Classification des Tweets.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# install tweepy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tweepy in /opt/conda/lib/python3.7/site-packages (3.9.0)\n",
      "Requirement already satisfied: requests[socks]>=2.11.1 in /opt/conda/lib/python3.7/site-packages (from tweepy) (2.23.0)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in /opt/conda/lib/python3.7/site-packages (from tweepy) (1.2.0)\n",
      "Requirement already satisfied: six>=1.10.0 in /opt/conda/lib/python3.7/site-packages (from tweepy) (1.14.0)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in /opt/conda/lib/python3.7/site-packages (from requests-oauthlib>=0.7.0->tweepy) (3.0.1)\n",
      "Requirement already satisfied: requests>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from requests-oauthlib>=0.7.0->tweepy) (2.23.0)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests>=2.0.0->requests-oauthlib>=0.7.0->tweepy) (1.25.9)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests>=2.0.0->requests-oauthlib>=0.7.0->tweepy) (2.9)\n",
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      "Requirement already satisfied: chardet<4,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests>=2.0.0->requests-oauthlib>=0.7.0->tweepy) (3.0.4)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests>=2.0.0->requests-oauthlib>=0.7.0->tweepy) (1.25.9)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests>=2.0.0->requests-oauthlib>=0.7.0->tweepy) (2.9)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests>=2.0.0->requests-oauthlib>=0.7.0->tweepy) (2020.12.5)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests>=2.0.0->requests-oauthlib>=0.7.0->tweepy) (3.0.4)\n",
      "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /opt/conda/lib/python3.7/site-packages (from requests[socks]>=2.11.1->tweepy) (1.7.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install tweepy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# import libraries python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* used NLP and NLTK with wordnet et stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /usr/share/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to /usr/share/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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read our dasasets**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets = pd.read_csv('../input/tweetsdatasets/data.csv' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "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>user</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Education1939</td>\n",
       "      <td>This past week The Staff Recognition Committee...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alexanderrusso</td>\n",
       "      <td>RT @UNICEFmedia: \"Evidence shows that schools ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TaleamSystems</td>\n",
       "      <td>Taleam Systems' CEO implemented the Student e-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>javrsda</td>\n",
       "      <td>Ya quiero que salga la nueva temporada de Sex ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Calpe19</td>\n",
       "      <td>RT @LizzyJPrice: @JonnyGeller A great loss. Wh...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             user                                               Text\n",
       "0   Education1939  This past week The Staff Recognition Committee...\n",
       "1  alexanderrusso  RT @UNICEFmedia: \"Evidence shows that schools ...\n",
       "2   TaleamSystems  Taleam Systems' CEO implemented the Student e-...\n",
       "3         javrsda  Ya quiero que salga la nueva temporada de Sex ...\n",
       "4         Calpe19  RT @LizzyJPrice: @JonnyGeller A great loss. Wh..."
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20000, 2)"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* checking columns and total records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20000 entries, 0 to 19999\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   user    20000 non-null  object\n",
      " 1   Text    20000 non-null  object\n",
      "dtypes: object(2)\n",
      "memory usage: 312.6+ KB\n"
     ]
    }
   ],
   "source": [
    "datasets.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* dataset description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>17593</td>\n",
       "      <td>11960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>tr_mechanical</td>\n",
       "      <td>RT @sapphiresdust: To sum up: the right didn’t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>57</td>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 user                                               Text\n",
       "count           20000                                              20000\n",
       "unique          17593                                              11960\n",
       "top     tr_mechanical  RT @sapphiresdust: To sum up: the right didn’t...\n",
       "freq               57                                                246"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user    0\n",
       "Text    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Getting all Coulmn names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['user', 'Text'], dtype='object')"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Text Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.  Cleaning the tweets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "lemmatization, tokenizing, removing stopwards, punctuations, hashtags and mentions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>user</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Education1939</td>\n",
       "      <td>this past week the staff recognition committee...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alexanderrusso</td>\n",
       "      <td>evidence shows that schools are not the main...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TaleamSystems</td>\n",
       "      <td>taleam systems ceo implemented the student ere...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>javrsda</td>\n",
       "      <td>ya quiero que salga la nueva temporada de sex ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Calpe19</td>\n",
       "      <td>a great loss what a wonderful enlightened a...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             user                                               Text\n",
       "0   Education1939  this past week the staff recognition committee...\n",
       "1  alexanderrusso    evidence shows that schools are not the main...\n",
       "2   TaleamSystems  taleam systems ceo implemented the student ere...\n",
       "3         javrsda  ya quiero que salga la nueva temporada de sex ...\n",
       "4         Calpe19     a great loss what a wonderful enlightened a..."
      ]
     },
     "execution_count": 185,
     "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(datasets, 'Text')\n",
    "clean_tweets.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "remove stopwords, punctuations, lemmatize and tokenize word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:23: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#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",
    "datasets.tweets = datasets.Text.apply(furnished)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "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>user</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>GusDP2</td>\n",
       "      <td>le problme ne vient pas de la pauvret mais d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6820</th>\n",
       "      <td>noticebard</td>\n",
       "      <td>online fdp on ml applications in mechanical en...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13195</th>\n",
       "      <td>JohnBothwellPA</td>\n",
       "      <td>less than half of registered voters say majo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6770</th>\n",
       "      <td>QuantumWorkshop</td>\n",
       "      <td>did you know that all electrical devices are f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2352</th>\n",
       "      <td>TruzzzTrump</td>\n",
       "      <td>its a doctorate in education my mother ha...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  user                                               Text\n",
       "658             GusDP2    le problme ne vient pas de la pauvret mais d...\n",
       "6820        noticebard  online fdp on ml applications in mechanical en...\n",
       "13195   JohnBothwellPA    less than half of registered voters say majo...\n",
       "6770   QuantumWorkshop  did you know that all electrical devices are f...\n",
       "2352       TruzzzTrump       its a doctorate in education my mother ha..."
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 2. Defining the set of words."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Related Words runs on several different algorithms which compete to get their results higher in the list. One such algorithm uses ***word embedding*** to convert words into many dimensional vectors which represent their meanings\n",
    "\n",
    "Word embedding is any of a set of language modeling and feature learning techniques in natural language processing (NLP) where words or phrases from the vocabulary are mapped to vectors of real numbers. Conceptually it involves a mathematical embedding from a space with many dimensions per word to a continuous vector space with a much lower dimension.\n",
    "\n",
    "Link to obtain Related Words: https://relatedwords.org/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "education_related_words = '''teaching pedagogy instruction school curriculum learning college\n",
    "university educational activity didactics vocational education kindergarten department of education\n",
    "secondary education training secondary school knowledge education department scholarship literacy\n",
    "special education schooling tuition teacher student undergraduate academic homeschooling higher education\n",
    "adult skill high school experience medicine science humanities coeducation educational\n",
    "autodidacticism lesson research society course profession homework breeding primary school classwork\n",
    "classroom preschool lyceum middle school educationist junior school apprenticeship europe confucius\n",
    "programs health welfare vocational colleges teachers students basic universities vocational training\n",
    "professor funding institutes arts courses classes graduates unesco entrepreneurship management elementary school\n",
    "graduate school diploma agriculture academic degree open university primary education public school\n",
    "education for all internet liberal arts tertiary education mathematics syllabus socialization montessori method\n",
    "value belief habit storytelling discussion formality etymologically homonym plato mastering\n",
    "class preparation assimilation grounding erudition enlightenment activity prep foundation content\n",
    "acculturation upbringing acquisition extension alexandria united states country china analects world\n",
    "educate didactic caliphate generation indus pedagogical teach teachable scholar scholarly democracy schools\n",
    "self-cultivation self-education encyclopaedism eruditeness encyclopedism learnedness educative instructional social\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "fashion_related_words = ''' style lifestyle haute couture clothing vogue trend tailor footwear form\n",
    "couture dress costume manner way forge mode cut fad retro fit pattern rage make dresses makeup menswear\n",
    "kind popular stylist designer show art look beauty styles looks brand photography hollywood fun design\n",
    "advertising clothes sustainable fashion decor sewing pop novelty business order streetwear ready-to-wear\n",
    "sweden hairstyle baroque rococo kairos chronos practice property idiom drape setup signature touch wise\n",
    "response craze cult furor furore tie craft sew fashion week india caliphate perfume sophistication life-style\n",
    "tailor-make shōgun fashions chic designers shows ziryab glamour fashionable stylish apparel inspired france\n",
    "picture garments fancy casual elegant glamorous theatrical boutique attire contemporary trendy\n",
    "sexy moda bourgeoisie garment lingerie styling image theme showcase flashy cravat armani fashion accessory\n",
    "celebrity parisian showing costumes familiar best boutiques creative creations trends quirky genre\n",
    "focus salon focusing musical looking artistic collection tastes presents outfits performance scene\n",
    "like magazines print cinema designing magazine sensibility usual focused concept reality eclectic '''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "sport_related_words = '''game disport play foul player mountaineer hunt fence archery sportive fencing jest SUV Boxer sporting\n",
    "sporty pastime fishing attack big game falconry gaud sportless ring sports sailing cross-country hunting skateboarding\n",
    "caving shikar ski surfing turf lake racing preserve\n",
    "professional box angling discus spectator sport toy dive pothunter boxing foil crew shoot pro course fisherman skiing fun rowing fancy\n",
    "hunter trapshooting skating mock judo sportswear mountaineering lark cycling running riding action sport bowl\n",
    "waggish sportful field multisport yachting titlist blood sport race snowboarding javelin sportsmanship wanton weightlifting mockery racket put fan shooting sail law\n",
    "letter championship coursing sporting fish skylark speed skating amateur laugh sports diving ploy climber climb sports diver season\n",
    "kendo letterman swordsman chase daff hammer ballooning surfboarding run wrestle angler the chase fandom paragliding semiprofessional mimic runner quarry slalom game fish\n",
    "activewear scuba diving trainer sportswoman venery luge score tobogganing kiteboarding autocross harlequin wrestling major league unsportsmanlike bait ball junior\n",
    "sportfishing hack synchronized swimming windsurfing falconer train crown stuff defender SUB sky divingtyro\n",
    "pickerel marlin gymnastics crewman netball rage challenge coasteering aficionado langlauf dandiprat spike fool rulebook cave fox hunt\n",
    "wrestler blinder sport vehicle track team joke tubing extreme freediving game bird horse riding referee\n",
    "snowmobiling kit rallying fall sportsperson DCMS speleology marlock rodeo kickball tussle spelunking slump pot racquetball canoeing block dodgeball turn pro\n",
    "broomball commissioner celebrity fumble the top flight sported split time fencer horse racing sport fish\n",
    "goody fox-hunting artistry all-rounder skeleton pro shop ice-skating duffer trash-talk spot bobsledding gamy pig-sticking retire\n",
    "surfer fight misplay rabbit sporting celebrity harness racing survivalist preseason\n",
    "sack try out fisher archer bouldering Dove devotee convert camogie mixed martial arts sim suit up mimicry throw hustle canyoneering sabre hoax pheasant dummy ringed rang AIS territory double bouncebackability mumm extreme sport rung sportingly\n",
    "jape fratch gamer scrimmage sideline ridicule bantam riflery widow close season base \n",
    "jumping allAmerican record  mountainboarding skijoring penalty gamesome merrymaking '''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "mechanical_related_words  = ''' mechanistic technical mechanic automatic machine machinery device\n",
    "apparatus mechanically skillful engine automation mechanized robotic gear equipment windup robotlike machinelike\n",
    "automatonlike turbine electrical electromechanical hydraulic faulty tool structural pneumatic technician\n",
    "malfunctioning mechanised machinist automaton mechanism automate cyborg mechanics welding zamboni\n",
    "contraption engineering instrument technique systems malfunction simulator hydraulics techniques\n",
    "invention components decoder processes brake system design electric physical tools vibration wiring\n",
    "invented method engineers pulley computational precision developed measurement braking used uses dynamics\n",
    "methods devices gravity robot machines conditioning combustion magnetic fluid malfunctions plumbing using\n",
    "thermal maintenance bearings conventional mathematical applied instrumentation instruments experimental\n",
    "propulsion quantum skill specialized skills experiments cognitive defects breathing basic sensors vacuum\n",
    "involves transmission theory combination tire processing characteristics repair utilizing type\n",
    "aerodynamics memory boiler circuitry valve use simulation\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "politics_related_words = '''diplomatic government law election aristotle diplomatical political science\n",
    "political parties politics ethics sovereign state suave smooth tribe republic dynasty expedient sagacious politician direct democracy political economy international relations\n",
    "state public policy power public administration city polis greek language social status border political system niccolò machiavelli thomas hobbes war nationalism technocracy society reactionary anarchism centrism russian empire ottoman empire absolute monarchy constitutions political geography bland monarchy pluralism legislature executive judiciary democracy comparative politics\n",
    "resource academy negotiation force warfare clans tribes company aristocracy plato political philosophy culture chanakya country confucius sic religion governance normative utopia decision-making\n",
    "chimpanzee confederation frontier federation\n",
    "chiefdom africa oasis sumer arthashastra mesopotamia terrorism river kleptocracy athens moralism nations christianism\n",
    "inveterately contradictive mobocracy equalitarian sermonise christianly mudslinger\n",
    "statesmanly sloganeer technocratic megalomanic westernism passivism russophile stateless society ancient civilizations psychodrama\n",
    "self-perpetuating first civilization balkanized piercer\n",
    "clubby internalised band society reflation nontransparent kafkaesque postindustrial unsustainability overregulated conflict theories\n",
    "atomized uruk period self-regulating predynastic egypt hidebound nonideological wrongness nile river dispassion fragmenting squishy heteronormative depersonalized metabolizes oligarchy persian gulf covenantal autocracy\n",
    "euphrates river microbiota legitimacy shapers\n",
    "tigris river solipsistic originalism seven-man standard-setting groupthink eviscerated\n",
    "146-nation classical antiquity neuropolitics greek city states ancient rome referendum nation-state athenian democracy peace of westphalia emer de vattel\n",
    "behavioralism position post-structuralism austrian empire\n",
    "plutocracy kingdom of france kingdom of hungary theocracy spanish empire dictatorship british empire muslim world death of muhammad\n",
    "revolution issue federalism drunk driving australia polity \n",
    "san marino mass media dutch republic steven weber david woodward michel fouc '''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "technology_related_words = '''engineering science biotechnology high technology internet nanotechnology\n",
    "robotics communication computing computer computer science industry automation wheel technological\n",
    "aeronautical engineering chemical engineering equipment manufacturing application bionics energy\n",
    "technical ergonomics applied science engineering science scientific telephone field of study development\n",
    "dolphin systems software scientific method transhumanism electronics digital tech printing press\n",
    "devices tools applications computers capabilities expertise biomedical innovations wireless products\n",
    "information technology solutions management second industrial revolution language engineer homo\n",
    "neolithic bioscience scientist industrial revolution biology skill good service knowledge prehistory lever\n",
    "weapon club miniaturization economy pollution value productivity discipline bailiwick machinery\n",
    "railroading source it bioengineering subject rocketry sink study field primates technologies crowbar\n",
    "spoon neo-luddism anarcho-primitivism ee medicine techno-progressivism physics reasoned innovation\n",
    "business phenomenal tool formality developed research utility integrated merriam-webster develop\n",
    "usability electronic safety micro focus developing mathematics communications advanced multimedia \n",
    "history marketing\n",
    " '''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "3. Preprocessing the datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tokenizing and removing stop words from the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "education = furnished(education_related_words)\n",
    "fashion  = furnished(fashion_related_words)\n",
    "mechanical = furnished(mechanical_related_words)\n",
    "technology = furnished(technology_related_words)\n",
    "politics = furnished(politics_related_words)\n",
    "sport = furnished(sport_related_words)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removing duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'game disport play foul player mountaineer hunt fence archery sportive fencing jest suv boxer sporting sporty pastime fishing attack big falconry gaud sportless ring sport sailing cross-country hunting skateboarding caving shikar ski surfing turf lake racing preserve professional box angling discus spectator toy dive pothunter boxing foil crew shoot pro course fisherman skiing fun rowing fancy hunter trapshooting skating mock judo sportswear mountaineering lark cycling running riding action bowl waggish sportful field multisport yachting titlist blood race snowboarding javelin sportsmanship wanton weightlifting mockery racket put fan shooting sail law letter championship coursing fish skylark speed amateur laugh diving ploy climber climb diver season kendo letterman swordsman chase daff hammer ballooning surfboarding run wrestle angler fandom paragliding semiprofessional mimic runner quarry slalom activewear scuba trainer sportswoman venery luge score tobogganing kiteboarding autocross harlequin wrestling major league unsportsmanlike bait ball junior sportfishing hack synchronized swimming windsurfing falconer train crown stuff defender sub sky divingtyro pickerel marlin gymnastics crewman netball rage challenge coasteering aficionado langlauf dandiprat spike fool rulebook cave fox wrestler blinder vehicle track team joke tubing extreme freediving bird horse referee snowmobiling kit rallying fall sportsperson dcms speleology marlock rodeo kickball tussle spelunking slump pot racquetball canoeing block dodgeball turn broomball commissioner celebrity fumble top flight sported split time fencer goody fox-hunting artistry all-rounder skeleton shop ice-skating duffer trash-talk spot bobsledding gamy pig-sticking retire surfer fight misplay rabbit harness survivalist preseason sack try fisher archer bouldering dove devotee convert camogie mixed martial art sim suit mimicry throw hustle canyoneering sabre hoax pheasant dummy ringed rang ais territory double bouncebackability mumm rung sportingly jape fratch gamer scrimmage sideline ridicule bantam riflery widow close base jumping allamerican record mountainboarding skijoring penalty gamesome merrymaking'"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string1 = sport\n",
    "words = string1.split()\n",
    "sport = \" \".join(sorted(set(words), key=words.index))\n",
    "sport"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'diplomatic government law election aristotle diplomatical political science party politics ethic sovereign state suave smooth tribe republic dynasty expedient sagacious politician direct democracy economy international relation public policy power administration city polis greek language social status border system niccolò machiavelli thomas hobbes war nationalism technocracy society reactionary anarchism centrism russian empire ottoman absolute monarchy constitution geography bland pluralism legislature executive judiciary comparative resource academy negotiation force warfare clan company aristocracy plato philosophy culture chanakya country confucius sic religion governance normative utopia decision-making chimpanzee confederation frontier federation chiefdom africa oasis sumer arthashastra mesopotamia terrorism river kleptocracy athens moralism nation christianism inveterately contradictive mobocracy equalitarian sermonise christianly mudslinger statesmanly sloganeer technocratic megalomanic westernism passivism russophile stateless ancient civilization psychodrama self-perpetuating first balkanized piercer clubby internalised band reflation nontransparent kafkaesque postindustrial unsustainability overregulated conflict theory atomized uruk period self-regulating predynastic egypt hidebound nonideological wrongness nile dispassion fragmenting squishy heteronormative depersonalized metabolizes oligarchy persian gulf covenantal autocracy euphrates microbiota legitimacy shaper tigris solipsistic originalism seven-man standard-setting groupthink eviscerated 146-nation classical antiquity neuropolitics rome referendum nation-state athenian peace westphalia emer de vattel behavioralism position post-structuralism austrian plutocracy kingdom france hungary theocracy spanish dictatorship british muslim world death muhammad revolution issue federalism drunk driving australia polity san marino mass medium dutch steven weber david woodward michel fouc'"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string1 = politics\n",
    "words = string1.split()\n",
    "politics = \" \".join(sorted(set(words), key=words.index))\n",
    "politics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'teaching pedagogy instruction school curriculum learning college university educational activity didactics vocational education kindergarten department secondary training knowledge scholarship literacy special schooling tuition teacher student undergraduate academic homeschooling higher adult skill high experience medicine science humanity coeducation autodidacticism lesson research society course profession homework breeding primary classwork classroom preschool lyceum middle educationist junior apprenticeship europe confucius program health welfare basic professor funding institute art class graduate unesco entrepreneurship management elementary diploma agriculture degree open public internet liberal tertiary mathematics syllabus socialization montessori method value belief habit storytelling discussion formality etymologically homonym plato mastering preparation assimilation grounding erudition enlightenment prep foundation content acculturation upbringing acquisition extension alexandria united state country china analects world educate didactic caliphate generation indus pedagogical teach teachable scholar scholarly democracy self-cultivation self-education encyclopaedism eruditeness encyclopedism learnedness educative instructional social'"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# delete duplicates\n",
    "string1 = education\n",
    "words = string1.split()\n",
    "education = \" \".join(sorted(set(words), key=words.index))\n",
    "education"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'style lifestyle haute couture clothing vogue trend tailor footwear form dress costume manner way forge mode cut fad retro fit pattern rage make makeup menswear kind popular stylist designer show art look beauty brand photography hollywood fun design advertising clothes sustainable fashion decor sewing pop novelty business order streetwear ready-to-wear sweden hairstyle baroque rococo kairos chronos practice property idiom drape setup signature touch wise response craze cult furor furore tie craft sew week india caliphate perfume sophistication life-style tailor-make shōgun chic ziryab glamour fashionable stylish apparel inspired france picture garment fancy casual elegant glamorous theatrical boutique attire contemporary trendy sexy moda bourgeoisie lingerie styling image theme showcase flashy cravat armani accessory celebrity parisian showing familiar best creative creation quirky genre focus salon focusing musical looking artistic collection taste present outfit performance scene like magazine print cinema designing sensibility usual focused concept reality eclectic'"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string1 = fashion \n",
    "words = string1.split()\n",
    "fashion = \" \".join(sorted(set(words), key=words.index))\n",
    "fashion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'mechanistic technical mechanic automatic machine machinery device apparatus mechanically skillful engine automation mechanized robotic gear equipment windup robotlike machinelike automatonlike turbine electrical electromechanical hydraulic faulty tool structural pneumatic technician malfunctioning mechanised machinist automaton mechanism automate cyborg welding zamboni contraption engineering instrument technique system malfunction simulator hydraulics invention component decoder process brake design electric physical vibration wiring invented method engineer pulley computational precision developed measurement braking used us dynamic gravity robot conditioning combustion magnetic fluid plumbing using thermal maintenance bearing conventional mathematical applied instrumentation experimental propulsion quantum skill specialized experiment cognitive defect breathing basic sensor vacuum involves transmission theory combination tire processing characteristic repair utilizing type aerodynamics memory boiler circuitry valve use simulation'"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string1 = mechanical\n",
    "words = string1.split()\n",
    "mechanical = \" \".join(sorted(set(words), key=words.index))\n",
    "mechanical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'engineering science biotechnology high technology internet nanotechnology robotics communication computing computer industry automation wheel technological aeronautical chemical equipment manufacturing application bionics energy technical ergonomics applied scientific telephone field study development dolphin system software method transhumanism electronics digital tech printing press device tool capability expertise biomedical innovation wireless product information solution management second industrial revolution language engineer homo neolithic bioscience scientist biology skill good service knowledge prehistory lever weapon club miniaturization economy pollution value productivity discipline bailiwick machinery railroading source bioengineering subject rocketry sink primate crowbar spoon neo-luddism anarcho-primitivism ee medicine techno-progressivism physic reasoned business phenomenal formality developed research utility integrated merriam-webster develop usability electronic safety micro focus developing mathematics advanced multimedia history marketing'"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string1 = technology\n",
    "words = string1.split()\n",
    "technology = \" \".join(sorted(set(words), key=words.index))\n",
    "technology"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "4. Vectorizing and Standadization.¶\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vectorizing the sets of words, then standardizing them. TFIDF will be used in order to take care of the least frequent words. Standardizing is cause TFIDF favors long sentences and there'll be inconsistencies between the length of the tweets and the length of set of words.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:70: FutureWarning: Pass input=['style lifestyle haute couture clothing vogue trend tailor footwear form dress costume manner way forge mode cut fad retro fit pattern rage make makeup menswear kind popular stylist designer show art look beauty brand photography hollywood fun design advertising clothes sustainable fashion decor sewing pop novelty business order streetwear ready-to-wear sweden hairstyle baroque rococo kairos chronos practice property idiom drape setup signature touch wise response craze cult furor furore tie craft sew week india caliphate perfume sophistication life-style tailor-make shōgun chic ziryab glamour fashionable stylish apparel inspired france picture garment fancy casual elegant glamorous theatrical boutique attire contemporary trendy sexy moda bourgeoisie lingerie styling image theme showcase flashy cravat armani accessory celebrity parisian showing familiar best creative creation quirky genre focus salon focusing musical looking artistic collection taste present outfit performance scene like magazine print cinema designing sensibility usual focused concept reality eclectic'] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  FutureWarning)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:70: FutureWarning: Pass input=['teaching pedagogy instruction school curriculum learning college university educational activity didactics vocational education kindergarten department secondary training knowledge scholarship literacy special schooling tuition teacher student undergraduate academic homeschooling higher adult skill high experience medicine science humanity coeducation autodidacticism lesson research society course profession homework breeding primary classwork classroom preschool lyceum middle educationist junior apprenticeship europe confucius program health welfare basic professor funding institute art class graduate unesco entrepreneurship management elementary diploma agriculture degree open public internet liberal tertiary mathematics syllabus socialization montessori method value belief habit storytelling discussion formality etymologically homonym plato mastering preparation assimilation grounding erudition enlightenment prep foundation content acculturation upbringing acquisition extension alexandria united state country china analects world educate didactic caliphate generation indus pedagogical teach teachable scholar scholarly democracy self-cultivation self-education encyclopaedism eruditeness encyclopedism learnedness educative instructional social'] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  FutureWarning)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:70: FutureWarning: Pass input=['mechanistic technical mechanic automatic machine machinery device apparatus mechanically skillful engine automation mechanized robotic gear equipment windup robotlike machinelike automatonlike turbine electrical electromechanical hydraulic faulty tool structural pneumatic technician malfunctioning mechanised machinist automaton mechanism automate cyborg welding zamboni contraption engineering instrument technique system malfunction simulator hydraulics invention component decoder process brake design electric physical vibration wiring invented method engineer pulley computational precision developed measurement braking used us dynamic gravity robot conditioning combustion magnetic fluid plumbing using thermal maintenance bearing conventional mathematical applied instrumentation experimental propulsion quantum skill specialized experiment cognitive defect breathing basic sensor vacuum involves transmission theory combination tire processing characteristic repair utilizing type aerodynamics memory boiler circuitry valve use simulation'] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  FutureWarning)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:70: FutureWarning: Pass input=['engineering science biotechnology high technology internet nanotechnology robotics communication computing computer industry automation wheel technological aeronautical chemical equipment manufacturing application bionics energy technical ergonomics applied scientific telephone field study development dolphin system software method transhumanism electronics digital tech printing press device tool capability expertise biomedical innovation wireless product information solution management second industrial revolution language engineer homo neolithic bioscience scientist biology skill good service knowledge prehistory lever weapon club miniaturization economy pollution value productivity discipline bailiwick machinery railroading source bioengineering subject rocketry sink primate crowbar spoon neo-luddism anarcho-primitivism ee medicine techno-progressivism physic reasoned business phenomenal formality developed research utility integrated merriam-webster develop usability electronic safety micro focus developing mathematics advanced multimedia history marketing'] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  FutureWarning)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:70: FutureWarning: Pass input=['diplomatic government law election aristotle diplomatical political science party politics ethic sovereign state suave smooth tribe republic dynasty expedient sagacious politician direct democracy economy international relation public policy power administration city polis greek language social status border system niccolò machiavelli thomas hobbes war nationalism technocracy society reactionary anarchism centrism russian empire ottoman absolute monarchy constitution geography bland pluralism legislature executive judiciary comparative resource academy negotiation force warfare clan company aristocracy plato philosophy culture chanakya country confucius sic religion governance normative utopia decision-making chimpanzee confederation frontier federation chiefdom africa oasis sumer arthashastra mesopotamia terrorism river kleptocracy athens moralism nation christianism inveterately contradictive mobocracy equalitarian sermonise christianly mudslinger statesmanly sloganeer technocratic megalomanic westernism passivism russophile stateless ancient civilization psychodrama self-perpetuating first balkanized piercer clubby internalised band reflation nontransparent kafkaesque postindustrial unsustainability overregulated conflict theory atomized uruk period self-regulating predynastic egypt hidebound nonideological wrongness nile dispassion fragmenting squishy heteronormative depersonalized metabolizes oligarchy persian gulf covenantal autocracy euphrates microbiota legitimacy shaper tigris solipsistic originalism seven-man standard-setting groupthink eviscerated 146-nation classical antiquity neuropolitics rome referendum nation-state athenian peace westphalia emer de vattel behavioralism position post-structuralism austrian plutocracy kingdom france hungary theocracy spanish dictatorship british muslim world death muhammad revolution issue federalism drunk driving australia polity san marino mass medium dutch steven weber david woodward michel fouc'] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  FutureWarning)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:70: FutureWarning: Pass input=['game disport play foul player mountaineer hunt fence archery sportive fencing jest suv boxer sporting sporty pastime fishing attack big falconry gaud sportless ring sport sailing cross-country hunting skateboarding caving shikar ski surfing turf lake racing preserve professional box angling discus spectator toy dive pothunter boxing foil crew shoot pro course fisherman skiing fun rowing fancy hunter trapshooting skating mock judo sportswear mountaineering lark cycling running riding action bowl waggish sportful field multisport yachting titlist blood race snowboarding javelin sportsmanship wanton weightlifting mockery racket put fan shooting sail law letter championship coursing fish skylark speed amateur laugh diving ploy climber climb diver season kendo letterman swordsman chase daff hammer ballooning surfboarding run wrestle angler fandom paragliding semiprofessional mimic runner quarry slalom activewear scuba trainer sportswoman venery luge score tobogganing kiteboarding autocross harlequin wrestling major league unsportsmanlike bait ball junior sportfishing hack synchronized swimming windsurfing falconer train crown stuff defender sub sky divingtyro pickerel marlin gymnastics crewman netball rage challenge coasteering aficionado langlauf dandiprat spike fool rulebook cave fox wrestler blinder vehicle track team joke tubing extreme freediving bird horse referee snowmobiling kit rallying fall sportsperson dcms speleology marlock rodeo kickball tussle spelunking slump pot racquetball canoeing block dodgeball turn broomball commissioner celebrity fumble top flight sported split time fencer goody fox-hunting artistry all-rounder skeleton shop ice-skating duffer trash-talk spot bobsledding gamy pig-sticking retire surfer fight misplay rabbit harness survivalist preseason sack try fisher archer bouldering dove devotee convert camogie mixed martial art sim suit mimicry throw hustle canyoneering sabre hoax pheasant dummy ringed rang ais territory double bouncebackability mumm rung sportingly jape fratch gamer scrimmage sideline ridicule bantam riflery widow close base jumping allamerican record mountainboarding skijoring penalty gamesome merrymaking'] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "fashion_vector = get_vectors(fashion)\n",
    "education_vector = get_vectors(education)\n",
    "mechanical_vector = get_vectors(mechanical)\n",
    "technology_vector = get_vectors(technology)\n",
    "politics_vector = get_vectors(politics)\n",
    "sport_vector = get_vectors(sport)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "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(datasets.tweets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Jaccard Similarity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "\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": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.75,\n",
       " 0.7142857142857143,\n",
       " 0.7241379310344828,\n",
       " 0.6666666666666666,\n",
       " 0.6428571428571429,\n",
       " 0.7857142857142857,\n",
       " 0.75,\n",
       " 0.6428571428571429,\n",
       " 0.42857142857142855,\n",
       " 0.5]"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sport scores\n",
    "sp_scores = get_scores(sport, datasets.tweets.to_list())\n",
    "sp_scores[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.65625,\n",
       " 0.625,\n",
       " 0.6875,\n",
       " 0.6363636363636364,\n",
       " 0.5625,\n",
       " 0.6875,\n",
       " 0.65625,\n",
       " 0.5625,\n",
       " 0.375,\n",
       " 0.4375]"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# politics scores\n",
    "p_scores = get_scores(politics, datasets.tweets.to_list())\n",
    "p_scores[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.75,\n",
       " 0.7142857142857143,\n",
       " 0.7241379310344828,\n",
       " 0.6666666666666666,\n",
       " 0.6428571428571429,\n",
       " 0.7857142857142857,\n",
       " 0.75,\n",
       " 0.6428571428571429,\n",
       " 0.42857142857142855,\n",
       " 0.5]"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# education scores\n",
    "e_scores = get_scores(education, datasets.tweets.to_list())\n",
    "e_scores[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.75,\n",
       " 0.7142857142857143,\n",
       " 0.6666666666666666,\n",
       " 0.6666666666666666,\n",
       " 0.6428571428571429,\n",
       " 0.7857142857142857,\n",
       " 0.75,\n",
       " 0.6428571428571429,\n",
       " 0.42857142857142855,\n",
       " 0.5]"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fashion scores\n",
    "f_scores = get_scores(fashion, datasets.tweets.to_list())\n",
    "f_scores[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.8076923076923077,\n",
       " 0.7692307692307693,\n",
       " 0.7142857142857143,\n",
       " 0.7142857142857143,\n",
       " 0.6923076923076923,\n",
       " 0.8461538461538461,\n",
       " 0.8076923076923077,\n",
       " 0.6923076923076923,\n",
       " 0.46153846153846156,\n",
       " 0.5384615384615384]"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mechanical scores\n",
    "m_scores = get_scores(mechanical, datasets.tweets.to_list())\n",
    "m_scores[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.75,\n",
       " 0.7142857142857143,\n",
       " 0.7241379310344828,\n",
       " 0.6666666666666666,\n",
       " 0.6428571428571429,\n",
       " 0.7857142857142857,\n",
       " 0.75,\n",
       " 0.6428571428571429,\n",
       " 0.42857142857142855,\n",
       " 0.5]"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# technology scores\n",
    "t_scores = get_scores(technology, datasets.tweets.to_list())\n",
    "t_scores[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "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>education_score</th>\n",
       "      <th>mechanical_score</th>\n",
       "      <th>technology_score</th>\n",
       "      <th>fashion_scores</th>\n",
       "      <th>politics_scores</th>\n",
       "      <th>sport_scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Education1939</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.807692</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.656250</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alexanderrusso</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TaleamSystems</td>\n",
       "      <td>0.724138</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.724138</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.724138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>javrsda</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Calpe19</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.562500</td>\n",
       "      <td>0.642857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MohdHus97063247</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.785714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SherriKuczeryk</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.807692</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.656250</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>csmatteson</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.562500</td>\n",
       "      <td>0.642857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Jintu41851351</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>shamasi1968</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             names  education_score  mechanical_score  technology_score  \\\n",
       "0    Education1939         0.750000          0.807692          0.750000   \n",
       "1   alexanderrusso         0.714286          0.769231          0.714286   \n",
       "2    TaleamSystems         0.724138          0.714286          0.724138   \n",
       "3          javrsda         0.666667          0.714286          0.666667   \n",
       "4          Calpe19         0.642857          0.692308          0.642857   \n",
       "5  MohdHus97063247         0.785714          0.846154          0.785714   \n",
       "6   SherriKuczeryk         0.750000          0.807692          0.750000   \n",
       "7       csmatteson         0.642857          0.692308          0.642857   \n",
       "8    Jintu41851351         0.428571          0.461538          0.428571   \n",
       "9      shamasi1968         0.500000          0.538462          0.500000   \n",
       "\n",
       "   fashion_scores  politics_scores  sport_scores  \n",
       "0        0.750000         0.656250      0.750000  \n",
       "1        0.714286         0.625000      0.714286  \n",
       "2        0.666667         0.687500      0.724138  \n",
       "3        0.666667         0.636364      0.666667  \n",
       "4        0.642857         0.562500      0.642857  \n",
       "5        0.785714         0.687500      0.785714  \n",
       "6        0.750000         0.656250      0.750000  \n",
       "7        0.642857         0.562500      0.642857  \n",
       "8        0.428571         0.375000      0.428571  \n",
       "9        0.500000         0.437500      0.500000  "
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''new df with names, and the jaccard scores for each group'''\n",
    "\n",
    "data  = {'names':datasets.user.to_list(), 'education_score':e_scores,\n",
    "         'mechanical_score': m_scores, 'technology_score':t_scores,\n",
    "         'fashion_scores':f_scores,'politics_scores':p_scores,\n",
    "         'sport_scores':sp_scores}\n",
    "scores_df = pd.DataFrame(data)\n",
    "scores_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Actual assigning of classes to the tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_clusters(l1, l2, l3, l4, l5, l6):\n",
    "    educ = []\n",
    "    mech = []\n",
    "    tech = []\n",
    "    fash = []\n",
    "    poli = []\n",
    "    spor = []\n",
    "    for i, j, k, l, p, s in zip(l1, l2, l3, l4,l5, l6):\n",
    "        m = max(i, j, k, l, p, s)\n",
    "        if m == i:\n",
    "            educ.append(1)\n",
    "        else:\n",
    "            educ.append(0)\n",
    "        if m == j:\n",
    "            mech.append(1)\n",
    "        else:\n",
    "            mech.append(0)        \n",
    "        if m == k:\n",
    "            tech.append(1)\n",
    "        else:\n",
    "            tech.append(0)  \n",
    "        if m == l:\n",
    "            fash.append(1)\n",
    "        else:\n",
    "            fash.append(0)   \n",
    "        if m == p:\n",
    "            poli.append(1)\n",
    "        else:\n",
    "            poli.append(0) \n",
    "        if m == s:\n",
    "            spor.append(1)\n",
    "        else:\n",
    "            spor.append(0) \n",
    "    return educ, mech, tech, fash, poli, spor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "l1 = scores_df.education_score.to_list()\n",
    "l2 = scores_df.mechanical_score.to_list()\n",
    "l3 = scores_df.technology_score.to_list()\n",
    "l4 = scores_df.fashion_scores.to_list()\n",
    "l5 = scores_df.politics_scores.to_list()\n",
    "l6 = scores_df.sport_scores.to_list()\n",
    "educ, mech, tech, fash, poli, spor = get_clusters(l1, l2, l3, l4, l5, l6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>education</th>\n",
       "      <th>mechanical</th>\n",
       "      <th>technology</th>\n",
       "      <th>fashion</th>\n",
       "      <th>politics</th>\n",
       "      <th>sport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Education1939</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alexanderrusso</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TaleamSystems</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>javrsda</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Calpe19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MohdHus97063247</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SherriKuczeryk</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>csmatteson</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Jintu41851351</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>shamasi1968</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              name  education  mechanical  technology  fashion  politics  \\\n",
       "0    Education1939          0           1           0        0         0   \n",
       "1   alexanderrusso          0           1           0        0         0   \n",
       "2    TaleamSystems          1           0           1        0         0   \n",
       "3          javrsda          0           1           0        0         0   \n",
       "4          Calpe19          0           1           0        0         0   \n",
       "5  MohdHus97063247          0           1           0        0         0   \n",
       "6   SherriKuczeryk          0           1           0        0         0   \n",
       "7       csmatteson          0           1           0        0         0   \n",
       "8    Jintu41851351          0           1           0        0         0   \n",
       "9      shamasi1968          0           1           0        0         0   \n",
       "\n",
       "   sport  \n",
       "0      0  \n",
       "1      0  \n",
       "2      1  \n",
       "3      0  \n",
       "4      0  \n",
       "5      0  \n",
       "6      0  \n",
       "7      0  \n",
       "8      0  \n",
       "9      0  "
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'name': scores_df.names.to_list(), 'education':educ, 'mechanical':mech, 'technology':tech, 'fashion': fash, 'politics': poli, 'sport': spor}\n",
    "cluster_df = pd.DataFrame(data)\n",
    "cluster_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Due to the close similarity between the education, mechanical and technology 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[['education', 'mechanical', 'technology', 'fashion','politics', 'sport']].sum(axis = 1) > 1\n",
    "c = cluster_df[['education', 'mechanical', 'technology', 'fashion','politics', 'sport']].sum(axis = 1)\n",
    "# b = cluster_df.copy()\n",
    "cluster_df.loc[(a), ['education','mechanical', 'technology', 'fashion','politics', 'sport']] = 1/c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20000, 7)"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Clustered Datasets: Question 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17594, 7)\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>education</th>\n",
       "      <th>mechanical</th>\n",
       "      <th>technology</th>\n",
       "      <th>fashion</th>\n",
       "      <th>politics</th>\n",
       "      <th>sport</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>zzcat11</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zzexolove</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zzwwppp</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>5.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zzzbeeme</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>1131.666667</td>\n",
       "      <td>16826.666667</td>\n",
       "      <td>1131.666667</td>\n",
       "      <td>1131.666667</td>\n",
       "      <td>1520.666667</td>\n",
       "      <td>1131.666667</td>\n",
       "      <td>22874.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             education    mechanical   technology      fashion     politics  \\\n",
       "name                                                                          \n",
       "zzcat11       0.000000      1.000000     0.000000     0.000000     0.000000   \n",
       "zzexolove     0.333333      0.333333     0.333333     0.333333     0.333333   \n",
       "zzwwppp       0.166667      5.166667     0.166667     0.166667     0.166667   \n",
       "zzzbeeme      0.000000      1.000000     0.000000     0.000000     0.000000   \n",
       "Total      1131.666667  16826.666667  1131.666667  1131.666667  1520.666667   \n",
       "\n",
       "                 sport    total  \n",
       "name                             \n",
       "zzcat11       0.000000      1.0  \n",
       "zzexolove     0.333333      2.0  \n",
       "zzwwppp       0.166667      6.0  \n",
       "zzzbeeme      0.000000      1.0  \n",
       "Total      1131.666667  22874.0  "
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_clusters = cluster_df.groupby(['name']).sum()\n",
    "#pivot_clusters['education'] = pivot_clusters['education'].astype(int)\n",
    "#pivot_clusters['mechanical'] = pivot_clusters['mechanical'].astype(int)\n",
    "#pivot_clusters['technology'] = pivot_clusters['technology'].astype(int)\n",
    "#pivot_clusters['fashion'] = pivot_clusters['fashion'].astype(int)\n",
    "#pivot_clusters['politics'] = pivot_clusters['politics'].astype(int)\n",
    "#pivot_clusters['sport'] = pivot_clusters['sport'].astype(int)\n",
    "pivot_clusters['total'] = pivot_clusters['education'] + pivot_clusters['mechanical'] + pivot_clusters['technology'] +  pivot_clusters['fashion'] + pivot_clusters['politics'] + pivot_clusters['sport']\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": [
    "* A pie chart to display the total number of tweets in each category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize =(10, 15)) \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 mechanical tweets follow, this could be related to the set of words defined as words related to mechanical. Most of these terms are general, thus if a tweet that maybe was more technology related could have more mechanical words thantechnologyt words and thus classified as mechanical, so this is mainnly a bias in the classification method.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Users with most tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/pandas/core/frame.py:4174: 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: https://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": 220,
     "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": [
    "d = pivot_clusters.sort_values(by = 'total', ascending  = False)\n",
    "e = d.head(15)\n",
    "e.drop(e.head(1).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('user')\n",
    "plt.ylabel('total tweets')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'sport tweets')"
      ]
     },
     "execution_count": 221,
     "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": [
    "d = pivot_clusters.sort_values(by = 'sport', ascending  = False)\n",
    "e = d.head(15)\n",
    "e.drop(e.head(1).index, inplace=True)\n",
    "\n",
    "plt.figure(figsize=(12,10))\n",
    "sns.barplot(x = e.index, y = e.sport)\n",
    "plt.title('A bar plot showing top tweeps based on volume of sport tweets')\n",
    "plt.xticks(rotation=50)\n",
    "plt.xlabel('user')\n",
    "plt.ylabel('sport tweets')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Users with most politics tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'politics tweets')"
      ]
     },
     "execution_count": 222,
     "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": [
    "d = pivot_clusters.sort_values(by = 'politics', ascending  = False)\n",
    "e = d.head(15)\n",
    "e.drop(e.head(2).index, inplace=True)\n",
    "\n",
    "plt.figure(figsize=(12,10))\n",
    "sns.barplot(x = e.index, y = e.politics)\n",
    "plt.title('A bar plot showing top tweeps based on volume of politics tweets')\n",
    "plt.xticks(rotation=60)\n",
    "plt.xlabel('user')\n",
    "plt.ylabel('politics tweets')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  Users with most education tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'education tweets')"
      ]
     },
     "execution_count": 223,
     "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": [
    "d = pivot_clusters.sort_values(by = 'education', ascending  = False)\n",
    "e = d.head(15)\n",
    "e.drop(e.head(2).index, inplace=True)\n",
    "\n",
    "plt.figure(figsize=(12,10))\n",
    "sns.barplot(x = e.index, y = e.education)\n",
    "plt.title('A bar plot showing top tweeps based on volume of education tweets')\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel('user')\n",
    "plt.ylabel('education tweets')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  Users with most mechanical tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mechanical tweets')"
      ]
     },
     "execution_count": 224,
     "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": [
    "d = pivot_clusters.sort_values(by = 'mechanical', ascending  = False)\n",
    "e = d.head(15)\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.mechanical)\n",
    "plt.title('A bar plot showing top tweeps based on volume of mechanical tweets')\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel('user')\n",
    "plt.ylabel('mechanical tweets')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Users with most technology tweets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/pandas/core/frame.py:4174: 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: https://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, 'technology tweets')"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = pivot_clusters.sort_values(by = 'technology', ascending  = False)\n",
    "e = d.head(15)\n",
    "e.drop(e.head(1).index, inplace=True)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12,12))\n",
    "sns.barplot(x = e.index, y = e.technology)\n",
    "plt.title('A bar plot showing top tweeps based on volume of technology tweets')\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel('user')\n",
    "plt.ylabel('technology tweets')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  Users with most fashion tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'fashion tweets')"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = pivot_clusters.sort_values(by = 'fashion', ascending  = False)\n",
    "e = d.head(15)\n",
    "e.drop(e.head(1).index, inplace=True)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12,12))\n",
    "sns.barplot(x = e.index, y = e.fashion)\n",
    "plt.title('A bar plot showing top tweeps based on volume of fashion tweets')\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel('user')\n",
    "plt.ylabel('fashion tweets')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution test and skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17594, 7)\n",
      "(17593, 7)\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": "markdown",
   "metadata": {},
   "source": [
    "# KMeans Clustering."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2D Clusters\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Politics-Sport"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "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[:, [0,1]].values\n",
    "X = pivot_clusters[['politics', 'sport']].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": 231,
   "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 politics and sport groups')\n",
    "plt.xlabel('politics tweets')\n",
    "plt.ylabel('sport tweets')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Fashion-Politics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "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[:, [0,1]].values\n",
    "X = pivot_clusters[['politics', 'fashion']].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": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yyim9yrzyyiusWrWKL37xiwB0dnayzz777Lz/7LPP7vUYY0xxqhpRRXhaeMB58Jm42CmrAV5VW4A+J+Bn1PaBiwDJl9/tx7hx4wiHwwOWGzJkCF1dH3/LdM8532OPPVi5ciV/+ctfuPnmm1mwYMHOnnk3VWXcuHEsXbq0z33vtluvc9PGmCI2ZewUIjMizHlqDvMj83deyTo9NJ2GoxoydiWrP3LR7OKx3NDB7/qkk05i27Zt/O53v9u57dlnn+01ZDJ69GhaWlro6urirbfe4plnngFgw4YNdHV1ceaZZ/KTn/yEFStWALD77rvT1tYGwMEHH8z69et3BvhYLMYLL7ww+MYaY4pG1Ygq5k6dy5YrttD54062XLGFuVPnZiy4QxHkovFi6GFDndkzyYZpypxygyUi3H///Vx22WVcd911DBs2jNGjR3PDDTf0KHfMMccwZswYDjvsMMaPH88RRzjnk99++20uuOCCnb37WbNmAXD++eczY8YMdt11V5YuXUo4HOY73/kOW7ZsYceOHVx22WWMGzcuadvee+89qqur2bp1K2VlZdxwww28+OKLBIPBQR+nMcZ/Cj4XzUsvvcQhhxyS9HGdrZ1svWWrM4umPwEI1gUpH1GegZYWJy/PpTGmuOQtF02ulI8op7KmEgL0PqIyIACVNZUlHdyNMaXHF0M0AIEDAwTrgmx7epszq2YbMNQZlhn6+aEW3I0xJcc3AR6cnnzFlIqcT4U0xphC5IshGmOMMb1ZgDfGGJ+yAG+MMT7lqwAfBeqBIM6BBd2/010XpVDTBd95552EQiFCoRBHH300K1euTGt/xhh/8U2AbwZCQCPQBqj7s9Hd3pzifgs5XfCYMWNYsmQJkUiEH/3oR9TV1aXVJmOMv/giwEeBGqCD3tc6xdztNaTWky/kdMFHH300e+yxBwBHHXUUa9euTeEIjTF+5YtpkrNJfhEr7v1zgLmD3HexpAu+9dZbmTLF2yrsxpjS4IsA34S3AD+fwQf4dOUiXfCjjz7KrbfeypNPPpnZxhtjipovArzXlQtTWeGw0NMFRyIRLrroIpqbm9lzzz29HJIxpkT4Ygze68qFqaxwWMjpgv/1r3/xta99jfnz53PQQQelcHTGGD/zRQ++Fme2zADJJEllhcNCThd87bXXsnHjRurr6wHnv4jEbJzGmNLli3TBUZypkB1JylQAESBzqfSLj6ULNsZ/fJ8uuAoI4wTxxLXKA+72MKUd3I0xpccXAR5gCk4PvY6eV7LWudttAqExptT4Ygy+WxXONMhcT4U0xphC5JsevDHGmJ4swBtTYKKtUeoX1ROcFaTsmjKCs4LUL6on2ppu2jxTaizAG1NAmlc3E5oXonFFI23b21CUtu1tNK5oJDQvRPPqVNPmmVKU1QAvImtE5HkRaRGRrE/QzlbPp1DTBf/pT38iFAoxYcIEqqurLVVBkYu2RqlZWENHrINYV8+rOmJdMTpiHdQsrLGevPEsFz34E1V1Qn/zNDMlWz2fQk4XfPLJJ7Ny5UpaWlq47bbbuOiii9Jqk8mv2UtnE+tMnlUp1hljzlNzctQiU+x8MUSTzZ5PIacLrqysREQA+OCDD3b+bopTU6Sp1/s3UawrxvzI/By1yBS7bE+TVGCxiCjwW1W9JbGAiNThTFdnv/32S6mSwfR85k4d3CTKQk8XfP/993PllVeybt06Fi1alHI7Tf61b/eWDs9rOWOy3YM/RlWPwLnO6GIROT6xgKreoqrVqlo9cuTIlCop5J5PfLrghx9+mGAw2KtMfLrgCRMm8NOf/rTH4h3J0gWfccYZvPzyyzzwwAP86Ec/ysoxmNyo3MVbOjyv5YzJaoBX1Xfcn+uA+4HPZaOebPZ8xo0bx/LlywcsN1C64MmTJ3PzzTf3OU7enS64paWFlpYWnn/+eRYvXrzz/mTpgrsdf/zxRKNRNmzY4OWwTAGqDdUSKEtMttFToCzA9FAqafNMKcpagBeR3URk9+7fgVOAVdmoK5s9n0JOF/zaa6/RnSxuxYoVbN++3XLCF7GZk2YSKB8gwJcHaDiqIUctMsUum2PwnwTud0/8DQHuUtWHs1FRbaiWxhWNSYdpUu35FHK64Pvuu48//OEPBAIBdt11V+6991470VrEqkZUEZ4WpmZhDbHOWI/3c6AsQKA8QHhamKoRljbPeOOPdMGtUULzQnTE+k8YXBGoIDIjUtIfDksXXByirVHmPDWH+ZH5tG9vp3KXSqaHptNwVENJv39N35KlC/ZFsjHr+Rg/qRpRxdypcwc948uYRL6YBw8wZewUIjMi1E2sIzg0SJmUERwapG5iHZEZEaaMtYTBxpjS4osefDfr+RhjzMd804M3xhjTkwV4Y4zxKQvwxhjjU74K8NFolPr6eoLBIGVlZQSDQerr64lGs5MueLBSTRE8depUNm/e3Gv71VdfzfXXXz/o/RljSoNvAnxzczOhUIjGxkba2tpQVdra2mhsbCQUCtHcnP90wckCfGdnZ7+Pe+ihhxg+fPig6zPGlDZfBPhoNEpNTQ0dHR3EYgnpgmMxOjo6qKmpSaknnyxd8C9/+UuOPPJIQqEQV111FQBr1qzhkEMO4Vvf+hbjxo3jlFNO4cMPP+wzRfDo0aO59tprOfbYY1m4cCF33333zithL7/88p31jR49emeOmZ/97GccfPDBfOELX+CVV17ZWeamm27i0EMPJRQKcc455wz6OI0x/uOLAD979uxegT1RLBZjzpzBL5TQX7rgxYsXs3r1ap555hlaWlpYvnw5jz/+OACrV6/m4osv5oUXXmD48OHcd9991NTUUF1dzZ133klLSwu77rorAMOGDePJJ5/k+OOP5/LLL+eRRx6hpaWFZ599lgceeKBHncuXL+eee+7hueee449//CPPPvvszvuuu+46nnvuOSKRCPPmzRv0cRpj/McXAb6pqclTgJ8/P3PpghcvXszixYs5/PDDOeKII3j55ZdZvXo1AGPGjGHChAkATJw4kTVr1vS7n+5UwM8++yyTJ09m5MiRDBkyhPPOO2/nF0a3J554gjPOOIOKigqCwSBf+cpXdt4XCoU477zzaGpqYsgQX13eYIxJkS8CfHu7x3TBHsvF6y9dsKpy5ZVX7kzx+9prr3HhhRcCMHTo0J3lysvLey21F687FbDXnED9JRNbtGgRF198McuXL2fixIlJ6zTGlAZfBPjKSo/pgj2Wi9dfuuBgMMhtt92280vj7bffZt26dUn3FZ8iONHnP/95lixZwoYNG+js7OTuu+/mhBNO6FHm+OOP5/777+fDDz+kra2NP//5zwA7UxSfeOKJ/OIXv2Dz5s0pfZkZY/zFF//L19bW0tjYmHSYJhAIMH16ZtMFDx8+nEmTJgHOl0dTUxPl5eX97isxRXC8ffbZh1mzZnHiiSeiqkydOpXTTz+9R5kjjjiCs88+mwkTJrD//vvvXBe2s7OT2tpatmzZgqrS0NBgs26MMT5JFxyNEgqF6OhIki64ooJIJEJVVelmlLR0wcb4T7J0wb4YoqmqqiIcDlNRUUEg0HNFnEAgQEVFBeFwuKSDuzGm9PgiwANMmTKFSCRCXV1djytZ6+rqiEQiTJli6YKNMaWlKMbgVdXTUnRVVVXMnTuXuXMtXXCiQhqKM8bkRsH34IcNG8bGjRstQKVBVdm4cSPDhg3Ld1OMMTlU8D34UaNGsXbtWtavX5/vphS1YcOGMWrUqHw3wxiTQwUf4AOBAGPGjMl3M4wxpugU/BCNMcaY1FiAN8YYn7IAb4wxPpX1AC8i5SLynIg8mO26jDHGfGzAAC8ix4jIbu7vtSLyKxHZfxB1fBd4KdUGGmOMSY2XHvxvgA4R+Szw38CbwB+87FxERgGnAo0pt9AYY0xKvAT4HepcZXQ6cKOq3gjs7nH/N+B8KXT1V0BE6kRkmYgss7nuxhiTOV4CfJuIXAnUAotEpBwIDPAYROQ0YJ2q9l4tI46q3qKq1apaPXLkSE+NNsYYMzAvAf5sYBtwoaq+B3wK+KWHxx0DfEVE1gD3ACeJSFOqDTXGGDM4XgJ8g6r+SlWfAFDVfwHjBnqQql6pqqNUdTRwDvCIqtam1VpjjDGeeQnwX+xjm+XeNcaYAtdvLhoR+U+gHjhARCJxd+0O/HMwlajqY8BjKbTPGGNMipIlG7sLaAZmAVfEbW9T1dastsoYY0za+h2iUdUtqrpGVc8FPg2cpKpvAmUiYukdjTGmwHm5kvUq4HLgSnfTLoDNhjHGmALn5STrGcBXgA8AVPUdvF/oZIwxJk+8BPjt7pWsCtCdl8YYY0xh8xLgF4jIb4HhIvIt4G/A77LbLGOMMekacMk+Vb1eRL4IbAUOBn6sqn/NesuMMcakxeuarK8Cqqp/E5EKEdldVduy2TBjjDHp8TKL5ltAGPitu+lTwAPZbJQxxpj0eRmDvxgncdhWAFVdDeydzUYZY4xJn5cAv01Vt3f/ISJDcGfUGGOMKVxeAvwSEfkBsKt7snUh8OfsNssYY3qLtkapX1RPcFaQsmvKCM4KUr+onmhrNN9NK0jiTHFPUkCkDLgQOAUQ4C9Aow70wBRUV1frsmXLMr1bY4wPNK9upmZhDbHOGLGu2M7tgbIAgfIA4WlhpowtvUS3IrJcVav7vM9DgD8JeEpVO7LRuHgW4I0xfYm2RgnNC9ER6z8MVQQqiMyIUDWiKocty79kAd7LEM35QIuILBWRX4jIl0Vkj4y20Bhjkpi9dDaxzljSMrHOGHOempOjFhWHAQO8qn5dVQ8CzgTWAjcDtjq2MSZnmiJNPYZl+hLrijE/Mj9HLSoOA17oJCK1wHHAYcAGYC7wRJbbZYwxO7Vvb89ouVLh5UrWG4AoMA94VFXXZLVFxhiToHKXStq2D3zxfOUulTloTfHwMkSzF/BNYBjwMxF5RkTs/yBjTM7UhmoJlAWSlgmUBZgemp6jFhUHL6kKgsB+wP7AaOAT2IVOxpgcmjlpJoHyAQJ8eYCGoxpy1KLi4GUWzZPAl4EIcLaqHqyqX89us4wx5mNVI6oITwtTEajo1ZMPlAWoCFQQnhYuuSmSA/ES4H+qqvWqepeqrgUQkWlZbpcxxvQwZewUIjMi1E2sIzg0SJmUERwapG5iHZEZkZK8yGkgXi50WqGqRwy0LRPsQidjjBmcZBc69TuLRkSmAFOBT4nITXF3BYEdmW2iMcaYTEs2TfIdYBnOgtvL47a3AQOeyRCRYcDjwFC3nrCqXpV6U40xxgxGvwFeVVcCK0XkLlVNfglZ37YBJ6lqu4gEgCdFpFlVn0q1scYYY7zzsiZrKsEdN9tk92VlAfdm0yuNMSZHvMyiSZmIlItIC7AO+KuqPt1HmToRWSYiy9avtxQ3xhiTKVkN8KraqaoTgFHA50RkfB9lblHValWtHjlyZDabY4wxJcVLsrGDgO/jXMm6s7yqnuS1ElXdLCKPAV8CVg2+mcYYYwbLS7KxhTiJxn4HdHrdsYiMBGJucN8V+ALwvym10hhjzKB5CfA7VPU3Kex7H+D3IlKOMxS0QFUfTGE/xhhjUuAlwP9ZROqB+3GmPgKgqq3JHqSqEeDw9JpnjDEmVV4C/Dfcn9+P26bAAZlvjjHGT6KtUWYvnU1TpIn27e1U7lJJbaiWmZNmWmKwHBgwF00uWS4aY/yjeXUzNQtriHXGeiy3FygLECgPEJ4WtgRhGZDWotsiEhCR74hI2L1d4l6ZaowxfYq2RqlZWENHrKPXWqqxrhgdsQ5qFtYQbY3mqYWlwcs8+N8AE4Ffu7eJ7jZjTAGIRqPU19cTDAYpKysjGAxSX19PNJq/4Dl76WxinQMskt0ZY85Tc3LUotLkJV3wSlX97EDbMsGGaIwZnObmZmpqaojFYsRiccMggQCBQIBwOMyUKbkfBgnOCnpaQzU4NMiWK7bkoEX+ldYQDdApIjvPhojIAQxiPrwxJjui0Sg1NTV0dHT0CO4AsViMjo4Oampq8tKTb9/ePnChQZQzqfES4L8PPCoij4nIEuARYGZ2m2WMGcjs2bN7BfZEsViMOXNyPwxSuUtlRsuZ1AwY4FX178BY4Dvu7WBVfTTbDTPGJNfU1OQpwM+fPz9HLfpYbai219qpiWyqwLUAABVCSURBVAJlAaaHpueoRaWp3wAvIie5P78GnAocCFQBp7rbjDF51N7ucRjEY7lMmjlpJoHyAQJ8eYCGowZcO8ikIVkP/gT355f7uJ2W5XYZYwZQWelxGMRjuUyqGlFFeFqYikBFr558oCxARaCC8LSwXeyUZf0G+O7l9VT1gj5u38xdE40xfamtrSUQGKCXHAgwfXp+hkGmjJ1CZEaEuol1BIcGKZMygkOD1E2sIzIjYhc55YCXaZJDgTOB0fRMF3xtphtj0yQz5xGcEyYvxG0bB9wEeM7zXGRK7bL4aDRKKBSio6Oj3zIVFRVEIhGqqvx3/MaR7jTJPwGnAzuAD+JupkBdC5xMz+CO+/fJ7v1+07y6mdC8EI0rGmnb3oaitG1vo3FFI6F5IZpXN+e7iRlXVVVFOBymoqKiV08+EAhQUVFBOBy24F7CvPTgV6lqr5WYssF68Ol7BCeID+Tv+KcnH22NEpoXoiOWpCcbqCAyI+LbnvycOXOYP38+7e3tVFZWMn36dBoaGiy4l4B0e/D/FJHDMtwmkyXf8Vjuu1ltRW6V+mXxVVVVzJ07ly1bttDZ2cmWLVuYO3euBXeTdJrk8yISAY4FVojIKyISidtuClDisEx//LRuYlOkqVdCq0SxrhjzI7mfD25MPiXLB29TIU1RsMvijelbsmmSb6rqmzhfAu+5v4/BOeFq2YFMwbDL4o3pm5cx+PtwEo4dCNyKE+TvymqrTMrGeSyXk7PmOWKXxRvTNy8BvktVdwBfA25Q1QacBbVNAbrJY7kbs9qK3LLL4o3pm5cAHxORc4GvAw+622xFpwJ1EnDNAGWuwT9TJMEuizemP14C/AXAJOBnqvqGiIwBmrLbLJOOH+PMc08chhnvbv9xzluUfXZZvDG92aLbRaYUUxAYY/qX7qLbY93Ftl8Ukde7b5lvphlIKaYgMMakzssQze04i2zvAE4E/gAMeMWIiHxaRB4VkZdE5AUR8dPFkzn3CHDVAGWucssZYwx4C/C7uqs6iTs3/mq8jQbsAGaq6iHAUcDFInJo6k0tbaWYgsAYk55kV7J2+0hEyoDVInIJ8Daw90APUtV3gXfd39tE5CXgU8CLabS3ZJViCgJjTHq89OAvAypwOpETgVrgG4OpRERGA4cDT/dxX52ILBORZevXrx/Mbo0xxiSRLNlY9zj70ararqpr3dWczlTVp7xWICKVOFfDXqaqWxPvV9VbVLVaVatHjhw56AMwxhjTt2Q9+Ikisj/wTRHZQ0RGxN+87FxEAjjB/U5V/WMmGlyqSjEFgTEmPcnG4OcBDwMHAMsBibtP3e39EhHByV3zkqr+Ks12lryb8LaQh59SEBhj0pMsm+RN7gyY21T1AFUdE3dLGtxdxwDTgZNEpMW9Tc1Uw0tNKaYgMMakZ8BZNKr6n6nsWFWfpGev36Tpxzirr3yXnrNlxuP03C24G2PieZlFY3IgCtQDQZwXJej+HU0odxLwPM4YWffteYovuEdbo9Qvqic4K0jZNWUEZwWpX1RPtDXxiP1VtzG5ZLloCkAzUAPE3Fu3gHsLA35KldW8upmahTXEOmM9ltoLlAUIlAcITwtnLTlYPus2JhvSXXTbZFEUJ7h30DO44/7d4d7vl75ltDVKzcIaOmIdvdZRjXXF6Ih1ULOwJiu96XzWbUw+WIDPs9n0DuyJYsCcHLQlF2YvnU2sc4AFsjtjzHkq80ecz7qNyQcL8HnWhLcAP2B2tyLRFGnq1XtOFOuKMT+S+SPOZ93G5IMF+Dxrz3C5Qte+3duReC1XLHUbkw8W4POsMsPlCl3lLt6OxGu5YqnbmHywAJ9ntQy8wG0A54oxP6gN1fZaNzVRoCzA9FDmjzifdRuTDxbg82wm3gJ8Qw7akgszJ80kUD5AkC0P0HBU5o84n3Ubkw8W4POsCmeeewW9A33A3R52y/lB1YgqwtPCVAQqevWmA2UBKgIVhKeFqRqR+SPOZ93G5IMF+AIwBYgAdfS8krXO3e63y26mjJ1CZEaEuol1BIcGKZMygkOD1E2sIzIjktULjfJZtzG5ZleyZtG5wD19bD8HuDvHbTHG+JNdyZoHn6Tv4I67/ZM5bIsxpjRZgM+Cc4F1A5RZ55YzxphssQCfBf313FMtZ4wxqbAAb4wxPmUB3hhjfMoCvDHG+JQFeGOM8SkL8FlwTobLGWNMKizAZ8HdwN4DlNkbu9jJGJNdFuCz5H3676Gf495vjDHZZAE+BRMA6eM2IaHc3YD2cbOeuzEmF4bkuwHFJgDs6Oe+le79Ay3BZ4wxuZC1HryI3CYi60RkVbbqyLUJ9B/cu+2gd0/eGGPyIZtDNHcAX8ri/nNuZYbLGWNMNmUtwKvq40BrtvZvjDEmubyfZBWROhFZJiLL1q9fn+/m9NIEjMA5iVoqokA9PRcfqXe3G2OKR94DvKreoqrVqlo9cuTIfDenh+nubVO+G5JDzUAIaATacGb9tLl/h9z7jTHFIe8BvlA1ubdSEgVqgA56zwSKudtrsJ68McXCAnw/vpPGYz+bsVbk1mwGnuIZA+bkoC3GmPRlc5rk3cBS4GARWSsiF2arrmxIdVhmCNCSyYbkUBPeAvz8HLTFGJO+rF3opKoltyLdZyne4A7QnuFyxpj8siGaOPFpBwajOwVBOsE9frZO920E2T8P8Agw3q1PPT6mMnvNMcZkkAV4V6rTIEdkoO7+ZutsirsvG64FTgZeGMRjAmSvPcaYzLIAT3pz3G9Ms24vs3WyMaPnEeCqFB4XABoy3BZjTHZYgE9DrXtLh9fZOt9Ns55U6+0WACqAMFCV4bYYY7LDAnwKRuDMJMnEbBKvs3UynfNhMMMyQaAOiABTMtwOY0z2lGyAP4TUT6huJPWee/xJzVynP4hPQTAYW4C5pN5zt9QHpSHaGqV+UT3BWUHKrikjOCtI/aJ6oq32SueLqHqdO5F91dXVumzZsqzXUw50pfjYdJ6ta0lt3DsTdTfjXIUaY/D56rNRb8C9hbH/CvygeXUzNQtriHXGiHV9/EoHygIEygOEp4WZMtZe6WwQkeWqWt3XfSXXgz+E1IN7OlI9qdktndk6yVIQDGR8luq11Af+EW2NUrOwho5YR4/gDhDritER66BmYY315POg5AL8y3mqN53UB5DebB0vKQjyVa+lPih+s5fOJtaZ/JWOdcaY85S90rlWcgE+HekMVQzmpGaidGfreElB0JdrgJOyXK+lPih+TZGmXj33RLGuGPMj9krnmgV4j/JxpiJTs3UGm1pgPPB34Mc5qtdSHxS39u3eXkGv5UzmlESATzcFQarBfUaa9aYzWyc+9YHX9gfdss+Tes89fsaMpT4oDZW7eHsFvZYzmeP7AN8ruEajUF8PwSCUlTk/6+ud7Rk0BvhtCo9L56Rmt1QWKslECoLExUJyVa/Jr9pQLYGyQNIygbIA00P2SuearwN8r+De3AyhEDQ2QlsbqDo/Gxud7c0fr1f0mTTqnQGsSfGxuUh90Jd0UxCkOlPHUh8Uv5mTZhIoHyDAlwdoOMpe6VzzdYDvIRqFmhro6IBYQgiKxZztNTUQjVIGvJRGVan03CH9k5qQvxQEg52pY6kP/KNqRBXhaWEqAhW9evKBsgAVgQrC08JUjbBXOtdKJ8DPnt07sCeKxRg+Zw6duWnRTpk6qQmDG5bJZAqCwczUsdQH/jNl7BQiMyLUTawjODRImZQRHBqkbmIdkRkRu8gpT3x3Jeumn3wc4kb8z3AQd6AmGHSGYwYQDAbZsmXLoOs9EXhs0I/K/OycwZzQzWTdZR73VwY5/wLNpGhrlNlLZ9MUaaJ9ezuVu1RSG6pl5qSZ1kM1eVEyV7LGB/de2j1O5fJYLt5upBbc/cTr/IhinkfRvLqZ0LwQjSsaadvehqK0bW+jcUUjoXkhmlc3D7wTY3LINwG+3+De/R9KpcepXB7LdTsR58RiodjDY7lMLFQSrxZnXD2ZYp4xY5fjm2LkmwDfp+3bP/69thYCA5zpDwSYPn1wIeixFJrV7dtpPLY/N3ksl+5snUQz8Rbgi3UehV2Ob4qRrwN86y86oKvL6cV/73ueAnxDQ25C0GhgXhb26yWtQSYWKklUhTMjpoLegd4PM2bscnxTjHwd4AFaf77V6clXVcHChVBR0SvQBwIBKioqCIfDVFVlPwR9G3gji/vvTm+QOAyTyYVK+jIFZ2ZMHT1zv/thxoxdjm+KUVHPolnRsIKb/3EzCyIL+GDbB+w2dDfOCp3FxcdczJgRY/p8zButb3Bb7Dbmz59Pe3s7lZWVTJ8+nYaGBs/BPdWFOjLxTO8NrO9j+0hgXQb2b/oWnBWkbbuHWVhDg2y5YvCzsIxJVbJZNEUb4BdMX8AF917Q7wIDt599O1886It9PnaPH3k9FdlbOqswpftMe6m7cF5Nf6lfVE/jisakwzSBsgB1E+uYO3VuDltmSl3epkmKyJdE5BUReU1ErsjUflc0rOCCey9IOqPhgnsv4I3WzA6EpBPcJ6dZ994ZLmcGxy7HN8UoawFeRMqBm3GGXg8FzhWRQzOx75v/cbOnGQ2//ueve21Pp/eeqgrg0TT30dewTDrlzODY5fimGGWzB/854DVVfV1VtwP3AKdnYscLIgs8zWhYsHJBj235CO6TgQ9yXqvJBrsc3xSbrI3Bi0gN8CVVvcj9ezrweVW9JKFcHc5EC/bbb7+Jb7755oD7LpMy1MNos4jQ1ZW5FVjzlQYg33UbYwpXvsbg+4pJvWKPqt6iqtWqWj1y5EhPO95t6G6eytkCA8aYUpbNAL8W+HTc36OAdzKx47NCZ3laYOCsz56VieqMMaYoZTPAPwuMFZExIrILcA7wf5nY8cXHXOxpRkP90fWZqK4gePvfxns5Y4z/ZS3Aq+oO4BLgLzjrZyxQ1Rcyse8j5hzB7WffnnRGw+1n384Rc47IRHU7eR3bzsYYuNeLmOxiJ2NMtyHZ3LmqPgQ8lI19nzX/LA7c60B+/c9fs2Dlgp25uc/67FnUH12f8eDeTUl+wjObJzgVu5LVGONd0V7JaowxpoQW/DDGGPMxC/DGGONTFuCNMcanCmoMXkTWAwNfytq3vYANGWxOMbBj9r9SO16wYx6s/VW1zxnSBRXg0yEiy/o70eBXdsz+V2rHC3bMmWRDNMYY41MW4I0xxqf8FOBvyXcD8sCO2f9K7XjBjjljfDMGb4wxpic/9eCNMcbEsQBvjDE+VfQBPlsLexcqEfm0iDwqIi+JyAsi8t18tylXRKRcRJ4TkQfz3ZZcEJHhIhIWkZfd13tSvtuUbSLS4L6vV4nI3SIyLN9tyjQRuU1E1onIqrhtI0TkryKy2v2ZkfVFizrAZ3Nh7wK2A5ipqocARwEXl8Axd/suTurpUnEj8LCqfgb4LD4/dhH5FPAdoFpVxwPlOOtI+M0dwJcStl0B/F1VxwJ/d/9OW1EHeLK4sHehUtV3VXWF+3sbzof+U/ltVfaJyCjgVKAx323JBREJAscDtwKo6nZV3ZzfVuXEEGBXERkCVJChVeAKiao+DrQmbD4d+L37+++Br2airmIP8J8C3or7ey0lEOy6icho4HDg6fy2JCduAP4byNwq6oXtAJzU/7e7w1KNIuJtMeIipapvA9cD/wLeBbao6uL8tipnPqmq74LTicNZ+iFtxR7gPS3s7UciUgncB1ymqlvz3Z5sEpHTgHWqujzfbcmhIcARwG9U9XDgAzL0b3uhcsedTwfGAPsCu4lIbX5bVdyKPcBnbWHvQiYiAZzgfqeq/jHf7cmBY4CviMganGG4k0SkKb9Nyrq1wFpV7f7vLIwT8P3sC8AbqrpeVWPAH4Gj89ymXHlfRPYBcH9mZIG2Yg/wWVvYu1CJiOCMy76kqr/Kd3tyQVWvVNVRqjoa5zV+RFV93bNT1feAt0TkYHfTycCLeWxSLvwLOEpEKtz3+cn4/MRynP8DvuH+/g3gT5nYaVbXZM02Vd0hIt0Le5cDt2VqYe8CdgwwHXheRFrcbT9w1781/nIpcKfbeXkduCDP7ckqVX1aRMLACpzZYs/hw7QFInI3MBnYS0TWAlcB1wELRORCnC+6aRmpy1IVGGOMPxX7EI0xxph+WIA3xhifsgBvjDE+ZQHeGGN8ygK8Mcb4lAV44xsi8piIVLu/P+RmYxwuIvVxZfZ1p+KlU89kEcnaBTgiMlpE/iNb+zelwwK88SVVneom5xoO1Mdtf0dVa9Lc/WSye4XlaMACvEmbBXhTkNxe7Msi8nsRibh50Svc+052E3A97+bWHtrH49eIyF44F5BUiUiLiPzS3e8qt0y5iFzv7iciIpe6268TkRfdbdcntguYATS4+zxBRF4Xx3AR6RKR492yT4jIgSKym9vOZ912nx5X/y/d7RER+bZbzXXAce7+G0RknIg84/4dEZGx2XjOjf8U9ZWsxvcOBi5U1X+IyG1AvYjMxcmnfbKqvioifwD+EyfbZF+uAMar6gTYGaC71eEktjrcvSp6hIiMAM4APqOqKiLD43emqmtEZB7QrqrXu/t8FWc9gjHAcpzg/DQwSlVfE5Gf46RX+Ka7v2dE5G/AeTgZE490v6T+ISKL3Tb/l6qe5u7//wE3qmr3Va3lKT6fpsRYD94UsrdU9R/u703AsThB/w1VfdXd/nucvOmp+AIwT1V3AKhqK7AV+AhoFJGvAR0e9vOE24bjgVluO4/EyZUEcApwhZta4jFgGLCfu/3r7vangT2BvnrnS4EfiMjlwP6q+uHgD9WUIgvwppAl5tFQ+k4RnSpJrMMN9p/Dydb5VeBhD/t5AjjOfdxDOOP+k4HH4+o5U1UnuLf9VPUld/ulcdvH9JX/XFXvAr4CfAj8RUROGvyhmlJkAd4Usv3k43VIzwWeBF4GRovIge726cCSJPtoA3bv577FwAx39aDudTErgU+4ydsuAyZ42OfTOCddu1T1I6AF+DZO4AcnGd6lboZEROTwuO3/6aZ/RkQOchf16LF/ETkAeF1Vb8LJOhhKcrzG7GQB3hSyl4BviEgEGIGz+MVHOFkVF4rI8zgrPM3rbwequhFnbHuViPwy4e5GnMx9ERFZiTNzZXfgQbfOJUBDH7v9M3CGe9LzOFXdhrOy2FPu/U+4+3ne/fsnQMCtZ5X7d3f9LwIr3O2/xTkvFgF2iMhKEWkAzgZWuUM5nwH+kPxpM8Zh2SRNQXJPhj7oLr5sjEmB9eCNMcanrAdvjDE+ZT14Y4zxKQvwxhjjUxbgjTHGpyzAG2OMT1mAN8YYn/r/8pgKB2Owos0AAAAASUVORK5CYII=\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 fashion and politics groups')\n",
    "plt.xlabel('politics tweets')\n",
    "plt.ylabel('fashion tweets')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Technology-Mechanical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "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": [
    "\n",
    "# X = pivot_clusters.iloc[:, [2,3]].values\n",
    "X = pivot_clusters[['technology', 'mechanical']].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": 235,
   "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 technology and mechanical groups')\n",
    "plt.xlabel('technology tweets')\n",
    "plt.ylabel('mechanical tweets')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " > Technology-Fashion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "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[['technology', 'fashion']].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": 237,
   "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 fashion and technology groups')\n",
    "plt.xlabel('technology tweets')\n",
    "plt.ylabel('fashion tweets')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Technology-Mechanical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "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[['technology', 'mechanical']].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": 239,
   "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 technology and mechanical groups')\n",
    "plt.xlabel('technology tweets')\n",
    "plt.ylabel('mechanical tweets')\n",
    "plt.legend()  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> \n",
    "Education-Politics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "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[['education', 'politics']].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": 241,
   "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 education and politics groups')\n",
    "plt.xlabel('education tweets')\n",
    "plt.ylabel('politics tweets')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "* Fashion*Mechanical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "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[['fashion', 'mechanical',]].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": 243,
   "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 mechanical and fashion groups')\n",
    "plt.xlabel('fashion tweets')\n",
    "plt.ylabel('mechanical tweets')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}