaR0aDipkXRsX+3Lt9laPT8I1abIt4KpsAAAANiPCH0dEo86mlmq6kXXj+vex+f16ExJXhgq5ji6ZiKtZx4d0QPnFlXxAo2lYnpivqzhZFTBUCjXeSq0GUl1P9Tff21Gx8fTuiqX1HAypsdmSyrWPHl+0Gzysn+ak2xlrV7EdWSax9thKiwAAAD2I0Jfh4ylYrpuIqO7v3hO1x0Y0ktvmFypSJ1fqOjuL5zVK77ukBwjzZU9JaKuyrVAS7VAw8mngkgkYvTwhSWVVu6Laige0TOPjsjzQ4XWKhV312z5MMi2ulbvtqtzSsVdlTaY4rmfpsJi97AlCAAA2GsGPzV0iR+E8q3VbUdyurRY1UPTi1IoyZESrtGtR0cUhI2Om4mI0WLNKlSj2cua8/hWC1V/pRGM1Khe1f3GtERjjKr1UIu1QBP7IPRtda1esdk99fKK4LL9NBUWu4ctQQAAwF40+KmhS/IlT3U/kOdbXVis6dxCRX4oRRyjq4YTyqXiqvuBEhGnuedeo7On66xtzxlaq+WmnZfft2w/rU3bzlq91fsdclGOTmNLEAAAsFcR+jrEONJcua7PPDqrahDKdYzcxlZ9ulCsaq5U1785MaF4JKIgrCqXjKrihxqKu2vO4xgjY6R4xFl338praf+sTdvuWr3V+x0uT78bSUX3zdcLu2Or04xX770JAADQLwh9HWKt1cMXllT1Qx3IJjQ2lJBjGheD+aWqpheqOj1Tkhwr1zW6ZnxI+VJ9TRMXqbGmbyQZ0VAitu6+ZftpbVouFd32Wr2I67AtAzqKLUEAAMBeRujrEGulwIa65eqczsyU9JlHZ5tr+KTDuaRuvTqnQqWuIJCumxzSicmMrNZPRYy7jp537bimF6qsTZMUdZ2VtXrWNkKx71uF1soxRrGI2VdfD/QGW4IAAIC9jNDXIY7TqN59/NRFleqhHEkR0wiDT85VlC/V9E03HFQs4uj2a8dXQkq7qYi5VIy1aU1TuaSMpK9dKurhC0UVqr5kpUTM0TXj6V4PD/sAW4IAAIC9jNDXIamoq3OFiqIRV/FQqjW7bTqS4jFHsYir6cWKRtJr15e1m4o4nonLMVKh4svIKpeKaTQd27cXlVZSzQuUS8eVScbkOkZDcVeu49BEowW2FeisnUwzBgAA6BeEvg6pBaHSMVd1L5BjpPSq7RRCG6ruBUpHI6rUN5/+1aotfL5UVxDuvyqf9FQTDWOcNXsaLhukJhqdCGtsK9B5q6cZM+0aAADsNYS+Dql7oUaH4rpqJKVLi1UVa75CK7lGGkpENJlJKJeOquZv3AyCtvDr7ZcmGp0Ia7x/dg9bggAAgL2K0NchrmN0caGiqeGEUjFXixVfobVyHaNMIqLhZFQzxZpc074SQFv41vZDE41OhDXeP7uPLUEAAMBeROjrkETU0UQmqYdninKNND4Uk4yRsVYVP9BsqaYTExklY+0vDvdSRauba8YGvYnG6rDmh6FKtUBB2PiFQTruKuI4Wwpre+n9s5exJQgAANhrCH0dY3TToawemVnSfNmT1OgwKdMIJMPJqG48lG1bhZH2TkWr22vGBr2JxnJYmyvVdalYVc0Ln9rCI+poMpOQUWzTsLZX3j8AAADorr1ZGulDuXRMD51f1HOPjerIaEqOlQJrFXGMjo+ndfs1o/rK9KJG0+2DyXJFayO9rmgtT0MsNQOftHYa4nSh0vHXXG6i4bT54uz1Jhp+ECpfquvsfFlVL1zzda16oc7Ol5Uv1TcNa3vh/QMAAIDuo9LXIWFolYg6+uuHLujGgxm99KYDco0UWOnxmaL+4kvn9fzrxvXEXFWxSKRlRawTFa3dnHbZyzVjg9xEwxhpZqm64dd1dqnaNvQuG/SKKAAAAHaG0NchpbqvbDKmmw8Na7Hq6dGLRdWbV/E2tHrG4ZzSsYhmihV5zYrN5UHlStvC7/a0y1Zrxi5fgxaE4a6tGRvUJhqOMUq4jipqX8mLR1yZTVIf2woAAACgFUJfh9R9q8VqXddODOlrFxf16GxZ9SBU1DE6PJrSNeNpzSzW5IUbV8R2WtHqRqv+y9eMtVuDdmQ0tWuNLgaxiUYQWh0ZS6t4flF+i7QWcYyOjKYUBJut2BvsiigAAAB2htDXIdmYq1wqro8+MK3RdEzZZES2ecV9vlDR6YtFvewZBzUUdSU91UUxl4qum4653YpWt6Zdru6iOddcg3Z5yKz7oaYXqsqlYgSMLYq4jqKuo5sOZfV4vqSFqi9rG5W5bDKqo6MpRV1ny/92g1oRBQAAwM4Q+jrEs1ZVrzH18dJiTZeK1ZUqS8R1lIlHGmutmtfdjmM0W6zq9KVi24rMVita3WrVv7xmbKHi6VKx9Rq0bLIRLtgPbutyqajiUUdBaHXiYFaeHyq0Vo4xikYceX6oeNTZ1lq8QayIAgAAYGcIfR1ijXRpoaJrJoZUrfvKJCKysjIyiriNDdofnyvpG5xJGUlBEOqrF5Y0lHjqn2Cn0zG71ap/ec3Yvz46p5q3/lwRx+joaEqeH8rzxX5wW7R6LV7db3xdjTErlVPW4gEAAOBKcBXZKVYq1QOVa75CK8UjjlzHketIruOo5oeq1kKFkiIRo+nFatuN2penY241pHWzVf9ULqmnHRzScDIi03xRxzSqVTcdyiriOrJiP7jtmsoldcNUVum4u/JvaSSl465umMoyVRYAAAA7RqWvQ6y1Gh+K66HpRZXqvqKus9LgxFpPybirp09m5AWhooGjRMSV67QPYduZjtntVv3jmYSedjCreotpiEFzzif7wW0fa/EAAACwGwh9HRJzHKXjrkJrVfMae+UZSX4oJaOOkjFXuVRMqairqeFE26Yry7ZTKet2q/5cKqqIa1Tz105DXI394HaGtXgAAADoNEJfh9SCUImYK0dGE5m4somojLEyxqhY9RUGViOpqA4MxzWeSeh8obrhOrztVsq62ap/v+4Ht5sb3wMAAAC7hdDXIUFo9eilkp533ZgePL+o05eW5IVWUdfo+HhaNx3N6qsXivqGGw/s2nTMbk4P3G/7we32xvcAAADAbiH0dYgXhhofiumehy7qYDauZx7NrYSD/FJN9zx0UV9/3bjqfrCrlbJuTg/sdMjs10paNza+BwAAAHYLoa9DUlFXFS/U8fG0npgr68vnFxWEVq5jdDCb0PHxtKpeoHS8Ub0blEpZp0Jmv1bSurXxPQAAALBbCH0d4kZcpeOuPvtYURU/VNx1ZN1GeClUPNUuFvWSp0/KdZ7aXGG5Uja7VNNCxZeRVS4d00gq1rtPpAf6uZLWrY3vAQAAgN1C6OuQmGs0U6wrl4rJVH2Va75krYxjlIpFNJyIaKZYV9xdu6PeTLG2psL15FylLypc3dLvlbRubXwPAAAA7BZCX4eUaoHiESPXkdIxV0Mxd2WaopXkOlI8YlRcVTXq5wpXt/R7JW154/tOdloFAAAAuokr1Q6JOUajQwkdyCaVjkcUWCs/sAqsVToe0YFsUqNDCcWb4WCrFa5BryD1eyVtudPqRtiTEAAAAP2s70KfMeaXjTHnjDH3Nf+8vNdj2opIxNXEUExlz5e1VkOJqLLJqIYSUVlrVfZ8TWZicpuhbzsVrkG2XEnbSC8racudVp02gxzUPQkBAAAwOPp1eud/t9b+Zq8HsR2T2Zi8MNTR0bQuLlZVKHsKJbnGKJeOajKTkB+Emso2mrT0e4WrW3Zrz8JOGpROqwAAANif+jX07TmVeqhjY2k9PltSzHV0KJeQrJFMY5pnpe7ryNiolrxQqQRrxZbt5p6FndTNje8BAACATurXK9YfNsbcb4z5A2PMSK8HsxWFcl3luq9j42l5QaivXVzSqQuLevjSkvww1LHxtMp1X4VSXRJrxVabyiV1w1RW6bi7MtXTSErHXd0wle2bStrynoTLAZDABwAAgL2gJ5U+Y8zHJR1scdfPS/odSb+qRhHsVyW9Q9L3tjjHXZLukqQjR47s2li3zuirF5Y0X67rmvG0bjyUlbWSMVK1HuiRmZLmS56efWxM0t6pcHULlTQAAABgd/Qk9Flrv3ErjzPGvFvSX7Y5x7skvUuSTp48udnyuF1njNVSzVMQSucWai0fs1TzZFZN6GSt2FrLlTQAAAAAndN3a/qMMVPW2unmzddIeqCX49mqRDSiyUxC5+YrikSM6p5VaEM5xlEsauT7VhOZhBLRtV9yKlwAAAAAdlPfhT5J/7cx5lY1pneekfTmno5mixJRV9eMD+nSYlUPXywqX6orDCXHkcbSMV0/mdE1E0OKR9ev46PCBQAAAGC39F3os9Z+d6/HsBO5VFRBGKrqh4q4ruIRtxn6jKKuq6ofKgjDTRuzeEGowqqqXy4VVZSqH9BRfJ8BAID9pO9C315V9XyV6r4KFU/ZZEQTQ09V7mpBoELFU6nmq+75irixlueYLlRY3wfsMr7PAADAfsOvtjvkzGxZX71Y1M1X5TQ+FJOVlR82NlafGIrr5qty+trFoh6ZLbd8/nSholPTiyo1L0SlxvzWUi3QqelFTRcq3flEgAHG9xkAANiPqPR1SM0PFISSDUNNDSd1dNRZ2bKhHoSqe6H8sPG4y3lBqDP5UsutGyQptI0On+wNB+wc32cAAGC/IvR1SCLi6nAuqWLN1xcemdOZ2ZL8MFTEcXRsPK2bDw/r6pGU4pH1jVwKZU/l2vowuFq5Fmi+7NHwBdghvs8AAMB+RejrkKlcQvGooz/9wgVZNRq7LJteqOjCQkX//rlHdGQkse65fhBqs40GbfNxAHaG7zMAALBfEfo6pFDy9OjMkrLJqFxHja0ZrJGMVdJzFITS6dmS8mVPw+m1wS/iOjLShhekpvk4ADvD9xkAANivuLrpkNmlmopVXweycS1WfH357KK++OS8vnx2UYuVxvGliq+Zxdq65+ZSUaXi66d9So2L0GjEKBFzVPMCzRRr8qhEANu20ffZslTc3XRbFQAAgL2G0Nchpvn3g+cXVaz5SkYdDcUjSkYdFWu+Hjy/2LbCEHUdHRtLyzFrj7uOUWitHrlY1Pn5qr5yoaj7nyzo3jNzdBkEtqnd99kyx0jHxtJU+gAAwMBhemeHJKKu8qW66n4oP7QK7VMRzwmNQsdorlzXUKJ1pWF5f7Dl/cMkKQhCPTKzpNF0XCPpxt5+q9vLr34egM1d/n3GPn0AAGA/IPR1irEaTcd0WloT+NS8bWU0loq1bRcvNS5IJzJxzZc9eX6gB84v6vh4Wq6zvvJAe3lgZ1Z/n/lBqIjraCQV5fsIAAAMLEJfh/iBdDAb17HRtC4UqypVfYWycozRUDyiA5mEJrMx1f2N+wdGXEcTmbhmijW5xsiaNnPRRHt5YKeWv88AAAD2A0Jfh8QiRoWKrxsOZZSdj2h2qabAShHHaCwd01UjSS1UAiWiW6sm0F4eAAAAQCcQ+jpkNBXT8bG07n18XiOpqKZyKclayRiVap6emKvo5LERjaS31hmQ9vIAAAAAOoHQ1yHRaEQ3Hx5WvlzT2fmKzhYqCsNGB85cKqLjEyndfNWwou7WvuTL7eVLzaYurdBeHgAAAMBmCH0dkktFNZKK6bnHxhRzC7qwUFHQDH1TwwndenVOI6nYlkPacnv5U9OLLZu/0F4eAAAAwFYQ+jok6jpKxVw9OVfSdeNpnTiQURiGchxHYRA2jk8ObSuk0V4eAAAAwJUi9HWIF4Qq1nxdfyCrx/MlLRRrsrZRkcsmo7r+QFbFmr/SIn6rLm8v7zpG1lqFVpop1pRLRRWl2gcAAACgDUJfhxTKnooVX44xOnEwK88PFdrGlg3RiCPPD1Ws+DvaYmG5vfx0oaJHZpao+gEAAADYMkJfhyxvsdDouLl2Ed7y7SvZYmG6UFm3vs9KKtUCnZpelCSCHwAAAIB1CH0dEnEdRRwjLwj18IUlLVT95R0bNJyI6OhYWlHX2VHjFS8IdSZfatnQRZJC21j3N5GJ09gFAAAAwBokhA7JpaJyHOnB84sqVBqBT2ps1Veo+Hrw/KIcRzvaYqFQ9lTeYOsGSSrXAs2XvZ0MHQAAAMAAI/R1UKnmK7Sty3GhtSrV/B2dd3nq6EauZOooAAAAgMFF6OuQQtlTzHV1eCSlRNSRaR43khJRR4dHUoq57o6qcRH3qfO1Y5qPAwAAAIDVWNPXIcvVuNF0TMPJiJZqgYLQynWMhuKuXMfZcTUul4oqFXdV2mCKZyru7mjqKAAAAIDBRmmoQ1ZX41zH0XAy2gyAUblO48u802pc1HV0bCwtp025zzHSsbE0lT4AAAAA61Dp65DdrsYtb8dwJl9inz4AAAAAW0bo65Dlatzle+kt60Q1biqX1EQmrvmyJz8IFXEdjaSiVPgAAAAAtEXo66BuVOMirqOJTPyKzwMAAABgfyD0dRjVOAAAAAD9hNC3C6jGAQAAAOgXhL5d4AWhCqsqfblUVFEqfQAAAAB6gNDXYdOFCh02AQAAAPQNQl8HTRcq67p3WkmlWqBT04uSRPADAAAA0FXMOewQLwh1Jl9quV2DJIW20dXTD8LuDgwAAADAvkbo65BC2VN5g43ZJalcCzRf9ro0IgAAAAAg9HWMH4Qra/iiESMjyVq79nbzcQAAAADQLazp65CI6yjiGHlBqIcvLGmh6stayRhpOBHR0bG0oq7Dfn0AAAAAuooE0iG5VFSOIz14flGFSiPwSZK1UqHi68Hzi3IcaSQV7e1AAQAAAOwrhL4OKtV8hbZ1J5fQWpVqfpdHBAAAAGC/I/R1SKHsKea6OjySUiLqyDSPG0mJqKPDIynFXJdGLgAAAAC6ijV9HbLcyGUsHdNkJqZizZcXWEVdo0w8oiCkkQsAAACA7iP0dcjqRi6PXSrRyAUAAABAXyCBdAiNXAAAAAD0I0JfB9HIBQAAAEC/IfR1yOpGLtGIUaXua6nqq1L3FY0YGrkAAAAA6AnW9HXIciMXSYq7jtKxiEJr5RijeHMdH41cAAAAAHQboa9DIq6j+VJdZ+fLCpfTnzEKJS3VApXrZRkjGrkAAAAA6CoSSIcMxV1V/eCpwHeZ0EpVL1A27nZ3YAAAAAD2NUJfhyzVAk1lE4o4puX9EcdoKpvQYi3o8sgAAAAA7GeEvg7xg1Cu6+imQ1nlkhGZZvZzTGM7h5sOZeW6Dmv6AAAAAHRVT9b0GWO+XdIvS7pB0nOstfeuuu9nJb1JUiDpR621H+vFGLcr4joKw0bjlhMHs/L8cKWRSzTiNG6HljV9AAAAALqqVwnkAUnfJunTqw8aY26UdKekmyS9TNL/MsbsiUVwuVRUqbgrK6nuNzp5GmPW3E7FXTZnBwAAANBVPQl91tpT1tqvtrjrVZI+aK2tWWsfk3Ra0nO6O7qdibqOjo2l1WZJnxwjHRtLU+kDAAAA0FX9tmXDVZL+ZdXts81je8JULilJOpMvqVwLGtU+NSp8x8bSK/cDAAAAQLfsWugzxnxc0sEWd/28tfbPO3D+uyTdJUlHjhy50tN1zFQuqYlMXPNlT34QKuI6GklFqfABAAAA6IldC33W2m/cwdPOSbp61e3DzWOtzv8uSe+SpJMnT7bZHa83Iq6jiUy818MAAAAAgL7bsuEjku40xsSNMcclXS/psz0eEwAAAADsWT0JfcaY1xhjzkp6nqS/MsZ8TJKstQ9K+mNJD0n6G0k/ZK1lN3MAAAAA2KGeNHKx1t4t6e429/0XSf+luyMCAAAAgMHUb9M7AQAAAAAd1G9bNgADyQtCFVZ1dM2loorS0RUAAABdQOgDdtl0ocLejQAAAOgZQh+wi6YLFZ2aXlS4alMRK6lUC3RqelGSCH4AAADYVYS+XcBUPkiN98GZfGlN4FsttNKZfEkTmbgivD8AAACwSwh9HcZUPiwrlD2VaxvvOFKuBZove5rIxLs0KgAAAOw3hL4OYiofVvODUG2KfCts83EAAADAbmFOWYdsdSofF/j7R8R1ZDZ5jGk+DgAAANgtXG12yHam8mF/yKWiSsXdDR+TirsaSUW7NCIAAADsR4S+DmEqHy4XdR0dG0vLaVPuc4x0bCxNpQ8AAAC7ijV9HbI8lW+j4MdUvv1neQ0nzX0AAADQK4S+DlmeylfaYIonU/n2p6lcUhOZuOZXbeMxkoryCwAAAAB0BaGvQ5an8l3evXMZU/n2t4jrsC0DAAAAeoLQ10FM5QMAAADQbwh9HcZUPgAAAAD9hNC3C5jKBwAAAKBfUH4CAAAAgAFG6AMAAACAAUboAwAAAIABRugDAAAAgAFG6AMAAACAAUboAwAAAIABRugDAAAAgAFG6AMAAACAAcbm7LvAC0IVyp78IFTEdZRLRRV1ydcAAAAAuo/Q12HThYpOzyxpZrEmP7SKOEYT2biumxjSVC7Z6+EBAAAA2GcIfR00Xajonx/Ja3qhopoXykoyks4vVHRpsabnXztG8AMAAADQVYS+DvGCUPc9Oa/H8yWF9qnjVlLVC/V4vqRUzNFEJq4IUz0BAAAAdAnpo0PySzU9Ors28K0WWumx2ZLyS7XuDgwAAADAvkbo65CFiq9qPdzwMZV6qELF79KIAAAAAIDQ1zFGtrGAb6PHmObjAAAAAKBLCH0dkkvFlEtsvERyOBlVLh3r0ogAAAAAgNDXMSPpmK4/mFHEaV3uizhG1x8Y0miK0AcAAACge+je2SFR19GJyYzqfqgzsyUtVH1ZKzlGyiajOjaW0onJDJ07AQAAAHQVoa+DDuaSspJyqagWy74Ca+Uao2wqoqtyKR1kjz4AAAAAXUbo67CpXFITmbjmy578IFTEdTSSilLhAwAAANAThL5dEHEbm7ADAAAAQK9RfgIAAACAAUboAwAAAIABRugDAAAAgAFG6AMAAACAAUYjl13gBaEKq7p35lJRReneCQAAAKAHCH0dNl2o6Ey+pHItkJVkJKXiro6NpTXFPn0AAAAAuozQ10HThYpOTS8qtE8ds5JKtUCnphclieAHAAAAoKuYc9ghXhDqTL60JvCtFlrpTL4kPwi7OzAAAAAA+xqhr0MKZU/lWrDhY8q1QPNlr0sjAgAAAABCX8f4Qag2Rb4Vtvk4AAAAAOgWQl+HRFxHZpPHmObjAAAAAKBbSCAdkktFlYq7Gz4mFXc1kop2aUQAAAAA0KPQZ4z5dmPMg8aY0BhzctXxY8aYijHmvuaf3+3F+HYi6jo6NpaW06bc5xjp2FiaSh8AAACArurVlg0PSPo2Sf+7xX2PWGtv7e5wOmN5Owb26QMAAADQL3oS+qy1pyTJmM1Wwe09U7mkJjJxzZc9+UGoiOtoJBWlwgcAAACgJ/pxc/bjxpgvSlqU9AvW2n/o9YC2K+I6msjEez0MAAAAANi90GeM+bikgy3u+nlr7Z+3edq0pCPW2rwx5lmS/swYc5O1drHF+e+SdJckHTlypFPDBgAAAICBsmuhz1r7jTt4Tk1Srfnx540xj0g6IeneFo99l6R3SdLJkyc32yIPAAAAAPalvlpoZoyZMMa4zY+vkXS9pEd7OyoAAAAA2Lt6tWXDa4wxZyU9T9JfGWM+1rzrRZLuN8bcJ+nDkr7fWjvXizECAAAAwCDoVffOuyXd3eL4n0j6k+6PCAAAAAAGU19N7wQAAAAAdBahDwAAAAAGGKEPAAAAAAYYoQ8AAAAABhihDwAAAAAGGKEPAAAAAAYYoQ8AAAAABhihDwAAAAAGmLHW9noMV8wYMyPp8V6Po4VxSbO9HgT6Hu8TbIb3CDbDewSb4T2CzfAe2fuOWmsnWt0xEKGvXxlj7rXWnuz1ONDfeJ9gM7xHsBneI9gM7xFshvfIYGN6JwAAAAAMMEIfAAAAAAwwQt/uelevB4A9gfcJNsN7BJvhPYLN8B7BZniPDDDW9AEAAADAAKPSBwAAAAADjNC3S4wxLzPGfNUYc9oY8zO9Hg+6xxjzB8aYS8aYB1YdGzXG3GOMebj590jzuDHG/HbzfXK/MeaZq57zxubjHzbGvLEXnwt2hzHmamPMJ40xDxljHjTG/Mfmcd4nkCQZYxLGmM8aY77UfI/8SvP4cWPMvzbfCx8yxsSax+PN26eb9x9bda6fbR7/qjHmm3v0KWGXGGNcY8wXjTF/2bzNewQrjDFnjDFfNsbcZ4y5t3mMnzX7EKFvFxhjXEnvlPRvJd0o6fXGmBt7Oyp00XskveyyYz8j6RPW2uslfaJ5W2q8R65v/rlL0u9Ijf+QJb1V0nMlPUfSW5f/U8ZA8CX9uLX2Rkm3S/qh5v8RvE+wrCbppdbar5N0q6SXGWNul/RfJf13a+11kuYlvan5+DdJmm8e/+/Nx6n5vrpT0k1q/L/0v5o/ozA4/qOkU6tu8x7B5V5irb111XYM/KzZhwh9u+M5kk5bax+11tYlfVDSq3o8JnSJtfbTkuYuO/wqSe9tfvxeSa9edfz/tQ3/IilnjJmS9M2S7rHWzllr5yXdo/VBEnuUtXbaWvuF5sdFNS7YrhLvEzQ1/62XmjejzT9W0kslfbh5/PL3yPJ758OSvsEYY5rHP2itrVlrH5N0Wo2fURgAxpjDkr5F0u81bxvxHsHm+FmzDxH6dsdVkp5cdfts8xj2rwPW2unmxxckHWh+3O69wnton2hOsbpN0r+K9wlWaU7bu0/SJTUush6RVLDW+s2HrP73XnkvNO9fkDQm3iOD7rck/ZSksHl7TLxHsJaV9LfGmM8bY+5qHuNnzT4U6fUAgP3GWmuNMbTNhYwxQ5L+RNJ/stYuNn7p3sD7BNbaQNKtxpicpLslPb23I0I/Mca8QtIla+3njTEv7vFw0L9eYK09Z4yZlHSPMeYrq+/kZ83+QaVvd5yTdPWq24ebx7B/XWxOkVDz70vN4+3eK7yHBpwxJqpG4HuftfZPm4d5n2Ada21B0iclPU+N6VbLv7Bd/e+98l5o3j8sKS/eI4Ps6yW90hhzRo1lJC+V9D/EewSrWGvPNf++pMYvj54jftbsS4S+3fE5Sdc3O2jF1Fgg/ZEejwm99RFJy92u3ijpz1cdf0OzY9btkhaaUy4+JukOY8xIc7H0Hc1jGADNdTS/L+mUtfa/rbqL9wkkScaYiWaFT8aYpKRvUmPt5yclvbb5sMvfI8vvnddK+jvb2Ij3I5LubHZuPK5Gg4bPduWTwK6y1v6stfawtfaYGtcZf2et/ffiPYImY0zaGJNZ/liNnxEPiJ81+xLTO3eBtdY3xvywGt8QrqQ/sNY+2ONhoUuMMR+Q9GJJ48aYs2p0vHq7pD82xrxJ0uOSXtd8+EclvVyNhfNlSf+XJFlr54wxv6rGLxAk6T9bay9vDoO96+slfbekLzfXbEnSz4n3CZ4yJem9zS6KjqQ/ttb+pTHmIUkfNMa8TdIX1fjlgZp//3/GmNNqNJK6U5KstQ8aY/5Y0kNqdI39oea0UQyunxbvETQckHR3c+lARNL7rbV/Y4z5nPhZs++Yxi95AAAAAACDiOmdAAAAADDACH0AAAAAMMAIfQAAAAAwwAh9AAAAADDACH0AAAAAMMAIfQAAAAAwwAh9AAC0YYz5DWPMg8aY3+jR6/9z8+9jxpjv7MUYAAB7H/v0AQD2NWNMxFrrt7lvQdJotzervnxMxpgXS/oJa+0rujkOAMBgoNIHANhTmlWvB1bd/gljzC8bY37UGPOQMeZ+Y8wHm/eljTF/YIz5rDHmi8aYVzWPf48x5iPGmL+T9Ik2r/MRSUOSPm+M+Q5jzLcaY/61eZ6PG2MOGGMcY8wZY0xu1fMebt53zBjzd83xfMIYc6R5/3uMMa9d9fil5t8vNsb8Q/N1H1p9n6S3S3qhMeY+Y8yPGWPcZhXyc83zv7n5+CljzKebj3vAGPPCDn3ZAQB7WKTXAwAAoEN+RtJxa21tVQj7eUl/Z6393uaxzxpjPt6875mSbrHWzrU6mbX2lcaYJWvtrZJkjBmRdLu11hpjvk/ST1lrf9wY8+eSXiPpD40xz5X0uLX2ojHmLyS911r7XmPM90r6bUmv3uRzeKakZ1hrH2vxua1U+owxd0lasNY+2xgTl/RPxpi/lfRtkj5mrf0vxhhXUmrTrxoAYOAR+gAAg+J+Se8zxvyZpD9rHrtD0iuNMT/RvJ2QdKT58T3tAl8bhyV9yBgzJSkmaTmYfUjSL0n6Q0l3Nm9L0vPUCGGS9P9J+r+38BqfbRH4WrlD0i2rKobDkq6X9DlJf2CMiUr6M2vtfVs4FwBgwDG9EwCw1/ha+/Mr0fz7WyS9U41q2eeMMRFJRtK/s9be2vxzxFp7qvn40jZf939K+n+stTdLevOq1/2MpOuMMRNqVPL+dKvjN8Y4agTIZVsdk5H0I6s+r+PW2r+11n5a0osknZP0HmPMG7Z4PgDAACP0AQD2mouSJo0xY82pja9Q4+fZ1dbaT0r6aTUqX0OSPibpR4wxRpKMMbddwesOqxGmJOmNywdtoyPa3ZL+m6RT1tp8865/VqPyJ0n/XtI/ND8+I+lZzY9fKSm6hdcuSsqsuv0xST/QrOjJGHOiuX7xqKSL1tp3S/o9NQIwAGCfY3onAGBPsdZ6xpj/LOmzaoSwr0hyJf2RMWZYjSrYb1trC8aYX5X0W5Lub1bVHlMjJO7EL0v6P8aYeUl/J+n4qvs+pMbUyu9ZdexH1Fjn95OSZiT9X83j75b058aYL0n6G22tune/pKD5nPdI+h+Sjkn6QjPQzqhRZXyxpJ80xniSliRR6QMAsGUDAAAAAAwypncCAAAAwABjeicAYF8zxtysRnfN1WrW2uf2YjwAAHQa0zsBAAAAYIAxvRMAAAAABhihDwAAAAAGGKEPAAAAAAYYoQ8AAAAABhihDwAAAAAG2P8Pa2reRc8bdnEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the orginal data (as before)\n",
    "sns.scatterplot(x='count', y='change', alpha=0.3, data=data, s=100)\n",
    "\n",
    "# Generate and plot the model fit line\n",
    "xs = np.arange(data['count'].min(), data['count'].max())\n",
    "ys = 0.0009 * xs + 0.1719 \n",
    "plt.plot(xs, ys, '--k', linewidth=4, label='Model')\n",
    "\n",
    "plt.xlabel('user_favourites')\n",
    "plt.ylabel('volume')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the report, the p-value is 0.052 and R-squared is 0.012. Both p-value and R-squared suggest that there is no relationship between the variables we chose, and we should retain the null hypothesis. The visulization of the model shows the data points are slightly aligned to the line, but the line is also highly affected by the outliers.We did not find a correlation between the count of tweets that mentioned Elon Musk and the Bitcoin change on a daily basis. What about user favourites and buying volume on a daily basis?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## user favourites vs. volume change% daily\n",
    "ax = data.plot(x='date', y='volume', legend=False, color = 'darkslateblue')\n",
    "ax2 = ax.twinx()\n",
    "data.plot(x='date', y='user_favourites', ax=ax2, legend=False, color=\"lightcoral\")\n",
    "plt.title('bitcoin buying volumn and the number of total user favorites on tweets about Elon Musk')\n",
    "ax.figure.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                 volume   R-squared:                       0.001\n",
      "Model:                            OLS   Adj. R-squared:                 -0.002\n",
      "Method:                 Least Squares   F-statistic:                    0.3119\n",
      "Date:                Tue, 07 Dec 2021   Prob (F-statistic):              0.577\n",
      "Time:                        20:29:30   Log-Likelihood:                -1701.0\n",
      "No. Observations:                 304   AIC:                             3406.\n",
      "Df Residuals:                     302   BIC:                             3413.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "Intercept         112.4008      3.869     29.051      0.000     104.787     120.015\n",
      "user_favourites  -6.17e-07    1.1e-06     -0.558      0.577   -2.79e-06    1.56e-06\n",
      "==============================================================================\n",
      "Omnibus:                      184.225   Durbin-Watson:                   0.839\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1814.366\n",
      "Skew:                           2.337   Prob(JB):                         0.00\n",
      "Kurtosis:                      14.018   Cond. No.                     3.62e+06\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 3.62e+06. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "## generate a model\n",
    "outcome, predictors = patsy.dmatrices('volume ~ user_favourites', data)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit() \n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the orginal data (as before)\n",
    "sns.scatterplot(x='user_favourites', y='volume', alpha=0.3, data=data, s=100)\n",
    "# Generate and plot the model fit line\n",
    "xs = np.arange(data['user_favourites'].min(), data['user_favourites'].max())\n",
    "ys = -6.17e-07 * xs + 112.4008 \n",
    "plt.plot(xs, ys, '--k', linewidth=4, label='Model')\n",
    "\n",
    "plt.xlabel('user_favourites')\n",
    "plt.ylabel('volume')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately, both the OLS Regression model and the visualization of the model indicate that there does not exist a relationship between the daily user favourites count and daily buying volumn. Still, we failed to find a positive correaltion between the user favourites gained by tweets that mentioned Elon Musk and the buying volume of Bitcoin on that particular day. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above graphs and OLS Regression Results, we found out more tweets that mentioned Elon Musk likely do not increase the bitcoin value of that particular day. We also found out that the total of user favorites on tweets about Elon Musk of the day does not impact the buying volumes of that day. Also, on a weekly basis, the above correlations also don't hold true according to linear models. For futhure research, we want to look at the tweets from Elon Musk directly and their impacts on the Bitcoin price change and buying volumes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data being analyzed by our group contains information related to the user's description and location, but when preprocessing the data, we discarded user's name, location and description which are all the columns that could potentially violate user's privacy. As a result, we did not use any user's personal information in our analysis process. Because of its publicity on the internet, there would be no privacy violation as the datasets online are being accessed and downloaded in open-source for the intended purpose of data analysis. Potential issues could be that users and organizations on Twitter could be unaware that their information and tweets content are being collected for data analysis through Twitter's API. Another issue regarding these data involves the primary use of English as opposed to other languages, which increases the likelihood of bias towards English speakers comparatively to other languages. Hence, the results of this project will not be representative or accurate globally."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, we tried to explore potential relationships between tweets that mention Elon Musk and the Bitcoin value changes, and between user favourites gained by those tweets and the Bitcoin buying volume from 2021/1/1 to 2021/10/31. We hypothesized that more tweets that mentioned Elon Musk would increase the Bitcoin value change on both daily and weekly basis, and more user favourites gained by the tweets that mentioned Elon Musk would increase the Bitcoin buying volume on both daily and weekly basis. \n",
    "\n",
    "Two online datasets were recruited for the subsequent analysis. The first dataset contains all bitcoin tweets with #Bitcoin and #btc hashtag. The second dataset contains the Bitcoin value change of the year 2021. We combined the two datasets based on dates and filter out necessary data for the analysis, and generated OLS models for the variables that we are interested in. We first fitted an OLS model using the number of weekly tweets that mentioned Elon Musk to predict the weekly Bitcoin value change, and we gained an R-squared value of 0.010 and a p-value of 0.516, which is greater than 0.05. Another OLS model was fitted using the weekly count of user favourites gained by the tweets to predict the weekly buying volume of Bitcoin, resulting in an R-squared value of 0.026 and p-values of 0.296, which is also greater than 0.0005. We then trained the OLS model using the same variables, but examined them on a daily basis, getting R-squared value and p-value pairs of (0.012,0.052) for the number of tweets mentioned Elon Musk and the Bitcoin value, (0.001, 0.577) for the count of user favourites and the buying volume of Bitcoin. Overall, the reported R-squared values and p-values of all the above models do not support our hypothesis.\n",
    "\n",
    "Based on the analyzations and graphs, from 2021/1/1 to 2021/10/31, more tweets that mentioned Elon Musk do not increase the Bitcoin value and buying volume. This result might be due to the amount of factors that actually contribute to bitcoin prices, and that reveals too much variance. A future improvement that may yield better results would be to look at tweets posted directly by Elon Musk, and perform the same analysis using this dataset.  However, The reason we didn’t do this directly is because there was no involvement of Elon Musk tweets and bitcoin within the 2021 dataset. We would have to manually use some twitter api to collect his tweets from 2020 (we couldn’t find any dataset having his tweets from that year and forward), and use that to analyze the prices of bitcoin of that same year. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Team Contributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Jasmine Chen**: Overview, Data cleaning, EDA & Inferential Analysis, Ethics & Privacy, create video slides.\n",
    "- **Jingwen Liu**: Hypothesis, Data cleaning, EDA & Inferential Analysis, Ethics & Privacy, create video slides.\n",
    "- **Hoang Nguyen**: Overview, Data cleaning, EDA & Inferential Analysis, Ethics & Privacy, Conclusion & Discussion, create video slides, video presentaion.\n",
    "- **Ruoxuan Li**: Overview, Background, Hypothesis, Data cleaning, EDA & Inferential Analysis, Conclusion & Discussion, create video slides."
   ]
  }
 ],
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