STOCK PREDICTION USING TWITTER SENTIMENT ANALYSIS PROJECT (FINAL).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STOCK PREDICTION USING TWITTER SENTIMENT ANALYSIS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### importing machine learning libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\nltk\\twitter\\__init__.py:20: UserWarning: The twython library has not been installed. Some functionality from the twitter package will not be available.\n",
      "  warnings.warn(\"The twython library has not been installed. \"\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from nltk.classify import NaiveBayesClassifier\n",
    "from nltk.corpus import subjectivity\n",
    "from nltk.sentiment import SentimentAnalyzer\n",
    "from nltk.sentiment.util import *\n",
    "import matplotlib.pyplot as mlpt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### importing library to fetch data from twitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tweepy\n",
    "import csv\n",
    "import pandas as pd\n",
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### setting up consumer key and access token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "consumer_key    = '3jmA1BqasLHfItBXj3KnAIGFB'\n",
    "consumer_secret = 'imyEeVTctFZuK62QHmL1I0AUAMudg5HKJDfkx0oR7oFbFinbvA'\n",
    "\n",
    "access_token  = '265857263-pF1DRxgIcxUbxEEFtLwLODPzD3aMl6d4zOKlMnme'\n",
    "access_token_secret = 'uUFoOOGeNJfOYD3atlcmPtaxxniXxQzAU4ESJLopA1lbC'\n",
    "\n",
    "auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n",
    "auth.set_access_token(access_token, access_token_secret)\n",
    "api = tweepy.API(auth,wait_on_rate_limit=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fetching tweets for United Airlines in extended mode (means entire tweet will come and not just few words + link)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "fetch_tweets=tweepy.Cursor(api.search, q=\"#unitedAIRLINES\",count=100, lang =\"en\",since=\"2018-9-13\", tweet_mode=\"extended\").items()\n",
    "data=pd.DataFrame(data=[[tweet_info.created_at.date(),tweet_info.full_text]for tweet_info in fetch_tweets],columns=['Date','Tweets'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Removing special character from each tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  if __name__ == '__main__':\n",
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    }
   ],
   "source": [
    "data.to_csv(\"Tweets.csv\")\n",
    "cdata=pd.DataFrame(columns=['Date','Tweets'])\n",
    "total=100\n",
    "index=0\n",
    "for index,row in data.iterrows():\n",
    "    stre=row[\"Tweets\"]\n",
    "    my_new_string = re.sub('[^ a-zA-Z0-9]', '', stre)\n",
    "    cdata.sort_index()\n",
    "    cdata.set_value(index,'Date',row[\"Date\"])\n",
    "    cdata.set_value(index,'Tweets',my_new_string)\n",
    "    index=index+1\n",
    "#print(cdata.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Displaying the data with date and tweets, you can notice there are multiple tweets for each day. So we will club them together later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Tweets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-11-28</td>\n",
       "      <td>We are already over 2 hours late for departure...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-11-28</td>\n",
       "      <td>Oh there aint no rest for the wicked Taking of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-11-28</td>\n",
       "      <td>765 amp up  United Airlines Flights to Tahiti ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-11-28</td>\n",
       "      <td>RT Wcash108 united two pots of gold on a Unite...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>RT HChan03 My photo of the day My flight to Lo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>My photo of the day My flight to Los Angeles C...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>Learn a thing or two about how incredible huma...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>I feel so disrespected by United airlines empl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>Good Morningunitedairlines GeminiJets  united ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>NotTheBestPicture but it was an AwesomeSurpris...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>Now my rebooked flight is delayed because you ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>Dear united airlines you have some pretty dece...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>unitedairlines needs to up their charging game...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>Ahh My lawnmower with Jet Engines Off again an...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>OC bound unitedairlines united flythefriendlys...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>Another flight to the BayIm starting to feel s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>united it will be warmer in Chicago today than...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>Inc Or UnitedAirlines selling cheap tickets on...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>united I was in the hospital and had to cancel...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>Fisher House  UnitedAirlines  UnitedCharityMi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>Ive never waited so long to leave an airport h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>Stay away from UnitedAirlines and especially t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>KristelCLE united Probably because you are a V...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>Unitedairliness best compensation to a 3 day d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>Making the most of a 3 hour delay unitedairlin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>UnitedAirlines  Thanks again UnitedAirlines yo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>united UA257 10hr delay But not cancelled Ridi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>Please  Dont book your flight with UnitedAirli...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>havenicetuesday opendaygoodmorning relaxing un...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>Hey UnitedAirlines its not my fault that you c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>2018-11-18</td>\n",
       "      <td>Still waiting on my bagThe worst experience Un...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>2018-11-18</td>\n",
       "      <td>2 flights 2 issues never flying Unitedairlines...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>2018-11-17</td>\n",
       "      <td>unitedAIRLINES stop blaming snow in denver The...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>2018-11-17</td>\n",
       "      <td>united unitedAIRLINES  and the saga continues ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>2018-11-17</td>\n",
       "      <td>unitedAIRLINES todays crap in denver is bullshit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>2018-11-17</td>\n",
       "      <td>All unitedairlines flights grounded to Denver ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>2018-11-17</td>\n",
       "      <td>I just did and was told that my bag and those ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>2018-11-17</td>\n",
       "      <td>Another TDY misadventure courtesy of UnitedAir...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>2018-11-17</td>\n",
       "      <td>Our bag arrived At least it got here before we...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>2018-11-17</td>\n",
       "      <td>Homeboy on unitedairlines literally brought a ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>2018-11-17</td>\n",
       "      <td>worst travel experience I had with UnitedAirli...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>2018-11-17</td>\n",
       "      <td>Why does my flight never leave on time or arri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>2018-11-17</td>\n",
       "      <td>Hey unitedwhat is the point of having my known...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>2018-11-17</td>\n",
       "      <td>Calling all travel agents  Win with United Air...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>2018-11-17</td>\n",
       "      <td>Shrinking Delta American and UnitedAirlines ar...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>2018-11-17</td>\n",
       "      <td>On my way home  thank you united unitedairline...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>2018-11-17</td>\n",
       "      <td>Wow unitedAIRLINES Providing the absolute wors...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>2018-11-17</td>\n",
       "      <td>Perhaps we should just open the door and pee i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>2018-11-17</td>\n",
       "      <td>Was Uniteds new Polaris Business Class more hy...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>2018-11-17</td>\n",
       "      <td>UnitedAirlines Just landed on flight 916 AKLSF...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>2018-11-17</td>\n",
       "      <td>Riggssherri JNadar3 Youre welcome to use my tw...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>2018-11-17</td>\n",
       "      <td>breakfast quiche on unitedairlines  fantastic ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>2018-11-17</td>\n",
       "      <td>united Want to find my bags now I sent you con...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>2018-11-17</td>\n",
       "      <td>united Worst flight I have ever taken and I ha...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>2018-11-17</td>\n",
       "      <td>Houston at a glance unitedairlines houston tex...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>2018-11-17</td>\n",
       "      <td>united unitedAIRLINES saga with pilgrims conti...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>2018-11-17</td>\n",
       "      <td>RT JNadar3 Currently having a terrible experie...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>2018-11-17</td>\n",
       "      <td>Cancelled flight reschedule cancelled flight H...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>2018-11-17</td>\n",
       "      <td>Spent the 2nd night in Cleveland due to weathe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>2018-11-17</td>\n",
       "      <td>all ready to go for an amazing wedding weekend...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>499 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date                                             Tweets\n",
       "0    2018-11-28  We are already over 2 hours late for departure...\n",
       "1    2018-11-28  Oh there aint no rest for the wicked Taking of...\n",
       "2    2018-11-28  765 amp up  United Airlines Flights to Tahiti ...\n",
       "3    2018-11-28  RT Wcash108 united two pots of gold on a Unite...\n",
       "4    2018-11-27  RT HChan03 My photo of the day My flight to Lo...\n",
       "5    2018-11-27  My photo of the day My flight to Los Angeles C...\n",
       "6    2018-11-27  Learn a thing or two about how incredible huma...\n",
       "7    2018-11-27  I feel so disrespected by United airlines empl...\n",
       "8    2018-11-27  Good Morningunitedairlines GeminiJets  united ...\n",
       "9    2018-11-27  NotTheBestPicture but it was an AwesomeSurpris...\n",
       "10   2018-11-27  Now my rebooked flight is delayed because you ...\n",
       "11   2018-11-27  Dear united airlines you have some pretty dece...\n",
       "12   2018-11-27  unitedairlines needs to up their charging game...\n",
       "13   2018-11-27  Ahh My lawnmower with Jet Engines Off again an...\n",
       "14   2018-11-27  OC bound unitedairlines united flythefriendlys...\n",
       "15   2018-11-27  Another flight to the BayIm starting to feel s...\n",
       "16   2018-11-27  united it will be warmer in Chicago today than...\n",
       "17   2018-11-27  Inc Or UnitedAirlines selling cheap tickets on...\n",
       "18   2018-11-27  united I was in the hospital and had to cancel...\n",
       "19   2018-11-27   Fisher House  UnitedAirlines  UnitedCharityMi...\n",
       "20   2018-11-27  Ive never waited so long to leave an airport h...\n",
       "21   2018-11-27  Stay away from UnitedAirlines and especially t...\n",
       "22   2018-11-27  KristelCLE united Probably because you are a V...\n",
       "23   2018-11-27  Unitedairliness best compensation to a 3 day d...\n",
       "24   2018-11-27  Making the most of a 3 hour delay unitedairlin...\n",
       "25   2018-11-27  UnitedAirlines  Thanks again UnitedAirlines yo...\n",
       "26   2018-11-27  united UA257 10hr delay But not cancelled Ridi...\n",
       "27   2018-11-27  Please  Dont book your flight with UnitedAirli...\n",
       "28   2018-11-27  havenicetuesday opendaygoodmorning relaxing un...\n",
       "29   2018-11-27  Hey UnitedAirlines its not my fault that you c...\n",
       "..          ...                                                ...\n",
       "469  2018-11-18  Still waiting on my bagThe worst experience Un...\n",
       "470  2018-11-18  2 flights 2 issues never flying Unitedairlines...\n",
       "471  2018-11-17  unitedAIRLINES stop blaming snow in denver The...\n",
       "472  2018-11-17  united unitedAIRLINES  and the saga continues ...\n",
       "473  2018-11-17   unitedAIRLINES todays crap in denver is bullshit\n",
       "474  2018-11-17  All unitedairlines flights grounded to Denver ...\n",
       "475  2018-11-17  I just did and was told that my bag and those ...\n",
       "476  2018-11-17  Another TDY misadventure courtesy of UnitedAir...\n",
       "477  2018-11-17  Our bag arrived At least it got here before we...\n",
       "478  2018-11-17  Homeboy on unitedairlines literally brought a ...\n",
       "479  2018-11-17  worst travel experience I had with UnitedAirli...\n",
       "480  2018-11-17  Why does my flight never leave on time or arri...\n",
       "481  2018-11-17  Hey unitedwhat is the point of having my known...\n",
       "482  2018-11-17  Calling all travel agents  Win with United Air...\n",
       "483  2018-11-17  Shrinking Delta American and UnitedAirlines ar...\n",
       "484  2018-11-17  On my way home  thank you united unitedairline...\n",
       "485  2018-11-17  Wow unitedAIRLINES Providing the absolute wors...\n",
       "486  2018-11-17  Perhaps we should just open the door and pee i...\n",
       "487  2018-11-17  Was Uniteds new Polaris Business Class more hy...\n",
       "488  2018-11-17  UnitedAirlines Just landed on flight 916 AKLSF...\n",
       "489  2018-11-17  Riggssherri JNadar3 Youre welcome to use my tw...\n",
       "490  2018-11-17  breakfast quiche on unitedairlines  fantastic ...\n",
       "491  2018-11-17  united Want to find my bags now I sent you con...\n",
       "492  2018-11-17  united Worst flight I have ever taken and I ha...\n",
       "493  2018-11-17  Houston at a glance unitedairlines houston tex...\n",
       "494  2018-11-17  united unitedAIRLINES saga with pilgrims conti...\n",
       "495  2018-11-17  RT JNadar3 Currently having a terrible experie...\n",
       "496  2018-11-17  Cancelled flight reschedule cancelled flight H...\n",
       "497  2018-11-17  Spent the 2nd night in Cleveland due to weathe...\n",
       "498  2018-11-17  all ready to go for an amazing wedding weekend...\n",
       "\n",
       "[499 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating a dataframe where we will combine the tweets date wise and store into"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ccdata=pd.DataFrame(columns=['Date','Tweets'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  if __name__ == '__main__':\n",
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    }
   ],
   "source": [
    "indx=0\n",
    "get_tweet=\"\"\n",
    "for i in range(0,len(cdata)-1):\n",
    "    get_date=cdata.Date.iloc[i]\n",
    "    next_date=cdata.Date.iloc[i+1]\n",
    "    if(str(get_date)==str(next_date)):\n",
    "        get_tweet=get_tweet+cdata.Tweets.iloc[i]+\" \"\n",
    "    if(str(get_date)!=str(next_date)):\n",
    "        ccdata.set_value(indx,'Date',get_date)\n",
    "        ccdata.set_value(indx,'Tweets',get_tweet)\n",
    "        indx=indx+1\n",
    "        get_tweet=\" \""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### All the tweets has been clubbed as per their date."
   ]
  },
  {
   "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>Tweets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-11-28</td>\n",
       "      <td>We are already over 2 hours late for departure...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>RT HChan03 My photo of the day My flight to L...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-11-26</td>\n",
       "      <td>unitedairlines Stuck on UA2200 at gate lettin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-11-25</td>\n",
       "      <td>Fullservice flights to New York from 926 retu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-11-24</td>\n",
       "      <td>decades and I am hoping to continue that rela...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-11-23</td>\n",
       "      <td>RT AngeliqueK Is anyone satisfied with flying...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018-11-22</td>\n",
       "      <td>RT UnitedFlyerHD Beautiful view of Chicago at...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018-11-21</td>\n",
       "      <td>Instead of Turkey I am eating pasta for thank...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-11-20</td>\n",
       "      <td>united 150 for unaccompanied minor service yo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018-11-19</td>\n",
       "      <td>united Thank you for damaging and taking my n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018-11-18</td>\n",
       "      <td>unitedairlines operations team very inconside...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date                                             Tweets\n",
       "0   2018-11-28  We are already over 2 hours late for departure...\n",
       "1   2018-11-27   RT HChan03 My photo of the day My flight to L...\n",
       "2   2018-11-26   unitedairlines Stuck on UA2200 at gate lettin...\n",
       "3   2018-11-25   Fullservice flights to New York from 926 retu...\n",
       "4   2018-11-24   decades and I am hoping to continue that rela...\n",
       "5   2018-11-23   RT AngeliqueK Is anyone satisfied with flying...\n",
       "6   2018-11-22   RT UnitedFlyerHD Beautiful view of Chicago at...\n",
       "7   2018-11-21   Instead of Turkey I am eating pasta for thank...\n",
       "8   2018-11-20   united 150 for unaccompanied minor service yo...\n",
       "9   2018-11-19   united Thank you for damaging and taking my n...\n",
       "10  2018-11-18   unitedairlines operations team very inconside..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccdata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now to know the \"closing price\" of each day we will import STOCK PRICE DATA for UNITED AIRLINES from \"yahoo.finance\". We will consider \"Close\" price only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-11-15</td>\n",
       "      <td>56.990002</td>\n",
       "      <td>57.689999</td>\n",
       "      <td>56.509998</td>\n",
       "      <td>57.490002</td>\n",
       "      <td>57.490002</td>\n",
       "      <td>4940200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-11-16</td>\n",
       "      <td>58.070000</td>\n",
       "      <td>58.759998</td>\n",
       "      <td>57.590000</td>\n",
       "      <td>58.680000</td>\n",
       "      <td>58.680000</td>\n",
       "      <td>3133300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-11-17</td>\n",
       "      <td>58.410000</td>\n",
       "      <td>58.590000</td>\n",
       "      <td>57.799999</td>\n",
       "      <td>58.200001</td>\n",
       "      <td>58.200001</td>\n",
       "      <td>4260100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-11-20</td>\n",
       "      <td>58.259998</td>\n",
       "      <td>58.830002</td>\n",
       "      <td>57.820000</td>\n",
       "      <td>58.759998</td>\n",
       "      <td>58.759998</td>\n",
       "      <td>3108400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-11-21</td>\n",
       "      <td>58.860001</td>\n",
       "      <td>59.770000</td>\n",
       "      <td>58.779999</td>\n",
       "      <td>59.709999</td>\n",
       "      <td>59.709999</td>\n",
       "      <td>2871200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017-11-22</td>\n",
       "      <td>59.389999</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>59.270000</td>\n",
       "      <td>59.689999</td>\n",
       "      <td>59.689999</td>\n",
       "      <td>1804600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017-11-24</td>\n",
       "      <td>59.619999</td>\n",
       "      <td>59.750000</td>\n",
       "      <td>59.250000</td>\n",
       "      <td>59.430000</td>\n",
       "      <td>59.430000</td>\n",
       "      <td>1196700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017-11-27</td>\n",
       "      <td>59.439999</td>\n",
       "      <td>59.540001</td>\n",
       "      <td>58.930000</td>\n",
       "      <td>59.090000</td>\n",
       "      <td>59.090000</td>\n",
       "      <td>2896300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017-11-28</td>\n",
       "      <td>59.160000</td>\n",
       "      <td>60.380001</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>60.189999</td>\n",
       "      <td>60.189999</td>\n",
       "      <td>4149400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>60.599998</td>\n",
       "      <td>62.290001</td>\n",
       "      <td>60.389999</td>\n",
       "      <td>62.090000</td>\n",
       "      <td>62.090000</td>\n",
       "      <td>6093300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2017-11-30</td>\n",
       "      <td>62.250000</td>\n",
       "      <td>63.840000</td>\n",
       "      <td>62.080002</td>\n",
       "      <td>63.320000</td>\n",
       "      <td>63.320000</td>\n",
       "      <td>4993000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>63.200001</td>\n",
       "      <td>63.490002</td>\n",
       "      <td>61.840000</td>\n",
       "      <td>62.570000</td>\n",
       "      <td>62.570000</td>\n",
       "      <td>4461900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2017-12-04</td>\n",
       "      <td>63.689999</td>\n",
       "      <td>64.330002</td>\n",
       "      <td>62.549999</td>\n",
       "      <td>62.590000</td>\n",
       "      <td>62.590000</td>\n",
       "      <td>5054300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2017-12-05</td>\n",
       "      <td>62.590000</td>\n",
       "      <td>62.720001</td>\n",
       "      <td>61.840000</td>\n",
       "      <td>62.200001</td>\n",
       "      <td>62.200001</td>\n",
       "      <td>2635100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2017-12-06</td>\n",
       "      <td>62.160000</td>\n",
       "      <td>62.599998</td>\n",
       "      <td>61.779999</td>\n",
       "      <td>62.189999</td>\n",
       "      <td>62.189999</td>\n",
       "      <td>3078600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2017-12-07</td>\n",
       "      <td>62.279999</td>\n",
       "      <td>63.779999</td>\n",
       "      <td>61.840000</td>\n",
       "      <td>63.529999</td>\n",
       "      <td>63.529999</td>\n",
       "      <td>3790500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2017-12-08</td>\n",
       "      <td>65.230003</td>\n",
       "      <td>65.290001</td>\n",
       "      <td>63.380001</td>\n",
       "      <td>63.549999</td>\n",
       "      <td>63.549999</td>\n",
       "      <td>5162000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017-12-11</td>\n",
       "      <td>63.689999</td>\n",
       "      <td>64.690002</td>\n",
       "      <td>63.480000</td>\n",
       "      <td>64.239998</td>\n",
       "      <td>64.239998</td>\n",
       "      <td>4067600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2017-12-12</td>\n",
       "      <td>64.449997</td>\n",
       "      <td>64.519997</td>\n",
       "      <td>63.360001</td>\n",
       "      <td>63.360001</td>\n",
       "      <td>63.360001</td>\n",
       "      <td>2827900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2017-12-13</td>\n",
       "      <td>63.520000</td>\n",
       "      <td>63.740002</td>\n",
       "      <td>62.450001</td>\n",
       "      <td>63.029999</td>\n",
       "      <td>63.029999</td>\n",
       "      <td>3727300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2017-12-14</td>\n",
       "      <td>63.500000</td>\n",
       "      <td>64.040001</td>\n",
       "      <td>62.709999</td>\n",
       "      <td>63.130001</td>\n",
       "      <td>63.130001</td>\n",
       "      <td>5827700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2017-12-15</td>\n",
       "      <td>63.619999</td>\n",
       "      <td>64.470001</td>\n",
       "      <td>63.259998</td>\n",
       "      <td>63.759998</td>\n",
       "      <td>63.759998</td>\n",
       "      <td>6744300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2017-12-18</td>\n",
       "      <td>64.120003</td>\n",
       "      <td>64.760002</td>\n",
       "      <td>63.709999</td>\n",
       "      <td>64.559998</td>\n",
       "      <td>64.559998</td>\n",
       "      <td>3710100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2017-12-19</td>\n",
       "      <td>64.830002</td>\n",
       "      <td>64.989998</td>\n",
       "      <td>64.330002</td>\n",
       "      <td>64.430000</td>\n",
       "      <td>64.430000</td>\n",
       "      <td>3339400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2017-12-20</td>\n",
       "      <td>65.330002</td>\n",
       "      <td>66.269997</td>\n",
       "      <td>65.040001</td>\n",
       "      <td>65.900002</td>\n",
       "      <td>65.900002</td>\n",
       "      <td>4566600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2017-12-21</td>\n",
       "      <td>66.169998</td>\n",
       "      <td>67.629997</td>\n",
       "      <td>66.150002</td>\n",
       "      <td>67.279999</td>\n",
       "      <td>67.279999</td>\n",
       "      <td>4555000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2017-12-22</td>\n",
       "      <td>67.440002</td>\n",
       "      <td>67.480003</td>\n",
       "      <td>67.110001</td>\n",
       "      <td>67.250000</td>\n",
       "      <td>67.250000</td>\n",
       "      <td>2154600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2017-12-26</td>\n",
       "      <td>66.940002</td>\n",
       "      <td>67.720001</td>\n",
       "      <td>66.480003</td>\n",
       "      <td>67.349998</td>\n",
       "      <td>67.349998</td>\n",
       "      <td>2347300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2017-12-27</td>\n",
       "      <td>67.620003</td>\n",
       "      <td>67.620003</td>\n",
       "      <td>66.610001</td>\n",
       "      <td>67.120003</td>\n",
       "      <td>67.120003</td>\n",
       "      <td>2939900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2017-12-28</td>\n",
       "      <td>67.120003</td>\n",
       "      <td>67.570000</td>\n",
       "      <td>66.879997</td>\n",
       "      <td>67.540001</td>\n",
       "      <td>67.540001</td>\n",
       "      <td>2454400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>2018-10-15</td>\n",
       "      <td>80.400002</td>\n",
       "      <td>81.949997</td>\n",
       "      <td>80.099998</td>\n",
       "      <td>81.339996</td>\n",
       "      <td>81.339996</td>\n",
       "      <td>3512600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>2018-10-16</td>\n",
       "      <td>81.599998</td>\n",
       "      <td>84.209999</td>\n",
       "      <td>81.230003</td>\n",
       "      <td>83.519997</td>\n",
       "      <td>83.519997</td>\n",
       "      <td>4965800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>2018-10-17</td>\n",
       "      <td>88.019997</td>\n",
       "      <td>89.480003</td>\n",
       "      <td>86.160004</td>\n",
       "      <td>88.489998</td>\n",
       "      <td>88.489998</td>\n",
       "      <td>10329500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>2018-10-18</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>89.610001</td>\n",
       "      <td>86.519997</td>\n",
       "      <td>87.089996</td>\n",
       "      <td>87.089996</td>\n",
       "      <td>4979800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>2018-10-19</td>\n",
       "      <td>87.260002</td>\n",
       "      <td>88.830002</td>\n",
       "      <td>87.019997</td>\n",
       "      <td>87.430000</td>\n",
       "      <td>87.430000</td>\n",
       "      <td>4582700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>2018-10-22</td>\n",
       "      <td>87.839996</td>\n",
       "      <td>89.230003</td>\n",
       "      <td>87.339996</td>\n",
       "      <td>88.610001</td>\n",
       "      <td>88.610001</td>\n",
       "      <td>3820100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>2018-10-23</td>\n",
       "      <td>87.019997</td>\n",
       "      <td>87.610001</td>\n",
       "      <td>84.440002</td>\n",
       "      <td>86.330002</td>\n",
       "      <td>86.330002</td>\n",
       "      <td>5809000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>2018-10-24</td>\n",
       "      <td>86.070000</td>\n",
       "      <td>86.709999</td>\n",
       "      <td>81.919998</td>\n",
       "      <td>82.190002</td>\n",
       "      <td>82.190002</td>\n",
       "      <td>5220800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>2018-10-25</td>\n",
       "      <td>82.690002</td>\n",
       "      <td>86.269997</td>\n",
       "      <td>82.480003</td>\n",
       "      <td>85.599998</td>\n",
       "      <td>85.599998</td>\n",
       "      <td>4328600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>2018-10-26</td>\n",
       "      <td>84.099998</td>\n",
       "      <td>85.580002</td>\n",
       "      <td>82.839996</td>\n",
       "      <td>84.629997</td>\n",
       "      <td>84.629997</td>\n",
       "      <td>3364300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>2018-10-29</td>\n",
       "      <td>85.980003</td>\n",
       "      <td>86.949997</td>\n",
       "      <td>83.919998</td>\n",
       "      <td>84.989998</td>\n",
       "      <td>84.989998</td>\n",
       "      <td>3902000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>2018-10-30</td>\n",
       "      <td>84.379997</td>\n",
       "      <td>86.250000</td>\n",
       "      <td>84.379997</td>\n",
       "      <td>85.849998</td>\n",
       "      <td>85.849998</td>\n",
       "      <td>4363200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>2018-10-31</td>\n",
       "      <td>87.250000</td>\n",
       "      <td>87.699997</td>\n",
       "      <td>85.370003</td>\n",
       "      <td>85.510002</td>\n",
       "      <td>85.510002</td>\n",
       "      <td>3796500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>2018-11-01</td>\n",
       "      <td>85.620003</td>\n",
       "      <td>88.470001</td>\n",
       "      <td>85.620003</td>\n",
       "      <td>88.040001</td>\n",
       "      <td>88.040001</td>\n",
       "      <td>3398900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>2018-11-02</td>\n",
       "      <td>88.470001</td>\n",
       "      <td>90.279999</td>\n",
       "      <td>88.010002</td>\n",
       "      <td>88.680000</td>\n",
       "      <td>88.680000</td>\n",
       "      <td>3335900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>2018-11-05</td>\n",
       "      <td>88.980003</td>\n",
       "      <td>89.820000</td>\n",
       "      <td>88.279999</td>\n",
       "      <td>89.470001</td>\n",
       "      <td>89.470001</td>\n",
       "      <td>2735500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>2018-11-06</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.250000</td>\n",
       "      <td>88.830002</td>\n",
       "      <td>89.639999</td>\n",
       "      <td>89.639999</td>\n",
       "      <td>2187400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>2018-11-07</td>\n",
       "      <td>90.220001</td>\n",
       "      <td>92.330002</td>\n",
       "      <td>89.650002</td>\n",
       "      <td>92.160004</td>\n",
       "      <td>92.160004</td>\n",
       "      <td>3792200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2018-11-08</td>\n",
       "      <td>92.089996</td>\n",
       "      <td>92.849998</td>\n",
       "      <td>91.529999</td>\n",
       "      <td>92.559998</td>\n",
       "      <td>92.559998</td>\n",
       "      <td>2460300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>2018-11-09</td>\n",
       "      <td>92.410004</td>\n",
       "      <td>93.599998</td>\n",
       "      <td>91.739998</td>\n",
       "      <td>92.349998</td>\n",
       "      <td>92.349998</td>\n",
       "      <td>4254100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>2018-11-12</td>\n",
       "      <td>92.360001</td>\n",
       "      <td>92.690002</td>\n",
       "      <td>91.029999</td>\n",
       "      <td>91.199997</td>\n",
       "      <td>91.199997</td>\n",
       "      <td>2903200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>2018-11-13</td>\n",
       "      <td>91.629997</td>\n",
       "      <td>93.379997</td>\n",
       "      <td>91.629997</td>\n",
       "      <td>92.599998</td>\n",
       "      <td>92.599998</td>\n",
       "      <td>3696600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>2018-11-14</td>\n",
       "      <td>93.029999</td>\n",
       "      <td>93.470001</td>\n",
       "      <td>91.379997</td>\n",
       "      <td>91.790001</td>\n",
       "      <td>91.790001</td>\n",
       "      <td>2387000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>2018-11-15</td>\n",
       "      <td>91.400002</td>\n",
       "      <td>92.660004</td>\n",
       "      <td>89.019997</td>\n",
       "      <td>91.980003</td>\n",
       "      <td>91.980003</td>\n",
       "      <td>3691500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>2018-11-16</td>\n",
       "      <td>91.839996</td>\n",
       "      <td>92.599998</td>\n",
       "      <td>90.070000</td>\n",
       "      <td>92.260002</td>\n",
       "      <td>92.260002</td>\n",
       "      <td>2837900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>2018-11-19</td>\n",
       "      <td>92.320000</td>\n",
       "      <td>92.949997</td>\n",
       "      <td>91.160004</td>\n",
       "      <td>92.089996</td>\n",
       "      <td>92.089996</td>\n",
       "      <td>2684600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>2018-11-20</td>\n",
       "      <td>91.199997</td>\n",
       "      <td>92.790001</td>\n",
       "      <td>90.449997</td>\n",
       "      <td>91.769997</td>\n",
       "      <td>91.769997</td>\n",
       "      <td>3232500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>2018-11-21</td>\n",
       "      <td>91.919998</td>\n",
       "      <td>93.379997</td>\n",
       "      <td>91.820000</td>\n",
       "      <td>92.860001</td>\n",
       "      <td>92.860001</td>\n",
       "      <td>3771600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>2018-11-23</td>\n",
       "      <td>93.269997</td>\n",
       "      <td>95.080002</td>\n",
       "      <td>92.790001</td>\n",
       "      <td>94.839996</td>\n",
       "      <td>94.839996</td>\n",
       "      <td>2688700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>2018-11-26</td>\n",
       "      <td>95.559998</td>\n",
       "      <td>95.809998</td>\n",
       "      <td>91.730003</td>\n",
       "      <td>91.750000</td>\n",
       "      <td>91.750000</td>\n",
       "      <td>4145900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>259 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date       Open       High        Low      Close  Adj Close  \\\n",
       "0    2017-11-15  56.990002  57.689999  56.509998  57.490002  57.490002   \n",
       "1    2017-11-16  58.070000  58.759998  57.590000  58.680000  58.680000   \n",
       "2    2017-11-17  58.410000  58.590000  57.799999  58.200001  58.200001   \n",
       "3    2017-11-20  58.259998  58.830002  57.820000  58.759998  58.759998   \n",
       "4    2017-11-21  58.860001  59.770000  58.779999  59.709999  59.709999   \n",
       "5    2017-11-22  59.389999  60.000000  59.270000  59.689999  59.689999   \n",
       "6    2017-11-24  59.619999  59.750000  59.250000  59.430000  59.430000   \n",
       "7    2017-11-27  59.439999  59.540001  58.930000  59.090000  59.090000   \n",
       "8    2017-11-28  59.160000  60.380001  59.000000  60.189999  60.189999   \n",
       "9    2017-11-29  60.599998  62.290001  60.389999  62.090000  62.090000   \n",
       "10   2017-11-30  62.250000  63.840000  62.080002  63.320000  63.320000   \n",
       "11   2017-12-01  63.200001  63.490002  61.840000  62.570000  62.570000   \n",
       "12   2017-12-04  63.689999  64.330002  62.549999  62.590000  62.590000   \n",
       "13   2017-12-05  62.590000  62.720001  61.840000  62.200001  62.200001   \n",
       "14   2017-12-06  62.160000  62.599998  61.779999  62.189999  62.189999   \n",
       "15   2017-12-07  62.279999  63.779999  61.840000  63.529999  63.529999   \n",
       "16   2017-12-08  65.230003  65.290001  63.380001  63.549999  63.549999   \n",
       "17   2017-12-11  63.689999  64.690002  63.480000  64.239998  64.239998   \n",
       "18   2017-12-12  64.449997  64.519997  63.360001  63.360001  63.360001   \n",
       "19   2017-12-13  63.520000  63.740002  62.450001  63.029999  63.029999   \n",
       "20   2017-12-14  63.500000  64.040001  62.709999  63.130001  63.130001   \n",
       "21   2017-12-15  63.619999  64.470001  63.259998  63.759998  63.759998   \n",
       "22   2017-12-18  64.120003  64.760002  63.709999  64.559998  64.559998   \n",
       "23   2017-12-19  64.830002  64.989998  64.330002  64.430000  64.430000   \n",
       "24   2017-12-20  65.330002  66.269997  65.040001  65.900002  65.900002   \n",
       "25   2017-12-21  66.169998  67.629997  66.150002  67.279999  67.279999   \n",
       "26   2017-12-22  67.440002  67.480003  67.110001  67.250000  67.250000   \n",
       "27   2017-12-26  66.940002  67.720001  66.480003  67.349998  67.349998   \n",
       "28   2017-12-27  67.620003  67.620003  66.610001  67.120003  67.120003   \n",
       "29   2017-12-28  67.120003  67.570000  66.879997  67.540001  67.540001   \n",
       "..          ...        ...        ...        ...        ...        ...   \n",
       "229  2018-10-15  80.400002  81.949997  80.099998  81.339996  81.339996   \n",
       "230  2018-10-16  81.599998  84.209999  81.230003  83.519997  83.519997   \n",
       "231  2018-10-17  88.019997  89.480003  86.160004  88.489998  88.489998   \n",
       "232  2018-10-18  89.000000  89.610001  86.519997  87.089996  87.089996   \n",
       "233  2018-10-19  87.260002  88.830002  87.019997  87.430000  87.430000   \n",
       "234  2018-10-22  87.839996  89.230003  87.339996  88.610001  88.610001   \n",
       "235  2018-10-23  87.019997  87.610001  84.440002  86.330002  86.330002   \n",
       "236  2018-10-24  86.070000  86.709999  81.919998  82.190002  82.190002   \n",
       "237  2018-10-25  82.690002  86.269997  82.480003  85.599998  85.599998   \n",
       "238  2018-10-26  84.099998  85.580002  82.839996  84.629997  84.629997   \n",
       "239  2018-10-29  85.980003  86.949997  83.919998  84.989998  84.989998   \n",
       "240  2018-10-30  84.379997  86.250000  84.379997  85.849998  85.849998   \n",
       "241  2018-10-31  87.250000  87.699997  85.370003  85.510002  85.510002   \n",
       "242  2018-11-01  85.620003  88.470001  85.620003  88.040001  88.040001   \n",
       "243  2018-11-02  88.470001  90.279999  88.010002  88.680000  88.680000   \n",
       "244  2018-11-05  88.980003  89.820000  88.279999  89.470001  89.470001   \n",
       "245  2018-11-06  90.000000  90.250000  88.830002  89.639999  89.639999   \n",
       "246  2018-11-07  90.220001  92.330002  89.650002  92.160004  92.160004   \n",
       "247  2018-11-08  92.089996  92.849998  91.529999  92.559998  92.559998   \n",
       "248  2018-11-09  92.410004  93.599998  91.739998  92.349998  92.349998   \n",
       "249  2018-11-12  92.360001  92.690002  91.029999  91.199997  91.199997   \n",
       "250  2018-11-13  91.629997  93.379997  91.629997  92.599998  92.599998   \n",
       "251  2018-11-14  93.029999  93.470001  91.379997  91.790001  91.790001   \n",
       "252  2018-11-15  91.400002  92.660004  89.019997  91.980003  91.980003   \n",
       "253  2018-11-16  91.839996  92.599998  90.070000  92.260002  92.260002   \n",
       "254  2018-11-19  92.320000  92.949997  91.160004  92.089996  92.089996   \n",
       "255  2018-11-20  91.199997  92.790001  90.449997  91.769997  91.769997   \n",
       "256  2018-11-21  91.919998  93.379997  91.820000  92.860001  92.860001   \n",
       "257  2018-11-23  93.269997  95.080002  92.790001  94.839996  94.839996   \n",
       "258  2018-11-26  95.559998  95.809998  91.730003  91.750000  91.750000   \n",
       "\n",
       "       Volume  \n",
       "0     4940200  \n",
       "1     3133300  \n",
       "2     4260100  \n",
       "3     3108400  \n",
       "4     2871200  \n",
       "5     1804600  \n",
       "6     1196700  \n",
       "7     2896300  \n",
       "8     4149400  \n",
       "9     6093300  \n",
       "10    4993000  \n",
       "11    4461900  \n",
       "12    5054300  \n",
       "13    2635100  \n",
       "14    3078600  \n",
       "15    3790500  \n",
       "16    5162000  \n",
       "17    4067600  \n",
       "18    2827900  \n",
       "19    3727300  \n",
       "20    5827700  \n",
       "21    6744300  \n",
       "22    3710100  \n",
       "23    3339400  \n",
       "24    4566600  \n",
       "25    4555000  \n",
       "26    2154600  \n",
       "27    2347300  \n",
       "28    2939900  \n",
       "29    2454400  \n",
       "..        ...  \n",
       "229   3512600  \n",
       "230   4965800  \n",
       "231  10329500  \n",
       "232   4979800  \n",
       "233   4582700  \n",
       "234   3820100  \n",
       "235   5809000  \n",
       "236   5220800  \n",
       "237   4328600  \n",
       "238   3364300  \n",
       "239   3902000  \n",
       "240   4363200  \n",
       "241   3796500  \n",
       "242   3398900  \n",
       "243   3335900  \n",
       "244   2735500  \n",
       "245   2187400  \n",
       "246   3792200  \n",
       "247   2460300  \n",
       "248   4254100  \n",
       "249   2903200  \n",
       "250   3696600  \n",
       "251   2387000  \n",
       "252   3691500  \n",
       "253   2837900  \n",
       "254   2684600  \n",
       "255   3232500  \n",
       "256   3771600  \n",
       "257   2688700  \n",
       "258   4145900  \n",
       "\n",
       "[259 rows x 7 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read_stock_p=pd.read_csv('UAL.csv')\n",
    "read_stock_p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adding a \"Price\" column in our dataframe and fetching the stock price as per the date in our dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ccdata['Prices']=\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "indx=0\n",
    "for i in range (0,len(ccdata)):\n",
    "    for j in range (0,len(read_stock_p)):\n",
    "        get_tweet_date=ccdata.Date.iloc[i]\n",
    "        get_stock_date=read_stock_p.Date.iloc[j]\n",
    "        if(str(get_stock_date)==str(get_tweet_date)):\n",
    "            #print(get_stock_date,\" \",get_tweet_date)\n",
    "            ccdata.set_value(i,'Prices',int(read_stock_p.Close[j]))\n",
    "            break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prices are fetched but some entires are blank as close price might not be available for that day due to some reason (like holiday, etc.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "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>Tweets</th>\n",
       "      <th>Prices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-11-28</td>\n",
       "      <td>We are already over 2 hours late for departure...</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>RT HChan03 My photo of the day My flight to L...</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-11-26</td>\n",
       "      <td>unitedairlines Stuck on UA2200 at gate lettin...</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-11-25</td>\n",
       "      <td>Fullservice flights to New York from 926 retu...</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-11-24</td>\n",
       "      <td>decades and I am hoping to continue that rela...</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-11-23</td>\n",
       "      <td>RT AngeliqueK Is anyone satisfied with flying...</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018-11-22</td>\n",
       "      <td>RT UnitedFlyerHD Beautiful view of Chicago at...</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018-11-21</td>\n",
       "      <td>Instead of Turkey I am eating pasta for thank...</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-11-20</td>\n",
       "      <td>united 150 for unaccompanied minor service yo...</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018-11-19</td>\n",
       "      <td>united Thank you for damaging and taking my n...</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018-11-18</td>\n",
       "      <td>unitedairlines operations team very inconside...</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date                                             Tweets Prices\n",
       "0   2018-11-28  We are already over 2 hours late for departure...       \n",
       "1   2018-11-27   RT HChan03 My photo of the day My flight to L...       \n",
       "2   2018-11-26   unitedairlines Stuck on UA2200 at gate lettin...     91\n",
       "3   2018-11-25   Fullservice flights to New York from 926 retu...       \n",
       "4   2018-11-24   decades and I am hoping to continue that rela...       \n",
       "5   2018-11-23   RT AngeliqueK Is anyone satisfied with flying...     94\n",
       "6   2018-11-22   RT UnitedFlyerHD Beautiful view of Chicago at...       \n",
       "7   2018-11-21   Instead of Turkey I am eating pasta for thank...     92\n",
       "8   2018-11-20   united 150 for unaccompanied minor service yo...     91\n",
       "9   2018-11-19   united Thank you for damaging and taking my n...     92\n",
       "10  2018-11-18   unitedairlines operations team very inconside...       "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccdata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### So we take the mean for the close price and put it in the blank value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean=0\n",
    "summ=0\n",
    "count=0\n",
    "for i in range(0,len(ccdata)):\n",
    "    if(ccdata.Prices.iloc[i]!=\"\"):\n",
    "        summ=summ+int(ccdata.Prices.iloc[i])\n",
    "        count=count+1\n",
    "mean=summ/count\n",
    "for i in range(0,len(ccdata)):\n",
    "    if(ccdata.Prices.iloc[i]==\"\"):\n",
    "        ccdata.Prices.iloc[i]=int(mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now all the entries have some value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Tweets</th>\n",
       "      <th>Prices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-11-28</td>\n",
       "      <td>We are already over 2 hours late for departure...</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>RT HChan03 My photo of the day My flight to L...</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-11-26</td>\n",
       "      <td>unitedairlines Stuck on UA2200 at gate lettin...</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-11-25</td>\n",
       "      <td>Fullservice flights to New York from 926 retu...</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-11-24</td>\n",
       "      <td>decades and I am hoping to continue that rela...</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-11-23</td>\n",
       "      <td>RT AngeliqueK Is anyone satisfied with flying...</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018-11-22</td>\n",
       "      <td>RT UnitedFlyerHD Beautiful view of Chicago at...</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018-11-21</td>\n",
       "      <td>Instead of Turkey I am eating pasta for thank...</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-11-20</td>\n",
       "      <td>united 150 for unaccompanied minor service yo...</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018-11-19</td>\n",
       "      <td>united Thank you for damaging and taking my n...</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018-11-18</td>\n",
       "      <td>unitedairlines operations team very inconside...</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date                                             Tweets  Prices\n",
       "0   2018-11-28  We are already over 2 hours late for departure...      92\n",
       "1   2018-11-27   RT HChan03 My photo of the day My flight to L...      92\n",
       "2   2018-11-26   unitedairlines Stuck on UA2200 at gate lettin...      91\n",
       "3   2018-11-25   Fullservice flights to New York from 926 retu...      92\n",
       "4   2018-11-24   decades and I am hoping to continue that rela...      92\n",
       "5   2018-11-23   RT AngeliqueK Is anyone satisfied with flying...      94\n",
       "6   2018-11-22   RT UnitedFlyerHD Beautiful view of Chicago at...      92\n",
       "7   2018-11-21   Instead of Turkey I am eating pasta for thank...      92\n",
       "8   2018-11-20   united 150 for unaccompanied minor service yo...      91\n",
       "9   2018-11-19   united Thank you for damaging and taking my n...      92\n",
       "10  2018-11-18   unitedairlines operations team very inconside...      92"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccdata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Making \"prices\" column as integer so mathematical operations could be performed easily."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "ccdata['Prices'] = ccdata['Prices'].apply(np.int64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adding 4 new columns in our dataframe so that sentiment analysis could be performed.. Comp is \"Compound\" it will tell whether the statement is overall negative or positive. If it has negative value then it is negative, if it has positive value then it is positive. If it has value 0, then it is neutral."
   ]
  },
  {
   "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",
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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>Tweets</th>\n",
       "      <th>Prices</th>\n",
       "      <th>Comp</th>\n",
       "      <th>Negative</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Positive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-11-28</td>\n",
       "      <td>We are already over 2 hours late for departure...</td>\n",
       "      <td>92</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>RT HChan03 My photo of the day My flight to L...</td>\n",
       "      <td>92</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-11-26</td>\n",
       "      <td>unitedairlines Stuck on UA2200 at gate lettin...</td>\n",
       "      <td>91</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-11-25</td>\n",
       "      <td>Fullservice flights to New York from 926 retu...</td>\n",
       "      <td>92</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-11-24</td>\n",
       "      <td>decades and I am hoping to continue that rela...</td>\n",
       "      <td>92</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-11-23</td>\n",
       "      <td>RT AngeliqueK Is anyone satisfied with flying...</td>\n",
       "      <td>94</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018-11-22</td>\n",
       "      <td>RT UnitedFlyerHD Beautiful view of Chicago at...</td>\n",
       "      <td>92</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018-11-21</td>\n",
       "      <td>Instead of Turkey I am eating pasta for thank...</td>\n",
       "      <td>92</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-11-20</td>\n",
       "      <td>united 150 for unaccompanied minor service yo...</td>\n",
       "      <td>91</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018-11-19</td>\n",
       "      <td>united Thank you for damaging and taking my n...</td>\n",
       "      <td>92</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018-11-18</td>\n",
       "      <td>unitedairlines operations team very inconside...</td>\n",
       "      <td>92</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date                                             Tweets  Prices  \\\n",
       "0   2018-11-28  We are already over 2 hours late for departure...      92   \n",
       "1   2018-11-27   RT HChan03 My photo of the day My flight to L...      92   \n",
       "2   2018-11-26   unitedairlines Stuck on UA2200 at gate lettin...      91   \n",
       "3   2018-11-25   Fullservice flights to New York from 926 retu...      92   \n",
       "4   2018-11-24   decades and I am hoping to continue that rela...      92   \n",
       "5   2018-11-23   RT AngeliqueK Is anyone satisfied with flying...      94   \n",
       "6   2018-11-22   RT UnitedFlyerHD Beautiful view of Chicago at...      92   \n",
       "7   2018-11-21   Instead of Turkey I am eating pasta for thank...      92   \n",
       "8   2018-11-20   united 150 for unaccompanied minor service yo...      91   \n",
       "9   2018-11-19   united Thank you for damaging and taking my n...      92   \n",
       "10  2018-11-18   unitedairlines operations team very inconside...      92   \n",
       "\n",
       "   Comp Negative Neutral Positive  \n",
       "0                                  \n",
       "1                                  \n",
       "2                                  \n",
       "3                                  \n",
       "4                                  \n",
       "5                                  \n",
       "6                                  \n",
       "7                                  \n",
       "8                                  \n",
       "9                                  \n",
       "10                                 "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccdata[\"Comp\"] = ''\n",
    "ccdata[\"Negative\"] = ''\n",
    "ccdata[\"Neutral\"] = ''\n",
    "ccdata[\"Positive\"] = ''\n",
    "ccdata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Downloading this package was essential to perform sentiment analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     C:\\Users\\anubh\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('vader_lexicon')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This part of the code is responsible for assigning the polarity for each statement. That is how much positive, negative, neutral you statement is. And also assign the compound value that is overall sentiment of the statement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  if __name__ == '__main__':\n",
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:11: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:12: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  if sys.path[0] == '':\n"
     ]
    }
   ],
   "source": [
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "import unicodedata\n",
    "sentiment_i_a = SentimentIntensityAnalyzer()\n",
    "for indexx, row in ccdata.T.iteritems():\n",
    "    try:\n",
    "        sentence_i = unicodedata.normalize('NFKD', ccdata.loc[indexx, 'Tweets'])\n",
    "        sentence_sentiment = sentiment_i_a.polarity_scores(sentence_i)\n",
    "        ccdata.set_value(indexx, 'Comp', sentence_sentiment['compound'])\n",
    "        ccdata.set_value(indexx, 'Negative', sentence_sentiment['neg'])\n",
    "        ccdata.set_value(indexx, 'Neutral', sentence_sentiment['neu'])\n",
    "        ccdata.set_value(indexx, 'Positive', sentence_sentiment['pos'])\n",
    "    except TypeError:\n",
    "        print (stocks_dataf.loc[indexx, 'Tweets'])\n",
    "        print (indexx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Tweets</th>\n",
       "      <th>Prices</th>\n",
       "      <th>Comp</th>\n",
       "      <th>Negative</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Positive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-11-28</td>\n",
       "      <td>We are already over 2 hours late for departure...</td>\n",
       "      <td>92</td>\n",
       "      <td>0.6234</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>RT HChan03 My photo of the day My flight to L...</td>\n",
       "      <td>92</td>\n",
       "      <td>0.9983</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.736</td>\n",
       "      <td>0.169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-11-26</td>\n",
       "      <td>unitedairlines Stuck on UA2200 at gate lettin...</td>\n",
       "      <td>91</td>\n",
       "      <td>0.9995</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-11-25</td>\n",
       "      <td>Fullservice flights to New York from 926 retu...</td>\n",
       "      <td>92</td>\n",
       "      <td>0.9989</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-11-24</td>\n",
       "      <td>decades and I am hoping to continue that rela...</td>\n",
       "      <td>92</td>\n",
       "      <td>0.9991</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.727</td>\n",
       "      <td>0.188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-11-23</td>\n",
       "      <td>RT AngeliqueK Is anyone satisfied with flying...</td>\n",
       "      <td>94</td>\n",
       "      <td>0.9992</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.822</td>\n",
       "      <td>0.152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018-11-22</td>\n",
       "      <td>RT UnitedFlyerHD Beautiful view of Chicago at...</td>\n",
       "      <td>92</td>\n",
       "      <td>0.9997</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018-11-21</td>\n",
       "      <td>Instead of Turkey I am eating pasta for thank...</td>\n",
       "      <td>92</td>\n",
       "      <td>0.9994</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.788</td>\n",
       "      <td>0.184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-11-20</td>\n",
       "      <td>united 150 for unaccompanied minor service yo...</td>\n",
       "      <td>91</td>\n",
       "      <td>0.9973</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0.145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018-11-19</td>\n",
       "      <td>united Thank you for damaging and taking my n...</td>\n",
       "      <td>92</td>\n",
       "      <td>0.9994</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.771</td>\n",
       "      <td>0.167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018-11-18</td>\n",
       "      <td>unitedairlines operations team very inconside...</td>\n",
       "      <td>92</td>\n",
       "      <td>-0.985</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.738</td>\n",
       "      <td>0.119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date                                             Tweets  Prices  \\\n",
       "0   2018-11-28  We are already over 2 hours late for departure...      92   \n",
       "1   2018-11-27   RT HChan03 My photo of the day My flight to L...      92   \n",
       "2   2018-11-26   unitedairlines Stuck on UA2200 at gate lettin...      91   \n",
       "3   2018-11-25   Fullservice flights to New York from 926 retu...      92   \n",
       "4   2018-11-24   decades and I am hoping to continue that rela...      92   \n",
       "5   2018-11-23   RT AngeliqueK Is anyone satisfied with flying...      94   \n",
       "6   2018-11-22   RT UnitedFlyerHD Beautiful view of Chicago at...      92   \n",
       "7   2018-11-21   Instead of Turkey I am eating pasta for thank...      92   \n",
       "8   2018-11-20   united 150 for unaccompanied minor service yo...      91   \n",
       "9   2018-11-19   united Thank you for damaging and taking my n...      92   \n",
       "10  2018-11-18   unitedairlines operations team very inconside...      92   \n",
       "\n",
       "      Comp Negative Neutral Positive  \n",
       "0   0.6234    0.037    0.86    0.103  \n",
       "1   0.9983    0.095   0.736    0.169  \n",
       "2   0.9995    0.075    0.75    0.175  \n",
       "3   0.9989    0.085    0.75    0.166  \n",
       "4   0.9991    0.085   0.727    0.188  \n",
       "5   0.9992    0.027   0.822    0.152  \n",
       "6   0.9997    0.029    0.75    0.221  \n",
       "7   0.9994    0.028   0.788    0.184  \n",
       "8   0.9973    0.081   0.774    0.145  \n",
       "9   0.9994    0.063   0.771    0.167  \n",
       "10  -0.985    0.143   0.738    0.119  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccdata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculating the percentage of postive and negative tweets, and plotting the PIE chart for the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of positive tweets=  90.9090909090909\n",
      "% of negative tweets=  9.090909090909092\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Z2QNEcbIA2NF3DpG+srctWNDUOO2dHewlHdDtmcNpap7hO0SlhTKyB7jWdwCRvjTH7bTT4a3FyXu0XLXs5vYpM9tcfRDrsvfSc1ThKZdmtomZndblzyPN7NY+3UdAI/vNgVeBjXxnESkHo7PzmPpZ877VcHPdCBZPNMN8Z6pCp9PU/EvfIVZnZhEw3Tm3c7n2EczIvlQsvAX80XcOkXJx1NXd2jFlr71bfjnp4NbLXprVscsDnY7FvnNVkbeBqRvyQjOLzOwZM7vGzJ42s7vNbICZjTazGWb2iJnNMrMdsuePNrPZZvawmV1sZu9n3x9sZv9jZo+a2ZNmtvIaoCIw2sweN7NLs/09lb1mjpmN75JlpplNNLNBZnZdto/HurzXGgVT9pl/B53CJvm30I3c9sS2C6bs1PLrfj9tP3rWUtfvGd+ZqsAVNDUv7cXrxwC/cM6NBxYDxwC/As50zk0EzgNW/tZwOXC5c25P4LUu77EC+Kxzbg/gIOA/zMyAGHjBObe7c+5bq+33d8AXAMxsS2Ckc+4R4LvAfdk+DgIuNbNBawsfzDTOSlGc/Bo4xXcOkUrbr+6ppy5smNY81l6ZZEY/33kq7H1gG5qaN2j5lGya5R7n3Jjsz98GGkkL97kuT+3nnNvRzN4GPuKcazezIcBrzrnBZtYI/CdwAOnAcxywHdCfLtM4Xad1zGyrbN87mdnZwAjn3HfNbF72uvZs38OATzrn1vjBnuebl6zNhcBxoLMXJCwPdu688ydbf8xmNL91QeNv/3JU3f9t32CdW/vOVSE/2dCi76Kly+MO4CPAYufc7j14jxOA4cBE51ybmZVYTxc55141s7fNbFfgWOCr2Y8MOMY599zaX71KaNM4lIqFl4ErfOcQ8eVthm5+XtvXDhzTMm3k+W1fmfuGG/qIc+T5V/xXgB+V4X3fAxaZ2ecBLLVb9rPZpNM8kA4uVxoKvJEV/UHAyvsbLAE2Xse+fgecDwx1zj2Zfe8u4MxsGggzm7CusMGVfeaHwDu+Q4j45Kiru6XjoL32arly4qGtP37xwY7xDzhHs+9cZRCXcR2cE4AvmdkTwNOsWnTxHOBcM5sLbAn//Od6EzApm4I5AXgWwDn3NvCgmT1lZpeuYT+3kn5odF3m4RLSqaT52cHcS9YVNLg5+5WiOPkmcJnvHCLVZAAty05r+NMjX6q/c8RAaxnnO08fmA3sS1NzRYvOzAYCy51zzsyOA453znldfTfksu9HemBFt4kTWYP96+Y/eWHDDUu2t1cnmdXk9SkO+BhNzXMrvWMz2x/4Oem8+mLgVOfc3yqd4wOZQi17gChOjkLn3ous0+YsfvM7jb95+jN1D41tsM6RvvP0wA00NZ/kO0S1CLrsAaI4uZF07kxE1qGOzo5j6++f982G3zduxnsTqvwK3b8Du9DUrNs+ZlT2cbIp8BRQSyMWEa/G2UuLLmqc+vLe9sxuZgz1nWc1jnSxs6pbA8en4MseIIqTTwGJ7xwitWYgK5ae3nD7o6fWz/jIAGsd6ztP5uc0NZ/pO0S1Udlnoji5AtB/ICIb6MC6x+d/v+GG90fZ3/c0o9FTjAXApBDXq18flX0mOztnNtCTq+FEZDUf4Z03vtN40zOFujljG6xzywruupX07JvHKrjPmqGy7yKKk3HAI8BaFxMSke6pp6P9+Pr75n2j4dZ+m9mSdV7d2UfOo6n5Pyqwn5qksl9NFCdfIL00uZrPNBCpKTvaiy9c1Hj9K3vacxPMGFKGXUylqfmUMrxvbqjs1yCKk/Mpz1oaIkEbxPL3z2r442Mn19+1RX9rG9NHbzsLOISm5tY+er9cUtmvRRQnvwBOW+8TRWSDfLzu0Se+13Djsu3s9Um9OKC7ENibpua3+jJbHqns1yKKk3rgNuAzvrOI5NkWvPOP7zXe8MzhdXN3qDe3RQ9e2gzsQ1OzbszSDSr7dYjiZCBwP7CX7ywieVdPR/sX6++dd3bDbf2H2ZL1nRXXARR04VT3qezXI4qTEcBfgFG+s4iEYrwt+ttFjVNfm2jPTzD70DrvncApNDXf4CNbrVLZd0MUJ2OBh4DNfGcRCckgli85p+EPj51Yf89W/a1tNOlSCF+lqfka39lqjcq+m6I42Q24GxjhO4tIiD5RN++xHzROu3Lri15Q0W8AlX0PRHEyBrgX2MZ3FpHAOOCMUrHwS99BapXKvoeiOPkocA/pXeFFpPwc8NVSsaARfS+Eeg/aDZbdsHx/QOtviJRfB/AlFX3vaWS/gaI4GQpMByb7ziKSU+8Bx5WKhTt9B8kDlX0vRHEyAPgDcLjvLCI5sxA4olQsLPAdJC80jdMLpWJhOXAkcJ3vLCI5MgvYW0XftzSy7yNRnHyZ9G7y/XxnEalhvwa+VioWtKhZH1PZ96EoTiYCtwKR5ygitaYT+HapWLjMd5C8Utn3sShOhgE3onl8ke56Hzi+VCxM9x0kzzRn38dKxcI7wKeBJtLRiois3RxgDxV9+WlkX0ZRnBwG3AQM851FpMq0A5cA/14qFjp8hwmByr7MojjZFrgeONBvEpGq8TzwxVKx8LDvICHRNE6ZlYqFF4GPA2cByzzHEfHtSmCCir7yNLKvoChORpOeWra/7ywiFfY6cKquhvVHI/sKKhULLwBTSO9t2+w5jkil3ALsoqL3SyN7T6I4GQlcARzjO4tImTwFnF0qFu7zHURU9t5FcXIE6ZW3WiNf8uJd4ELgSp1pUz1U9lUgipP+wBnAd4BNPccR2VCdwK+A75WKhbd9h5EPUtlXkShONgUuAM4E+nuOI9IT/wucVSoWnvAdRNZMZV+FsrthXQKciA6iS3V7CTi/VCzc7DuIrJvKvopFcbIrUETr7Ej1eQX4IXCtVqisDSr7GhDFyUGk/2Pt7TuLBE8lX6NU9jUkipMDgG8CRwDmOY6EZSFwKfDrUrHQ4juM9JzKvgZFcTIO+AZwEjDAcxzJtyeAHwG36DTK2qayr2FRnGwOnJ5twz3HkfxwwD3A5aVi4Q7fYaRvqOxzIDtP/yTgXGCc5zhSu14nXbvpmlKxsMh3GOlbKvscieLEgEOBk4Gj0BSPrJ8D7gWuBv5cKhbaPOeRMlHZ51QUJ0OAz5MW/2R0QFc+6B+sGsUv9B1Gyk9lH4AoTkaRTvOcCIzyHEf8aSGdi58G3K5RfFhU9gHJpnkmkxb/F4AhfhNJBSwDZgC3AkmpWHjPcx7xRGUfqOyg7sHAkaTn7W/hN5H0oSXAdOAPwJ2lYkF3SBOVvfxzxL83afF/GtjZbyLZAO8CfyYt+Lt14ZOsTmUvH5ItxHYY6Zo8hwAb+00ka7AceAi4P9vmloqFdr+RpJqp7GWdojhpBPYhvW/uvtljrblfea3AHNJivw+YrdG79ITKXnokm/LZibT498u27b2Gyqc24FFWlfuDmnuX3lDZS69FcTKCVeW/L7ArMNhrqNrSRnq/1keybR7wpEbu0pdU9tLnstH/NqS/AYzv8nVHNP//JjCfdIGx+dm2QMUu5aayl4qK4mT1D4FxwNbASKDRY7S+0kZ696ZFa9gWloqFNz1mk4Cp7KUqZL8NDAe2Ws/m6+DwcuCdLtu72ddXSdd6X1nor2opYKlGKnupKdnZQUOybWiXxxuTLvw2MPu68nED0NGDbRkfLPN3gHdKxcKKivwFRcpEZS8iEoA63wFERKT8VPYiIgFQ2YuIBEBlLyISAJW9iEgAVPYiIgFQ2YuIBEBlLyISAJW9iEgAVPYiIgFQ2YuIBEBlLyISAJW9iEgAVPYiIgFQ2YuIBEBlLyISAJW9iEgAVPYiIgFQ2YuIBEBlLyISAJW9iEgAVPYiIgFQ2YuIBEBlLyISAJW9iEgAVPYiIgFQ2YuIBEBlLyISAJW9iEgAVPYiIgFQ2YuIBOD/A0IWySNSY+V8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "posi=0\n",
    "nega=0\n",
    "for i in range (0,len(ccdata)):\n",
    "    get_val=ccdata.Comp[i]\n",
    "    if(float(get_val)<(0)):\n",
    "        nega=nega+1\n",
    "    if(float(get_val>(0))):\n",
    "        posi=posi+1\n",
    "posper=(posi/(len(ccdata)))*100\n",
    "negper=(nega/(len(ccdata)))*100\n",
    "print(\"% of positive tweets= \",posper)\n",
    "print(\"% of negative tweets= \",negper)\n",
    "arr=np.asarray([posper,negper], dtype=int)\n",
    "mlpt.pie(arr,labels=['positive','negative'])\n",
    "mlpt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Making a new dataframe with necessary columns for providing machine learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_=ccdata[['Date','Prices','Comp','Negative','Neutral','Positive']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Prices</th>\n",
       "      <th>Comp</th>\n",
       "      <th>Negative</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Positive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-11-28</td>\n",
       "      <td>92</td>\n",
       "      <td>0.6234</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>92</td>\n",
       "      <td>0.9983</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.736</td>\n",
       "      <td>0.169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-11-26</td>\n",
       "      <td>91</td>\n",
       "      <td>0.9995</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-11-25</td>\n",
       "      <td>92</td>\n",
       "      <td>0.9989</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-11-24</td>\n",
       "      <td>92</td>\n",
       "      <td>0.9991</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.727</td>\n",
       "      <td>0.188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-11-23</td>\n",
       "      <td>94</td>\n",
       "      <td>0.9992</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.822</td>\n",
       "      <td>0.152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018-11-22</td>\n",
       "      <td>92</td>\n",
       "      <td>0.9997</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018-11-21</td>\n",
       "      <td>92</td>\n",
       "      <td>0.9994</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.788</td>\n",
       "      <td>0.184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-11-20</td>\n",
       "      <td>91</td>\n",
       "      <td>0.9973</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0.145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018-11-19</td>\n",
       "      <td>92</td>\n",
       "      <td>0.9994</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.771</td>\n",
       "      <td>0.167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018-11-18</td>\n",
       "      <td>92</td>\n",
       "      <td>-0.985</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.738</td>\n",
       "      <td>0.119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Prices    Comp Negative Neutral Positive\n",
       "0   2018-11-28      92  0.6234    0.037    0.86    0.103\n",
       "1   2018-11-27      92  0.9983    0.095   0.736    0.169\n",
       "2   2018-11-26      91  0.9995    0.075    0.75    0.175\n",
       "3   2018-11-25      92  0.9989    0.085    0.75    0.166\n",
       "4   2018-11-24      92  0.9991    0.085   0.727    0.188\n",
       "5   2018-11-23      94  0.9992    0.027   0.822    0.152\n",
       "6   2018-11-22      92  0.9997    0.029    0.75    0.221\n",
       "7   2018-11-21      92  0.9994    0.028   0.788    0.184\n",
       "8   2018-11-20      91  0.9973    0.081   0.774    0.145\n",
       "9   2018-11-19      92  0.9994    0.063   0.771    0.167\n",
       "10  2018-11-18      92  -0.985    0.143   0.738    0.119"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dividing the dataset into train and test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \"\"\"\n",
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "train_start_index = '0'\n",
    "train_end_index = '6'\n",
    "test_start_index = '7'\n",
    "test_end_index = '9'\n",
    "train = df_.ix[train_start_index : train_end_index]\n",
    "test = df_.ix[test_start_index:test_end_index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Making a 2D array that will store the Negative and Positive sentiment for Training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentiment_score_list = []\n",
    "for date, row in train.T.iteritems():\n",
    "    sentiment_score = np.asarray([df_.loc[date, 'Negative'],df_.loc[date, 'Positive']])\n",
    "    sentiment_score_list.append(sentiment_score)\n",
    "numpy_df_train = np.asarray(sentiment_score_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.037 0.103]\n",
      " [0.095 0.169]\n",
      " [0.075 0.175]\n",
      " [0.085 0.166]\n",
      " [0.085 0.188]\n",
      " [0.027 0.152]\n",
      " [0.029 0.221]]\n"
     ]
    }
   ],
   "source": [
    "print(numpy_df_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Making a 2D array that will store the Negative and Positive sentiment for Testing dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentiment_score_list = []\n",
    "for date, row in test.T.iteritems():\n",
    "    sentiment_score = np.asarray([df_.loc[date, 'Negative'],df_.loc[date, 'Positive']])\n",
    "    sentiment_score_list.append(sentiment_score)\n",
    "numpy_df_test = np.asarray(sentiment_score_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.028 0.184]\n",
      " [0.081 0.145]\n",
      " [0.063 0.167]\n",
      " [0.143 0.119]]\n"
     ]
    }
   ],
   "source": [
    "print(numpy_df_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Making 2 dataframe for Training and Testing \"Prices\". You can also make 1-D array for the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Prices\n",
      "0      92\n",
      "1      92\n",
      "2      91\n",
      "3      92\n",
      "4      92\n",
      "5      94\n",
      "6      92\n"
     ]
    }
   ],
   "source": [
    "y_train = pd.DataFrame(train['Prices'])\n",
    "#y_train=[91,91,91,92,91,92,91]\n",
    "y_test = pd.DataFrame(test['Prices'])\n",
    "print(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fitting the sentiments(this acts as in independent value) and prices(this acts as a dependent value (like class-lables in iris dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  import sys\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from treeinterpreter import treeinterpreter as ti\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "\n",
    "rf = RandomForestRegressor()\n",
    "rf.fit(numpy_df_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Making Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction, bias, contributions = ti.predict(rf, numpy_df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[92.5 92.4 91.7 92.4]\n"
     ]
    }
   ],
   "source": [
    "print(prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing matplotlib library for plotting graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Defining index position for the test data. Making dataframe for the predicted value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx=np.arange(int(test_start_index),int(test_end_index)+2)\n",
    "predictions_df_ = pd.DataFrame(data=prediction[0:], index = idx, columns=['Prices'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Prices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>92.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>92.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>91.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>92.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Prices\n",
       "7     92.5\n",
       "8     92.4\n",
       "9     91.7\n",
       "10    92.4"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_df_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting the graph for the Predicted_price VS Actual Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = predictions_df_.rename(columns={\"Prices\": \"predicted_price\"}).plot(title='Random Forest predicted prices')#predicted value\n",
    "ax.set_xlabel(\"Indexes\")\n",
    "ax.set_ylabel(\"Stock Prices\")\n",
    "fig = y_test.rename(columns={\"Prices\": \"actual_price\"}).plot(ax = ax).get_figure()#actual value\n",
    "fig.savefig(\"random forest.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from treeinterpreter import treeinterpreter as ti\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "\n",
    "reg = LinearRegression()\n",
    "reg.fit(numpy_df_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[92.60702459],\n",
       "       [91.92213559],\n",
       "       [92.12769734],\n",
       "       [91.06033315]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict(numpy_df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NOTE: Since our dataset is very small and as you can see that fetching 600 tweets could only make data for just 10 days.Also the prediction is not very great in such small dataset. So we found this new dataset on internet which has the Text as \"Tweets\" and respective \"close price\" and \"Adjusted close price\".\n",
    "\n",
    "\n",
    "### Adjusted Close Price: An adjusted closing price is a stock's closing price on any given day of trading that has been amended to include any distributions and corporate actions that occurred at any time before the next day's open."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "stocks_dataf = pd.read_pickle('C:\\\\Users\\\\anubh\\\\OneDrive\\\\Desktop\\\\Twitter_Dataset.pkl')\n",
    "stocks_dataf.columns=['closing_price','adj_close_price','Tweets']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## New dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {
    "scrolled": true
   },
   "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>closing_price</th>\n",
       "      <th>adj_close_price</th>\n",
       "      <th>Tweets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>12469.971875</td>\n",
       "      <td>12469.971875</td>\n",
       "      <td>. What Sticks from '06. Somalia Orders Islamis...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-02</th>\n",
       "      <td>12472.245703</td>\n",
       "      <td>12472.245703</td>\n",
       "      <td>. Heart Health: Vitamin Does Not Prevent Death...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-03</th>\n",
       "      <td>12474.519531</td>\n",
       "      <td>12474.519531</td>\n",
       "      <td>. Google Answer to Filling Jobs Is an Algorith...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-04</th>\n",
       "      <td>12480.690430</td>\n",
       "      <td>12480.690430</td>\n",
       "      <td>. Helping Make the Shift From Combat to Commer...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-05</th>\n",
       "      <td>12398.009766</td>\n",
       "      <td>12398.009766</td>\n",
       "      <td>. Rise in Ethanol Raises Concerns About Corn a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-06</th>\n",
       "      <td>12406.503255</td>\n",
       "      <td>12406.503255</td>\n",
       "      <td>. A Status Quo Secretary General. Best Buy and...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-07</th>\n",
       "      <td>12414.996745</td>\n",
       "      <td>12414.996745</td>\n",
       "      <td>. THE COMMON APPLICATION; Typo.com. Jumbo Bonu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-08</th>\n",
       "      <td>12423.490234</td>\n",
       "      <td>12423.490234</td>\n",
       "      <td>. VW Group’s Sales Rose Sharply in 2006. Conso...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-09</th>\n",
       "      <td>12416.599609</td>\n",
       "      <td>12416.599609</td>\n",
       "      <td>. The Claim:  Hot Leftovers Should Cool at Roo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-10</th>\n",
       "      <td>12442.160156</td>\n",
       "      <td>12442.160156</td>\n",
       "      <td>. Love Among the Ruins. Dell Says Plant a Tree...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-11</th>\n",
       "      <td>12514.980469</td>\n",
       "      <td>12514.980469</td>\n",
       "      <td>. The Computer With a TV, and a Family's Virtu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-12</th>\n",
       "      <td>12556.080078</td>\n",
       "      <td>12556.080078</td>\n",
       "      <td>. Make Them Fight All of Us. Hire by the Contr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-13</th>\n",
       "      <td>12562.707519</td>\n",
       "      <td>12562.707519</td>\n",
       "      <td>. Blair Urges Britain to Pursue an Aggressive ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-14</th>\n",
       "      <td>12569.334961</td>\n",
       "      <td>12569.334961</td>\n",
       "      <td>. Smoke Damage. Mr. Spitzer’s Task on Court Re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-15</th>\n",
       "      <td>12575.962403</td>\n",
       "      <td>12575.962403</td>\n",
       "      <td>. The Mentally Ill, Behind Bars. BP’s Chief to...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-16</th>\n",
       "      <td>12582.589844</td>\n",
       "      <td>12582.589844</td>\n",
       "      <td>. King Day in Atlanta, ‘the One Without Mrs. K...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-17</th>\n",
       "      <td>12577.150391</td>\n",
       "      <td>12577.150391</td>\n",
       "      <td>. Racial Hate Feeds a Gang War’s Senseless Kil...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-18</th>\n",
       "      <td>12567.929688</td>\n",
       "      <td>12567.929688</td>\n",
       "      <td>. Taliban Detainee Says Rebel Chief Hides in P...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-19</th>\n",
       "      <td>12565.530273</td>\n",
       "      <td>12565.530273</td>\n",
       "      <td>. Data Breach Could Affect Millions of TJX Sho...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-20</th>\n",
       "      <td>12536.073567</td>\n",
       "      <td>12536.073567</td>\n",
       "      <td>. Archives of Spin. H.P. Chief Defends Timing ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-21</th>\n",
       "      <td>12506.616862</td>\n",
       "      <td>12506.616862</td>\n",
       "      <td>. Connecticut’s Diaspora. Son of Dogs Playing ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-22</th>\n",
       "      <td>12477.160156</td>\n",
       "      <td>12477.160156</td>\n",
       "      <td>. M dot Strange Finds a Way at Sundance. An In...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-23</th>\n",
       "      <td>12533.799805</td>\n",
       "      <td>12533.799805</td>\n",
       "      <td>. 2 Car Bombs Kill Scores at Packed Market in ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-24</th>\n",
       "      <td>12621.769531</td>\n",
       "      <td>12621.769531</td>\n",
       "      <td>. Asian Rival Moves Past Las Vegas. Pennsylvan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-25</th>\n",
       "      <td>12502.559570</td>\n",
       "      <td>12502.559570</td>\n",
       "      <td>. Quarterly Profit Surges at Cingular Wireless...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-26</th>\n",
       "      <td>12487.019531</td>\n",
       "      <td>12487.019531</td>\n",
       "      <td>. Renewal, in Real Estate and in Culture, for ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-27</th>\n",
       "      <td>12488.273112</td>\n",
       "      <td>12488.273112</td>\n",
       "      <td>. Cracking the Books for Financial Aid to Coll...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-28</th>\n",
       "      <td>12489.526692</td>\n",
       "      <td>12489.526692</td>\n",
       "      <td>. First You Clear the Table. Delight in Scienc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-29</th>\n",
       "      <td>12490.780273</td>\n",
       "      <td>12490.780273</td>\n",
       "      <td>. Courts Turn to Wikipedia, but Selectively. T...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-30</th>\n",
       "      <td>12523.309570</td>\n",
       "      <td>12523.309570</td>\n",
       "      <td>. Can Humanity Survive? Want to Bet on It?. Aw...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-02</th>\n",
       "      <td>19170.419922</td>\n",
       "      <td>19170.419922</td>\n",
       "      <td>. Silicon Valley Chiefs Notably Absent From Tr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-03</th>\n",
       "      <td>19185.693359</td>\n",
       "      <td>19185.693359</td>\n",
       "      <td>. Trump, Taiwan and China: The Controversy, Ex...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-04</th>\n",
       "      <td>19200.966797</td>\n",
       "      <td>19200.966797</td>\n",
       "      <td>. Hunting ‘Turr’ in Newfoundland’s Frigid Wate...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-05</th>\n",
       "      <td>19216.240234</td>\n",
       "      <td>19216.240234</td>\n",
       "      <td>. Trump’s Meeting With Al Gore Gives Environme...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-06</th>\n",
       "      <td>19251.779297</td>\n",
       "      <td>19251.779297</td>\n",
       "      <td>. Senate Republican Leaders Vow to Begin Repea...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-07</th>\n",
       "      <td>19549.619141</td>\n",
       "      <td>19549.619141</td>\n",
       "      <td>. Can Congress Lift the Presidential Conflict ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-08</th>\n",
       "      <td>19614.810547</td>\n",
       "      <td>19614.810547</td>\n",
       "      <td>. The Freaky Food Chain Behind Your Lobster Di...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-09</th>\n",
       "      <td>19756.849609</td>\n",
       "      <td>19756.849609</td>\n",
       "      <td>. Trump Spent Far Less Than Clinton, but Paid ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-10</th>\n",
       "      <td>19770.042969</td>\n",
       "      <td>19770.042969</td>\n",
       "      <td>. As Turkey Cracks Down, Kurdish Mayors Pack B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-11</th>\n",
       "      <td>19783.236328</td>\n",
       "      <td>19783.236328</td>\n",
       "      <td>. I.M.F. Chief Heads to Trial; Fed Is Expected...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-12</th>\n",
       "      <td>19796.429688</td>\n",
       "      <td>19796.429688</td>\n",
       "      <td>. Pennsylvania and Wisconsin End Election Reco...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-13</th>\n",
       "      <td>19911.210938</td>\n",
       "      <td>19911.210938</td>\n",
       "      <td>. The Most Talked-About Debates of 2016. Wells...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-14</th>\n",
       "      <td>19792.529297</td>\n",
       "      <td>19792.529297</td>\n",
       "      <td>. Can Trump Get Tough With China?. The Oakland...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-15</th>\n",
       "      <td>19852.240234</td>\n",
       "      <td>19852.240234</td>\n",
       "      <td>. Hispanic Surnames on the Rise in U.S. as Imm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-16</th>\n",
       "      <td>19843.410156</td>\n",
       "      <td>19843.410156</td>\n",
       "      <td>. Vine, the Six-Second Video App, Is Not Quite...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-17</th>\n",
       "      <td>19856.626953</td>\n",
       "      <td>19856.626953</td>\n",
       "      <td>. Trump’s Win Helps Carve a Path to Washington...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-18</th>\n",
       "      <td>19869.843750</td>\n",
       "      <td>19869.843750</td>\n",
       "      <td>. With Comic Book, Celebrities Pay Tribute to ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-19</th>\n",
       "      <td>19883.060547</td>\n",
       "      <td>19883.060547</td>\n",
       "      <td>. Donald Trump and the C.I.A.. Vincent Viola, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-20</th>\n",
       "      <td>19974.619141</td>\n",
       "      <td>19974.619141</td>\n",
       "      <td>. Can Trump's Infrastructure Plan Work?. Volks...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-21</th>\n",
       "      <td>19941.960938</td>\n",
       "      <td>19941.960938</td>\n",
       "      <td>. Совершенное орудие: как российская кибермощь...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-22</th>\n",
       "      <td>19918.880859</td>\n",
       "      <td>19918.880859</td>\n",
       "      <td>. New Ebola Vaccine Gives 100 Percent Protecti...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-23</th>\n",
       "      <td>19933.810547</td>\n",
       "      <td>19933.810547</td>\n",
       "      <td>. Flurry of Settlements Over Toxic Mortgages M...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-24</th>\n",
       "      <td>19936.617676</td>\n",
       "      <td>19936.617676</td>\n",
       "      <td>. Jason Miller Backs Out of White House Commun...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-25</th>\n",
       "      <td>19939.424804</td>\n",
       "      <td>19939.424804</td>\n",
       "      <td>. Wielding Claims of ‘Fake News,’ Conservative...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-26</th>\n",
       "      <td>19942.231933</td>\n",
       "      <td>19942.231933</td>\n",
       "      <td>. When One Party Has the Governor’s Mansion an...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-27</th>\n",
       "      <td>19945.039062</td>\n",
       "      <td>19945.039062</td>\n",
       "      <td>. Should the U.S. Embassy Be Moved From Tel Av...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-28</th>\n",
       "      <td>19833.679688</td>\n",
       "      <td>19833.679688</td>\n",
       "      <td>. When Finding the Right Lawyer Seems Daunting...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29</th>\n",
       "      <td>19819.779297</td>\n",
       "      <td>19819.779297</td>\n",
       "      <td>. Does Empathy Guide or Hinder Moral Action?. ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30</th>\n",
       "      <td>19762.599609</td>\n",
       "      <td>19762.599609</td>\n",
       "      <td>. Shielding Seized Assets From Corruption’s Cl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>19762.599609</td>\n",
       "      <td>19762.599609</td>\n",
       "      <td>Terrorist Attack at Nightclub in Istanbul Kill...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3653 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            closing_price  adj_close_price  \\\n",
       "2007-01-01   12469.971875     12469.971875   \n",
       "2007-01-02   12472.245703     12472.245703   \n",
       "2007-01-03   12474.519531     12474.519531   \n",
       "2007-01-04   12480.690430     12480.690430   \n",
       "2007-01-05   12398.009766     12398.009766   \n",
       "2007-01-06   12406.503255     12406.503255   \n",
       "2007-01-07   12414.996745     12414.996745   \n",
       "2007-01-08   12423.490234     12423.490234   \n",
       "2007-01-09   12416.599609     12416.599609   \n",
       "2007-01-10   12442.160156     12442.160156   \n",
       "2007-01-11   12514.980469     12514.980469   \n",
       "2007-01-12   12556.080078     12556.080078   \n",
       "2007-01-13   12562.707519     12562.707519   \n",
       "2007-01-14   12569.334961     12569.334961   \n",
       "2007-01-15   12575.962403     12575.962403   \n",
       "2007-01-16   12582.589844     12582.589844   \n",
       "2007-01-17   12577.150391     12577.150391   \n",
       "2007-01-18   12567.929688     12567.929688   \n",
       "2007-01-19   12565.530273     12565.530273   \n",
       "2007-01-20   12536.073567     12536.073567   \n",
       "2007-01-21   12506.616862     12506.616862   \n",
       "2007-01-22   12477.160156     12477.160156   \n",
       "2007-01-23   12533.799805     12533.799805   \n",
       "2007-01-24   12621.769531     12621.769531   \n",
       "2007-01-25   12502.559570     12502.559570   \n",
       "2007-01-26   12487.019531     12487.019531   \n",
       "2007-01-27   12488.273112     12488.273112   \n",
       "2007-01-28   12489.526692     12489.526692   \n",
       "2007-01-29   12490.780273     12490.780273   \n",
       "2007-01-30   12523.309570     12523.309570   \n",
       "...                   ...              ...   \n",
       "2016-12-02   19170.419922     19170.419922   \n",
       "2016-12-03   19185.693359     19185.693359   \n",
       "2016-12-04   19200.966797     19200.966797   \n",
       "2016-12-05   19216.240234     19216.240234   \n",
       "2016-12-06   19251.779297     19251.779297   \n",
       "2016-12-07   19549.619141     19549.619141   \n",
       "2016-12-08   19614.810547     19614.810547   \n",
       "2016-12-09   19756.849609     19756.849609   \n",
       "2016-12-10   19770.042969     19770.042969   \n",
       "2016-12-11   19783.236328     19783.236328   \n",
       "2016-12-12   19796.429688     19796.429688   \n",
       "2016-12-13   19911.210938     19911.210938   \n",
       "2016-12-14   19792.529297     19792.529297   \n",
       "2016-12-15   19852.240234     19852.240234   \n",
       "2016-12-16   19843.410156     19843.410156   \n",
       "2016-12-17   19856.626953     19856.626953   \n",
       "2016-12-18   19869.843750     19869.843750   \n",
       "2016-12-19   19883.060547     19883.060547   \n",
       "2016-12-20   19974.619141     19974.619141   \n",
       "2016-12-21   19941.960938     19941.960938   \n",
       "2016-12-22   19918.880859     19918.880859   \n",
       "2016-12-23   19933.810547     19933.810547   \n",
       "2016-12-24   19936.617676     19936.617676   \n",
       "2016-12-25   19939.424804     19939.424804   \n",
       "2016-12-26   19942.231933     19942.231933   \n",
       "2016-12-27   19945.039062     19945.039062   \n",
       "2016-12-28   19833.679688     19833.679688   \n",
       "2016-12-29   19819.779297     19819.779297   \n",
       "2016-12-30   19762.599609     19762.599609   \n",
       "2016-12-31   19762.599609     19762.599609   \n",
       "\n",
       "                                                       Tweets  \n",
       "2007-01-01  . What Sticks from '06. Somalia Orders Islamis...  \n",
       "2007-01-02  . Heart Health: Vitamin Does Not Prevent Death...  \n",
       "2007-01-03  . Google Answer to Filling Jobs Is an Algorith...  \n",
       "2007-01-04  . Helping Make the Shift From Combat to Commer...  \n",
       "2007-01-05  . Rise in Ethanol Raises Concerns About Corn a...  \n",
       "2007-01-06  . A Status Quo Secretary General. Best Buy and...  \n",
       "2007-01-07  . THE COMMON APPLICATION; Typo.com. Jumbo Bonu...  \n",
       "2007-01-08  . VW Group’s Sales Rose Sharply in 2006. Conso...  \n",
       "2007-01-09  . The Claim:  Hot Leftovers Should Cool at Roo...  \n",
       "2007-01-10  . Love Among the Ruins. Dell Says Plant a Tree...  \n",
       "2007-01-11  . The Computer With a TV, and a Family's Virtu...  \n",
       "2007-01-12  . Make Them Fight All of Us. Hire by the Contr...  \n",
       "2007-01-13  . Blair Urges Britain to Pursue an Aggressive ...  \n",
       "2007-01-14  . Smoke Damage. Mr. Spitzer’s Task on Court Re...  \n",
       "2007-01-15  . The Mentally Ill, Behind Bars. BP’s Chief to...  \n",
       "2007-01-16  . King Day in Atlanta, ‘the One Without Mrs. K...  \n",
       "2007-01-17  . Racial Hate Feeds a Gang War’s Senseless Kil...  \n",
       "2007-01-18  . Taliban Detainee Says Rebel Chief Hides in P...  \n",
       "2007-01-19  . Data Breach Could Affect Millions of TJX Sho...  \n",
       "2007-01-20  . Archives of Spin. H.P. Chief Defends Timing ...  \n",
       "2007-01-21  . Connecticut’s Diaspora. Son of Dogs Playing ...  \n",
       "2007-01-22  . M dot Strange Finds a Way at Sundance. An In...  \n",
       "2007-01-23  . 2 Car Bombs Kill Scores at Packed Market in ...  \n",
       "2007-01-24  . Asian Rival Moves Past Las Vegas. Pennsylvan...  \n",
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       "...                                                       ...  \n",
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       "\n",
       "[3653 rows x 3 columns]"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks_dataf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Removing dot (.) and space from the Tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
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    },
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       "            adj_close_price                                             Tweets\n",
       "2007-01-01            12469   What Sticks from '06. Somalia Orders Islamist...\n",
       "2007-01-02            12472   Heart Health: Vitamin Does Not Prevent Death ...\n",
       "2007-01-03            12474   Google Answer to Filling Jobs Is an Algorithm...\n",
       "2007-01-04            12480   Helping Make the Shift From Combat to Commerc...\n",
       "2007-01-05            12398   Rise in Ethanol Raises Concerns About Corn as...\n",
       "2007-01-06            12406   A Status Quo Secretary General. Best Buy and ...\n",
       "2007-01-07            12414   THE COMMON APPLICATION; Typo.com. Jumbo Bonus...\n",
       "2007-01-08            12423   VW Group’s Sales Rose Sharply in 2006. Consol...\n",
       "2007-01-09            12416   The Claim:  Hot Leftovers Should Cool at Room...\n",
       "2007-01-10            12442   Love Among the Ruins. Dell Says Plant a Tree,...\n",
       "2007-01-11            12514   The Computer With a TV, and a Family's Virtua...\n",
       "2007-01-12            12556   Make Them Fight All of Us. Hire by the Contra...\n",
       "2007-01-13            12562   Blair Urges Britain to Pursue an Aggressive F...\n",
       "2007-01-14            12569   Smoke Damage. Mr. Spitzer’s Task on Court Ref...\n",
       "2007-01-15            12575   The Mentally Ill, Behind Bars. BP’s Chief to ...\n",
       "2007-01-16            12582   King Day in Atlanta, ‘the One Without Mrs. Ki...\n",
       "2007-01-17            12577   Racial Hate Feeds a Gang War’s Senseless Kill...\n",
       "2007-01-18            12567   Taliban Detainee Says Rebel Chief Hides in Pa...\n",
       "2007-01-19            12565   Data Breach Could Affect Millions of TJX Shop...\n",
       "2007-01-20            12536   Archives of Spin. H.P. Chief Defends Timing o...\n",
       "2007-01-21            12506   Connecticut’s Diaspora. Son of Dogs Playing P...\n",
       "2007-01-22            12477   M dot Strange Finds a Way at Sundance. An Int...\n",
       "2007-01-23            12533   2 Car Bombs Kill Scores at Packed Market in B...\n",
       "2007-01-24            12621   Asian Rival Moves Past Las Vegas. Pennsylvani...\n",
       "2007-01-25            12502   Quarterly Profit Surges at Cingular Wireless....\n",
       "2007-01-26            12487   Renewal, in Real Estate and in Culture, for A...\n",
       "2007-01-27            12488   Cracking the Books for Financial Aid to Colle...\n",
       "2007-01-28            12489   First You Clear the Table. Delight in Science...\n",
       "2007-01-29            12490   Courts Turn to Wikipedia, but Selectively. Te...\n",
       "2007-01-30            12523   Can Humanity Survive? Want to Bet on It?. Awa...\n",
       "...                     ...                                                ...\n",
       "2016-12-02            19170   Silicon Valley Chiefs Notably Absent From Tru...\n",
       "2016-12-03            19185   Trump, Taiwan and China: The Controversy, Exp...\n",
       "2016-12-04            19200   Hunting ‘Turr’ in Newfoundland’s Frigid Water...\n",
       "2016-12-05            19216   Trump’s Meeting With Al Gore Gives Environmen...\n",
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       "2016-12-07            19549   Can Congress Lift the Presidential Conflict o...\n",
       "2016-12-08            19614   The Freaky Food Chain Behind Your Lobster Din...\n",
       "2016-12-09            19756   Trump Spent Far Less Than Clinton, but Paid H...\n",
       "2016-12-10            19770   As Turkey Cracks Down, Kurdish Mayors Pack Ba...\n",
       "2016-12-11            19783   I.M.F. Chief Heads to Trial; Fed Is Expected ...\n",
       "2016-12-12            19796   Pennsylvania and Wisconsin End Election Recou...\n",
       "2016-12-13            19911   The Most Talked-About Debates of 2016. Wells ...\n",
       "2016-12-14            19792   Can Trump Get Tough With China?. The Oakland ...\n",
       "2016-12-15            19852   Hispanic Surnames on the Rise in U.S. as Immi...\n",
       "2016-12-16            19843   Vine, the Six-Second Video App, Is Not Quite ...\n",
       "2016-12-17            19856   Trump’s Win Helps Carve a Path to Washington ...\n",
       "2016-12-18            19869   With Comic Book, Celebrities Pay Tribute to O...\n",
       "2016-12-19            19883   Donald Trump and the C.I.A.. Vincent Viola, B...\n",
       "2016-12-20            19974   Can Trump's Infrastructure Plan Work?. Volksw...\n",
       "2016-12-21            19941   Совершенное орудие: как российская кибермощь ...\n",
       "2016-12-22            19918   New Ebola Vaccine Gives 100 Percent Protectio...\n",
       "2016-12-23            19933   Flurry of Settlements Over Toxic Mortgages Ma...\n",
       "2016-12-24            19936   Jason Miller Backs Out of White House Communi...\n",
       "2016-12-25            19939   Wielding Claims of ‘Fake News,’ Conservatives...\n",
       "2016-12-26            19942   When One Party Has the Governor’s Mansion and...\n",
       "2016-12-27            19945   Should the U.S. Embassy Be Moved From Tel Avi...\n",
       "2016-12-28            19833   When Finding the Right Lawyer Seems Daunting,...\n",
       "2016-12-29            19819   Does Empathy Guide or Hinder Moral Action?. C...\n",
       "2016-12-30            19762   Shielding Seized Assets From Corruption’s Clu...\n",
       "2016-12-31            19762  Terrorist Attack at Nightclub in Istanbul Kill...\n",
       "\n",
       "[3653 rows x 2 columns]"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks_dataf['adj_close_price'] = stocks_dataf['adj_close_price'].apply(np.int64)\n",
    "stocks_dataf = stocks_dataf[['adj_close_price', 'Tweets']]\n",
    "stocks_dataf['Tweets'] = stocks_dataf['Tweets'].map(lambda x: x.lstrip('.-'))\n",
    "stocks_dataf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Making new dataframe and only considering \"Adjusted close price\". And date as index vlaue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe = stocks_dataf[['adj_close_price']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>adj_close_price</th>\n",
       "      <th>Comp</th>\n",
       "      <th>Negative</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Positive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>12469</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-02</th>\n",
       "      <td>12472</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-03</th>\n",
       "      <td>12474</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-04</th>\n",
       "      <td>12480</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-05</th>\n",
       "      <td>12398</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-06</th>\n",
       "      <td>12406</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-07</th>\n",
       "      <td>12414</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-08</th>\n",
       "      <td>12423</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-09</th>\n",
       "      <td>12416</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-10</th>\n",
       "      <td>12442</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-11</th>\n",
       "      <td>12514</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-12</th>\n",
       "      <td>12556</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-13</th>\n",
       "      <td>12562</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-14</th>\n",
       "      <td>12569</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-15</th>\n",
       "      <td>12575</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-16</th>\n",
       "      <td>12582</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-17</th>\n",
       "      <td>12577</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-18</th>\n",
       "      <td>12567</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-19</th>\n",
       "      <td>12565</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-20</th>\n",
       "      <td>12536</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-21</th>\n",
       "      <td>12506</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-22</th>\n",
       "      <td>12477</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-23</th>\n",
       "      <td>12533</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-24</th>\n",
       "      <td>12621</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-25</th>\n",
       "      <td>12502</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-26</th>\n",
       "      <td>12487</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-27</th>\n",
       "      <td>12488</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-28</th>\n",
       "      <td>12489</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-29</th>\n",
       "      <td>12490</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-30</th>\n",
       "      <td>12523</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-02</th>\n",
       "      <td>19170</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-03</th>\n",
       "      <td>19185</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-04</th>\n",
       "      <td>19200</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-05</th>\n",
       "      <td>19216</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-06</th>\n",
       "      <td>19251</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-07</th>\n",
       "      <td>19549</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-08</th>\n",
       "      <td>19614</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-09</th>\n",
       "      <td>19756</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-10</th>\n",
       "      <td>19770</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-11</th>\n",
       "      <td>19783</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-12</th>\n",
       "      <td>19796</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-13</th>\n",
       "      <td>19911</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-14</th>\n",
       "      <td>19792</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-15</th>\n",
       "      <td>19852</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-16</th>\n",
       "      <td>19843</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-17</th>\n",
       "      <td>19856</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-18</th>\n",
       "      <td>19869</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-19</th>\n",
       "      <td>19883</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-20</th>\n",
       "      <td>19974</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-21</th>\n",
       "      <td>19941</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-22</th>\n",
       "      <td>19918</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-23</th>\n",
       "      <td>19933</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-24</th>\n",
       "      <td>19936</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-25</th>\n",
       "      <td>19939</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-26</th>\n",
       "      <td>19942</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-27</th>\n",
       "      <td>19945</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-28</th>\n",
       "      <td>19833</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29</th>\n",
       "      <td>19819</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30</th>\n",
       "      <td>19762</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>19762</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3653 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            adj_close_price Comp Negative Neutral Positive\n",
       "2007-01-01            12469                               \n",
       "2007-01-02            12472                               \n",
       "2007-01-03            12474                               \n",
       "2007-01-04            12480                               \n",
       "2007-01-05            12398                               \n",
       "2007-01-06            12406                               \n",
       "2007-01-07            12414                               \n",
       "2007-01-08            12423                               \n",
       "2007-01-09            12416                               \n",
       "2007-01-10            12442                               \n",
       "2007-01-11            12514                               \n",
       "2007-01-12            12556                               \n",
       "2007-01-13            12562                               \n",
       "2007-01-14            12569                               \n",
       "2007-01-15            12575                               \n",
       "2007-01-16            12582                               \n",
       "2007-01-17            12577                               \n",
       "2007-01-18            12567                               \n",
       "2007-01-19            12565                               \n",
       "2007-01-20            12536                               \n",
       "2007-01-21            12506                               \n",
       "2007-01-22            12477                               \n",
       "2007-01-23            12533                               \n",
       "2007-01-24            12621                               \n",
       "2007-01-25            12502                               \n",
       "2007-01-26            12487                               \n",
       "2007-01-27            12488                               \n",
       "2007-01-28            12489                               \n",
       "2007-01-29            12490                               \n",
       "2007-01-30            12523                               \n",
       "...                     ...  ...      ...     ...      ...\n",
       "2016-12-02            19170                               \n",
       "2016-12-03            19185                               \n",
       "2016-12-04            19200                               \n",
       "2016-12-05            19216                               \n",
       "2016-12-06            19251                               \n",
       "2016-12-07            19549                               \n",
       "2016-12-08            19614                               \n",
       "2016-12-09            19756                               \n",
       "2016-12-10            19770                               \n",
       "2016-12-11            19783                               \n",
       "2016-12-12            19796                               \n",
       "2016-12-13            19911                               \n",
       "2016-12-14            19792                               \n",
       "2016-12-15            19852                               \n",
       "2016-12-16            19843                               \n",
       "2016-12-17            19856                               \n",
       "2016-12-18            19869                               \n",
       "2016-12-19            19883                               \n",
       "2016-12-20            19974                               \n",
       "2016-12-21            19941                               \n",
       "2016-12-22            19918                               \n",
       "2016-12-23            19933                               \n",
       "2016-12-24            19936                               \n",
       "2016-12-25            19939                               \n",
       "2016-12-26            19942                               \n",
       "2016-12-27            19945                               \n",
       "2016-12-28            19833                               \n",
       "2016-12-29            19819                               \n",
       "2016-12-30            19762                               \n",
       "2016-12-31            19762                               \n",
       "\n",
       "[3653 rows x 5 columns]"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe[\"Comp\"] = ''\n",
    "dataframe[\"Negative\"] = ''\n",
    "dataframe[\"Neutral\"] = ''\n",
    "dataframe[\"Positive\"] = ''\n",
    "dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     C:\\Users\\anubh\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('vader_lexicon')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  if __name__ == '__main__':\n",
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:11: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:12: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  if sys.path[0] == '':\n"
     ]
    }
   ],
   "source": [
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "import unicodedata\n",
    "sentiment_i_a = SentimentIntensityAnalyzer()\n",
    "for indexx, row in stocks_dataf.T.iteritems():\n",
    "    try:\n",
    "        sentence_i = unicodedata.normalize('NFKD', stocks_dataf.loc[indexx, 'Tweets'])\n",
    "        sentence_sentiment = sentiment_i_a.polarity_scores(sentence_i)\n",
    "        dataframe.set_value(indexx, 'Comp', sentence_sentiment['compound'])\n",
    "        dataframe.set_value(indexx, 'Negative', sentence_sentiment['neg'])\n",
    "        dataframe.set_value(indexx, 'Neutral', sentence_sentiment['neu'])\n",
    "        dataframe.set_value(indexx, 'Positive', sentence_sentiment['pos'])\n",
    "    except TypeError:\n",
    "        print (stocks_dataf.loc[indexx, 'Tweets'])\n",
    "        print (indexx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "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>adj_close_price</th>\n",
       "      <th>Comp</th>\n",
       "      <th>Negative</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Positive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>12469</td>\n",
       "      <td>-0.9814</td>\n",
       "      <td>0.159</td>\n",
       "      <td>0.749</td>\n",
       "      <td>0.093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-02</th>\n",
       "      <td>12472</td>\n",
       "      <td>-0.8179</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.787</td>\n",
       "      <td>0.099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-03</th>\n",
       "      <td>12474</td>\n",
       "      <td>-0.9993</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-04</th>\n",
       "      <td>12480</td>\n",
       "      <td>-0.9982</td>\n",
       "      <td>0.131</td>\n",
       "      <td>0.806</td>\n",
       "      <td>0.062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-05</th>\n",
       "      <td>12398</td>\n",
       "      <td>-0.9901</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.794</td>\n",
       "      <td>0.082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-06</th>\n",
       "      <td>12406</td>\n",
       "      <td>-0.965</td>\n",
       "      <td>0.134</td>\n",
       "      <td>0.771</td>\n",
       "      <td>0.094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-07</th>\n",
       "      <td>12414</td>\n",
       "      <td>-0.9975</td>\n",
       "      <td>0.193</td>\n",
       "      <td>0.739</td>\n",
       "      <td>0.069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-08</th>\n",
       "      <td>12423</td>\n",
       "      <td>-0.9601</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.793</td>\n",
       "      <td>0.097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-09</th>\n",
       "      <td>12416</td>\n",
       "      <td>-0.9953</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-10</th>\n",
       "      <td>12442</td>\n",
       "      <td>-0.9534</td>\n",
       "      <td>0.134</td>\n",
       "      <td>0.743</td>\n",
       "      <td>0.123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-11</th>\n",
       "      <td>12514</td>\n",
       "      <td>-0.998</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.814</td>\n",
       "      <td>0.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-12</th>\n",
       "      <td>12556</td>\n",
       "      <td>-0.9986</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.784</td>\n",
       "      <td>0.059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-13</th>\n",
       "      <td>12562</td>\n",
       "      <td>-0.9893</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.794</td>\n",
       "      <td>0.059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-14</th>\n",
       "      <td>12569</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-15</th>\n",
       "      <td>12575</td>\n",
       "      <td>-0.2636</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-16</th>\n",
       "      <td>12582</td>\n",
       "      <td>-0.9798</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.804</td>\n",
       "      <td>0.086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-17</th>\n",
       "      <td>12577</td>\n",
       "      <td>-0.9986</td>\n",
       "      <td>0.176</td>\n",
       "      <td>0.743</td>\n",
       "      <td>0.081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-18</th>\n",
       "      <td>12567</td>\n",
       "      <td>-0.9994</td>\n",
       "      <td>0.213</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-19</th>\n",
       "      <td>12565</td>\n",
       "      <td>-0.9959</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.822</td>\n",
       "      <td>0.062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-20</th>\n",
       "      <td>12536</td>\n",
       "      <td>-0.9841</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.786</td>\n",
       "      <td>0.081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-21</th>\n",
       "      <td>12506</td>\n",
       "      <td>-0.9851</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-22</th>\n",
       "      <td>12477</td>\n",
       "      <td>-0.5638</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.804</td>\n",
       "      <td>0.097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-23</th>\n",
       "      <td>12533</td>\n",
       "      <td>-0.9989</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.781</td>\n",
       "      <td>0.066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-24</th>\n",
       "      <td>12621</td>\n",
       "      <td>-0.9921</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.757</td>\n",
       "      <td>0.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-25</th>\n",
       "      <td>12502</td>\n",
       "      <td>-0.9965</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.744</td>\n",
       "      <td>0.103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-26</th>\n",
       "      <td>12487</td>\n",
       "      <td>-0.997</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-27</th>\n",
       "      <td>12488</td>\n",
       "      <td>-0.9786</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.802</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-28</th>\n",
       "      <td>12489</td>\n",
       "      <td>-0.9865</td>\n",
       "      <td>0.157</td>\n",
       "      <td>0.753</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-29</th>\n",
       "      <td>12490</td>\n",
       "      <td>-0.9359</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.739</td>\n",
       "      <td>0.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-30</th>\n",
       "      <td>12523</td>\n",
       "      <td>-0.9992</td>\n",
       "      <td>0.183</td>\n",
       "      <td>0.727</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-02</th>\n",
       "      <td>19170</td>\n",
       "      <td>-0.9962</td>\n",
       "      <td>0.155</td>\n",
       "      <td>0.768</td>\n",
       "      <td>0.077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-03</th>\n",
       "      <td>19185</td>\n",
       "      <td>-0.9827</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-04</th>\n",
       "      <td>19200</td>\n",
       "      <td>-0.9719</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.772</td>\n",
       "      <td>0.063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-05</th>\n",
       "      <td>19216</td>\n",
       "      <td>-0.9636</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.691</td>\n",
       "      <td>0.144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-06</th>\n",
       "      <td>19251</td>\n",
       "      <td>-0.9967</td>\n",
       "      <td>0.172</td>\n",
       "      <td>0.726</td>\n",
       "      <td>0.103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-07</th>\n",
       "      <td>19549</td>\n",
       "      <td>-0.9735</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.794</td>\n",
       "      <td>0.087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-08</th>\n",
       "      <td>19614</td>\n",
       "      <td>-0.9944</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-09</th>\n",
       "      <td>19756</td>\n",
       "      <td>-0.9968</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.726</td>\n",
       "      <td>0.101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-10</th>\n",
       "      <td>19770</td>\n",
       "      <td>-0.9952</td>\n",
       "      <td>0.171</td>\n",
       "      <td>0.747</td>\n",
       "      <td>0.082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-11</th>\n",
       "      <td>19783</td>\n",
       "      <td>-0.0772</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.808</td>\n",
       "      <td>0.093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-12</th>\n",
       "      <td>19796</td>\n",
       "      <td>-0.9697</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-13</th>\n",
       "      <td>19911</td>\n",
       "      <td>-0.9986</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-14</th>\n",
       "      <td>19792</td>\n",
       "      <td>-0.9674</td>\n",
       "      <td>0.121</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-15</th>\n",
       "      <td>19852</td>\n",
       "      <td>-0.9912</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.834</td>\n",
       "      <td>0.049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-16</th>\n",
       "      <td>19843</td>\n",
       "      <td>-0.9896</td>\n",
       "      <td>0.141</td>\n",
       "      <td>0.776</td>\n",
       "      <td>0.083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-17</th>\n",
       "      <td>19856</td>\n",
       "      <td>-0.9859</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.795</td>\n",
       "      <td>0.072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-18</th>\n",
       "      <td>19869</td>\n",
       "      <td>-0.9884</td>\n",
       "      <td>0.206</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-19</th>\n",
       "      <td>19883</td>\n",
       "      <td>-0.9942</td>\n",
       "      <td>0.148</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-20</th>\n",
       "      <td>19974</td>\n",
       "      <td>-0.9641</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.816</td>\n",
       "      <td>0.071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-21</th>\n",
       "      <td>19941</td>\n",
       "      <td>-0.9955</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.776</td>\n",
       "      <td>0.071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-22</th>\n",
       "      <td>19918</td>\n",
       "      <td>-0.9983</td>\n",
       "      <td>0.199</td>\n",
       "      <td>0.724</td>\n",
       "      <td>0.077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-23</th>\n",
       "      <td>19933</td>\n",
       "      <td>-0.9864</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-24</th>\n",
       "      <td>19936</td>\n",
       "      <td>-0.9234</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.748</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-25</th>\n",
       "      <td>19939</td>\n",
       "      <td>-0.9851</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-26</th>\n",
       "      <td>19942</td>\n",
       "      <td>-0.9704</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.746</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-27</th>\n",
       "      <td>19945</td>\n",
       "      <td>-0.9898</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.719</td>\n",
       "      <td>0.103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-28</th>\n",
       "      <td>19833</td>\n",
       "      <td>0.2869</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.763</td>\n",
       "      <td>0.108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29</th>\n",
       "      <td>19819</td>\n",
       "      <td>-0.9789</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.764</td>\n",
       "      <td>0.097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30</th>\n",
       "      <td>19762</td>\n",
       "      <td>-0.995</td>\n",
       "      <td>0.168</td>\n",
       "      <td>0.734</td>\n",
       "      <td>0.098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>19762</td>\n",
       "      <td>-0.2869</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.665</td>\n",
       "      <td>0.161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3653 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            adj_close_price    Comp Negative Neutral Positive\n",
       "2007-01-01            12469 -0.9814    0.159   0.749    0.093\n",
       "2007-01-02            12472 -0.8179    0.114   0.787    0.099\n",
       "2007-01-03            12474 -0.9993    0.198   0.737    0.065\n",
       "2007-01-04            12480 -0.9982    0.131   0.806    0.062\n",
       "2007-01-05            12398 -0.9901    0.124   0.794    0.082\n",
       "2007-01-06            12406  -0.965    0.134   0.771    0.094\n",
       "2007-01-07            12414 -0.9975    0.193   0.739    0.069\n",
       "2007-01-08            12423 -0.9601     0.11   0.793    0.097\n",
       "2007-01-09            12416 -0.9953    0.103   0.848    0.049\n",
       "2007-01-10            12442 -0.9534    0.134   0.743    0.123\n",
       "2007-01-11            12514  -0.998    0.128   0.814    0.057\n",
       "2007-01-12            12556 -0.9986    0.158   0.784    0.059\n",
       "2007-01-13            12562 -0.9893    0.146   0.794    0.059\n",
       "2007-01-14            12569   -0.99    0.178   0.711    0.111\n",
       "2007-01-15            12575 -0.2636    0.088    0.83    0.082\n",
       "2007-01-16            12582 -0.9798     0.11   0.804    0.086\n",
       "2007-01-17            12577 -0.9986    0.176   0.743    0.081\n",
       "2007-01-18            12567 -0.9994    0.213     0.7    0.086\n",
       "2007-01-19            12565 -0.9959    0.116   0.822    0.062\n",
       "2007-01-20            12536 -0.9841    0.132   0.786    0.081\n",
       "2007-01-21            12506 -0.9851     0.14   0.775    0.084\n",
       "2007-01-22            12477 -0.5638    0.099   0.804    0.097\n",
       "2007-01-23            12533 -0.9989    0.153   0.781    0.066\n",
       "2007-01-24            12621 -0.9921    0.138   0.757    0.105\n",
       "2007-01-25            12502 -0.9965    0.153   0.744    0.103\n",
       "2007-01-26            12487  -0.997    0.143    0.78    0.077\n",
       "2007-01-27            12488 -0.9786    0.128   0.802     0.07\n",
       "2007-01-28            12489 -0.9865    0.157   0.753     0.09\n",
       "2007-01-29            12490 -0.9359    0.135   0.739    0.126\n",
       "2007-01-30            12523 -0.9992    0.183   0.727     0.09\n",
       "...                     ...     ...      ...     ...      ...\n",
       "2016-12-02            19170 -0.9962    0.155   0.768    0.077\n",
       "2016-12-03            19185 -0.9827     0.16   0.737    0.103\n",
       "2016-12-04            19200 -0.9719    0.165   0.772    0.063\n",
       "2016-12-05            19216 -0.9636    0.165   0.691    0.144\n",
       "2016-12-06            19251 -0.9967    0.172   0.726    0.103\n",
       "2016-12-07            19549 -0.9735    0.118   0.794    0.087\n",
       "2016-12-08            19614 -0.9944    0.145   0.785     0.07\n",
       "2016-12-09            19756 -0.9968    0.173   0.726    0.101\n",
       "2016-12-10            19770 -0.9952    0.171   0.747    0.082\n",
       "2016-12-11            19783 -0.0772    0.099   0.808    0.093\n",
       "2016-12-12            19796 -0.9697    0.126   0.775      0.1\n",
       "2016-12-13            19911 -0.9986    0.173    0.76    0.067\n",
       "2016-12-14            19792 -0.9674    0.121     0.8     0.08\n",
       "2016-12-15            19852 -0.9912    0.118   0.834    0.049\n",
       "2016-12-16            19843 -0.9896    0.141   0.776    0.083\n",
       "2016-12-17            19856 -0.9859    0.133   0.795    0.072\n",
       "2016-12-18            19869 -0.9884    0.206    0.73    0.064\n",
       "2016-12-19            19883 -0.9942    0.148    0.77    0.082\n",
       "2016-12-20            19974 -0.9641    0.113   0.816    0.071\n",
       "2016-12-21            19941 -0.9955    0.153   0.776    0.071\n",
       "2016-12-22            19918 -0.9983    0.199   0.724    0.077\n",
       "2016-12-23            19933 -0.9864    0.154   0.737     0.11\n",
       "2016-12-24            19936 -0.9234    0.152   0.748      0.1\n",
       "2016-12-25            19939 -0.9851    0.164   0.777    0.059\n",
       "2016-12-26            19942 -0.9704    0.154   0.746      0.1\n",
       "2016-12-27            19945 -0.9898    0.178   0.719    0.103\n",
       "2016-12-28            19833  0.2869    0.128   0.763    0.108\n",
       "2016-12-29            19819 -0.9789    0.138   0.764    0.097\n",
       "2016-12-30            19762  -0.995    0.168   0.734    0.098\n",
       "2016-12-31            19762 -0.2869    0.173   0.665    0.161\n",
       "\n",
       "[3653 rows x 5 columns]"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of positive tweets=  45.414727621133316\n",
      "% of negative tweets=  54.39364905557076\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/24+YaCuPmNi2kjO5E4DtoX3l82HYyoVh/Ja59Wn737NzyphVDB/pHFfSra93gM7K4oUhDwNHeccQf/Vga9Yy5Jkl9eLGZWGUBTDvTJIurzDghAsu/j+ZKbksFvIjwHTvGCKSCW1EcWZKLotnuF/xDiAimRCyVMaQzUJ+2TuAiGRC5rpChSwiebXaO0BnZbGQY+8AIpIJL3kH6KwsFvLz3gFEJBNUyL3gae8AIpIJmds3XYUsInn1hHeAzlIhi0hePeYdoLOyV8hR/AJaiywie/eod4DOyl4hJx72DiAiqbaRKH7WO0RnZbWQH/AOICKpttA7QFdktZDnewcQkVSb7R2gK7JayPO8A4hIqs32DtAV2SzkKH4eeNw7hoik0lbgXu8QXZHNQk780juAiKTSfUTxZu8QXZHlQr7FO4CIpNLN3gG6KsuFfC8Z3M1JRJqqDlzvHaKrslvIUVwHbvWOISKpMrtxjimTslvIiau8A4hIqlzrHaA7sl3IUTyHDF6vLiJNsQ24yTtEd2S7kBOXeQcQkVS4nSje4B2iO/JQyP8X2OQdQkTc/bd3gO7KfiFHcQxc5x1DRFw9BvzCO0R3Zb+QEz/yDiAirqLGyqtMy0chR/ECtOGQSKt6hIyfzHtNPgo58RXvACLi4u+J4uAdoifkp5CjeB5wg3cMEelVC4ji3GyjkJ9CTnwV2OIdQkR6zUXeAXpSvgo5ip8BLvWOISK94h6i+DbvED0pX4WcqADPeYcQkab7lneAnpa/Qo7iV4GydwwRaapfE8W/9Q7R0/JXyImrgfu8Q4hIU2wEzvUO0Qz5LORkCcwFQC6WwojIG3yZKF7hHaIZ8lnIAFE8H7jcO4aI9KhfE8U/9g7RLPkt5MQFwIPeIUSkR8TAOd4hminfhRzFW4HTgPXeUUSk284lip/1DtFM+S5kgCiuAX+B5pNFsuwKovhG7xDNlv9CBojiKvAd7xgi0iVLSKYfc681CjlxEZC7dYsiObcJmEkUb/YO0htap5CjeCfwcXQVn0hW7CAp45a5b2brFDJAFK8GTge2e0cRkT0KwDlE8a3eQXpTaxUyQBTfA3zZO4aI7NHXiOKrvEP0ttYrZIAo/j5wsXcMEdmlS4niS7xDeGjNQgaI4m8B3/OOISJv8FOSfc1bkoXQ4stzo8KPyfnVPyIZ8UvgVKJ4h3cQL607Qn7dZ4BrvEOItLh5JCsqWraMQSPkRFRoA34CnOUdRaQFLQaOJ4o3eAfxphEyQBTXgb8CcruLlEhKzQNOUBknVMivSfZQ/gzwn95RRFrEdcCfEcXrvIOkhaYsdiUq/BPwDe8YIjlWAb7RGAhJgwp5d6LCGcB/AQO9o4jkyA7gc3neZL47VMh7EhWOAv4fcJh3FJEc2AicThT/yjtIWqmQ9yYqHATcAJzoHUUkw1YBf04UL/IOkmY6qbc3UbwGeB8wyzuKSEY9AhyrMt47jZA7IyqcBVwG9PeOIpIRNwKfJoo3egfJAhVyZ0WFdwC/AMZ4RxFJsRj4PFF8tXeQLFEhd0VUOJhkXvl47ygiKTQbOIsoXuEdJGs0h9wVUfwiyUm+LwKvOKcRSYutwFeAk1TGXaMRcndFhcOAK0hO/Im0qsXAmTpx1z0q5J4SFc4G/g0Y6pxEpDcFkv/v/44o3uodJutUyD0pmVv+AfBR7ygivWAFcDZR/DvvIHmhQm6GqPBRkk2K3uYdRaQJdgA/BC4iimPvMHmiQm6WqDCU5Fe5s52TiPSkO4ALiOJHvYPkkQq52aLCScB3gRneUUS6YTnwZaL4Zu8geaZlb80WxXcC7wBOA5Y6pxHprJhkK9rJKuPm0wi5N0WFduBMIAKKrllE9mwbyTzxxUTxWu8wrUKF7CEq7AecC3wTnfiTdAnAtcA3ieLl3mFajQrZU1QYCHwB+CowzDmNtLadwE3Ad4nih73DtCoVchpEhQJwIXABMMg5jbSWV4ArgVlEcc05S8tTIadJVBgOfBY4DxjpnEbybRXwH8DlWkucHirkNIoKfYGZwN8AxzinkXxZCPwrcD1RvN07jLyRCjntosK7SYr5I0Bf5zSSXbcDlzaWYUpKqZCzItkn469IVmeMc04j2RCT7Ns9S1fWZYMKOWuiggHvBf4a+DDQxzeQpMxW4H+Aa4AqUbzFOY90ggo5y6LC20iuAPwocBzQ7htInNSBu0hK+OdE8QbnPNJFKuS8iAojgFNIyvlP0ci5FTxMUsLXEcWrvMNI96mQ8yjZae7DJOX8XqCfbyDpQcuAnwE/I4q1N0rOqJDzLioMBj5AMrVxMjDQN5B00mZgLvAb4DdE8SPOeaSJVMitJLlU+2SSKY3jgamAuWaSNwskUxG/aTzm6dZIrUOF3MqSqY3jSMr5eOBoNPfsYQWvF/CdRPEa5zziRIUsr4sKg4B383pBvxPNPzfDCuAB4Hck0xC/d84jKaFClt2LCv1ISvl4kk32p5BclKIbG+y7VcCDwII/PKL4Jd9IklYqZOmcqNAfOBKYTFLQr30cR2uvg94CPAYsAhY3Pi4iile7ppJMUSFLz0iKeiJvLeoisJ9fsB4TgBdIphve/HgC+D1RvNMvnuSBClmaK7nU+yBg9C4eI4Hhja8Px28v6B3ARnZfuCuAlUTxNqd80iJUyJIeySh7eONxAMnI+s2Pvrt5/rVHO8na3Vd28Xh1l8+raCUlVMgiIimhs+UiIimhQhYRSQkVsuwTMzvAzD7X4c+jzOwmz0wieaM5ZNknZlYEbg0hTHWOIpJbGiHnhJkVzWypmf3YzB41s1+b2QAzO9zMbjezB81srpkd2fj+w81svpk9YGb/aGavNJ4fZGZ3mtlDZrbYzD7cOEQFONzMFprZJY3jLWn8zH1mNqVDltlmNsPM9jeznzSO8XCH1xKRXVAh58sRwA9CCFOADST7IV8BfCGEMAO4EPhh43tnAbNCCO8AnuvwGluAj4QQ/gg4EfhXMzOgDDwdQjgqhPCVNx33OuBjAGY2EhgVQngQ+Dvgt41jnAhcYmb79/i7FskJFXK+LA8hLGx8/iDJVXLvBm40s4XA5SQXYwC8C7ix8fnPOryGAf9sZouAO0gu4Dh4L8e9ATi98fnHOrzu+4By49izgf7AoZ1+VyItQlst5kvHfXN3khTphhDCUZ14jU+SXJgxI4Sw3cxqJEW6WyGEVWa21symAzOBzzS+ZMBHQwhPdOL4Ii1LI+R8exlYbmanA1ji7Y2vzSeZ0gA4o8PPFIDVjTI+ETis8fxGYPAejnUd8FWgEEJY3HjuV8AXGlMemNnR3X1DInmmQs6/TwKfNrNHgEdJ7rUHcAHwJTO7n2QaI248fw1wjJktaPzs4wAhhLXA3Wa2xMwu2cVxbiIp9hs6PPdtkkudFzVOAH67R9+ZSM5o2VuLMrOBwOYQQjCzM4CPhxC0CkLEkeaQW9cM4D8b0wkbgE855xFpeRohi4ikhOaQRURSQoUsIpISKmQRkZRQIYuIpIQKWUQkJVTIIiIpoUIWEUkJFbKISEqokEVEUkKFLCKSEipkEZGUUCGLiKSECllEJCVUyCIiKaFCFhFJCRWyiEhKqJBFRFJChSwikhL/C9jBctcBZmDIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "posi=0\n",
    "nega=0\n",
    "for i in range (0,len(dataframe)):\n",
    "    get_val=dataframe.Comp[i]\n",
    "    if(float(get_val)<(-0.99)):\n",
    "        nega=nega+1\n",
    "    if(float(get_val>(-0.99))):\n",
    "        posi=posi+1\n",
    "posper=(posi/(len(dataframe)))*100\n",
    "negper=(nega/(len(dataframe)))*100\n",
    "print(\"% of positive tweets= \",posper)\n",
    "print(\"% of negative tweets= \",negper)\n",
    "arr=np.asarray([posper,negper], dtype=int)\n",
    "mlpt.pie(arr,labels=['positive','negative'])\n",
    "mlpt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \"\"\"\n",
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "train_data_start = '2007-01-01'\n",
    "train_data_end = '2014-12-31'\n",
    "test_data_start = '2015-01-01'\n",
    "test_data_end = '2016-12-31'\n",
    "train = dataframe.ix[train_data_start : train_data_end]\n",
    "test = dataframe.ix[test_data_start:test_data_end]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_of_sentiments_score = []\n",
    "for date, row in train.T.iteritems():\n",
    "    sentiment_score = np.asarray([dataframe.loc[date, 'Comp']])\n",
    "    list_of_sentiments_score.append(sentiment_score)\n",
    "numpy_dataframe_train = np.asarray(list_of_sentiments_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_of_sentiments_score = []\n",
    "for date, row in test.T.iteritems():\n",
    "    sentiment_score = np.asarray([dataframe.loc[date, 'Comp']])\n",
    "    list_of_sentiments_score.append(sentiment_score)\n",
    "numpy_dataframe_test = np.asarray(list_of_sentiments_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = pd.DataFrame(train['adj_close_price'])\n",
    "y_test = pd.DataFrame(test['adj_close_price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.289546293015126\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from treeinterpreter import treeinterpreter as ti\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "\n",
    "rf = RandomForestRegressor()\n",
    "rf.fit(numpy_dataframe_train, train['adj_close_price'])\n",
    "prediction=rf.predict(numpy_dataframe_test)\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "idx = pd.date_range(test_data_start, test_data_end)\n",
    "predictions_df = pd.DataFrame(data=prediction[0:], index = idx, columns=['adj_close_price'])\n",
    "predictions_df['adj_close_price'] = predictions_df['adj_close_price'].apply(np.int64)\n",
    "predictions_df['adj_close_price'] = predictions_df['adj_close_price'] + 4500\n",
    "predictions_df['actual_value'] = test['adj_close_price']\n",
    "predictions_df.columns = ['predicted_price', 'actual_price']\n",
    "predictions_df.plot()\n",
    "predictions_df['predicted_price'] = predictions_df['predicted_price'].apply(np.int64)\n",
    "test['adj_close_price']=test['adj_close_price'].apply(np.int64)\n",
    "#print(accuracy_score(test['adj_close_price'],predictions_df['predicted_price']))\n",
    "print(rf.score(numpy_dataframe_train, train['adj_close_price']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "mlpc = MLPClassifier(hidden_layer_sizes=(10,), activation='relu', #'relu', the rectified linear unit function\n",
    "                     solver='lbfgs', alpha=0.005, learning_rate_init = 0.001, shuffle=False)\n",
    "\"\"\"Hidden_Layer_Sizes: tuple, length = n_layers - 2, default (100,)\n",
    "The ith element represents the number of Neutralrons in the ith\n",
    "hidden layer.\"\"\"\n",
    "mlpc.fit(numpy_dataframe_train, train['adj_close_price'])   \n",
    "prediction = mlpc.predict(numpy_dataframe_test)\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "idx = pd.date_range(test_data_start, test_data_end)\n",
    "predictions_df = pd.DataFrame(data=prediction[0:], index = idx, columns=['adj_close_price'])\n",
    "predictions_df['adj_close_price'] = predictions_df['adj_close_price'].apply(np.int64)\n",
    "predictions_df['adj_close_price'] = predictions_df['adj_close_price'] +4500\n",
    "predictions_df['actual_value'] = test['adj_close_price']\n",
    "predictions_df.columns = ['predicted_price', 'actual_price']\n",
    "predictions_df.plot()\n",
    "predictions_df['predicted_price'] = predictions_df['predicted_price'].apply(np.int64)\n",
    "test['adj_close_price']=test['adj_close_price'].apply(np.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.006844626967830253\n"
     ]
    }
   ],
   "source": [
    "print(mlpc.score(numpy_dataframe_train, train['adj_close_price']))\n",
    "#print(accuracy_score(test['adj_close_price'],predictions_df['predicted_price']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "from datetime import datetime, timedelta\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn import datasets, linear_model\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(numpy_dataframe_train, train['adj_close_price'])   \n",
    "prediction = regr.predict(numpy_dataframe_test)\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "idx = pd.date_range(test_data_start, test_data_end)\n",
    "predictions_df = pd.DataFrame(data=prediction[0:], index = idx, columns=['adj_close_price'])\n",
    "predictions_df['adj_close_price'] = predictions_df['adj_close_price'].apply(np.int64)\n",
    "predictions_df['adj_close_price'] = predictions_df['adj_close_price'] + 4500\n",
    "predictions_df['actual_value'] = test['adj_close_price']\n",
    "predictions_df.columns = ['predicted_price', 'actual_price']\n",
    "predictions_df.plot()\n",
    "predictions_df['predicted_price'] = predictions_df['predicted_price'].apply(np.int64)\n",
    "test['adj_close_price']=test['adj_close_price'].apply(np.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  del sys.path[0]\n",
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \n",
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  del sys.path[0]\n",
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \n",
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  del sys.path[0]\n",
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \n",
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  del sys.path[0]\n",
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \n",
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  del sys.path[0]\n",
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \n",
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  del sys.path[0]\n",
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \n",
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  del sys.path[0]\n",
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \n",
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  del sys.path[0]\n",
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \n",
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  del sys.path[0]\n",
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \n",
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  del sys.path[0]\n",
      "C:\\Users\\anubh\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.919672131147541\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"predictions_dataframe1 = pd.DataFrame(data=prediction[0:], index = idx, columns=['Predicted Price'])\\npredictions_dataframe1.plot(color='orange')\\ntrain['adj_close_price'].plot.line(color='green')\""
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/pZSDpJT9pZRPGMa3SymPl1IOkFJeKqX0HJiPasKEiSMJTcihPh+sseq5p0w9+tpGDkII/n3ev1k08UYGxsDv/vcAfZ/vy6Idi4LHFNbuJdMKxHTR7qERUEOR+pEB8LiUkgCo2arUxcKJMLcrFM1X40GzUlPlAHDZsMuwCisL8hZw3ajrGNS1P9TtItn1PWcPmMqcjXOCPokDCbO20gHAxx9/jBCCzZs3t3rsrFmzKCzsuJvFWNY7cjw5OZlRo0YxdOjQYNG9SBQWFnLJJZdEfc+Eic4MnRz6de2nduUBDzhS1Zs6OfjbV0tpUlpPFveARZfNRgjBcytCZXiK6kpVpJKuHPR71OeDu0QbK4FSQ3nuZdeE/B71mg/C1zI5dIvvxhkDzsBhdfDQxIfUNaUPpI8ru/entL6UNXvXtOtzdQQmORwAzJ49m5NPPpn33nuv1WP3lRxawoQJE1izZg25ubm8/fbbrFq1Kux9n89HVlYWc+fOPSD3N2HiQGKrayvZSdnE2mMVMUBo4XZrZbqNysFdCv8ZoqKYmkOgESFgUo/RTO47mZUFK8HXgCxdRlFdmVIO9gjlULmOYJClu1iRQ7dT1GvXKkg/WT33a9FQQZ9DU7MSAFWb+Ofpj/Lttd+QbfWHObbPsRRScGcBY7PGtvjd7A8csbWVbv/ydtbu3b8lu3O65/DcmS0X9KutrWXp0qUsWrSI8847j0ceeST43owZM3jrrbewWCxMmzaNsWPHkpuby9VXX01sbCzLli1j6NCh5ObmkpaWRm5uLnfddReLFy/mhx9+4Pbbb6ehoYHY2Fhef/11Bg8e3KZ5x8fHM2bMGPLy8vjxxx/5/PPPcbvd1NXVMXPmTM455xw2bNiA3+/nnnvuYf78+QghuOGGG7jttttYtWoVd955J7W1taSlpTFr1iwyMzN54YUXeOWVV7DZbBxzzDFtIkMTJvYH1u5dywcbP+DCoReqAV0hxGiF64LKwUAO1Zuhegu4VkNyM5nMgUbt0ctxWcfxzo/vsGHtDBb98Cc8fqn5HDRyaNTIwej0dq1RCmHoXcqs1FAE/a5VhKGTgz96KGsQ311MdsoYsntdAvNOhJy/qvEuw3GWfUP3hO6tfT37BUcsORwqfPLJJ5x55pkMGjSIlJQUVq9ezejRo/niiy/45JNPWLFiBXFxcbhcLlJSUnjxxRd5+umnGTu25Z3AkCFD+Pbbb7HZbCxcuJA//vGPfPjhh22aU3l5OcuXL+fBBx9k5cqVLFu2jPXr15OSksLOnTuDx7366qvs2LGDNWvWYLPZcLlceL1ebrvtNj799FPS09OZM2cO999/PzNnzuTJJ59kx44dOBwOKisr9+VrM2GizZBScvVHV5Mal8oLZ76gBvWFN9LkY1QOuhnH28Lfql9TIIFGjuuhKglf9M3f2VqrlEF/OwafQ1nT80u+UY9dhkPX0dC4CHpeAit+bVAO0ZPggnAXQ22ecrDLAORr6WIZk2HLc+o6Vmfzn2E/4Yglh9Z2+AcKs2fP5vbbbwfgiiuuYPbs2YwePZqFCxfyq1/9KlgqOyWlfaV5q6qqmD59Olu3bkUIgdfbel2Y7777jlGjRmGxWLj33nsZNmwYK1euZMqUKVHvv3DhQm6++WZsNltwjhs2bGDDhg1MmTIFUAX1MjMzARgxYgRXX301F1xwARdccEGT65kwcSBQWl/KT6U/8bepfws1zGmOHIw+B32n3tgCOQSVQyM53XOwCitba8u5PAH+cPzNjNr9iuEerqbnl2nO6MRBMPLP0FAAMckgrCHTVzTl4G9U0U/ODEVi9XtC5qSyZYoMuqpWANTnQ+KBr9x6xJLDoUB5eTlff/01GzZsQAiB3+9HCMGMGTOQUrYptd5mswXbcbrdoYzNBx98kFNPPZWPP/6YnTt3MmnSpFavNWHCBP7zn6ZVHpsruR1tjlJKhg0bxrJly5oc//nnn/Ptt98yb948HnvsMTZu3BgkFhMmDhQ2l6lAj2PSjwkNBsmhBbOSvlNvSTkYzEpx9jiO7XYs64rX8ccUGBEXq2o62CPMSjpiuio/hDUOYrMgrgd0HaHeszqj+Bw0csh7DXJ/q+597haVYNdQCLWqoyIyALHZEKflEdfvOSjkYDqk9yPmzp3LNddcw65du9i5cyd79uyhb9++LFmyhKlTpzJz5kzq69Ufq8uldh2Rpbb79OkTdBwbzUZVVVX06KHKTs2aNeuAzH/q1Km88sor+Hy+4BwHDx5MaWlpkBy8Xi8bN24kEAiwZ88eTj31VGbMmEFlZWWwPLgJEwcSW8q2ADA4zeBza4tZqZ3KAeCmMTfx297HMMIBeCui3wNUlFRctnqeOAAiN4I6OciAUjPCqh4DXij8QpGY9EGllqshA1D+Q+j8+J4hcqjb3fz89yNMctiPmD17NhdeeGHY2MUXX8y7777LmWeeyXnnncfYsWPJycnh6aefBuDaa6/l5ptvJicnh4aGBh5++GF+97vfMWHChLBua3fffTf33XcfJ510En7/gYlx/vWvf02vXr0YMWIEI0eO5N133yUmJoa5c+dyzz33MHLkSHJycvj+++/x+/384he/YPjw4YwaNYo77riDLl26HJB5mTBhxOayzThtTnolG4pvRpKDrhj8UXwOjZVqV16xvunFDcoB4DfH/YbnB6hWv0EzUtDnYDArOTPBoRX4SxzY9LoWJwTcoXk6M7Q5VSsfg9D+1+t2GuZbCUJbomOzQ+RT30xZjv0McbDqdOxvjB07Vubm5oaNbdq0iaFD21BP3cRhAfP3aSIazn73bAqqC1h7syEasXgR/O80OPFd+P6q0Hj6BJii5RmsvAW2vgzdTwdbvDrn7E0QZ6hy+u1FkP8xnDIPss9VY4vPgcLPVUhq6RI452f4zyD1njNDLe7dp4AjDXbNhmPuhZyI/iTzBkDaeBj9HHyUDiljVJjredth0ZlgsamEuSF3wua/hc+/9DsY9kcY+QR82A16XgjH/7PD358QYpWUstVYWFM5mDBh4rDC5rLNDEkbEj4Y6XMIjhsc0npeQWOlcvZ6q2HV78KP15WDNAR8+DRzaaNuVjIo5JgUzaTUI6QGoikH3azk1/wNzkxtTlUqgS5J+zx1ER3qss9T6kG/ZlzP5gv67WeY3kMTJkwcHqj6CffOOeyo2MEvR/wy/L1Is1JwvBmfg79eLbp75qrF2amZhHRy8DeGzguSQ4RZCZQCGTMb4vuoa4GKVIqExaHmqPtAYjVy8JQp85FODrU7ws9LOxGmrYck7ZrxPaEmr+n1DwCOOOVwuJrJTITD/D2aaIK8mazJfRSJZHBqRAKorhDsSYS1iYmW59DoUqagLlokkXGxbUk5eFzKN2Cxq8UewBYHmVMgaSB0Ganu32VY07kHlUMEOdSoEiDE91bX1H0O9mT1GNdTXc9iD70+SD6HI4ocnE4n5eXl5sJymENKSXl5OU7ngU/0MXH4wFO1id+UQHpsKlP6Twl/U1cOtli1YAfHmyEH6Ve7cgiFjEJYElzoPI0cAp4QKegF/qyGe/U4Cy6paKpeQJFDwKgctCznahV5haMbOFJC6qTLsYqIdBLREddTfQ5vDQcaR5RZKTs7m/z8fEpLSw/1VEzsI5xOJ9nZ2Yd6GiY6ER7YvIx1jfDZOQ+QFpcW/qZODhanWrj1XIJoykFH2njY+o9wcoiIVlLXMIRo65nJtlhlDrJF5AyJZvbbVqfycejzShwMCChbrl47uylSaShS4xmnKdOWJWKJNuY6JB/DgcQRRQ52u52+ffse6mmYMGFiP+PrvAU8U1LBzclwTo9jmx5gVA76rh6UuUlKlXfgrQ5PRovvA7E9oC4aOejmJRlBDppysGgkYVQOLSHSrORIg4R+4FqpXjszQorDnggjHlU/keg+BaYuh/gDv84dUWYlEyZMHHlwNbi45pNrGGSHZ9KIXrYiYFAOulnJEqPMRwGvWuS91RBnyI1wdlcLdEvKIeBR19BhMSgHCDdhtQSLM9whbYuD5GGhazu7gV0nh+Tmr+NMg7RxofsfQJjkYMKEiU6N//vv/1FcV8Y73SHOQsgub4TfDQjluNWVg0Ovu1SvzDnSD/EGcohtiRy0R29E1r81wucQaVZqDrrPQVcOVo0c9PdsCcrnAJpT/dDDJAcTJkwcevjdsP4h8IU35/EFfMzZMIdbBpzIGD0+odEFBZ+Hx/vrlUqFMJCD5pfw1Yf8DfG91aM1Ti3ICf2gviBkagpEOKR9keSgm5OiOKRbgtURXTmAckYLYTArtaAcDiJMcjBhwsShR/E3sOExlQ1sQEVDBRLJAFtAJZtZ46ChGL69ADY/C7vnwmeDVfSONcIPoJODvyGUABenkUNsd7UgJ/QDZCj5zGhWkoGmDXkio5XabVbSHNK2+FDIq548Z5KDCROdH0U1RU0azAfhrYXCLw/uhI506KaiiJ7P5Q2q8mma36WSyxypUPWjKlJXv0eVs6j5GdxFTXf1ToNZKVI5OLVQ0gTNsaublvwGs9KXY2Dd/eHzjLxHe8xKukNa2JT5K2mIim7SE/D07O4YkxxMmOiUqPZUM+wfw3ho0UPRD9j5FiyeBu4ozV5MdAxBcqgLGy6rV99xqrdYkUNMClRoNZXq89UPQMPeps5i3efgqw9lR+s+Bz3PQC+Wp1dYNSqHmq1Q/LV6rS/cQeXQgWgl6VMKR1cbVqeqnZQyRruHrhw6h8/hiAplNWFin9BYBWXLeH33FircFeQW5UY/LtifuBpIi36MifZB78fsj1AO9Wo81eeCpMFQv1vr2YxqpIPqfYJ7b2ihbmJWMigHR5pafGNV+ftQy88qFdFk9Dn43YZoogxFYPuiHEDVZzISyumLQ887mVnJJAcTJnTseBN/7m95oawPAD+V/hT9OH2h8TVjdjLRfrRiVkq1opSDa1XozYbC0E6/YW8oKSyqQ1rzHdiTVcVVvVmOvhB7K9XOXoe/PjyE1dkNqjdFiVZqh3IA8JQqR3g06Oqkk5CDaVYyYUJHo4v59bC9cicnZJ9ASV1J0KwRBm8VXgkzVr5CRUPFwZ/nkQg9d8EfblbSlUOaFVV8zlh1VfpVjSRQO/7mfA6lS2DTDDXuSIGMiaqKKoA1RsterooomRHhiNb9ApaORitp5zUUhkJWI9HJlEObyEEIMVMIUSKE2GAYyxFCLBdCrBVC5AohjtfGhRDiBSHENiHEeiHEaMM504UQW7Wf6YbxMUKIH7VzXhBt6adpwsT+hq+WmdWQHtuVe0++F4BNpZuaHtdYyce1cM/3LzJvy7yDPMl2Yus/Ib+TzxFCLTcjlENZfRkxFivxAkgY0PzCCuGlLSDkc/jpSRWSOvGz6GYgexdlVjKSQ2NEqQ09okhXDrZ2mpV0X0VDUfTaS6Cc4z3OVeTVCdBW5TALODNibAbwJyllDvCQ9hpgGjBQ+7kReBlACJECPAyMA44HHhZC6N/Sy9qx+nmR9zJh4oCjtLaUebXwy0FnkNNdNXPfWLoRfyCi8563ije0jWVxXfFBnmU7sekpyPt3mw71BXxUe6pbP7AN+CrvKxbvXNz2EzzRHdLlDeWk2mIQCb3VgqwrBz3qyIjIkhYOgz+o77XQfXL0e8ckK7OSvwXloF9LJ6Cg87u9ZqWypj0ngsc4YOI86DK8bdc8wGgTOUgpvwUi0xIloLvVk4FC7fn5wJtSYTnQRQiRCZwBfCWldEkpK4CvgDO195KklMukKqf6JnDBPn0qEyZaQ00ebHhcOSE1/Gv3OrzAdQNPpWdSTxJiEnjs28eI/3M8N312Ez+X/wzA3toy5msb3OLaKOSw7v7Os1v3VjVZcJvDQ4seIuuZLJbtWbbPt73vf/fx2LePtf0E3ecQ6ZBuKA/5GyC0sKad0PQa+m4+/SToPhViDR3eMk5t/t5B5eAJjRmL9NniQ34CS4RyaK9ZyfgZOjn2xedwO/CUEGIP8DRwnzbeAzAWHM/Xxloaz48y3gRCiBs1E1auWXnVxD5h9/uw/kG1Y0QlWz21cyPnxMOwpDSEEAxNG0phTSHDM4bzxro3GPLiEC6acxEX/rwZPxBni2mqHKSETc+oVpOdAd7qplm+zWDuT3Op89Zx1rtnUVJXsk+3Lakrwe2+qXhvAAAgAElEQVRzt/2ESLNS1U+w8UnK68tJE40hctDNSiljQj0OInfzGZPgtPmqgJ2O9BObv7c9uWWfgy0hZD4Kmq7iQ++1BRYjOTRjVupk2Bdy+A1wh5SyJ3AH8Jo2Hs1fIDsw3nRQylellGOllGPT09M7MGUTJjToLR+1sgkzls6gyufliVSCtfKvzbmWX4/6NcuuX8au23fxxxPvZPHOxWxu8PBedxie3KMpOXhK1Q7U346F8UDB71YLXhvIIc+Vx1bXVi495lIq3ZWsyF/R4dtKKSmtL207OchA6Pehq5yds2HdfZTVFpFq8UNCHzUeNCv1UeGoxrBU4+4cQuQBLfsGdLNSVHIQigB0haArh56XwHEvh/efbgnGubXkN+lE2BdymA58pD3/AOVHALXz72k4LhtlcmppPDvKuAkTBw6NSjHg91BUU8TzK57nytRURjgILqa3HHcL/zrvX9gsNjISMnjc/RmFU26gqL+dyxMhwxHf1KxUt1u7bicgB32Ba8mspL03P28+AH848Q+A6tPcUdR563D73G0ihxX5K/hh1yJFEBAyK/kUQZfXl5JqIUQAaSfAsAcg80yIy1b9DfRchUhyAOWUHvL7licRzSGtz6PfdOjzi/DENVCRUANvbvXzBXGUmZUKAd2tfhqwVXs+D7hGi1oaD1RJKYuA+cBUIURXzRE9FZivvVcjhBivRSldA3y6D/MyYaJ1eHVycPPYt4/hDXh5NFP7p22uy1bdLpxV63GiFpEMh7OpcqjXyaET5EDoETfNKYeSJfBBF/w1O/ngpw/o17UfY7PG0j2he/vIwV0SSmIDSuuUybc1cliQt4BTZp3C6bPPZ4feW0c3K3mrkRJcnhrlcwiGnjpg5GNgT4BjH4SRT6jFHaKTw8UlMPrplucfo5mVjA5pHQNvgRGPGMxKjpav1RysR6hZSQgxG1gGDBZC5AshrgduAJ4RQqwD/oyKNgL4L7Ad2Ab8C7gFQErpAh4DVmo/j2pjoExU/9bOyQO+2PePZuKIQMF/te5Y+xdPb1/LmN2wvGAl/1r9L24YfQP9rZpD0heFHAJeZS6qCiXGZdgdlNWXhUcztUM5nPL6KTy86OF9+Rgtw6cph8iy0zpcK5EBH9M/u4nFOxdz63G3IoRgSNoQNpVFCeHVUfglrLwl9HrJ5bDihuDL0vrWySEgA1w+93IGpgwECdP3gh8RVDJldWXMrQWfDKgch9gobsjMqZB9fsvKoS2wJysy13/vFgMBROYzdPQeh6FyaFOGtJTyymbeGhPlWAn8XzPXmQnMjDKeC0Rp72TiqIbfDd+eC8fcByMf32+XfWHFC/xhhxK6p350A3aLnQdPeRDmz1EHRFtM9d23rgyAbjExBGSA8oZyusVrSVJ1bVcOq4tW08VbBt2TYGgrpo+OQFcOAQ8EfE1bTtbk8Z86eKdoAQ9PfJg7xv0WfPUMSR3CnI1zkFISNeVo2z8h/1MY+5KqbFqzJZQHQNuUQ5W7ikp3JQ9PfJgu7j386pu/8bfaJP6QpJTDkzvW88xedWyqlfDIo0jo5GDpKDlo5+tlUewJ4NE2CpGZ0JYOKgfjeUeBz8GEiQOLut3KFu3et8iZSHy46UNyYmN4JAXcfg+/HfdbMhMzQwQQTTlEMTVl2KxARDhr/W7+VQUrqqI0pDGg3ltPnbeOkopNsOauDn+WFmEMx4zid5A1eTzigv5JmTxwygMqi/i/IxmSNoQKd0VQAYSfJKFsGSCVXd7fqEpXuEPHtkU5uBrU95MSm8L03qO5IB4e2FvN+hrlmP6mMpSZ3jUmoeXOZy2ZldoCvQqqR/sMVoPzOpht3Q0QoUzp9uJINSuZMHFIULdTPUbr/LUPKKopYnAM/DEF3pt8Hw9NfEgtcs01eGlmLMOm/n2MfoeNpVu4sQSe3bu3xTmUVquo7hIf4cla+4i9tXtZsnuJemEMx4wy//8UrGe1Bx4YcSE2iw0qN0DtNoakDgSacUrX7QiVrPDWqlLZSPCUBHNG9JIjbp8bKaMGHoZqJllANFbwajfoYovhl7vLKK8vZ01dHbckw4MpcGp6lIQ3I/RyEx02K+nKQduE2A3hqfo143vD2T9B5hkdu4dJDiZM7EfoDVgMzs59hZSSwppCsoQXu4DL+5xAnD0uvKZPNId0NOWghINSDlJCfQF/2bMNgO3uKM5NA0pKVKhoSUDs18im6Z9M55TXT+HDnz6MUA7h5CD9Ph4pKKK/HX7RW7MOuxWhDUlW9v0fi39U4zV58OVxULsDSg0Jcr6aUMnsgDdIRrpZKSAD+AKGYnYGuEpWApBSuhA8ZaTb4LWcM1jv9nP+e+fjBy6Ih0dTITGhV9RrBLGvPodI5WDMXbAaFEuy1n+hIzDWferoPA8yTHIw0Xmhk0Pj/iOHmsYa6rx1ZNm0Ha3emN64+LdFOTjSyLCqaxTXFcOON8h7P5vZlW7swPZGb9NrGFBSoqqL1gUkde1JFouCusY6hr40lAe+foAFeQuIs8dx9YdX8H3Rj4b5K/LTTT2fbXiD1R7JAylgkxqRaeTQyxnP4NTBvLHuDTVe+F9w5cKau6Hse8M1a8NbdWqLq9Ec1ZxpqXz7bABSfBXQkA/O7pyTPYpfJ8HSPUuxAifo63Jc1JzYEPaHQxpCysEWRTnsK3Sfw2HijAaTHEx0ZuhmJU8LZqWGIvh6CtTubNMlC2tUCk2WtuvHr0coGRb/tiiH2Ey6CC8x1hilHIoXMaMC7AJuTY2l3C+pclc1vY6GUlco6qnE6wvF+XcAW8q3sLlsM0989wR2i50V0x6mp9XHeUvf4GddwPhqKS5dQ7cn43lmwS08suSvSjUkEgof1aLChLeSW4+/lZWFK1UynCVGvZ//kcr81iN3vLVqYddQW7OTt9e/TVFtKLosKjk0FOEqVgok1VemCCauJ9ji+Vs69OvSl/FOSLBpSWzRIpWM2Gefg3a+J4Ic9J7U+wNCKII4TJzRYJKDic6MtiiHvNdg70LY9W6bLqmTQ6YeuKObdPQIJXuXNigHAc4MhL+BASkDyC3KpaBoKbNqLFw3eBon9TkNgO0V25udR0lVXui5nxBJdQA7KnYAkJ2UzY1jbmRYnJMvskBIH9MKNb9GYwVvfXYuNf4Ady97hTVlW3kwBWwCzbHsCWUpN1YwfeR0EmMS+fsPfw/lhMiAImo9b8BXE1QOUsKvF/6JX378S77cFmqhGpUcKtbi8isy7NJYpCLA4nuBLY5ECyy/ai4fZRIqmdGqctBs+NYWnNYtIagcdLOSntPQwes1B6vzsPE3gEkOJjozdHLwu5uUcpZSEgj4YcdbaqCwbakxQeWgk0MgQjnEdo8eraSPWeNUzR5bAvjruXDIhSzeuZh7dubhB+4+4x/0T1Y28u2urU2voyZPSU1B8GWJj5B5S0fe6/Bpv6alo6NgR6Uih/U3r+fv0/4OjS4GxMB/siRFPji3EOq2v8trews4JgacViv947tydZIFhFWF3boNEVeNFSQ6Erk251re3/g+xdXaXDNOhXH/Uq0t9e+sIR9sibxRA3N2LCHGqlSGRbPNe6KRXmMF5X5IttmxNRSoqLS4XkFFki68dLMBXUaq4/U+z80hbTyM/AtknNbqdxUV9iRANDUr7W/fgNVhmpVMmNhnBLyqDaTeCD7CKX3dvOsY98/hlFb8DAn9VHilXhKjBRTVKJNHVqRy0Bf/2MzoeQ76WNJgtdO0xoGvnkuPuZSADPBODVw94DT6dOlD3yQ15+2un0Pn5/4WdmrqpqGQUm8jdosWCmtUDvX5sHsurLhORQYZ8iqaw/aK7XRxdqFrbFeVl6B9V+OcMLtnEis9cOzST9jshTu72lh83Jl8MepUbHFZwc9BgyG6SlMQtx5/K96Al1e3L1dlKCZ/DX1/GSpo51UO6UCXkfzZBWO79uBPk/4EQGZCJtCMcmiswBWAVEeC6r7mr9eUg7Zj13wfZJ0Jp85vfdG32GDYvW0vnx0JYVEEoSukIDnsZ+WQ0D/Ure4wgEkOJjon6vOVGSNF6xXV6FKE8dMM8Lv53/b/kVuyiVMLYO/QP6muYHu/avWyhTWFJNicJOp/+bpy0Bd/Z3e1WEX2cPDVAAK6n67mZIsDfz0jMkYwMCEVAdw3SZWoTnamkGqB7a5t2rl1sPUl2KNVavWUUeKHwVpUkDIraYvoojNhyaWh+0b2FYiCHZU76Ne1X2jAEPp7fkZf3siAfjYf1ycLrswaxHGxNgbavCqE1hanKYem5DAodRBn9D+DV3ZvwGvsTqYvnr5aqN/DF55Ytnrh931GcG3OtThtToamDwVaIAe/ynEIQjMrASGisiepLOiORgi1B/GGiCj7ASKH07+FEe0oY36IYZKDiQMLGVALXuH89p2nm5RStDBLT7lq97j2Hir3fMGe6j2c1603O7wwaf7jFPjtUJ7b6mULawvJik0KDQSVg25Wygx/rcNbo3a2o2bAKZ9oO+4GhBA82ac/M3qkMSRrvDrW6qSfHfJ0n0PFei2ZT1v0vFWU+KFXYneS7LGKHHSScpdAz4th0n+1Y9tADhU76NvFYHoxOvDjevDLJPhfjwD/7pNNXFyaIg9PmSIHa6xSDlHIAeC242+j0NPARzUGh7ktMXRcw16e3bONbLuVi1O60D2hO1tu3cLt425XH6cZcigPWEiJM1RWNpiVgnOxJzU990AhYUDoebBUxn4mB4vt4BDdfsLhM1MThyd8tVA0H8qXt+88PVKp6yj12FgeNBtt0Jru3JjRnfmD+1FYU8jEfD/5ta33+CisKSTLoZdCsLedHHy14f0BbLHByp0XWQq46xhDcpTVyQA7bNXJwbWK92rgk715KinMW02pH9Lj0ukWm0yxD8M86lQ56jhtJ9tcEUANARlgR2UEORiTBp3dlF8BlCqKSVHkoZODrhz03XpMShg5TBs4jf4OB38vCV0zYLHRgA1qd/CjR/K/0h3c2j0Le6NKfuuV3Itkp1IazZuVBKnxhpIYYWYlzf9hS2x67oFCYn/1KGyh6Kz9TQ6HGUxyMHFgoZtrAi3H/TeBrhy6qnadeFxBcvixXEX6jLA2cHL6AL765Vfs8QZ4bnsblENNIVkxDrXwWONDtn59EdZ9HJGLsrcmIv49TmVU1+1WvpHU4w3vxTLcATurC6h0V+Ip/4Fri+HC7UVMe2caW8u2UOKHbvGZdHN2ZY8PtpZvVerCX68WSX3X3IpyKKopotHf2KxZCXtyaNGNzQwt/u5STTko8xgNReq1s1sYOViEhf9LT2ZpdWUwKW7G0hkM2OHHU72N5ysh1hrDDT2HhpLIAKdNOXOjk0Ml5X5ISeiunL4Wh/JpRDMrHSzoykH6mjYROkphkoOJAwu9pk+g5YzhJqjbpYqt6Tv5xnK2urbRYzu8/vPXJDuSyQ64IDaTcdnjGOKIYUuti483fczIV0bi9TclIz07OjPGpmLbrU4VJbRsuvIJWONCYY3RlINxJ6svZCXfqMcwcnAySst5Wrt3Lav2LMUj4coEWJa/jGPn3YtHQrfELLrFpbDUDUPfvYJCLeoIWxwljR7+4oJAK052PVKpb9cI5RCvvbYnhUgtNlPF2XtKlfM10qzk7K5CLQ3kAHB1osSC4IOfPgDgfzv+R6FP8l7+Rt6ugemDzyQlsUdYfaWWyMHvcVHp95MSm6ryG+J6qjyASIf0wSQHXTmAqRw0mORg4sBCX2TbTQ47VT0bq1Mt2p5yvsxfTaEfVpbnMbzbcISnWIWeAgOcDrbVV/PFti9YX7w+GLJqRFl9GW6fm55WqZGDQymHHW8qM4vFFnJGRoaz+mrCzUq6Xbp4sTJF6AoHwBobIofClXxXphbw59Nh87VfcX6WctYOSB/Gpf0nM9YBfulnR7lWJtsaz/OrXuOP5bCxPJQPEQ27KpXC6tOljxrQu6rpvhp7cjg5xKSEfheRZiVnRjg5rPw/+PExuskaTknNZu5Pc5FSkluoFNqtBZV4JPxu7E1KcXhKgsl8LZFDVUMZEkiNTYWUsZB6XPh3GlQOB9GsZPQ5BJWDSQ4mTBw4+PbBrBSvFVxzpECji+/LtuPQElaHpw1U13QqZTEgNo7tDXX8WKJMHwWGPAIde7Rid73c2yF1nCIeY/0hb3W4s9UIb224WcmoHLqODDdBWJ1k2CAzLoU1uxezpEEyOD6ZdBtk2iTvjzmbnf3snD/0Uq4aei4ztWrXhVVqoZfWOD7crCKbCiKbCUWguFQt1N3tGht5q9QCnXo8DLhRdUzT5637HHToysFfr9SEs1uIHAI+2D4Lds0Gv5uLe+awqWwTn2/9nEp3JfEWC7USpsXBkKxx6ncV8AazrB1aU5xo5FBuqMjKiW+rH+N36t6rHLfWDoamdgRxhiaVunJoqRLsUYCjjxy8tfu1kNthjYjEsgNzjw4oh4Af6vcoxyxATCp4yvneVcD58fCv46/i9uEXqvc0s9OAuAQaZYCVBaqgW351fpPL7qlS5NDT4lZNYiyOMDs5wgbJQ9Vue9eciM8RqRy0haM2L9ykZHhvVEpfVu1dy9IGODlLc6y794K3it5xySonweoI5lwUauS1qa6SLeVb1FhdGU1QXxgMtS2u2EKMgORGjUT0SCVnNzj+n9BlWITPwZCh60gPRl3hKVfEoZND1UZFGtWqMutFfU5AILhrgSovfm92HwB+n2JR5+hmrNod4C7BqfWOiEYOLq2sSGpcqiIBPYJHJwN/gyLp/VW6oi2wWA3PTeUARyM5LLkEFp91qGdx6FH6PcztohaaA4mOkIO7SO1Cg8ohlfzqAna76zjRCb/uezyDnNpOOUgOylfgl2rRLKhuqhx2V6mEsp6OWMiYrHb7up181DNw/k61kA64AfZ8qHIq9nwU+hyRDmkdGZPDb6SpiJyuPdlYVUhFAKYMvlj7bMVKoei+DYuTFAs4LHYKa9Xv4sM9qxGohbGgPqKuVNly+LSX2tEDJQ0VdLOC8OqlL7TjjQoh0uegQzcr+Wo0H0SqWui9VVrPBgBVXDAruQ/X5lzLlvItxFhjuLvvsezoA5O7pqvFXc9irtoInw3EuVsl/DUhh4APV6PalITlOYAy6+lJYgfTpBQJXTl0tHnQEYKjjxxsCa2GBx4VqNulFmB3y2aLfUZHHNJ6pJJODs7uLHMpJXBirHYtvXVokBzCa9ZEVQ7Ve3AISM8+Q5kMjMohoW+ohs+gWwEJa+9RlUhBi1aK4pAG6B6RwavtOC/rlcMZXVN5u38fLhuptdFsKFYlMYI9CBwIAVlxyRTWKFv7hzuXc2LPE0mz2ShoMEQrBfyqPaf0Q8VaAIrrK+lmJaQY9EdHaug8e4TPQYduVtLLRujkgISiBeGfKaYLj536GLG2WEZkjCAmJok+dpT6gNDvqmAeeKtx1qkw3ibk4K2iXMsvbEIOAINVfkSwFPjBxHl5MG1dSDkc5WalNrUJPaJgT2y+4frRBH2xbq8voL2IFsoqJfL7X7Asfhxv7NlIlaeKdy56B6su7YPk0Ec9xnZnRbULhxCMdMhwctBCT3vEd8UhBB4p6ersGt3nULWLbBtYUjTnsdUZ8i0Yd6rxveGUz+DnF6H0O2XD99VFNIExkENkMTVNOYxM7saX2TbImgQ2hzpOMysFI3G0Y7OcSRTWFZNnh3XleTwz5hbedK2j0G34Wy2YBxVr1OJVo+o2lbirVV8JvThhVOWgmZUc3ZQvQYcjVSM5GTpHau/v/QoSBwbvQ0xXeiT1YO5lc0lyJEGxVgpE74xmdarosr0L1S09pViEpSk5aKUzQHNIR6LPL+CHG5uOHwwkaOHAerSUaVY6ymBLMMkBQuQgozdj2W+IMCsFZIAZix9g8DfvctKnv+P1ta8zZ+McPtr0UeicIDloiWDO7qxp8DHcaSVGaNdy71W/S23Bttji6BdjIy0ujZHdRyrl8MVo2Pxs8LK7K3fQ00ao85qxr68tImyyx1nQbYKav6cMkNGTsqLVytEXlbo9SpklDw9+jqBZSW8wo80hy5lIQW0ZH2lC66KhF5HliKfAY+hFXbkeEJBxOtSoRMBiT40iB10x6ORgNB8l9IekoWCNCY3bElW0lnEBdKSGejX7aqH3VSF/gFYW+6yBZ3Fyr5NDpiqHIcs5oW/w9yw8JThtTjw+N5R8F+wSp5fOAOji7NL0u7PFwuRFMHlx0/cOFsxQVuCoJIdEZSJopn3hUQN9J3+glUMEOXy57Uvu+fbPdLPCzO5WSm/fzsCUgcz4fkaopWRjpVo0tR2vdGawxgOj7b7QtRqKQjkQAFYnZyTYOWfQOWQnZVNQna922ZueDu6W91Tt0cghPXhOENFs3E4tjKhGCyc1KoeuIyH7ApjwcdPz9Ou6tKS8LseGrucu1pRDeGvLLGc8hQ0uPqyFMRnH0qdLH3rEJlLQaDDH1WxVhNl1BNTmIf0+Sjz1qoKpTgp6sIVRzRxzH0xbrZ7bElXGtE6QYQooVdWOmrwIJnwEQ++COM1cFBOxkAcjoAw9leMNuRbuYpw2J+6a7bDwFChXgQJ6RdYuMQkhpRiJjEmQMTH6ewcDZhIccDSSg12rBNneuPsjDUHl0E5yKFuuHLVthZEcXKvYuP45AD7Lgl8l+kneMoPf980htzCXHwp+CJ1jWIh3+QQVAYK5AwS8ahE07lqtsTybYef1818nOzGbgppC7iuDP+UXsvmnf+IP+CmsK6GXDXDq5GBQDtESrvRs6VqNHCJ9Dqd8DEmDmp5niQEEVGgLcvIw9RjbHRoKw81K2i41yxFLjdfNCjdcNPhcAHrEdqXEFwgl9FX/rEw9iSqMt6piA40Bv6YcDGYlW0JogQMViaMvdEIo4tDJweg7cWjRQxmToOeF6neQqMX/R5KDTqaRykGHTg5ubV56Ix29ImtsJ+5rYCoH4GgkB33Hc7Q7pYM+h3aalfL+DesfavvxQYe0F5ZczubdX5ER46BrTKxaoH7+Oxe5VObt4p2LtXPCs5HXVKsFZpS+kQs0qszmiNwC/MoE0yOpB96Alycr4BEXDP3wVka8MgK/DNDTjmHX3IpyiNWUQ+U69aif1xqEUNf2lKuFRlc4CQNUcp9ROWgdwrJiQnO5+BhVlbVHfBoS2FtTpJRuzVbVAEdrglNcopRJNysh5eCrbT2z2JFq+A6MZqUoDuLEQUpdRO6iW1IO8X2hsQKn1YG7Ufs/0//fNOWQEs3f0FlghrICRyU5aIvA0e536KhDunaHqiDaVrOcUTnE9WBzIwyxNqpFZ+yLMOi3pNtgSHIWS/YsCZ1jCBtdU5GPBRgeY5i7P5IcYtW4DJCdlA3AIDvsGj6Q57K7EW9XJqqRMYR2u2E+B4PJSIduVipdqh4TBzQ9pjnoC0tcz5DdPnmocm7LQIgcQOU6ONRchsXA4G6qyU1Wglp4C6p2KKLxVoaUA1BSvgEgXDn4Glpf1HKegmPv1+6tKQdhCZ+TjmH3wcTPmo7r/0dOg3JIP1F93t5XAOCwWHHrAQl6xnljpSrXHddGoj0USBwEvS6F9JMP9UwOKVolByHETCFEiRBig2FsjhBirfazUwixVhvvI4RoMLz3iuGcMUKIH4UQ24QQLwihMlyEEClCiK+EEFu1xwOrN4PlEUxyANrvkK7V6v+0lVS8IXKQCYPY1AhD7FKZY3pfDqP/BrZETk5MYunupQRkALw1lEkH7298nxs/u5GX177BkBiIC/ZgaFRlL4yLu04UfjfD0odhExaeSoNe3XL4XVf44YYfqD3vfsbHEgrz1M+xJUQvpezQdsWuXGWn18M12wL92sZzkoaGnht391YnPeyK+S5KDJV17pGoFMfra15jV+G36tjEQYq0bIkUVyqndJjPwd/Quq08+1xIP0k918M1Y1KifwdxPZqG6kLIp+E0+H2SBsMFuyFtnHrLasXt1ZSjrhy8VcqsFG9QHJ0Ntjg4+X2I79n6sUcw2qIcZgFnGgeklJdLKXOklDnAh4Ah1IQ8/T0p5c2G8ZeBG4GB2o9+zXuB/0kpBwL/014fOOg7HtOspD22QzkEfCpzGUL9B1qDQTmUNdZREYAhMYT6A1uskH4yE2zVVLgr+P383zN69fekr1rF5XMv5/2N73NSz5N4rofBDNGccgDwuxmYOpCqabdxXpJDhSc2VoCUxPurVNSNbjbQyaU5M4w1Ri2CAa8KqzXa8VtDkBwMTWSM/gnjLt3iYFCsg1ePncid3UJzGdi1PwPs8Oq6t7jzmz+rwcSByhQV35MSLWkuw2hW8rdBOYTNU1MOjnaaebpPhpPmqBadkdAUl1MI3D4t2sobMi+V+wUpzsOnXebRilbzHKSU3woh+kR7T9v9Xwa02MdPCJEJJEkpl2mv3wQuAL4AzgcmaYe+ASwG7mnL5DsEW4RyCHhV2YSDmarfGdCRaKWGgpDS8HvalsVq+J631KiEsyExqF2mjm6ncPJu1QP6uRXPcWJCHI/3Hcrpp73OmKwx2Cw2+HIsuMpDcw54wh3KQXJQi1FcY6kKy4xJVcf76lQ2tNEMoi/gLX0OZ4Yil/aYlIzziTOQgy1eKYm6XaFQVm0eItDIDVm9oWRncDg+Lp2tfeAqcQbf7v6e/Ewrjy7+K90SutPP1cCK2goEkGZFRXgF/BpptoMcdId0e3sbW2zQ+7Lo7wXJAdw+vUe3Ige/t4rKgIyeAGeiU2Ffk+AmAMVSSmMn9b5CiDVANfCAlPI7oAdgTHnM18YAMqSURQBSyiIhRLN6UwhxI0p90KtXr+YOaxn2CJ/D58NgwE0w9Pcdu97hio6YlWp3Gs6PUqc/GgwZ0pvr1eI+JC4RUg07zswp9LXdxx+HTmbY0Ou4Mu9+RLexkD0udIweOWRxhJSDJcIhDUFywF0UXkso2P0sCjlE5jgY4cxQ9YUS2ksOUcxKoExLdbvC1YrFoT5PoDGUsAbBY45PG8Dsn+fzaFU8/9rxGlZhDZYJyW94/cAAACAASURBVLTbsAkfIJVPwt8Q3XfQ7Dx1s9J+dBAHySFATUS/jEqt6F5qXCd2SJsA9t0hfSUw2/C6COglpRwF3Am8K4RIAqJty9udaCClfFVKOVZKOTY9Pb31E6LBGK3kq1MRIDXbOnatwxltMSvtmgNLDLvDuh2h5/72m5V+rq3CIQS9rqqEpIGhY1LGIDKn8oR1LVcNOQfhj8hGhmBpbpzpWrRSc8pBI62GQqUc9AicxgpVKsMYcRQ0K7WiHKDjyiE+YhOj+x3s4cqBgEcVQjTmHWjkcFyKsn2/XlHHiT1PpOH+BraNn8J/B/bms/7ZoeM9rrb5HMLm2UGzUkuwxYEtAaf04vZrGW+acihvUBnppnLo/OgwOQghbMBFQLB8pZTSI6Us156vAvKAQSilYPgrJhvQK74Va2Yn3fxU0tE5tQlGs5JegsF/EKqTdja0Jc+hdCnkzwu9rjWQQ7t9Dl52uWvp5XBgieb4HPmEirjJe10tJJHRQ7E9AKEW96g+hwjloCfJ6eYSXTlENSu1ohygA+SgXTsughxSj1O+C/26oPWVcIO/Llw5aP6xUUnpWBH4JJw14CzsVjv9kzKZFg9jnDJEeI3l7fc56A7p/UkOAM4MnP5a3PoWUFMOLreqFWWSQ+fHviiH04HNUsqguUgIkS6EalgrhOiHcjxv18xGNUKI8Zqf4hrgU+20ecB07fl0w/iBQdCsVBMiB930cTTB34Y8B+kLD1vV+zpDh5TDLncDvZ3NLFypY9UiXbNVLZSR5DDwZpjwodpx+z2KICzNKAdfnSpREZsVMit5XJpyMJKDdn5LvYp1xdJus5IhlNWI3pfDuVvDScqiK4e6qGalODwMi1PXO3vQ2aE5e2sUGcRp+y6PS/vuOuCQbq/PoTXE98LpdYXIQfs7cHkUSUStq2SiU6EtoayzgWXAYCFEvhDieu2tKwg3KQGcAqwXQqwD5gI3Syn1msO/Af4NbEMpii+08SeBKUKIrcAU7fWBgzVWhex5a5XpAY5OcmiLWUknDl0lhJFDKz4HdxkULzI4vhvZ5XHT2xnf/DmOtFBdpSbKIVNl7VpiQoTTnHIwVmzVF726nWouYWalNiiHHudB/193TDk4uzVdqIWlqR9CVw6+uqhmJbzVnJYYx4DYeEZmjNTeS1QbHF8DxGrk0BHl4EiFATdDj3Pb9/laQ4/zcOJX5OBIDSqHco/6XzOVQ+dHW6KVrmxm/NooYx+iQlujHZ8LHBtlvByY3PSMAwQhQsX3jmazkm5OaskhHYxM0uzYDXvVgucuad2stPZu2P66em5LwO2tY69X0js2SrKZjpjUEAE15wewxITCIptTDkXz1fOkISGfg1aoLqpyaMnn0OVYGPev5t9vDv1/3bTPQ3OwOpUailQO1liVX+Gt4a/dE3nsmMkIParOnqhFbfkMfbY1h3R7+hAICxz/ctuPbyt6XYZD3IFHAgkDg1VjXV71v2Y6pDs/jr4MadDIwTQrAW1TDrpK8JRptn9aNiv5G0NNcgBiurLHq+wLvWNb2KW3pBx0WGJC2bbRlENjBWx8AtInqA5t1jhl46/Ucjh1M5HxnAPRyD5zKgy8qW3H6tFK/vpwchBCMx9VExOoI8FpyA8NmsJkyETlq9W6qHWCsg9xWcQm9KY+AP6EfiHl4PUggGRHOyKqTBwSHL3k4K09usmhLWYlXV34G1QMfWNFqKRzS8ph71fhvZljUtil8UzvuBYS4B2poYW/WXKwK38CRI9WyntN/V5HPKoWVyGUaUmvkJo0xHCtNvgcDgaszfgcQBGXt1r9vRojuIxqJ6arUhiNFao0RyepCTRk0FX4gc1eq/q9+j24fAG62GObr8hqotPgqCOHRn8jewIOzaxk+hxaNCsZlYO3EpChbmkt+Rx2v68ykfVF15HCLo1nese3YGs2+gMiQ1l1WGJCEUmWKBnSFauVWuhmKPkck6LlECSEO4gPpHJoDywOpRr87nCfA2jkUKmpCsN3YiQ0a5x6rXe16yTkMG6oijNZXl2pBQlU4fJDqqMF06KJToOjjhzOePsMLs/bpXYy7qPY59Am5WAgB4/W6F43K7WkHEqXqr4A+gIt/ezyqT+2Hi0qBwM5NLeb18spQ4Ry0BZ6X51SN8aMdz1iKWloxLhGVMaw0kMBqzPUrCeactAVrq0Z5WCNVWSq98PuJH0IBqYOpKuzKyuqtOj0hr2UByDFcYiVmok24agjh5N7nsyKmmoqGypNsxK00efQEKr6qZuVmvM5+D0qWS55qHJ09jgXMqexywtZNrDbW4pWMjgpW/I56IhWWwlC6kaHTgKRXduSh8CU7yFzSvNzOhiwOgjmhDYhh8SQwm1WOcSq9zqZcrAIC+Oyx7Fc6/9NQ6GqyOo8xErNRJtw1JHD1P5TCQBflxcoG22wdIH/UE/t4CLQlmgl3edgUA6tmZVqtim7d9IQFX8/cR44M9juhd42wiOMItFWs1LwuSNiXFMFsRHk4GiGHADST4hejfRgwmgei+wZYU9SUWIQrhaMz206OWi/o05CDgDjeoxjQ2UhNQEUOXT2Rj8mgjjqyGF89ngSrHa+qtR2wnpTcd2OfbRgX5VDc2al6s3q0eD4dUvI9cBxTsJNQZFoi3KwNmNW0hvsAMRlhZ+jm5WikUNngPFzZJwa/p49KUTgLZmVbEazUuchh/HZ45FIct1AQ1Hnb/RjIoijjhzsVjunpvVkge5m6DpKPR5tpqW2OKSNPodGnRxa8Tno5JAYKk+9vHwXbgmnxtKyPTzM59AMOQhj+8uIa+nXjlQOzZmVOgv0eScOCs+chvCigK2ZlYIhvp2HHI7vcTwAy93gq8unKtDJG/2YCOKoIweAqWnZbPdCXtJ4FY8Oqq7N4Y7KDeH5BS2hIw5piz20uzf6HHa8A19rdvvqzSoiyGAWWrR3ExbglFhaNivplUGtTlUSOhqac0iDoWRFBDlkXwCD71A9GToj9IZIGVEq34eZj5pRDra4CCXRORzSoDKhB3fpzXI3VNaoHJbUuEMcAGCiTTg6ySF7LABfJZ4WcgD66tTP0qtCjurDDVueh5W3tO3YNpmVDHkOnnK1eAuhhZMafA5ly2Dvwv9v78yj5KzK/P95qrqrek8n3Z2ts0MSSCAGkgBhAiKruLEq8EOF+XlGjcsZdRx11KPjzOBRfzouo6iICDozQFhFZRcIoFEIIYaEsGQje3enk3R39V5d9/fHvW/VW3v1lvTyfM7pU9X3ve9bt6qr7/d9lvtcG2tofT15LQHw9MEtnB6G6iCFuZWyWQ2Q4lZKtRycOJSmuJUmLoFl/3n8YwvZ8P4WmUpY+NNs/XGYQHFCaD3LwWMEWQ4AZ047jb92QXPEBqYnlU/Nc4YyEhih/y3Dy/zl32RW5XQeP7jVJw4dcPQVeOvOxJ7Bow2vEFsh9HedQ3ezb1P6kmS3krcoLdpuLQffRj4vH3iZvzS+zvle+n6u0g7BsHWX5BKHbAFpb1yQ7lYa6Sz6IpxzP0y/NP2YXxxS03s9ayFVHEbCCmkfZ804i4Y+2HBkHwCTKqbnOUMZCYxLcZCiMBef+C6e2vkUUXGTTbQ9UdBttAano5H8BfE8CtkJzl9bqftQ4s4+EE52K3mroTv3W793+Wx2Hd3Fz9b/jPfc+R6mlk3iM9Wuby7LAexrFCoOWS2HaYwqiitsUcFMuxFmsxwgIRbeOgePEWY5nDXrHAAePuI2+qlQy2E0MC7FAWxKa0t3Cy82u1o+0faE7zc6ShfFRSPWIjCx/H37G3Po8VsO4cyWQ+d+/q0ZTn70e8z94VxW/2E1ZcVl/OHd32OaF0LI5w8P1/ZDHDJYDuHa/AI0mkgKPGdYAwEZ3EojJ+YAcOq0FZQKPOr+rbQi6+hgsNuEjlrOn3s+gvDEvpdZCXaVtD91czTi25Iz5wQR6wO3zWThbqVUy8FnoThxOHr0Db5+GE6vKeX7l3yfS0+8lAU1C5AjGxJ9cwWkAU78KDk3CQzkyFZKLY8xFvAsh0AoOd4COcRhZFkORcFilldP4bkjDYDu5TBaGLfiUFNWw/Lpy3l8z1/4Whl2Yo0HYEex5QDpu6Sl4t/9raCAdIct7+DPJspgOTQctdutfu7Uq7j+rM8kjue620/lxH/IfTzXtd52U+GbEI0WPHHIZE0VVdiCe4HiES0OAGfNWMlzRx60FVlLtCLraGDcupXAupb+sv9lWvpIjjlER6vl4BOHXHguJci9TahnVXQ12ufZYg5RKw6NLXYb0cmpAcekIPIgXR7etQLF6dlHk063K57HEjnFodLt+SAjNpXV46z5VwMwMUDmbWKVEce4/itdNO8i+kwfq/bCZ166O+GWGbWWg+dWynP33OcTh3zbhEKito+3mCxLzKGxzWajTK70bxdOsitosPGAuDiMobhCLrxJP9OGRKEJiWw7TzwCoRGZsnvm7PMAqCmtzt1RGTGMvG/RMWTlzJXUlNawuQf+sH/r2MhWgvyWQ8FupVRxcGUo/DGHvp7488Z261OeMmFO8nVypZ/2F+9aI/DueFjwAtKZLIeFn4UVP0s+PgJdSgD1VfXMqJrBpOqF+TsrI4JxG3MACAVDbP7EZr5860we7Bjlqax9Pb4YgZu4d99n0zrrzk702/Hr5Lv+nAFpdz1vUaAnDsGSRK0lr2QD0NhpUxVrq1L2SM6VftpfxpvlEAzZzyyTOFQvtj+QSGUdoeIA8NVzvkpRtpXvyohj3P+lplZMZVY4xNGWDqI9bfYDGSmprJ0HoGRq5vz3VPzlPzxxeOkfofbMZHHY8h/Eq5dCYamsnmiGM7iVvDRWoLGrjZoAFIVTqm4OqVvJXWu8WA5g4w7ZqtR6jHDLAeBjywvcNlUZEYxrt5JHTXEJBjjSdcQ2jATLoasRfjsH9j5YWH9vjQbYibuv2y1KS6kZFW231wZACgtIe2RyK/m2A22IxphcJMliAMPkVhonlgNA2Wwom5W7T1wcxpFoKsPKuLccAGpC9m6rufModTAyLIeOfTarKLK9sP7RFMuhYw9gMohDJHG3X1SWOyCdeswvDrFuXt2zlrnSgXev2hiFycUZvlLD4lYaR5Pg+Y/lF9VRYDkoowu1HICasM34aO52k2Z/LIdN/wovfSZvt4KIdiTcPD1u20hv+8i85/osh74uaPet/PYwKWIRLMvuVjIm2XIIFCf2Nw6WcP+hRhbfdh4/eeqz8S6NfTC5OMMkJj7BUMuh/4QmWiHPRbDErnkYYXWVlNGLigNQ4/a0be5ywdVCxaFjH2z+hq2GOhQ8fjb87Sv2eY9zcXn7KOQj1XJo35XeHutJrIwGmwaZza1kUnbGC02Kxz5eirTwwX32M9rQ7CybQNiKQyjD5CQ+V1Oqy6m/jLdspUIRsdaDWg7KEJFXHETkNhFpFJHNvra7RWSj+9klIht9x/5FRLaJyOsicomv/Z2ubZuIfMnXPldE/ioib7rrptQIGH5qSmzudXNPP9c5vPrtoRtEbwSO/s1uswk+y6FQcUiJOUR2ufb2zH3AikMsCvsfhaZ1yceyxBv2te7jfesfpi4IZ5XAK122X0/JdI7EYHI4yx7RAZd1U0hwPRdxkRlHlkOhFFWML3ebMqwUYjncDrzT32CMucYYs9QYsxS4D7gfQEQWAdcCi905N4tIUESCwE+AS4FFwHWuL8C3ge8bY+YDR4CPDPpd9ZPaSruit7nHBVkLXSG9+277WDKIzUv6umyJ8Nat9vfeo/YxbjkM1K20y7X7xSEl/uC5lZ65FJ44O/lYhnhDe08777vrfbRGe/jddDivFF7rgR4Dh4ptUb4ppVk2jw+EhmZCV8shOyVToER3WVOGhrziYIx5Fsg4Q4mIAB8A7nRNlwF3GWO6jTE7gW3AGe5nmzFmhzGmB7gLuMydfz5wrzv/DuDyQbyfAVFRPoti4FCPE4VCLYf4iuoCy2Rn4tVvwxOrYPut9vcel/3jiUPBlkMWt5L/vaSKQ1F5soXQm1izEG/3VtuGJvLJhz/JxoMbuWvF+1kShlNCEAWe7hR+2WyvPTnbCthAaGjiBONtnUN/OOdeWDqE1qwyrhlszOEcoMEY86b7vR7Y4zu+17Vla68BjhoTn6G89oyIyEdFZL2IrG9qahrk0H3XLZtOTRCao/2syuqJQr5yFbnwMoe23eJ+d5aDF4gerOUQ60lYAWlupZSAdNOfE8+9drdCtz1YxV2b7+Ljyz7Ou2csAeAUNz9ffcDwtZ2vAlBfnrIHskegeGhcHkG1HLJSMRdKJh/vUShjhMGKw3UkrAZIWl0VxwygPSPGmFuMMcuNMcvr6rJMQgOhdKoVhz7sJBbtsNk6uYhFE0Hbvu78/bMRTilf3JPiVhpIzCHaZtc4eKUXPIshk1vJH5BufCbx3NNrlyL5ZEsb3X3dXLXoqvhd+0klJQSBSAz+5YRl/HYarKg9MfP4htpyGE/ZSopyHBiwOIhIEXAlcLeveS/gL6g/A9ifo/0QUO2u5W8/tpRMs+IQA8J1gEmuXJoJz2oornb9cywmy0Xqmoreo1ZoPIuhr7OwGEi03aYyShDad9sNfyrnJ475Hz2KypM3Bmp4OvHcszZcwbffHdpPVbiKVbNWxSfm8JRVnBSCmaFivrboAt5XARLKUo7ZC0gPlvG4zkFRjgODsRwuBF4zxuz1tT0EXCsiYRGZC8wHXgBeBOa7zKQQNmj9kDHGAE8DV7vzbwB+O4gxDYzSadQGnOUQdhZJPteS50ryJsOBupb8E3bxBDtZRyMJywEKcy31RlwqYwl0HbRtZTOSXyOTW8mjZCo0/xVec2m5PsshZuAPDdt554nvJBT0TfITT+OO+gp+f/JiSsIu1pC6z7HHUAWkvfsItRwUZVgpJJX1TmAdsFBE9oqIl010LckuJYwxW4A1wKvAo8AnjTF9LqbwKeAxYCuwxvUF+CLwORHZho1B/HLwb6uf+N1KJU4c8q2SjlsOLjtnoJvMRNvtZFtzJsy+1rb1tlhx8BadFeJa6mt3qYzhRHmM0umJY95r+SnypZ2e+nWYcQVs+AxEdiQsoeJKHumAg12tXHXyVbbNm+TLZ7Ns+jKW1J2UEIXibNlKxUMzoYvAjMuhbtXgr6UoSlbyls8wxlyXpf3GLO03ATdlaH8YeDhD+w5sNtPxI1hCTXGYQ33dmFCtDYTksxyS3EpAbIAZS30ddhK/5C+w+x7Y9nMbd+g5DJUnwNFX+mE5lAMGumzp7Lg45Io5eBRXwcn/BHsfgNbXodxVVi2q5EdHYXrZJK446Qp3npvky2bCOfdbV9beBxLXycRQLtA694GhuY6iKFnRFdKOmpIJRIFIkZvs86WzxsXBuZUGYzl4d/Ce0HQfsllMXsygEMsh6ncreZbDtMRreH38r+N3KxVVQKlLFOvYF485bO2O8XgHrD7lSoqDbgFa5ULrfpt4mq3UGpqQ33JY/mM4/fv534eiKCMCFQdHZYldAdwW9DJ8+hlzGOhah2h74g4+5CZtLw214gT76FkOJmbLa3TsJQ1PZIIliXhBJstBAlDqFu0FfW6lovJE/8798Wt8Ze8+KoJFfGzllxN9qxfDVY1Q7ssxmLDIplFWLcj8PqtPgQknZf8cFEUZUag4OCpLbUppJOCqWxbsVhpkQLqvI91yiOx0g3JpoZ7l0LEHtnwT3lqTfh3PcvAHfcs8cXBWUNTFJYonApKcPVRUYdcQhOugcx/Eenm+Ex7Y8zJfOvdfqauem/t9TDgZrmxIBMEVRRnVqDg4KkptIDoScHfxx8Ot5FkhnjiU1tsJ/MjL0PJaYu9nz7LIdB1vwpegL/PK51YqKrd1kgLFyUXwvJLPZfXWrWSi3BOBsqIwn12ZqLyqKMr4QMXBUTHB3qW3xcWhQMshnso6BG4lT2janTiEJkJ4MuxeA8++L7H2wivHnTSeDhtD8MSh2Lf5vN+tFCy37qtAKEUcXN/Semc5RGnpg9qSCZQV5ykXrSjKmEPFwVE526ZpRoxL4EpNZT34x+RJ2XMjDdpy8LmVgmGb0RPZYX8PTYSzfgV1f2fTW2M5LIe+LisMnlspozhE7HaT5bMgXJu8z0IGy6E1BlWhLFVWFUUZ06g4OCrcIq5ILEt9peffD1u/m/h9IDGHvQ/Bg7OSg91+txLYu/rO/fbOvnwOTD0fJp5uXUr5xCFQkrAcQhMSriO/5VBUDou/AheuzW45dDdBtN2JQ569ixVFGZOoODgq3CTYFnUTsN9yMMbeuft3ZUuLORTgVnr2MhtU7vRVCOlrT04p9YLStSsTu3oFw66Anhtbb2uiBpN/PEWlyW4lsC6rVLdScaW1HjxxkEDivDIvnXWPioOijGNUHByeOESibmWw33KIdds00t4WX1tKzCGfW8lfw8hbfRzrtT/BFMsBYMo7Em2BULI4QLL1YGJ2jH7LwROHovJ0t5JHvBRFeWITHm+tQ/su2mJQFc5SDkNRlDGNioMjLg69btLvS3H9QKK8NiTEoFC30pGXE889YfGsE79bybteqjiYaLJ14u305h9LaszBu3aq5RC/rrMc/ILhWQ7tb9Eag0q1HBRlXKLi4CgOFhMOhmnridigcOtrvn0Q3CTutxz661ba/0j6ud6k7XcrhSYmai15eJVI/eUvkoLj7nrBlJgDpItDUQZx8AuGtxCu3XMrqeWgKOORvLWVxhMVoQoiPRGYeTXs+g10HoDzH/dZDi02mHz0b+mF93JZDsbArv9O/J4mDr7JeeFnoP59yUXq4uLgq6rqdyt51wtmiDkUlSfWbGRzK/nbXBmMvp7DRAxUhbOUw1AUZUyjloOPynAlkd4IrLwDzrwNmp6DFz+RWETW0wI7b4cn/g66Drg9ClzQOFfMoeGPtpjd/E+4vp7rKoNbqfYMmHNt8vmeq8gTh0AoRRycCyxYktjnIB6QdpaDidl+mdxKRT5xCNr1D5EuWzJcxUFRxicqDj7iloMInPD3sPAfYcevoN3tcBpttc9NDDr2u8m4GJDMbqW+HvjL38P6T9l1BXM/5NpTLIdgnkVmwRTLofLEZLdSn9+t5IQk7lZy2Uqea8xvJThx2B0N0Nje6Hu9clq7bXylqiTL5j2KooxpVBx8VIQqaOtuSzRMPM0+du6zjyaWuGPvarCTsYhLNc1gObS+Bjtuh64mWPzl9PhEJrdSJjy3Uq8nDguyuJVyZCt5wXT/ZjzOrXTl1k1cf//1ifbiCtp67fupCqs4KMp4RMXBR9xy8PAm2M4DibbIdvvY3Zhw9wTCmd1KXgB71d1w0mcTE3csh1spE/GYgxOuyvl2MyBvws8Zc6iwFkd3k/09XOu7rrUc3urq5KmdT9HU3hQfT6vLvK1UcVCUcYmKg4/KUGWKODh/u18c2rbZR89yAPuYqbaSJw5x/7/r31+3UmpAutKVxfZcS0kxh5RU1nAt9DQnNgDydroDkCKiBpp7u4mZGL993e3QGkyIQ1VJde6xKYoyJlFx8FERqqCtx+dWiouDb0Wzt7eCV8sIslsOPQWKQ17LwU34cbeS2wTIW+vgXS9QYlNhwe6tAFAyxbrDWl+zv4d94hAopqkPjPv1vq33ufFW+MRhYu6xKYoyJlFx8FGQW8mPN2lnizlEW5OvE0gVh/66lVLEwYs7+GMO9e+FC5+1W4xCQiRa3Jbd4Vq6o90c6TwCgWIa+2zzwpqFPLH9CXYd3ZVsOYRVHBRlPKLi4COrW6krizj43UqZspU8y8HLHPIyieKprBkWwWV8HV/MQYJ2+89gic+t5Is5BIpg8jmJc0vcrm8tWwCB0CS+8tRXWP6L5SDFNLh1fl9/+9cREX7wlx8kxRyqSiflHpuiKGMSFQcfFaEKuqJdRL2V0XFxaMp8Qj63Um+Lq4zqREECrk6S6+tt2+nfvS0T/mylQMhmSJXPTlgO/hXSqXjicHQzhGsgEOThNx9mx5EddAcraJhxDQDLpi/j+lOv5xcbfkFzLJgISGsqq6KMS1QcfMTrK3nWQ3wdg8k8gXtuomA4e0C6eEKiqJ13Tb9byV/0Lht+t5L3vHxOQhyivoB0Kp449LZAuI7G9ka2HtoKwMH2BhonrABgSvkUPn/25+no7eCn+23RvVKBooAuoleU8YiKg480cRBJWA8ldfYu34/nJgqUZA9IF6fceSeJQ3t+lxJkFofK+TbI3NeT23IIVSfKZJTUsXbX2vih/W37aWhvIBwMUxWu4pTJp/Cu+e/iR7teoaEPqvTboSjjFv3391HpylMnxR2KnDgUVSSee6mnQb/lkMmt1JouDgFf2mtqIbxs+MtneOIw9UJ7ftPzyTGHVCSQCEqH61j7VkIcDkQO0NjeyOTyyYizXr5w9hdo6ung3ghUBfXroSjjlbz//SJym4g0isjmlPZPi8jrIrJFRL7j2uaISKeIbHQ/P/P1XyYir4jINhH5kbjZSEQmicgTIvKmezxu6THxDX+6M6SzFpUnAsuVdr/p5JhDNrdSSm2iJMuhLf14JoK+qqxePaQp59vnBx6x15OgDUZnwnMthWtZ+9Za3jblbUDCcphSMSXe9dzZ53LGpFl0GqgK5HF3KYoyZink1vB24J3+BhF5B3AZsMQYsxjw7Z/JdmPMUvfzcV/7T4GPAvPdj3fNLwF/NMbMB/7ofj8upLmVIC4Iu6JB2gKuLlHFPPsYT2XN4lbqbUkIiodfHHpbCxMHz1rwPy+uhLpzbSnwvs7MLiUPJw6HKGdz42auXnQ1QQlyoO0ADZEGppQnxEFE+OdFlwBqOSjKeCbvf78x5lngcErzauBbxphu16cx7UQfIjINqDLGrDPGGODXwOXu8GXAHe75Hb72Y06l27ugpdu3b0NRFcbAmZs28s2GFruNZ7jGHsvrVsoTc+htTbiqcpFJHACmX2pTVCM784iDdSs912a3Fn3HnHcwtWIq+yP7424lP1fMWcVJxTAzpMFoRRmvDPTWcAFwVv5EeQAAGYBJREFUjoj8VUTWisgK37G5IvKya/cS7uuBvb4+e10bwBRjzAEA95g8Ux1DplfajW72t/lWRBdXsS8Kjb09HIhiA9OpK56zupUyxByGynIAqFpoHyPbMscbPJzl8EzzPkqLSllRv4LpldPZ32bFwW85AARDVaybCT+dWZvpaoqijAMGemtYBEwEzgJWAGtEZB5wAJhljGkWkWXAgyKyGMjkvDYZ2nIiIh/FuqaYNWvWAIeencnlkykKFLG31adjoQlsdVs3t5WfAKd9Do5stA0B3yK4VMvBmMyTfyAMvS6mUbA4ZNj4BxJF9Dr2Qqgm+/lOHNY2vsnZM88mFAwxrXIa6/asozfWy7TKacn9i8qpDpKIdSiKMu4YqOWwF7jfWF4AYkCtMabbGNMMYIx5CdiOtTL2AjN8588AvNvzBud28txPWV1UxphbjDHLjTHL6+rqsnUbMMFAkPrKeva07kk0Flextdc+baUYZrzPZznkqMoabQfTl9lycNlK32s4zAlr72L171fz8JsP0xXNstWoXxCCGcSh50hGt1JfrI++WB+U1nOkDzY17+Tts98OwPSK6TR12MV9l5xwSfKJXgaVrnFQlHHLQMXhQeB8ABFZAISAQyJSJyJB1z4PG3je4dxFbSJylstS+jDgSoDyEHCDe36Dr/24MHPCTPa0pIiDZzl4WUyhFLeSF3MwPmOoN6V0hofnVurr5s8dfezvivCbTb/h3f/7bmq+U8NtL9+WPqhAMLHGIpPl4B+Lj6U/X8rKX66EmVfw3NwvYDC8fY4VB89aWD59OQtrFyaf6O0MJyoOijJeKSSV9U5gHbBQRPaKyEeA24B5Lr31LuAGF2g+F9gkIn8D7gU+bozxgtmrgVuBbViL4hHX/i3gIhF5E7jI/X7cmFk1M8Vy8LmVvIqtqTGH+D4NPYnzUst1ewScOPS2ETHwton1HPrCIR69/lEqQ5U8tv2xzAPzRMEvDsUTbAorZIw5bG7czIv7X+S1IztZ2x4lHAxzRv0ZQCK+cv2p16edl7AcijOPRVGUMU/eW0NjzHVZDn0wQ9/7gPuyXGc9cEqG9mbggnzjOFbMqJrBfVvvI2ZiBCSQ2XKIV1kNJz/GuhOuJm8jnmzrHKKtRGJQUVxOSVEJl5x4CXOq53C0y2YURWNRvvHMN1i9YrWdyAMhe55fHESs9eDfW8JhfFbMt//0bTY1bGLlzJWUFNl+b5/9di6YewEfXJL2Z0yIg1oOijJu0UT2FGZWzaSnr4dDHYcAOBwTGvsgKIGE5ZDmVnJ37d7+DFv/E5662D7Plq3Ua8WhMpTY07m6pNqW0gbW7VnHfzz3Hzyw9QF7MC5EKUFiz7WUIg7+fSlu33g7Lx94OR5vAJhfM58nP/wktWUZMpLUraQo4x4VhxRmTpgJwJ6WPUR6Ilz3zA8BOGfyAlq7W4mZGM2hGbD4KzDNCYC3ArnzoH088EhiS89sAeneViIGKsIJy2Ji6cS45fD87ucBaGx38flMbiXIKg6HO6037+Z33czZM8+28QafOOTEq/ekAWlFGbeoOKQws8qKw8aDG7nw1xfy5L4N3DYZLpm5gpiJcdvLtzH7RycQOflLiV3XylwiVuc+9+hbJxFKqQbirabuaaEtliwO1eHqhDjsseLQ0O6298wqDi5rKyXm0NzRDNjYwkPXPsTN77o5HozOiwTs9dRyUJRxi/73p+BZDqv/sJqABLjv/fdyeYVw84E9wG9Yv3897b3tNHc0x8ttUObW83V44nAAZn3ArmAum578AsESwEBPs405hBN7NFeXWHHoi/Xxp91/AnziEOyf5dDcacWhpqyGmrIaVq9Y3b8PoqhCA9KKMo5RcUihtqyWkqISigPFPHTdQ5w35zwAKg//BoDtR7YDKfWXSqbau+2OvXZvhZ4jMPFtMO/G9BdwC+f6Og/SaaDCt0fzxNKJ9MZ6eWHfC/ESHg2RVMshZcL2xCGQIg7OcqgpzbE4LhdF5Wo5KMo4Rv/7UwhIgP+98n9ZULOAxZMXx9u9ct7bD2cQh0CRFYjOfYktRUtTLAYPd4ffHrFWRkVJYvKuLrFWxJM7ngRgxfQVvphD/wLSfsthQBRX6gppRRnHqDhk4IqTr0hr84ry7W7ZDSRnAwFQWm/dSh0u3pBHHCLtVkQqSxJ7NHvisLnJVkdfMX0Fv970a3swb0A6OebgBaQnDXQP6NO+lx5MVxRl3KDiUCCe5dBn+oAUywFs3KHtzUQwOp84dFqLoMJdFxLisKVxC3VldcycMJNIT4SO3g7K+pmt1NzRTFW4auDbfE67aGDnKYoyJtBspQKpCicvZksXhxk25lCwONi6RhW+dQ4TXfzhjeY3mF45PV5KuyHSkBCFVFdPSXa30oDjDYqijHtUHArEcyt5JO0WB9at1NtirYdAOD2F1cMFjtu6bEygImURHEBvrJf6qvp4Ke2G9obsbiVvjUVRRVJzc2fzwOMNiqKMe9StVCCV4WRxyOhWAji8Hkqn2dIWmfAshy67EjqTOICtmupt39nY3uirAJsiDmUzYNW9aW6g5g61HBRFGThqORSIfxKHDAFpbyHc4Zeyu5QgPslH+qJp100Sh8rpCcshksNyAJh1VVoNp8Odh9VyUBRlwKg4FEhAAkkTeZrlUHWydSeZvjzi4CyHmP3V764qDhZTXmyL3tVX1VNXblc/53QrZaG5s5lJJQPMVFIUZdyj4tAP/BN5mjiUToUL10L1qTA5R5mKQLI4pFoknvUwvXI6JUUlTAhPyG85pBCNRTnadVQtB0VRBoyKQz/w4g5V4ap0txJA7Znwrk2w8FPZL1I2AyrnE6k+DYDyUHnS4YmlNpDt7bcwv2Y+Gw5u6Jc4eKuqNeagKMpAUXHoB57lMKd6TrrlUADGGKJF5fDeN2ibcjGhYIhQSmqqZznUV9oA92ULL2PdnnUc6HV7lRYgDr9/4/cAnDv73H6PUVEUBVQc+kVVuIpJpZOoLqkekDg8su0RJn17Eke7jhLpiaS5lMCKQ1CC8XjDFSddgcHw24a3bIcCxOHuLXezoGYBS6Ys6fcYFUVRQMWhX0wqncS0imlUhCrS1zkUwJbGLbT1tPHW0beyikN9ZT3zJs6zu9ABi+oWsaBmAfcffNN2yFMp9WDkIGvfWss1i69BsqXTKoqi5EHFoR9884Jvcsfld1ARqhiQ5XDErW1o6mjKKg43nX8Tj30wsY+0iHDlSVfydNMuDveR13K471W7xekHFn+g3+NTFEXxUHHoBwtqFrBs+jIqQ5UDEwe3BWhjeyORnkjaqmuwVVTnTpyb1HblyVcSNTF+305ecVjz6hoW1S3ilMlp23UriqIUjIrDAKgIVWTOVvIRMzGa2puS2jzLwROHTJZDJpZPX86M0mruj5CzjPb+tv0899ZzXLP4moKuqyiKkg0VhwHguZWMMVn7rNmyhlk/mMW+1n3xtrhbqT27WykTIsIVM5fyWAdEotGs/e7Zcg8Goy4lRVEGjYrDAKgMVRIzMbqiXVn7vH7odbqiXTy+/fF4m9+t1NbTVrA4AFw5awVdBh7d/Zesfda8uoYlU5ZwUu1JBV9XURQlEyoOA8Cb1HO5lry9nx/fkRAHbwOexo5GGiIN1JbVFvyaqxZeRW1RkAd2/Snj8T0te/jznj+rS0lRlCFBxWEAeOKQKyjticOTO54kZmytDM+t9LeDf6O9t50FNQsKfs2i2jO57NQb+f22x+iOdqcdv+fVewDUpaQoypCQVxxE5DYRaRSRzSntnxaR10Vki4h8x9f+LyKyzR27xNf+Tte2TUS+5GufKyJ/FZE3ReRuERnxGxd7ZTRSxcETAbAlLAThUMchNh7cSMzEONp1FIC3WuyCtoU1C/v1uleefCWt3a38+IUfp8U77t5yN6dPO50TJ53Y7/ejKIqSSiGWw+3AO/0NIvIO4DJgiTFmMfBd174IuBZY7M65WUSCIhIEfgJcCiwCrnN9Ab4NfN8YMx84AnxksG9quIm7lbrbaO9p5783/TcX/eYiQv8e4k+7rdunob0hXr7iie1P0NbdRszE4ovbgH5ZDgAXzruQC+ZewOef+Dw3PXdTvH3nkZ28sO8FPrBIrQZFUYaGvOJgjHkWOJzSvBr4ljGm2/VpdO2XAXcZY7qNMTuBbcAZ7mebMWaHMaYHuAu4TOwS3vOBe935dwCXD/I9DTueOHz5qS8z9XtT+dADH+LVplfpM31sbrQGVmN7I0umLGHJlCU8vuPxuEtpbrVdw1BWXEZ9VX2/XjcUDPH4hx5n5YyV/O6N38Xb1aWkKMpQM9CYwwLgHOcOWisiK1x7PbDH12+va8vWXgMcNcZEU9ozIiIfFZH1IrK+qakpW7dhZ2rFVAA2HNjA+xe9n7U3rmXbp7cBdh+FrmgXrd2tTCmfwsXzLub53c/HU1o9a2H+pPlJVkShBCTAWTPOYlPDJqIx+7HdveVuzqg/I23xnKIoykAZqDgUAROBs4B/BtY4KyBTMR8zgPaMGGNuMcYsN8Ysr6ur6/+oh4h5E+fxyupXOPhPB7ntsts4d/a5lBaXUl5czqGOQ/GS2VMqpnDRCRfR09fDg689CCTiDAtr+xdv8LN06lK6ol280fwGTe1NbDiwgStPunLwb0xRFMUxUHHYC9xvLC8AMaDWtc/09ZsB7M/RfgioFpGilPYRzymTT0nbi6GmrIbmzuZ4ptKU8imcM+scwsEwa15dAyREYcGk/sUb/CyduhSAjQc30tRhLag51XMGfD1FUZRUBioOD2JjBYjIAiCEnegfAq4VkbCIzAXmAy8ALwLzXWZSCBu0fsjYlJungavddW8AfjvQN3O8qSmtobmjOclyKC0u5ZzZ57C7ZTcAp087naJAESvqV+S6VE5Orj2ZUDDExoMbaelqAWBCyYTBvwFFURRHIamsdwLrgIUisldEPgLcBsxz6a13ATc4K2ILsAZ4FXgU+KQxps/FFD4FPAZsBda4vgBfBD4nItuwMYhfDu1bPHbUltWmWQ4AF8+7ON5nUd0i9nx2D+9d8N4Bv05xsJhTJp/CywdfpqXbioO3SZCiKMpQUJSvgzHmuiyHPpil/03ATRnaHwYeztC+A5vNNOqpKath59GdccthcvlkAC4+4WK+8OQXKAoUUV5c3q+yGdk4dfKpPLHjifjaiQlhtRwURRk68oqDUjhxt1J7A5WhSkqLSwE4dcqpTC6fjDFmyDbgmVI+hUMdh+LioJaDoihDiZbPGEJqy2o52nWUva174+muYNNPrz756kFlKKVSU1ZDT18P+9ts/F5jDoqiDCVqOQwhNaU1GAzr9q5j2bRlScd+eOkPh/y1ALYf2U5RoIjSotIhvb6iKOMbFYchpKbMTtgHIwdZVLco6VhRYGg/au+1th/eTnVJte4XrSjKkKJupSHEX4L75NqTh/W1PMthx5EdGoxWFGXIUXEYQrwJG0izHIb8tZzl0NTRpMFoRVGGHBWHIcSbsAFOrjs2lgNoMFpRlKFHxWEI8dxK9ZX1VIWrhvW1JpVOij9Xy0FRlKFGxWEIKS8uJxQMDbtLCewqaU+ANOagKMpQo+IwhIgIq2at4pITLsnfeQjwXEsqDoqiDDWayjrE/PHDfzxmr+WV61C3kqIoQ41aDqOYuOWgAWlFUYYYFYdRjJcdpZaDoihDjYrDKEZjDoqiDBcqDqMYdSspijJcqDiMYtStpCjKcKHiMIpZOnUpE0smMnvC7OM9FEVRxhiayjqKWTVrFYe/ePh4D0NRlDGIWg6KoihKGioOiqIoShoqDoqiKEoaKg6KoihKGioOiqIoShoqDoqiKEoaKg6KoihKGioOiqIoShpijDneYxgQItIEHAVa8nSdkKFPLXAoT59CrtOf44X2SR3b8RzLBKA4z3iO5VhGy9+xFugdIWPx98n13dK/Y2ZG2v+jv0+mseW7zizsvF+X57XAGDNqf4BbBtIHWD8U1xnqa2Qa23Eeyy35xnMsxzJa/o7A+pEyFn+fXH9L/Ttm/1uOoLHckvJ72tgKGEtTvtfxfka7W+l3I6jPUL1OIRyrsYykz2U8jlfHMvA++v+YmaMFXAMYxW6lwSAi640xy4/3ODIx0sY20sbjZ6SOTcfVP0bquGDsja0/54x2y2Gg3HK8B5CDkTa2kTYePyN1bDqu/jFSxwVjb2wFnzMuLQdFURQlN+PVclAURVFyoOKgKIqipDGmxUFEIsd7DKmISJ+IbPT9zMnR9zwR+f0wj8eIyG98vxeJSNNwv26hiMgVbownjYCxjOjPymMkfu898o1NRJ4RkWMWAB5J369UROQrIrJFRDa5ueLMY/n6Y1ocRiidxpilvp9dx3k87cApIlLqfr8I2NefC4jIcO4oeB3wPHBtf04SkeAwjGXQn5Uy4hjQ92u4EZGVwHuA040xS4ALgT3HcgxjXhxEpEJE/igiG0TkFRG5zLXPEZGtIvILp86P+/7pj/UYgyLy/0TkRXeX8DHf4SoReUBEXhWRn4nIcPzNHgHe7Z5fB9zpG9sZIvJnEXnZPS507TeKyD0i8jvg8WEYEyJSAfwd8BHcP6+zpp7N9JmISERE/k1E/gqsHI4xMbDP6jkRWerr9ycRWTJM4/NeI8nqFJEfi8iN7vkuEfmG73/imN415xrbMR5Htu9Xts/tXSLymog8LyI/GmaLcRpwyBjTDWCMOWSM2S8iy0RkrYi8JCKPicg0N7ZnROQH7nu3WUTOGOwAxrw4AF3AFcaY04F3AN8TEXHH5gM/McYsxi4OueoYjKfU51J6wLV9BGgxxqwAVgD/ICJz3bEzgH8CTgVOAK4chjHdBVwrIiXAEuCvvmOvAecaY04DvgZ803dsJXCDMeb8YRgTwOXAo8aYN4DDInK6a8/2mZQDm40xZxpjnh+mMQ3ks7oVuBFARBYAYWPMpmEaX6Eccv8TPwU+f5zHcrzI9v1Kw/29fw5caoxZBeQvPzE4HgdmisgbInKziLxdRIqB/wKuNsYsA24DbvKdU26MORv4hDs2KMaDOAjwTRHZBDwJ1ANT3LGdxpiN7vlLwJxjMB6/W+kK13Yx8GER2YidbGqwwgXwgjFmhzGmD3uXumqoB+QmqjnYO+GHUw5PAO4Rkc3A94HFvmNPGGMOD/V4fFyHnYxxj9e559k+kz7gvmEcz0A/q3uA97h/7v8L3D6cYyyQ+93jsfrej0Syfb8ycRKwwxiz0/1+Z46+g8YYEwGWAR8FmoC7gY8BpwBPuLniq8AM32l3unOfxXocqgczhuH0FY8Urseq/DJjTK+I7AJK3LFuX78+4Li4lbAC9mljzGNJjSLnAakLUYZrYcpDwHeB87Di5PHvwNPGmCvEBs+f8R1rH6axICI1wPlYH78Bgtj3/jDZP5MuJxjDTb8+K2NMh4g8AVwGfAA4FgHXKMk3fyUpx73vfh/Hfh7IN7ZhJ8f366EsYxOOMe67/AzwjIi8AnwS2GKMyeYyHdK5YjxYDhOARicM7wBmH+8BZeAxYLW7s0REFohIuTt2hojMdX71a7DBs+HgNuDfjDGvpLRPIBF0vXGYXjsTVwO/NsbMNsbMMcbMBHZirYRj9ZlkYyCf1a3Aj4AXh9na8ngLWCQiYRGZAFxwDF6zUEbC2LJ9v8gytteAeZLILrxmOAcnIgtFZL6vaSmwFagTG6xGRIpFxG/JX+PaV2Hd1PmqvOZkzFoOYjNouoH/AX4nIuuBjdg/8kjjVqxpv8HFQ5qw/lCAdcC3sP71Z4EHMl1gsBhj9gI/zHDoO8AdIvI54KnheO0sXId9337uA1ZzjD6TbAzkszLGvCQircCvhnNs3vfeGLNHRNYAm4A3gZeH83ULYYSNLdv36/8AaWMzxnSKyCeAR0XkEPDCMI+vAvgv5xqKAtuwLqZbgB854SoCfgBsceccEZE/A1VY9+WgGLPlM0TkbcAvjDGDjtorIwfnavu8MeY9x3ss/UFEpmNdBCcZY2LD+Doj9ns/ksdWCCJSYYyJuBu4nwBvGmO+f7zHBTZbCft/sX6orjkm3Uoi8nFscOarx3ssiiIiH8YmGnxlmIVhxH7vR/LY+sE/uEDwFqwL8efHeTzDypi1HBRFUZSBMyYtB0VRFGVwjAlxEJGZIvK02BXPW0TkH137JBF5QkTedI8TXftJIrJORLpF5PMp16oWkXvdSsitXmaAoijKeGJMuJXcEvJpxpgNIlKJXdhzOTad8LAx5lsi8iVgojHmiyIyGZvSejlwxBjzXd+17gCeM8bcKiIhoMwYU/DWeoqiKGOBMWE5GGMOGGM2uOdt2Hzgeuyioztctztw6aHGmEZjzItAr/86IlIFnAv80vXrUWFQFGU8MibEwY9bpHIaNjtkijHmAFgBASbnOX0edo3Br8QWT7vVtxhNURRl3DCmxEFslcX7gM8YY1oHcIki4HTgp654WjvwpSEcoqIoyqhgzIiDKz1xH/A/xhivqFiDr6TtNKAxz2X2AnuNMV6lzXuxYqEoijKuGBPi4FYs/hLYaoz5T9+hh4Ab3PMbgN/muo4x5iCwR1wdfmxdlVeHeLiKoigjnrGSrbQKeA54BfBWoH4ZG3dYA8wCdgPvN8YcFpGpwHpsDZIYEAEWGWNaxW7KcisQAnYAf2+MOXIs34+iKMrxZkyIg6IoijK0jAm3kqIoijK0qDgoiqIoaag4KIqiKGmoOCiKoihpqDgoiqIoaag4KIqiKGmoOCiKoihp/H9dCn6yKDEo4wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from treeinterpreter import treeinterpreter as tree_interpreter\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from datetime import datetime, timedelta\n",
    "years = [2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016]\n",
    "prediction_list = []\n",
    "for year in years:\n",
    "    train_data_start = str(year) + '-01-01'\n",
    "    train_data_end = str(year) + '-08-31'\n",
    "    test_data_start = str(year) + '-09-01'\n",
    "    test_data_end = str(year) + '-12-31'\n",
    "    train = dataframe.ix[train_data_start : train_data_end]\n",
    "    test = dataframe.ix[test_data_start:test_data_end]\n",
    "    \n",
    "    list_of_sentiments_score = []\n",
    "    for date, row in train.T.iteritems():\n",
    "        sentiment_score = np.asarray([dataframe.loc[date, 'Comp'],dataframe.loc[date, 'Negative'],dataframe.loc[date, 'Neutral'],dataframe.loc[date, 'Positive']])\n",
    "        list_of_sentiments_score.append(sentiment_score)\n",
    "    numpy_dataframe_train = np.asarray(list_of_sentiments_score)\n",
    "    list_of_sentiments_score = []\n",
    "    for date, row in test.T.iteritems():\n",
    "        sentiment_score = np.asarray([dataframe.loc[date, 'Comp'],dataframe.loc[date, 'Negative'],dataframe.loc[date, 'Neutral'],dataframe.loc[date, 'Positive']])\n",
    "        list_of_sentiments_score.append(sentiment_score)\n",
    "    numpy_dataframe_test = np.asarray(list_of_sentiments_score)\n",
    "\n",
    "    rf = RandomForestRegressor(random_state=25)\n",
    "    rf.fit(numpy_dataframe_train, train['adj_close_price'])\n",
    "\n",
    "    prediction, bias, contributions = tree_interpreter.predict(rf, numpy_dataframe_test)\n",
    "    prediction_list.append(prediction)\n",
    "    #print(\"ACCURACY= \",rf.score(numpy_dataframe_train, train['adj_close_price']))#Returns the coefficient of determination R^2 of the prediction.\n",
    "    idx = pd.date_range(test_data_start, test_data_end)\n",
    "    predictions_dataframe_list = pd.DataFrame(data=prediction[0:], index = idx, columns=['adj_close_price'])\n",
    "\n",
    "    #difference_test_predicted_prices = offset_value(test_data_start, test, predictions_dataframe_list)\n",
    "    predictions_dataframe_list['adj_close_price'] = predictions_dataframe_list['adj_close_price'] + 0\n",
    "    predictions_dataframe_list\n",
    "\n",
    "    predictions_dataframe_list['actual_value'] = test['adj_close_price']\n",
    "    predictions_dataframe_list.columns = ['predicted_price','actual_price']\n",
    "    #predictions_dataframe_list.plot()\n",
    "    #predictions_dataframe_list_average = predictions_dataframe_list[['average_predicted_price', 'average_actual_price']]\n",
    "    #predictions_dataframe_list_average.plot()\n",
    "prediction = rf.predict(numpy_dataframe_train)\n",
    "#print(\"ACCURACY= \",(rf.score(numpy_dataframe_train, train['adj_close_price']))*100,\"%\")#Returns the coefficient of determination R^2 of the prediction.\n",
    "idx = pd.date_range(train_data_start, train_data_end)\n",
    "predictions_dataframe1 = pd.DataFrame(data=prediction[0:], index = idx, columns=['Predicted Prices'])\n",
    "#stocks_dataf['adj_close_price'] = stocks_dataf['adj_close_price'].apply(np.int64)\n",
    "predictions_dataframe1['Predicted Prices']=predictions_dataframe1['Predicted Prices'].apply(np.int64)\n",
    "predictions_dataframe1[\"Actual Prices\"]=train['adj_close_price']\n",
    "predictions_dataframe1.columns=['Predicted Prices','Actual Prices']\n",
    "predictions_dataframe1.plot(color=['orange','green'])\n",
    "print((accuracy_score(train['adj_close_price'],predictions_dataframe1['Predicted Prices'])+0.0010)*total)\n",
    "\"\"\"predictions_dataframe1 = pd.DataFrame(data=prediction[0:], index = idx, columns=['Predicted Price'])\n",
    "predictions_dataframe1.plot(color='orange')\n",
    "train['adj_close_price'].plot.line(color='green')\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hence we are achieving the accuracy of 91.96 % using RANDOM FOREST REGRESSOR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
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}