analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cluster and Time Series Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The kernel involves 2 major analysis:\n",
    "### 1. Clustering:\n",
    "*  Involves grouping tweets into 4 categories: ***Economic, Social, Culture*** and ***health*** then performing KMeans clustering on the grouped tweets to observe how they behave.\n",
    "* There are several ways of approaching the problem of grouping the tweets such as use of hashtags, user description to check on someone's inclination et.c . My approach uses the technique of creating a set of words that can be confidently classified as belonging to a particular category for each of the 4 classes. \n",
    "* So the tweets are each compared with the 4 sets and assigned a similarity score. There're 2 main techniques I considered for computing similarity score:\n",
    "    1. Cosine Similarity: Cosine similarity is a metric used to measure how similar the documents are irrespective of their size.     Mathematically, it measures the cosine of the angle between two vectors projected in a multi-dimensional space. This would involve creating word vectors for the set of words and all the tweets then performing the cosine similarity. TFIDF (bag of words model) Vectorizer would be ideal for this.\n",
    "    2. Jaccard Similarity: Jaccard similarity or intersection over union is defined as size of intersection divided by size of union of two sets.\n",
    "    * Jaccard similarity takes only unique set of words for each sentence / document while cosine similarity takes total length of the vectors. Jaccard similarity is good for cases where duplication does not matter, cosine similarity is good for cases where duplication matters while analyzing text similarity. In our case, context matters more than duplication thus making Jaccard similarity the most appropriate method to use.\n",
    "* After getting a tweet's score for the 4 classes, the class with the highest score (highest similarity) is where the tweet is classified. There may be a thin line between ***social, economic*** and ***health*** classes given that the words involved in the 3 classes deeply intermarry, thus the scores among the 3 classes will be really close.\n",
    "* After classifiying the tweets, sums are done across the categories per user then K Means clustering comes in.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Time Series Analysis:\n",
    "* Involves use of time data (created_at) to observe the trends in volume of tweets across the months and days of the week. The analysis might be biased given that we're working with most recent 500 tweets of each user, thus majority of the tweets will be from June and July and lowest records being August. Same goes for the weekly analysis, majority of the tweets being recorded on Tuesday, given the data was scraped on Wednesday and lowest record being on Thursday."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***NB***: The tweets collected are not for all the 1k top users. I stopped at around 400 users iInternet issues) thus the data might miss some of the most important figures. But the technique would be the same if I had the full figures, only a different output wold be observed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "1. [Libraries](#Libraries)\n",
    "2. [Merging the Tweets](#Merging-the-tweets-files-into-a-Mega-df.)\n",
    "3. [Text Classification](#Text-Classification): \n",
    "4. [Jaccard Similarity](#Jaccard-Similarity)\n",
    "5. [Clustered DF](#Clustered-DF)\n",
    "6. [KMeans](#KMeans-CLustering.)\n",
    "7. [KMeans with PCA](#KMeans-with-PCA)\n",
    "8. [Time Series Analysis](#Time-Series-Analysis)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /home/ada/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to /home/ada/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import tweepy\n",
    "import csv\n",
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import re\n",
    "import spacy\n",
    "from sklearn.model_selection import train_test_split\n",
    "import nltk\n",
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')\n",
    "from nltk.tokenize import RegexpTokenizer, WhitespaceTokenizer\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.corpus import stopwords\n",
    "import string\n",
    "from string import punctuation\n",
    "import collections\n",
    "from collections import Counter\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "import en_core_web_sm\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "# !pip3 install -U spacy\n",
    "# !python3 -m spacy download en_core_web_sm\n",
    "\n",
    "from sklearn.metrics import jaccard_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging the tweets files into a Mega df."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make one mega dataframe of all the tweets\n",
    "\n",
    "def pivoted_df(paths):\n",
    "    global dataframes\n",
    "    dataframes = []\n",
    "    for path in paths:\n",
    "        frame = pd.read_csv(path)\n",
    "\n",
    "        names = path.split('/')\n",
    "        names= names[-1]\n",
    "        names = names.split('_')\n",
    "        global name\n",
    "        name = names[0]\n",
    "\n",
    "        df = pd.DataFrame()\n",
    "        df['tweets'] = frame['text']\n",
    "        df.insert(0, 'screen_name', name)\n",
    "        \n",
    "        dataframes.append(df)\n",
    "    mega = pd.concat(dataframes)\n",
    "    return mega\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "consumer_key = os.environ.get('consumer_key')\n",
    "consumer_secret = os.environ.get('consumer_secret')\n",
    "access_token = os.environ.get('access_token')\n",
    "access_token_secret = os.environ.get('access_token_secret')\n",
    "\n",
    "auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n",
    "auth.set_access_token(access_token, access_token_secret)\n",
    "api = tweepy.API(auth)\n",
    "\n",
    "# fetch the unique handles from the superbowl\n",
    "# convert to list.\n",
    "handles = pd.read_csv('/home/ada/10academy/training /week2/monday/kernels/csvs/superbowl.csv')\n",
    "names = handles['handles'].to_list()\n",
    "#divide into groups\n",
    "n = 100\n",
    "x = [names[i:i + n] for i in range(0, len(names), n)]  \n",
    "batch_1 = x[0]\n",
    "batch_2 = x[1]\n",
    "batch_3 = x[2]\n",
    "batch_4 = x[3]\n",
    "# I only have data for the first 4 batches and half of the 5th.\n",
    "accounts = batch_1 + batch_2 + batch_3 + batch_4\n",
    "n = []\n",
    "\n",
    "for user in accounts:\n",
    "    try:\n",
    "        u=api.get_user(user)\n",
    "        n.append(u.screen_name)\n",
    "    except Exception:\n",
    "            pass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "##DO NOT DELETE, DO NOT RUN.\n",
    "\n",
    "# n.remove('SokoAnalyst')\n",
    "# len(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_paths = ['/home/ada/10academy/training /week2/monday/csvs/mega csvs tweets/@' + name + '_tweets.csv' for name in n]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(197802, 2)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_bowl = pivoted_df(file_paths)\n",
    "tweets_bowl.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>screen_name</th>\n",
       "      <th>tweets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>RT @yola_plaatjie: Friends, Family, romantic r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>Been off IG for a while. I’m really loving it 🥺</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>The scariest thing is not knowing what a perso...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>Koko has this habit of licking off my lotion o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>Indlela endonqena ngayo, and all for convenien...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  screen_name                                             tweets\n",
       "0      @nanga  RT @yola_plaatjie: Friends, Family, romantic r...\n",
       "1      @nanga    Been off IG for a while. I’m really loving it 🥺\n",
       "2      @nanga  The scariest thing is not knowing what a perso...\n",
       "3      @nanga  Koko has this habit of licking off my lotion o...\n",
       "4      @nanga  Indlela endonqena ngayo, and all for convenien..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_bowl.to_csv('tweet bowl batch 1 - 4.csv')\n",
    "tweets_bowl.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Text Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Cleaning the tweets\n",
    "   lemmatization, tokenizing, removing stopwards, punctuations, hashtags and mentions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>screen_name</th>\n",
       "      <th>tweets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>plaatjie friends family romantic relationships</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>been off ig for a while im really loving it</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>the scariest thing is not knowing what a perso...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>koko has this habit of licking off my lotion o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>indlela endonqena ngayo and all for convenienc...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  screen_name                                             tweets\n",
       "0      @nanga     plaatjie friends family romantic relationships\n",
       "1      @nanga       been off ig for a while im really loving it \n",
       "2      @nanga  the scariest thing is not knowing what a perso...\n",
       "3      @nanga  koko has this habit of licking off my lotion o...\n",
       "4      @nanga  indlela endonqena ngayo and all for convenienc..."
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove the hashtags, mentions and unwanted characters from the tweet texts\n",
    "def clean_text(df, text_field):\n",
    "    df[text_field] = df[text_field].str.lower()\n",
    "    df[text_field] = df[text_field].apply(lambda elem: re.sub(r\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)|^rt|http.+?\", \"\", elem))  \n",
    "    return df\n",
    "\n",
    "clean_tweets = clean_text(tweets_bowl, 'tweets')\n",
    "clean_tweets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove stopwords, punctuations, lemmatize and tokenize word\n",
    "# nlp = spacy.load(\"en_core_web_sm\")   #working with english only, no support for swahili\n",
    "nlp = en_core_web_sm.load()\n",
    "tokenizer = RegexpTokenizer(r'\\w+')\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "stop = set(stopwords.words('english'))\n",
    "punctuation = list(string.punctuation) #already taken care of with the cleaning function.\n",
    "stop.update(punctuation)\n",
    "w_tokenizer = WhitespaceTokenizer()\n",
    "\n",
    "            \n",
    "def furnished(text):\n",
    "    final_text = []\n",
    "    for i in w_tokenizer.tokenize(text):\n",
    "#     for i in text.split():\n",
    "        if i.lower() not in stop:\n",
    "            word = lemmatizer.lemmatize(i)\n",
    "            final_text.append(word.lower())\n",
    "    return \" \".join(final_text)\n",
    "\n",
    "\n",
    "            \n",
    "tweets_bowl.tweets = tweets_bowl.tweets.apply(furnished)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>screen_name</th>\n",
       "      <th>tweets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>@KibetClinton</td>\n",
       "      <td>arsenal back</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>399</td>\n",
       "      <td>@SafeWayRightWay</td>\n",
       "      <td>kenya covid19 update july 10 2020new case 473 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>373</td>\n",
       "      <td>@DN</td>\n",
       "      <td>kpl player starring bleak future</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>480</td>\n",
       "      <td>@idringp</td>\n",
       "      <td>movie chaiii</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>219</td>\n",
       "      <td>@ipsnews</td>\n",
       "      <td>covid19 left india fishery lurch however india...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          screen_name                                             tweets\n",
       "43      @KibetClinton                                       arsenal back\n",
       "399  @SafeWayRightWay  kenya covid19 update july 10 2020new case 473 ...\n",
       "373               @DN                   kpl player starring bleak future\n",
       "480          @idringp                                       movie chaiii\n",
       "219          @ipsnews  covid19 left india fishery lurch however india..."
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_bowl.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Defining the set of words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "economy_related_words = '''agriculture infrastructure capitalism trading service sector technology  economical supply \n",
    "                          industrialism efficiency frugality retrenchment downsizing   credit debit value \n",
    "                         economize   save  economically\n",
    "                         economies sluggish rise   rising spending conserve trend \n",
    "                         low-management  decline   industry impact poor  \n",
    "                            profession    surplus   fall\n",
    "                         declining  accelerating interest sectors balance stability productivity increase rates\n",
    "                            pushing expanding stabilize  rate industrial borrowing struggling\n",
    "                           deficit predicted    increasing  data\n",
    "                          economizer analysts investment market-based economy   debt free enterprise\n",
    "                         medium  exchange metric savepoint scarcity capital bank company stockholder fund business  \n",
    "                         asset treasury tourism incomes contraction employment jobs upturn deflation  macroeconomics\n",
    "                         bankruptcies exporters hyperinflation dollar entrepreneurship upswing marketplace commerce devaluation \n",
    "                         quicksave deindustrialization stockmarket reflation downspin dollarization withholder bankroll venture capital\n",
    "                         mutual fund plan economy mortgage lender unemployment rate credit crunch central bank financial institution\n",
    "                         bank rate custom duties mass-production black-market developing-countries developing economic-growth gdp trade barter \n",
    "                         distribution downturn economist'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "social_related_words = '''sociable, gregarious societal friendly society socialization political  sociality \n",
    "                        interpersonal  ethnic socially party welfare public community socialist societies development\n",
    "                            network humans socialism collective personal corporation social constructivism\n",
    "                        relations volition citizenship brute   attitude rights socio \n",
    "                        socioeconomic ethics civic communal marital  sociale socialized communities     \n",
    "                         policy   unions        \n",
    "                        institutions values     governmental   organizations jamboree \n",
    "                         festivity    fairness  support  care  \n",
    "                         sides   activism     unsocial psychosocial \n",
    "                        socializing psychological distributional  demographic  participation reunion \n",
    "                        partygoer partyism festive power network gala housewarming celebration counterparty   social-war\n",
    "                        particularist interactional ideational asocial'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "culture_related_words  = ''' ethnicity heritage modernity spirituality marxismmaterial culture \n",
    "                           ethos nationality humanism romanticism civilisation traditionalism genetics\n",
    "                        kinship heredity marriage   indigenous  archeology  acculturate  \n",
    "                       ontogenesis viniculture modern clothes     rooted \n",
    "                       cicero societies history roots influence geography historical folk origins \n",
    "                       phenomenon teleology ancient aspects perspective liberalism nowadays community style unique prevalent describes \n",
    "                         today  origin   modernity beliefs  genre barbarian ethnic \n",
    "                       colonization cultural universal organization western-civilization structuralism  culture \n",
    "                       heathen pagan transculturation culture peasant classicist nativism anarchy ungrown philosophic cult  \n",
    "                       consciousness islamist bro-culture evolve cultic diaspora aftergrowth native cultural-relativism  \n",
    "                       mongolian cosmopolitan epistemology lifestyles diversity chauvinism westernization materialism vernacular \n",
    "                       homogeneity otherness holism tusculanae disputationes primitivism superficiality hedonism discourse\n",
    "                       puritanism modernism intellectualism  exclusiveness elitism  colonialism  \n",
    "                       pentecostalism paganism nationwide expansion rural  auxesis kimono \n",
    "                       culturize alethophobia nettlebed japanification  dongyi clannishness insularity hybridity\n",
    "                       westernisation foreignness worldview exclusionism enculturation ethnocentrism  confucianist vulgarization\n",
    "                       shintoism  westernism denominationalism    deracination\n",
    "                        eurocentrism  cosmologies  emotiveness bohemianism territorialism\n",
    "                       philosophical-doctrine ethnic minority social-darwinism  theory cultural evolution belief systemfolk music \n",
    "                       traditional art house karl-marx   theorymedia  \n",
    "                       film-theory art history museum studies cultural artifact'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "health_related_words = '''disease obesity world health organization medicine nutrition well-being exercise welfare wellness health care public health \n",
    "                     nursing stress safety hygiene research social healthy condition aids epidemiology healthiness wellbeing\n",
    "                     care illness medical dieteducation infectious disease environmental healthcare physical fitness hospitals \n",
    "                     health care provider doctors healthy community design insurance sanitation human body patient mental health\n",
    "                      medicare agriculture health science fitnesshealth policy  weight loss physical therapy psychology pharmacy\n",
    "                     metabolic organism human lifestyle status unhealthy upbeat vaccination sleep condom alcohol smoking water family\n",
    "                     eudaimonia eudaemonia air house prevention genetics public families poor needs treatment communicable disease \n",
    "                     study protection malaria development food priority management healthful mental provide department administration\n",
    "                     programs help assistance funding environment improving emergency need program affected schools private mental illness \n",
    "                     treat diseases preparedness perinatal fertility sickness veterinary sanitary pharmacists behavioral midwives\n",
    "                     gerontology infertility hospitalization midwifery cholesterol childcare pediatrician pediatrics medicaid asthma \n",
    "                     pensions sicknesses push-up physical education body-mass-index eat well gymnastic apparatus tune up good morning \n",
    "                     bathing low blood-pressure heart attack health club ride-bike you feel good eczema urticaria dermatitis sunburn overwork \n",
    "                     manufacturing medical sociology need exercise run'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Preprocessing the sets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " #### Tokenizing and removing stop words from the sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = en_core_web_sm.load()\n",
    "tokenizer = RegexpTokenizer(r'\\w+')\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "stop = set(stopwords.words('english'))\n",
    "punctuation = list(string.punctuation)\n",
    "stop.update(punctuation)\n",
    "w_tokenizer = WhitespaceTokenizer()\n",
    "\n",
    "# clean the set of words\n",
    "            \n",
    "def furnished(text):\n",
    "    final_text = []\n",
    "    for i in text.split():\n",
    "        if i.lower() not in stop:\n",
    "            word = lemmatizer.lemmatize(i)\n",
    "            final_text.append(word.lower())\n",
    "    return \" \".join(final_text)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "economy = furnished(economy_related_words)\n",
    "social = furnished(social_related_words)\n",
    "culture = furnished(culture_related_words)\n",
    "health = furnished(health_related_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Removing duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'agriculture infrastructure capitalism trading service sector technology economical supply industrialism efficiency frugality retrenchment downsizing credit debit value economize save economically economy sluggish rise rising spending conserve trend low-management decline industry impact poor profession surplus fall declining accelerating interest balance stability productivity increase rate pushing expanding stabilize industrial borrowing struggling deficit predicted increasing data economizer analyst investment market-based debt free enterprise medium exchange metric savepoint scarcity capital bank company stockholder fund business asset treasury tourism income contraction employment job upturn deflation macroeconomics bankruptcy exporter hyperinflation dollar entrepreneurship upswing marketplace commerce devaluation quicksave deindustrialization stockmarket reflation downspin dollarization withholder bankroll venture mutual plan mortgage lender unemployment crunch central financial institution custom duty mass-production black-market developing-countries developing economic-growth gdp trade barter distribution downturn economist'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# delete duplicates\n",
    "string1 = economy\n",
    "words = string1.split()\n",
    "economy = \" \".join(sorted(set(words), key=words.index))\n",
    "economy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'sociable, gregarious societal friendly society socialization political sociality interpersonal ethnic socially party welfare public community socialist development network human socialism collective personal corporation social constructivism relation volition citizenship brute attitude right socio socioeconomic ethic civic communal marital sociale socialized policy union institution value governmental organization jamboree festivity fairness support care side activism unsocial psychosocial socializing psychological distributional demographic participation reunion partygoer partyism festive power gala housewarming celebration counterparty social-war particularist interactional ideational asocial'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string1 = social\n",
    "words = string1.split()\n",
    "social = \" \".join(sorted(set(words), key=words.index))\n",
    "social"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ethnicity heritage modernity spirituality marxismmaterial culture ethos nationality humanism romanticism civilisation traditionalism genetics kinship heredity marriage indigenous archeology acculturate ontogenesis viniculture modern clothes rooted cicero society history root influence geography historical folk origin phenomenon teleology ancient aspect perspective liberalism nowadays community style unique prevalent describes today belief genre barbarian ethnic colonization cultural universal organization western-civilization structuralism heathen pagan transculturation peasant classicist nativism anarchy ungrown philosophic cult consciousness islamist bro-culture evolve cultic diaspora aftergrowth native cultural-relativism mongolian cosmopolitan epistemology lifestyle diversity chauvinism westernization materialism vernacular homogeneity otherness holism tusculanae disputationes primitivism superficiality hedonism discourse puritanism modernism intellectualism exclusiveness elitism colonialism pentecostalism paganism nationwide expansion rural auxesis kimono culturize alethophobia nettlebed japanification dongyi clannishness insularity hybridity westernisation foreignness worldview exclusionism enculturation ethnocentrism confucianist vulgarization shintoism westernism denominationalism deracination eurocentrism cosmology emotiveness bohemianism territorialism philosophical-doctrine minority social-darwinism theory evolution systemfolk music traditional art house karl-marx theorymedia film-theory museum study artifact'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string1 = culture\n",
    "words = string1.split()\n",
    "culture = \" \".join(sorted(set(words), key=words.index))\n",
    "culture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'disease obesity world health organization medicine nutrition well-being exercise welfare wellness care public nursing stress safety hygiene research social healthy condition aid epidemiology healthiness wellbeing illness medical dieteducation infectious environmental healthcare physical fitness hospital provider doctor community design insurance sanitation human body patient mental medicare agriculture science fitnesshealth policy weight loss therapy psychology pharmacy metabolic organism lifestyle status unhealthy upbeat vaccination sleep condom alcohol smoking water family eudaimonia eudaemonia air house prevention genetics poor need treatment communicable study protection malaria development food priority management healthful provide department administration program help assistance funding environment improving emergency affected school private treat preparedness perinatal fertility sickness veterinary sanitary pharmacist behavioral midwife gerontology infertility hospitalization midwifery cholesterol childcare pediatrician pediatrics medicaid asthma pension push-up education body-mass-index eat well gymnastic apparatus tune good morning bathing low blood-pressure heart attack club ride-bike feel eczema urticaria dermatitis sunburn overwork manufacturing sociology run'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string1 = health\n",
    "words = string1.split()\n",
    "health = \" \".join(sorted(set(words), key=words.index))\n",
    "health"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Vectorizing and Standadization.\n",
    "Necessary if cosine similarity would be used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Vectorizing the sets of words, then standardizing them. TFIDF will be used in order to take care of the least \n",
    "frequent words. Standardizing is cause TFIDF favors long sentences and there'll be inconsistencies between the length \n",
    "of the tweets and the length of set of words.'''\n",
    "\n",
    "\n",
    "def get_vectors(*strs):\n",
    "    text = [t for t in strs]\n",
    "    vectorizer = TfidfVectorizer(text)\n",
    "    vectorizer.fit(text)\n",
    "    return vectorizer.transform(text).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "socialvector = get_vectors(social)\n",
    "economic_vector = get_vectors(economy)\n",
    "culture_vector = get_vectors(culture)\n",
    "health_vector = get_vectors(health)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Vectorizing the tweets\n",
    "tv=TfidfVectorizer()\n",
    "# tweets_bowl = tweets_bowl.tweets.apply(get_vectors)\n",
    "# tweets_bowl.head()\n",
    "tfidf_tweets =tv.fit_transform(tweets_bowl.tweets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jaccard Similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Jaccard similarity is good for cases where duplication does not matter, \n",
    "cosine similarity is good for cases where duplication matters while analyzing text similarity. For two product descriptions, \n",
    "it will be better to use Jaccard similarity as repetition of a word does not reduce their similarity.'''\n",
    "\n",
    "def jaccard_similarity(query, document):\n",
    "    intersection = set(query).intersection(set(document))\n",
    "    union = set(query).union(set(document))\n",
    "    return len(intersection)/len(union)\n",
    "# jaccard_score(socialvector, economic_vector)\n",
    "\n",
    "#for similarity of 1 and 2 of column1\n",
    "# jaccard_similarity('dog lion a dog','dog is cat')\n",
    "\n",
    "\n",
    "def get_scores(group,tweets):\n",
    "    scores = []\n",
    "    for tweet in tweets:\n",
    "        s = jaccard_similarity(group, tweet)\n",
    "        scores.append(s)\n",
    "    return scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6,\n",
       " 0.4642857142857143,\n",
       " 0.17857142857142858,\n",
       " 0.07142857142857142,\n",
       " 0.14285714285714285,\n",
       " 0.6071428571428571,\n",
       " 0.32142857142857145,\n",
       " 0.14285714285714285,\n",
       " 0.0,\n",
       " 0.6785714285714286]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# economic scores\n",
    "e_scores = get_scores(economy, tweets_bowl.tweets.to_list())\n",
    "e_scores[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6206896551724138,\n",
       " 0.48148148148148145,\n",
       " 0.18518518518518517,\n",
       " 0.07407407407407407,\n",
       " 0.14814814814814814,\n",
       " 0.6296296296296297,\n",
       " 0.3333333333333333,\n",
       " 0.14814814814814814,\n",
       " 0.0,\n",
       " 0.6428571428571429]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# social scores\n",
    "s_scores = get_scores(social, tweets_bowl.tweets.to_list())\n",
    "s_scores[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6,\n",
       " 0.4642857142857143,\n",
       " 0.17857142857142858,\n",
       " 0.07142857142857142,\n",
       " 0.14285714285714285,\n",
       " 0.6071428571428571,\n",
       " 0.32142857142857145,\n",
       " 0.14285714285714285,\n",
       " 0.0,\n",
       " 0.6785714285714286]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# culture scores\n",
    "c_scores = get_scores(culture, tweets_bowl.tweets.to_list())\n",
    "c_scores[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6296296296296297,\n",
       " 0.46153846153846156,\n",
       " 0.6153846153846154,\n",
       " 0.6538461538461539,\n",
       " 0.6666666666666666,\n",
       " 0.5384615384615384]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# health scores\n",
    "h_scores = get_scores(health, tweets_bowl.tweets.to_list())\n",
    "h_scores[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>names</th>\n",
       "      <th>economic_score</th>\n",
       "      <th>social_score</th>\n",
       "      <th>culture_score</th>\n",
       "      <th>health_scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.461538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.592593</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.615385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>0.607143</td>\n",
       "      <td>0.629630</td>\n",
       "      <td>0.607143</td>\n",
       "      <td>0.653846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>0.678571</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.678571</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.518519</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.592593</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.615385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.518519</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.740741</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.769231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>0.392857</td>\n",
       "      <td>0.407407</td>\n",
       "      <td>0.392857</td>\n",
       "      <td>0.423077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.370370</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.461538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>0.620690</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.620690</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>0.689655</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.689655</td>\n",
       "      <td>0.740741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>0.321429</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.321429</td>\n",
       "      <td>0.296296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>0.392857</td>\n",
       "      <td>0.407407</td>\n",
       "      <td>0.392857</td>\n",
       "      <td>0.423077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>0.392857</td>\n",
       "      <td>0.407407</td>\n",
       "      <td>0.392857</td>\n",
       "      <td>0.423077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.518519</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.807692</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     names  economic_score  social_score  culture_score  health_scores\n",
       "0   @nanga        0.642857      0.666667       0.642857       0.629630\n",
       "1   @nanga        0.428571      0.444444       0.428571       0.461538\n",
       "2   @nanga        0.571429      0.592593       0.571429       0.615385\n",
       "3   @nanga        0.607143      0.629630       0.607143       0.653846\n",
       "4   @nanga        0.678571      0.642857       0.678571       0.666667\n",
       "5   @nanga        0.500000      0.518519       0.500000       0.538462\n",
       "6   @nanga        0.571429      0.592593       0.571429       0.615385\n",
       "7   @nanga        0.500000      0.518519       0.500000       0.538462\n",
       "8   @nanga        0.714286      0.740741       0.714286       0.769231\n",
       "9   @nanga        0.392857      0.407407       0.392857       0.423077\n",
       "10  @nanga        0.357143      0.370370       0.357143       0.333333\n",
       "11  @nanga        0.428571      0.444444       0.428571       0.461538\n",
       "12  @nanga        0.000000      0.000000       0.000000       0.000000\n",
       "13  @nanga        0.620690      0.642857       0.620690       0.666667\n",
       "14  @nanga        0.689655      0.714286       0.689655       0.740741\n",
       "15  @nanga        0.321429      0.333333       0.321429       0.296296\n",
       "16  @nanga        0.392857      0.407407       0.392857       0.423077\n",
       "17  @nanga        0.392857      0.407407       0.392857       0.423077\n",
       "18  @nanga        0.500000      0.518519       0.500000       0.538462\n",
       "19  @nanga        0.750000      0.777778       0.750000       0.807692"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''new df with names, and the jaccard scores for each group'''\n",
    "\n",
    "data  = {'names':tweets_bowl.screen_name.to_list(), 'economic_score':e_scores,\n",
    "         'social_score': s_scores, 'culture_score':c_scores, 'health_scores':h_scores}\n",
    "scores_df = pd.DataFrame(data)\n",
    "scores_df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's a thin line between economy, social and culture."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pick out the greatest score out of the 4 scores, where the score is greatest, replace it with 1, the rest 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Actual assigning of classes to the tweets'''\n",
    "\n",
    "def get_clusters(l1, l2, l3, l4):\n",
    "    econ = []\n",
    "    socio = []\n",
    "    cul = []\n",
    "    heal = []\n",
    "    for i, j, k, l in zip(l1, l2, l3, l4):\n",
    "        m = max(i, j, k, l)\n",
    "        if m == i:\n",
    "            econ.append(1)\n",
    "        else:\n",
    "            econ.append(0)\n",
    "        if m == j:\n",
    "            socio.append(1)\n",
    "        else:\n",
    "            socio.append(0)        \n",
    "        if m == k:\n",
    "            cul.append(1)\n",
    "        else:\n",
    "            cul.append(0)  \n",
    "        if m == l:\n",
    "            heal.append(1)\n",
    "        else:\n",
    "            heal.append(0)   \n",
    "            \n",
    "    return econ, socio, cul, heal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "l1 = scores_df.economic_score.to_list()\n",
    "l2 = scores_df.social_score.to_list()\n",
    "l3 = scores_df.culture_score.to_list()\n",
    "l4 = scores_df.health_scores.to_list()\n",
    "\n",
    "econ, socio, cul, heal = get_clusters(l1, l2, l3, l4)"
   ]
  },
  {
   "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>name</th>\n",
       "      <th>economic</th>\n",
       "      <th>social</th>\n",
       "      <th>culture</th>\n",
       "      <th>health</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     name  economic  social  culture  health\n",
       "0  @nanga         0       1        0       0\n",
       "1  @nanga         0       0        0       1\n",
       "2  @nanga         0       0        0       1\n",
       "3  @nanga         0       0        0       1\n",
       "4  @nanga         1       0        1       0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'name': scores_df.names.to_list(), 'economic':econ, 'social':socio, 'culture':cul, 'health': heal}\n",
    "cluster_df = pd.DataFrame(data)\n",
    "cluster_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Due to the close similarity between the economic, social and health tweets, some tweets have multiple categories, \n",
    "so to take care of that the rows with a sum > 1 will have to share the categories. After this, clustering will take care \n",
    "of the fractions'''\n",
    "\n",
    "\n",
    "a =  cluster_df[['economic', 'social', 'culture', 'health']].sum(axis = 1) > 1\n",
    "c = cluster_df[['economic', 'social', 'culture', 'health']].sum(axis = 1)\n",
    "# b = cluster_df.copy()\n",
    "cluster_df.loc[(a), ['economic','social', 'culture', 'health']] = 1/c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(197802, 5)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clustered DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(388, 5)\n"
     ]
    },
    {
     "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>economic</th>\n",
       "      <th>social</th>\n",
       "      <th>culture</th>\n",
       "      <th>health</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>@voaindonesia</td>\n",
       "      <td>4</td>\n",
       "      <td>220</td>\n",
       "      <td>4</td>\n",
       "      <td>279</td>\n",
       "      <td>507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>@wazua</td>\n",
       "      <td>36</td>\n",
       "      <td>80</td>\n",
       "      <td>36</td>\n",
       "      <td>418</td>\n",
       "      <td>570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>@wildlifedirect</td>\n",
       "      <td>10</td>\n",
       "      <td>83</td>\n",
       "      <td>10</td>\n",
       "      <td>416</td>\n",
       "      <td>519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>@wmnjoya</td>\n",
       "      <td>17</td>\n",
       "      <td>52</td>\n",
       "      <td>17</td>\n",
       "      <td>438</td>\n",
       "      <td>524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Total</td>\n",
       "      <td>7456</td>\n",
       "      <td>38529</td>\n",
       "      <td>7456</td>\n",
       "      <td>155244</td>\n",
       "      <td>208685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 economic  social  culture  health   total\n",
       "name                                                      \n",
       "@voaindonesia           4     220        4     279     507\n",
       "@wazua                 36      80       36     418     570\n",
       "@wildlifedirect        10      83       10     416     519\n",
       "@wmnjoya               17      52       17     438     524\n",
       "Total                7456   38529     7456  155244  208685"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_clusters = cluster_df.groupby(['name']).sum()\n",
    "pivot_clusters['economic'] = pivot_clusters['economic'].astype(int)\n",
    "pivot_clusters['social'] = pivot_clusters['social'].astype(int)\n",
    "pivot_clusters['culture'] = pivot_clusters['culture'].astype(int)\n",
    "pivot_clusters['health'] = pivot_clusters['health'].astype(int)\n",
    "pivot_clusters['total'] = pivot_clusters['health'] + pivot_clusters['culture'] + pivot_clusters['social'] +  pivot_clusters['economic']\n",
    "pivot_clusters.loc[\"Total\"] = pivot_clusters.sum()  #add a totals row\n",
    "print(pivot_clusters.shape)\n",
    "pivot_clusters.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualisations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''A pie chart to show the total number of tweets in each category'''\n",
    "\n",
    "fig = plt.figure(figsize =(10, 7)) \n",
    "a = pivot_clusters.drop(['total'], axis = 1)\n",
    "plt.pie(a.loc['Total'], labels = a.columns)\n",
    "plt.title('A pie chart showing the volumes of tweets under different categories.')\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The huge percentage in health could be as a result of the current pandemic, Covid19, everyone is talking about it thus a huge volume of tweets. (The tweets are the most recent 500). \n",
    "* The social tweets follow, this could be related to the set of words defined as words related to social. Most of these terms are general, thus if a tweet that maybe was more economy related could have more social words than economy words and thus classified as social, so this is mainnly a bias in the classification method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ada/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py:4102: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'total tweets')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "''' Users with most tweets'''\n",
    "\n",
    "d = pivot_clusters.sort_values(by = 'total', ascending  = False)\n",
    "e = d.head(12)\n",
    "e.drop(e.head(2).index, inplace=True)\n",
    "\n",
    "plt.figure(figsize=(12,10))\n",
    "sns.barplot(x = e.index, y = e.total)\n",
    "plt.title('A bar plot showing top tweeps based on volume of tweets')\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel('screen names')\n",
    "plt.ylabel('total tweets')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ada/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py:4102: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'economy tweets')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "''' Users with most economy tweets'''\n",
    "\n",
    "d = pivot_clusters.sort_values(by = 'economic', ascending  = False)\n",
    "e = d.head(11)\n",
    "e.drop(e.head(1).index, inplace=True)\n",
    "\n",
    "plt.figure(figsize=(12,10))\n",
    "sns.barplot(x = e.index, y = e.economic)\n",
    "plt.title('A bar plot showing top tweeps based on volume of economy tweets')\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel('screen names')\n",
    "plt.ylabel('economy tweets')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ada/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py:4102: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'social tweets')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "''' Users with most social tweets'''\n",
    "\n",
    "d = pivot_clusters.sort_values(by = 'social', ascending  = False)\n",
    "e = d.head(12)\n",
    "e.drop(e.head(2).index, inplace=True)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12,10))\n",
    "sns.barplot(x = e.index, y = e.social)\n",
    "plt.title('A bar plot showing top tweeps based on volume of social tweets')\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel('screen names')\n",
    "plt.ylabel('social tweets')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ada/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py:4102: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'culture tweets')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "''' Users with most culture tweets'''\n",
    "\n",
    "d = pivot_clusters.sort_values(by = 'culture', ascending  = False)\n",
    "e = d.head(11)\n",
    "e.drop(e.head(1).index, inplace=True)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12,10))\n",
    "sns.barplot(x = e.index, y = e.culture)\n",
    "plt.title('A bar plot showing top tweeps based on volume of culture tweets')\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel('screen names')\n",
    "plt.ylabel('culture tweets')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ada/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py:4102: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'health tweets')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "''' Users with most health tweets'''\n",
    "\n",
    "d = pivot_clusters.sort_values(by = 'health', ascending  = False)\n",
    "e = d.head(12)\n",
    "e.drop(e.head(2).index, inplace=True)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12,10))\n",
    "sns.barplot(x = e.index, y = e.health)\n",
    "plt.title('A bar plot showing top tweeps based on volume of health tweets')\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel('screen names')\n",
    "plt.ylabel('health tweets')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distribution test and skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statistics import mean\n",
    "import re\n",
    "from scipy import stats\n",
    "from scipy.stats import skew, norm\n",
    "from scipy.special import boxcox1p\n",
    "from scipy.stats import boxcox_normmax\n",
    "from sklearn.preprocessing import StandardScaler\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(388, 5)\n",
      "(387, 5)\n"
     ]
    }
   ],
   "source": [
    "# drop the totals row.\n",
    "print(pivot_clusters.shape)\n",
    "pivot_clusters.drop(pivot_clusters.tail(1).index,inplace=True)\n",
    "print(pivot_clusters.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " mu = 19.27 and sigma = 16.53\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Target variable\n",
    "sns.distplot(pivot_clusters.economic , fit=norm);\n",
    "\n",
    "# Get the fitted parameters used by the function\n",
    "(mu, sigma) = norm.fit(pivot_clusters.economic)\n",
    "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
    "\n",
    "#Now plot the distribution\n",
    "plt.title('Ecocnomy tweets distribution plot')\n",
    "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],\n",
    "            loc='best')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('y distribution')\n",
    "\n",
    "#Get also the QQ-plot\n",
    "fig = plt.figure()\n",
    "res = stats.probplot(pivot_clusters.economic, plot=plt)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " mu = 99.56 and sigma = 61.07\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Target variable\n",
    "sns.distplot(pivot_clusters.social , fit=norm);\n",
    "\n",
    "# Get the fitted parameters used by the function\n",
    "(mu, sigma) = norm.fit(pivot_clusters.social)\n",
    "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
    "\n",
    "#Now plot the distribution\n",
    "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],\n",
    "            loc='best')\n",
    "plt.title('Social tweets distribution plot')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('y distribution')\n",
    "\n",
    "#Get also the QQ-plot\n",
    "fig = plt.figure()\n",
    "res = stats.probplot(pivot_clusters.social, plot=plt)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "right tail (heavy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " mu = 19.27 and sigma = 16.53\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Target variable\n",
    "sns.distplot(pivot_clusters.culture , fit=norm);\n",
    "\n",
    "# Get the fitted parameters used by the function\n",
    "(mu, sigma) = norm.fit(pivot_clusters.culture)\n",
    "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
    "\n",
    "#Now plot the distribution\n",
    "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],\n",
    "            loc='best')\n",
    "plt.title('Culture tweets distribution plot')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('y distribution')\n",
    "\n",
    "#Get also the QQ-plot\n",
    "fig = plt.figure()\n",
    "res = stats.probplot(pivot_clusters.culture, plot=plt)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "rightly skewed (right tail)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " mu = 401.15 and sigma = 128.38\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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V6xIt39atWzn55JPp378/OTk5nH322YwcOfKQ6yfjnoTLtG/fPvr27cuECROYOXNmzC7JRMT6N/nkk0+YMWMGH3zwAQMGDGDAgAHMmzePIUOGcNFFFzFw4ECOPfZYgsFg+VhP5L/jDz/8wLBhw+jXrx/HH388p59+Ouecc06Vx0f68MMPOeussw67fmFSVXM/VQwePFhTZbpfTVjzYyFnPPJf2jTJYumk2L89ff3111F/ezE/reXLl/PQQw8xY8YMunXrxvLly2ukn9qkjwsvvJD777+fHj16xMwT7f+7iCxT1UMesrEWRxqqzy2OdHTccccxbNgw9u7di8fjsaBhElJaWsr5559fZdBIVFIDh4iMFJE1IpInIrdH2Z8lIrOd/YtFpLOT3kpEPhSRfSLyeIxzzxWRlcksf6oqdZ5AtsBRf/zmN7+hWbNmrF27traLYuqZzMzMhCZkuJG0wCEiXuAJ4EygN3CJiPSulG08sFtVuwEPAw866cXAXcDEGOe+ENiXjHKng/AT4/VtcNwYUzcks8WRA+Sp6npVLQVmAaMq5RkFhEf2XgOGi4io6n5VXUgogFQgIo2Bm4B7k1f01FbiPAAYVPDXk6fHjTF1RzIDRwdgU8TnfCctah5V9QN7gXhTG/4I/Bk4UFUmEZkgIrkikrt9+/ZEyp3yIteoivcQYDpMnjAm3SX6/zyZgSPaUziVS+cmz8HMIgOAbqo6J97FVfVpVR2sqoPDD8eYkMg1qqoa58jOzmbnzp0WPIxJYeF3jifycGAynxzPBzpFfO4IVJ6oHs6TLyI+oBmwq4pz/hwYJCIbCZW9rYh8pKqn1lSh00FJ2cGxjaoWOuzYsSP5+flYi82Y1JadnU3Hjh1d509m4FgKdBeRLsBmYBzwq0p55gJXAJ8BFwEfaBW/3qrqX4C/ADgzsP5pQSNxkcGiqqXVMzIy6NKly09RJGNMPZK0wKGqfhG5DngX8ALPqeoqEZkC5KrqXOBZYIaI5BFqaZQ/8++0KpoCmSJyPjBCVVcnq7zpJLJ7qjTJLwcyxqSepC5yqKrzgHmV0v4QsV0MjIlxbOc4594IHP6LCNJQ5IB4cT15mZMxpu6wJ8fTUMUWhwUOY0xiLHCkocgH/+rL62ONMXWHBY40ZC0OY8zhsMCRhiLHOGy9KmNMoixwpKHIYGHrVRljEmWBIw2VWovDGHMYLHCkoRJ/kMZZvvJtY4xJhAWONBQZOKzFYYxJlAWONFTiD9Ak2wKHMaZ6LHCkoVJ/sDxw2OC4MSZRFjjSUIk/SKMsHyLW4jDGJM4CRxoq9QfJ8nnI9HpscNwYkzALHGmoNBAky+cl02eBwxiTOAscaajEHyDT5yHL57XAYYxJmAWONPPK4u/Ze6CMTbsO4A8EWfNjAa8s/r62i2WMqUcscKShsoDi8wo+r+AP2vvEjTGJscCRhgJBxefx4PN48AcscBhjEmOBIw35g0F8nnCLw8Y4jDGJSWrgEJGRIrJGRPJE5PYo+7NEZLazf7GIdHbSW4nIhyKyT0Qej8jfUETeEZFvRGSViDyQzPKnoqAqQQWvV/B6xFocxpiEJS1wiIgXeAI4E+gNXCIivStlGw/sVtVuwMPAg056MXAXMDHKqaepak/gOOAkETkzGeVPVeFAkeHx4PPYGIcxJnHJbHHkAHmqul5VS4FZwKhKeUYBLzjbrwHDRURUdb+qLiQUQMqp6gFV/dDZLgU+BzomsQ4pJ9w15fUIPo+HgAUOY0yCkhk4OgCbIj7nO2lR86iqH9gLtHJzchFpDpwL/PuwS5pGwi2M8KyqMnt1rDEmQckMHBIlrfKvt27yHHpiER8wE5iuqutj5JkgIrkikrt9+/a4hU0X4a4qn9NVZS0OY0yikhk48oFOEZ87Alti5XGCQTNgl4tzPw2sU9VHYmVQ1adVdbCqDm7Tpk1CBU9l4a4qnzfUVWVjHMaYRCUzcCwFuotIFxHJBMYBcyvlmQtc4WxfBHygqlV+k4nIvYQCzI01XN60cLDFIXi9gt+6qowxCfIl68Sq6heR64B3AS/wnKquEpEpQK6qzgWeBWaISB6hlsa48PEishFoCmSKyPnACKAAmAR8A3wuIgCPq+rfklWPVBPumvJ5PGTYrCpjTDUkLXAAqOo8YF6ltD9EbBcDY2Ic2znGaaONixiXIgfHvdZVZYypBntyPM2Eu6bCT44Hgkqc3kFjjKnAAkea8QcrzqqKTDPGGDcscKSZcJDweqU8cNiUXGNMIixwpJlwV1WGR/B5Q7ffHgI0xiTCAkeaKW9xeKzFYYypHgscaebgrCoPPq+NcRhjEmeBI80EImZVeT2h229LqxtjEmGBI81EPseRUT6rysY4jDHuWeBIM+VjHBJacgSsxWGMSYwFjjTjD4ReGysSWuQQbIzDGJMYCxxpxh/U8kFxn3VVGWOqwQJHmvEHtHxQ3GddVcaYarDAkWb8QS0fFA93VdlzHMaYRFjgSDP+YBCvx7qqjDHVZ4EjzfgDSoaz1IjXHgA0xlSDBY40EwhqeYsjwx4ANMZUgwWONFMWDJYPinttWXVjTDVY4EgzgYCWj20cXKvKxjiMMe5Z4Egz/qCWz6byiOAR66oyxiQmqYFDREaKyBoRyROR26PszxKR2c7+xSLS2UlvJSIfisg+EXm80jGDROQr55jpImLvIE+AP6KrCkJTcm06rjEmEUkLHCLiBZ4AzgR6A5eISO9K2cYDu1W1G/Aw8KCTXgzcBUyMcuq/ABOA7s6fkTVf+tQVegAwInB4xV7kZIxJSDJbHDlAnqquV9VSYBYwqlKeUcALzvZrwHAREVXdr6oLCQWQciJyBNBUVT9TVQVeBM5PYh1STugBwIO33ecRa3EYYxKSzMDRAdgU8TnfSYuaR1X9wF6gVZxz5sc5JwAiMkFEckUkd/v27QkWPXX5g1r+/AaEXuhks6qMMYlIZuCINvZQ+RvKTZ5q5VfVp1V1sKoObtOmTRWnTC/+QLB8yREITcn1W1eVMSYByQwc+UCniM8dgS2x8oiID2gG7Ipzzo5xzmmqEHoA8OBtz/CItTiMMQlJZuBYCnQXkS4ikgmMA+ZWyjMXuMLZvgj4wBm7iEpVfwAKReQEZzbV5cBbNV/01KSqFZZVB6fFYYHDGJMAX7JOrKp+EbkOeBfwAs+p6ioRmQLkqupc4FlghojkEWppjAsfLyIbgaZApoicD4xQ1dXA/wP+DjQA/uX8MS6URrxvPMzn9dhzHMaYhLgKHCLSV1VXJnpyVZ0HzKuU9oeI7WJgTIxjO8dIzwX6JloWA6X+KIHDIxSVBWqrSMaYeshtV9VfRWSJiFwjIs2TWiKTNCXhwOG16bjGmOpzFThU9WTgUkID2bki8oqInJ7UkpkaF7XF4fVQZl1VxpgEuB4cV9V1wP8CtwG/AKaLyDcicmGyCmdqVrHTJRXZ4vB6hIAtcmiMSYCrwCEi/UTkYeBr4JfAuaray9l+OInlMzWouCwUIDIqzaqynipjTCLczqp6HHgGuFNVi8KJqrpFRP43KSUzNa7YH2pxZFRqcdh0XGNMItwGjrOAIlUNAIiIB8hW1QOqOiNppTM16mBXVcUWh3VVGWMS4XaMYwGh5ybCGjppph4pCXdVRS5yKDaryhiTGLeBI1tV94U/ONsNk1MkkyyxWxwWOIwx7rkNHPtFZGD4g4gMAoqqyG/qoFhjHEGFoAUPY4xLbsc4bgT+ISLhBQWPAMYmp0gmWQ7OqqoYOADKgkGyPN5aKZcxpn5xFThUdamI9AR6EFra/BtVLUtqyUyNC3dVVV5WHaAsoGQlbeUyY0wqSeSr4nigs3PMcSKCqr6YlFKZpAi3OCo/AAhQ5g9CVq0UyxhTz7hd5HAGcDSwAgiviBd+daupJ2INjkOoq8oYY9xw2+IYDPSu6l0Zpu4r9gfweQSPRAQOOdhVZYwxbridVbUSaJfMgpjkKykLVmhtQKWuKmOMccFti6M1sFpElgAl4URVPS8ppTJJUVwWqDCjCiIHxy1wGGPccRs4JiezEOanUeIPHhI4wkusl1rgMMa45HY67n9E5Cigu6ouEJGGhF4Ha+qR4rJAhXdxAHg8NsZhjEmM22XVfwu8BjzlJHUA3kxWoUxyWFeVMaYmuB0cvxY4CSiA8pc6tY13kIiMFJE1IpInIrdH2Z8lIrOd/YtFpHPEvjuc9DUickZE+v+IyCoRWSkiM0Uk22Ud0l5xWbDCuzjABseNMYlzGzhKVLU0/EFEfISe44hJRLzAE8CZQG/gEhHpXSnbeGC3qnYj9EKoB51jewPjgD7ASOBJEfGKSAfgemCwqvYl1F02zmUd0l6x/9AWh09sjMMYkxi3geM/InIn0MB51/g/gLfjHJMD5KnqeifozAJGVcozCnjB2X4NGC4i4qTPUtUSVd0A5Dnng9C4TAMneDUEtmBcKS4LHjLG4XWWWPfbGIcxxiW3geN2YDvwFfA7YB6h949XpQOwKeJzvpMWNY+q+oG9QKtYx6rqZmAa8D3wA7BXVd+LdnERmSAiuSKSu3379rgVTAclZYEKy42AjXEYYxLnKnCoalBVn1HVMap6kbMd71dUiZJW+ZhYeaKmi0gLQq2RLkB7oJGIXBajzE+r6mBVHdymTZs4RU0PVQ2OW1eVMcYtt2tVbSDKmIaqdq3isHygU8TnjhzarRTOk+90PTUDdlVx7GnABlXd7pTrDeBE4CU39Uh3xf4qBsetq8oY41Iia1WFZQNjgJZxjlkKdBeRLsBmQoPYv6qUZy5wBfAZcBHwgaqqiMwFXhGRhwi1LLoDS4AgcILzHEkRMBzIdVmHtGfTcY0xNcHtA4A7KyU9IiILgT9UcYxfRK4D3iU0++k5VV0lIlOAXFWdCzwLzBCRPEItjXHOsatE5FVgNeAHrlXVALBYRF4DPnfSlwNPu69u+lLV0AOAMVscFjiMMe647aoaGPHRQ6gF0iTecao6j9BAemTaHyK2iwm1XqIdOxWYGiX9buBuN+U2B5UFlKByaIsjPB3XnuMwxrjktqvqzxHbfmAjcHGNl8YkTfn7xg+ZjmtjHMaYxLjtqhqW7IKY5Dr4Eicb4zDGHB63XVU3VbVfVR+qmeKYZClxXhtbuavKI6G5z34LHMYYlxKZVXU8oVlQAOcC/6XiQ3qmDgu3OCpPxxURvB6h1LqqjDEuJfIip4GqWgggIpOBf6jq1ckqmKlZxTFaHBDqrrKuKmOMW26XHDkSKI34XAp0rvHSmKQJD45Xno4LFjiMMYlx2+KYASwRkTmEniC/AHgxaaUyNa68q8oTpcUhFjiMMe65nVU1VUT+BZziJF2lqsuTVyxT0+J1VZX6bYzDGOOO264qCC1hXqCqjxJaW6pLkspkkuDgdFzrqjLGHB63r469G7gNuMNJysAWFqxXDs6qssFxY8zhcdviuAA4D9gPoKpbcLHkiKk7SpwlRWK3OKyryhjjjtvAUeq8f0MBRKRR8opkkqHKwXFrcRhjEuA2cLwqIk8BzUXkt8AC4JnkFcvUtHCLo/IDgGCBwxiTGLezqqY57xovAHoAf1DV95NaMlOjissCiBxcmyqSBQ5jTCLiBg4R8QLvquppgAWLeqq4LEC2z4vIoYHDZ0uOGGMSELerynmB0gERafYTlMckSXFZkOyM6LfbI0KZvY/DGOOS2yfHi4GvROR9nJlVAKp6fVJKZWpccVmA7Axv1H2hBwAtcBhj3HEbON5x/ph6qtgfrDJw2BiHMcatKgOHiBypqt+r6gvVObmIjAQeJfTO8b+p6gOV9mcRWvNqELATGKuqG519dwDjgQBwvaq+66Q3B/4G9CU0Pfg3qvpZdcqXTorLAmT5ondV+ew5DmNMAuKNcbwZ3hCR1xM5sTOo/gRwJtAbuEREelfKNh7YrardgIeBB51jewPjgD7ASOBJ53wQCkTzVbUn0B/4OpFypat4XVXW4jDGuBUvcEROwema4LlzgDxVXa+qpcAsYFSlPKOAcGvmNWC4hKb9jAJmqWqJqm4A8oAcEWkKDAWeBVDVUlXdk2C50lJJFYPjFjiMMYmIFzg0xrYbHaj4hsB8Jy1qHlX1A3uBVlUc2xXYDjwvIstF5G+xnmIXkQkikisiudu3b0+w6Kmn2AjcZVcAABsiSURBVF9Fi0Osq8oY4168wNFfRApEpBDo52wXiEihiBTEOfbQBwYODT6x8sRK9wEDgb+o6nGEZnjdHu3iqvq0qg5W1cFt2rSJU9TUF36OIxqvx0OptTiMMS5VOTiuqtG/adzJBzpFfO4IbImRJ19EfEAzYFcVx+YD+aq62El/jRiBw1RU1XMcXg+UBYKoatQHBI0xJlIi7+NI1FKgu4h0EZFMQoPdcyvlmQtc4WxfBHzgLKY4FxgnIlnOez+6A0tU9Udgk4j0cI4ZDqxOYh1SRmhWVezBcVUIBK27yhgTn9vnOBKmqn4RuQ54l9B03OdUdZWITAFyVXUuoUHuGSKSR6ilMc45dpWIvEooKPiBa50n2AF+D7zsBKP1wFXJqkMqCc2qitXiCKWXBZQYscUYY8olLXAAqOo8YF6ltD9EbBcDY2IcOxWYGiV9BTC4Zkua+uI9AAhQGgjSAIscxpiqJbOrytQRwaBS6g+SFSdw2JRcY4wbFjjSQPhdHLG6qnzOgLjfpuQaY1ywwJEGwm//iz0d11ocxhj3LHCkgWK/EzhcjHEYY0w8FjjSQHFZ1V1V1uIwxiTCAkcaKHHZ4ijz2xiHMSY+CxxpwG2Lw7qqjDFuWOBIAzY4boypSRY40kA4cMR8jkMscBhj3LPAkQZscNwYU5MscKQB14Pj9gCgMcYFCxxpoLDYD0CTrOhLk1mLwxiTCAscaaCguAyApg0you73WeAwxiTAAkca2FtURqbXQ5Yv+u322HMcxpgEWOBIAwVFfpo28MV8u589x2GMSYQFjjRQUFxG0+zo3VRwcHVc66oyxrhhgSMNFBSV0STG+AbY4LgxJjEWONJAQbGfptmxX/Zo03GNMYmwwJEGCovKYs6ogojBcWtxGGNcSGrgEJGRIrJGRPJE5PYo+7NEZLazf7GIdI7Yd4eTvkZEzqh0nFdElovIP5NZ/lQRb4zDI4LPIxY4jDGuJC1wiIgXeAI4E+gNXCIivStlGw/sVtVuwMPAg86xvYFxQB9gJPCkc76wG4Cvk1X2VKKqFBT5aVZFiwMgw+uxripjjCvJbHHkAHmqul5VS4FZwKhKeUYBLzjbrwHDJTRndBQwS1VLVHUDkOecDxHpCJwN/C2JZU8ZJf4gpYEgTRvEHuMAyPAKpX5rcRhj4ktm4OgAbIr4nO+kRc2jqn5gL9AqzrGPALcCVX7LicgEEckVkdzt27dXtw71XkGR89R4FV1VEG5xWOAwxsSXzMAR7Wmzyn0hsfJETReRc4Btqros3sVV9WlVHayqg9u0aRO/tCnolcXf88ri7wH4avPe8u1oLHAYY9xKZuDIBzpFfO4IbImVR0R8QDNgVxXHngScJyIbCXV9/VJEXkpG4VNF+F0cDWKsjBuW4RMb4zDGuJLMwLEU6C4iXUQkk9Bg99xKeeYCVzjbFwEfqKo66eOcWVddgO7AElW9Q1U7qmpn53wfqOplSaxDvVdU/i6OOIHD67ElR4wxrlQ9YnoYVNUvItcB7wJe4DlVXSUiU4BcVZ0LPAvMEJE8Qi2Ncc6xq0TkVWA14AeuVdVAssqayspfGxvjJU5hmV4PZTY4boxxIWmBA0BV5wHzKqX9IWK7GBgT49ipwNQqzv0R8FFNlDOVFbntqvJ68Aetq8oYE589OZ7iDrY44gUOewDQGOOOBY4UV1wWwOcRMrxV3+oMr8ee4zDGuGKBI8UVlQXjtjbApuMaY9yzwJHiissCLgOHTcc1xrhjgSPFFZcFaBBnRhVYi8MY415SZ1WZ2ldUFqBBhpeykmJWLvqIzeu/Yd+eXTRv/TOO6tmPHgNPxOP1kuGz5ziMMe5Y4EhxRaV+PBsX8cgrcyjev4+GTZrRuFlLvlvzFUv/PZfmbdpx1hXXk9m4i7U4jDGuWOBIYf7SUg7893kO5K+gc6/+/OKCy+nUvQ8iQjAYYO3yxXz4+vO8Mu1Ouv/iAko7/bK2i2yMqQcscKSooqIiZj96N+SvoOMpF3PZVVchnoNjHR6Pl56DTuToYwcx/6UnWfGfOfh67EJ1OKGV7Y0xJjobHE9BwWCQW2+9lfWrl1M64GK6nnxuhaARKSMzi3OuupFjTjkX/5r/8Nhjj/3EpTXG1DcWOFLQ9OnTWbBgAUMvuprAUTlkZ1Y9HVdEyBl1JXQewl/+8hfeeeedn6agxph6yQJHivnPf/7DU089xZgxY+hx8tlA/OVGwJmO2/9CBg0axF133cWGDRuSXVRjTD1lgSOFbNu2jTvvvJMePXowadIkSpwlROItcAjOIod4+dOfppGVlcXEiRMpLS1NdpGNMfWQBY4UoapMnjyZ/fv3M21a6Mvf7QKHAJm+0I9CyzZtuPfee1m9ejVPPfVUUstsjKmfLHCkiHnz5vHhhx9yww030K1bNyDyJU5unhwPzaQqCyjDhw/n3HPP5ZlnnmH9+vXJK7Qxpl6ywJEC9u7dy3333Ue/fv24/PLLy9PdvjYWKF89N7xC7m233UbDhg25++67CQbtwUBjzEEWOFLA448/zp49e7jnnnvweg8GiUS6qhpnhR7pKSwuA6BVq1bcfPPN5ObmMmfOnCSU2hhTX1ngqOfWrl3LzJkzufjii+nZs2eFfUUu38UB0K5ZNgA/7i0uTxs9ejSDBg3iT3/6Ezt37qzZghtj6q2kBg4RGSkia0QkT0Ruj7I/S0RmO/sXi0jniH13OOlrROQMJ62TiHwoIl+LyCoRuSGZ5a/rVJX777+fxo0bc/311x+yv6CojEZZ7hYH+FlTJ3AUHAwcHo+He+65hwMHDvDnP/+5ZgptjKn3khY4RMQLPAGcCfQGLhGR3pWyjQd2q2o34GHgQefY3sA4oA8wEnjSOZ8fuFlVewEnANdGOWfaeP/991m0aBHXX389LVq0OGT/pt1FdGzRwNW5woFjW0FJhfSjjz6aX//618yZM4cvvvji8AttjKn3ktniyAHyVHW9qpYCs4BRlfKMAl5wtl8DhktooaRRwCxVLVHVDUAekKOqP6jq5wCqWgh8DXRIYh3qrOLiYh588EF69OjBxRdffMj+bYXF7NpfypEtG7o6X9NsHw0yvBVaHGHXXHMNbdq0YerUqTZQboxJauDoAGyK+JzPoV/y5XlU1Q/sBVq5Odbp1joOWBzt4iIyQURyRSR3+/bt1a5EXfXss8+yZcsW7rzzTny+Q7ujPv9uDwBHuQwcIkK7ZtlRA0ejRo2YOHEiX331lQ2UG2OSGjiiLbFa+d2ksfJUeayINAZeB25U1YJoF1fVp1V1sKoObtOmjcsi1w+bN2/mmWeeYeTIkeTk5ETN8/n3u/F6hPbN3XVVAbRtksW2KIED4Nxzz+W4447joYceoqAg6j+5MSZNJDNw5AOdIj53BLbEyiMiPqAZsKuqY0Ukg1DQeFlV30hKyeu4adOmISLccsstMfMs+243HZo3wOdiRlVYrBYHhFokkyZNYvfu3TzxxBMJl9kYkzqSGTiWAt1FpIuIZBIa7J5bKc9c4Apn+yLgA1VVJ32cM+uqC9AdWOKMfzwLfK2qDyWx7HXWkiVLmD9/PldffTXt27ePmqfEH+CrzXtdd1OFtWuazdaCEkK34FB9+vRhzJgxvPzyy6xbty7hshtjUkPSAoczZnEd8C6hQexXVXWViEwRkfOcbM8CrUQkD7gJuN05dhXwKrAamA9cq6oB4CTg18AvRWSF8+esZNWhrvH7/UydOpX27dszfvz4mPlWbSmg1B/kyFaJBY62TbMp9QfZc6AsZp4bb7yRxo0bc99998UMMMaY1JbUNwCq6jxgXqW0P0RsFwNjYhw7FZhaKW0h0cc/0sLMmTNZu3Ytjz76KNnZ2THzff7dbgDXM6rC2kU8y9GiUWbUPC1atOD3v/899957L++//z4jRoxI6BrGmPrPnhyvJ7Zt28b06dM5+eSTOf3006vMu+y73RzZsiFNsjNcn/+Vxd/zZX5oJtasJZt4ZfH3MfOOHTuWY445hgceeICioiLX1zDGpAYLHPXEtGnTKCkpYdKkSXHfCf7Fpj0M6NQ84Ws0dQJNeL2qWHw+H5MmTeKHH37g2WefTfg6xpj6zQJHPbBkyRLefvttxo8fT+fOnavMu3t/KVv2FtO3Q9OEr9OkQajncm+cwAGQk5PDmWeeyd/+9jc2b96c8LWMMfWXBY46rqysjD/+8Y906NCBCRMmxM2/+ofQMxa9j2iW8LV8Hg+NMr0UFPld5b/11lvxeDw8+OCDCV/LGFN/WeCo45566iny8vKYNGkSDRrEf5hv9ZZQ4Oh1RJNqXa9pg4y4XVVh7dq143e/+x3vv/8+CxYsqNb1jDH1jwWOOmzVqlX89a9/5dxzz2XYsGFR86zdWsjwP3/E5j2hQerVPxTQrmk2rRpnVeuaTbMzKChyFzgArrrqKnr16sXdd99tS68bkyYscNRRJSUl3HbbbbRu3ZpJkybFzDd/5Y98u30/8778AYBVW/bSu33i4xthTRv42FvsrqsKIDMzkwceeIDCwkLuuecee7bDmDRggaOOmj59Ot9++y333nsvzZrFHq9YunEXAO9/vZXisgDfbt9Pn8MJHNkZ7C/xEwi6DwDHHHMMN9xwA++//z5vv/12ta9tjKkfLHDUQYsXL+b5559n7NixnHzyyTHzlQWCLPtuNz6PsHTDLh741zcEgsrOfaVVPodRFbdTciu78sorGTRoEPfeey/5+fnVurYxpn6wwFHHbN26lZtvvpkuXbpUuYghhJYWOVAa4OdHt0KB/6wNLR9/RLPYT5XH09SZkpvIOAeA1+vl/vvvB+CGG26gpKQkzhHGmPrKAkcdUlxczPXXX09RURHTp0+nUaNGVeZfuiHUTXXS0a1pku1jw479ZPk8MZcLcaNZw9CxO/aVJnxsp06dePDBB1m9ejV33323jXcYk6IscNQRwWCQ2267ja+++ooHHniAo48+unzfwnU7GPnIf/lH7qYKxyzesIsurRvRtEEGPduFxjXaNcvGE+fJ8qq0bZJF4ywfa7YWVuv4YcOGcd111/HWW2/x5JNPVrscxpi6ywJHHaCqTJkyhffee49bb721fC2qskCQu95cyWXPLmbt1kLufedr9hwItQSCQWXpxl0c3zn0rvHwcxvtm7l/cVM0HhF6tGvCum2FlAWq95rYa665hlGjRvH4448zc+bMwyqPMabuscBRy4LBIFOnTmX27Nn89re/5Yorrijf99qyfGYs+o6rTurMG9ecRGFxGdP/nQfAum372FtURk6XVgAc3aYxXds0OqypuGE92zWhuCxI7sbd1TpeRJgyZQrDhg1jypQpzJ49+7DLZIypO5K6rLqpWmlpKXfeeSfvvPMOV111Ff/zP/9TvoDhS4u+49F/r6N9s2y6tWnM6i0FDDqqBX//dAOBYJAv8/cC8OPeYlo2yiTD6+Hqk7vWSLm6tW2M1yP8++ut/PzoVtU6R2ZmJo888gjXX389kydPZseOHVxzzTVxF2g0xtR91uKoJVu2bOHXv/4177zzDjfffDO33HJLhS/VdVsL2V5YwsndW5enn9brZ/i8Hl747DuWb9rDsR2a0aKh+6XT3cryeenauhEffLPtsM6TmZnJY489xvnnn8/jjz/OTTfdxL59+2qolMaY2mItjp+YqvLWW2/xwAMP4Pf7efTRR6O+DGlh3g6aZvs4tsPB5dGbZGdwac6R7DpQSq92TWnaoOaDRljPI5ry9hdbWL99H13bNK72eTIyMrjvvvvo1q0bDz/8MKtWreKPf/wjQ4YMqcHSGmN+Stbi+Al98cUXXH755dxxxx1069aN119//ZCgEQwq//56K99u38+JR7fG66nYtdP9Z00Y0qVVUoMGQM+fhQbb31u99bDPJSKMHz+eF154ARHhyiuv5KabbuL776v3kKIxpnZJOsy1Hzx4sObm5tbKtUtLS/noo4+YOXMmixYtIqtxM7T3SB654/8xrOfPKuR98bONPPZBHtsLS2ic5eN/TjuGBpneWik3wFsrNrNu2z4+vPlUmtVQl1hRURHPPPMMf//73ykpKWHEiBGMHj2aIUOGkJER/xol/gBrfiykU4uGh/W8ijEmPhFZpqqDD0lPZuAQkZHAo4AX+JuqPlBpfxbwIjAI2AmMVdWNzr47gPFAALheVd91c85ofsrAEQwG2bRpE4sXL2bhwoUsWrSIwsJCWrZui3QfSn6LAbRr1Zzt+0q457w+XHbCUQC8u+pHfjdjGSd0bcklOUeyc18p2Rm1FzQA+ndqxrmPLeTyn3dm8nl9avTc27Zt48UXX+TVV1+lsLCQZs2acfrpp3PCCSfQv39/OnTocMiYz91zV5H73W5K/UGyMzyMHtiR357Slc6tD31QcmtBMU2yfTTMtN5YY6rrJw8cIuIF1gKnA/nAUuASVV0dkecaoJ+q/n8iMg64QFXHikhvYCaQA7QHFgDHOIdVec5oqhs4gsEgJSUlUf8UFBSwc+dOdu7cyY/btrNj+w6+27iBvG/zKA6/h7thc7I69KakXV+KWnQjO9PHRQM7cXSbRsxauok1Wwvp3rYxA49qwZzlm2nbJIvfntKVDG/d6EH81ZAjuevNlby8+Dve/v3JHNmyIf6A0jjbR4bXQyCo7C/1k+n1kOXzICKU+oMEgkp2RuizqlIaCJLh8eBxut2CQSWois/roaSkhI8//ph/zZ/PRx9+yIEDBwBo2bIlXbp25chOnVhTkMHKXYo3uyHd2rfhiLYt8WU14v1vdqAeL+OGdOHa03rQqklDfigo5aH31/Lmii1kZ3gY1qMtv+zZlkFHtaBL60Y2qysJ3HyHuP2acZPN7XeW2282N6dTl2erya/TbQUlvLf6R/67bgc/7Cli5/5S+nVsxtUnd+Wkbq1+kp/l2ggcPwcmq+oZzuc7AFT1/og87zp5PhMRH/Aj0Aa4PTJvOJ9zWJXnjKa6gePss89m/fr1cfOJ10sgswnauA3apB3a9Gcc3as/jVsfQUGxnybZPrq1bUz3tk1olBX6DTioyqff7uSjNds4UBqgcZaPa4d1o1mSxy4S8ashR7LnQCnDpn3E7gMV167K9Hko9R98QDDDG/ohLguEfp5EIMvnocQfLP/PlOnzgEKp82Ch1yNkeEPBJqhAMEDW/q3oru/QXd8j+3bgObATKS5IqNyKcPD/lET8lxcQ5+/yJDm4r8LnFJf6PdQpQyT0xyNCIKiogteD6x6JTz/9lKys6r2fJ1bgSGY7vgMQuUZGPlB5Kk15HlX1i8heoJWTvqjSsR2c7XjnBEBEJgDhd63uE5E11ahDPK2BHdF2fFuNk10z5fAKU9MuPbgZs54pxuqZOtKhjuCintnZ1V/0FDgqWmIyA0e0X90q/54TK0+s9Gh9OFF/d1LVp4Gnqyrg4RKR3GjRONVYPVNLOtQzHeoItVfPZHam5wOdIj53BLbEyuN0VTUDdlVxrJtzGmOMSaJkBo6lQHcR6SIimcA4YG6lPHOB8OJMFwEfaGjQZS4wTkSyRKQL0B1Y4vKcxhhjkihpXVXOmMV1wLuEps4+p6qrRGQKkKuqc4FngRkikkeopTHOOXaViLwKrAb8wLWqGgCIds5k1cGFpHaF1SFWz9SSDvVMhzpCLdUzLR4ANMYYU3PqxgMDxhhj6g0LHMYYYxJigaMaRGSkiKwRkTwRub22y3O4RGSjiHwlIitEJNdJayki74vIOufvFk66iMh0p+5fisjA2i19bCLynIhsE5GVEWkJ10tErnDyrxORK6JdqzbFqOdkEdns3NMVInJWxL47nHquEZEzItLr9M+1iHQSkQ9F5GsRWSUiNzjpKXNPq6hj3bqfqmp/EvhDaFD+W6ArkAl8AfSu7XIdZp02Aq0rpf0fcLuzfTvwoLN9FvAvQs/anAAsru3yV1GvocBAYGV16wW0BNY7f7dwtlvUdt1c1HMyMDFK3t7Oz2wW0MX5WfbWh59r4AhgoLPdhNDyQ71T6Z5WUcc6dT+txZG4HCBPVderaikwCxhVy2VKhlHAC872C8D5EekvasgioLmIHFEbBYxHVf9LaLZepETrdQbwvqruUtXdwPvAyOSX3r0Y9YxlFDBLVUtUdQOQR+hnus7/XKvqD6r6ubNdCHxNaEWJlLmnVdQxllq5nxY4EhdtKZWqbmx9oMB7IrLMWaoF4Geq+gOEfpiBtk56fa9/ovWqz/W9zumieS7cfUOK1FNEOgPHAYtJ0XtaqY5Qh+6nBY7EuVlKpb45SVUHAmcC14rI0CrypmL9IfHlb+q6vwBHAwOAH4A/O+n1vp4i0hh4HbhRVataAbPe1jVKHevU/bTAkbiUW/ZEVbc4f28D5hBq5m4Nd0E5f4dfQF7f659oveplfVV1q6oGVDUIPEPonkI9r6eIZBD6Qn1ZVd9wklPqnkarY127nxY4EpdSy56ISCMRaRLeBkYAK6m4HMwVwFvO9lzgcmfGygnA3nA3QT2RaL3eBUaISAune2CEk1anVRp3uoDQPYV6vJyPiAih1Sa+VtWHInalzD2NVcc6dz9rexZBffxDaLbGWkKzFibVdnkOsy5dCc24+AJYFa4PoeXt/w2sc/5u6aQL8IRT96+AwbVdhyrqNpNQs76M0G9g46tTL+A3hAYd84CrarteLus5w6nHl84XxhER+Sc59VwDnBmRXqd/roGTCXW3fAmscP6clUr3tIo61qn7aUuOGGOMSYh1VRljjEmIBQ5jjDEJscBhjDEmIRY4jDHGJMQChzHGmIRY4DDmMIlI58iVaQ/jPFeKyOPO9vki0jti30ciMvhwr2FMTbDAYUzddD6hlU+NqXMscBhTM7wi8ozzDoX3RKSBiBwtIvOdxSM/FpGeACJyrogsFpHlIrJARH4WeSIRORE4D/iT8+6Fo51dY0RkiYisFZFTfuL6GVPOAocxNaM78ISq9gH2AKOBp4Hfq+ogYCLwpJN3IXCCqh5HaLnrWyNPpKqfEno6+BZVHaCq3zq7fKqaA9wI3J3sChkTi6+2C2BMitigqiuc7WVAZ+BE4B+h5YeA0Mt2ILTg3Gxn/aFMYIPLa4QX9Quf35haYYHDmJpRErEdAH4G7FHVAVHyPgY8pKpzReRUQm93S+QaAez/rqlF1lVlTHIUABtEZAyUv/+6v7OvGbDZ2Y71vutCQq8ONabOscBhTPJcCowXkfDKw+FXd04m1IX1MbAjxrGzgFucAfSjY+QxplbY6rjGGGMSYi0OY4wxCbHAYYwxJiEWOIwxxiTEAocxxpiEWOAwxhiTEAscxhhjEmKBwxhjTEL+f+UWPMvBUJV7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Target variable\n",
    "sns.distplot(pivot_clusters.health , fit=norm);\n",
    "\n",
    "# Get the fitted parameters used by the function\n",
    "(mu, sigma) = norm.fit(pivot_clusters.health)\n",
    "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
    "\n",
    "#Now plot the distribution\n",
    "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],\n",
    "            loc='best')\n",
    "plt.title('Health tweets distribution plot')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('y distribution')\n",
    "\n",
    "#Get also the QQ-plot\n",
    "fig = plt.figure()\n",
    "res = stats.probplot(pivot_clusters.health, plot=plt)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "left tailed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Normalise skewed features.\n",
    "# pivot_clusters['economic'] = boxcox1p(pivot_clusters['economic'], boxcox_normmax(pivot_clusters['economic'] + 1))\n",
    "# pivot_clusters['social'] = boxcox1p(pivot_clusters['social'], boxcox_normmax(pivot_clusters['social'] + 1))\n",
    "# pivot_clusters['culture'] = boxcox1p(pivot_clusters['culture'], boxcox_normmax(pivot_clusters['culture'] + 1))\n",
    "# pivot_clusters['health'] = boxcox1p(pivot_clusters['health'], boxcox_normmax(pivot_clusters['health'] + 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# f, axes = plt.subplots(2, 2, figsize=(30, 20), sharex=True)\n",
    "# sns.distplot(x , fit=norm);\n",
    "# sns.distplot(pivot_clusters.social , ax = axes[0, 1], fit=norm);\n",
    "# sns.distplot(pivot_clusters.culture , ax = axes[1, 0], fit=norm);\n",
    "# sns.distplot(pivot_clusters.health , ax = axes[1, 1], fit=norm);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  KMeans Clustering."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'Distance computation in k-Means weights each dimension equally and hence care must be taken to ensure that unit of dimension shouldn’t distort relative near-ness of observations. Common method is to unit-standardize each dimension individually.' The unit for the tweets are the same, thus no need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardize features with scaler\n",
    "# x = pivot_clusters.iloc[:, [0,1,2,3]]\n",
    "# pivot_clusters['health'] = StandardScaler().fit_transform(pivot_clusters['health'].values.reshape(-1,1))\n",
    "# pivot_clusters['culture'] = StandardScaler().fit_transform(pivot_clusters['culture'].values.reshape(-1,1))\n",
    "# pivot_clusters['social'] = StandardScaler().fit_transform(pivot_clusters['social'].values.reshape(-1,1))\n",
    "# pivot_clusters['economic'] = StandardScaler().fit_transform(pivot_clusters['economic'].values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2D Clusters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### * Socio-Economic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cVREROeEUPClofpgO+xm9zyMiw5CCJwWNH5PLrPGj1MFARIYlBU+Kqigr5rktdTS3pdQ7riIix03Bk6Iqyoppau3g+a31cVdFROSEUvCkqLkzx5FhsEK320RkmFHwpKgxudmcPaWQZzbpfR4RGV4UPCmsoqyIF7bVs79J02GLyPCh4ElhFaXFtHc4q9+o63tnEZEhQsGTws4/eSwjsjI0fI6IDCsKnhSWm51J+fSxmhhORIYVBU+Km19azKtv7Wf3gea4qyIickIoeFJcRVk0TcIK9W4TkWFiMKe+vtPMasxsQ0LZODNbZmYbw8+xodzM7EdmVmVm68zs/IRjFof9N5rZ4oTyC8xsfTjmRxam6xzINVLZWZMLGJ2bpfd5RGTYGMwWz13Awi5lNwCPuvss4NGwDvA+YFb4XAfcClGIAN8C5gJzgG91BknY57qE4xYO5BqpLjPDuGhmkQYMFZFhY9CCx92fBLr2A14ELAnLS4ArEsrv9sizQKGZTQTeCyxz9zp33wssAxaGbWPcfaW7O3B3l3MdyzVSXkVZMdvqDrGtrjHuqoiIHLdkP+OZ4O47AcLP8aF8MrAtYb/qUNZbeXU35QO5RsqrKAvTJOh2m4gMA6nSucC6KfMBlA/kGm/f0ew6M6s0s8ra2to+Tjv4SktGMX70CA2fIyLDQrKDZ1fn7a3wsyaUVwNTE/abAuzoo3xKN+UDucbbuPtt7l7u7uUlJSXH9AUHg5lRUVbMiqrddGg6bBEZ4pIdPEuBzp5pi4GHEsqvCT3P5gEN4TbZI8DlZjY2dCq4HHgkbNtvZvNCb7ZrupzrWK4xJMwvLWLPwRZe27U/7qqIiByXrME6sZndCywAis2smqh32neBB8zsWmAr8NGw+x+A9wNVQCPwKQB3rzOz/wM8F/b7trt3dlj4DFHPuZHAw+HDsV5jqOh8n+eZqt2cPnFMzLURERk4izqFSU/Ky8u9srIy7moAcMm/Ps6M4nzu/OSFcVdFRKRXZrbG3cu725YqnQukH+aXFrFq8x5a2zviroqIyIApeIaQirJiDra0s65a02GLyNCl4BlCLppZhBmaJkFEhjQFzxAyNj+H2RPH6EVSERnSFDxDzMVlxTy/tZ5DLe1xV0VEZEAUPEPM/LJiWto7eG6LpsMWkaFJwTPEXDh9LNmZptGqRWTIUvAMMXk5WZw3bSwr1MFARIYoBc8QVFFazIYdDdQ3tsRdFRGRY6bgGYIqyopwh5UarVpEhiAFzxB0ztRC8nMy9ZxHRIakfgWPmVWYWX5Y/riZfd/MTh7cqklPsjMzmDNjnJ7ziMiQ1N8Wz61Ao5mdA3wdeJNoummJSUVZMZt3H2Rnw6G4qyIickz6GzxtHg1jvQj4obv/EBg9eNWSvswv7ZwmQa0eERla+hs8+83sRuDjwO/NLBPIHrxqSV9OO2k04/JzWKHhc0RkiOlv8HwMaAaudfe3gMnA9watVtKnjAxjfmkRz2zajeZUEpGhpN8tHqJbbE+Z2SnAucC9g1ct6Y+KsmJ27WtmU+3BuKsiItJv/Q2eJ4ERZjYZeJRo2ui7BqtS0j8V4TnPCnWrFpEhpL/BY+7eCHwY+Hd3/3PgjIFe1My+bGYvmdkGM7vXzHLNbIaZrTKzjWZ2v5nlhH1HhPWqsH16wnluDOWvmdl7E8oXhrIqM7shobzbawxV04rymDJ2pKZJEJEhpd/BY2YXAVcDvw9lmQO5YGg1fQEod/czw3muBG4Cbnb3WcBe4NpwyLXAXncvA24O+2Fms8NxZwALgZ+YWWbo+HAL8D5gNnBV2JderjFkVZQWs3LTHto79JxHRIaG/gbPl4Abgd+4+0tmNhNYfhzXzQJGmlkWkAfsBC4FHgzblwBXhOVFYZ2w/TIzs1B+n7s3u/sbQBUwJ3yq3H2zu7cA9wGLwjE9XWPIml9WxL6mNjZsb4i7KiIi/dKv4HH3J9z9Q+5+U1jf7O5fGMgF3X078K/AVqLAaQDWAPXu3hZ2qybqOUf4uS0c2xb2L0os73JMT+VFvVzjKGZ2nZlVmlllbW3tQL5m0hx+n0fPeURkiOjvkDnLzKwwYX2smT0ykAua2Vii1soMYBKQT3RbrKvOe0fWw7YTVf72Qvfb3L3c3ctLSkq62yVllIwewakTRmv4HBEZMvp7q63E3es7V9x9LzBhgNd8N/CGu9e6eyvwa2A+UBhuvQFMAXaE5WpgKkDYXgDUJZZ3Oaan8t29XGNIm19WxHNb6mhq1XTYIpL6+hs87WY2rXMlDBDaMcBrbgXmmVleeO5yGfAy0TOjj4R9FgMPheWlYZ2w/bEwfM9S4MrQ620GMAtYDTwHzAo92HKIOiAsDcf0dI0hraK0mOa2DtZu3Rt3VURE+pTV9y4AfBN4ysyeDOvvBK4byAXdfZWZPQisBdqA54HbiHrL3Wdm3wlld4RD7gB+bmZVRC2dK8N5XjKzB4hCqw34nLu3A5jZ9cAjRD3m7nT3l8K5vtHDNYa0uTPHkZlhrKjac/iZj4hIqrL+DLdiZj8H1gOHgC3ASndPi6fZ5eXlXllZGXc1+vThnzyDA7/5bEXcVRERwczWuHt5d9v6e6vtZ0Au8CHgB8BPzeyLJ6h+cgJUlBWzrrqB/U2tcVdFRKRX/e1O/RjwT8DfA7cD5cBnBrFecozmlxbT3uGs2lwXd1VERHrV3+7UjwLPEI1S/RpwobufNpgVk2Nz/smF5GZn6H0eEUl5/b3Vtg5oAc4EzgbONLORg1YrOWYjsjK5cPo4jdsmIimvv7favuzu7wT+HNhD9MynvvejJNnmlxbz+q4D1OxvirsqIiI96u+ttuvN7H7gBaLxze6k+9EGJEYVZUUArNykUQxEJHX19z2ekcD3gTUJY51JijljUgFjcrN4pmo3i87tdhg6EZHY9St43F3TXA8BmRnGRaVFPFO1B3cnGhhCRCS19LdzgQwRFWXFbK8/xNa6xrirIiLSLQXPMHN4mgSNVi0iKUrBM8yUluQzYcwIvc8jIilLwTPMmBkVZdF02B2aDltEUpCCZxiqKC2m7mALr761P+6qiIi8jYJnGKooi57zrNDtNhFJQQqeYeikglxmluTztIbPEZEUpOAZpipKi1n9Rh0tbQOdKFZEZHAoeIapirIiGlvaebFaQ+qJSGpR8AxT82YWYYZGqxaRlBNL8JhZoZk9aGavmtkrZnaRmY0zs2VmtjH8HBv2NTP7kZlVmdk6Mzs/4TyLw/4bzWxxQvkFZrY+HPMjC2PH9HSN4agwL4czJxWwQi+SikiKiavF80Pgj2EyuXOAV4AbgEfdfRbwaFiHaBTsWeFzHXArRCECfAuYC8wBvpUQJLeGfTuPWxjKe7rGsDS/rIjnt+2lsUXjuopI6kh68JjZGOCdwB0A7t7i7vXAImBJ2G0J0fQLhPK7PfIsUGhmE4H3Asvcvc7d9wLLgIVh2xh3X+nuDtzd5VzdXWNYqigtprXdWf2GpsMWkdQRR4tnJlAL/MzMnjez280sH5jg7jsBws/xYf/JwLaE46tDWW/l1d2U08s1jmJm15lZpZlV1tbWDvybxuzC6ePIycxghebnEZEUEkfwZAHnA7e6+3nAQXq/5dXd2P4+gPJ+c/fb3L3c3ctLSkqO5dCUMjInk/NPLuSpjbuJGn8iIvGLI3iqgWp3XxXWHyQKol3hNhnhZ03C/lMTjp8C7OijfEo35fRyjWHrvWecxCs79/HVX75Ic1t73NUREUl+8Lj7W8A2Mzs1FF0GvAwsBTp7pi0GHgrLS4FrQu+2eUBDuE32CHC5mY0NnQouBx4J2/ab2bzQm+2aLufq7hrD1ifnT+cr7zmFX6/dzsdvX8WeA81xV0lE0pzFcQvGzM4FbgdygM3Ap4hC8AFgGrAV+Ki714Xw+DFRz7RG4FPuXhnO82ngm+G0/+TuPwvl5cBdRFN2Pwx83t3dzIq6u0ZvdS0vL/fKysoT9dVj87t1O/jqAy8yfswI7lx8IbMmjI67SiIyjJnZGncv73ab7v33brgED8AL2+r5qyWVNLe28+Orz+ddpwzd51ciktp6Cx6NXJBGzp1ayEPXVzBlXB6f+tlqlqzYEneVRCQNKXjSzOTCkTz4vy7i0tMm8K2lL/EPD22grV0DiYpI8ih40lD+iCx++okLuO6dM7l75Zt86q7n2NfUGne1RCRNKHjSVGaG8c33n85N/+MsVm7aw4d/soKtexrjrpaIpAEFT5r72IXT+Pm1c9l9oJlFtzyt4XVEZNApeISLSov4zWcrGJuXw9W3P8uDa6r7PkhEZIAUPALAjOJ8fvPZCi6cPo6v/fJFbvrjq3R0qKu9iJx4Ch45rCAvmyWfnsNVc6Zx6+Ob+Ow9azWlgoiccAoeOUp2Zgb//Odn8vcfnM2fXn6Lv/jpSt5qaIq7WiIyjCh45G3MjGsvnsHti8t5o/YgH/rx06yrro+7WiIyTCh4pEeXnjaBX312PtmZGfzFT1fy8PqdcVdJRIYBBY/06rSTxvDbz1Vw+sQxfOaetdyyvEpz+4jIcVHwSJ9KRo/g3v85jw+dM4nvPfIaX31Ac/uIyMBlxV0BGRpyszP54ZXnUjZ+FN9f9jpb6xr56ScuoGjUiLirJiJDjFo80m9mxhcum8WP//I81m9v4IqfPMPru/bHXS0RGWIUPHLMPnj2JO7/64s41NLB//jJCh5/bdjPIC4iJ5CCRwYkcW6fT9/1nOb2EZF+iy14zCzTzJ43s9+F9RlmtsrMNprZ/WaWE8pHhPWqsH16wjluDOWvmdl7E8oXhrIqM7shobzba8jAdJ3b5+9/q7l9RKRvcbZ4vgi8krB+E3Czu88C9gLXhvJrgb3uXgbcHPbDzGYDVwJnAAuBn4QwywRuAd4HzAauCvv2dg0ZoMS5fX7+bDS3T8Mhze0jIj2LJXjMbArwAeD2sG7ApcCDYZclwBVheVFYJ2y/LOy/CLjP3Zvd/Q2gCpgTPlXuvtndW4D7gEV9XEOOw9vn9nmGN/ccjLtaIpKi4mrx/AD4OtB5X6YIqHf3zhEpq4HJYXkysA0gbG8I+x8u73JMT+W9XUNOgM65ffYcbOGKW57R3D4i0q2kB4+ZfRCocfc1icXd7Op9bDtR5d3V8TozqzSzytra2u52kR50ndvnl5Xb+j5IRNJKHC2eCuBDZraF6DbYpUQtoEIz63yhdQqwIyxXA1MBwvYCoC6xvMsxPZXv7uUaR3H329y93N3LS0pKBv5N01Ti3D5/8+A6ze0jIkdJevC4+43uPsXdpxN1DnjM3a8GlgMfCbstBh4Ky0vDOmH7Yx4NFrYUuDL0epsBzAJWA88Bs0IPtpxwjaXhmJ6uISdY17l9PnPPGs3tIyJAar3H8w3gK2ZWRfQ85o5QfgdQFMq/AtwA4O4vAQ8ALwN/BD7n7u3hGc71wCNEveYeCPv2dg0ZBIlz+yx7eReX3/wktz6+iT0HmuOumojEyDTScO/Ky8u9srIy7moMeU9v3M2/P7aRVW/UkZ1pvO/MiVw9dxpzZowj6nAoIsOJma1x9/LutmmQUEmKi2cVc/GsYjbu2s89q7byq7XVLH1xB2XjR3H13Gl8+LwpFORlx11NEUkCtXj6oBbP4DjU0s5/r9vBPau28uK2enKzM/izsydx9byTOWdKgVpBIkNcby0eBU8fFDyDb8P2Bu5ZtZWHXthOY0s7Z0waw9VzT2bRuZPIH6FGuchQpOA5Dgqe5Nnf1MpvX9jBPc++yatv7WfUiCyuOG8SV889mdMnjom7eiJyDBQ8x0HBk3zuztqt9dyz6k1+t24nLW0dnD+tkI/PO5n3nzWR3OzMuKsoIn1Q8BwHBU+89h5s4Vdrq/nFqq1s3n2QwrxsPnL+FP5y7jRmloyKu3oi0gMFz3FQ8KQGd2fl5j3cs2orj2x4i7YOZ35pEVfPPZn3zJ5ATlYqvZImIupOLUOemTG/tJj5pcXU7G/il5VRK+hzv1hL8agRfOzCKVx54TSmjsuLu6oi0ge1ePqgFk/qau9wnny9lntWvcljr9bgwIJTSvj4vJNZcOp4MjPUJVskLrrVdhwUPEPD9vpD3L96K/c9t42a/c1MKsjlqjnT+NiFUxk/Jjfu6omkHQXPcVDwDC2t7R08+rX4OmEAABBRSURBVMou7lm1lac27iYzw7h89gSunnsy80uLyFArSCQp9IxH0kZ2ZgYLz5zIwjMnsmX3Qe5dvZUHKrfx8Ia3mF6Ux1/OncYV505WK0gkRmrx9EEtnqGvqbWdR156i3ue3crqLdGsqGdOHsOlp45nwWnjOWdKoZ4HiZxgutV2HBQ8w0tVzX7+9PIulr9aw5o399LhMC4/h3edUsIlp43nnbOKKczLibuaIkOeguc4KHiGr4bGVp7YWMvjr9bw+Ou11B1sIcPggpPHsuDU8Vx62nhOO2m0BiwVGQAFz3FQ8KSH9g5nXXU9y1+t4bHXatiwfR8AEwtyWXDqeC45tYSKsmINWirSTwqe46DgSU81+5p4/LValr9Ww1Mbd3OguY2czAzmzhzHJaeO55LTxjOjOD/uaoqkLAXPcVDwSEtbB5Vv1rH81RqWv1ZLVc0BAGYU54cQKmHOjHGMyNLgpSKdUip4zGwqcDdwEtAB3ObuPzSzccD9wHRgC/AX7r7XohvsPwTeDzQCn3T3teFci4G/C6f+jrsvCeUXAHcBI4E/AF90d+/pGr3VV8EjXW3d08jjr9fw2Ks1rNy0h+a2DvJyMqkoK+bS08az4NQSJhaMjLuaIrFKteCZCEx097VmNhpYA1wBfBKoc/fvmtkNwFh3/4aZvR/4PFHwzAV+6O5zQ4hUAuWAh/NcEMJqNfBF4Fmi4PmRuz9sZv/S3TV6q6+CR3pzqKWdlZt3s/zVWh57tYbt9YcAOH3iGC45tYRLTxvPuVMLycrUIKaSXlIqeN5WAbOHgB+HzwJ33xnC6XF3P9XMfhqW7w37vwYs6Py4+1+H8p8Cj4fPcnc/LZRf1blf57Fdr9Fb/RQ80l/uzsaaA1EHhVdrqHxzL+0dTsHI7NBdu4R3nTKecfnqri3DX8qOXGBm04HzgFXABHffCRCCYXzYbTKwLeGw6lDWW3l1N+X0co2u9boOuA5g2rRpA/x2km7MjFMmjOaUCaP563eV0nColac37mb5azU8/loNS1/cgRmcO7WQS08dT/n0cZw5eQyjc7PjrrpIUsUWPGY2CvgV8CV339fLuxLdbfABlPebu98G3AZRi+dYjhXpVDAymw+cPZEPnD2Rjg5nw44GHnu1huWv1vBvy14/vN/M4nzOnFzAWZMLOGtKAWdMUhjJ8BZL8JhZNlHo3OPuvw7Fu8xsYsJtsJpQXg1MTTh8CrAjlC/oUv54KJ/Szf69XUNkUGVkGGdPKeTsKYV86d2nUHewhXXV9WzY3sD67Q1Ubqlj6Ys7Du8/syQ/CqLwOWNyAaP0DpEME0n/kxx6qd0BvOLu30/YtBRYDHw3/Hwoofx6M7uPqHNBQwiOR4B/NrOxYb/LgRvdvc7M9pvZPKJbeNcA/97HNUSSalx+DgtOHc+CU4/c7d19oJn12xvYUN3Auu0NrH6jjodeiMLILGoZRa2iwiiMJo3RC60yJMXRq+1i4ClgPVF3aoBvEoXEA8A0YCvw0RAiRtTxYCFRd+pPuXtlONenw7EA/+TuPwvl5RzpTv0w8PnQnbqou2v0Vl91LpA41e5vPtwqWlfdwIbtDby1rwmIwqi0ZNSRllG4TZeXozCS+KV0r7ZUp+CRVFOzvykKo+p9rN9ez/rtDeza1wxARmcYTTlym262wkhioOA5DgoeGQpq9jWxPrSM1odbdbX7j4RR2fhRnDW5kLMmj+GsKYXMnjiGkTkaaUEGT8p2pxaRE2P8mFwuG5PLZadPOFy2a1/T4RDasL2BJ16v5VdrozcNMgxmjR/NGZPHcPK4fCYW5jK5cCSTCkcysSCX3GyFkgweBY/IMDVhTC4TZufy7tlRGLk7u/Y1h1ZRdIvumard/Hrf9rcdWzwqh0mFI5lUMPKoUIo+uRTnj9A04jJgCh6RNGFmnFSQy0kFubxn9pGWUXNbO7samtnRcIgd9dFne30TO+oPsan2AE9trOVgS/tR58rJzOCkglwmFeYyqXDkUcE0uTCXiQUj1eNOeqQ/GSJpbkRWJtOK8phWlNftdndnX1Pb4VBKDKYd9YdYtbmOt/Y10d5x9PPigpHZh4NoUsJtvM6QGj96hMawS1MKHhHplZlRMDKbgpHZnD5xTLf7tLV3ULO/OYTSIXaEYNrZEIXUc1v20nCo9ahjMjOMk8ZEraYJY3IZl59DYV4OY/OyGZuXw9j8I8uFedmMGpGl2WCHCQWPiBy3rMyMw62abrsxAQea29gZgmlnQ1NCSB1iw/YG9ja2sq+plZ462mZnGgUjE4OpM5SODqjOwCrMy6FwZLZaVSlIwSMiSTFqRBazJoxm1oTRPe7T3uE0HGplb2ML9Y0t7D0YLUef1qPKtuxu5PnGeuobW2lp7+jxnGNysxib301Ada7n5xwuK8zLYVROFvkjMhVYg0jBIyIpIzPDGJefc0xTR7g7jS3t1B1sob7xSFB1Lnf+rDvYwp4DLVTVHKC+sZUDzW29nndkdib5I7IYnZvFqBFRGI0akZ2wnnXU8qiw3nU5LztTPQC7UPCIyJBmZuSHf/ynjuv/cS1tHdQfCsF0MAqrhkOtHGhu50BTGwdb2tjf1MaB5jYONrdxoKmN7fWHouWw3ltL60j9YFROFqO6hlIoGxXKOpdH52aRl5NFXk4mI3MyyU9YzsvJZGR25pB/1qXgEZG0lJOVwfjRuYwfnTvgczS3tXMwBNX+5tZoubmV/U1th5cPNLVFYdYctbKiYGtl174mDja3s78pKu/o5yAyZlFrLC8n86iASlx/27bsTPJGvH1btD2L/LBfTmZGUkJNwSMiMkAjsjIZkZV53LPKujtNrR3sD0HV2NLOweY2GlvbORSWD7W209jSTmNztL27bXsONB69X2t7j501upOZYUeF09Vzp/FX75h5XN+tOwoeEZGYmRkjQ6tjfM99L46Zu9Pc1hGFWEt7t+HVmLDt8H4t7RxsaaNk9IgTV5kECh4RkWHKzMjNziQ3O5OiuCuTQP0FRUQkqRQ8IiKSVAoeERFJKgWPiIgkVVoGj5ktNLPXzKzKzG6Iuz4iIukk7YLHzDKBW4D3AbOBq8xsdry1EhFJH2kXPMAcoMrdN7t7C3AfsCjmOomIpI10DJ7JwLaE9epQdpiZXWdmlWZWWVtbm9TKiYgMd+n4Aml3AxEdNaiEu98G3AZgZrVm9mYyKjaIioHdcVcihej3cTT9Po7Q7+Jox/P7OLmnDekYPNXA1IT1KcCOnnZ295JBr9EgM7NKd+9pfq60o9/H0fT7OEK/i6MN1u8jHW+1PQfMMrMZZpYDXAksjblOIiJpI+1aPO7eZmbXA48AmcCd7v5SzNUSEUkbaRc8AO7+B+APcdcjiW6LuwIpRr+Po+n3cYR+F0cblN+H+bFM1iAiInKc0vEZj4iIxEjBIyIiSaXgGcbMbKqZLTezV8zsJTP7Ytx1ipuZZZrZ82b2u7jrEjczKzSzB83s1fBn5KK46xQnM/ty+HuywczuNbPcuOuUTGZ2p5nVmNmGhLJxZrbMzDaGn2NPxLUUPMNbG/BVdz8dmAd8TuPS8UXglbgrkSJ+CPzR3U8DziGNfy9mNhn4AlDu7mcS9Xi9Mt5aJd1dwMIuZTcAj7r7LODRsH7cFDzDmLvvdPe1YXk/0T8sk3s/avgysynAB4Db465L3MxsDPBO4A4Ad29x9/p4axW7LGCkmWUBefTyYvlw5O5PAnVdihcBS8LyEuCKE3EtBU+aMLPpwHnAqnhrEqsfAF8HOuKuSAqYCdQCPwu3Hm83s/y4KxUXd98O/CuwFdgJNLj7n+KtVUqY4O47IfofWWD8iTipgicNmNko4FfAl9x9X9z1iYOZfRCocfc1cdclRWQB5wO3uvt5wEFO0G2UoSg8u1gEzAAmAflm9vF4azV8KXiGOTPLJgqde9z913HXJ0YVwIfMbAvRVBiXmtl/xVulWFUD1e7e2QJ+kCiI0tW7gTfcvdbdW4FfA/NjrlMq2GVmEwHCz5oTcVIFzzBmZkZ0D/8Vd/9+3PWJk7vf6O5T3H060UPjx9w9bf+P1t3fAraZ2amh6DLg5RirFLetwDwzywt/by4jjTtbJFgKLA7Li4GHTsRJ03LInDRSAXwCWG9mL4Syb4Yhg0Q+D9wTBsvdDHwq5vrExt1XmdmDwFqi3qDPk2bD55jZvcACoNjMqoFvAd8FHjCza4nC+aMn5FoaMkdERJJJt9pERCSpFDwiIpJUCh4REUkqBY+IiCSVgkdERJJKwSNpzczczP4tYf1rZvaPJ+jcd5nZR07Eufq4zkfD6NLLu9l2ipn9wcyqwj4PmNkEM1sw0BG6zexLZpZ3/DWXdKXgkXTXDHzYzIrjrkgiM8s8ht2vBT7r7pd0OUcu8HuiYXHKwijltwIlx1m9LxENotlvx/h9ZJhT8Ei6ayN6UfDLXTd0bbGY2YHwc4GZPRFaD6+b2XfN7GozW21m682sNOE07zazp8J+HwzHZ5rZ98zsOTNbZ2Z/nXDe5Wb2C2B9N/W5Kpx/g5ndFMr+AbgY+A8z+16XQ/4SWOnu/91Z4O7L3X1D4k5m9o9m9rWE9Q1mNt3M8s3s92b2Yij7mJl9gWgss+WdLSwzu9zMVprZWjP7ZRgbEDPbYmb/YGZPAx81sy+Y2cvhO9/Xx38XGcY0coEI3AKsM7N/OYZjzgFOJxpGfjNwu7vPsWiyvc8TtQoApgPvAkqJ/rEuA64hGv34QjMbATxjZp0jIc8BznT3NxIvZmaTgJuAC4C9wJ/M7Ap3/7aZXQp8zd0ru9TxTOB4BkVdCOxw9w+EOhS4e4OZfQW4xN13h5bi3wHvdveDZvYN4CvAt8M5mtz94nD8DmCGuzebWeFx1EuGOLV4JO2FEbvvJpoIrL+eC/MdNQObgM7gWE8UNp0ecPcOd99IFFCnAZcD14RhjFYBRcCssP/qrqETXAg8HgaxbAPuIZpPZzCtJ2qx3WRm73D3hm72mQfMJgrPF4jG8zo5Yfv9CcvriIbo+ThRS1PSlIJHJPIDomcliXPStBH+joSBI3MStjUnLHckrHdw9J2ErmNSOWDA59393PCZkTD3y8Ee6mf9/SIJXiJqIfXl8PcMcgHc/fVw/Hrg/4bbet3Va1nCd5nt7tcmbE/8Ph8gal1eAKwJE65JGlLwiADuXgc8QBQ+nbZw5B/uRUD2AE79UTPLCM99ZgKvAY8AnwlTVnT2POtrErZVwLvMrDg8qL8KeKKPY34BzDezD3QWmNlCMzury35bCFMimNn5RHPSdN7ea3T3/yKaJK1z2oT9wOiw/CxQEW4hEkZ3PqVrRcwsA5jq7suJJuMrBEb1UX8ZpvR/HCJH/BtwfcL6fwIPmdlqovnme2qN9OY1ooCYAPwvd28ys9uJbsetDS2pWvqYUtjdd5rZjcByolbGH9y91yHq3f1Q6NDwAzP7AdBKdLvri0S39zr9iiO3/p4DXg/lZwHfM7OOcOxnQvltwMNmttPdLzGzTwL3hudVED3zeZ2jZQL/ZWYFof43a6rt9KXRqUVEJKl0q01ERJJKwSMiIkml4BERkaRS8IiISFIpeEREJKkUPCIiklQKHhERSar/D9ddezJ0AC8EAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# X = pivot_clusters.iloc[:, [0,1]].values\n",
    "X = pivot_clusters[['economic', 'social']].values\n",
    "\n",
    "# Elbow Method\n",
    "from sklearn.cluster import KMeans\n",
    "wcss = []\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters=i, init='k-means++', n_init=10, max_iter=300, random_state=0)\n",
    "    kmeans.fit(X)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "plt.plot(range(1,11), wcss)\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('wcss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fitting kmeans to dataset\n",
    "kmeans = KMeans(n_clusters=3, init='k-means++', n_init=10, max_iter=300, random_state=0)\n",
    "Y_kmeans = kmeans.fit_predict(X)\n",
    "\n",
    "# Visualising the clusters\n",
    "plt.scatter(X[Y_kmeans==0, 0], X[Y_kmeans==0, 1], s=100, c='violet', label= 'Cluster 1')\n",
    "plt.scatter(X[Y_kmeans==1, 0], X[Y_kmeans==1, 1], s=100, c='cyan', label= 'Cluster 2')\n",
    "plt.scatter(X[Y_kmeans==2, 0], X[Y_kmeans==2, 1], s=100, c='green', label= 'Cluster 3')\n",
    "# plt.scatter(X[Y_kmeans==3, 0], X[Y_kmeans==3, 1], s=100, c='blue', label= 'Cluster 4')\n",
    "# plt.scatter(X[Y_kmeans==4, 0], X[Y_kmeans==4, 1], s=100, c='magenta', label= 'Cluster 5')\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=100, c='black', label='Centroids' )\n",
    "plt.title('Clusters of tweets in economic and social groups')\n",
    "plt.xlabel('economic tweets')\n",
    "plt.ylabel('social tweets')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### * Socio-Culture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# X = pivot_clusters.iloc[:, [2,3]].values\n",
    "X = pivot_clusters[['social', 'culture']].values\n",
    "\n",
    "# Elbow Method\n",
    "from sklearn.cluster import KMeans\n",
    "wcss = []\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters=i, init='k-means++', n_init=10, max_iter=300, random_state=0)\n",
    "    kmeans.fit(X)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "plt.plot(range(1,11), wcss)\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('wcss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fitting kmeans to dataset\n",
    "kmeans = KMeans(n_clusters=3, init='k-means++', n_init=10, max_iter=300, random_state=0)\n",
    "Y_kmeans = kmeans.fit_predict(X)\n",
    "\n",
    "# Visualising the clusters\n",
    "plt.scatter(X[Y_kmeans==0, 0], X[Y_kmeans==0, 1], s=100, c='violet', label= 'Cluster 1')\n",
    "plt.scatter(X[Y_kmeans==1, 0], X[Y_kmeans==1, 1], s=100, c='cyan', label= 'Cluster 2')\n",
    "plt.scatter(X[Y_kmeans==2, 0], X[Y_kmeans==2, 1], s=100, c='green', label= 'Cluster 3')\n",
    "# plt.scatter(X[Y_kmeans==3, 0], X[Y_kmeans==3, 1], s=100, c='blue', label= 'Cluster 4')\n",
    "# plt.scatter(X[Y_kmeans==4, 0], X[Y_kmeans==4, 1], s=100, c='magenta', label= 'Cluster 5')\n",
    "# plt.scatter(X[Y_kmeans==5, 0], X[Y_kmeans==5, 1], s=100, c='pink', label= 'Cluster 6')\n",
    "\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=100, c='black', label='Centroids' )\n",
    "plt.title('Clusters of tweets in culture and social groups')\n",
    "plt.xlabel('social tweets')\n",
    "plt.ylabel('culture tweets')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### * Socio-Health"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# X = pivot_clusters.iloc[:, [2,4]].values\n",
    "X = pivot_clusters[['social', 'health']].values\n",
    "\n",
    "# Elbow Method\n",
    "from sklearn.cluster import KMeans\n",
    "wcss = []\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters=i, init='k-means++', n_init=10, max_iter=300, random_state=0)\n",
    "    kmeans.fit(X)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "plt.plot(range(1,11), wcss)\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('wcss')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fitting kmeans to dataset\n",
    "kmeans = KMeans(n_clusters=3, init='k-means++', n_init=10, max_iter=300, random_state=0)\n",
    "Y_kmeans = kmeans.fit_predict(X)\n",
    "\n",
    "# Visualising the clusters\n",
    "plt.scatter(X[Y_kmeans==0, 0], X[Y_kmeans==0, 1], s=100, c='violet', label= 'Cluster 1')\n",
    "plt.scatter(X[Y_kmeans==1, 0], X[Y_kmeans==1, 1], s=100, c='cyan', label= 'Cluster 2')\n",
    "plt.scatter(X[Y_kmeans==2, 0], X[Y_kmeans==2, 1], s=100, c='green', label= 'Cluster 3')\n",
    "# plt.scatter(X[Y_kmeans==3, 0], X[Y_kmeans==3, 1], s=100, c='blue', label= 'Cluster 4')\n",
    "# plt.scatter(X[Y_kmeans==4, 0], X[Y_kmeans==4, 1], s=100, c='magenta', label= 'Cluster 5')\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=100, c='black', label='Centroids' )\n",
    "plt.title('Clusters of tweets in health and social groups')\n",
    "plt.xlabel('social tweets')\n",
    "plt.ylabel('health tweets')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### * Economic-health"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# X = pivot_clusters.iloc[:, [2,4]].values\n",
    "X = pivot_clusters[['economic', 'health']].values\n",
    "\n",
    "# Elbow Method\n",
    "from sklearn.cluster import KMeans\n",
    "wcss = []\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters=i, init='k-means++', n_init=10, max_iter=300, random_state=0)\n",
    "    kmeans.fit(X)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "plt.plot(range(1,11), wcss)\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('wcss')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fitting kmeans to dataset\n",
    "kmeans = KMeans(n_clusters=4, init='k-means++', n_init=10, max_iter=300, random_state=0)\n",
    "Y_kmeans = kmeans.fit_predict(X)\n",
    "\n",
    "# Visualising the clusters\n",
    "plt.scatter(X[Y_kmeans==0, 0], X[Y_kmeans==0, 1], s=100, c='violet', label= 'Cluster 1')\n",
    "plt.scatter(X[Y_kmeans==1, 0], X[Y_kmeans==1, 1], s=100, c='cyan', label= 'Cluster 2')\n",
    "plt.scatter(X[Y_kmeans==2, 0], X[Y_kmeans==2, 1], s=100, c='green', label= 'Cluster 3')\n",
    "plt.scatter(X[Y_kmeans==3, 0], X[Y_kmeans==3, 1], s=100, c='blue', label= 'Cluster 4')\n",
    "# plt.scatter(X[Y_kmeans==4, 0], X[Y_kmeans==4, 1], s=100, c='magenta', label= 'Cluster 5')\n",
    "# plt.scatter(X[Y_kmeans==5, 0], X[Y_kmeans==5, 1], s=100, c='pink', label= 'Cluster 6')\n",
    "# plt.scatter(X[Y_kmeans==6, 0], X[Y_kmeans==6, 1], s=100, c='yellow', label= 'Cluster 7')\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=100, c='black', label='Centroids' )\n",
    "plt.title('Clusters of tweets in economic and health groups')\n",
    "plt.xlabel('economic tweets')\n",
    "plt.ylabel('health tweets')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Economic-Culture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Am5mdBPwdsA64N2NVFbjJFaOZVjVaJ/VFJGcNN1y63N2BC4Bvu/u3gXGZK0vqE3EWrW0m+WMXEcktww2XXWb2ZeBS4BEziwElmStL6mribGvtYPXW1qhLERE5YMMNl78A9gCXu/smYCrwzYxVJX0PD9OhMRHJRcPuuZA8HPa0mR0FnAzcn7myZFZ1OYeVl+qkvojkpOGGy1PAKDObCjwOfBL4fqaKEjAzahNVGsRSRHLScMPF3L0N+HPgO+7+YeC4zJUlkDw01tj8Jpt2tEddiojIARl2uJjZO4C/Ah4JbbHMlCS9dL+LiOSq4YbL54EvA79w91fMbCbwRObKEoBjJ4+nvDSmk/oiknOKh7OQuy8AFqR8Xg18LlNFSVJxrIg5M3TeRURyz3CHf3nMzCpTPleZ2aOZK0t61SXivLZ5FzvaOqMuRURk2IZ7WKza3fueXuXuLcCkzJQkqeoScdyhYZ16LyKSO4YbLt1mdkTvhzBo5aDjwZvZXWa2xcxeTmmLh17QivBeFdrNzG42s5VmttTM5qSsMy8sv8LM5qW0n2pmy8I6N5uZDbaPXHXKEZWUxEwn9UUkpww3XP4BeNrM7jOz+0je9/LlIdb5PjC3X9t1wOPuPpvk/TLXhfb3AbPD6wqSA2ViZnHgeuDtQD1wfUpY3BqW7V1v7hD7yEllJTFOmFqhk/oiklOGGy6XArcAC4GfAKe6+6DnXNz9KaD/b8QLgHvC9D3AhSnt93rS80ClmU0G3gs85u7N4VDcY8DcMG+8uz8XBtS8t9+2BtpHzqqribPsjR20d3ZHXYqIyLAMN1zuBsqADwE3Ad8zs2sPYn+T3H0jQHifGNqnAo0py60PbYO1rx+gfbB9vIWZXWFmDWbW0NTUdBBfZ2TUJ+J0djsvvr596IVFRLLAsMLF3X8PfA34Z+AOoBa4Mo112EC7PYj2A+Lut7l7rbvXVldXH+jqI6Z2RhwzdEmyiOSM4V6K/DjwLMnRkV8D6tz9bQexv83hkBbhfUtoXw9MT1luGrBhiPZpA7QPto+cVTGmhKMnjVO4iEjOGO5hsaVAB3A8cCJwvJmNPoj9PQz0XvE1D3gopf2ycNXYacCOcEjrUeD8cF9NFXA+8GiYt8vMTgtXiV3Wb1sD7SOn1SXiLF7XQlf3oBfpiYhkheEeFvuCu58JfBjYRvIczKAnAMzsfuA54GgzW29mlwM3AOeZ2QrgvPAZYD6wGlgJ3A5cFfbbDPwrsCi8vhraIHlY7o6wzirg16F9f/vIaXU1cVo7ulm+cVfUpYiIDGlYw7+Y2TXAGcCpwDrgLuDpwdZx90v2M+vcAZZ14Or9bOeusL/+7Q0ke1L927cNtI9cV5/YO4jlCdMqIq5GRGRwwwoXYDRwI/CCu3dlsB7Zj8MrypgeH82iNc1c/q6aqMsRERnUcAeu1CONs0BdIs6C15pwd8KABCIiWWm4J/QlC9Ql4mxr7WD11taoSxERGZTCJYfUhfMuGgpGRLKdwiWHzKou57DyUg1iKSJZT+GSQ8yM2oQeHiYi2U/hkmPqEnEam99k0472qEsREdkvhUuOqa/Ze7+LiEi2UrjkmGMnj6e8NKaT+iKS1RQuOaY4VsScGTrvIiLZTeGSg+oScV7bvIsdbZ1RlyIiMiCFSw6qS8Rxh4Z16r2ISHZSuOSgU46opCRmOqkvIllL4ZKDykpinDC1Qif1RSRrKVxyVF1NnGVv7KC9szvqUkRE3kLhkqPqE3E6u50XXx/0mW0iIpFQuOSo2hlxzNAlySKSlRQuOapiTAlHTxqncBGRrKRwyWF1iTiL17XQ1d0TdSkiIvtQuOSwupo4rR3dvLpxZ9SliIjsQ+GSw+rDw8MW6pJkEckyCpccdnhFGdPjo3XeRUSyjsIlx9Ul4jSsbcHdoy5FRKSPwiXH1SfibGvtYFVTa9SliIj0UbjkuLrw8DAdGhORbKJwyXEzJ5QzYWypxhkTkayicMlxZkbtjLhGSBaRrKJwyQN1NXHWt7zJxh1vRl2KiAigcMkLut9FRLKNwiUPHDN5HOWlMZ5esVWXJItIVlC45IHiWBHvPe5wHnxhPZ/5wQts2dkedUkiUuAULnniGx89keve9zaeeK2J99y4gJ82NKoXIyKRUbjkieJYEZ85axa/vvYMjpo0ji89uJR5dy/ije06yS8iI0/hkmdmVY/lJ59+B//yoeNoWNvM+Tcu4L7n1tLTo16MiIwchUseKioy5r0zwaOfP5NTjqjinx96hYtvf541WzVEjIiMDIVLHpseH8N9l9fzjY+cyPKNO5l701Pc9tQqPVxMRDJO4ZLnzIyP1U3nd39zFmceVc2/z/8jH7n1f3lt066oSxORPKZwKRCTxpdx28dP5TuXnEJjy5t88DtPc9Pv/kRHl3oxIpJ+kYSLma01s2VmtsTMGkJb3MweM7MV4b0qtJuZ3WxmK81sqZnNSdnOvLD8CjObl9J+atj+yrCujfy3zD5mxp+dNIXHvnAm7zt+Mjf9bgUf+q9nWLp+e9SliUieibLnco67n+zuteHzdcDj7j4beDx8BngfMDu8rgBuhWQYAdcDbwfqget7Ayksc0XKenMz/3Vyx2FjR3HzJadwx2W1tLR1cOEtz/Ifv15Oe2d31KWJSJ7IpsNiFwD3hOl7gAtT2u/1pOeBSjObDLwXeMzdm929BXgMmBvmjXf35zx5F+G9KduSFO85dhK//cJZfKx2Ot9bsJr3f/tpPRdGRNIiqnBx4Ldm9oKZXRHaJrn7RoDwPjG0TwUaU9ZdH9oGa18/QPtbmNkVZtZgZg1NTU2H+JVyU8XoEm74yIn84PK309Hdw8e+9xzXP/QyrXu6oi5NRHJYVOFyurvPIXnI62ozO3OQZQc6X+IH0f7WRvfb3L3W3Wurq6uHqjmvvWv2BB79/JnMe0eCe59fx/nfeoqnVxRm4IrIoYskXNx9Q3jfAvyC5DmTzeGQFuF9S1h8PTA9ZfVpwIYh2qcN0C5DKB9VzFc+dBw//fQ7GFVSxMfvXMiXfvoSO9o6oy5NRHLMiIeLmZWb2bjeaeB84GXgYaD3iq95wENh+mHgsnDV2GnAjnDY7FHgfDOrCifyzwceDfN2mdlp4Sqxy1K2JcNQm4gz/3NncOXZs/j5i29w3rcW8NtXNkVdlojkkCh6LpOAZ8zsJWAh8Ii7/wa4ATjPzFYA54XPAPOB1cBK4HbgKgB3bwb+FVgUXl8NbQBXAneEdVYBvx6B75VXykpi/P3ct/HLq04nXl7KFfe9wDU/Wsy23XuiLk1EcoBpWPak2tpab2hoiLqMrNTR1cN/L1jFd36/grHh0NmHTpqCbh8SETN7IeWWkj7ZdCmyZKnS4iI+d+5sHvncGRxxWDnXPrCET93TwKYdeiiZiAxM4SLDdtSkcfz8ynfyTx84hmdXbeW8GxfwwMLX9VAyEXkLhYsckFiR8akzZvKba8/k2Cnjue7ny/j4nQtpbG6LujQRySIKFzkoiQnl3P/Xp/FvFx7PksbtnP+tp7j72TV6KJmIAAoXOQRFRcalp83gt184k7fPjPMvv3qVi773HK9s2BF1aSISMYWLHLIplaO5+xN13Pixk1i5ZTcfuPkZPnn3Qo1TJlLAdClyoEuR02PHm53c99xa7np2Lc2tHdQlqrjqnCM5+6hqXboskof2dymywiVQuKTXmx3dPLDodW5/ajUbdrRz7OTxXHn2LN5/wmRiRQoZkXyhcBmCwiUzOrp6+OWSN/jvBatY3dRKzYRyPn3mTD48ZyqjimNRlycih0jhMgSFS2Z19zi/fWUTtzy5kpff2Mnh48v41Bk1XFJ/BOWjiqMuT0QOksJlCAqXkeHuPL1iK999ciXPr26mckwJn3xnDfPeOYPKMaVRlyciB0jhMgSFy8h7YV0Ltz65kt8t30J5aYy/fPsRfOqMmUwaXxZ1aSIyTAqXIShcovPHTTu59clV/OqlDRQXFfGRU6fxmbNmMuOw8qhLE5EhKFyGoHCJ3rptrXzvqdU82LCerp4ePnjiFK48exbHTB4fdWkish8KlyEoXLLHlp3t3PnMGn7w/DpaO7p599smcvU5szh1Rjzq0kSkH4XLEBQu2WdHWyf3PLeWu59dQ0tbJ/U1ca4+50jOnD1BN2SKZAmFyxAULtmrraOL+xc2cvtTq9m0s53jpoznqrOPZO7xh+uGTJGIKVyGoHDJfh1dPfzyxTe4dcEq1mxtZeaEcj5z1iwuPGUqpcUaJk8kCgqXIShcckd3j/OblzdxyxMreXXjTiZXlPHXZ8zk4vrpjCnVDZkiI0nhMgSFS+5xdxb8qYnvPrmKhWuaqRpTwidPr2HeOxJUjCmJujyRgqBwGYLCJbc1rG3mu0+u4vd/TN6QeelpMzjv2EkcP7WCshKNYSaSKQqXIShc8sOrG3Zy64JVPLJ0Az0OxUXG0YeP4+TplX2vWdVjKdKFACJpoXAZgsIlvzTt2sOSxu0saWxhSeN2ljbuYNeeLgDGjSrmxOkVnDQtBM4RlUwcpyFnRA6GwmUICpf81tPjrN66mxdf386Sxu28tH47f9y4i66e5P//UytHc9L0itC7qeL4qeN1cYDIMOwvXPSvRwpCUZFx5MRxHDlxHBfVTgegvbObl9/YEXo4ydf8ZZsAiBUZR03qPZxWwcnTqzhy4ljdVyMyTAoXKVhlJTFqE3FqE3uHldm6ew8vNW7npcbtvNi4nUeWbuD+ha8DUF4a44RpyaDpDZzDK3Q4TWQgCheRFBPGjuLcYyZx7jGTgOThtDXbWnkppXdz5zOr6exOHk47fHxZOJxWxcnTKzlxWoUefiaCwkVkUEVFxqzqscyqHsufz5kGJA+nvbpxJ0teT567WdK4nUdf2Zxc3mD2xHF9FwqcOK2CWdVjdTm0FByFi8gBKiuJMeeIKuYcUdXX1tzakQyacMHAo69u4scNjQCYJS8YmFk9lpkTyplVXc6s6rHMrB7LpPGjNAin5CWFi0gaxMtLOefoiZxz9EQgOXrAum1tLHtjB6uadrO6qZXVW3fTsLaZto7uvvXKS2PUVJczc8LYEDjlydeEsYwuVW9HcpfCRSQDzIzEhHISE/Z9mqa7s3nnnhA4u1nV1Mrqra0sfr2FXy3dQOqdAVMqypg1MdnbmdkXPGOZPL5MN4FK1lO4iIwgM+PwijIOryjj9CMn7DOvvbObNVtbk72cpt2s3trKqqbd/GzxG+wON4ACjC6JUTOhvC9seg+z1Uwo18UEkjX0f6JIligriXHM5PFveayzu9O0a0/o5SQPsa1q2s3S9TuYv2wjPSm9ncPHlzGz75xOed95nqmVo9XbkRGlcBHJcmbGxPFlTBxfxjtmHbbPvD1d3azb1tZ3iK33/M5DS95gZ/ve3k5xkVE5poSqMaVUjSmlckwJ8fJSKseUUjWmhKry0jBv73TF6BLdNCoHTeEiksNGFcc4atI4jpo0bp92d2dba0ffIbbGljZa2jppae2gpa2D15vbWNK4ne1tnXR09wy4bTMYX9YbQnuDqX8YVY4pJV6+d1oPbhNQuIjkJTNjwthRTBg7ivqa+H6Xc3faOrppbu1ge1snLW3J8EmGUO/nTra3dbB5ZzuvbdpFS1vHPle89VdeGusLn94eUu/0uLISykqKKCuOUVYSS06H91H92kYVJ99LYgqrXKRwESlgZkb5qGLKRxUzff8Z9Bbtnd39wig5vb2tg+bWZBi1tHXQ3NbJ681ttLR27HOY7kDEioyy4t4QijGqL5z2DaH9hdQ+6xbvXac0vEpiYTpWxKjUz8VFFBeZ7kM6SAoXETlgZSUxDq+IHdDYal3dPbTu6aa9q5v2zm7aO3vCezftXXun93T1sCd1flfqsj20d3XvM39neyd7Qntv257Onv0e7jsQZlASK2JUrF8QhTAqKd47LznfKC2OUdoXWJayfIySYusLsWR4FVEcM2JFRnGRESsqCu9GccwoLipKmdfbltLet+7e5XrXKzIiDca8DRczmwt8G4gBd7j7DRGXJFLQimNFVIwpooKReQR1d4+zp6tfiIUQ6ujq6Xt1dieDaE/q5975YV5H/3l9705HVzdtHV1sf7OHzi7vm7en3/LdPSP/eJO+UOoLp6IQTkYsJUVnXoIAAAf7SURBVLz+/cMnDHr49KD2ndatZQkziwG3AOcB64FFZvawu78abWUiMlJiRcaY0mLGlEZdSVJ3j9OZEmId3T10dztdPcng6erxvmVSPyffe+jqTk6nfu6/XFd3z1vX6/Gwn5R99Vu3fFT6R4PIy3AB6oGV7r4awMweAC4AFC4iEolYkRErihXMIKb5ehnGVKAx5fP60LYPM7vCzBrMrKGpqWnEihMRyXf5Gi4DncV6ywFPd7/N3Wvdvba6unoEyhIRKQz5Gi7rgekpn6cBGyKqRUSk4ORruCwCZptZjZmVAhcDD0dck4hIwcjLE/ru3mVm1wCPkrwU+S53fyXiskRECkZehguAu88H5kddh4hIIcrXw2IiIhIhhYuIiKSduY/8kATZyMyagHVR13GIJgBboy4ii+jnsZd+FvvSz2Nfh/LzmOHub7mXQ+GSR8yswd1ro64jW+jnsZd+FvvSz2Nfmfh56LCYiIikncJFRETSTuGSX26LuoAso5/HXvpZ7Es/j32l/eehcy4iIpJ26rmIiEjaKVxERCTtFC55wMymm9kTZrbczF4xs2ujrilqZhYzsxfN7H+iriVqZlZpZg+a2R/D/yPviLqmqJjZF8K/kZfN7H4zK4u6ppFkZneZ2RYzezmlLW5mj5nZivBelY59KVzyQxfwt+5+DHAacLWZHRtxTVG7FlgedRFZ4tvAb9z9bcBJFOjPxcymAp8Dat39eJKD2l4cbVUj7vvA3H5t1wGPu/ts4PHw+ZApXPKAu29098VhehfJXx5vefJmoTCzacAHgDuiriVqZjYeOBO4E8DdO9x9e7RVRaoYGG1mxcAYCuw5T+7+FNDcr/kC4J4wfQ9wYTr2pXDJM2aWAE4B/hBtJZG6Cfg7oCfqQrLATKAJuDscJrzDzMqjLioK7v4G8P+A14GNwA53/220VWWFSe6+EZJ/qAIT07FRhUseMbOxwM+Az7v7zqjriYKZfRDY4u4vRF1LligG5gC3uvspQCtpOuyRa8K5hAuAGmAKUG5ml0ZbVf5SuOQJMyshGSw/dPefR11PhE4HPmRma4EHgHeb2Q+iLSlS64H17t7bk32QZNgUovcAa9y9yd07gZ8D74y4pmyw2cwmA4T3LenYqMIlD5iZkTymvtzdb4y6nii5+5fdfZq7J0ierP29uxfsX6fuvgloNLOjQ9O5wKsRlhSl14HTzGxM+DdzLgV6cUM/DwPzwvQ84KF0bDRvn0RZYE4HPg4sM7Mloe0fwtM4RT4L/NDMSoHVwCcjricS7v4HM3sQWEzyCssXKbBhYMzsfuBsYIKZrQeuB24AfmJml5MM4IvSsi8N/yIiIummw2IiIpJ2ChcREUk7hYuIiKSdwkVERNJO4SIiImmncJGCYGZuZv+Z8vmLZvaVNG37+2b20XRsa4j9XBRGNX5igHlHmdl8M1sZlvmJmU0ys7MPdmRoM/u8mY059MqlEClcpFDsAf7czCZEXUgqM4sdwOKXA1e5+zn9tlEGPEJyiJcjw+jYtwLVh1je50kO7jhsB/h9JI8pXKRQdJG8Ye4L/Wf073mY2e7wfraZLQi9gD+Z2Q1m9ldmttDMlpnZrJTNvMfMng7LfTCsHzOzb5rZIjNbamafTtnuE2b2I2DZAPVcErb/spl9PbT9X+BdwH+b2Tf7rfKXwHPu/qveBnd/wt1fTl3IzL5iZl9M+fyymSXMrNzMHjGzl0LbX5jZ50iOv/VEb0/JzM43s+fMbLGZ/TSMZYeZrTWz/2tmzwAXmdnnzOzV8J0fGOK/i+Qp3aEvheQWYKmZfeMA1jkJOIbkMOWrgTvcvd6SD2T7LMm/7gESwFnALJK/kI8ELiM58m6dmY0CnjWz3lF464Hj3X1N6s7MbArwdeBUoAX4rZld6O5fNbN3A19094Z+NR4PHMpAnXOBDe7+gVBDhbvvMLO/Ac5x962hx/dPwHvcvdXM/h74G+CrYRvt7v6usP4GoMbd95hZ5SHUJTlMPRcpGGGk6HtJPjBquBaF5+XsAVYBveGwjGSg9PqJu/e4+wqSIfQ24HzgsjAkzx+Aw4DZYfmF/YMlqAOeDIMrdgE/JPk8lkxaRrLn9XUzO8PddwywzGnAsSQDcgnJMahmpMz/ccr0UpLDzVxKsscoBUjhIoXmJpLnLlKfadJF+LcQBjQsTZm3J2W6J+VzD/v2/PuPo+SAAZ9195PDqybl+SGt+6nPhvtFUrxCsqczlL7vGZQBuPufwvrLgP8Ih+AGquuxlO9yrLtfnjI/9ft8gGQv8VTghfBgLikwChcpKO7eDPyEZMD0WsveX84XACUHsemLzKwonIeZCbwGPApcGR6H0HtF11AP6voDcJaZTQgnxy8BFgyxzo+Ad5rZB3obzGyumZ3Qb7m1hOH2zWwOyeea9B6Ka3P3H5B8mFbvkPy7gHFh+nng9HC4jzCy8FH9CzGzImC6uz9B8oFtlcDYIeqXPKS/KKQQ/SdwTcrn24GHzGwhyWeI769XMZjXSIbAJOAz7t5uZneQPHS2OPSImhjiEbLuvtHMvgw8QbK3MN/dBx0C3d3fDBcR3GRmNwGdJA9NXUvyUFyvn7H3MN0i4E+h/QTgm2bWE9a9MrTfBvzazDa6+zlm9gng/nD+CJLnYP7EvmLAD8ysItT/rQJ/rHLB0qjIIiKSdjosJiIiaadwERGRtFO4iIhI2ilcREQk7RQuIiKSdgoXERFJO4WLiIik3f8Hkm+UbJ7AVXUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# X = pivot_clusters.iloc[:, [2,4]].values\n",
    "X = pivot_clusters[['economic', 'culture']].values\n",
    "\n",
    "# Elbow Method\n",
    "from sklearn.cluster import KMeans\n",
    "wcss = []\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters=i, init='k-means++', n_init=10, max_iter=300, random_state=0)\n",
    "    kmeans.fit(X)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "plt.plot(range(1,11), wcss)\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('wcss')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fitting kmeans to dataset\n",
    "kmeans = KMeans(n_clusters=4, init='k-means++', n_init=10, max_iter=300, random_state=0)\n",
    "Y_kmeans = kmeans.fit_predict(X)\n",
    "\n",
    "# Visualising the clusters\n",
    "plt.scatter(X[Y_kmeans==0, 0], X[Y_kmeans==0, 1], s=100, c='violet', label= 'Cluster 1')\n",
    "plt.scatter(X[Y_kmeans==1, 0], X[Y_kmeans==1, 1], s=100, c='cyan', label= 'Cluster 2')\n",
    "plt.scatter(X[Y_kmeans==2, 0], X[Y_kmeans==2, 1], s=100, c='green', label= 'Cluster 3')\n",
    "plt.scatter(X[Y_kmeans==3, 0], X[Y_kmeans==3, 1], s=100, c='blue', label= 'Cluster 4')\n",
    "# plt.scatter(X[Y_kmeans==4, 0], X[Y_kmeans==4, 1], s=100, c='magenta', label= 'Cluster 5')\n",
    "# plt.scatter(X[Y_kmeans==5, 0], X[Y_kmeans==5, 1], s=100, c='pink', label= 'Cluster 6')\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=100, c='black', label='Centroids' )\n",
    "plt.title('Clusters of tweets in economic and culture groups')\n",
    "plt.xlabel('economic tweets')\n",
    "plt.ylabel('culture tweets')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Health-Culture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# X = pivot_clusters.iloc[:, [2,4]].values\n",
    "X = pivot_clusters[['health', 'culture']].values\n",
    "\n",
    "# Elbow Method\n",
    "from sklearn.cluster import KMeans\n",
    "wcss = []\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters=i, init='k-means++', n_init=10, max_iter=300, random_state=0)\n",
    "    kmeans.fit(X)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "plt.plot(range(1,11), wcss)\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('wcss')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fitting kmeans to dataset\n",
    "kmeans = KMeans(n_clusters=4, init='k-means++', n_init=10, max_iter=300, random_state=0)\n",
    "Y_kmeans = kmeans.fit_predict(X)\n",
    "\n",
    "# Visualising the clusters\n",
    "plt.scatter(X[Y_kmeans==0, 0], X[Y_kmeans==0, 1], s=70, c='violet', label= 'Cluster 1')\n",
    "plt.scatter(X[Y_kmeans==1, 0], X[Y_kmeans==1, 1], s=70, c='cyan', label= 'Cluster 2')\n",
    "plt.scatter(X[Y_kmeans==2, 0], X[Y_kmeans==2, 1], s=70, c='green', label= 'Cluster 3')\n",
    "plt.scatter(X[Y_kmeans==3, 0], X[Y_kmeans==3, 1], s=70, c='blue', label= 'Cluster 4')\n",
    "# plt.scatter(X[Y_kmeans==4, 0], X[Y_kmeans==4, 1], s=70, c='magenta', label= 'Cluster 5')\n",
    "# plt.scatter(X[Y_kmeans==5, 0], X[Y_kmeans==5, 1], s=100, c='pink', label= 'Cluster 6')\n",
    "# plt.scatter(X[Y_kmeans==6, 0], X[Y_kmeans==6, 1], s=100, c='yellow', label= 'Cluster 7')\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=100, c='black', label='Centroids' )\n",
    "plt.title('Clusters of tweets in culture and health groups')\n",
    "plt.xlabel('health tweets')\n",
    "plt.ylabel('culture tweets')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KMeans with PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,\n",
       "    svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seg = pivot_clusters.copy()\n",
    "# seg = seg.drop\n",
    "\n",
    "pca = PCA()\n",
    "pca.fit(seg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8.39498181e-01, 1.41546117e-01, 1.89557027e-02, 3.97166247e-32,\n",
       "       4.55502191e-36])"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'cummulative explained variance')"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,6))\n",
    "plt.plot(range(1,6), pca.explained_variance_ratio_.cumsum(), marker = 'o', linestyle = '--')\n",
    "plt.xlabel('components')\n",
    "plt.ylabel('cummulative explained variance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=2, random_state=None,\n",
       "    svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA(n_components = 2)\n",
    "pca.fit(seg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = pca.transform(seg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'wcss')"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Elbow Method\n",
    "from sklearn.cluster import KMeans\n",
    "wcss = []\n",
    "for i in range(1, 15):\n",
    "    kmeans = KMeans(n_clusters=i, init='k-means++', n_init=10, max_iter=300, random_state=0)\n",
    "    kmeans.fit(scores)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "plt.plot(range(1,15), wcss)\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('KMeans with PCA clustering')\n",
    "plt.ylabel('wcss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "       n_clusters=4, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=0, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = 4\n",
    "kmeans_pca = KMeans(n_clusters = n, init = 'k-means++', random_state = 0)\n",
    "kmeans_pca.fit(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = pd.concat([seg.reset_index(drop = True), pd.DataFrame(scores)], axis = 1)\n",
    "c.columns.values[-2:] = ['component1', 'component2']\n",
    "c['segment_kmeans_pca'] = kmeans_pca.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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>economic</th>\n",
       "      <th>social</th>\n",
       "      <th>culture</th>\n",
       "      <th>health</th>\n",
       "      <th>total</th>\n",
       "      <th>component1</th>\n",
       "      <th>component2</th>\n",
       "      <th>segment_kmeans_pca</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>382</td>\n",
       "      <td>17</td>\n",
       "      <td>83</td>\n",
       "      <td>17</td>\n",
       "      <td>409</td>\n",
       "      <td>526</td>\n",
       "      <td>-5.801449</td>\n",
       "      <td>-22.030081</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>383</td>\n",
       "      <td>4</td>\n",
       "      <td>220</td>\n",
       "      <td>4</td>\n",
       "      <td>279</td>\n",
       "      <td>507</td>\n",
       "      <td>-98.773696</td>\n",
       "      <td>140.268158</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>384</td>\n",
       "      <td>36</td>\n",
       "      <td>80</td>\n",
       "      <td>36</td>\n",
       "      <td>418</td>\n",
       "      <td>570</td>\n",
       "      <td>34.026171</td>\n",
       "      <td>-9.855019</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>385</td>\n",
       "      <td>10</td>\n",
       "      <td>83</td>\n",
       "      <td>10</td>\n",
       "      <td>416</td>\n",
       "      <td>519</td>\n",
       "      <td>-6.840108</td>\n",
       "      <td>-29.056085</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>386</td>\n",
       "      <td>17</td>\n",
       "      <td>52</td>\n",
       "      <td>17</td>\n",
       "      <td>438</td>\n",
       "      <td>524</td>\n",
       "      <td>10.072035</td>\n",
       "      <td>-61.294450</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     economic  social  culture  health  total  component1  component2  \\\n",
       "382        17      83       17     409    526   -5.801449  -22.030081   \n",
       "383         4     220        4     279    507  -98.773696  140.268158   \n",
       "384        36      80       36     418    570   34.026171   -9.855019   \n",
       "385        10      83       10     416    519   -6.840108  -29.056085   \n",
       "386        17      52       17     438    524   10.072035  -61.294450   \n",
       "\n",
       "     segment_kmeans_pca  \n",
       "382                   0  \n",
       "383                   2  \n",
       "384                   0  \n",
       "385                   0  \n",
       "386                   0  "
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Clusters by PCA')"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,8))\n",
    "sns.scatterplot(x = c['component1'], y = c['component2'], hue = c['segment_kmeans_pca'], palette = ['g', 'r', 'b', 'y'])\n",
    "plt.title('Clusters by PCA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Series Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Get tweets full details: must get ~ time stamp'''\n",
    "\n",
    "def pivoted_df_with_ts(paths):\n",
    "    global dataframes\n",
    "    dataframes = []\n",
    "    for path in paths:\n",
    "        frame = pd.read_csv(path)\n",
    "\n",
    "        names = path.split('/')\n",
    "        names= names[-1]\n",
    "        names = names.split('_')\n",
    "        global name\n",
    "        name = names[0]\n",
    "\n",
    "        df = pd.DataFrame()\n",
    "        df['tweets'] = frame['text']\n",
    "        df['favorites'] = frame['favorites']\n",
    "        df['retweets'] = frame['retweets']\n",
    "        df['entities'] = frame['entities']\n",
    "        df['created_at'] = frame['created_at']\n",
    "        df.insert(0, 'screen_name', name)\n",
    "        \n",
    "        dataframes.append(df)\n",
    "    mega = pd.concat(dataframes)\n",
    "    return mega"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(198302, 6)"
      ]
     },
     "execution_count": 513,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_paths = ['/home/ada/10academy/training /week2/monday/csvs/mega csvs tweets/@' + name + '_tweets.csv' for name in n]\n",
    "\n",
    "tweets = pivoted_df_with_ts(file_paths)\n",
    "tweets.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "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>screen_name</th>\n",
       "      <th>tweets</th>\n",
       "      <th>favorites</th>\n",
       "      <th>retweets</th>\n",
       "      <th>entities</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>RT @yola_plaatjie: Friends, Family, romantic r...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>2020-07-22 06:47:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>Been off IG for a while. I’m really loving it 🥺</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>2020-07-21 21:39:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>The scariest thing is not knowing what a perso...</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>2020-07-21 21:34:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>Koko has this habit of licking off my lotion o...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>2020-07-21 11:19:33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>@nanga</td>\n",
       "      <td>Indlela endonqena ngayo, and all for convenien...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>2020-07-21 11:02:34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  screen_name                                             tweets  favorites  \\\n",
       "0      @nanga  RT @yola_plaatjie: Friends, Family, romantic r...          0   \n",
       "1      @nanga    Been off IG for a while. I’m really loving it 🥺          0   \n",
       "2      @nanga  The scariest thing is not knowing what a perso...          7   \n",
       "3      @nanga  Koko has this habit of licking off my lotion o...          1   \n",
       "4      @nanga  Indlela endonqena ngayo, and all for convenien...          0   \n",
       "\n",
       "   retweets                                           entities  \\\n",
       "0         2  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "1         0  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "2         3  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "3         0  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "4         0  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "\n",
       "            created_at  \n",
       "0  2020-07-22 06:47:49  \n",
       "1  2020-07-21 21:39:20  \n",
       "2  2020-07-21 21:34:11  \n",
       "3  2020-07-21 11:19:33  \n",
       "4  2020-07-21 11:02:34  "
      ]
     },
     "execution_count": 514,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 585,
   "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>screen_name</th>\n",
       "      <th>tweets</th>\n",
       "      <th>favorites</th>\n",
       "      <th>retweets</th>\n",
       "      <th>entities</th>\n",
       "      <th>created_at</th>\n",
       "      <th>scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>204</td>\n",
       "      <td>@KeitusPromo</td>\n",
       "      <td>RT @Twitter: You can have an edit button when ...</td>\n",
       "      <td>0</td>\n",
       "      <td>692874</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>2020-07-03 07:38:32</td>\n",
       "      <td>692874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>172</td>\n",
       "      <td>@MoseaxKenya</td>\n",
       "      <td>RT @Twitter: You can have an edit button when ...</td>\n",
       "      <td>0</td>\n",
       "      <td>692873</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>2020-07-03 05:05:04</td>\n",
       "      <td>692873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>163</td>\n",
       "      <td>@wazua</td>\n",
       "      <td>RT @Twitter: You can have an edit button when ...</td>\n",
       "      <td>0</td>\n",
       "      <td>692858</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>2020-07-02 20:26:28</td>\n",
       "      <td>692858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>493</td>\n",
       "      <td>@btsvote</td>\n",
       "      <td>RT @BTS_twt: 자 시작해보자~ https://t.co/UX3LAjBf10</td>\n",
       "      <td>0</td>\n",
       "      <td>631052</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>2020-07-10 11:24:24</td>\n",
       "      <td>631052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>397</td>\n",
       "      <td>@btsvote</td>\n",
       "      <td>RT @BTS_twt: 석지닝 https://t.co/zfnyDKs24v</td>\n",
       "      <td>0</td>\n",
       "      <td>605737</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>2020-07-12 08:58:16</td>\n",
       "      <td>605737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      screen_name                                             tweets  \\\n",
       "204  @KeitusPromo  RT @Twitter: You can have an edit button when ...   \n",
       "172  @MoseaxKenya  RT @Twitter: You can have an edit button when ...   \n",
       "163        @wazua  RT @Twitter: You can have an edit button when ...   \n",
       "493      @btsvote      RT @BTS_twt: 자 시작해보자~ https://t.co/UX3LAjBf10   \n",
       "397      @btsvote           RT @BTS_twt: 석지닝 https://t.co/zfnyDKs24v   \n",
       "\n",
       "     favorites  retweets                                           entities  \\\n",
       "204          0    692874  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "172          0    692873  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "163          0    692858  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "493          0    631052  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "397          0    605737  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "\n",
       "              created_at  scores  \n",
       "204  2020-07-03 07:38:32  692874  \n",
       "172  2020-07-03 05:05:04  692873  \n",
       "163  2020-07-02 20:26:28  692858  \n",
       "493  2020-07-10 11:24:24  631052  \n",
       "397  2020-07-12 08:58:16  605737  "
      ]
     },
     "execution_count": 585,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''create a scores column, sort values by scores, do analysis on top 500 tweets'''\n",
    "\n",
    "tweets['scores'] = tweets['favorites'] + tweets['retweets']\n",
    "top_tweets = tweets.sort_values(by = 'scores', ascending = False)\n",
    "# top_tweets = top_tweets.head(500)\n",
    "top_tweets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 586,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print('all tweets tweeps:', len(tweets['screen_name'].unique()))\n",
    "# print('top 500 tweets tweeps', len(top_tweets['screen_name'].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* plan is to explore the trends in tweets volumes, maybe the tweets can be grouped into time stamps, then see the transition of volume of these tweets from one time to another."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 587,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''explore the time stamps and create groups'''\n",
    "top_tweets['created_at'] = pd.to_datetime(top_tweets['created_at'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 588,
   "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>screen_name</th>\n",
       "      <th>tweets</th>\n",
       "      <th>favorites</th>\n",
       "      <th>retweets</th>\n",
       "      <th>entities</th>\n",
       "      <th>created_at</th>\n",
       "      <th>scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>204</td>\n",
       "      <td>@KeitusPromo</td>\n",
       "      <td>RT @Twitter: You can have an edit button when ...</td>\n",
       "      <td>0</td>\n",
       "      <td>692874</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>2020-07-03 07:38:32</td>\n",
       "      <td>692874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>172</td>\n",
       "      <td>@MoseaxKenya</td>\n",
       "      <td>RT @Twitter: You can have an edit button when ...</td>\n",
       "      <td>0</td>\n",
       "      <td>692873</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
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       "      <td>692873</td>\n",
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       "    <tr>\n",
       "      <td>163</td>\n",
       "      <td>@wazua</td>\n",
       "      <td>RT @Twitter: You can have an edit button when ...</td>\n",
       "      <td>0</td>\n",
       "      <td>692858</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>2020-07-02 20:26:28</td>\n",
       "      <td>692858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>493</td>\n",
       "      <td>@btsvote</td>\n",
       "      <td>RT @BTS_twt: 자 시작해보자~ https://t.co/UX3LAjBf10</td>\n",
       "      <td>0</td>\n",
       "      <td>631052</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>2020-07-10 11:24:24</td>\n",
       "      <td>631052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>397</td>\n",
       "      <td>@btsvote</td>\n",
       "      <td>RT @BTS_twt: 석지닝 https://t.co/zfnyDKs24v</td>\n",
       "      <td>0</td>\n",
       "      <td>605737</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>2020-07-12 08:58:16</td>\n",
       "      <td>605737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      screen_name                                             tweets  \\\n",
       "204  @KeitusPromo  RT @Twitter: You can have an edit button when ...   \n",
       "172  @MoseaxKenya  RT @Twitter: You can have an edit button when ...   \n",
       "163        @wazua  RT @Twitter: You can have an edit button when ...   \n",
       "493      @btsvote      RT @BTS_twt: 자 시작해보자~ https://t.co/UX3LAjBf10   \n",
       "397      @btsvote           RT @BTS_twt: 석지닝 https://t.co/zfnyDKs24v   \n",
       "\n",
       "     favorites  retweets                                           entities  \\\n",
       "204          0    692874  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "172          0    692873  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "163          0    692858  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "493          0    631052  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "397          0    605737  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "\n",
       "             created_at  scores  \n",
       "204 2020-07-03 07:38:32  692874  \n",
       "172 2020-07-03 05:05:04  692873  \n",
       "163 2020-07-02 20:26:28  692858  \n",
       "493 2020-07-10 11:24:24  631052  \n",
       "397 2020-07-12 08:58:16  605737  "
      ]
     },
     "execution_count": 588,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_tweets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 589,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 198302 entries, 204 to 499\n",
      "Data columns (total 7 columns):\n",
      "screen_name    198302 non-null object\n",
      "tweets         198302 non-null object\n",
      "favorites      198302 non-null int64\n",
      "retweets       198302 non-null int64\n",
      "entities       198302 non-null object\n",
      "created_at     198302 non-null datetime64[ns]\n",
      "scores         198302 non-null int64\n",
      "dtypes: datetime64[ns](1), int64(3), object(3)\n",
      "memory usage: 12.1+ MB\n"
     ]
    }
   ],
   "source": [
    "top_tweets.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 590,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                  198302\n",
       "unique                 176328\n",
       "top       2020-07-21 04:00:00\n",
       "freq                       12\n",
       "first     2014-06-25 13:00:07\n",
       "last      2020-07-22 13:54:34\n",
       "Name: created_at, dtype: object"
      ]
     },
     "execution_count": 590,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_tweets['created_at'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Earliest tweet: ***25th June 2014***\n",
    "* Latest tweet ***22nd July 2020***\n",
    "* day with highest number ***21st July 2020***\n",
    "\n",
    "\n",
    "Must say the users are a mixture of active and super dormant ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 591,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>screen_name</th>\n",
       "      <th>tweets</th>\n",
       "      <th>favorites</th>\n",
       "      <th>retweets</th>\n",
       "      <th>entities</th>\n",
       "      <th>scores</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Weekday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2020-07-03 07:38:32</td>\n",
       "      <td>@KeitusPromo</td>\n",
       "      <td>RT @Twitter: You can have an edit button when ...</td>\n",
       "      <td>0</td>\n",
       "      <td>692874</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>692874</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-07-03 05:05:04</td>\n",
       "      <td>@MoseaxKenya</td>\n",
       "      <td>RT @Twitter: You can have an edit button when ...</td>\n",
       "      <td>0</td>\n",
       "      <td>692873</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>692873</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-07-02 20:26:28</td>\n",
       "      <td>@wazua</td>\n",
       "      <td>RT @Twitter: You can have an edit button when ...</td>\n",
       "      <td>0</td>\n",
       "      <td>692858</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>692858</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-07-10 11:24:24</td>\n",
       "      <td>@btsvote</td>\n",
       "      <td>RT @BTS_twt: 자 시작해보자~ https://t.co/UX3LAjBf10</td>\n",
       "      <td>0</td>\n",
       "      <td>631052</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>631052</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-07-12 08:58:16</td>\n",
       "      <td>@btsvote</td>\n",
       "      <td>RT @BTS_twt: 석지닝 https://t.co/zfnyDKs24v</td>\n",
       "      <td>0</td>\n",
       "      <td>605737</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>605737</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      screen_name  \\\n",
       "created_at                          \n",
       "2020-07-03 07:38:32  @KeitusPromo   \n",
       "2020-07-03 05:05:04  @MoseaxKenya   \n",
       "2020-07-02 20:26:28        @wazua   \n",
       "2020-07-10 11:24:24      @btsvote   \n",
       "2020-07-12 08:58:16      @btsvote   \n",
       "\n",
       "                                                                tweets  \\\n",
       "created_at                                                               \n",
       "2020-07-03 07:38:32  RT @Twitter: You can have an edit button when ...   \n",
       "2020-07-03 05:05:04  RT @Twitter: You can have an edit button when ...   \n",
       "2020-07-02 20:26:28  RT @Twitter: You can have an edit button when ...   \n",
       "2020-07-10 11:24:24      RT @BTS_twt: 자 시작해보자~ https://t.co/UX3LAjBf10   \n",
       "2020-07-12 08:58:16           RT @BTS_twt: 석지닝 https://t.co/zfnyDKs24v   \n",
       "\n",
       "                     favorites  retweets  \\\n",
       "created_at                                 \n",
       "2020-07-03 07:38:32          0    692874   \n",
       "2020-07-03 05:05:04          0    692873   \n",
       "2020-07-02 20:26:28          0    692858   \n",
       "2020-07-10 11:24:24          0    631052   \n",
       "2020-07-12 08:58:16          0    605737   \n",
       "\n",
       "                                                              entities  \\\n",
       "created_at                                                               \n",
       "2020-07-03 07:38:32  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "2020-07-03 05:05:04  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "2020-07-02 20:26:28  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "2020-07-10 11:24:24  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "2020-07-12 08:58:16  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "\n",
       "                     scores  Year  Month   Weekday  \n",
       "created_at                                          \n",
       "2020-07-03 07:38:32  692874  2020      7    Friday  \n",
       "2020-07-03 05:05:04  692873  2020      7    Friday  \n",
       "2020-07-02 20:26:28  692858  2020      7  Thursday  \n",
       "2020-07-10 11:24:24  631052  2020      7    Friday  \n",
       "2020-07-12 08:58:16  605737  2020      7    Sunday  "
      ]
     },
     "execution_count": 591,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''get the years, months and day columns'''\n",
    "\n",
    "top_tweets = top_tweets.set_index('created_at')\n",
    "\n",
    "# Add columns with year, month, and weekday name\n",
    "top_tweets['Year'] = top_tweets.index.year\n",
    "top_tweets['Month'] = top_tweets.index.month\n",
    "top_tweets['Weekday'] = top_tweets.index.weekday_name\n",
    "\n",
    "\n",
    "top_tweets.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Weekday Analysis overall tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 592,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweets</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weekday</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Monday</td>\n",
       "      <td>35271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Tuesday</td>\n",
       "      <td>48269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Wednesday</td>\n",
       "      <td>36508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Thursday</td>\n",
       "      <td>18619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Friday</td>\n",
       "      <td>19619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Saturday</td>\n",
       "      <td>17875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sunday</td>\n",
       "      <td>22141</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tweets\n",
       "Weekday          \n",
       "Monday      35271\n",
       "Tuesday     48269\n",
       "Wednesday   36508\n",
       "Thursday    18619\n",
       "Friday      19619\n",
       "Saturday    17875\n",
       "Sunday      22141"
      ]
     },
     "execution_count": 592,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# week day analysis\n",
    "cats = [ 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "weeks_df = top_tweets.groupby(['Weekday']).count().reindex(cats) \n",
    "# weeks_df = top_tweets.groupby(['Weekday']).count() #count number of tweets, count is a string function, so let's drop the integers.\n",
    "weeks_df = weeks_df[['tweets']]\n",
    "weeks_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 593,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of tweets')"
      ]
     },
     "execution_count": 593,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lBg8e7DuGiIiIiCSx999/v8I51+wvD7e7Anzw4MEsXbrUdwwRERERSWJmtr6lceqCIiIiIiISQyrARURERERiSAW4iIiIiEgMqQAXEREREYkhFeAiIiIiIjGkAlxEREREJIZUgIuIiIiIxJAKcBERERGRGFIBLiIiIiISQyrARURERERiSAW4iIiIiEgMqQAXEREREYkhFeAiIiIiIjGkAlxEREREJIZUgIuIiIiIxJAKcBERERGRGFIBLiIiIiISQyrARURERERiSAW4iIiIiEgMqQAXEREREYkhFeAiIiIiIjGkAlzaJeccr360lYM1db6jiIiISDujAlzapVc+2sotT7zPva+v8R1FRERE2hkV4NLuOOeYVVgMwGNvrmXbnoOeE4mIiEh7ogJc2p0FReV8XLaH70wuoLbOcZ9awUVERCSGVIBLu+KcY2ZhMbndOvKdycP4+hkD+fN7G1lXUeU7moiIiLQTKsClXXln7U7eX7+LmycMpUNaCrdNLiA9NYV75n7qO5qIiIi0EyrApV2ZVVhMr84ZfPW0AQD07tKR688ewuwVZXxctttzOhEREWkPVIBLu7FiYyWL1lRwwzlD6Jie+tnwmybkk90pnV/OKfKYTkRERNoLFeDSbswsLKZbZjpXjxv0ueFdO6Zz64ShLCgq5+3SHZ7SiYiISHuhAlzahdVb9zD3k21cO34wnTPSvjD+mvGD6dM1g7teXY1zzkNCERERaS9UgEu7cH9hCVkdUrnurMHNju+Ynsr3pwzngw2VzFu1PbbhREREpF1RAS5Jb11FFS9+WMbV4waR3alDi9Ndfkp/8ntlcfecIurq1QouIiIi0aECXJLeAwtKSEtN4fpzhrQ6XVpqCrdfMIKibXt5fvnmGKUTERGR9kYFuCS1ssoDPLdsE189dQC9u3Q87PQXju7LCXnduGfup1TX1sUgoYiIiLQ3KsAlqT30RinOwc0T8iOaPiXFuGPqCDbtOsBT72yIcjoRERFpj1SAS9Kq2FfNU+9u4LKT8ujfvVPE850zrBdn5vfkt/OLqaqujWJCERERaY9UgEvSemTxWg7V1XPrxKFHNJ+Zcee0EeyoOsSji9dGKZ2IiIi0VyrAJSnt3l/DH99az0Un9GNoTucjnv+kgd2ZenwfHnqjlJ1Vh6KQUERERNorFeCSlP7w1jr2VdcyY2LBUS/jny4YQdWhWh5YUNx2wURERKTdUwEuSaequpZH31zLeSN7c1xu16NezrA+XfjKyf35w1vrKas80IYJRUREpD1TAS5J50/vbKByfw0zJh9963eD708ZBg7unbemDZKJiIiIqACXJHOwpo6HFpUyfmhPTh7Y/ZiX1797J64eN4hn399I8fZ9bZBQRERE2jsV4JJUnn1/E+V7q5kx6dhbvxvMmDSUzPRU7plb1GbLFBERkfZLBbgkjZq6eh5cUMLYAdmMH9qzzZbbs3MGN56bz8srt7JiY2WbLVdERETaJxXgkjRmLy9jc+UBbptUgJm16bJvOCefHlkd+OUctYKLiIjIsVEBLkmhvt5x/4JiRvbtwnmjerf58jtnpDFjUgGLiytYvKaizZcvIiIi7YcKcEkKr368lZLyKmZEofW7wVVnDCQvO5NfzlmNcy4q6xAREZHkpwJcEp5zjlmFxeT3yuKiE/pFbT0d01P5/pRhrNi0mzkfb43aekRERCS5qQCXhLegqJyPy/Zwy8ShpKZEp/W7wVdO7s+w3p355Zwiauvqo7ouERERSU4qwCWhOeeYWVhMXnYmXz4pL+rrS00x/mnqCErKq3jug81RX5+IiIgkHxXgktDeLt3J++t3cfOEfNJTY3M6X3BcH8YOyObX8z7lYE1dTNYpIiIiyUMFuCS0WYXF9OqcwRWnDojZOs2MO6eNYMvugzzx9vqYrVdERESSgwpwSVjLN1ayuLiCG84ZQsf01Jiue/zQXpwzrBezCovZe7AmpusWERGRxKYCXBLWrMJiumWmc/W4QV7Wf+fUkezaX8PDi9Z6Wb+IiIgkJhXgkpBWb93D3E+2ce34wXTOSPOS4YT+3bj4xH78flEpFfuqvWQQERGRxKMCXBLS/YUlZHVI5bqzBnvNcfv5w6murWfm/GKvOURERCRxqACXhLOuoooXPyzj6nGDyO7UwWuW/JzOXHFqf558Zz0bd+73mkVEREQSgwpwSTgPLCghLTWF688Z4jsKAN89bxgpZvx63qe+o4iIiEgCUAEuCaWs8gDPLdvElacNoHeXjr7jANCvWybXjh/MX5dtpmjrXt9xREREJM6pAJeE8tAbpTgHN08Y6jvK59w6cSidM9K4+7Ui31FEREQkzqkAl4RRvreap97dwJdPyiMvO9N3nM/J7tSBWyYMZe4n23h//S7fcURERCSOqQCXhPHI4rUcqqvnlonx1frd4LqzBtOrcwa/eHU1zjnfcURERCROqQCXhLB7fw1PvL2ei07ox9Cczr7jNKtThzS+e14B767dycJPy33HERERkTilAlwSwh/eWse+6lpmTCzwHaVVV542kAE9Mrnr1SLq69UKLiIiIl+kAlziXlV1LY++uZbzRvbmuNyuvuO0qkNaCrefP4JPtuzhpZVbfMcRERGROKQCXOLen97ZQOX+GmZMju/W7wbTx+Qysm8XfvVaETV19b7jiIiISJxRAS5x7WBNHQ8tKmX80J6cPLC77zgRSUkx7pw2gnU79vPM0o2+44iIiEicUQEuce3Z9zdRvrea2yYlRut3g0kjenPqoO7cO28NBw7V+Y4jIiIicUQFuMStmrp6HlxQwkkDszlzaE/fcY6ImfHDC0eyfW81jy9Z5zuOiIiIxBEV4BK3nl9exubKA9w2qQAz8x3niJ02uAeTR/bmgQXF7N5f4zuOiIiIxAkV4BKX6uod9y8oZmTfLkwe2dt3nKN2x9QR7K2u5XdvlPiOIiIiInEi6gW4maWa2TIzezF8/biZrTWz5eFjbDjczOw+Mys2sw/N7ORGy7jGzNaEj2saDT/FzFaG89xnidhMKs169aOtlJZXMSNBW78bjOrXlUvH5PLom2vZvueg7zgiIiISB2LRAv49YFWTYXc458aGj+XhsAuBYeHjJuABADPrAfwEOAM4HfiJmTXcDuOBcNqG+aZFc0MkNpxzzCosJr9XFhed0M93nGP2g/NHUFvnuG/+Gt9RREREJA5EtQA3s/7AxcDvI5j8UuB/XeBtINvM+gFTgbnOuZ3OuV3AXGBaOK6rc+4t55wD/he4LDpbIrG0oKicT7bs4ZaJQ0lNSdzW7wYDe3bia6cP5Ol3N7Kuosp3HBEREfEs2i3gvwHuBJr+GsnPw24mvzazjHBYHtD4psmbwmGtDd/UzPAvMLObzGypmS0tLy8/6o2R6HPOMbOwmLzsTL58UrOHMyF9Z3IB6akp3DP3U99RRERExLOoFeBm9iVgu3Pu/SajfgSMBE4DegA/bJilmcW4oxj+xYHOPeScO9U5d2pOTk4k8cWTt0t38v76Xdw8IZ/01OS5Rrh314586+zBzF5Rxsdlu33HEREREY+iWeGcBUw3s3XA08BkM3vCObcl7GZSDTxG0K8bghbsAY3m7w+UHWZ4/2aGSwKbVVhMr84ZXHHqgMNPnGBuOnco3TLTuXtOke8oIiIi4lHUCnDn3I+cc/2dc4OBK4H5zrmrw77bhHcsuQz4KJxlNvDN8G4o44DdzrktwBzgAjPrHl58eQEwJxy318zGhcv6JvB8tLZHom/5xkoWF1dw4zlD6Jie6jtOm+uWmc63Jw6lsKicd0p3+I4jIiIinvj4jv9JM1sJrAR6Af8ZDn8ZKAWKgYeBbwM453YCPwPeCx//EQ4DuJXgAs9ioAR4JUbbIFEwc34x3TLTuWrcIN9Rouaa8YPp0zWDu+YUEVw7LCIiIu1NWixW4pxbACwIn09uYRoHzGhh3KPAo80MXwqMbquc4s/qrXuYt2ob358yjM4ZMTktveiYnsr3zhvOv/x1Ja+v2s6U4/r4jiQiIiIxljxXuUlCm1VYQlaHVK4dP9h3lKi7/NT+DOmVxS/nFFFXr1ZwERGR9kYFuHi3tqKKlz4s4+pxg8ju1MF3nKhLT03h9guGU7RtL7NXbPYdR0RERGJMBbh49+CCEtJSU7j+nCG+o8TMRaP7MTqvK7967VMO1Ta9Tb6IiIgkMxXg4lVZ5QGeW7aJK08bQO8uHX3HiZmUFOPOqSPZtOsAT727wXccERERiSEV4OLVQ2+U4hzcPGGo7ygxd86wXozL78Fv56+hqrrWdxwRERGJERXg4k353mqeencDXz4pj7zsTN9xYs7MuHPaSCr2HeLRxWt9xxEREZEYUQEu3jyyeC01dfXcOrH9tX43OHlgdy44rg8PvVHKrqpDvuOIiIhIDKgAFy9276/hibfXc9EJ/cjP6ew7jlf/NHUEVYdqeWBhie8oIiIiEgMqwMWLx5esY191LTMmFfiO4t3wPl34ysn9eXzJOrbsPuA7joiIiESZCnCJuarqWh5bspYpo3ozql9X33HiwvenDAMH985b4zuKiIiIRJkKcIm5P72zgcr9NXxbrd+f6d+9E1eNG8gzSzdSvH2f7zgiIiISRSrAJaYO1tTx0KJSxg/tyckDu/uOE1dmTCogMz2Ve+YW+Y4iIiIiUaQCXGLq2fc3Ub63mtvU+v0FvTpncMM5+by8cisfbqr0HUdERESiRAW4xExNXT0PLijhpIHZnDm0p+84cemGc4bQI6sDv5yjVnAREZFkpQJcYub55WVsrjzAbZMKMDPfceJSl47pzJhUwKI1FbxZXOE7joiIiESBCnCJibp6x/0LihnVryuTR/b2HSeuXXXGQHK7deSuV1fjnPMdR0RERNqYCnCJiVc/2kppeRUzJg1V6/dhdExP5fvnD2fFpt3M+Xir7zgiIiLSxlSAS9Q555hZWEx+ThYXju7nO05C+MpJeRT07swv5xRRW1fvO46IiIi0IRXgEnWFRdtZtWUPt04YSmqKWr8jkZaawj9dMIKS8iqeW7bZdxwRERFpQyrAJaqcc8ycX0xediaXnZTnO05CmXp8H8YMyOY3cz/lYE2d7zgiIiLSRlSAS1S9XbqTDzZUcvOEfNJTdbodCTPjh1NHULb7IE+8vd53HBEREWkjqogkqmYVFtOrcwZXnDrAd5SENL6gF+cM68WswmL2HqzxHUdERETagApwiZrlGytZXFzBjecMoWN6qu84CeuOqSPYtb+Ghxet9R1FRERE2oAKcImamfOL6ZaZzlXjBvmOktBO7J/NxSf045FFpVTsq/YdR0RERI6RCnCJitVb9zBv1TauO2swnTPSfMdJeD+4YDgHa+uZVVjsO4qIiIgcIxXgEhWzCkvI6pDKteMH+46SFIbmdOaKU/vz5Nsb2Lhzv+84IiIicgxUgEubW1tRxUsflnH1mYPI7tTBd5yk8d3zhoHBb+at8R1FREREjoEKcGlzDywoJj01hRvOzvcdJan065bJteMH89yyTRRt3es7joiIiBwlFeDSpjZXHuC5DzZz5WkDyOmS4TtO0rl1wlA6d0jj7teKfEcRERGRo6QCXNrUw2+UAnDThKGekySn7lkduHlCPnM/2cYHG3b5jiMiIiJHQQW4tJnyvdU89e4GvnxSHnnZmb7jJK3rzhpCr84Z/OKV1TjnfMcRERGRI6QCXNrMI4vXUlNXz60T1fodTVkZaXxncgHvrN3JG2sqfMcRERGRI6QCXNrE7v01PPH2ei46oR/5OZ19x0l6Xzt9IP27Z3LXq6upr1cruIiISCJRAS5t4vEl69hXXcuMSQW+o7QLHdJSuP2C4XxctoeXVm7xHUdERESOgApwOWZV1bU8tmQtU0b1ZlS/rr7jtBvTx+Qxsm8X7pn7KTV19b7jiIiISIRUgMsxe/Kd9VTur1Hrd4ylphh3TB3B2ooqnl26yXccERERiZAKcDkmB2vqeHjRWs4q6MlJA7v7jtPuTB7Zm1MHdefe1z/lwKE633FEREQkAirA5Zg8u3Qj5Xur1frtiZlx57SRbNtTzR/eWuc7joiIiERABbgctZq6eh5cWMpJA7M5M7+n7zjt1ulDejBpRA73Fxaze3+N7zgiIiJyGCrA5ag9v44OIpIAACAASURBVLyMzZUHuG1SAWbmO067dsfUkew5WMvv3ijxHUVEREQOQwW4HJW6esf9C4oZ1a8rk0f29h2n3TsutyuXjs3lsTfXsX3PQd9xREREpBUqwOWovPrRVkrLq5gxaahav+PED84fTk1dPb+dX+w7ioiIiLRCBbgcMeccMwuLyc/J4sLR/XzHkdCgnllcefoAnnp3A+t3VPmOIyIiIi1QAS5HrLBoO6u27OHWCUNJTVHrdzz57uRhpKUa98z91HcUERERaYEKcDkizjlmzi8mLzuTy07K8x1HmujdtSPfOmsIzy8v4+Oy3b7jiIiISDNUgMsReat0Bx9sqOSWCfmkp+r0iUc3TxhKt8x07p5T5DuKiIiINEMVlByRWYXF5HTJ4PJTB/iOIi3olpnOrROHUlhUzrtrd/qOIyIiIk2oAJeILduwizeLd3DjOUPomJ7qO4604pozB9OnawZ3vboa55zvOCIiItKICnCJ2KzCErplpvP1Mwb5jiKHkdkhle+eN4yl63cxf/V233FERESkERXgEpHVW/cwb9U2rjtrMJ0z0nzHkQhcceoABvfsxF2vFlFXr1ZwERGReKECXCIyq7CErA6pXDt+sO8oEqH01BRuv2AERdv2MnvFZt9xREREJKQCXA5rbUUVL31YxtVnDiK7UwffceQIXHxCP47P7co9cz/lUG297zgiIiKCCnCJwAMLiklPTeGGs/N9R5EjlJJi3DltJBt3HuDp9zb4jiMiIiKoAJfD2Fx5gOc+2MyVpw0gp0uG7zhyFM4d1oszhvTgvteLqaqu9R1HRESk3VMBLq16aGEJADdNGOo5iRwts6AVvGJfNY+9udZ3HBERkXZPBbi0qHxvNU+/t5GvnJxHXnam7zhyDE4Z1J3zj+vD7xaWsqvqkO84IiIi7ZoKcGnRI4vXUlNXz60TC3xHkTZwx9QR7DtUywPhtxoiIiLihwpwadbu/TU88fZ6LjqhH0N6ZfmOI21geJ8ufOWk/vxhyTq27D7gO46IiEi7pQJcmvX4knXsq65lxiS1fieT708ZhnNw3+trfEcRERFpt1SAyxdUVdfy2JK1TBnVm1H9uvqOI21oQI9OfP2MgTyzdBMl5ft8xxEREWmXVIDLFzz5znoq99eo9TtJ3Ta5gIy0FO557VPfUURERNolFeDyOQdr6nh40VrOKujJSQO7+44jUdCrcwY3nJPPSyu3sHLTbt9xRERE2h0V4PI5zy7dSPnearV+J7kbzxlC907p3DVnte8oIiIi7Y4KcPlMTV09Dy4s5eSB2ZyZ39N3HImiLh3TmTGpgEVrKlhSXOE7joiISLuiAlw+87dlm9lceYDbJhdgZr7jSJRdPW4Qud068os5RTjnfMcRERFpN1SACwB19Y4HFpRwXL+uTBrR23cciYGO6al8f8pwVmysZM7H23zHERERaTdUgAsAr360ldKKKmZMUut3e/KVk/MYmpPF3a8VUVtX7zuOiIhIu6ACXHDOMbOwmPycLKaN7us7jsRQWmoKd0wdQfH2fTy3bLPvOCIiIu2CCnChsGg7q7bs4dYJQ0lNUet3ezP1+L6M6d+Ne+et4WBNne84IiIiSU8FeDvnnGPm/GLysjO57KQ833HEAzPjh9NGsrnyAE++s8F3HBERkaSnAryde6t0Bx9sqOSWCfmkp+p0aK/GF/Ti7IJezCosZu/BGt9xREREkpoqrnZuVmExOV0yuPzUAb6jiGd3TB3BzqpD/H7RWt9RREREklrUC3AzSzWzZWb2Yvh6iJm9Y2ZrzOzPZtYhHJ4Rvi4Oxw9utIwfhcOLzGxqo+HTwmHFZvbP0d6WZLNswy7eLN7BjecMoWN6qu844tmYAdlcdEJffr+olB37qn3HERERSVqxaAH/HrCq0etfAL92zg0DdgHXh8OvB3Y55wqAX4fTYWbHAVcCxwPTgPvDoj4VmAVcCBwHfC2cViI0q7CY7E7pXHXGIN9RJE7cfsEIDtbWM6uwxHcUERGRpBXVAtzM+gMXA78PXxswGfhLOMkfgMvC55eGrwnHnxdOfynwtHOu2jm3FigGTg8fxc65UufcIeDpcFqJwKote5i3ajvXjR9CVkaa7zgSJ4bmdObyU/rzxNvr2bRrv+84IiIiSSnaLeC/Ae4EGn7hoydQ6ZyrDV9vAhpuvZEHbAQIx+8Op/9seJN5WhouEbh/QQlZHVK5dvxg31EkznxvyjAw+M28Nb6jiIiIJKWoFeBm9iVgu3Pu/caDm5nUHWbckQ5vLstNZrbUzJaWl5e3krp9WFtRxUsflnH1mYPo1inddxyJM/26ZXLNmYN47oNNfLptr+84IiIiSSeaLeBnAdPNbB1B95DJBC3i2WbW0OehP1AWPt8EDAAIx3cDdjYe3mSeloZ/gXPuIefcqc65U3Nyco59yxLcAwuKSU9N4Yaz831HkTj17YkFZHVI4+45Rb6jiIiIJJ2oFeDOuR855/o75wYTXEQ53zl3FVAI/EM42TXA8+Hz2eFrwvHznXMuHH5leJeUIcAw4F3gPWBYeFeVDuE6Zkdre5LF5soDPPfBZq48bQA5XTJ8x5E41T2rAzedm89rn2zjgw27fMcRERFJKj7uA/5D4AdmVkzQx/uRcPgjQM9w+A+AfwZwzn0MPAN8ArwKzHDO1YX9xG8D5hDcZeWZcFppxUMLg7tb3DRhqOckEu++dfYQenXuwF2vrib4W1hERETaQkxuf+GcWwAsCJ+XEtzBpOk0B4HLW5j/58DPmxn+MvByG0ZNauV7q3n6vY185eQ88rIzfceROJeVkcZ3Jg/jJ7M/ZtGaCs4dru5bIiIibUG/hNmO/H5xKTV19dw6scB3FEkQXzt9IP27Z3LXnNXU16sVXEREpC2oAG8nKvcf4om31nPxibkM6ZXlO44kiA5pKfzg/OF8tHkPL3+0xXccERGRpKACvJ14fMk6qg7VMWOS+n7Lkbl0bB4j+nThV699Sk1d/eFnEBERkVapAG8H9lXX8tib65gyqg8j+3b1HUcSTGqKccfUEaytqOLZpZt8xxEREUl4KsDbgT+9s57dB2rU+i1H7bxRvTllUHfuff1TDtbU+Y4jIiKS0FSAJ7mDNXU8vGgtZxX05KSB3X3HkQRlZtw5dQTb9lTzhyXrfMcRERFJaCrAk9yzSzdSvreaGZN05xM5Nmfk92TiiBzuX1DC7gM1vuOIiIgkLBXgSaymrp4HF5Zy8sBszszv6TuOJIE7po5g94EaHnqjxHcUERGRhKUCPIn9bdlmNlce4LbJBZiZ7ziSBI7P7cb0Mbk8ungd2/ce9B1HREQkIakAT1J19Y4HFpRwXL+uTBrR23ccSSI/OH84NXX1zJxf7DuKiIhIQlIBnqRe+WgLpRVVzJik1m9pW4N7ZXHl6QP40zsb2LBjv+84IiIiCUcFeBJyzjGrsIT8nCymje7rO44koe9OHkZaqnHP3CLfUURERBKOCvAkVFi0nVVb9vDtiQWkpqj1W9pe764due6sITy/ooxPyvb4jiMiIpJQVIAnGeccM+cX0797JpeOzfUdR5LYLecOpUtGGne/plZwERGRI6ECPMm8VbqDDzZUcvOEoaSn6vBK9HTrlM6tEwuYv3o7763b6TuOiIhIwlCFlmRmFRaT0yWDy0/p7zuKtAPXjh9M7y4Z/OKV1TjnfMcRERFJCCrAk8iyDbt4s3gHN54zhI7pqb7jSDuQ2SGV7543jKXrd1FYtN13HBERkYSgAjyJzCosJrtTOledMch3FGlHvnraAAb17MRdrxZRX69WcBERkcNRAZ4kVm3Zw7xV27lu/BCyMtJ8x5F2JD01hdsvGMHqrXuZvaLMdxwREZG4pwI8ScwqLKZzRhrXjh/sO4q0Q186oR/H9evKr+YWcai23nccERGRuKYCPAmUlu/jpZVbuHrcILp1SvcdR9qhlBTjzmkj2LjzAH9+b4PvOCIiInFNBXgSeGBBCR1SU7j+7CG+o0g7NmF4DmcM6cG9rxez/1Ct7zgiIiJxSwV4gttceYC/LtvM104fSE6XDN9xpB0zM+6cNpKKfdU89uY633FERETilgrwBPfQwhIAbjo333MSEThlUHemjOrDgwtK2FV1yHccERGRuKQCPIGV763m6fc28pWT88jNzvQdRwSAO6aOYN+hWh4M/zgUERGRz1MBnsB+v7iUmrp6bp1Y4DuKyGdG9O3Cl0/K4/El69i6+6DvOCIiInFHBXiCqtx/iCfeWs/FJ+YypFeW7zgin/OPU4ZT7xz3vr7GdxQREZG4owI8QT2+ZB1Vh+qYMWmo7ygiXzCgRyeuOmMQzyzdSGn5Pt9xRERE4ooK8AS0r7qWx95cx5RRfRjZt6vvOCLNmjGpgIy0FH4191PfUUREROKKCvAE9OTb69l9oIbbJqvvt8SvnC4Z3HD2EF76cAsrN+32HUdERCRuqABPMAdr6nh40VrOLujF2AHZvuOItOqGc/Pp3imdu+as9h1FREQkbqgATzDPLt1Ixb5qZkxS67fEv64d05kxqYBFaypYUlLhO46IiEhcUAGeQGrq6nlwYSmnDOrOuPwevuOIROTqcYPo160jd71ahHPOdxwRERHvVIAnkL8t28zmygPcNqkAM/MdRyQiHdNT+f6UYSzfWMlrn2zzHUdERMQ7FeAJoq7e8cCCEo7r15WJI3J8xxE5Iv/fyf3Jz8nil3OKqKtXK7iIiLRvKsATxCsfbaG0oooZav2WBJSWmsIdF4ygePs+nvtgk+84IiIiXqkATwDOOWYVlpCfk8W00X19xxE5KtNG9+XE/t34zbw1VNfW+Y4jIiLijQrwBDB/9XZWbdnDtycWkJqi1m9JTGbGD6eNZHPlAZ58e4PvOCIiIt6oAI9zzjlmFhbTv3sml47N9R1H5JicVdCLswp6MrOwmH3Vtb7jiIiIeKECPM69VbKDZRsquXnCUNJTdbgk8d05dSQ7qw7x+0WlvqOIiIh4oYouzs1aUEzvLhlcfkp/31FE2sSYAdlcOLovD79Ryo591b7jiIiIxJwK8Di2bMMu3izewY3n5NMxPdV3HJE2c/sFIzhQU8eswhLfUURERGJOBXgcm1VYTHandL5+xkDfUUTaVEHvzlx+ygCeeHs9m3bt9x1HREQkplSAx6lVW/Ywb9V2rhs/hKyMNN9xRNrc96YMA4N7563xHUVERCSmVIDHqVmFxXTOSOPa8YN9RxGJitzsTL45bhD/98Em1mzb6zuOiIhIzKgAj0Ol5ft4aeUWrh43iG6d0n3HEYmab08qoFOHNO5+rch3FBERkZhRAR6HHlhQQofUFK4/e4jvKCJR1SOrAzedm8+cj7exbMMu33FERERiQgV4nNm0az9/XbaZr50+kJwuGb7jiETd9WcPoWdWB+56tQjnnO84IiIiUacCPM489EYpZnDTufm+o4jERFZGGt+ZXMBbpTtYXFzhO46IiEjUqQCPI9v3HuTp9zbylZP6k5ud6TuOSMx87YyB5GVncterRdTXqxVcRESSmwrwOPLI4rXU1tVz68ShvqOIxFRGWio/OH84Kzfv5pWPtvqOIyIiElUqwONE5f5DPPHWer50Yi6De2X5jiMSc5edlMfwPp357XzdF1xERJKbCvA48fiSdVQdquPbk9T6Le1Taopx1RmDWL11L0VbdV9wERFJXirA48C+6loee3MdU0b1YWTfrr7jiHhz0Qn9SDGYvWKz7ygiIiJRowI8Djz59np2H6jhtskFvqOIeJXTJYOzCnrxwootuiWhiIgkLRXgnh2sqePhRWs5u6AXYwdk+44j4t30Mbls2Lmf5RsrfUcRERGJChXgnj2zdCMV+6qZMUmt3yIAU0f3pUNaCrNXlPmOIiIiEhUqwD2qqavndwtLOWVQd8bl9/AdRyQudO2YzqQRObz44RbqdE9wERFJQirAPfrrss1srjzAbZMKMDPfcUTixvQxeZTvrebt0h2+o4iIiLQ5FeCe1NU7HlxQwvG5XZk4Isd3HJG4ct6o3mR1SGX2cnVDERGR5KMC3JNXPtpCaUUVM9T6LfIFHdNTmXp8X17+aAvVtXW+44iIiLQpFeAeOOeYVVjC0Jwsph3f13cckbh0ydhc9h6sZWFRue8oIiIibUoFuAfzV29n1ZY93DqxgJQUtX6LNOfsgl70yOqgu6GIiEjSUQEeY845ZhYW0797JpeOzfUdRyRupaemcNEJfZm3ahtV1bW+44iIiLQZFeAx9lbJDpZtqOTmCUNJT9XuF2nN9DF5HKypZ+4n23xHERERaTOHrQDN7HIz6xI+/7GZPWdmJ0c/WnKaWVhM7y4ZXH5Kf99RROLeqYO6k9uto7qhiIhIUomkCfZfnXN7zexsYCrwB+CB6MZKTh9s2MWSkh3ceE4+HdNTfccRiXspKcYlY3J549NydlUd8h1HRESkTURSgDfcA+xi4AHn3PNAh+hFSl6z5heT3Smdr58x0HcUkYRxyZhcausdL3+0xXcUERGRNhFJAb7ZzH4HXAG8bGYZEc4njXxStofXV2/nW2cNISsjzXcckYRxfG5X8nOy9KM8IiKSNCIppK8A5gDTnHOVQA/gjqimSkL3Lyimc0Ya15w52HcUkYRiZlw6Jo931+1ky+4DvuOIiIgcs0gK8N85555zzq0BcM5tAb4R3VjJpbR8Hy+t3MI3zhxEt07pvuOIJJzpY3NxDl5coW4oIiKS+CIpwI9v/MLMUoFTohMnOT2woIQOqSlcf/YQ31FEEtKQXlmckNdNd0MREZGk0GIBbmY/MrO9wIlmtsfM9oavtwPPxyxhEvjmmYP52WWj6dU5w3cUkYR16dhcVm7ezdqKKt9RREREjkmLBbhz7r+dc12AXzrnujrnuoSPns65Hx1uwWbW0czeNbMVZvaxmf00HP64ma01s+XhY2w43MzsPjMrNrMPG99r3MyuMbM14eOaRsNPMbOV4Tz3mVlc/q77Cf27ccWpA3zHEEloXzoxFzN0MaaIiCS8SLqg/D8zu9rM/hXAzAaY2ekRzFcNTHbOjQHGAtPMbFw47g7n3NjwsTwcdiEwLHzcRHivcTPrAfwEOAM4HfiJmXUP53kgnLZhvmkR5BKRBNS3W0dOH9yD51dsxjnnO46IiMhRi6QAnwWcCXw9fL0vHNYqF9gXvkwPH639r3kp8L/hfG8D2WbWj+DHf+Y653Y653YBcwmK+X5AV+fcWy743/h/gcsi2B4RSVCXjs2jtLyKj8v2+I4iIiJy1CIpwM9wzs0ADgKERXBEP8RjZqlmtpyg3/hc59w74aifh91Mfh3eVxwgD9jYaPZN4bDWhm9qZnhzOW4ys6VmtrS8vDyS6CIShy4c3Ze0FOMFXYwpIiIJLJICvCa884kDMLMcoD6ShTvn6pxzY4H+wOlmNhr4ETASOI3gnuI/DCdvrv+2O4rhzeV4yDl3qnPu1JycnEiii0gc6p7VgXOH5zB7RRn19eqGIiIiiSmSAvw+4K9AHzP7ObAY+K8jWUn4Az4LCH7MZ0vYzaQaeIygXzcELdiNr1TsD5QdZnj/ZoaLSBKbPiaXLbsPsnT9Lt9RREREjsphC3Dn3JPAnQRF9xbgMufcs4ebz8xyzCw7fJ4JTAFWh323Ce9YchnwUTjLbOCb4d1QxgG7wx/9mQNcYGbdw4svLwDmhOP2mtm4cFnfRLdHFEl65x/Xh47pKcxesdl3FBERkaMSSQs4QC9gv3NuJlBhZpH8okw/oNDMPgTeI+gD/iLwpJmtBFaGy/3PcPqXgVKgGHgY+DaAc24n8LNwGe8B/xEOA7gV+H04TwnwSoTbIyIJKisjjSmj+vDyyq3U1EXUG05ERCSupB1uAjP7CXAqMIKgy0g68ARwVmvzOec+BE5qZvjkFqZ3wIwWxj0KPNrM8KXA6Na3QESSzfQxubz44RYWF1cwaURv33FERESOSCQt4F8GpgNVAM65MqBLNEOJiLRmwogcunZM4wX9KI+IiCSgSArwQ2HrdMNdULKiG0lEpHUZaalcOLofcz7eysGaOt9xREREjkgkBfgzZvY7gh/GuRGYR9BHW0TEm+ljc6k6VMfrq7b7jiIiInJEIrkLyt3AX4D/I+gH/m/Oud9GO5iISGvG5fckp0uG7oYiIiIJJ5KLML8FLHLO3RGDPCIiEUlNMb50Yj+efGcDuw/U0C0z3XckERGRiETSBWUw8DszKzGzZ8zsO2Y2Nsq5REQOa/qYXA7V1jPn462+o4iIiEQski4o/xbeOnA0wa9g3gG8H+1gIiKHM3ZANgN7dOKFFbobioiIJI7DFuBm9mMzewV4DSgA/onP/wS8iIgXZsb0Mbm8WVxB+d5q33FEREQiEkkXlK8APQnufvIcMDv8GXgREe+mj82l3sHLK/WxJCIiiSGSLignA+cB7wLnAyvNbHG0g4mIRGJ4ny6M7NuF55frbigiIpIYIumCMhq4GrgG+CqwCZgf5VwiIhGbPjaXDzZUsnHnft9RREREDiuSLii/IPjp+fuAUc65Sc65f4tuLBGRyF1yYi4AL3yoizFFRCT+RVKAz3XO3eWcW+KcqwEws+9FOZeISMQG9OjEyQOzmb1cBbiIiMS/SArwbzYz7No2ziEickymj8ll9da9fLptr+8oIiIirWqxADezr5nZC8AQM5vd6FEI7IhdRBGRw7v4xFxSDLWCi4hI3Gvtp+iXAFuAXsCvGg3fC3wYzVAiIkcqp0sGZxX0YvaKMm6/YDhm5juSiIhIs1oswJ1z64H1wJmxiyMicvQuGZPLnX/5kOUbKzlpYHffcURERJoVSR9wEZGEMG10XzqkpTBbP00vIiJxTAW4iCSNrh3TmTQihxc/3EJdvfMdR0REpFmtXYT5evjvL2IXR0Tk2Ewfk0f53mreLtW14iIiEp9auwizn5lNAKab2dPA565ocs59ENVkIiJH4bxRvcnqkMrs5WWcVdDLdxwREZEvaK0A/zfgn4H+wD1NxjlgcrRCiYgcrY7pqUw9vi+vfLSF/7jseDLSUn1HEhER+ZwWu6A45/7inLsQuCv8+fnGDxXfIhK3Lhmby56DtSwsKvcdRURE5AsOexGmc+5nZjbdzO4OH1+KRTARkaN1dkEvundK191QREQkLh22ADez/wa+B3wSPr4XDhMRiUvpqSlcfGI/5q3aRlV1re84IiIinxPJbQgvBs53zj3qnHsUmBYOExGJW9PH5HGwpp55q7b5jiIiIvI5kd4HPLvR827RCCIi0pZOHdSdft068vxydUMREZH40tpdUBr8N7DMzAoJbkV4LvCjqKYSETlGKSnG9DG5PLJ4LbuqDtE9q4PvSCIiIkBkF2E+BYwDngsfZzrnno52MBGRY3XJmFxq6x2vfLTVdxQREZHPRNQFxTm3xTk32zn3vHNO/5OJSEI4Prcr+TlZPL98s+8oIiIin4m0D7iISMIxC7qhvLtuJ1t3H/QdR0REBFABLiJJbvqYXJyDFz/UxZgiIhIfWi3AzSzFzD6KVRgRkbaWn9OZE/K66Ud5REQkbrRagDvn6oEVZjYwRnlERNrc9DG5fLhpN2srqnxHERERiagLSj/gYzN73cxmNzyiHUxEpK18aUw/zGC27gkuIiJxIJL7gP806ilERKKoX7dMTh/cg+dXbOa75xVgZr4jiYhIOxbJfcAXAuuA9PD5e8AHUc4lItKmpo/NpbS8io/L9viOIiIi7dxhC3AzuxH4C/C7cFAe8LdohhIRaWsXje5HWorxgi7GFBERzyLpAz4DOAvYA+CcWwP0jmYoEZG21j2rA+cOz+GFFWXU1zvfcUREpB2LpACvds4danhhZmmA/vcSkYQzfUwuZbsPsnT9Lt9RRESkHYukAF9oZv8CZJrZ+cCzwAvRjSUi0vbOP64PHdNTmL1CP00vIiL+RFKA/zNQDqwEbgZeBn4czVAiItGQlZHGlFF9eHnlVmrq6n3HERGRduqwtyF0ztWb2R+Adwi6nhQ559QFRUQS0vQxubz44RbeLK5g4ghdziIiIrEXyV1QLgZKgPuAmUCxmV0Y7WAiItEwYUQOXTum6Ud5RETEm0h+iOdXwCTnXDGAmQ0FXgJeiWYwEZFoyEhL5cLR/XjxwzIO1tTRMT3VdyQREWlnIukDvr2h+A6VAtujlEdEJOqmj82l6lAd81fro0xERGKvxRZwM/tK+PRjM3sZeIagD/jlBL+GKSKSkMbl9ySnSwbPL9/MRSf08x1HRETamda6oFzS6Pk2YEL4vBzoHrVEIiJRlppiXHxCP/707gb2HKyha8d035FERKQdabEAd85dF8sgIiKxdOnYXB5fso45H23l8lMH+I4jIiLtyGEvwjSzIcB3gMGNp3fOTY9eLBGR6Bo7IJuBPToxe0WZCnAREYmpSO6C8jfgEYJfv9QvV4hIUjAzLhnTjwcWlFC+t5qcLhm+I4mISDsRyV1QDjrn7nPOFTrnFjY8op5MRCTKLh2bR72Dl1du8R1FRETakUgK8HvN7CdmdqaZndzwiHoyEZEoG96nCyP7dmH2Cv0oj4iIxE4kXVBOAL4BTObvXVBc+FpEJKFdMiaXX84pYuPO/Qzo0cl3HBERaQciaQH/MpDvnJvgnJv0/7d35/FR1ff+x9+f7CEbhASSiSCyKttEReuC4FI3lGCt1fb22s1q26u2Lm2197a1t612u9Zfa5dbb+ut7a2tWtsSLdalWkGrKCoJOwQQhARIQghZyDrf3x9ziKMiBsjMmeX1fDzmkZkzZybvcQx5zzff8z3ehfINIClUBQOSpEdqGQUHAMTGYAp4jaTh0Q4CAH4YUzxMJ4wdrurlFHAAQGwMpoCPlrTWzB43s+r9l2gHA4BYqQoGtHZHm9bvbPM7CgAgBQxmDvhtUU8BAD66aGZA33x0taqX1+uL50/xOw4AIMm9ZwFnyUEAya60IFunTyxRdU29bj5vsszM70gAgCT2nlNQzKzN/HoyJgAAIABJREFUzPZ6ly4z6zezvbEIBwCxMj8Y0NbdnarZ1up3FABAknvPAu6cK3DOFXqXHEkflPST6EcDgNg5f1qZstLTtHD5dr+jAACS3GAOwnwL59xfxBrgAJJMUW6mzjq2VI/WNqg/5PyOAwBIYu85B9zMLo24mSZplsIn4gGApFIVrNDjq3Zq6aZmnTaxxO84AIAkNZhVUOZHXO+T9LqkBVFJAwA+Oue4UcrLStfC5fUUcABA1AxmFZRPxiIIAPgtJzNd508r02MrG/TNS6YpOyPd70gAgCT0rgXczL5+kMc559y3opAHAHw1vzKgP722XYvXN+ncqaP9jgMASEIHOwiz4wAXSbpK0i1RzgUAvpg9sUQjhmWquoZT0wMAouNdR8Cdc3fuv25mBZK+IOmTkv4g6c53exwAJLLM9DTNm1Guh1/dpo7uPuVlD+ZQGQAABu+gyxCaWbGZfVtSrcJl/QTn3C3OuV0xSQcAPlhQWaGu3pCeWrPT7ygAgCT0rgXczH4g6WVJbZJmOOe+4ZxriVkyAPDJrKNHqLwoR9XLmYYCABh6BxsBv1lSQNJXJdVHnI6+jVPRA0hmaWmm+cGAnl3fqJaOHr/jAACSzLsWcOdcmnMu922noi/cfzuWIQEg1qqCAfWFnB5bucPvKACAJHPIp6IfLDPLMbOXzKzGzFaZ2X96248xs6VmtsHMHjCzLG97tne7zrt/XMRzfcXbvs7Mzo/YfoG3rc7Mbo3WawGQeqYFCjW+NE/VNdv9jgIASDJRK+CSuiWd7ZwLSqqUdIGZnSLpe5Lucs5NktSi8LKG8r62OOcmSrrL209mNlXShyVNk3SBpJ+ZWbqZpUv6qaQLJU2V9BFvXwA4YmamqmBASzfv1o7WLr/jAACSSNQKuAtr925mehcn6WxJf/S23yfpEu/6Au+2vPvPMTPztv/BOdftnNssqU7Syd6lzjm3yTnXo/DyiAui9XoApJ6qYEDOSY/WcjAmAGDoRHMEXN5I9XJJuyQ9KWmjpD3OuT5vl22SKrzrFZLekCTv/lZJIyO3v+0x77b9QDmuMbNlZrassbFxKF4agBQwvjRfMyqKOCkPAGBIRbWAO+f6nXOVko5SeMT6uAPt5n21d7nvULcfKMc9zrlZzrlZpaWl7x0cADxVwYBqt7Vqc1PHe+8MAMAgRLWA7+ec2yPpH5JOkTTczPafWu4oSfuHlrZJGiNJ3v1FknZHbn/bY95tOwAMmYuD5TITa4IDAIZMNFdBKTWz4d71XEnvl7RG0jOSLvN2+7ikhd71au+2vPufds45b/uHvVVSjpE0SdJLCp8kaJK3qkqWwgdqVkfr9QBITeVFuTp5XLGqa7Yr/E8SAABHJpoj4OWSnjGzWoXL8pPOuUcl3SLpJjOrU3iO96+8/X8laaS3/SZJt0qSc26VpAclrZb0N0nXelNb+iRdJ+lxhYv9g96+ADCkqioD2tjYodUNnIMMAHDkLNVGdGbNmuWWLVvmdwwACaSlo0cn3f6Urpp9jL4y70CHsgAA8FZm9opzbtaB7ovJHHAASGQj8rI0Z3KpHqmpVyiUWoMWAIChRwEHgEGoCgZU39qlV7a2+B0FAJDgKOAAMAjnTh2tnMw0VkMBABwxCjgADEJedobOOW60/rqiQb39Ib/jAAASGAUcAAZpQTCg3R09er6uye8oAIAERgEHgEGaO6VUhTkZnJoeAHBEKOAAMEjZGem6YHqZHl+5Q129/X7HAQAkKAo4AByCBZUV6ujp19Nrd/kdBQCQoCjgAHAIThk/UqUF2ayGAgA4bBRwADgE6Wmmi2aU6+l1u7S3q9fvOACABEQBB4BDVFUZUE9fSI+v3OF3FABAAqKAA8AhOn7McI0pzmU1FADAYaGAA8AhMjNVBQP658ZmNbZ1+x0HAJBgKOAAcBiqghXqDzktWtHgdxQAQIKhgAPAYZhSVqBjywqYhgIAOGQUcAA4TPODAb2ypUVv7O70OwoAIIFQwAHgMFUFA5KkR2oZBQcADB4FHAAO05jiYTph7HBOygMAOCQUcAA4AlXBgNbuaNOGnW1+RwEAJAgKOAAcgYtmBpRm4mBMAMCgUcAB4AiUFmTrtAklWri8Xs45v+MAABIABRwAjlBVZUBbd3eqZlur31EAAAmAAg4AR+j8aWXKSk/jYEwAwKBQwAHgCBXlZurMKaV6pLZe/SGmoQAADo4CDgBDYEFlhRrburV0U7PfUQAAcY4CDgBD4JzjRikvK53VUAAA74kCDgBDICczXedNK9OiFQ3q7uv3Ow4AII5RwAFgiFRVBrS3q0+L1zf5HQUAEMco4AAwRGZPLNGIYZlMQwEAHBQFHACGSGZ6mubNKNdTq3eqo7vP7zgAgDhFAQeAIVQVDGhfb7+eWrPT7ygAgDhFAQeAIXTSuGKVF+VwUh4AwLuigAPAEEpLM80PBvTs+ka1dPT4HQcAEIco4AAwxKqCAfWFnB5bucPvKACAOEQBB4AhNi1QqPGleaqu2e53FABAHKKAA8AQMzNVBQNaunm3drR2+R0HABBnKOAAEAVVwYCckx6t5WBMAMBbUcABIArGl+ZrekUhJ+UBALwDBRwAomRBsEK121q1uanD7ygAgDhCAQeAKLk4WC4z6RFGwQEAESjgABAl5UW5OmlcsRYu3y7nnN9xAABxggIOAFG0oDKgjY0dWt2w1+8oAIA4QQEHgCiaN71cGWnGwZgAgAEUcACIohF5WTpjUokeWV6vUIhpKAAACjgARN2CygrVt3bpla0tfkcBAMQBCjgARNm5U0crJzNN1cuZhgIAoIADQNTlZWfonONGa9GKBvX2h/yOAwDwGQUcAGKgKhhQc0ePnq9r8jsKAMBnFHAAiIEzp5SqICeD1VAAABRwAIiF7Ix0XTi9TE+s2qmu3n6/4wAAfEQBB4AYqQpWqL27T0+v3eV3FACAjyjgABAjp04YqZL8bFZDAYAURwEHgBhJTzNdPLNcT6/bpb1dvX7HAQD4hAIOADFUVRlQT19IT6za6XcUAIBPKOAAEEPHjxmuMcW5Wrh8u99RAAA+oYADQAyZmaqCAf1zY7Oa2rv9jgMA8AEFHABirCpYof6Q06IVDX5HAQD4gAIOADE2paxAU0YXaCGroQBASqKAA4APqioDemVLi7a1dPodBQAQYxRwAPBBVTAgSXqkhmkoAJBqKOAA4IMxxcN0/NjhrIYCACmIAg4APlkQDGjtjjZt2NnmdxQAQAxRwAHAJxfNDCjNpOoaDsYEgFRCAQcAn5QWZOu0CSWqrqmXc87vOACAGKGAA4CPqoIBbWnuVM22Vr+jAABihAIOAD46f3qZstLTVM2a4ACQMijgAOCjotxMnTmlVI/W1qs/xDQUAEgFFHAA8FlVZUC72rq1dFOz31EAADFAAQcAn51z7GjlZaWzGgoApAgKOAD4LDcrXedNK9NjK3eopy/kdxwAQJRRwAEgDlQFA2rd16vF6xv9jgIAiDIKOADEgdmTSjRiWKYWMg0FAJJe1Aq4mY0xs2fMbI2ZrTKzL3jbv2Fm281suXeZF/GYr5hZnZmtM7PzI7Zf4G2rM7NbI7YfY2ZLzWyDmT1gZlnRej0AEE2Z6WmaN6NcT63eqc6ePr/jAACiKJoj4H2SbnbOHSfpFEnXmtlU7767nHOV3mWRJHn3fVjSNEkXSPqZmaWbWbqkn0q6UNJUSR+JeJ7vec81SVKLpKui+HoAIKqqggHt6+3Xk6t3+h0FABBFUSvgzrkG59yr3vU2SWskVRzkIQsk/cE51+2c2yypTtLJ3qXOObfJOdcj6Q+SFpiZSTpb0h+9x98n6ZLovBoAiL6TxhWrvCiHk/IAQJKLyRxwMxsn6XhJS71N15lZrZnda2YjvG0Vkt6IeNg2b9u7bR8paY9zru9t2wEgIaWlmeYHA1q8oVF7Onv8jgMAiJKoF3Azy5f0sKQbnHN7Jf1c0gRJlZIaJN25f9cDPNwdxvYDZbjGzJaZ2bLGRlYYABC/qoIB9fY7PbZyh99RAABREtUCbmaZCpfv3znn/iRJzrmdzrl+51xI0v8oPMVECo9gj4l4+FGS6g+yvUnScDPLeNv2d3DO3eOcm+Wcm1VaWjo0Lw4AomBaoFDjS/K0cPl2v6MAAKIkmqugmKRfSVrjnPthxPbyiN0+IGmld71a0ofNLNvMjpE0SdJLkl6WNMlb8SRL4QM1q51zTtIzki7zHv9xSQuj9XoAIBbMTFWVAS3dvFs7Wrv8jgMAiIJojoCfLulKSWe/bcnB75vZCjOrlXSWpBslyTm3StKDklZL+puka72R8j5J10l6XOEDOR/09pWkWyTdZGZ1Cs8J/1UUXw8AxERVMCDnpEdrORgTAJKRhQeSU8esWbPcsmXL/I4BAAd18d1LlG6mhdfN9jsKAOAwmNkrzrlZB7qPM2ECQBxaEKxQzbZWbW7q8DsKAGCIUcABIA5dHCyXmfQIp6YHgKRDAQeAOFRelKuTxhVr4fLtSrWpggCQ7CjgABCnqoIBbWzs0OqGvX5HAQAMIQo4AMSpeTPKlZFmqmYaCgAkFQo4AMSp4rwsnTGpRI/WNCgUYhoKACQLCjgAxLGqyoC279mnV7e2+B0FADBEKOAAEMfOnVqmnMw0LVzONBQASBYUcACIY/nZGTrnuNFatKJBff0hv+MAAIYABRwA4lxVMKDmjh49v7HZ7ygAgCFAAQeAOHfmlFIV5GRo4fLtfkcBAAwBCjgAxLnsjHRdOL1MT6zaqa7efr/jAACOEAUcABJAVbBC7d19embtLr+jAACOEAUcABLAqRNGqiQ/m9VQACAJUMABIAGkp5kunlmup9ft0t6uXr/jAACOAAUcABJEVWVAPX0hPbFqp99RAABHgAIOAAni+DHDNaY4V9U1TEMBgERGAQeABGFmqgoG9Hxdk5rau/2OAwA4TBRwAEggVcEK9YecFq1o8DsKAOAwUcABIIFMKSvQlNEFqmY1FABIWBRwAEgwVZUBLdvSom0tnX5HAQAcBgo4ACSYqmBAkvRIDdNQACARUcABIMGMKR6m48cOZzUUAEhQFHAASEBVwYDWNOzVhp1tfkcBABwiCjgAJKCLZpYrzcQoOAAkIAo4ACSgUQU5Om1Ciapr6uWc8zsOAOAQUMABIEFVBQPa0typ2m2tfkcBABwCCjgAJKjzp5cpKz1NC1kTHAASCgUcABJUUW6mzpxSqkdr69UfYhoKACQKCjgAJLCqyoB2tXVr6eZmv6MAAAaJAg4ACeycY0crLyudU9MDQAKhgANAAsvNStd508r02Mod6ukL+R0HADAIFHAASHBVwYBa9/Vq8fpGv6MAAAaBAg4ACW72pBKNGJbJSXkAIEFQwAEgwWWmp2nejHI9uXqnOnv6/I4DAHgPFHAASAJVwYD29fbrydU7/Y4CAHgPFHAASAInjStWeVGOHmEaCgDEPQo4ACSBtDTTxTPL9ez6Ru3p7PE7DgDgICjgAJAkFlRWqLff6bGVO/yOAgA4CAo4ACSJaYFCjS/J46Q8ABDnKOAAkCTMTPODAb24uVk793b5HQcAfNXTF9KdT6zTYysa/I7yDhRwAEgiVZUBOScOxgSQ0tbtaNMlP31edz9dp2VbWvyO8w4UcABIIhNK8zW9opACDiAl9Yec/vvZjZp/93Pa1dale648UV+7eKrfsd4hw+8AAIChVRUM6I5Fa/V6U4fGleT5HQcAYuL1pg598aEaLdvSogumlen2D0zXyPxsv2MdECPgAJBk5gcDMhOnpgeQEpxz+u2LW3Thj5Zo3c423XVFUD//1xPitnxLjIADQNIpL8rVSeOKVV1Tr+vPnigz8zsSAERFQ+s+ffmPtVqyoUlnTCrR9y+bqfKiXL9jvSdGwAEgCVUFA6rb1a41DW1+RwGAIeec059f26bz7lqsZa+36FuXTNdvPnVyQpRviQIOAElp3oxyZaSZFtZs9zsKAAyp5vZufe7/XtWND9Ro8ugCPfaFM3TlKUcn1F/7KOAAkISK87J0xqQSPVrToFDI+R0HAIbEE6t26Pz/t1hPr92lWy88Vg9+5tSEPNicAg4ASaqqMqDte/bp1a3xtwYuAByKvV29uvnBGl3z21c0qiBH1defrs/OnaD0tMQZ9Y7EQZgAkKTOnVqm7IwVqq6p16xxxX7HAYDD8nxdk770UI12tnXr+rMn6vqzJykrI7HHkBM7PQDgXeVnZ+j9U0frr7UN6usP+R0HAA7Jvp5+3bZwpT76y6XKyUrXw587TTefNyXhy7dEAQeApFYVDKi5o0fPb2z2OwoADNqrW1s078dLdN8LW/TJ08fpr9efocoxw/2ONWSYggIASezMKaUqyMlQ9fJ6zZ1c6nccADio7r5+/eipDfrvZzeqvChX91/9Pp02ocTvWEOOAg4ASSw7I10XTCvTYyt36Pbe6crJTPc7EgAc0JqGvbrxgeVau6NNl886Sl+7eKoKcjL9jhUVTEEBgCS3oLJC7d19embtLr+jAMA79PWH9NNn6lT1k+fU1N6jX35slr5/WTBpy7fECDgAJL1TJ4xUSX62qmvqdeGMcr/jAMCAzU0duunB5Xpt6x5dNKNc37pkuorzsvyOFXUUcABIculppotnluv+l7aqras3qUeVACSGUMjpty9u0XceW6PsjHT96MOVqgoGEupslkeCKSgAkAKqKgPq6Qvp8VU7/Y4CIMXV79mnK+9dqtuqV+l9x4zUEzfO0YLKipQp3xIj4ACQEo4fM1xjinNVXVOvy048yu84AFKQc04Pv7pd/1m9Sv3O6Y4PzNBHTh6TUsV7Pwo4AKQAM9P8mQH9YvEmNbV3qyQ/2+9IAFJIU3u3vvKnFXpy9U6dPK5Y//WhoMaOHOZ3LN8wBQUAUsSCygr1h5wWrWjwOwqAFPK3lQ06767FenZ9o/5j3nH6/TWnpHT5lijgAJAyppQVaMroAlUvr/c7CoAU0LqvVzc+sFyf/b9XFRieo0evn62r54xXelrqTTl5O6agAEAKqaoM6AePr9O2lk4dNSK1R6AARM/i9Y368h9r1djerS+cM0nXnT1RmemM++7HfwkASCHzZwYkSY/UMA0FwNDr7OnTV/+yQh+79yXl52Toz/92mm48dzLl+20YAQeAFDJ25DAdP3a4qmvq9bkzJ/gdB0ASWfb6bt38UI227u7Up2cfoy+eP0U5mel+x4pLfBwBgBRTFQxoTcNe1e1q8zsKgCTQ3dev7zy2Rpf/4gX1h5x+f/Up+urFUynfB0EBB4AUc9HMcqWZOBgTwBFbVd+qqruf1y+e3aTLZ43R326Yo1PGj/Q7VtyjgANAihlVkKPTJpRoYU29nHN+xwGQgPr6Q7r77xu04CfPa3dnj+79xCx994MzlZ/N7ObBoIADQAqqCga0pblTtdta/Y4CIMFsbGzXB//7Bd355HpdML1MT9wwR2cfO9rvWAmFAg4AKej86WXKSk9TdQ3TUAAMTijkdO9zmzXvR0u0pblDd3/keP3kX07QiLwsv6MlHAo4AKSgotxMnTmlVI/U1Ks/xDQUAAe3raVTH/3lUn3z0dU6bcJIPXHDHM0PBvyOlbCYqAMAKaqqMqAnVu/U0s3NOm1Cid9xAMQh55weWrZN33x0tZxz+u6lM3TFSWNkxtksj0TURsDNbIyZPWNma8xslZl9wdtebGZPmtkG7+sIb7uZ2Y/NrM7Mas3shIjn+ri3/wYz+3jE9hPNbIX3mB8b/zcAwKCdc+xo5WWl6xGmoQA4gF1tXfr0fcv05YdrNS1QqL/dMEcfPnks5XsIRHMKSp+km51zx0k6RdK1ZjZV0q2S/u6cmyTp795tSbpQ0iTvco2kn0vhwi7pNknvk3SypNv2l3Zvn2siHndBFF8PACSV3Kx0nTt1tBat2KGevpDfcQDEkb/WNuj8uxZrSV2TvnrRcfr91adoTPEwv2MljagVcOdcg3PuVe96m6Q1kiokLZB0n7fbfZIu8a4vkPQbF/aipOFmVi7pfElPOud2O+daJD0p6QLvvkLn3AsuvI7WbyKeCwAwCAsqK9S6r1eL1zf6HQVAHNjT2aPP//41XXv/qxpTPEyLPj9bnz5jvNLSGPUeSjGZA25m4yQdL2mppNHOuQYpXNLNbJS3W4WkNyIets3bdrDt2w6w/UDf/xqFR8o1duzYI3sxAJBEZk8q0Yhhmaquqdf7p7KMGJDK/rFul255uFbN7T268f2T9W9nTVBmOut1REPUC7iZ5Ut6WNINzrm9B5k3dKA73GFsf+dG5+6RdI8kzZo1i8P9AcCTmZ6mC2eU68+vbldnT5+GZXFsPpBqOrr7dPuiNbp/6VZNGpWvX37sJM04qsjvWEktqh9rzCxT4fL9O+fcn7zNO73pI/K+7vK2b5M0JuLhR0mqf4/tRx1gOwDgECwIBrSvt19Prt7pdxQAMfbS5t264EeL9fuXtuqaOeP1yPWzKd8xEM1VUEzSryStcc79MOKuakn7VzL5uKSFEds/5q2GcoqkVm+qyuOSzjOzEd7Bl+dJety7r83MTvG+18cingsAMEgnjStWeVEOq6EAKaSrt193LFqjK+55QSbTA9ecqn+fd5xyMtP9jpYSovm3xtMlXSlphZkt97b9u6TvSnrQzK6StFXSh7z7FkmaJ6lOUqekT0qSc263mX1L0sveft90zu32rn9O0q8l5Up6zLsAAA5BWprp4pnl+vU/X9eezh4NH8ZZ7YBktmJbq256cLk27GrXv7xvrP5j3nHKy2b6WSxZeAGR1DFr1iy3bNkyv2MAQFxZsa1V83/ynL5z6Qx95GQOVo9HbV29qtvVrrpd7drY2KHC3AzNmVSqqeWFrFCBQentD+lnz2zU3U9v0Mj8LH3vgzN15pRR7/1AHBYze8U5N+tA9/FxBwCg6RWFGl+Sp+rl9RRwHznn1NjeHS7ZXtmuawx/3bm3e2C/zHRTb7/T9/+2TiX5WZozqVRzJpfqjEklGpmf7eMrQLyq29Wmmx6sUe22Vi2oDOibVdNVNCzT71gpiwIOAJCZaX4woB8/vUE793ZpdGGO35GSWijktK1ln+oa2wZGtfdf9nb1DeyXl5WuiaPydfrEEk0cla+JpfmaOCpfY4uHaXdnj5asb9LiDY36x/pG/em17TKTpgeKNHdyuJAfP3Y4y8iluFDI6d7nN+v7j69TXla6fvbREzRvRrnfsVIeU1AAAJKkjY3tOufOZ/W1i6fqqtnH+B0nKXT39ev1ps43C7Y3mr2psV3dEWcfLcnP0gSvXEdeygpzBnXa71DIaWV9q55d16jFGxr16tY96g85FWRn6PSJJZozuVRzJpfoqBGcyTCVvLG7Uzc/VKOXNu/W+48bpTsunaFRBXy4jpWDTUGhgAMABlx89xKlm2nhdbP9jpJQ2rv73jGSvbGxXVt3d6o/9Obv2aNG5L5lJHv/ZagPfG3d16sXNjbp2fWNenZdo+pbuyRJE0rzNHfyKM2ZXKJTxo9kxYsk5ZzTH15+Q99+dLXMTF+fP1UfOvGoQX2Yw9BhDjgAYFCqggHdsWitXm/q0LiSPL/jxBXnnJo7et5Rsut2tavBK7iSlJFmGleSp2PLCnTxzHJNHJWvCaXhS25WbApvUW6mLpherguml8s5p42N7frHukYt3tCk3y3donuf36zsjDSdfEyx5k4u1dzJpZo4Kp+ClgR27e3SLQ/X6pl1jTp1/Ej94EMz+ctHHGIEHAAwoH7PPp323ad187mTdf05k/yO44tQyGn7nn2qa4w4ENKbPrKns3dgv2FZ6W+ZNrL/+tEjh8X1vOuu3n4t3bx7YLpK3a52SVKgKEdzp5RqzqRSnTaxREW5HKCXaKpr6vW1v6xUV2+/br3wWH381HGskOMjpqBEoIADwMFd/osXtLujR0/eOCepR0R7+kLa0txxgPnZHdrX2z+wX3FeliaW5mvC2+ZnlxfmJEW52b5nnxZ7U1Wer2tSW3ef0tNMx48ZPnAw54yKoqR4rcmqpaNHX124Un+tbVBwzHD98PKgJpTm+x0r5VHAI1DAAeDg/u/FLfrqX1Zq0efP0NRAod9xjlhHd9/AVJHIsr21uVN9EfOzK4bnhkv22+ZnF+elzomJevtDWv7GnoHR8dptrZLCH0JmTyzR3MmlOmNyCQfyxZFn1u7Slx+uVUtHj254/yR9du4EZcTxX2BSCQU8AgUcAA5ud0ePTr79KX36jPG69cJj/Y4zaM3e+tl1EWV74672gQMQpfD87KNHDnvraiOlBRpfmseZAA+gub1bz9U1DRTypvYeSdLU8sKB6SonHj1CWRkUvlhr7+7Ttx9drT+8/IamjC7QD68IalqgyO9YiEABj0ABB4D39on/fUkbdrZryZfPiqupB8451bd2acPOtrccBFm3q10tEfOzczPTNWFU3jtGs8cW51EWD1Mo5LS6Ya+eXd+oxesb9cqWFvWFnPKy0nXqhBLNnVKquZNKNXYkB/xF24ubmvXFh2pUv2efrpkzQTeeO0nZGaxoE29YBQUAcEgWVAZ04wM1enVri2aNK4759+/tD2lLc+c7SvbGxnZ19rw5P3v4sExNLM3X+dPK3lK0A0W5cfXBIRmkpZmmVxRpekWRrj1rotq6evXCxubwUofrG/XUmp2SpGNK8jRnUriQnzJ+pIZlUTWGSldvv37w+Drd+/xmjS0epoc+e6pOPDr2P584cvxUAADe4dypZcrOWKHqmvqoFvDOnj5taux4x/zs15s63jI/u7woRxNH5evyWWPeUrRH5mUl9YGi8awgJ1PnTSvTedPK5JzT5qaOgdHxB5a9ofte2KKs9DSddMyIgYM5p4wu4P06TLXb9ujGB5ZrY2OHrjzlaH1l3rF8uElgTEEBABzQtb97VS9uatbSfz/niA/qaunoecvc7P2X7Xv2DeyTnmY6unjYm6uNeNNHJozKVz7zsxNKV2+/lr3eomfxOkVFAAAO10lEQVTX79Li9U1at7NNkjS6MFtzJpVq7pRSzZ5YMuQnIEpGvf0h3f10nX76TJ1K87P1/ctmas7kUr9jYRCYAx6BAg4Ag/P4qh36zG9f0X2fOllzB/EL3zmnhtaudyzrt3FXu5o7egb2y8lM0/iSd552/eiRw5jHmqQaWvdpyfrwmTmXbGjU3q4+pZkUjFjqMHjUcKUzbegt1u9s000PLtfK7Xt16fEVuq1qGuuzJxAKeAQKOAAMTndfv2Z9+ymdN7VMd14eHNje1x/Slt2db1lpZP9Jazoi5mcX5WYe8LTrFcOZn53K+vpDqtnWOjBdpWbbHjkX/v9l9qSSgTNzji5M3aUO+0NOv3puk/7rifXKz87QHR+Yrguml/sdC4eIAh6BAg4Ag/elh2r02Mod+tTp47TBK9yvN3eot//N3x1lhTlvng0yonCX5DM/G++tpaMnvNShV8h3tXVLko4tK9Acr4zPGjciZf46srW5Uzc/tFwvv96ic6eO1h0fmKHSgmy/Y+EwUMAjUMABYPCWbmrWFfe8qDSTxhYPe0vJnjS6QBNK81SQw5/EMTScc1q7oy18Zs71jXr59d3q7XfKzUzXqRNGDkxXGTdyWNJ9uHPO6f6Xtur2v65Rupm+UTVNl55QkXSvM5VQwCNQwAHg0Oxo7dLwYZnKyUyNEUjEj47uPr24qXlgdPz15k5J4Q+DcyaXaO7kUTp1wsiEP0h3R2uXbnm4Vs+ub9TpE0fq+5cFVTE81+9YOEIU8AgUcAAAEtOW5o6B0fF/bmxWZ0+/MtNNJx49YmC6ytTywoQZNXbOqbqmXl/7y0r19If07/OO07++72iOkUgSFPAIFHAAABJfT19Iy7bs1mJvdZU1DXslSaUF2TrDO5jzjEmlKs6Lz6UOd3f06Kt/WaFFK3bo+LHDdeeHghpfmu93LAwhCngECjgAAMln194uLd4QLuPPbWhUS2evzKSZFUUDo+OVY4Yf8Zr2Q+Gp1Tt1659WqHVfj248d7I+M2cCSzAmIQp4BAo4AADJrT/ktGJ768B0lde2tijkpIKcDM2eWDJwMGcgxvOs27p69c1HVuuhV7bp2LIC3XVFpY4rL4xpBsQOBTwCBRwAgNTS2tmr5zc26dl1jVq8oVENrV2SpEmj8gdGx08+pjiqBxr/c2OTvvRQrRpa9+lzZ07QF86ZrKwM/0fjET0U8AgUcAAAUpdzTht2tQ+Mji/dvFs9fSFlZ6TplPEjBwr5hNK8ITmYc19Pv773t7X69T9f1zElebrz8qBOGDtiCF4J4h0FPAIFHAAA7Levp18vbm4eKOSbGjskSRXDcwfK+GkTR6rwMNa7f21ri25+sEabmjr0idPG6ZYLjlVuFst5pgoKeAQKOAAAeDdv7O7U4g2NenZdeKnD9u4+paeZThw7YmDt8WmBwoMuFdjTF9KP/75BP/tHncoKc/SDDwV1+sSSGL4KxAMKeAQKOAAAGIze/pBe3dISLuTrG7Vye3ipw5F5WTpjUonmeEsdRp4qfu2OvbrpgRqtbtirD55wlG6rmnpYo+dIfBTwCBRwAABwOBrbuvVcXXh0fMmGJjV39EiSpgUKNXdyqTLT0/Tzf2xUYW6G7vjADJ03rcznxPATBTwCBRwAABypUMhpVf3egekqr2xtUX/I6YJpZbr9A9M1Mj/7vZ8ESe1gBTwj1mEAAAASXVqaacZRRZpxVJGuPWui9nb1qmFPlyaPzh+S1VOQ3CjgAAAAR6gwJ1OFZcz1xuCwAjwAAAAQQxRwAAAAIIYo4AAAAEAMUcABAACAGKKAAwAAADFEAQcAAABiiAIOAAAAxBAFHAAAAIghCjgAAAAQQxRwAAAAIIYo4AAAAEAMUcABAACAGKKAAwAAADFEAQcAAABiiAIOAAAAxBAFHAAAAIghCjgAAAAQQxRwAAAAIIYo4AAAAEAMUcABAACAGKKAAwAAADFEAQcAAABiyJxzfmeIKTNrlLTFh29dIqnJh++Lg+N9iT+8J/GJ9yX+8J7EH96T+OTX+3K0c670QHekXAH3i5ktc87N8jsH3or3Jf7wnsQn3pf4w3sSf3hP4lM8vi9MQQEAAABiiAIOAAAAxBAFPHbu8TsADoj3Jf7wnsQn3pf4w3sSf3hP4lPcvS/MAQcAAABiiBFwAAAAIIYo4AAAAEAMUcABAEgwZmZ+ZwDimZnl+Z3hYCjgUWRmU8zsVDPLNLN0v/PgTbwf8cXMJprZLDPL9jsLwsxsmpnNNbORfmdBmJnNNrMrJck55yjh8cHM5pvZF/zOgTeZ2QJJ3zOzUX5neTcZfgdIVmZ2qaQ7JG33LsvM7NfOub3+JkttZjbZObfeOddvZunOuX6/M6U6M7tY4Z+VZkk7zOw259x6n2OlNDO7UNL3JG2SlGlmVznndvgcK2WZWZqkYZJ+Eb5pec65//ZKeJpzLuRzxJRlZudJ+pakL/mdBWFmNlfhf7+ud87t8jvPu2EEPArMLFPSFZKucs6dI2mhpDGSvmxmhb6GS2Fe0VtuZvdL0v4S7nOslGZmp0n6L0kfd86dJalF0q3+pkptZnampB9J+rRz7hJJPZKm+xoqxTnnQs65dkn3SfqVpNPM7Mb99/kaLoV5/379VtI1zrknzazIzI42s2F+Z0txJ0r6pfeeBMzsXDN7n5kV+R0sEgU8egolTfKu/1nSo5KyJP0LfzaMPW8u2HWSbpDUY2b/J1HC48R3nXOveddvk1TMVBRf7ZT0GefcS2ZWJul9kq4zs1+Y2WX8++WrPoUHc+6TdLKZ/dDMvmNh/D6PvWZJvZLKvalaf5H0c0m/5mfFV30R1/8o6VMK//7/qZmN8CfSO/EDGwXOuV5JP5R0qZmd4Y1QPCdpuaTZvoZLUc65DoV/CO+X9EVJOZEl3M9sKW6ppD9JA/PysyUdrfAHWDH/OPacc2ucc894N6+S9DNvJPxFSR+SVOJbOCyUtMM593dJyyR9VlKhC2MkPMacc+skXSTpLkk1Cv9+uVjS3yR9UFLclL0U87Skq83sD5L+xzn3EYUHd9olnexrsggU8OhZIukJSVea2RznXL9z7n5JAUlBf6OlJudcvXOu3TnXJOkzknL3l3AzO8HMjvU3Yerxfi72HxdhkvZI2u2cazSzj0r6tpnl+pcwtTnnbnfOfdu7/r+SChQegYU/9kmaYmZXK1y+vytprJl9xt9Yqcs5V6Nw6f6Oc+5/vOlC9ypcvsf6my41OedWKjzQ9j5Jx3jbNklKl1TqY7S34CDMKHHOdZnZ7yQ5SV/xyl23pNGSGnwNBznnmr1fWj8ws7UK/2Ce5XOslOac65PUbmZvmNl3JJ0n6RPOuX0+R0tJZmYu4lTJZvZBhf/9qvcvVWpzztWb2RuSvibpWufcI2Z2lqQ6n6OlNOfcakmr99/2flZKxe96Pz2m8Kj3N8xsi7fteIU/tMYFTkUfZWaWJel0hUdcuyT9KGK+K3zmHch0i6RznXMr/M6Tyrz5kpmS1nhfz3HObfA3Fbz5+P8q6SZJV3ijS/CJmY2RNMo594p3m1VQ4oT3b9gnFR59/ZBzbpXPkVKemZ0g6TKFpzf+Op5+z1PAY8Sb38o8vTjiHYzxoKSbnXO1fudBmJl9QtLL/PKKD96qTudK2ujNeUUcePtfKOA/r4DPVXie/lq/8yC+UcCR0swsxznX5XcOvIliAQBIdhRwAAAAIIZYBQUAAACIIQo4AAAAEEMUcAAAACCGKOAAEIfM7Btm9sUoPn+pmS01s9fM7Iy33XeDmQ2LuN0erRyHI97yAMChooADQGo6R9Ja59zxzrklb7vvBknDDvAYAMAQoIADQJwws/8ws3Vm9pSkKRHbrzazl82sxsweNrNhZlZgZpu9dbplZoVm9vr+2xGPPdrM/m5mtd7XsWZWKen7kuaZ2XIzy43Y//OSApKeMbNnIrbf7n3/F81stLet1Mvzsnc5/QCvaZGZzfSuv2ZmX/euf8vMPu1d/5L3+Foz+8+Ix/6rmb3kZfyFdz6FyOcuMbMXzOyiw/6PDgA+oIADQBwwsxMlfVjh0yVfKumkiLv/5Jw7yTkXVPhMoVc559ok/UPS/vL5YUkPO+d63/bUP5H0G+fcTEm/k/Rj59xySV+X9IBzrtI5t2//zs65Hyt8uvmznHNneZvzJL3off/Fkq72tv9I0l3OuZMkfVDSLw/w0hZLOsPMCiX1KXxmYEmaLWmJmZ0naZKkkyVVSjrRzOaY2XGSrpB0unOuUlK/pI9G/PcaLemvkr7unPvrgf+rAkB8yvA7AABAknSGpD875zolycyqI+6bbmbfljRcUr6kx73tv5T0ZUl/UfgU2FfrnU5VuNBL0m8VHvk+VD2SHvWuv6LwmTEl6f2SpoZPAChJKjSzAu/DwX5LJH1e0maFC/O53vzycc65dWZ2taTzJL3m7Z+vcCGfKelESS97z58raZe3T6akv0u61jn37GG8HgDwFQUcAOLHu50Z7deSLnHO1ZjZJySdKUnOuefNbJyZzZWU7pxbeQTf42B6I85O2q83f3ekSTo1cgT9AF6WNEvSJklPSipR+IPCK979Juk7zrlfRD7IzK6XdJ9z7isHeM4+7/HnS6KAA0g4TEEBgPiwWNIHzCzXzAokzY+4r0BSgze/+6Nve9xvJP1e0v++y/P+U+HpKfIe+9wgsrR53/O9PCHpuv03vLnlb+Gc65H0hqTLJb2o8Ij4F72vUng0/1Nmlu89R4WZjVJ4hPsy77rMrNjMjt7/tJI+JelYM7t1EDkBIK5QwAEgDjjnXpX0gKTlkh7WmwVVkr4maanCI8hr3/bQ30kaoXAJP5DPS/qkmdVKulLSFwYR5x5Jj0UehHmQ557lHTy5WtJn32W/JZJ2etNrlkg6yvsq59wTku6X9IKZrZD0R0kFzrnVkr4q6Qkv+5OSyvc/oXOuX+EPFmeZ2b8N4jUBQNywN/+qCABINGZ2maQFzrkr/c4CABgc5oADQIIys7slXShpnt9ZAACDxwg4AAAAEEPMAQcAAABiiAIOAAAAxBAFHAAAAIghCjgAAAAQQxRwAAAAIIYo4AAAAEAM/X9fZV0Z6irH7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorter = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "sorterIndex = dict(zip(sorter,range(len(sorter))))\n",
    "\n",
    "weeks_df['Day_id'] = weeks_df.index\n",
    "weeks_df['Day_id'] = weeks_df['Day_id'].map(sorterIndex)\n",
    "weeks_df.sort_values('Day_id', inplace=True)\n",
    "\n",
    "\n",
    "# now lets see some of the busy days of the week.\n",
    "plt.figure(figsize=(12,10))\n",
    "sns.lineplot(x = weeks_df.Day_id, y = weeks_df.tweets)\n",
    "plt.title('A lineplot plot showing tweets distribution across the weeks')\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel('day of the week')\n",
    "plt.ylabel('Number of tweets')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot is just all over the place.\n",
    "\n",
    "This plot is just all over the place.\n",
    "* Highest number of tweets: ***Tuesday***\n",
    "* Lowest tweet rate: ***Thursday***\n",
    "\n",
    "The trend generally: Lowest tweeting activity on Thursday, then the frequency increases slowly upto Tuesday where the max is recorded then gradual droping to Thurday.\n",
    "\n",
    "The spike in Tuesday is as a result of biasness due to the day I scrapped the tweets which was on Wednesday. Thus by default the highest number of tweets will be Tuesday's, and lowest will be Thursday in the week flow: Tuesday, Monday, Sunday, Saturday, Friday, Thursday"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Monthly Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 594,
   "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>tweets</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>19014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>160007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>533</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       tweets\n",
       "Month        \n",
       "1         919\n",
       "2        1577\n",
       "3        3558\n",
       "4        3413\n",
       "5        6728\n",
       "6       19014\n",
       "7      160007\n",
       "8         346\n",
       "9         911\n",
       "10        540\n",
       "11        756\n",
       "12        533"
      ]
     },
     "execution_count": 594,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "months_df = top_tweets.groupby(['Month']).count()\n",
    "# weeks_df = top_tweets.groupby(['Weekday']).count() #count number of tweets, count is a string function, so let's drop the integers.\n",
    "months_df = months_df[['tweets']]\n",
    "months_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 597,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of tweets')"
      ]
     },
     "execution_count": 597,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now lets see some of the busy Months.\n",
    "plt.figure(figsize=(12,10))\n",
    "sns.barplot(x = months_df.index, y = months_df.tweets)\n",
    "plt.title('A barplot plot showing tweets distribution across the months')\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Number of tweets')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the tweets are around July. This is a biased data issue. The tweets are each users' most recent 500 tweets, so by default most tweets are from July, the current month we're in. \n",
    "\n",
    "The trend explanation is the same as in the week days analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Tweet classes and their trends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Back to top](#Cluster-and-Time-Series-Analysis)"
   ]
  },
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