MBTI personality classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MBTI Personality Classifier\n",
    "\n",
    "This programme will classify people into mbti personality types based on their past 50 posts on social media using the basic naivebayesclassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import nltk\n",
    "import string\n",
    "from nltk.classify import NaiveBayesClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>type</th>\n",
       "      <th>posts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8670</th>\n",
       "      <td>ISFP</td>\n",
       "      <td>'https://www.youtube.com/watch?v=t8edHB_h908||...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8671</th>\n",
       "      <td>ENFP</td>\n",
       "      <td>'So...if this thread already exists someplace ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8672</th>\n",
       "      <td>INTP</td>\n",
       "      <td>'So many questions when i do these things.  I ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8673</th>\n",
       "      <td>INFP</td>\n",
       "      <td>'I am very conflicted right now when it comes ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8674</th>\n",
       "      <td>INFP</td>\n",
       "      <td>'It has been too long since I have been on per...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      type                                              posts\n",
       "8670  ISFP  'https://www.youtube.com/watch?v=t8edHB_h908||...\n",
       "8671  ENFP  'So...if this thread already exists someplace ...\n",
       "8672  INTP  'So many questions when i do these things.  I ...\n",
       "8673  INFP  'I am very conflicted right now when it comes ...\n",
       "8674  INFP  'It has been too long since I have been on per..."
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_set = pd.read_csv(\"mbti_1.csv\")\n",
    "data_set.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking the dataset for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type     False\n",
       "posts    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_set.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The size of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8675, 2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_set.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explroing the posts in posts field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"'http://www.youtube.com/watch?v=qsXHcwe3krw\",\n",
       " 'http://41.media.tumblr.com/tumblr_lfouy03PMA1qa1rooo1_500.jpg',\n",
       " 'enfp and intj moments  https://www.youtube.com/watch?v=iz7lE1g4XM4  sportscenter not top ten plays  https://www.youtube.com/watch?v=uCdfze1etec  pranks',\n",
       " 'What has been the most life-changing experience in your life?',\n",
       " 'http://www.youtube.com/watch?v=vXZeYwwRDw8   http://www.youtube.com/watch?v=u8ejam5DP3E  On repeat for most of today.',\n",
       " 'May the PerC Experience immerse you.',\n",
       " 'The last thing my INFJ friend posted on his facebook before committing suicide the next day. Rest in peace~   http://vimeo.com/22842206',\n",
       " \"Hello ENFJ7. Sorry to hear of your distress. It's only natural for a relationship to not be perfection all the time in every moment of existence. Try to figure the hard times as times of growth, as...\",\n",
       " '84389  84390  http://wallpaperpassion.com/upload/23700/friendship-boy-and-girl-wallpaper.jpg  http://assets.dornob.com/wp-content/uploads/2010/04/round-home-design.jpg ...',\n",
       " 'Welcome and stuff.',\n",
       " 'http://playeressence.com/wp-content/uploads/2013/08/RED-red-the-pokemon-master-32560474-450-338.jpg  Game. Set. Match.',\n",
       " \"Prozac, wellbrutin, at least thirty minutes of moving your legs (and I don't mean moving them while sitting in your same desk chair), weed in moderation (maybe try edibles as a healthier alternative...\",\n",
       " \"Basically come up with three items you've determined that each type (or whichever types you want to do) would more than likely use, given each types' cognitive functions and whatnot, when left by...\",\n",
       " 'All things in moderation.  Sims is indeed a video game, and a good one at that. Note: a good one at that is somewhat subjective in that I am not completely promoting the death of any given Sim...',\n",
       " 'Dear ENFP:  What were your favorite video games growing up and what are your now, current favorite video games? :cool:',\n",
       " 'https://www.youtube.com/watch?v=QyPqT8umzmY',\n",
       " 'It appears to be too late. :sad:',\n",
       " \"There's someone out there for everyone.\",\n",
       " 'Wait... I thought confidence was a good thing.',\n",
       " \"I just cherish the time of solitude b/c i revel within my inner world more whereas most other time i'd be workin... just enjoy the me time while you can. Don't worry, people will always be around to...\",\n",
       " \"Yo entp ladies... if you're into a complimentary personality,well, hey.\",\n",
       " '... when your main social outlet is xbox live conversations and even then you verbally fatigue quickly.',\n",
       " 'http://www.youtube.com/watch?v=gDhy7rdfm14  I really dig the part from 1:46 to 2:50',\n",
       " 'http://www.youtube.com/watch?v=msqXffgh7b8',\n",
       " 'Banned because this thread requires it of me.',\n",
       " 'Get high in backyard, roast and eat marshmellows in backyard while conversing over something intellectual, followed by massages and kisses.',\n",
       " 'http://www.youtube.com/watch?v=Mw7eoU3BMbE',\n",
       " 'http://www.youtube.com/watch?v=4V2uYORhQOk',\n",
       " 'http://www.youtube.com/watch?v=SlVmgFQQ0TI',\n",
       " \"Banned for too many b's in that sentence. How could you! Think of the B!\",\n",
       " 'Banned for watching movies in the corner with the dunces.',\n",
       " 'Banned because Health class clearly taught you nothing about peer pressure.',\n",
       " 'Banned for a whole host of reasons!',\n",
       " 'http://www.youtube.com/watch?v=IRcrv41hgz4',\n",
       " \"1) Two baby deer on left and right munching on a beetle in the middle.  2) Using their own blood, two cavemen diary today's latest happenings on their designated cave diary wall.  3) I see it as...\",\n",
       " 'a pokemon world  an infj society  everyone becomes an optimist',\n",
       " '49142',\n",
       " 'http://www.youtube.com/watch?v=ZRCEq_JFeFM',\n",
       " 'http://discovermagazine.com/2012/jul-aug/20-things-you-didnt-know-about-deserts/desert.jpg',\n",
       " 'http://oyster.ignimgs.com/mediawiki/apis.ign.com/pokemon-silver-version/d/dd/Ditto.gif',\n",
       " 'http://www.serebii.net/potw-dp/Scizor.jpg',\n",
       " \"Not all artists are artists because they draw. It's the idea that counts in forming something of your own... like a signature.\",\n",
       " \"Welcome to the robot ranks, person who downed my self-esteem cuz I'm not an avid signature artist like herself. :proud:\",\n",
       " 'Banned for taking all the room under my bed. Ya gotta learn to share with the roaches.',\n",
       " 'http://www.youtube.com/watch?v=w8IgImn57aQ',\n",
       " 'Banned for being too much of a thundering, grumbling kind of storm... yep.',\n",
       " \"Ahh... old high school music I haven't heard in ages.   http://www.youtube.com/watch?v=dcCRUPCdB1w\",\n",
       " \"I failed a public speaking class a few years ago and I've sort of learned what I could do better were I to be in that position again. A big part of my failure was just overloading myself with too...\",\n",
       " \"I like this person's mentality. He's a confirmed INTJ by the way. http://www.youtube.com/watch?v=hGKLI-GEc6M\",\n",
       " \"Move to the Denver area and start a new life for myself.'\"]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_set.iloc[0,1].split('|||')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finding the number of posts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data_set.iloc[1,1].split('|||'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finding the unique vales from type of personality column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ENFJ', 'ENFP', 'ENTJ', 'ENTP', 'ESFJ', 'ESFP', 'ESTJ', 'ESTP',\n",
       "       'INFJ', 'INFP', 'INTJ', 'INTP', 'ISFJ', 'ISFP', 'ISTJ', 'ISTP'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "types = np.unique(np.array(data_set['type']))\n",
    "types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The total number of posts for each type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "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>posts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENFJ</th>\n",
       "      <td>9500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENFP</th>\n",
       "      <td>33750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENTJ</th>\n",
       "      <td>11550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENTP</th>\n",
       "      <td>34250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ESFJ</th>\n",
       "      <td>2100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ESFP</th>\n",
       "      <td>2400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ESTJ</th>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ESTP</th>\n",
       "      <td>4450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INFJ</th>\n",
       "      <td>73500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INFP</th>\n",
       "      <td>91600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INTJ</th>\n",
       "      <td>54550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INTP</th>\n",
       "      <td>65200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ISFJ</th>\n",
       "      <td>8300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ISFP</th>\n",
       "      <td>13550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ISTJ</th>\n",
       "      <td>10250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ISTP</th>\n",
       "      <td>16850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      posts\n",
       "type       \n",
       "ENFJ   9500\n",
       "ENFP  33750\n",
       "ENTJ  11550\n",
       "ENTP  34250\n",
       "ESFJ   2100\n",
       "ESFP   2400\n",
       "ESTJ   1950\n",
       "ESTP   4450\n",
       "INFJ  73500\n",
       "INFP  91600\n",
       "INTJ  54550\n",
       "INTP  65200\n",
       "ISFJ   8300\n",
       "ISFP  13550\n",
       "ISTJ  10250\n",
       "ISTP  16850"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = data_set.groupby(['type']).count()*50\n",
    "total"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Graphing it for better visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Total posts for each personality type')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,6))\n",
    "\n",
    "plt.bar(np.array(total.index), height = total['posts'],)\n",
    "plt.xlabel('Personality types', size = 14)\n",
    "plt.ylabel('Number of posts available', size = 14)\n",
    "plt.title('Total posts for each personality type')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Organising the data to create a bag words model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Segrating all the posts by their personality types and ***creating a new dataframe to store all this in***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_posts= pd.DataFrame()\n",
    "for j in types:\n",
    "    temp1 = data_set[data_set['type']==j]['posts']\n",
    "    temp2 = []\n",
    "    for i in temp1:\n",
    "        temp2+=i.split('|||')\n",
    "    temp3 = pd.Series(temp2)\n",
    "    all_posts[j] = temp3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ENFJ</th>\n",
       "      <th>ENFP</th>\n",
       "      <th>ENTJ</th>\n",
       "      <th>ENTP</th>\n",
       "      <th>ESFJ</th>\n",
       "      <th>ESFP</th>\n",
       "      <th>ESTJ</th>\n",
       "      <th>ESTP</th>\n",
       "      <th>INFJ</th>\n",
       "      <th>INFP</th>\n",
       "      <th>INTJ</th>\n",
       "      <th>INTP</th>\n",
       "      <th>ISFJ</th>\n",
       "      <th>ISFP</th>\n",
       "      <th>ISTJ</th>\n",
       "      <th>ISTP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9283</th>\n",
       "      <td>I am VERY particular. And I have not dated cer...</td>\n",
       "      <td>'Very true.  The thing is to recognize and ove...</td>\n",
       "      <td>Um, totally off ROFL. I am female, 30, no opin...</td>\n",
       "      <td>Actually I was none of these labels of alpha o...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>'So, hey everyone, me again. :happy:  I want t...</td>\n",
       "      <td>Fantastic, I've avoided having to make my own ...</td>\n",
       "      <td>Arwen - Lord of the Rings  Her faithfulness, r...</td>\n",
       "      <td>i love romance. i love the idea of inside joke...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>If it still serves a meaningful purpose, I don...</td>\n",
       "      <td>Everything points to ISTJ for me, but then som...</td>\n",
       "      <td>Group One I tend to be sociable, but sometimes...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9284</th>\n",
       "      <td>Could you not tell the guy was INTP?  Is that ...</td>\n",
       "      <td>i'm not real sure it's a great idea to date un...</td>\n",
       "      <td>Hahah that's hilarious</td>\n",
       "      <td>Double post, dammit. &gt;_&lt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Put these all together, and you have a fair id...</td>\n",
       "      <td>I suppose in some respect, I used to be that w...</td>\n",
       "      <td>I'm taking this path myself right now, and I'm...</td>\n",
       "      <td>so not fair for those of us who are insecure a...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LOL I'm glad I could remind you of that. But y...</td>\n",
       "      <td>I think ESFJ is possible</td>\n",
       "      <td>https://www.youtube.com/watch?v=22wU-UITRIA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9285</th>\n",
       "      <td>I cannot speak for all ENFJ's, but I am hard-w...</td>\n",
       "      <td>Watch some comedies, kids are joyful and make ...</td>\n",
       "      <td>https://www.onlineassessmenttool.com/instinctu...</td>\n",
       "      <td>Radicalism 83 Socialism 100 Tenderness 71.875 ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I need to let go of my tendency to take too la...</td>\n",
       "      <td>I only know one in real life, but personally, ...</td>\n",
       "      <td>If only! My life would be so much more ruthles...</td>\n",
       "      <td>i hadn't heard of this, but i feel it's true i...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LOL. Thanks for making my day...INTPs are so f...</td>\n",
       "      <td>I disagree that Brandon is an INTJ. He cares a...</td>\n",
       "      <td>I'm trying to think... my first two detentions...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9286</th>\n",
       "      <td>Of course you do not see the point of the vide...</td>\n",
       "      <td>Meditation is great! Read up on TFT and EFT ta...</td>\n",
       "      <td>ENTJ - idunno...</td>\n",
       "      <td>Quoted for astonishing truth.  One thing thoug...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hello, and welcome to the forum!!! :D There's ...</td>\n",
       "      <td>Am I evil? is a question I've asked myself tim...</td>\n",
       "      <td>I don't know, maybe because I'm a Jungian mutt...</td>\n",
       "      <td>that's funny to me because i saved my chat log...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>That's fair I guess, but you're still trying t...</td>\n",
       "      <td>I thought so, but I don't think she rambles as...</td>\n",
       "      <td>I think you can.  You should never let your pe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9287</th>\n",
       "      <td>The types fit because the way they use their c...</td>\n",
       "      <td>2 Physical Touch   7 Quality Time   7 Words of...</td>\n",
       "      <td>I miscarried recently and was quite distraught...</td>\n",
       "      <td>Sure. I was taken aback by what I saw (and pre...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Oh that's just cruel. :D I'm going to go and G...</td>\n",
       "      <td>I've yet to meet an ENFJ in real life but you ...</td>\n",
       "      <td>I do not watch TV, period. I haven't since 200...</td>\n",
       "      <td>wow. i just fell in love with myself  lol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A lot of your responses seemed to indicate a p...</td>\n",
       "      <td>Cmart animalfromthesea you are both probably r...</td>\n",
       "      <td>I think it's just a part of human nature to ca...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   ENFJ  \\\n",
       "9283  I am VERY particular. And I have not dated cer...   \n",
       "9284  Could you not tell the guy was INTP?  Is that ...   \n",
       "9285  I cannot speak for all ENFJ's, but I am hard-w...   \n",
       "9286  Of course you do not see the point of the vide...   \n",
       "9287  The types fit because the way they use their c...   \n",
       "\n",
       "                                                   ENFP  \\\n",
       "9283  'Very true.  The thing is to recognize and ove...   \n",
       "9284  i'm not real sure it's a great idea to date un...   \n",
       "9285  Watch some comedies, kids are joyful and make ...   \n",
       "9286  Meditation is great! Read up on TFT and EFT ta...   \n",
       "9287  2 Physical Touch   7 Quality Time   7 Words of...   \n",
       "\n",
       "                                                   ENTJ  \\\n",
       "9283  Um, totally off ROFL. I am female, 30, no opin...   \n",
       "9284                             Hahah that's hilarious   \n",
       "9285  https://www.onlineassessmenttool.com/instinctu...   \n",
       "9286                                   ENTJ - idunno...   \n",
       "9287  I miscarried recently and was quite distraught...   \n",
       "\n",
       "                                                   ENTP ESFJ ESFP ESTJ ESTP  \\\n",
       "9283  Actually I was none of these labels of alpha o...  NaN  NaN  NaN  NaN   \n",
       "9284                           Double post, dammit. >_<  NaN  NaN  NaN  NaN   \n",
       "9285  Radicalism 83 Socialism 100 Tenderness 71.875 ...  NaN  NaN  NaN  NaN   \n",
       "9286  Quoted for astonishing truth.  One thing thoug...  NaN  NaN  NaN  NaN   \n",
       "9287  Sure. I was taken aback by what I saw (and pre...  NaN  NaN  NaN  NaN   \n",
       "\n",
       "                                                   INFJ  \\\n",
       "9283  'So, hey everyone, me again. :happy:  I want t...   \n",
       "9284  Put these all together, and you have a fair id...   \n",
       "9285  I need to let go of my tendency to take too la...   \n",
       "9286  Hello, and welcome to the forum!!! :D There's ...   \n",
       "9287  Oh that's just cruel. :D I'm going to go and G...   \n",
       "\n",
       "                                                   INFP  \\\n",
       "9283  Fantastic, I've avoided having to make my own ...   \n",
       "9284  I suppose in some respect, I used to be that w...   \n",
       "9285  I only know one in real life, but personally, ...   \n",
       "9286  Am I evil? is a question I've asked myself tim...   \n",
       "9287  I've yet to meet an ENFJ in real life but you ...   \n",
       "\n",
       "                                                   INTJ  \\\n",
       "9283  Arwen - Lord of the Rings  Her faithfulness, r...   \n",
       "9284  I'm taking this path myself right now, and I'm...   \n",
       "9285  If only! My life would be so much more ruthles...   \n",
       "9286  I don't know, maybe because I'm a Jungian mutt...   \n",
       "9287  I do not watch TV, period. I haven't since 200...   \n",
       "\n",
       "                                                   INTP ISFJ  \\\n",
       "9283  i love romance. i love the idea of inside joke...  NaN   \n",
       "9284  so not fair for those of us who are insecure a...  NaN   \n",
       "9285  i hadn't heard of this, but i feel it's true i...  NaN   \n",
       "9286  that's funny to me because i saved my chat log...  NaN   \n",
       "9287          wow. i just fell in love with myself  lol  NaN   \n",
       "\n",
       "                                                   ISFP  \\\n",
       "9283  If it still serves a meaningful purpose, I don...   \n",
       "9284  LOL I'm glad I could remind you of that. But y...   \n",
       "9285  LOL. Thanks for making my day...INTPs are so f...   \n",
       "9286  That's fair I guess, but you're still trying t...   \n",
       "9287  A lot of your responses seemed to indicate a p...   \n",
       "\n",
       "                                                   ISTJ  \\\n",
       "9283  Everything points to ISTJ for me, but then som...   \n",
       "9284                           I think ESFJ is possible   \n",
       "9285  I disagree that Brandon is an INTJ. He cares a...   \n",
       "9286  I thought so, but I don't think she rambles as...   \n",
       "9287  Cmart animalfromthesea you are both probably r...   \n",
       "\n",
       "                                                   ISTP  \n",
       "9283  Group One I tend to be sociable, but sometimes...  \n",
       "9284        https://www.youtube.com/watch?v=22wU-UITRIA  \n",
       "9285  I'm trying to think... my first two detentions...  \n",
       "9286  I think you can.  You should never let your pe...  \n",
       "9287  I think it's just a part of human nature to ca...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_posts.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a function to tokenize the words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "useless_words = nltk.corpus.stopwords.words(\"english\") + list(string.punctuation)\n",
    "def build_bag_of_words_features_filtered(words):\n",
    "    words = nltk.word_tokenize(words)\n",
    "    return {\n",
    "        word:1 for word in words \\\n",
    "        if not word in useless_words}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A random check of the function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Dear': 1,\n",
       " 'ENTJ': 1,\n",
       " 'sub': 1,\n",
       " 'Long': 1,\n",
       " 'time': 1,\n",
       " 'see': 1,\n",
       " 'Sincerely': 1,\n",
       " 'Alpha': 1}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "build_bag_of_words_features_filtered(all_posts['INTJ'].iloc[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating an array of features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "features=[]\n",
    "for j in types:\n",
    "    temp1 = all_posts[j]\n",
    "    temp1 = temp1.dropna() #not all the personality types have same number of files\n",
    "    features += [[(build_bag_of_words_features_filtered(i), j) \\\n",
    "    for i in temp1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because each number of personality types have different number of posts they must be splitted accordingle. Taking 80% for training and 20% for testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "split=[]\n",
    "for i in range(16):\n",
    "    split += [len(features[i]) * 0.8]\n",
    "split = np.array(split,dtype = int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7430, 7430, 7430, 7430, 1614, 1772, 1536, 3469, 7430, 7430, 7430,\n",
       "       7430, 6496, 7430, 7430, 7430])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=[]\n",
    "for i in range(16):\n",
    "    train += features[i][:split[i]] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentiment_classifier = NaiveBayesClassifier.train(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing the model on the dataset it was trained for accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43.904281855160065"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.classify.util.accuracy(sentiment_classifier, train)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "test=[]\n",
    "for i in range(16):\n",
    "    test += features[i][split[i]:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing the model on the test dataset which it has never seen before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.238794851632662"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.classify.util.accuracy(sentiment_classifier, test)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The model performs at efficieny of only 10% which is pretty bad.\n",
    "\n",
    "## Hence, instead of selecting all 16 types of personalitys as a unique feature I explored the dataset further and decided to simplify it.\n",
    "\n",
    "The Myers Briggs Type Indicator (or MBTI for short) is a personality type system that divides everyone into 16 distinct personality types across 4 axis:\n",
    "\n",
    "- Introversion (I) – Extroversion (E)\n",
    "- Intuition (N) – Sensing (S)\n",
    "- Thinking (T) – Feeling (F)\n",
    "- Judging (J) – Perceiving (P)\n",
    "<br><br>\n",
    "We will use this and create 4 classifyers to classify the person "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a classifyer for Introversion (I) and Extroversion (E)\n",
    "\n",
    "**Note:** The details for the steps over here are same as the ones while creating the model above, hence I will only explain the changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Features for the bag of words model\n",
    "features=[]\n",
    "for j in types:\n",
    "    temp1 = all_posts[j]\n",
    "    temp1 = temp1.dropna() #not all the personality types have same number of files\n",
    "    if('I' in j):\n",
    "        features += [[(build_bag_of_words_features_filtered(i), 'introvert') \\\n",
    "        for i in temp1]]\n",
    "    if('E' in j):\n",
    "        features += [[(build_bag_of_words_features_filtered(i), 'extrovert') \\\n",
    "        for i in temp1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=[]\n",
    "for i in range(16):\n",
    "    train += features[i][:split[i]] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "IntroExtro = NaiveBayesClassifier.train(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing the model on the dataset it was trained for accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81.12443979837917"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.classify.util.accuracy(IntroExtro, train)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "test=[]\n",
    "for i in range(16):\n",
    "    test += features[i][split[i]:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing the model on the test dataset which it has never seen before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58.20469312585358"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.classify.util.accuracy(IntroExtro, test)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seeing that this model has good somewhat good results, I shall repeat the same with the rest of the traits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a classifyer for Intuition (N) and Sensing (S)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Features for the bag of words model\n",
    "features=[]\n",
    "for j in types:\n",
    "    temp1 = all_posts[j]\n",
    "    temp1 = temp1.dropna() #not all the personality types have same number of files\n",
    "    if('N' in j):\n",
    "        features += [[(build_bag_of_words_features_filtered(i), 'Intuition') \\\n",
    "        for i in temp1]]\n",
    "    if('E' in j):\n",
    "        features += [[(build_bag_of_words_features_filtered(i), 'Sensing') \\\n",
    "        for i in temp1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=[]\n",
    "for i in range(16):\n",
    "    train += features[i][:split[i]] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "IntuitionSensing = NaiveBayesClassifier.train(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing the model on the dataset it was trained for accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70.14524215640667"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.classify.util.accuracy(IntuitionSensing, train)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "test=[]\n",
    "for i in range(16):\n",
    "    test += features[i][split[i]:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing the model on the test dataset which it has never seen before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54.46262259027357"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.classify.util.accuracy(IntuitionSensing, test)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a classifyer for Thinking (T) and Feeling (F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Features for the bag of words model\n",
    "features=[]\n",
    "for j in types:\n",
    "    temp1 = all_posts[j]\n",
    "    temp1 = temp1.dropna() #not all the personality types have same number of files\n",
    "    if('T' in j):\n",
    "        features += [[(build_bag_of_words_features_filtered(i), 'Thinking') \\\n",
    "        for i in temp1]]\n",
    "    if('F' in j):\n",
    "        features += [[(build_bag_of_words_features_filtered(i), 'Feeling') \\\n",
    "        for i in temp1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=[]\n",
    "for i in range(16):\n",
    "    train += features[i][:split[i]] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "ThinkingFeeling = NaiveBayesClassifier.train(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing the model on the dataset it was trained for accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.03456948570128"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.classify.util.accuracy(ThinkingFeeling, train)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "test=[]\n",
    "for i in range(16):\n",
    "    test += features[i][split[i]:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing the model on the test dataset which it has never seen before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59.41315234035509"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.classify.util.accuracy(ThinkingFeeling, test)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a classifyer for Judging (J) and Percieving (P)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Features for the bag of words model\n",
    "features=[]\n",
    "for j in types:\n",
    "    temp1 = all_posts[j]\n",
    "    temp1 = temp1.dropna() #not all the personality types have same number of files\n",
    "    if('J' in j):\n",
    "        features += [[(build_bag_of_words_features_filtered(i), 'Judging') \\\n",
    "        for i in temp1]]\n",
    "    if('P' in j):\n",
    "        features += [[(build_bag_of_words_features_filtered(i), 'Percieving') \\\n",
    "        for i in temp1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=[]\n",
    "for i in range(16):\n",
    "    train += features[i][:split[i]] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "JudgingPercieiving = NaiveBayesClassifier.train(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing the model on the dataset it was trained for accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.79341109742592"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.classify.util.accuracy(JudgingPercieiving, train)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "test=[]\n",
    "for i in range(16):\n",
    "    test += features[i][split[i]:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing the model on the test dataset which it has never seen before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54.40549600629061"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.classify.util.accuracy(JudgingPercieiving, test)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summarizing the results of the models\n",
    "***\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>Introvert - Extrovert</th>\n",
       "      <th>Intuition - Sensing</th>\n",
       "      <th>Thinking - Feeling</th>\n",
       "      <th>Judging - Percieiving</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>train</th>\n",
       "      <td>81.124440</td>\n",
       "      <td>70.145242</td>\n",
       "      <td>80.034569</td>\n",
       "      <td>79.793411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test</th>\n",
       "      <td>58.204693</td>\n",
       "      <td>54.462623</td>\n",
       "      <td>59.413152</td>\n",
       "      <td>54.405496</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Introvert - Extrovert  Intuition - Sensing  Thinking - Feeling  \\\n",
       "train              81.124440            70.145242           80.034569   \n",
       "test               58.204693            54.462623           59.413152   \n",
       "\n",
       "       Judging - Percieiving  \n",
       "train              79.793411  \n",
       "test               54.405496  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = {'train' : [81.12443979837917,70.14524215640667,80.03456948570128,79.79341109742592], 'test' : [58.20469312585358,54.46262259027357,59.41315234035509,54.40549600629061]}\n",
    "results = pd.DataFrame.from_dict(temp, orient='index', columns=['Introvert - Extrovert', 'Intuition - Sensing', 'Thinking - Feeling', 'Judging - Percieiving'])\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the results for better appeal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Total posts for each personality type')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kWpIkSRqSSbUkSZI0JJNqSZIkaUgm1ZIkSdKQeifVSZ6U5FtJTkuyYWt7eRIfMS5JkqQVWq+kOskLgC8BZwGbACu3SQuAt8xOaJIkSdJo6NtT/RZgt6p6I3DjQPvPgS1mPCpJkiRphPRNqjcFfjZB+7XAXWYuHEmSJGn09E2qLwbuP0H7NsA5MxeOJEmSNHr6JtUHAB9PsnUb3zDJLsAHgP1mJTJJkiRpRKzUZ6aq+kCSuwLHAqsCxwPXAx+qqk/OYnySJEnScq9XUg1QVW9P8l5gc7oe7tOq6tpZi0ySJEkaEb2TaoCq+iuwaJZikSRJkkbSpEl1kqP6rqSqnjYz4UiSJEmjZ2k91X+csygkSZKkETZpUl1VL5nLQCRJkqRRNa2a6iSrAfdto+dU1XUzH5IkSZI0WnrdpzrJHZN8FLgSOBk4BbgyyceSrDqbAUqSJEnLu7491fsBOwAv55bHlT8aeB+wBvDSmQ9NkiRJGg19k+pnA8+oqmMH2hYnuRz4KibVkiRJWoH1fUz5X4CLJmi/CLCuWpIkSSu0vkn1J4A924WKwD8uWnxHmyZJkiStsKbz8JfHAxclOaWNP6Qtv/rshCZJkiSNhuk8/OWr48bPneFYJEmSpJE0pw9/SbImcBDwYKDoLnA8EzgC2Bg4D3hOVV0109uWJEmSZkvfmuqZ8jHg6Kp6IPAw4HRgD+C4qtoUOK6NS5IkSSOj9xMVk7wEeD6wEbDK4LSquk+P5e8CbAPs2pa5AbghyU509doAhwInAG/tG5ckSZI03/o+UfE/gA8Dv6Yr0/gGcCqwFnBwz23dB1gCfCbJb5IclGR1YP2qugSg/Vxvkhh2T7IoyaIlS5b03KQkSZI0+/qWf+wG7F5VbwP+DuxbVU+jS7Tv3XMdKwFbAvtV1cPp7n3du9Sjqg6oqoVVtXDdddftu5gkSZI06/om1fcCftmGrwPu0oYPA57Zcx0XAhdW1S/a+FfokuzLktwDoP28vOf6JEmSpOVC36T6UmCdNnw+8Og2fD+6u3hMqaouBS5I8oDWtC1wGnAUsEtr2wU4smdMkiRJ0nKh74WKPwCeBpwIfBrYJ8lz6HqavzSN7b0W+EKSVYDFwEvoEvsvJXkZ8Afg2dNYnyRJkjTv+ibVu9N6tatq/yRXAVvTPRDmU303VlUnAQsnmLRt33VIkiRJy5teSXVV3QzcPDB+BN0DWyRJkqQV3qRJdZIt+66kqk6cmXAkSZKk0bO0nupFdBchZop1FLBgxiKSJEmSRszSkupN5iwKSZIkaYRNmlRX1flzGYgkSZI0qqaqqT6pqm6eqr7ammpJkiStyKaqqb473RMOl1ZfbU21JEmSVmhT1VQvGRiWJEmSNIFeNdXWV0uSJEmT6/tERQCS3BPYCFhlsL2qfjSTQUmSJEmjpFdS3ZLpLwLbcEttdQ3MYk21JEmSVlh36DnfR4GbgM2BvwKPBZ4NnA7sODuhSZIkSaOhb/nH44AnV9UZSQpYUlU/TXI98B7g2FmLUJIkSVrO9e2pXg24og1fCazXhk8DHjrTQUmSJEmjpG9SfQbwwDZ8EvDKJPcGXgNcNBuBSZIkSaOib/nHx+geBAPwbuBo4PnA9cAusxCXJEmSNDJ6JdVV9YWB4ROTbEzXc/2HqrpisuUkSZKkFUGv8o8kOyX5RwJeVX+tqhNNqCVJkqT+NdWHAZcm2S/J/5vNgCRJkqRR0zepXh/4D+B+wI+SLE7yniQPmL3QJEmSpNHQK6muqmuq6jNVtT2wIbAv8CTgtCS/nM0AJUmSpOVd37t//ENVXZJkX+B84L+AR8x4VJIkSdII6Vv+AUCSJyQ5CLgMOAj4DbDdbAQmSZIkjYpePdVJPgg8j+5JiscArwCOrKrrZzE2SZIkaST0Lf/YGngfcHhVXTmL8UiSJEkjp+/DX7yNniRJkjSJ3hcqtoe/bAVsBKwyOK2qPjvDcUmSJEkjo29N9QOBbwKbAAFuasv+HbgeMKmWJEnSCqvv3T8+CvwauCvwV2AzYCFwEvDM2QlNkiRJGg19yz8eCTyuqv6S5GZgpao6MclbgE8AD+2zkiTnAdfQ9XTfWFULk6wFHAFsDJwHPKeqrprWXkiSJEnzqG9Pdeh6qAGWABu04QvpHl0+HU+oqi2qamEb3wM4rqo2BY5r45IkSdLI6JtUnwo8rA3/EnhrkscB7wLOHjKGnYBD2/ChwM5Drk+SJEmaU32T6vfS9VZD92jyDYHjgR2A101jewV8L8mvk+ze2tavqkugewQ63QNmbiPJ7kkWJVm0ZMmSaWxSkiRJml1971N9zMDwYmDzVgt9VVXVNLa3dVVdnGQ94NgkZ/RdsKoOAA4AWLhw4XS2KUmSJM2qvj3Vt1FVV04zoaaqLm4/Lwe+Tnff68uS3AOg/bx8WWOSJEmS5sMyJ9XTlWT1JGuMDdOVjpwKHAXs0mbbBThyrmKSJEmSZkLvJyrOgPWBrycZ2+4Xq+roJL8CvpTkZcAfgGfPYUySJEnS0OYsqW612A+boP2PwLZzFYckSZI00+as/EOSJEm6veqVVCd5XJJ/GhjfNclPknwqyZ1nLzxJkiRp+de3p/qjwN0BkjwA+BRwCvBo4IOzE5okSZI0Gvom1fcFftuGnwkcW1WvBnYDnjobgUmSJEmjom9SXcCCNrwtcHQbvhRYe6aDkiRJkkZJ36T6V8A7krwIeCzw3da+MV1iLUmSJK2w+ibVbwS2APYF3ltV57T2ZwP/NxuBSZIkSaOi132qq+q3wEMnmPRm4MYZjUiSJEkaMX1vqfeDJGtOMGkV4HszG5IkSZI0WvqWfzyeLoEeb1W6GmtJkiRphbXU8o8kWw6MPjTJlQPjC4B/Bi6ajcAkSZKkUTFVTfUiutvpFROXeVwHvHamg5IkSZJGyVRJ9SZAgMXAVsCSgWk3AJdX1U2zFJskSZI0EpaaVFfV+W2wb+21JEmStMLpe/eP5yTZYWD8nUkuTHJMknvMXniSJEnS8q9vD/ReYwPt4sX/BD4OrAx8eObDkiRJkkZHr4e/APcGzmzDTwe+UVUfSPI94JhZiUySJEkaEX17qv8GrNGGtwW+34b/NNAuSZIkrZD69lT/GPhwkp8AC4Fntfb7AxfMRmCSJEnSqOjbU/1vdLfQexbwyqq6uLU/Ccs/JEmStILr1VNdVRcCT52g/Q0zHpEkSZI0YvqWfwCQ5InA5nRPWDytqo6flagkSZKkEdIrqU6yAfB14BHAWOnHPZMsAp4+UA4iSZIkrXD61lR/HLgJuF9VbVhVGwKbtraPz1ZwkiRJ0ijoW/6xPfD4qjp3rKGqFid5HXDcrEQmSZIkjYi+PdWTuXlGopAkSZJGWN+k+jjg40k2HGtIshHwMeypliRJ0gqub1L9OuBOwOIk5yc5Dzintb1ulmKTJEmSRkLf+1RfAGyZZHvggUDobqn3/aUvKUmSJN3+Tes+1VV1LHDsMBtMsgBYBFxUVU9JsglwOLAWcCLwoqq6YZhtSJIkSXOp94WKSXZO8qMkV7TXj5M8fRm2+Xrg9IHx9wP7VNWmwFXAy5ZhnZIkSdK86ZVUJ/l34AjgTOAt7XUG8MUkb+67sST3Ap4MHNTGAzwR+Eqb5VBg577rkyRJkpYHfcs/3gz8W1UdONB2cJJfAu8GPtRzPR+lS8jXaONrA1dX1Y1t/EJgg57rkiRJkpYLfcs/7gwcP0H78W3alJI8Bbi8qn492DzBrDXJ8rsnWZRk0ZIlS/psUpIkSZoTfZPqbwDPmqD9mcBRPdexNfC0dju+w+nKPj4KrJlkrMf8XsDFEy1cVQdU1cKqWrjuuuv23KQkSZI0+/qWf5wN7JHkCcDPWtuj2usjSd40NmNVfWSiFVTV24C3ASR5PPDmqnpBki/TJeyHA7sARy7DfkiSJEnzpm9SvSvdnTnu315jrgJeMjBewIRJ9VK8FTg8yX8DvwE+Pc3lJUmSpHnV9+Evm8zkRqvqBOCENrwY2Gom1y9JkiTNpd73qZYkSZI0MZNqSZIkaUgm1ZIkSdKQTKolSZKkIU2aVCc5OMkabXibgXtJS5IkSRqwtJ7qFwKrt+HjgbVmPxxJkiRp9Cyt9/k84LVJvkf3OPFHJ7lqohmr6kezEJskSZI0EpaWVP8HcCDdUxAL+Pok8xWwYIbjkiRJkkbGpEl1VR0JHJlkTeBK4EHA5XMVmCRJkjQqprz4sKquTvIE4KyqunEOYpIkSZJGSt/HlP8wyR2TvBjYnK7k4zTgi1V1/WwGKEmSJC3vet2nOsnmwO+BjwD/BDwK2Af4fZLNZi88SZIkafnX9+EvHwNOAjaqqsdW1WOBjYCTgY/OVnCSJEnSKOj7QJetgUdW1Z/HGqrqz0neDvx8ViKTJEmSRkTfnuq/AWtO0H7XNk2SJElaYfVNqr8JHJhk6yQL2usxwKeAo2YvPEmSJGn51zepfj1wFvBjup7pvwE/pLt48Q2zE5okSZI0GvreUu9qYKck9wM2o3ts+WlVdfZsBidJkiSNgr4XKgLQkmgTaUmSJGlA3/IPSZIkSZMwqZYkSZKGZFItSZIkDWnKpDrJSkleneSecxGQJEmSNGqmTKqr6kbgg8DKsx+OJEmSNHr6ln/8HNhyNgORJEmSRlXfW+odCHw4yb2BXwN/GZxYVSfOdGCSJEnSqOibVH+x/fzIBNMKWDAz4UiSJEmjp29SvcmsRiFJkiSNsL6PKT9/tgORJEmSRlXv+1QneVKSbyU5LcmGre3lSbbtufyqSX6Z5OQkv0vyrta+SZJfJDkryRFJVlm2XZEkSZLmR6+kOskLgC8BZ9GVgozdXm8B8Jae27oeeGJVPQzYAtgxyaOA9wP7VNWmwFXAy/qHL0mSJM2/vj3VbwF2q6o3AjcOtP+cLkGeUnWubaMrt1cBTwS+0toPBXbuGZMkSZK0XOibVG8K/GyC9muBu/TdWJIFSU4CLgeOBc4Brm4PmAG4ENig7/okSZKk5UHfpPpi4P4TtG9Dlxj3UlU3VdUWwL2ArYDNJpptomWT7J5kUZJFS5Ys6btJSZIkadb1TaoPAD6eZOs2vmGSXYAPAPtNd6NVdTVwAvAoYM0kY3chuRddAj/RMgdU1cKqWrjuuutOd5OSJEnSrOmVVFfVB4Cv0ZVsrA4cD+wP7F9Vn+yzjiTrJlmzDa8GbAec3tb1rDbbLsCR09kBSZIkab71ffgLVfX2JO8FNqdLxk8buPCwj3sAhyZZ0Jb/UlV9K8lpwOFJ/hv4DfDpaaxTkiRJmne9k+qmgL+14ZumtWDVKcDDJ2hfTFdfLUmSJI2kvvepvmOSjwJXAicDpwBXJvlYklVnM0BJkiRpede3p3o/YAfg5dxya71HA+8D1gBeOvOhSZIkSaOhb1L9bOAZVXXsQNviJJcDX8WkWpIkSSuwvrfU+wtw0QTtFwHXzVw4kiRJ0ujpm1R/Atiz3QoP+Mdt8d7RpkmSJEkrrEnLP5IcNa7p8cBFSU5p4w9py68+O6FJkiRJo2FpNdV/HDf+1XHj585wLJIkSdJImjSprqqXzGUgkiRJ0qjqW1MtSZIkaRK9bqmX5G7AXsATgPUYl4xX1XozHpkkSZI0Ivrep/qzwIOAQ4HL6B5XLkmSJIn+SfXjgcdV1YmzGIskSZI0kvrWVJ8zjXklSZKkFUrfRPn1wPuSPCzJgtkMSJIkSRo1fcs/zgZWA04ESHKriVVloi1JkqQVVt+k+jDgrsDr8EJFSZIk6Vb6JtULga2q6tTZDEaSJEkaRX1rqk8D7jKbgUiSJEmjqm9S/V/AR5Jsl2T9JGsNvmYzQEmSJGl517f84zvt5/e4dT112rgXKkqSJGmF1TepfsKsRiFJkiSNsF5JdVX9cLYDkSRJkkZVr6Q6yZZLm+7jyyVJkrQi61v+sYiudnrwqS+DtdXWVEuSJGmF1Tep3mTc+MrAw4G3A2+b0YgkSZKkEdO3pvr8CZrPTvInYE/guzMalSRJkjRC+t6nejLnAlvMRCCSJEnSqOp7oeL4B7wEuAewF3DmDMckSZIkjZS+NdVXcOsLE6FLrC8AnjujEUmSJEkjZlkf/nIzsAQ4u6punNmQJEmSpNEyZw9/SbIh8Fng7nRJ+QFV9bFWWnIEsDFwHvAmXqy2AAAVeElEQVScqrpq2O1JkiRJc2WpFyomWavPq+e2bgT+vao2Ax4FvCbJ5sAewHFVtSlwXBuXJEmSRsZUPdUT1VKPVz3WQ1VdAlzShq9JcjqwAbAT8Pg226HACcBbp1qfJEmStLyYKhkeX0s9aEfg9XQ90NOSZGO6h8f8Ali/JdxU1SVJ1ptkmd2B3QE22mij6W5SkiRJmjVLTaonqqVOsiXwfmAb4FPAe6azwSR3Br4KvKGq/pxkqkXGYjkAOABg4cKFU/WeS5IkSXOm98NfkmyS5It0vctXAptX1euqask01rEyXUL9har6Wmu+LMk92vR7AJf3jl6SJElaDkyZVCdZO8nHgDPo7tzx6Kp6blWdM50NpeuS/jRwelV9ZGDSUcAubXgX4MjprFeSJEmab0st/0jyn8Bb6G51t1NVHT3EtrYGXgT8NslJre0/gb2BLyV5GfAH4NlDbEOSJEmac1NdqPjfwHXAhcCrk7x6opmq6mlTbaiqfkL3FMaJbDvV8pIkSdLyaqqk+rNMfUs9SZIkaYU21d0/dp2jOCRJkqSR1fvuH5IkSZImZlItSZIkDcmkWpIkSRqSSbUkSZI0JJNqSZIkaUgm1ZIkSdKQTKolSZKkIZlUS5IkSUMyqZYkSZKGZFItSZIkDcmkWpIkSRqSSbUkSZI0JJNqSZIkaUgm1ZIkSdKQTKolSZKkIZlUS5IkSUMyqZYkSZKGZFItSZIkDcmkWpIkSRqSSbUkSZI0JJNqSZIkaUgm1ZIkSdKQTKolSZKkIZlUS5IkSUMyqZYkSZKGZFItSZIkDcmkWpIkSRrSnCXVSQ5OcnmSUwfa1kpybJKz2s+7zVU8kiRJ0kyZy57qQ4Adx7XtARxXVZsCx7VxSZIkaaTMWVJdVT8CrhzXvBNwaBs+FNh5ruKRJEmSZsp811SvX1WXALSf681zPJIkSdK0zXdS3VuS3ZMsSrJoyZIl8x2OJEmS9A/znVRfluQeAO3n5ZPNWFUHVNXCqlq47rrrzlmAkiRJ0lTmO6k+CtilDe8CHDmPsUiSJEnLZC5vqXcY8DPgAUkuTPIyYG9g+yRnAdu3cUmSJGmkrDRXG6qq508yadu5ikGSJEmaDfNd/iFJkiSNPJNqSZIkaUgm1ZIkSdKQTKolSZKkIZlUS5IkSUMyqZYkSZKGZFItSZIkDcmkWpIkSRqSSbUkSZI0JJNqSZIkaUgm1ZIkSdKQTKolSZKkIZlUS5IkSUMyqZYkSZKGZFItSZIkDcmkWpIkSRqSSbUkSZI0JJNqSZIkaUgm1ZIkSdKQTKolSZKkIZlUS5IkSUMyqZYkSZKGZFItSZIkDcmkWpIkSRqSSbUkSZI0JJNqSZIkaUgm1ZIkSdKQTKolSZKkIZlUS5IkSUNaLpLqJDsmOTPJ2Un2mO94JEmSpOmY96Q6yQLgk8CTgM2B5yfZfH6jkiRJkvqb96Qa2Ao4u6oWV9UNwOHATvMckyRJktTb8pBUbwBcMDB+YWuTJEmSRsJK8x0AkAna6jYzJbsDu7fRa5OcOatRaSLrAFfMdxDzJe+f7wiE56Dml+ef5pPn3/y4d98Zl4ek+kJgw4HxewEXj5+pqg4ADpiroHRbSRZV1cL5jkMrLs9BzSfPP80nz7/l3/JQ/vErYNMkmyRZBXgecNQ8xyRJkiT1Nu891VV1Y5J/A44BFgAHV9Xv5jksSZIkqbd5T6oBquo7wHfmOw5NyfIbzTfPQc0nzz/NJ8+/5VyqbnNNoCRJkqRpWB5qqiVJkqSRZlI9h5Jc22OeNyS501zEM267uya55zIsd16S3yY5qb0+PsX8I7V/t2czeT4meWWSF7fhWx3rJAfN9lNSkzwqyS/aOXh6kr1meP3vTrLdTK5zRZdk7YHPjUuTXNSGr05y2iTLTPk+JNkryZuXZdlhJdk4yXUD+3VSuwB/WdZzahteONXnqpauz2fdwLyPT/KtHvP933BR9Yplr4Hfi1OTPG2G1jvlZ3LPeWb9GIwayz/mUJJrq+rOU8xzHrCwqm5zL8okC6rqplmIawFwHPDmqlo0zWXPY5J4pzv/8rh/t2fDno9LWeYE5vhYt/vWP6eqTm7v9wOqasLETMuf9k/QtVX1oSQbA9+qqgcPu64ZC7D/tjdmiNhnej3q9PmsG5j38XSfX0+Z3ah6xbIXt/xebAb8GFivqm7usexKVXXjbMeoW7Oneh60/4RPSPKVJGck+UI6rwPuCRyf5Pg277Wth+UXwKOTbJvkN613+OAkd0zypCRfGrf+b7bhHZL8LMmJSb6c5M6t/bwk70zyE+D5wELgC+0/4tWG3L+VkvyqfTiR5H1J3nt72b/bm+mejwPLPSvJIW14ryRvTvIsxh3rtu6Fbb7nt/f21OSWW/m38+C9SU5O8vMk609zN9YDLgGoqpvGEuokq7fz6FftvNqpte+a5GtJjk5yVpIPtPYFSQ5p8f02yRtb+yFt38bOrXe1c+63SR7Y2tdNcmxr/1SS85OsM839UGdBkgOT/C7J98Z+Z/u8D4OS7Jbku+08nLf3cCnn4YIkH2ztpyR5xQTL/qPntP2eHdx+pxa339Gx+d7Rfn+PTXJYJuitX5FlXA90kn2T7NqGd2zH7ifAMwbmmfR8GPssnOzzs037l7H1Jvl4evSAT6aqTgduBNZpcX21nTe/SrJ1295eSQ5I8j3gs+38+lA7x09J8to23+Bn8mR/Q09I9y3Jq8Y+H1v7rkk+MR/HYBSYVM+fhwNvADYH7gNsXVUfp3vwzROq6gltvtWBU6vqn4BFwCHAc6vqIXR3b3kVcCzwqCSrt2WeCxzRfvn/C9iuqrZsy79pIIa/VdVjqurzbdoLqmqLqrpumvtyfG75qvON7b/jXYH9kmwP7Ai8a4T3b0XQ93xcqqr6CpMc63QlIe8HnghsATwyyc5t8urAz6vqYcCPgN2mGf8+wJlJvp7kFUlWbe1vB35QVY8EngB8cOA82oLuXHoI8NwkG7a2Darqwe0c/Mwk27uinXP7AWPJy55tW1sCXwc2muY+6BabAp+sqgcBVwPPnGS+id4HANLdqvWpwM6T/M7P1nt434HPw0+2tsnOw5cBf2rtjwR2S7LJFOt/IPDPwFbAnklWbgnSM+l+j59B94+temifFQfSnSuPBe4+MLnv+XCbz8+23k8BT6qqxwDrDhnnPwE3A0uAjwH7tPPmmcBBA7M+Atipqv6V7inUmwAPr6qHAl8Yt86p/oYCfIWBfzRof38nCHHWj8EoMKmeP7+sqgvb1zgnARtPMt9NwFfb8AOAc6vq9238UGCblsQeDTw1yUrAk4EjgUfRneA/TXISsAu3ftzmRL8Yy+IJLYHaoqr2AWj3Gv8c8E3gpVV1wwjv34qg7/k4jEcCJ1TVkvaefgHYpk27ARjrwfj1dLdfVe+mSyS+B/wr3fkCsAOwRzs/TgBW5ZY/jMdV1Z+q6m/AaXTnzmLgPkk+kWRH4M+TbPJrE8T6GODwFs/RwFXT2QfdyrlVdVIbXtr5MNH7APAi4EnAM6vq+mksOxPv4TkDn4evaW2TnYc7AC9u7b8A1qb7h2Jpvl1V17eSrMuB9VvcR1bVdVV1Dd3nrvp5IN35dlZ19bCfH5jW93yY6PPzgcDiqjq3zXPYMsb3xnZ+fIiuw6mA7YB9W/tRwF2SrNHmP2rgn8jtgP3HykCq6spx657qbyhVtQRYnO66lbXp/k7/dII4Z/MYjIzl4j7VK6jBD/qbmPy9+NtAnXGWsr4jgNcAVwK/qqpr2tcvx1bV8ydZ5i9TBdl678Y+oPevqv2nWmbAQ+h6mZb2Vf687p/+oe/5OHgRxqqTzDOZpb2/f69bLvCYcPtJjqE7lxZV1ctvE1jVOXTfjhwILGl/AEKXWJ05bl3/xAT7XFVXJXkYXU/ga4DnAC+dIN7rB5frsX+anvHvzWQlWxO9DwCn0n3rcC/g3PELLWXZKd/DJE+n68EEeHnPawcmOw8DvLaqjhnXvvFS1jXR76rn3tRu5NYdiYOfX5NdXNb3uC7ze5LkM3S9vBdX1b9MMMs+E1wfcAfg0eO/gWkVF4N/98Lk+zY2fWl/Q8ccQfdZeAbw9YHP6kGel9hTvTy6BlhjkmlnABsnuV8bfxHwwzZ8ArAl3dfmYz20P6f7CuZ+AEnulOT+09luVV0w0OvSO6FO8gy6XpdtgI8nWXN53D9NafxxuyzJZknuADy95zJjfgE8Lsk66S4mfD63vL9Tqqp/bufhbRLqJE8eq+Gj6+m7ie4fumOA1w7U9z18adtoX4feoaq+CryD7pzr6yd0f3hIsgNwt2ksq5n1G+AVwFGZ3l1/pnwPq+rrA5+JfS/Gnew8PAZ4VZKVW/v9B8qTpuMndN/krdpqYp+8DOu4vTsf2DzddTp3BbZt7WcAmyS5bxsfTDCH+Z0+g+5br43b+HMnmqmqXtLOpYkS6sl8D/i3sZEkWyxlvle2b3hJsta46X3/hn4N2Jnu2EznG+Bex+D2xKR6+XMA8N20C8MGta+pXwJ8Oclv6eqr9m/TbqL7+vxJ7efY1za7AoclOYXuF+g2F/M0hwD7Z9ku5Busqf5sS0z2Bl7WSjn2pasBG9X9W5GNf7/2oDv+P6BdGDiBQ5jgWFfVJcDbgOOBk4ETq+rIGYrzRXQ11SfRlR29oJ0z7wFWBk5Jd4uy90yxng2AE9p6Dmnx9vUuYIckJ9Kdp5fQ/YOheVBVP6Grlf52+l9sOFvv4WTn4UF0pUcntvZPsQzfIFfVr+jKAE6mS4AWAX+agbhHXksor6+qC4AvAafQlZ79Bv7xd2d3uvPkJ3TJ95hlPh9aL/KrgaPbei9j5t6T1wEL0118eBrwyknmOwj4A915dzJdadxgjL3+hlbVVbQSuar6Zd8gZ/kYLJe8pZ4kzYAkdwRuqqobkzwa2K+qJutB0nJolN/DJHeuqmvT3Vf+R8DuVXXifMc131o514FVtdUyLDvU+TDwngT4JHDW2HVHK4oV7RhYUy1JM2Mj4EutNOYGpn8HE82/UX4PD0j3sI5VgUNNqCHJK+l6dd+wjKsY9nzYLckuwCp0PeOfWsY4RtkKdQzsqZYkSZKGZE21JEmSNCSTakmSJGlIJtWSJEnSkEyqJel2KsmuSa6dbFySNHNMqiWtsJIckqTa6+9JFif50DI+gGMUHAHcZ2wkyV7t/shDSfL4dgz73g9akm53vKWepBXd9+keHrMy8Fi6ByasDrxqWVaWZJWqumHmwps57WEM1005oyRp2uyplrSiu76qLq2qC6rqi3RPW9t5bGKSzZN8O8k1SS5PcliSuw9MPyTJt5K8NcmFwIWt/RntiWfXJbkyyQ+TrD+w3CuSnJ3khvbzVvfAbT2/uyf5cpK/tF70F46bZ+8kZ7ZtnJfkA0lWnWxHB8s/kuwK7Ak8aKC3ftckByf51rjl7pDkD0neNME6N6Z7SibAkraeQ5K8OMkf2wM0Buf/QpKj2vBeSU5N8vK2/uuSfGN8j3eSlyQ5Lcnfkvw+yRvbvYMHj+Xv2/QlSY5pT9KTpDljUi1Jt3YdXa81Se5B93S6U4GtgO2AOwNHDSZ1wOOAhwI7Atu2pPtw4FBgM2Abusen09b7dGBf4KPAg4GPAf+b5KnjYnkncCTwMLrSjYOT3Htg+l+Al7ZtvBp4HvD2nvt5BPBh4EzgHu11BHAgsGPb9zHbA3cf3IcBFwDPbMMPaut5PfBlur8xOw3s912BpwOfHlh+Y+CFbb7tgE2BgweW2Q34H7pjsRnw78Bb2/6SZCHdk9reBTygrePonsdAkmaM/8lLUpNkK+BfgeNa06uAk6vqrQPzvBi4ElgI/LI1/w14aVVd3+bZki4x/0pVnd/mGaxdfjPwuarat43/Pskj6JLFbw7M97mq+nxb5zvoktXHAucDVNV7BuY9L8n/tHW/Y6p9rarrWq/1jVV16cCknyU5A9gF2Lu1vRQ4qqqWTLCem5Jc2UYvr6orxqYl+UJb9kut6V+BPwPfHljFasCLq+oPbZlXAD9OsmlVndX25S1V9ZU2/7lJ9qZLqvele+rdX1p817Rjc/JU+y9JM82eakkruh2TXJvkb8DP6HqmX9umPQLYpk2/tiWhF7Rp9x1Yx6ljCXVzMl2t9qlJvprkVUnWHZi+GfDTcXH8BNh8XNspYwNVdSOwBFhvrC3Js5L8JMmlLbZ96JLMYR0IvKRtYy26XuRPL3WJydezfZJ7tfGX0j1C+8aBeS4aS6ibXwA3A5u1Y7Yh8Klx78He3HL8j6VLpM9tpSW7JFljGWKVpKGYVEta0f0I2IKudGDVqnpGVV3ept2Brld1i3GvTYHBuuO/DK6wqm4CdmivU4CXAWcledjgbBPEMr7t7xNMvwNAkkfRlZgcAzwVeDjwX7TSlSF9Drh3kscALwCuAL433ZVU1cnAicCuSR5M17t/8NKXupWxv1Gv5NbH/8F0pSa03uktgecAfwDeBpyR5J7TjVeShmH5h6QV3V+r6uxJpp1Il6ydX1XjE9ylqqqi6/n+WZJ3A78DnkvXi3068BhunWA+BjhtGpvYmq6X9x8lIOPqrfu4AVgwQexXJvkaXc/yw4FD2j8KS1sPE62Lrrf6LcA6wE+r6sxx0zdIsmFVjX0DsBVdMn16VV2W5CLgvlX12ck23nq+fwD8IMmewOXAU4ADlhKzJM0ok2pJmtwngd2AI5K8n6784j50ifa/t17S22i9yNvR9SJfRpeYbsgtSfMHgS8n+TVdD/COdD3Cz5hGbL+nS0hfQJe8/zPw/GntHZxH1yO9JV0v7zUDZSwH0l3wtzLwrCnWcz5dL/qTk3wTuK6qxh4ycxjwEbr69FdOsOx1wKHtziKrAfsD32711AB7AZ9IcjXwnRbPlsAGVfW+JE+hKwX5EV2t+xOANej+cZGkOWP5hyRNoqoupusRvpkuwfwdXaJ9fXtN5k9tuW8BZ9HdZeM9YxcdVtU36Oq230iXaL8eeHVVfXPi1U0Y2zfpkvOP0pWYbE93h4zp+Cpdonoc3T8Mg0n5CXS3Bzyhqs6ZIpaL6G7P9166fyL2HZh2Dd2FijdwywWLg86jK2P5Jl1v82JaPXdb/iC6HvMX0fXy/xjYHTi3zXI13S0Qvw+cQXeh5sur6sdL33VJmlnpvqGUJOkWSVYDLgJeW1VfGHJd3wUurKrx9+LeC3hWVT14mPVL0vLA8g9J0j+0+2+vT9eLfh3d/aaXdV1r0ZXB7EB3r21Jut0yqZYkDdqIrrTiQuAlQz5y/URgLeA/q+rUqWaWpFFm+YckSZI0JC9UlCRJkoZkUi1JkiQNyaRakiRJGpJJtSRJkjQkk2pJkiRpSCbVkiRJ0pD+P6rW1f2AlTGdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,6))\n",
    "\n",
    "plt.bar(np.array(results.columns), height = results.loc['train'],)\n",
    "plt.xlabel('Personality types', size = 14)\n",
    "plt.ylabel('Number of posts available', size = 14)\n",
    "plt.title('Total posts for each personality type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = np.array(results.columns)\n",
    "\n",
    "training = results.loc['train']\n",
    "ind = np.arange(4)\n",
    "width = 0.4\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "rects1 = ax.bar(ind, training, width, color='royalblue')\n",
    "\n",
    "testing = results.loc['test']\n",
    "rects2 = ax.bar(ind+width, testing, width, color='seagreen')\n",
    "\n",
    "fig.set_size_inches(12, 6)\n",
    "fig.savefig('Results.png', dpi=200)\n",
    "\n",
    "ax.set_xlabel('Model Classifying Trait', size = 18)\n",
    "ax.set_ylabel('Accuracy Percent (%)', size = 18)\n",
    "ax.set_xticks(ind + width / 2)\n",
    "ax.set_xticklabels(labels)\n",
    "ax.legend((rects1[0], rects2[0]), ('Tested on a known dataframe', 'Tested on an unknown dataframe'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing the models to predict my trait my feeding few of my quora writings\n",
    "\n",
    "link to my quora answers feed: https://www.quora.com/profile/Divya-Bramhecha"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Defining a functions that inputs the writings, tokenizes them and then predicts the output based on our earlier classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MBTI(input):\n",
    "    tokenize = build_bag_of_words_features_filtered(input)\n",
    "    ie = IntroExtro.classify(tokenize)\n",
    "    Is = IntuitionSensing.classify(tokenize)\n",
    "    tf = ThinkingFeeling.classify(tokenize)\n",
    "    jp = JudgingPercieiving.classify(tokenize)\n",
    "    \n",
    "    mbt = ''\n",
    "    \n",
    "    if(ie == 'introvert'):\n",
    "        mbt+='I'\n",
    "    if(ie == 'extrovert'):\n",
    "        mbt+='E'\n",
    "    if(Is == 'Intuition'):\n",
    "        mbt+='N'\n",
    "    if(Is == 'Sensing'):\n",
    "        mbt+='S'\n",
    "    if(tf == 'Thinking'):\n",
    "        mbt+='T'\n",
    "    if(tf == 'Feeling'):\n",
    "        mbt+='F'\n",
    "    if(jp == 'Judging'):\n",
    "        mbt+='J'\n",
    "    if(jp == 'Percieving'):\n",
    "        mbt+='P'\n",
    "    return(mbt)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building another functions that takes all of my posts as input and outputs the graph showing percentage of each trait seen in each posts and sums up displaying your personality as the graph title\n",
    "\n",
    "**Note:** The input should be an array of your posts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tellmemyMBTI(input, name, traasits=[]):\n",
    "    a = []\n",
    "    trait1 = pd.DataFrame([0,0,0,0],['I','N','T','J'],['count'])\n",
    "    trait2 = pd.DataFrame([0,0,0,0],['E','S','F','P'],['count'])\n",
    "    for i in input:\n",
    "        a += [MBTI(i)]\n",
    "    for i in a:\n",
    "        for j in ['I','N','T','J']:\n",
    "            if(j in i):\n",
    "                trait1.loc[j]+=1                \n",
    "        for j in ['E','S','F','P']:\n",
    "            if(j in i):\n",
    "                trait2.loc[j]+=1 \n",
    "    trait1 = trait1.T\n",
    "    trait1 = trait1*100/len(input)\n",
    "    trait2 = trait2.T\n",
    "    trait2 = trait2*100/len(input)\n",
    "    \n",
    "    \n",
    "    #Finding the personality\n",
    "    YourTrait = ''\n",
    "    for i,j in zip(trait1,trait2):\n",
    "        temp = max(trait1[i][0],trait2[j][0])\n",
    "        if(trait1[i][0]==temp):\n",
    "            YourTrait += i  \n",
    "        if(trait2[j][0]==temp):\n",
    "            YourTrait += j\n",
    "    traasits +=[YourTrait] \n",
    "    \n",
    "    #Plotting\n",
    "    \n",
    "    labels = np.array(results.columns)\n",
    "\n",
    "    intj = trait1.loc['count']\n",
    "    ind = np.arange(4)\n",
    "    width = 0.4\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    rects1 = ax.bar(ind, intj, width, color='royalblue')\n",
    "\n",
    "    esfp = trait2.loc['count']\n",
    "    rects2 = ax.bar(ind+width, esfp, width, color='seagreen')\n",
    "\n",
    "    fig.set_size_inches(10, 7)\n",
    "    \n",
    "    \n",
    "\n",
    "    ax.set_xlabel('Finding the MBTI Trait', size = 18)\n",
    "    ax.set_ylabel('Trait Percent (%)', size = 18)\n",
    "    ax.set_xticks(ind + width / 2)\n",
    "    ax.set_xticklabels(labels)\n",
    "    ax.set_yticks(np.arange(0,105, step= 10))\n",
    "    ax.set_title('Your Personality is '+YourTrait,size = 20)\n",
    "    plt.grid(True)\n",
    "    \n",
    "    \n",
    "    fig.savefig(name+'.png', dpi=200)\n",
    "    \n",
    "    plt.show()\n",
    "    return(traasits)\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing my quora answers from a text file\n",
    "\n",
    "I copied all my answer from the link i provided before (i broke down the paragraphs as separte posts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "My_writings = open(\"Myquora.txt\")\n",
    "my_writing = My_writings.readlines()\n",
    "#my_writing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_posts = my_writing[0].split('|||')\n",
    "len(my_posts)\n",
    "#my_posts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using the classifier to predict my personality type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trait=tellmemyMBTI(my_posts, 'Divy')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Concluding note\n",
    "\n",
    "My profile according to https://www.16personalities.com/ is INTJ.\n",
    "\n",
    "I am pretty happy that using such a basic model it was pretty close to my real profile, only 1 different. And even that difference was very close, between 10% inaccuary which pretty good.\n",
    "\n",
    "Although, I am not sure how the classifier will perform on all test cases in general. Specially, the data for some profiles was very less."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sanaya profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "My_writings = open(\"Sanayapoem.txt\")\n",
    "my_writing = My_writings.readlines()\n",
    "#my_writing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_posts = my_writing[0].split('|||')\n",
    "len(my_posts)\n",
    "#my_posts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0iO1C/WwTay7JlJK3+ThsSlV9rqoeWVVPBP4AXABckeTuAO3zlaOMUZIkaRTmZfKWZKv2+T7Ac4EjgeOBPdsqe9IMrUqSJK1V1ht1AOP4SpItgL8Ar6mqa5IcCBzdDqmuAJ430gglSZJGYF4mb1X1dwPKrgZ2HkE4kiRJ88a8HDaVJEnSYCZvkiRJHWLyJkmS1CEmb5IkSR1i8iZJktQhJm+SJEkdYvImSZLUISZvkiRJHWLyJkmS1CEmb5IkSR1i8iZJktQhJm+SJEkdYvImSZLUISZvkiRJHWLyJkmS1CEmb5IkSR1i8iZJktQhJm+SJEkdYvImSZLUISZvkiRJHWLyJkmS1CEmb5IkSR1i8iZJktQhJm+SJEkdYvImSZLUIeuNOgBpIdrp1StGHcKs23+PUUcgSQJ73iRJkjrF5E2SJKlDTN4kSZI6ZF4mb0nelOSXSX6R5MgkGyW5f5JTk1yQ5KgkG4w6TkmSpLk275K3JPcEXg8srqqHAesCLwA+BHykqrYGrgH2Hl2UkiRJozHvkrfWesDGSdYD7gD8FtgJOKZdfwSw24hikyRJGpl5d6uQqvp1koOAFcCNwAnAacC1VXVzW+1y4J6D3p9kH2AfgEWLFrFs2bKhx7wQrVy50mM3Ay/YYdWoQ5h1K1eusk1oNf5WqJ9tYvjmXfKW5C7ArsD9gWuBLwO7DKhag95fVYcChwIsXry4lixZMpxAF7hly5bhsVtzByzI+7xdbJvQavytUD/bxPDNx2HTJwOXVNVVVfUX4KvA44DN2mFUgHsBvxlVgJIkSaMyH5O3FcAOSe6QJMDOwNnA94Dd2zp7AseNKD5JkqSRmXfJW1WdSnNhwunAz2liPBR4G/DmJBcCWwCfG1mQkiRJIzLvznkDqKp3A+/uK74YePQIwpEkSZo35l3PmyRJksZn8iZJktQhJm+SJEkdYvImSZLUISZvkiRJHWLyJkmS1CEmb5IkSR1i8iZJktQhJm+SJEkdYvImSZLUISZvkiRJHWLyJkmS1CEmb5IkSR1i8iZJktQhJm+SJEkdYvImSZLUISZvkiRJHWLyJkmS1CEmb5IkSR1i8iZJktQhJm+SJEkdYvImSZLUIeuNOgBJkrRwnH3FxbzhoINHHcasO2vfpaMO4a/seZMkSeoQkzdJkqQOMXmTJEnqEJM3SZKkDjF5kyRJ6hCTN0mSpA6Zd8lbkgcnObPn8cckb0yyeZITk1zQPt9l1LFKkiTNtXmXvFXVeVW1XVVtB2wP3AAcC+wHnFRVWwMntcuSJElrlXmXvPXZGbioqn4F7Aoc0ZYfAew2sqgkSZJGJFU16hjGleQw4PSq+kSSa6tqs55111TVakOnSfYB9gFYtGjR9kuXzp87InfJypUr2WSTTUYdRmedv2LVqEOYdffYfJVtQqvxt0L9rrrmaq5add2ow5h12y56wND3seOOO55WVYsnqzdvk7ckGwC/AR5aVVdMNXnrtXjx4lq+fPmwQ12Qli1bxpIlS0YdRmft9OoVow5h1u2/x8W2Ca3G3wr1+9RRh3HIZSeMOoxZNxfTYyWZUvI2n4dNd6HpdbuiXb4iyd0B2ucrRxaZJEnSiExpYvok6wBPAJYADwW2Agq4CvgF8H3g5Kq6dRZjeyFwZM/y8cCewIHt83GzuC9JkqROmDB5S7IF8Hpgb+DuQNpVf2xfbwo8jyaR+22SzwIfr6qrZxJUkjsATwH+uaf4QODoJHsDK9r9SpIkrVXGHTZN8hbgQuAtwMnAy4BtgPWrarOqujOwPvCQdt2PgbcCFybZdyZBVdUNVbVFVV3XU3Z1Ve1cVVu3z3+YyT4kSZK6aKKet7cAHwAOqao/DqpQVbcA57WPI5LcCXhV+96DZjlWSZKktd5Eydv9q+pP09lYm+R9KMknZhaWJEmSBhl32HS6idtsvVeSJEnjm9LVpuNJ8nDg3jT3Yzuj5utN4yRJkhaINUrektyD5lYd27dFBfwiyW5VdclsBSdJkqTbW9Ob9H4C+APwt8BmwJOAjYBPz1JckiRJGmDC5C3Js8ZZ9Rhgv6r6ZVX9sapOBj4KPHa2A5QkSdJtJut5OyrJkUnu2le+Anjm2EI7A8NT23JJkiQNyWTJ2/8B7gqcm+SlPeXvBN6V5LwkJwGXAc8C3jGcMCVJkgSTJG9VdVFVPRn4F+Dfk5yU5G+q6iSaOU6XApcChwEPr6rjhx2wJEnS2mxKV5tW1eFJvg78B/CzJO8F/r2q3j3U6CRJknQ7U77atKquqqp/BHanmTD+9CSPGlpkkiRJWs2Ukrck90zyoCTrVtU3aYZMvwucnOQjSe441CglSZIETH6rkHsnOYXmKtJzgMuTPK2qbqiqNwFPAJYAv0yyy9CjlSRJWstN1vP2SWArmtuCPA5YDnwxyR0AquqnwGLgYOCYJF8aYqySJElrvcmStycCB1XVt6rqVOCNwObAtmMVquqWqjoQeDhNoidJkqQhmexq02uAB/Ys359mHtNr+itW1YXAk2cvNEmSJPWbLHk7GPhgkm1o5jL9e+C7VXXR0COTJEnSaia7Se+HgD2BPwNbAAcBu81BXJIkSRpg0pv0VtUXgC/MQSydtNOrF+Z0rvvvMeoIpIXF3wpJs2XKN+mVJEnS6I2bvCV5x5rcfDfJJkneObOwJEmSNMhEPW+vAi5J8r4kD55sQ0keluRDwCU002dJkiRplk10ztuDgP1o7u329iSXAj8BLqK58jQ093zbGtgBuCdwPfAR4MPDC1mSJGntNW7yVlU3APsn+TDNFae7A7sCG/VVvRH4EfA+4ItV9achxSpJkrTWm8rVpitppsn6ZJINgAcAd6W5We9VwMVV9ZehRilJkiRgCslbr6paBZzbPiRJkjTHvFWIJElSh5i8SZIkdci8TN6SbJbkmCTnJjknyWOTbJ7kxCQXtM93GXWckiRJc21eJm/AfwDfqqptgIcD59DctuSkqtoaOKldliRJWqvMu+QtyZ2AJwKfg+Yiiaq6luY2JUe01Y4AdhtNhJIkSaOTqppaxWQr4Nr2itNB6zcANquqK2cUULIdcChwNk2v22nAG4BfV9VmPfWuqarVhk6T7APsA7Bo0aLtly5dOpNwJnX+ioGHo/PusfkqNtlkk1GH0VkLsV3YJmZmIbYJsF1odVddczVXrbpu1GHMum0XPWDo+9hxxx1Pq6rFk9WbTvJ2C/CSqvrSOOufD3ypqtadVqSrb2cxcArw+Ko6Ncl/AH8EXjeV5K3X4sWLa/ny5TMJZ1I7vXrFULc/KvvvcTFLliwZdRidtRDbhW1iZhZimwDbhVb3qaMO45DLThh1GLPurH2H2xkEkGRKydt0hk0zhW1NLROc2OXA5VV1art8DPBI4Iokdwdon2fUwydJktRF0z3nbaLkbGtgxv2kVfU74LIkD26LdqYZQj2eZpou2ufjZrovSZKkrplwhoUkLwJe1FP01iQvGVB1c2B74H9mKa7XAV9sz6O7GHgpTaJ5dJK9gRXA82ZpX5IkSZ0x2fRYdwMe0b4u4H5tWa8CVgJHAm+bjaCq6kxg0JjvzrOxfUmSpK6aMHmrqv8L/F+AJLcCrxnvggVJkiQN33Qmpt8YWJjXukuSJHXElJO3qrppmIFIkiRpctPpeSPJbjQXE2wNbMHqtw+pqrrjLMUmSZKkPlNO3pK8keb8t+uA5cDVwwpKkiRJg02n5+31NFNV7VRVK4cUjyRJkiYwnZv03gM43MRNkiRpdKaTvF0M3GlYgUiSJGly00nePgq8LMnGwwpGkiRJE5vOOW/X0lykcHaSzwCXALf0V6qqo2cpNkmSJPWZTvK2tOf1+8apU4DJmyRJ0pBMJ3nbZWhRSJIkaUqmM8PCt4cZiCRJkiY3nQsW/irJOkm2SDKtGRokSZI0M9NK3pL8bZJvAH8CrgSe2JZvleTrSZbMfoiSJEkaM+XkLcnDgB8B2wHH9K6rqiuBLYG9ZjM4SZIk3d50et7eC1wFbAu8idUnpT8ReOwsxSVJkqQBppO8PRE4tKqupbklSL8VNFNoSZIkaUimk7zdAfjDBOs3mWEskiRJmsR05zZ9xATrlwDnzigaSZIkTWg6ydtRwJ5JnthTVgBJXgM8E/jiLMYmSZKkPtO5T9uHgacBJwE/p0ncPpRkS+C+wPeBj896hJIkSfqrKfe8VdWfgR2B/YENgFuBRwJ/acueXlWrTVQvSZKk2TOtGRKqahXwwfZBklTVoCtPJUmSNARrND3WGBM3SZKkuTWdGRbekeT0CdYvT/K22QlLkiRJg0yn5+15wA8mWP8D4PkzC0eSJEkTmU7y9gDg7AnWn9vWkSRJ0pBMJ3kLcOcJ1t8JWH9m4UiSJGki00nezgGeNcH6ZwPnzSycRpJLk/w8yZlJlrdlmyc5MckF7fNdZmNfkiRJXTKd5O1w4AlJDkmy2Vhhks2SHAw8HvjPWYxtx6rarqoWt8v7ASdV1dY0Nwrebxb3JUmS1AnTuc/bp2lu0vsK4KVJVtDMsnDfdjv/DXxi1iO8za4086cCHAEsA7y6VZIkrVWmM8NCVdXzgL2A79GcA7cuTS/YP1XVc2fxvm8FnJDktCT7tGWLquq3bSy/BbaapX1JkiR1RubjfXaT3KOqfpNkK+BE4HXA8VXVO1x7TVWtdt5bm+ztA7Bo0aLtly5dOtRYz1+xaqjbH5V7bL6KTTbZZNRhdNZCbBe2iZlZiG0CbBda3VXXXM1Vq64bdRizbttFw7+hxo477nhaz+li45pS8pZkE+AK4P1V9YFZiG/KkrwHWEkzXLukqn6b5O7Asqp68ETvXbx4cS1fvnyo8e306hVD3f6o7L/HxSxZsmTUYXTWQmwXtomZWYhtAmwXWt2njjqMQy47YdRhzLqz9h1uZxBAkiklb1MaNq2qlcBNwO9nGthkktwxyaZjr4GnAr8Ajgf2bKvtCRw37FgkSZLmm+lcsPB94AnAoUOKZcwi4Ngk0MT3par6VpKfAkcn2RtYQTPjgyRJ0lplOsnbW4D/TfJ24KNVdeMwAqqqi4GHDyi/Gth5GPuUJEnqiukkb8fTXGH6PuCAJL8GbuirU1X10NkKTpIkSbc3neTtj8B1wKXDCUWSJEmTmXLyVlU7DDMQSZIkTW4602NJkiRpxKYzbApAkkfT3L5jEfDxqjq/vaXHQ4Fzqur6WY5RkiRJrSn3vCVZJ8kRwI+BA4BXA/dqV98KnNCWSZIkaUimM2y6L/Bi4B3AdjRXngLQ3jbkWOBZsxqdJEmSbmc6ydtLgS9W1YHArwesPxv4m1mJSpIkSQNNJ3m7P3DyBOuvAbaYWTiSJEmayHSSt5XAZhOs/xvmYO5TSZKktdl0krcfAS8ctCLJnWiGVZfNQkySJEkax3SStw8AD03yLeApbdlDkuwJLAfuDBw4y/FJkiSpx3RmWDglyfOBz3Bb8vYxmqtOrwGeV1U/n/0QJUmSNGZaN+mtqmOTnADsAjyEJnG7APiaN+eVJEkavmnPsFBVfwKOGUIskiRJmsSk57wleUySLyc5I8mJSV48F4FJkiRpdRP2vCV5FM0VpBv2FO+UZPOq+tgwA5MkSdLqJut5eztQNNNi3RV4PHAh8K4k07lSVZIkSbNgsgTsscBnqupLVXV1Vf0YeDOwObDN0KOTJEnS7UyWvG0JnNlXdgbNVaZbDiUiSZIkjWuy5G1d4Ka+srHlaV+pKkmSpJmZSgK2QZI79CzfsX3esK8cgKq6YVYikyRJ0mqmkrx9rn30+9qAspriNiVJkrQGJku0jqZJyCRJkjQPTJi8VdUL5ioQSZIkTc57tUmSJHWIyZskSVKHmLxJkiR1iMmbJElSh5i8SZIkdci8Td6SrJvkjCRfa5fvn+TUJBckOSrJBqOOUZIkaa5NOXlL8tYkD5lg/TZJ3jo7YQHwBuCcnuUPAR+pqq2Ba4C9Z3FfkiRJnTCdnrcDgUdMsP7hwAdnFk4jyb2AZwKfbZcD7AQc01Y5AthtNvYlSZLUJbM5ldWGwC2ztK2PAm8FNm2XtwCuraqb2+XLgXsOemOSfYB9ABYtWsSyZctmKaTBXrDDqqFuf1RWrlw19GO3kC3EdmGbmJmF2CbAdqHV3XWDO/PP937qqMOYdfOpnU9E6p7eAAAdbElEQVSYvLUTz2/SU3SnJFsNqLo58HyapGpGkjwLuLKqTkuyZKx4QNWB03ZV1aHAoQCLFy+uJUuWDKo2aw549Yqhbn9U9t/jYoZ97BayhdgubBMzsxDbBNgutLpPHXUYh1x2wqjDmHVnPX/pqEP4q8l63t4C7N++LuCT7WOQAO+chZgeD/x9kmcAGwF3oumJ2yzJem3v272A38zCviRJkjplsuTtZODDNInZW4GvAb/sq1PASuCUqvruTAOqqrcDbwdoe972raoXJfkysDuwFNgTOG6m+5IkSeqaySamPwk4CSDJfYGPV9WP5iKwAd4GLE3yPuAM4HMjikOSJGlkpnzBQlW9cJiBjLPPZcCy9vXFwKPnOgZJkqT5ZNzkbezChKq6snd5MmP1JUmSNPsm6nn7HXBrkjtU1ap2eeAVnn3WnZXIJEmStJqJkrcP0yRrN/ctS5IkaUTGTd6qar+JliVJkjT35u3E9JIkSVrdGk2PlWR94M4MSP68YEGSJGl4ppW8JdmNZhaF7Rg8ZRV4wYIkSdLQTDl5S/JM4KvAJcDngb2AY4ANgGcAZwHfmf0QNQpnX3Exbzjo4FGHMavO2nf+zEsnLRT+VkhzbzrnvL0VOB/42/Y1wMFVtRuwA/Bg4AezG54kSZJ6TSd52w44vKpuAG7tfX9VnQ58ltmZmF6SJEnjmE7yth5wVfv6xvb5zj3rz6bplZMkSdKQTCd5+zVwH4CquhH4PfDInvVbc1tSJ0mSpCGYztWmPwZ2At7dLn8NeGOS62iSwNcA35rd8CRJktRrOsnbwcDzkmzc9rz9K82FCge2688H3jLL8UmSJKnHlJO3qvoxTe/b2PLvkjwMWAzcAvysqv4y+yFKkiRpzJSStyR3AF4LnFZVJ42VV9WtwE+GFJskSZL6TOmChfb2IO8FHjDccCRJkjSR6VxtejGw1bACkSRJ0uSmk7wdDLwsyZ0nrSlJkqShmM7Vpr8D/gicl+RzwAXADf2VquroWYpNkiRJfaaTvB3Z8/rt49QpwORNkiRpSKaTvO0ytCgkSZI0JRMmb0nuA1xVVTdW1bfnKCZJkiSNY7ILFi4BnjMXgUiSJGlykyVvmZMoJEmSNCXTuVWIJEmSRszkTZIkqUOmcrXp3yWZzgT2n59BPJIkSZrAVJKyfdrHZEJznzeTN0mSpCGZSvJ2KHDKsAMZk2Qj4H+BDWniO6aq3p3k/sBSYHPgdOAlVbVqruKSJEmaD6aSvP2gqr409EhucxOwU1WtTLI+cHKSbwJvBj5SVUuTHAzsDXx6DuOSJEkauXl3wUI1VraL67ePAnYCjmnLjwB2G0F4kiRJI5WqGn9lcivw4jnueSPJusBpwAOBTwL/DpxSVQ9s198b+GZVPWzAe/96jt6iRYu2X7p06VBjPX/Fwhy5vcum13PVqutGHcas2nbRA+ZsXwuxXdxj81Vssskmow6jsxZimwB/K7S6q665esG1CZibdrHjjjueVlWLJ6s3nblN50xV3QJsl2Qz4FjgIYOqjfPeQ2nO02Px4sW1ZMmSYYUJwAGvXjHU7Y/K7k/6DodcdsKow5hVZz1/uIl8r4XYLvbf42KG/e9pIVuIbQL8rdDqPnXUYQuuTcD8ahcTJm9VNdJh1aq6NskyYAdgsyTrVdXNwL2A34wyNkmSpFGYd+e8Jblr2+NGko2BJwPnAN8Ddm+r7QkcN5oIJUmSRmc+DpveHTiiPe9tHeDoqvpakrOBpUneB5wBfG6UQUqSJI3CvEvequpnwCMGlF8MPHruI5IkSZo/5t2wqSRJksZn8iZJktQhJm+SJEkdYvImSZLUIfPuggVJktYWOy3Amzfv/qRRR7Dw2fMmSZLUISZvkiRJHWLyJkmS1CEmb5IkSR1i8iZJktQhJm+SJEkdYvImSZLUISZvkiRJHWLyJkmS1CEmb5IkSR1i8iZJktQhJm+SJEkdYvImSZLUISZvkiRJHWLyJkmS1CEmb5IkSR1i8iZJktQhJm+SJEkdYvImSZLUISZvkiRJHWLyJkmS1CEmb5IkSR1i8iZJktQhJm+SJEkdMu+StyT3TvK9JOck+WWSN7Tlmyc5MckF7fNdRh2rJEnSXJt3yRtwM/AvVfUQYAfgNUm2BfYDTqqqrYGT2mVJkqS1yrxL3qrqt1V1evv6euAc4J7ArsARbbUjgN1GE6EkSdLopKpGHcO4ktwP+F/gYcCKqtqsZ901VbXa0GmSfYB9ABYtWrT90qVLhxrj+StWDXX7o3KXTa/nqlXXjTqMWbXtogfM2b4WYru4x+ar2GSTTUYdRmctxDYB/lbM1EJsFwuxTcDctIsdd9zxtKpaPFm9eZu8JdkE+D7w/qr6apJrp5K89Vq8eHEtX758qHHu9OoVQ93+qOz+pO9wyGUnjDqMWXXWvsNN5HstxHax/x4Xs2TJklGH0VkLsU2AvxUztRDbxUJsEzA37SLJlJK3eTdsCpBkfeArwBer6qtt8RVJ7t6uvztw5ajikyRJGpV5l7wlCfA54Jyq+n89q44H9mxf7wkcN9exSZIkjdp6ow5ggMcDLwF+nuTMtuxfgQOBo5PsDawAnjei+CRJkkZm3iVvVXUykHFW7zyXsUiSJM03827YVJIkSeObdz1vkuans6+4mDccdPCow5h1c3lloSTNBnveJEmSOsTkTZIkqUNM3iRJkjrE5E2SJKlDTN4kSZI6xORNkiSpQ0zeJEmSOsTkTZIkqUNM3iRJkjrE5E2SJKlDTN4kSZI6xORNkiSpQ0zeJEmSOsTkTZIkqUNM3iRJkjrE5E2SJKlDTN4kSZI6xORNkiSpQ0zeJEmSOsTkTZIkqUNM3iRJkjrE5E2SJKlDTN4kSZI6xORNkiSpQ0zeJEmSOsTkTZIkqUNM3iRJkjpkXiZvSQ5LcmWSX/SUbZ7kxCQXtM93GWWMkiRJozAvkzfgcODpfWX7ASdV1dbASe2yJEnSWmVeJm9V9b/AH/qKdwWOaF8fAew2p0FJkiTNA6mqUccwUJL7AV+rqoe1y9dW1WY966+pqtWGTpPsA+wDsGjRou2XLl061DjPX7FqqNsflbtsej1Xrbpu1GHMqm0XPWDO9rUQ28VCbBMwd+1iIbYJWJjtwt+KmVmIbQLmpl3suOOOp1XV4snqLbjkrdfixYtr+fLlQ41zp1evGOr2R2X3J32HQy47YdRhzKqz9h1uIt9rIbaLhdgmYO7axUJsE7Aw24W/FTOzENsEzE27SDKl5G1eDpuO44okdwdon68ccTySJElzrkvJ2/HAnu3rPYHjRhiLJEnSSMzL5C3JkcCPgQcnuTzJ3sCBwFOSXAA8pV2WJElaq6w36gAGqaoXjrNq5zkNRJIkaZ6Zlz1vkiRJGszkTZIkqUNM3iRJkjrE5E2SJKlDTN4kSZI6xORNkiSpQ0zeJEmSOsTkTZIkqUNM3iRJkjrE5E2SJKlDTN4kSZI6xORNkiSpQ0zeJEmSOsTkTZIkqUNM3iRJkjrE5E2SJKlDTN4kSZI6xORNkiSpQ0zeJEmSOsTkTZIkqUNM3iRJkjrE5E2SJKlDTN4kSZI6xORNkiSpQ0zeJEmSOsTkTZIkqUNM3iRJkjrE5E2SJKlDTN4kSZI6pFPJW5KnJzkvyYVJ9ht1PJIkSXOtM8lbknWBTwK7ANsCL0yy7WijkiRJmludSd6ARwMXVtXFVbUKWArsOuKYJEmS5lSqatQxTEmS3YGnV9XL2+WXAI+pqtf21dsH2KddfDBw3pwGunBsCfx+1EFoXrFNaBDbhfrZJtbcfavqrpNVWm8uIpklGVC2WuZZVYcChw4/nIUtyfKqWjzqODR/2CY0iO1C/WwTw9elYdPLgXv3LN8L+M2IYpEkSRqJLiVvPwW2TnL/JBsALwCOH3FMkiRJc6ozw6ZVdXOS1wLfBtYFDquqX444rIXMoWf1s01oENuF+tkmhqwzFyxIkiSpW8OmkiRJaz2TN0mSpA4xeRuCJCunUOeNSe4wF/H07XevJPdYg/ddmuTnSc5sHx+bpH6nPt8ozGY7SfLKJP/Uvr7dMUjy2WHPRpJkhySntm3jnCTvmeXtH5DkybO5zfkoyRY9/8Z+l+TX7etrk5w9znsmPTZJ3pNk3zV570wluV+SG3s+15ntRWdrsp1ftK8XT/Yb1FVT+V3oqbskydemUO9HM4tqSrG8p6e9/iLJ38/Sdif9/ZpinaEfg7nkOW9DkGRlVW0ySZ1LgcVVtdqNDJOsW1W3DCGudYGTgH2ravk033sp48Q73frz8fONwkzbyQTvWcYcH4Mk5wF7VNVZ7ffw4KoamGxoatoEeGVVHZTkfsDXquphM93WrAU49X3fjxnEPtvbme+m8rvQU3cJzb/1Zw03qinF8h5ua68PAX4AbFVVt07hvetV1c3DjnEhsedtiNr/FS1LckySc5N8MY3XA/cAvpfke23dle3/gk8FHptk5yRntL1dhyXZMMkuSY7u2/7/tK+fmuTHSU5P8uUkm7TllybZP8nJwAuBxcAX2/8dbTzDz7dekp+2PyAk+WCS9y+UzzdXpttOet63e5LD29fvSbJvmplIbncM2m0vbuu9sD3mv0jyoZ5trWy/u7OSnJJk0TQ/xlbAbwGq6paxxC3JHdvv96ft971rW75Xkq8m+VaSC5J8uC1fN8nhbXw/T/Kmtvzw9rONfef/1raFnyfZpi2/a5IT2/JDkvwqyZbT/Bzz2bpJPpPkl0lOGGvfUzk2vZK8Isk327YxsuM6QdtYN8m/t+U/S/LPA9771x6ntu0f1rbzi9t/N2P13tX+mzoxyZEZ0Ps4H6WvRy3JJ5Ls1b5+evuZTgae21Nn3O9p7HdjvN+adt0zxrab5GOZQo/eeKrqHOBmYMs2rq+03+dPkzy+3d97khya5ATg8+33flDb9n6W5HVtvd7fr/H+DixL0xv7qrHfkrZ8ryQfH8UxGLqq8jHLD5r/fQAsAa6juaHwOsCPgSe06y4Ftux5T9H0XABsBFwGPKhd/jzwRppbu6wA7tiWfxp4Mc1UJP/bU/42YP+e/by1Zz/LaHpypvuZLgV+DpzZPt7Ulj8UOAd4CnAGsEEXP1+H2snKnte7A4e3r99D8z/w1Y7B2DJNIrgCuGt7rL8L7Nbz/Ty7ff1h4J3T/Cz7A9cAxwL/DGzUln8AeHH7ejPgfOCOwF7AxcCd2/bwK5qbcG8PnNiz3c3a58OB3XuOyeva168GPtu+/gTw9vb109vPtOV0Psd8evR9p/ej+WO4Xbt8dM9xncqxeQ+wL/BamvtjbjhXx7WN/UZu++345CRtY5+x9gdsCCwH7t9u5xc9/2a+1vPZftTW3RK4Glifps2fCWwMbApcMHY85/MDWNn7+Xq+g7247bdza5pZh47uOQ7jfk9M8lvTs937t/WO7N3/GrTXx9DcRD/Al7jt9+w+wDk99U8DNm6XXwV8BVivXd68fV7WfpcT/R0Yq3NXmjnQx2L6Zs++h34M5vJhz9vw/aSqLq+m6/hMmh+gQW6habjQzMl6SVWd3y4fATyxmm7lbwHPTrIe8EzgOGAHYFvgh0nOBPYE7tuz7aNm6bPsWFXbtY+PAFRzr70vAP8DvKyqVnX4843SVNvJTDwKWFZVV7XH+ovAE9t1q4Cx/2WeNt39V9UBND+eJwD/SPM9AjwV2K/93pbR/EDep113UlVdV1V/Bs6m+U4vBh6Q5ONJng78cZxdfnVArE8AlrbxfIsmmVxILqmqM9vXE31Hg44NwEuAXYB/qKqbpvHe2TiuF/X8drymLRuvbTwV+Ke2/FRgC5pkZSJfr6qbqjm94EpgURv3cVV1Y1VdT/Mb1XXb0LSDC6rJMP6rZ91Uv6dBvzXbABdX1SVtnSPXML43td/bQcDz2xifDHyiLT8euFOSTdv6x1fVje3rJwMHt79NVNUf+rY92d8Bquoq4OI05+BuQfO35ocD4hzmMZgTnblJb4f1/kjewvjH/M9123lgg+ZxHXMU8BrgD8BPq+r6tsv3xKp64Tjv+dNkQSa5N7f9uB1cVQdP9p4efwtcS/ODOZ6Rfr4OmGo76T1JdaNp7mOi4/6X9od23P0n+TbNd7y8ql6+WmBVFwGfTvIZ4Kr2xzM0ycJ5fdt6DAM+c1Vdk+ThwNNo2sEewMsGxHtT7/um8PkWgv7jNd5pAYOODcAvgO1oehwu6X/TBO+d9LgmeQ7w7nbx5TW18y3Haxuh6QH8dl/5/SbY1qB/P11uDzdz+9Oaev+tj3ei+lQ/7xofqyT/CTwC+E1VPWNAlY/U6udVrgM8tidJG9sW3P63O4z/2cbWT/R3YMxRNL8b5wLH9vyu9ep8e7HnbXSup+nKH+Rc4H5JHtguvwT4fvt6GfBI4BXc1uN0CvD4sfpJ7pDkQdPZb1Vd1vM/4yknbkmeS/M/4ycCH0uy2Xz8fB3W/3muSPKQJOsAz5nie8acCjwpyZZpLip4Ibcd90lV1dPa9rFa4pbkmWPnjdD0ktxCk9B/G3hdzzklj5hoH+05OutU1VeAd9G0hak6meZHmyRPBe4yjfeuDc6gGdI+PtO7InvS41pVx/b8fkz1Qpnx2sa3gVclWb8tf1CSO04j3t64n51ko/bcqGeuwTZG5VfAtmnOBb4zsHNbfi5w/yR/0y73JjIzaf/n0vR4369dfv6gSlX10vY7HpS4jecEmuF62ti2m6DeK9tRF5Js3rd+qn8HvgrsRnNspjMqM6VjMF+YvI3OocA3056I3qsdRnop8OUkPwduBQ5u191CM7y1S/s81lW8F3Bkkp/RNPLVTlRuHQ4cnDU7of97ue1S/8+3f2gPBPZuh0A/AfxHhz/ffNR/HPejOS7fpb1AYIDDGXAMquq3wNuB7wFnAadX1XGzFOdLgPPa4YwvAC9qv8v30px/9LM0t3l47yTbuSewrN3O4W28U/VvwFOTnE7Tfn5Lk8iqVVUn05z79vVM/aKDYR3X8drGZ2mG0U9vyw9hDUaJquqnNMN0Z9H8QV9Oc67TvNUmLjdV1WU057P9jOb0hjPgr7+d+9B8fyfTJHlj1vh7anvFXg18q93uFczesXo9sDjNRQhnA68cp95nac7J/VmSs2hOv+iNcUp/B6rqGtrTMKrqJ1MNcsjHYNZ5qxBJC0KSDYFbqpkH+bHAp6tqvP/la4q6fFyTbFJVK9PcK/F/gX2q6vRRxzWe9pSBz1TVo9fgvTP6nnqOVYBPAheMndu8tujSMfCcN0kLxX2Ao9sh5VU0Q++auS4f10PT3Lx1I+CIeZ64vZKml+qNa7iJmX5Pr0iyJ7ABTU/fIWsYR5d15hjY8yZJktQhnvMmSZLUISZvkiRJHWLyJkmS1CEmb9IClGbe2Ury2SFs+4Httt85Udl8k+S/kjj5dUekmXty5eQ1pbWPyZvUIWkmVa4JHjuMOsZRSvLcJPuPOg6AJE/u+V4+Ok6duyW5ua3znb51Jw/4fq9KckqSfdqrCvv3M9njwvY9L2+Xd5sg/gdOY7tzkhQn2SPJv87FvqT5zFuFSN10JPCNAeUXArT3etqYZpqduXARzXRNf5mj/Y3nucALgANGHEevPwMvSvKWquo/PnvS3KR6PDfQzIoAzfQ9i2huXnoIzbyN/0Iz7dVL+t73KuBxNLee6J3jcjo31/3dgO3uDuxKc0Pd83vKJ/oMa+qFrN7BsAfNpOsfGML+pM4weZO66fSq+q+JKrR3Y58T7fyBc7a/jjmWJhF5NrdN/D5mL5o5hcebuukv/d9zkk8Cl7bv/Zeq+h23n6CcJE+nSd6OrarL1yToqlo5YLvb0CRvJ7SzNUyqnTVg/f65Laew/1H/R0Catxw2lRagQee89ZYleUKSHyS5IcnvkxyaAfNHJnlikh8luTHJ75J8DBhUb8Lz4JLsmuS0dju/SXJg+0e9fzt7pJlG589JfpXkXUme1m7nxZN85pOBFwHr9g3pvbiv3mZJDmmHIP/cDk8+asD21kny2iSnt8fp+iQnJXnSRHEM8BPglzRTwvVu/3E00/v853Q21iZB19DciHVeaY9XJXl8kvcluZQmqX9mu/5Z7blsl7bH/g9JvjFouD9957wlWQ78A3DHvu939zn6eNK8Yc+b1E13yOpzU95UVVMZFtueZlL7z9H0rOxEczf2m2nm9gP+mlycSDPB/IHAH2l6kJ4wzVifDbyOZqjvs+2+3wb8Afhwz/5e1MZzPvAemqG4PWl6eqbiAODdwA7t+8b8sOd1aD7Tb9p93BV4M81ckQ9oe5vGfJFmmO5ommO1MfBi4KQku1bV16cYFzQJ2oeS3L2dYxbgZTTzT35zgvel53sOsBVNEvhgmrks56tPts+fAv4EXNIuv4Lm7vWH0XwH92nLvp/kcVV12gTbfBfNZ/4/wMt7yqc8f6W0YFSVDx8+OvIAlgA1zmNpT7312rLPDii7BVjct91vAzcBG/eU/aQte2BP2YbAae123tlT/sAJylYC9+kpXwc4B7isp2x9mnOsfgNs1lO+Kc3k2wW8eArH57+AmydYV8DH+spf2Jbv3VP2vLbsZX1116eZNueCKcTy5HYbb6RJulYBb23X3ZEmGT6wXf4z8J2+9588zvd8M/CuKRyHAu41zvqXt+t3m2b7e1/7vieMs/617fozgQ0HrL/jgLJ700wAfnRf+THAysnKfPhYGx8Om0rddCjwlL7H+6b43pOranlf2XdpekTuC5DkHsCjgK9W1YVjlarqJmDglZMT+EpVrejZxq3AMuBe7UUVAI+mORn/P6vq2p661zP78wv2TzT93fZ5656yF9P0OP5Pki3HHsCdga8BD0zygKnusKquBL7ObUOnu9MkppMNmf6J23/HL2n3f8A8v+ryE21buZ2q+tPY6ySbJtkCuJEmIX7MHMYndZrDplI3XVBV35m82kAXDyi7un3eon0eS0zOHVD37Fnc3+bAr4H7t8vnDag7qGxN3UrTkzcoli16yh4CbAZcOcG2FjH4s43nP4Hj2vO7Xgb8qKom+2w3D/ie/yvJicB7kxxTVecPeuOIDYypveDh/TS9knfqWz2dK2GltZrJm7T2uWWCdel7rgnqDGN/w1Ztz99EsYy9HnSrjF7TTWK/0W7zvcDf0Zzrtaa+TZMALWGcRGnEbugvSLI58AOaY3sQzUUcK2kS6gNozmWTNAUmb5IGuah9fsiAdYPKZmrshPYHD1g3qGw8g5LNNXEBzTDlj6pqtURkTVRz770vAG+hGQ49agabW7993nTGgc2dXYAtgT2q6su9KzLOTYwHmK3vV+o0z3mTtJqq+g2wHHhukgeOlSfZkOYE/Nl2Ks0Q5UuTbNazv0257Sa1U7GS5lYh/UNy0/V5mv/cvn/QyiSL1nC7n6K5YvKVdfsrW6csSYC/bxcnujpzvhnrgb1dL2uS5wAPneI2VtJcab3hbAYmdY09b5LG82bgJOCHST5Fc0XgPzKEIc6q+kuStwBHAKcmOYxmOG0vmqTuPkyt1+UU4JXAwUm+STPjw4+rqv88t8niWZpkF+CNSRbTDHn+HrgX8Pg2ngdNZ5vtdi+luUXJVK3fd5+6RTT3OtsB+GZVLfv/7d09SkNREIbhdxpBwRUEtBMEwUa34A4kTcTOXhAluADBwlJEwUItBDsXkMZGBEHcRVyBqOFYTMCAAZPgTy6+T5dbnBy4t/hg5swZdg9/qEWOhzmMiHmyhLwE1MkS9OwAa9yS38Rx5HVir+QBnJEGEUtVZXiT1Fcp5SYiVoA9oEl3nAM5q+3hB/7vLCJegF2yB6oNnJBjRa7IU4lfuQAWyUBQJ6sLa3w+pDDIftYjokX2pjX5GGdyDxwNu96IpoDznt/PZEm3CRz80h6+RSnlqfs97QOb5Lu5I3v3thksvJ0CC2SAbXTXWCVHiEj/RpRiC4Gk8RURO+SQ4OU+I04k6d8xvEkaCxExAXRKKZ2eZ9PkxeuTQK1436UkWTaVNDbmgOuIuCRPn9bI/qYZYMPgJknJ8CZpXLTJHqgGeZ3UG/AIbJVS7GmSpC7LppIkSRXinDdJkqQKMbxJkiRViOFNkiSpQgxvkiRJFWJ4kyRJqpB3zE5RTxQRS2AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trait = tellmemyMBTI(my_posts,'sanaya')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Valentin Pyataev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "My_writings = open(\"Valentin pyatev.txt\")\n",
    "my_writing = My_writings.readlines()\n",
    "#my_writing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_posts = my_writing[0].split('|||')\n",
    "len(my_posts)\n",
    "#my_posts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trait=tellmemyMBTI(my_posts,'Valentin')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MIT gurukul people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "My_writings = open(\"All texts.txt\")\n",
    "my_writing = My_writings.readlines()\n",
    "a =[''];\n",
    "for i in my_writing:\n",
    "    a[0]=a[0]+i\n",
    "len(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_posts = a[0].split('&&&')\n",
    "len(my_posts)\n",
    "#my_posts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Posts for each person"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "alls = [None]*len(my_posts)\n",
    "for i in range(len(my_posts)):\n",
    "    alls[i] = my_posts[i].split('|||') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Email ID connection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "Names = open(\"Names.txt\")\n",
    "names = Names.readlines()\n",
    "#names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mearnav4\n",
      "amankhemlani12\n",
      "saumyarao2001\n",
      "pariza2008\n",
      "mishthifrooti\n",
      "aahanaagarwal02\n",
      "jahnavishahani01\n",
      "milap99shah\n",
      "arnavkadam44\n",
      "harlalkasanyam\n",
      "refan7401\n",
      "olafdange5\n",
      "raju.rastogi@gmail.com\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(names)):\n",
    "    names[i] = names[i].replace('@gmail.com\\n','')\n",
    "    print(names[i])\n",
    "names[len(names)-1]=names[len(names)-1].replace('@gmail.com','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UPm817DglSZLm2rxL3pI8GHgZ8EjgocAzkuwEHAycVlU7Aae17yVJkjYo8y55Ax4AnFFVN1TVzcC3gWcCewHHtnWOBfYeUnySJElDk6oadgy3k+QBwMnAo4EbaXrZVgEvrKote+pdU1XrDZ0mOQA4AGDJkiW7rFixYk7iXmjWrl3LokWLhh1GZ124Zt2wQ5hx2y9eZ5vQevyt0Gi2ialbvnz5mVW1dKJ68y55A0iyP/BKYC1wHk0S9+LJJG+9li5dWqtWrZrVWBeqlStXsmzZsmGH0Vm7Hbhm2CHMuEP2XW2b0Hr8rdBotompSzKp5G0+DptSVZ+oqodX1ROA3wEXAVcm2Q6gfb5qmDFKkiQNw7xM3pJs2z7fC3gWcDxwCrBfW2U/mqFVSZKkDcrGww5gDJ9PsjXwJ+CVVXVNksOAE9oh1TXAs4caoSRJ0hDMy+Stqh7fp+y3wO5DCEeSJGnemJfDppIkSerP5E2SJKlDTN4kSZI6xORNkiSpQ0zeJEmSOsTkTZIkqUNM3iRJkjrE5E2SJKlDTN4kSZI6ZF7OsCBJ0oZgtwPXDDuEGXfIvsOOYOGz502SJKlDTN4kSZI6xORNkiSpQ0zeJEmSOsTkTZIkqUNM3iRJkjrE5E2SJKlDTN4kSZI6xORNkiSpQ0zeJEmSOsTkTZIkqUNM3iRJkjrE5E2SJKlDTN4kSZI6xORNkiSpQ0zeJEmSOsTkTZIkqUPmZfKW5HVJfprkJ0mOT7Jpkvsk+X6Si5J8Nsmdhh2nJEnSXJt3yVuSuwP/ACytqgcDGwHPBd4HfKCqdgKuAfYfXpSSJEnDMe+St9bGwGZJNgbuDFwB7Aac2C4/Fth7SLFJkiQNTapq2DGsJ8lrgHcDNwJfB14DnFFVO7bL7wl8pe2ZG73uAcABAEuWLNllxYoVcxb3QrJ27VoWLVo07DA668I164YdwozbfvE624TW42/F9PhboV7Lly8/s6qWTlRv47kIZhBJtgL2Au4DXAt8DtizT9W+WWdVHQUcBbB06dJatmzZ7AS6wK1cuRKP3dS948A1ww5hxh2y72rbhNbjb8X0+FuhqZiPw6ZPAi6pqqur6k/AF4DHAFu2w6gA9wAuH1aAkiRJwzIfk7c1wK5J7pwkwO7AecC3gH3aOvsBJw8pPkmSpKGZd8lbVX2f5sKEs4Af08R4FPAm4PVJLga2Bj4xtCAlSZKGZN6d8wZQVW8H3j6qeDXwyCGEI0mSNG/Mu543SZIkjc3kTZIkqUNM3iRJkjrE5E2SJKlDTN4kSZI6xORNkiSpQ0zeJEmSOsTkTZIkqUNM3iRJkjrE5E2SJKlDTN4kSZI6xORNkiSpQ0zeJEmSOsTkTZIkqUNM3iRJkjrE5E2SJKlDTN4kSZI6xORNkiSpQ0zeJEmSOsTkTZIkqUM2nkylJHcAHgcsAx4EbAsUcDXwE+DbwOlV9efZCVOSJEkwQfKWZGvgH4D9ge2AtIv+0L7eAng2TSJ3RZKPAx+qqt/OWsSSJEkbsDGHTZO8AbgYeANwOvAS4P7AHatqy6q6K3BH4AHtsu8BbwQuTnLQbAcuSZK0IRqv5+0NwHuAI6vqD/0qVNUtwAXt49gkdwFe0a57+AzHKkmStMEbL3m7T1VdP8jG2iTvfUk+PL2wJEmS1M+YydugidtMrStJC9FuB64Zdgiz4pB9hx2BtOGZ1NWmY0nyUOCewOXA2VVVMxKVJEmS+prSfd6SbJ/kh8DZwCnAD4FzktxnugEluV+Sc3oef0jy2iSLk5ya5KL2eavp7kuSJKlrpnqT3g8DvwMeAmwJPBHYFPjodAOqqguqaueq2hnYBbgBOAk4GDitqnYCTmvfS5IkbVDGTd6SPGOMRY8CDq6qn1bVH6rqdODfgUfPcHy7Az+vql8AewHHtuXHAnvP8L4kSZLmvYx3mlqS62mGRf+hqq7uKf8e8KWqelf7/g7A54Edq+ohMxZccjRwVlV9OMm1VbVlz7Jrqmq9odMkBwAHACxZsmSXFStWzFQ4G5S1a9eyaNGiYYfRWReuWTfsEGbc9ovX2SamYSG2CbBdTNdCbBe2ialbvnz5mVW1dKJ6EyVvfwkcCTwMOKiqPtmW7w58GbgUuIzm5r3bAn9bVadMO/pmH3eiuRDiQVV15WSTt15Lly6tVatWzUQ4G5yVK1eybNmyYYfRWQvxysJD9l1tm5iGhdgmwHYxXQuxXdgmpi7JpJK3cYdNq+rnVfUk4B+Bf0lyWpK/rKrTaOY4XUGTwB0NPHSmErfWnjS9ble2769Msh1A+3zVDO5LkiSpEyZ1wUJVHUMzDdaVwI+SHAxcUlVvr6r9q+ptVXXeDMf2POD4nvenAPu1r/cDTp7h/UmSJM17k77atKqurqr/C+wD/D1wVpJHzEZQSe4MPBn4Qk/xYcCTk1zULjtsNvYtSZI0n00qeUty9yR/lWSjqvoKzZDpN4HTk3wgyeYzGVRV3VBVW1fV73vKfltVu1fVTu3z72Zyn5IkSV0w0a1C7pnkDGANcD5wWZKntsnV64DHAcuAnybZc9ajlSRJ2sBN1PP2EZqrSJ8OPAZYBRzXDmtSVT8ElgJHACcm+cwsxipJkrTBmyh5ewJweFV9taq+D7wWWAw8cKRCVd1SVYcBD6VJ9CRJkjRLJpqY/hpgx5739wGqLb+dqroYeNLMhSZJkqTRJkrejgDem+T+NHOZ/g3wzar6+axHJkmSpPVMdJPe99HcU+2PwNbA4TinqCRJ0tBM1PNGVX0a+PQcxCJJkqQJTPomvZIkSRq+MZO3JP80lZvvJlmU5K3TC0uSJEn9jNfz9grgkiTvSnK/iTaU5MFJ3gdcQjN9liRJkmbYeOe8/RVwMM293d6c5FLgB8DPaa48Dc0933YCdgXuDlwHfAB4/+yFLEmStOEaM3mrqhuAQ5K8n+aK032AvYBNR1W9Efgu8C7guKq6fpZilSRJ2uBN5mrTtTTTZH0kyZ2A+wJ3o7lZ79XA6qr606xGKUmSJGASyVuvqloH/Kx9SJIkaY55qxBJkqQOMXmTJEnqEJM3SZKkDjF5kyRJ6hCTN0mSpA6ZdPKWZNv2ViFjLb9Tkm1nJixJkiT1M0jP2xU0N+odyzPbOpIkSZolgyRvmcS2ahqxSJIkaQKDnvM2XnK2E/D7acQiSZKkCYw7w0KS5wPP7yl6Y5IX9qm6GNgF+O8ZjE2SJEmjTDQ91l8AD2tfF7BDW9argLXA8cCbZjI4SZIk3d64yVtV/SvwrwBJ/gy8sqo+MxeBSZIkaX2DTEy/GbButgKRJEnSxCZ9wUJV3VRVc3I1aZItk5yY5GdJzk/y6CSLk5ya5KL2eau5iEWSJGk+Gehq0yR7JzktyZok1ye5YdTj+hmK6z+Ar1bV/YGHAucDBwOnVdVOwGnte0mSpA3KpIdNk7yW5vy33wOrgN/ORkBJ7gI8AXgRQFWtA9Yl2QtY1lY7FliJF0hIkqQNTCY7EppkNfAbYLeqWjtrASU7A0cB59H0up0JvAb4VVVt2VPvmqpab+g0yQHAAQBLlizZZcWKFbMV6oK2du1aFi1aNOwwOuvCNQvv9NDtF6+zTUzDQmwTYLuYroXYLmwTU7d8+fIzq2rpRPUGSd7+CLy+qv5zusFNsJ+lwBnAY6vq+0n+A/gD8OrJJG+9li5dWqtWrZrNcBeslStXsmzZsmGH0Vm7Hbhm2CHMuEP2XW2bmIaF2CbAdjFdC7Fd2CamLsmkkrdBznlbDdxl6iFN2mXAZVX1/fb9icDDgSuTbAfQPl81B7FIkiTNK4Mkb/8OvCTJZrMVDEBV/Rr4ZZL7tUW70wyhngLs15btB5w8m3FIkiTNR4Pc5+1amosUzkvyMeAS4JbRlarqhBmI69XAcUnuRNPj92KaRPOEJPsDa4Bnz8B+JEmSOmWQ5K33zP93jVGngGknb1V1DtBvzHf36W5bkiSpywZJ3vactSgkSZI0KZNO3qrqa7MZiCRJkiY20AwLI5LcIcnWSQbpuZMkSdI0DTo91kOSfBm4nuZWHU9oy7dN8qUky2Y+REmSJI2YdPKW5MHAd4Gdae69dququgrYhnZKK0mSJM2OQYY93wlcTXPD3I2B549afirevmPBOO/K1bzm8COGHcaMOvcgp0qTJHXfIMOmTwCOqqpraW4JMtoaYPsZiUqSJEl9DZK83Rn43TjLnYVWkiRplg06t+nDxlm+DPjZtKKRJEnSuAZJ3j4L7JfkCT1lBZDklcDTgeNmMDZJkiSNMsgFC+8HngqcBvyYJnF7X5JtgHsD3wY+NOMRSpIk6VaT7nmrqj8Cy4FDgDsBf6a58vRPbdkeVbXeRPWSJEmaOQPNkFBV64D3tg+SpKr6XXkqSZKkWTCl6bFGmLhJkiTNrUFmWPinJGeNs3xVkjfNTFiSJEnqZ5Cet2cD3xln+XeA50wvHEmSJI1nkOTtvsB54yz/WVtHkiRJs2SQ5C3AXcdZfhfgjtMLR5IkSeMZJHk7H3jGOMv/GrhgeuFIkiRpPIMkb8cAj0tyZJItRwqTbJnkCOCxwCdnOD5JkiT1GOQ+bx+luUnvy4AXJ1lDM8vCvdvt/D/gwzMeoSRJkm41yAwLVVXPBl4EfIvmHLiNaKbL+ruqepb3fZMkSZpdA82wAFBVnwI+NQuxSJIkaQKT6nlLsijJ9UneMtsBSZIkaWyTSt6qai1wE/Cb2Q1HkiRJ4xnkatNvA4+brUAkSZI0sUGStzcAT0ry5iSbzVZAkiRJGtsgFyycQnOF6buAdyT5FXDDqDpVVQ+aqeAkSZJ0e4Mkb38Afg9cOjuh3CbJpcB1wC3AzVW1NMli4LPADm0M+1bVNbMdiyRJ0nwy6eStqnadzUD6WF5VvRdIHAycVlWHJTm4ff+mOY5JkiRpqAY5523Y9gKObV8fC+w9xFgkSZKGIoNOipDkkcBTgCXAh6rqwiSbAw8Czq+q66YdVHIJcA3N9FtHVtVRSa6tqt45Va+pqq36rHsAcADAkiVLdlmxYsV0w9kgXX3Nb7l63e+HHcaMeuCS+87Zvi5cs27O9jVXtl+8jkWLFg07jM5aiG0CbBfTtRDbhW1i6pYvX35mVS2dqN6kk7ckd6CZeP4FNBcuFPDkqvpme/XpFcB7q+p9Uw/71n1tX1WXJ9kWOBV4NXDKZJK3XkuXLq1Vq1ZNN5wN0n9+9miO/OXXhx3GjDr3oLlL5Hc7cM2c7WuuHLLvapYtWzbsMDprIbYJsF1M10JsF7aJqUsyqeRtkGHTg2gSt38CdqZJ4ACoqhuBk4BnDBhnX1V1eft8VbvdRwJXJtkOoH2+aib2JUmS1CWDJG8vBo6rqsOAX/VZfh7wl9MNKMnmSbYYeU0zRPsTmluV7NdW2w84ebr7kiRJ6ppBbhVyH+AD4yy/Bth6euEAzbl0JyWBJr7PVNVXk/wQOCHJ/sAa4NkzsC9JkqROGSR5WwtsOc7yv2QG5j6tqtXAQ/uU/xbYfbrblyRJ6rJBhk2/Czyv34Ikd6EZVl05AzFJkiRpDIMkb+8BHpTkq8CT27IHJNkPWAXcFThshuOTJElSj0FmWDgjyXOAj3Fb8vZBmqtOrwGeXVU/nvkQJUmSNGKQc96oqpOSfB3YE3gATeJ2EfDFmbg5ryRJksY3UPIGUFXXAyfOQiySJEmawITnvCV5VJLPJTk7yalJXjAXgUmSJGl94/a8JXkEzRWkm/QU75ZkcVV9cDYDkyRJ0vom6nl7M80cpi8A7gY8FrgYeFs716kkSZLm0EQJ2KOBj1XVZ6rqt1X1PeD1wGLg/rMenSRJkm5nouRtG+CcUWVn01xlus2sRCRJkqQxTZS8bQTcNKps5P3AV6pKkiRpeiaTgN0pyZ173m/ePm8yqhyAqrphRiKTJEnSeiaTvH2ifYz2xT5lNcltSpIkaQomSrROoEnIJEmSNA+Mm7xV1XPnKhBJkiRNzHu1SZIkdYjJmyRJUoeYvEmSJHWIyZskSVKHeFuPadrtwDXDDmFW7PPEYUcgSZL6sedNkiSpQyadvCV5Y5IHjLP8/kneODNhSZIkqZ9Bet4OAx42zvKHAu+dXjiSJEkaz0wOm24C3DKD25MkSdIo416w0E48v6in6C5Jtu1TdTHwHOCyGYxNkiQkkSgpAAAd+0lEQVRJo0x0tekbgEPa1wV8pH30E+CtMxSXJEmS+pgoeTsdeD9NYvZG4IvAT0fVKWAtcEZVfXPGI5QkSdKtJpqY/jTgNIAk9wY+VFXfnYvAkmwErAJ+VVXPSHIfYAXNEO1ZwAurat1cxCJJkjRfTPqChap63lwlbq3XAOf3vH8f8IGq2gm4Bth/DmORJEmaF8ZM3pJs23txwsj7iR4zEVSSewBPBz7evg+wG3BiW+VYYO+Z2JckSVKXpKr6L0j+DPwZuHNVrWvf96/co6o2mnZQyYk094zbAjgIeBHNOXU7tsvvCXylqh7cZ90DgAMAlixZssuKFSumG864LlyzMEdut9riOq5e9/thhzGjHrjkvnO2r4XYLrZfvI5FixZNXFF9LcQ2AbaL6VqI7cI2MXXLly8/s6qWTlRvvHPe3k+TrN086v2sSvIM4KqqOjPJspHiPlX7xlJVRwFHASxdurSWLVvWr9qMeceCndv0Gxz5y68PO4wZde5zZjeR77UQ28Uh+65mtv89LWQLsU2A7WK6FmK7sE3MvjGTt6o6eLz3s+ixwN8keRqwKXAX4N+BLZNsXFU3A/cALp+jeCRJkuaNeTcxfVW9uaruUVU7AM8FvllVzwe+BezTVtsPOHlIIUqSJA3NRPd56yvJHYG70if5q6qrphvUGN4ErEjyLuBs4BOztB9JkqR5a6DkLcneNLMo7Ez/89AApn3BwoiqWgmsbF+vBh45U9uWJEnqokkPmyZ5OvAFYCvgUzTJ2+eB/6aZkP4smosaJEmSNEsGOeftjcCFwEPa1wBHVNXewK7A/YDvzGx4kiRJ6jVI8rYzcExV3UBz/7db16+qs2huqOvE9JIkSbNokORtY+Dq9vWN7fNde5afR9MrJ0mSpFkySPL2K+BeAFV1I/Ab4OE9y3fitqROkiRJs2CQq02/RzO/6Nvb918EXpvk9zRJ4CuBr85seJIkSeo1SPJ2BPDsJJu1PW9voblQ4bB2+YXAG2Y4PkmSJPWYdPJWVd+j6X0bef/rJA8GltLcKuRHVfWnmQ9RkiRJIyaVvCW5M/Aq4MyqOm2kvKr+DPxglmKTJEnSKJO6YKG9Pcg7gfvObjiSJEkazyBXm64Gtp2tQCRJkjSxQZK3I4CXJLnrhDUlSZI0Kwa52vTXwB+AC5J8ArgIuGF0pao6YYZikyRJ0iiDJG/H97x+8xh1CjB5kyRJmiWDJG97zloUkiRJmpRxk7ck9wKurqobq+prcxSTJEmSxjDRBQuXAM+ci0AkSZI0sYmSt8xJFJIkSZqUQW4VIkmSpCEzeZMkSeqQyVxt+vgkg0xg/6lpxCNJkqRxTCYpO6B9TCQ093kzeZMkSZolk0nejgLOmO1AJEmSNLHJJG/fqarPzHokkiRJmpAXLEiSJHWIyZskSVKHmLxJkiR1yLjnvFXVnCd3STYF/gfYhCa+E6vq7UnuA6wAFgNnAS+sqnVzHZ8kSdIwzceet5uA3arqocDOwB5JdgXeB3ygqnYCrgH2H2KMkiRJQzHvkrdqrG3f3rF9FLAbcGJbfiyw9xDCkyRJGqpU1bBjWE+SjYAzgR2BjwD/ApxRVTu2y+8JfKWqHtxn3VtvKrxkyZJdVqxYMauxXrhmYY7cbrXFdVy97vfDDmNGPXDJfedsXwuxXWy/eB2LFi0adhidtRDbBNgupmshtgvbxNQtX778zKpaOlG9eZm8jUiyJXAScAjwyVHJ25er6iHjrb906dJatWrVrMa424FrZnX7w7LPE7/Bkb/8+rDDmFHnHjS7iXyvhdguDtl3NcuWLRt2GJ21ENsE2C6mayG2C9vE1CWZVPI274ZNe1XVtcBKYFdgy545Vu8BXD6suCRJkoZl3iVvSe7W9riRZDPgScD5wLeAfdpq+wEnDydCSZKk4ZnM9FhzbTvg2Pa8tzsAJ1TVF5OcB6xI8i7gbOATwwxSkiRpGOZd8lZVPwIe1qd8NfDIuY9IkjSW865czWsOP2LYYcyouTw/VpqKeTdsKkmSpLGZvEmSJHWIyZskSVKHmLxJkiR1iMmbJElSh5i8SZIkdYjJmyRJUoeYvEmSJHWIyZskSVKHmLxJkiR1iMmbJElSh5i8SZIkdYjJmyRJUoeYvEmSJHWIyZskSVKHmLxJkiR1iMmbJElSh5i8SZIkdYjJmyRJUoeYvEmSJHWIyZskSVKHmLxJkiR1iMmbJElSh5i8SZIkdYjJmyRJUoeYvEmSJHWIyZskSVKHzLvkLck9k3wryflJfprkNW354iSnJrmofd5q2LFKkiTNtXmXvAE3A/9YVQ8AdgVemeSBwMHAaVW1E3Ba+16SJGmDMu+St6q6oqrOal9fB5wP3B3YCzi2rXYssPdwIpQkSRqeVNWwYxhTkh2A/wEeDKypqi17ll1TVesNnSY5ADgAYMmSJbusWLFiVmO8cM26Wd3+sGy1xXVcve73ww5jRj1wyX3nbF8LsV1sv3gdixYtGnYYnbUQ2wT4WzFdC7Fd+FsxdcuXLz+zqpZOVG/eJm9JFgHfBt5dVV9Icu1kkrdeS5curVWrVs1qnLsduGZWtz8s+zzxGxz5y68PO4wZde5Bs5vI91qI7eKQfVezbNmyYYfRWQuxTYC/FdO1ENuFvxVTl2RSydu8GzYFSHJH4PPAcVX1hbb4yiTbtcu3A64aVnySJEnDMu+StyQBPgGcX1X/1rPoFGC/9vV+wMlzHZskSdKwbTzsAPp4LPBC4MdJzmnL3gIcBpyQZH9gDfDsIcUnSZI0NPMueauq04GMsXj3uYxFkiRpvpl3w6aSJEkam8mbJElSh5i8SZIkdYjJmyRJUoeYvEmSJHWIyZskSVKHmLxJkiR1iMmbJElSh5i8SZIkdYjJmyRJUoeYvEmSJHWIyZskSVKHmLxJkiR1iMmbJElSh5i8SZIkdcjGww5AkiQtHOdduZrXHH7EsMOYcecetGLYIdzKnjdJkqQOMXmTJEnqEJM3SZKkDjF5kyRJ6hCTN0mSpA4xeZMkSeoQkzdJkqQOMXmTJEnqEJM3SZKkDjF5kyRJ6hCTN0mSpA6Zl8lbkqOTXJXkJz1li5OcmuSi9nmrYcYoSZI0DPMyeQOOAfYYVXYwcFpV7QSc1r6XJEnaoMzL5K2q/gf43ajivYBj29fHAnvPaVCSJEnzwLxM3sawpKquAGiftx1yPJIkSXMuVTXsGPpKsgPwxap6cPv+2qrasmf5NVW13nlvSQ4ADgBYsmTJLitWrJjVOC9cs25Wtz8sW21xHVev+/2ww5hRD1xy3znb10JsF9svXseiRYuGHUZnLcQ2Af5WTNdCbBcLsU3A3LSL5cuXn1lVSyeqt/GsRzJzrkyyXVVdkWQ74Kp+larqKOAogKVLl9ayZctmNah3HLhmVrc/LPs88Rsc+cuvDzuMGXXuc2Y3ke+1ENvFIfuuZrb/PS1kC7FNgL8V07UQ28VCbBMwt+1iIl0aNj0F2K99vR9w8hBjkSRJGop5mbwlOR74HnC/JJcl2R84DHhykouAJ7fvJUmSNijzcti0qp43xqLd5zQQSZKkeWZe9rxJkiSpP5M3SZKkDjF5kyRJ6hCTN0mSpA4xeZMkSeoQkzdJkqQOMXmTJEnqEJM3SZKkDjF5kyRJ6hCTN0mSpA4xeZMkSeoQkzdJkqQOMXmTJEnqEJM3SZKkDjF5kyRJ6hCTN0mSpA4xeZMkSeoQkzdJkqQOMXmTJEnqEJM3SZKkDjF5kyRJ6hCTN0mSpA4xeZMkSeoQkzdJkqQOMXmTJEnqEJM3SZKkDjF5kyRJ6hCTN0mSpA7pVPKWZI8kFyS5OMnBw45HkiRprnUmeUuyEfARYE/ggcDzkjxwuFFJkiTNrc4kb8AjgYuranVVrQNWAHsNOSZJkqQ5laoadgyTkmQfYI+qemn7/oXAo6rqVaPqHQAc0L69H3DBnAa6cGwD/GbYQWhesU2oH9uFRrNNTN29q+puE1XaeC4imSHpU7Ze5llVRwFHzX44C1uSVVW1dNhxaP6wTagf24VGs03Mvi4Nm14G3LPn/T2Ay4cUiyRJ0lB0KXn7IbBTkvskuRPwXOCUIcckSZI0pzozbFpVNyd5FfA1YCPg6Kr66ZDDWsgcetZotgn1Y7vQaLaJWdaZCxYkSZLUrWFTSZKkDZ7JmyRJUoeYvM2CJGsnUee1Se48F/GM2u+Lkmw/hfUuTfLjJOe0jw9OUL9Tn28YZrKdJHl5kr9rX9/uGCT5+GzPRpJk1yTfb9vG+UkOneHtvyPJk2Zym/NRkq17/o39Osmv2tfXJjlvjHUmPDZJDk1y0FTWna4kOyS5sedzndNedDaV7fykfb10ot+grprM70JP3WVJvjiJet+dXlSTiuXQnvb6kyR/M0PbnfD3a5J1Zv0YzCXPeZsFSdZW1aIJ6lwKLK2q9W5kmGSjqrplFuLaCDgNOKiqVg247qWMEe+g9efj5xuG6baTcdZZyRwfgyQXAPtW1bnt93C/quqbbGhy2gR4bVUdnmQH4ItV9eDpbmvGApz8vndgGrHP9Hbmu8n8LvTUXUbzb/0ZsxvVpGI5lNva6wOA7wDbVtWfJ7HuxlV182zHuJDY8zaL2v8VrUxyYpKfJTkujX8Atge+leRbbd217f+Cvw88OsnuSc5ue7uOTrJJkj2TnDBq+//dvn5Kku8lOSvJ55IsassvTXJIktOB5wFLgePa/x1tNs3Pt3GSH7Y/ICR5b5J3L5TPN1cGbSc96+2T5Jj29aFJDkozE8ntjkG77aVtvee1x/wnSd7Xs6217Xd3bpIzkiwZ8GNsC1wBUFW3jCRuSTZvv98ftt/3Xm35i5J8IclXk1yU5P1t+UZJjmnj+3GS17Xlx7SfbeQ7/+e2Lfw4yf3b8rslObUtPzLJL5JsM+DnmM82SvKxJD9N8vWR9j2ZY9MrycuSfKVtG0M7ruO0jY2S/Etb/qMkf99n3Vt7nNq2f3Tbzle3/25G6r2t/Td1apLj06f3cT7KqB61JB9O8qL29R7tZzodeFZPnTG/p5HfjbF+a9plTxvZbpIPZhI9emOpqvOBm4Ft2rg+336fP0zy2HZ/hyY5KsnXgU+13/vhbdv7UZJXt/V6f7/G+juwMk1v7CtGfkva8hcl+dAwjsGsqyofM/yg+d8HwDLg9zQ3FL4D8D3gce2yS4FtetYpmp4LgE2BXwJ/1b7/FPBamlu7rAE2b8s/CryAZiqS/+kpfxNwSM9+3tizn5U0PTmDfqZLgR8D57SP17XlDwLOB54MnA3cqYufr0PtZG3P632AY9rXh9L8D3y9YzDyniYRXAPcrT3W3wT27vl+/rp9/X7grQN+lkOAa4CTgL8HNm3L3wO8oH29JXAhsDnwImA1cNe2PfyC5ibcuwCn9mx3y/b5GGCfnmPy6vb1gcDH29cfBt7cvt6j/UzbDPI55tNj1He6A80fw53b9yf0HNfJHJtDgYOAV9HcH3OTuTqubew3cttvx0cmaBsHjLQ/YBNgFXCfdjs/6fk388Wez/bdtu42wG+BO9K0+XOAzYAtgItGjud8fgBrez9fz3fwIm777dyJZtahE3qOw5jfExP81vRs9z5tveN79z+F9voompvoB/gMt/2e3Qs4v6f+mcBm7ftXAJ8HNm7fL26fV7bf5Xh/B0bq3I1mDvSRmL7Ss+9ZPwZz+bDnbfb9oKouq6br+ByaH6B+bqFpuNDMyXpJVV3Yvj8WeEI13cpfBf46ycbA04GTgV2BBwL/m+QcYD/g3j3b/uwMfZblVbVz+/gAQDX32vs08N/AS6pqXYc/3zBNtp1MxyOAlVV1dXusjwOe0C5bB4z8L/PMQfdfVe+g+fH8OvB/ab5HgKcAB7ff20qaH8h7tctOq6rfV9UfgfNovtPVwH2TfCjJHsAfxtjlF/rE+jhgRRvPV2mSyYXkkqo6p3093nfU79gAvBDYE/jbqrppgHVn4rj+vOe345Vt2Vht4ynA37Xl3we2pklWxvOlqrqpmtMLrgKWtHGfXFU3VtV1NL9RXXd/mnZwUTUZxn/1LJvs99Tvt+b+wOqquqStc/wU43td+70dDjynjfFJwIfb8lOAuyTZoq1/SlXd2L5+EnBE+9tEVf1u1LYn+jtAVV0NrE5zDu7WNH9r/rdPnLN5DOZEZ27S22G9P5K3MPYx/2Pddh5Yv3lcR3wWeCXwO+CHVXVd2+V7alU9b4x1rp8oyCT35LYftyOq6oiJ1unxEOBamh/MsQz183XAZNtJ70mqmw64j/GO+5/aH9ox95/kazTf8aqqeul6gVX9HPhoko8BV7c/nqFJFi4Yta1H0eczV9U1SR4KPJWmHewLvKRPvDf1rjeJz7cQjD5eY50W0O/YAPwE2Jmmx+GS0SuNs+6ExzXJM4G3t29fWpM733KsthGaHsCvjSrfYZxt9fv30+X2cDO3P62p99/6WCeqT/bzTvlYJfkk8DDg8qp6Wp8qH6j1z6u8A/DoniRtZFtw+9/uMPZnG1k+3t+BEZ+l+d34GXBSz+9ar863F3vehuc6mq78fn4G7JBkx/b9C4Fvt69XAg8HXsZtPU5nAI8dqZ/kzkn+apD9VtUve/5nPOnELcmzaP5n/ATgg0m2nI+fr8NGf54rkzwgyR2AZ05ynRHfB56YZJs0FxU8j9uO+4Sq6qlt+1gvcUvy9JHzRmh6SW6hSei/Bry655ySh423j/YcnTtU1eeBt9G0hck6neZHmyRPAbYaYN0Nwdk0Q9qnZLArsic8rlV1Us/vx2QvlBmrbXwNeEWSO7blf5Vk8wHi7Y37r5Ns2p4b9fQpbGNYfgE8MM25wHcFdm/LfwbcJ8lftu97E5nptP+f0fR479C+f06/SlX14vY77pe4jeXrNMP1tLHtPE69l7ejLiRZPGr5ZP8OfAHYm+bYDDIqM6ljMF+YvA3PUcBX0p6I3qsdRnox8LkkPwb+DBzRLruFZnhrz/Z5pKv4RcDxSX5E08jXO1G5dQxwRKZ2Qv+3ctul/p9q/9AeBuzfDoF+GPiPDn+++Wj0cTyY5rh8k/YCgT6Ooc8xqKorgDcD3wLOBc6qqpNnKM4XAhe0wxmfBp7ffpfvpDn/6EdpbvPwzgm2c3dgZbudY9p4J+ufgackOYum/VxBk8iqVVWn05z79qVM/qKD2TquY7WNj9MMo5/Vlh/JFEaJquqHNMN059L8QV9Fc67TvNUmLjdV1S9pzmf7Ec3pDWfDrb+dB9B8f6fTJHkjpvw9tb1iBwJfbbd7JTN3rP4BWJrmIoTzgJePUe/jNOfk/ijJuTSnX/TGOKm/A1V1De1pGFX1g8kGOcvHYMZ5qxBJC0KSTYBbqpkH+dHAR6tqrP/la5K6fFyTLKqqtWnulfg/wAFVddaw4xpLe8rAx6rqkVNYd1rfU8+xCvAR4KKRc5s3FF06Bp7zJmmhuBdwQjukvI5m6F3T1+XjelSam7duChw7zxO3l9P0Ur12ipuY7vf0siT7AXei6ek7copxdFlnjoE9b5IkSR3iOW+SJEkdYvImSZLUISZvkiRJHWLyJi1AaeadrSQfn4Vt79hu+63jlc03Sf4riZNfd0SauSfXTlxT2vCYvEkdkmZS5RrnseuwYxymJM9Kcsiw4wBI8qSe7+Xfx6jzF0lubut8Y9Sy0/t8v1cnOSPJAe1VhaP3M9Hj4nadl7bv9x4n/h0H2O6cJMVJ9k3ylrnYlzSfeasQqZuOB77cp/xigPZeT5vRTLMzF35OM13Tn+Zof2N5FvBc4B1DjqPXH4HnJ3lDVY0+PvvR3KR6LDfQzIoAzfQ9S2huXnokzbyN/0gz7dULR633CuAxNLee6J3jcpCb6/66z3b3AfaiuaHuhT3l432GqXoe63cw7Esz6fp7ZmF/UmeYvEnddFZV/dd4Fdq7sc+Jdv7AOdtfx5xEk4j8NbdN/D7iRTRzCo81ddOfRn/PST4CXNqu+49V9WtuP0E5SfagSd5OqqrLphJ0Va3ts9370yRvX29na5hQO2vAHUfPbTmJ/Q/7PwLSvOWwqbQA9TvnrbcsyeOSfCfJDUl+k+So9Jk/MskTknw3yY1Jfp3kg0C/euOeB5dkryRnttu5PMlh7R/10dvZN800On9M8oskb0vy1HY7L5jgM58OPB/YaNSQ3gtG1dsyyZHtEOQf2+HJR/TZ3h2SvCrJWe1xui7JaUmeOF4cffwA+CnNlHC9238MzfQ+nxxkY20SdA3NjVjnlfZ4VZLHJnlXkktpkvqnt8uf0Z7Ldml77H+X5Mv9hvsz6py3JKuAvwU2H/X97jNHH0+aN+x5k7rpzll/bsqbqmoyw2K70Exq/wmanpXdaO7GfjPN3H7ArcnFqTQTzB8G/IGmB+lxA8b618CraYb6Pt7u+03A74D39+zv+W08FwKH0gzF7UfT0zMZ7wDeDuzarjfif3teh+YzXd7u427A62nmirxv29s04jiaYboTaI7VZsALgNOS7FVVX5pkXNAkaO9Lsl07xyzAS2jmn/zKOOul53sOsC1NEng/mrks56uPtM//CVwPXNK+fxnN3euPpvkO7tWWfTvJY6rqzHG2+Taaz/x/gJf2lE96/kppwagqHz58dOQBLANqjMeKnnobt2Uf71N2C7B01Ha/BtwEbNZT9oO2bMeesk2AM9vtvLWnfMdxytYC9+opvwNwPvDLnrI70pxjdTmwZU/5FjSTbxfwgkkcn/8Cbh5nWQEfHFX+vLZ8/56yZ7dlLxlV94400+ZcNIlYntRu47U0Sdc64I3tss1pkuHD2vd/BL4xav3Tx/iebwbeNonjUMA9xlj+0nb53gO2v3e16z1ujOWvapefA2zSZ/nmfcruSTMB+Amjyk8E1k5U5sPHhvhw2FTqpqOAJ496vGuS655eVatGlX2Tpkfk3gBJtgceAXyhqi4eqVRVNwF9r5wcx+erak3PNv4MrATu0V5UAfBImpPxP1lV1/bUvY6Zn19w9ETT32yfd+opewFNj+N/J9lm5AHcFfgisGOS+052h1V1FfAlbhs63YcmMZ1oyPR6bv8dv7Dd/zvm+VWXH27byu1U1fUjr5NskWRr4EaahPhRcxif1GkOm0rddFFVfWPian2t7lP22/Z56/Z5JDH5WZ+6583g/hYDvwLu076/oE/dfmVT9Weanrx+sWzdU/YAYEvgqnG2tYT+n20snwRObs/vegnw3aqa6LPd3Od7/q8kpwLvTHJiVV3Yb8Uh6xtTe8HDu2l6Je8yavEgV8JKGzSTN2nDc8s4yzLqucapMxv7m23V9vyNF8vI6363yug1aBL75Xab7wQeT3Ou11R9jSYBWsYYidKQ3TC6IMli4Ds0x/Zwmos41tIk1O+gOZdN0iSYvEnq5+ft8wP6LOtXNl0jJ7Tfr8+yfmVj6ZdsTsVFNMOU362q9RKRqajm3nufBt5AMxz62Wls7o7t8xbTDmzu7AlsA+xbVZ/rXZAxbmLcx0x9v1Knec6bpPVU1eXAKuBZSXYcKU+yyf9v795do4iiAIx/p1BRCNiliMQuIAo2EQR7wUawkIBE1EZSqvhgsRbBwnIJCgGjRcDOJlWCIGIiCOJfYBuxTCHGcC3OSB6uZnbZ6A75ft3swp0LO8XZOY9LFuD32zKZorwWEYc33W+IjSG1daySo0K2p+S6NUv+uX3Q6cuIGO5x3TbZMTlVtna21hYRAZyvLv/WnTlofr2B3fKWNSIuAMdrrrFKdlof6OfGpKbxzZukP7kFLABvI6JNdgReYhdSnKWUtYi4AzwDliNihkynXSWDulHqvXVZAqaA6YiYJ098eFdK2V7nttN+5iLiHHAjIsbJlOdX4AhwptrPWDdrVut+JkeU1LVv25y6YXLW2WlgvpTyuts9/EeL5HiYdkQcI1PI48AEmYI+WmONJfKZeBJ5nNga2YDT0yBiqakM3iR1VEp5ExFngYdAi2qcAzmr7eMu3G82Ir4D98kaqBXgKTlW5CXZlbiTF8BJMiCYILMLl/m9SaHOfq5ExCJZm9ZiY5zJB2C62/V6dAh4vun6G5nSbQGP/9Ee+qKU8qV6nh4BN8nf5j1Zu3eXesHbDHCCDGAnqzUukiNEpD0jSrGEQNLgioh75JDgUx1GnEjSnmPwJmkgRMR+YL2Usr7psyHy4PWDwEjxvEtJMm0qaWCMAa8iYo7sPh0h65tGgesGbpKUDN4kDYoVsgZqkjxO6gfwCbhdSrGmSZIqpk0lSZIaxDlvkiRJDWLwJkmS1CAGb5IkSQ1i8CZJktQgBm+SJEkN8hMstq+UcAKsGAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(len(alls)):\n",
    "    trait=tellmemyMBTI(alls[i],names[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['INFJ',\n",
       " 'INTP',\n",
       " 'INTP',\n",
       " 'ENTJ',\n",
       " 'INFP',\n",
       " 'INTJ',\n",
       " 'INFJ',\n",
       " 'INTFP',\n",
       " 'INFJ',\n",
       " 'INTJP',\n",
       " 'INTJ',\n",
       " 'INTJ',\n",
       " 'ENTJ',\n",
       " 'ISFJ',\n",
       " 'ENFP',\n",
       " 'INTP']"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trait"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}