TTLproject.ipynb

{
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/isha-dutta/E-commerce_website/blob/main/TTLproject.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nejKWEhNcpMM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6cf6e228-c6e9-416d-8d4f-035fdc24b4dc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nGEl6zVjdFp3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "2924be9a-15ff-4cf1-a3e9-caf5e8c30359"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        type                                              posts\n",
              "count   8675                                               8675\n",
              "unique    16                                               8675\n",
              "top     INFP  'http://www.youtube.com/watch?v=qsXHcwe3krw|||...\n",
              "freq    1832                                                  1"
            ],
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              "      <th>type</th>\n",
              "      <th>posts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>8675</td>\n",
              "      <td>8675</td>\n",
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              "      <th>unique</th>\n",
              "      <td>16</td>\n",
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              "      <th>top</th>\n",
              "      <td>INFP</td>\n",
              "      <td>'http://www.youtube.com/watch?v=qsXHcwe3krw|||...</td>\n",
              "    </tr>\n",
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              "      <th>freq</th>\n",
              "      <td>1832</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"dataset\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          16,\n          \"1832\",\n          \"8675\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"posts\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"8675\",\n          \"'http://www.youtube.com/watch?v=qsXHcwe3krw|||http://41.media.tumblr.com/tumblr_lfouy03PMA1qa1rooo1_500.jpg|||enfp and intj moments  https://www.youtube.com/watch?v=iz7lE1g4XM4  sportscenter not top ten plays  https://www.youtube.com/watch?v=uCdfze1etec  pranks|||What has been the most life-changing experience in your life?|||http://www.youtube.com/watch?v=vXZeYwwRDw8   http://www.youtube.com/watch?v=u8ejam5DP3E  On repeat for most of today.|||May the PerC Experience immerse you.|||The last thing my INFJ friend posted on his facebook before committing suicide the next day. Rest in peace~   http://vimeo.com/22842206|||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...|||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 ...|||Welcome and stuff.|||http://playeressence.com/wp-content/uploads/2013/08/RED-red-the-pokemon-master-32560474-450-338.jpg  Game. Set. Match.|||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...|||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...|||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...|||Dear ENFP:  What were your favorite video games growing up and what are your now, current favorite video games? :cool:|||https://www.youtube.com/watch?v=QyPqT8umzmY|||It appears to be too late. :sad:|||There's someone out there for everyone.|||Wait... I thought confidence was a good thing.|||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...|||Yo entp ladies... if you're into a complimentary personality,well, hey.|||... when your main social outlet is xbox live conversations and even then you verbally fatigue quickly.|||http://www.youtube.com/watch?v=gDhy7rdfm14  I really dig the part from 1:46 to 2:50|||http://www.youtube.com/watch?v=msqXffgh7b8|||Banned because this thread requires it of me.|||Get high in backyard, roast and eat marshmellows in backyard while conversing over something intellectual, followed by massages and kisses.|||http://www.youtube.com/watch?v=Mw7eoU3BMbE|||http://www.youtube.com/watch?v=4V2uYORhQOk|||http://www.youtube.com/watch?v=SlVmgFQQ0TI|||Banned for too many b's in that sentence. How could you! Think of the B!|||Banned for watching movies in the corner with the dunces.|||Banned because Health class clearly taught you nothing about peer pressure.|||Banned for a whole host of reasons!|||http://www.youtube.com/watch?v=IRcrv41hgz4|||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...|||a pokemon world  an infj society  everyone becomes an optimist|||49142|||http://www.youtube.com/watch?v=ZRCEq_JFeFM|||http://discovermagazine.com/2012/jul-aug/20-things-you-didnt-know-about-deserts/desert.jpg|||http://oyster.ignimgs.com/mediawiki/apis.ign.com/pokemon-silver-version/d/dd/Ditto.gif|||http://www.serebii.net/potw-dp/Scizor.jpg|||Not all artists are artists because they draw. It's the idea that counts in forming something of your own... like a signature.|||Welcome to the robot ranks, person who downed my self-esteem cuz I'm not an avid signature artist like herself. :proud:|||Banned for taking all the room under my bed. Ya gotta learn to share with the roaches.|||http://www.youtube.com/watch?v=w8IgImn57aQ|||Banned for being too much of a thundering, grumbling kind of storm... yep.|||Ahh... old high school music I haven't heard in ages.   http://www.youtube.com/watch?v=dcCRUPCdB1w|||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...|||I like this person's mentality. He's a confirmed INTJ by the way. http://www.youtube.com/watch?v=hGKLI-GEc6M|||Move to the Denver area and start a new life for myself.'\",\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "import pandas as pd\n",
        "path = \"/content/drive/MyDrive/mbti_1.csv\"\n",
        "dataset = pd.read_csv(path)\n",
        "\n",
        "dataset.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LNv1t86Ec0Tx"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from numpy import asarray\n",
        "from numpy import savetxt\n",
        "from numpy import loadtxt\n",
        "import pickle as pkl\n",
        "from scipy import sparse"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nsztq38wdL9G"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.express as px\n",
        "import wordcloud\n",
        "from wordcloud import WordCloud, STOPWORDS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "53Iva3w3dpLh"
      },
      "outputs": [],
      "source": [
        "import re\n",
        "import itertools\n",
        "import string\n",
        "import collections\n",
        "from collections import Counter\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "import nltk\n",
        "from nltk.classify import NaiveBayesClassifier\n",
        "from nltk.corpus import stopwords\n",
        "from nltk import word_tokenize\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem import PorterStemmer, WordNetLemmatizer\n",
        "from tqdm import tqdm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6WK8SqgMdsgo"
      },
      "outputs": [],
      "source": [
        "import sklearn\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "import sklearn.cluster as cluster\n",
        "from sklearn.manifold import TSNE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yt7G_n36d2LB"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KR1qZwvWd5rw"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.svm import SVC,LinearSVC\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from xgboost import plot_importance\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.tree import DecisionTreeClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iCIlnzRreDmT"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error, accuracy_score, balanced_accuracy_score\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score, multilabel_confusion_matrix, confusion_matrix\n",
        "from sklearn.metrics import classification_report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xhIeTs9ieGe-"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cAI-icgIeR1M"
      },
      "source": [
        "## Loading Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "8EZhOU_dedYz",
        "outputId": "d16a86c6-7c35-481b-d7e0-c56179e421ec"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   type                                              posts\n",
              "0  INFJ  'http://www.youtube.com/watch?v=qsXHcwe3krw|||...\n",
              "1  ENTP  'I'm finding the lack of me in these posts ver...\n",
              "2  INTP  'Good one  _____   https://www.youtube.com/wat...\n",
              "3  INTJ  'Dear INTP,   I enjoyed our conversation the o...\n",
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I DID, but then I just got bored of them,...|||An INTJ friend of mine told me she really hates eating because it's so repetitive. As an INTP I don't really feel bothered, I don't really register eating I just do it while I'm thinking about...|||Similar Minds Advanced Test|||INTP 9 sx/so I have a lot of friends but I only have one I speak about how I actually feel with and only 1 other that I talk about what I am actually thinking about - with regards to...|||Getting close to someone and end up wanting to be infinitely close to them and end up merging every damn time.|||Interesting topic, I know two ENFP's, 1 is strange, as an INTP I view some of the stuff she does as 'coming onto me'?  - sort of. She gets really close and is very open to me, just what ENFP's do,...|||As far as I'm aware this is normal in all people, it's not so much your pessimistic self but just that humans are social creatures and you will feel better after socializing for that reason. Being...|||Vajrayaksa  How dare you use Lelouch as an INTP! He is an INTJ I say!|||I normally think about doing this stuff but don't actually do it.|||You'll probably find that commenting on their nihilistic thoughts logically would make them more open. Dismissing ideas will merely make them agree with that idea more as they'll feel a need to...|||Don't worry, calling me thinker is the same.|||INTJ - Winter - Coldest ENTJ - Summer - Actually doing things ENTP - Spring - Bringing out the fun in people INTP - Autumn - No one ever notices this season|||I've known a couple ENTP's, one ENTJ and an INTJ and it kind of went ENTP>ENTJ>ENTP>INTJ I love being able to constantly talk with ENTP's but also they need to know when to stop talking, if an ENTP...|||If you mean actually 1v1ing yourself I also have done this, maybe I'm wrong but I find it pretty easy to think about the most logical move the player I am playing should make with the information...|||Gender: female Age: 19-29 Intelligence: gifted Idea adoption: early-adopter MBTI personality profile: ESFP Closest alternative we found: ESFJ  I think I killed it when it told me to write...|||I'm fine with them, I'm more on shortage of people I'd ever want to do them with but that's on the grounds that any socialising that could be done could just as well be done over skype without the...|||I used to use pencils then I was told that my teachers wanted me to use pens only - so I used pens only. I haven't used a pencil - excluding my games of naughts and crosses earlier - in a couple...|||I'd love to own either a calm house out somewhere distant with a porch and a log cabin aesthetic - fuck it, I want a log cabin. OR I'd actually really like the idea of being in one of those smokey...|||Yeah I enjoy listening to lyrics but it doesn't make much of a difference if they're present or not, if they are there they're just another part of the instrumental I don't necessarily listen to...|||More often then not someone is only going to be fully entering my world if they invite me into theirs first and it's similar or we're friends on the internet - because then there are few...|||What did you get me today?  My head = Shit, shit, shit what did I miss, is it your birthday? Was I supposed to get you something? Are we that good friends that I should be buying you stuff? I...|||I frequently say it as an off hand thing, someone does something really awesome and I say fuck yeah I love you man or something like that. If I was to say it in a genuine way then it'd feel really...|||So something strange has been happening recently, my interest was drawn to naughts and crosses and how I could make it more enjoyable to play. I started by increasing the grid size and tried...|||You could tell them your thoughts. You could not tell them your thoughts and continue to brew over it. I think that there's no reason to be ashamed or annoyed about anything and if you don't...|||loudly, whilst he drew his|||and the baby platypi quivered|||particularly in the higher regions|||Starting with a lingering feeling|||What's the point of stopping? Why does it matter what they're called? Guilty pleasures are pleasures for a reason, does it matter that much?|||their own god dam story|||post multiple times to do|||It's impressive how people would|||Maximum of five words per line, feel free to say other stuff in the most but make sure it's clear which is the five words.     Once upon a time in|||someone who appreciates c.c. Fuck yeah!|||Yeezus can't we come to a reasonable conclusion without you picking a fight? It's not being open minded.|||More often then not I judge them purely by how they judge me, I have a dark sense of humor that I'm not exactly secretive about, I get along with most people on the grounds that I'm just friendly to...|||I chose my avatar as it's an awesome art work of Kaneki from Tokyo Ghoul and by god I loved the manga, love tragedies. Name is a slightly altered version of my name, George - Georgius. Liked the Halo...|||Violence in this situation would probably be a faster way to get things done for both parties involved and especially on the receiving end, trying to talk sense into someone who just said something...|||But surely the point that everyone would be worse off initially isn't relevant? Rebuilding a system would obviously have it's issues here and there.  I'm not crying revolution and I'm not being...|||We'd call it Diagon Alley!|||I'm with you, I make a pretty big point of not being a person for people to expect feelings from, I want to be that person but I just feel wrong, someones crying their eyes out and here I am lying...|||Good call but that's just how I read it ^^'|||What about a four dimensional plane.|||Why airplane why not aeroplane?|||Hit them for having the expectation that you should live up to their expectations which is infuriating in and of itself probably.|||It's just because hitting a women is politically incorrect and some women will use that to their advantage.|||What he's saying is that they should assume that if they say something worthy of being hit for, that they should be grateful when they're not hit for it, not expect not to be hit just on the basis...|||Is it particularly common amongst INTP's and other types to just acknowledge another sides point of view even though socially you shouldn't. What I mean by this is I can't have conversation with...'\",\n          \"'I said zero flexibility and little time for dating. Going around sifting through dates to find one that clicks isn't going to be as viable.  That has nothing to do with maintaining a relationship I...|||How you've come to the conclusion I have nothing to offer  merely because I am busy is beyond me.       No not really. Again, more made up stuff.|||okay, this isn't cool.  you're specifically looking to get into a relationship because you're about to enter a life phase where you will not have any resources to give to a relationship?   that is...|||I do take it slow (that's why I'm still single), but I'm not interested in making friends. I  have friends. Enough for my liking. I'm interested in going on dates with the intent of entering a...|||Absolutely not for me. I find the idea repugnant, actually.|||People could have been polite. Maybe average is a more conservative bet. And I'm not claiming I'm a social butterfly. I'm just a normal guy who is introverted but not a social doofus.  I don't...|||I'm probably giving up the online thing here when my next subscription runs out. After that I really don't know where to look. Bars are a definite no. I don't understand where people meet their...|||I've tried both ways, actually. The last few women I have gone out with have gotten really weird and/or ignored my backhanded compliments. I don't know what to do anymore. Seems I get screwed either...|||I'm actually pretty healthy.     Sorry you feel that way. Not sure which ones you met, but assholes exist everywhere. The number isn't anywhere near 99% though.|||If anything I err on the side of being indifferent. Definitely not overeager.|||Yeah my close friends don't live near me anymore.  The issue is that I have been meeting people, a good amount, and nothing works. I want to know why it is that deformed, infirm, mentally unstable,...|||My friends are in worse positions than I am. Asking them isn't going to help.   As far as feedback, not really because that seems like an extraordinarily awkward thing to ask someone you barely...|||So, I'm a 26 year old male who has been striking out time and time again when it comes to relationships. I've been in one relationship my entire life that lasted like 1.5 years and ended when I was...|||I don't think this sort of dating method is very reasonable, and at best, it is counterproductive. Being friends first just throws a wrench in what is normally a smooth dating process. When you fear...|||Never was a kiss.|||I appreciate the feedback. I think I'm just going to tell her we shouldn't hang out unless she can dedicate a decent amount of time to it. What is happening right now is dragging things out and...|||I don't know. After thinking more, I'm on the verge of giving up. It has nothing to do with seeing her infrequently, or not making it official or what have you. It's that she keeps things...|||I don't know. I'm not a model or anything but I'm decent looking. She's drop dead gorgeous and nice and smart. Guys are always after her it seems. But I'm also smarter and have better career...|||I like your answer, but maybe I'm just being optimistic. It is basically, what I've been doing. I can't shake the feeling that she wouldn't be giving me any of her time if she weren't interested and...|||How am I supposed to know if an INFJ just wants to be friends? Things have been very weird with an INFJ I've been seeing. I've been very upfront about my intentions and she essentially told me she...|||Interesting. I find it unusual INFJs are known for taking forever to open up and trust people yet all the responses so far have indicated kissing in 3 or fewer dates....|||My INFJ friend has been with an INTJ, and i don't want to bust your confidence but even though she liked him at first, she soon found out that he was putting her down and felt caged. I can see that...|||Enlighten us infjs. Also indicate your gender and if you initiated.|||Well there was more than that. She also said she was basically trying to figure out if I'm trustworthy and decent.|||Those two things seem at odds. How can you be simultaneously not saying things that need to be said but also being proactively aggressive?|||Pretty much yeah. Women just do not enjoy my company on any level. I'm in the same situation. Never had any female friends, dated one person in my life and it lasted a little less than two years. The...|||I don't think that is going to help me dig myself out of the very clingy seeming hole I dug myself. I basically asked her if she knew I was romantically interested and she responded of course lol...|||I should have just let things proceed at a natural pace. Pretty pissed at myself. About zero chance she speaks to me again.|||I read through most of that thread. From what I can tell I never did anything egregious. Good chance she lost interest after my bluntness though lol.|||I broke that off shortly after my post. I felt like a slime ball. I only did it because people were telling me she's a lost cause and I should see other people. BTW I have a very well developed...|||Update on this. Told her I liked her and got no reassurance she liked me or even that she wasn't interested. Those who said she was ambivalent were right. I'm pretty much calling it off at this point...|||Yeah so I told her and didn't get an affirmation she liked me nor that she didn't. She basically said she had known for a while I like her. Pretty much what I expected. Probably going to call it...|||Is the crystal ball reference supposed to mean your guess is as good as mine or what? Edit: guess not :p|||So I've gone on six or so dates with an INFJ over a 3 month period (she's pretty busy, and me too) and recently I feel like progress has been a little glacial. For a bit I thought she was warming up...|||She hates texting.  And yeah, she told me how picky she was about people recently and that normally she wouldn't give anyone the time of day.|||You would literally have to have an autism spectrum disorder. I would know.  Not to mention you don't tell someone who is just friends how much you like them. That would be more than bizarre.|||It's kind of hard to get to the point of officially dating when you have no idea what is going on in her head. For all I know an attempt to kiss her would result in abject horror and assault...|||Spoke to her last night, she essentially said she likes me a lot and would normally never make any time for people unless she was obligated to. But she also indirectly used the word friend but it...|||So I met an INFJ female a while back, and we've been out like five or so times on a date. They go well, we get along, everything seems nice. I find her very attractive and we have quite a bit in...|||'  I don't know, I kind of have a rule that I don't compliment a female on her appearance until she trusts me. I did compliment her personality a few times.  She is extremely cautious from what I...|||Well, now I have a new question. Went on a second date a few days ago and it went well and she said she wanted to meet up again. The problem is, there hasn't been an ounce of flirting or touching...|||She started initiating with me and asked if I'm free soon. -_-    If there is anything to be learned it is don't play the analysis game. You will lose.|||Expectations of....?|||I don't know. I can't really fault a strong introvert for being wary about someone she doesn't really know. Number 2 is cruel, but it is how modern dating works.    Pretty sure this isn't the...|||I don't know. I can't really fault a strong introvert for being wary about someone she doesn't really know. Number 2 is cruel, but it is how modern dating works.    Pretty sure this isn't the...|||Thanks, option 1 seems most likely. It seems whenever I finally get her to open up there is like a cascade of good conversation that finally ensues, but it is like pulling teeth initially. Then I...|||I'm socially retarded and would know that if a random stranger asked for my contact info then asked me out for drinks or lunch then the person probably isn't interested in just being friends.|||So I shouldn't be deterred by the fact she doesn't really ever reach out to me? I don't enjoy appearing like someone who can't take a hint or who puts someone else in an awkward situation. I worry...|||No this seems to fit her pretty well.|||Thanks for the advice everyone. I appreciate your taking the time to respond.'\",\n          \"'This has to be written with bias or something, I'm not sure how you could even say that truthfully. For some reason you put subjectivity for both God and man? It says nothing.    That makes no...|||??? How?  The main priority of the church is written throughout it's texts for the past nearly 2000 years and reverberated throughout the church's dialogues both before and after the incident about...|||That's an annoying phrasing, as it's just obtuse and confusing to newcomers. I don't blame you for your response there.    Why must the revealed knowledge not speak truth but contain a bias...|||There are no priestly desk jobs. The closest thing you have to being a priest that gets you away from people is being a monk and that in itself is very different.  I am telling you they did it out...|||No, you really won't. You can't just go to an INTP and assume their intelligence level. That would end in failure. Don't be afraid to ask big questions when musing with the INTP and just try to...|||Heresy is, like, the core reason most all Christian in-fighting occurs. Everything else is minor disputes about theological opinions.|||That's exceedingly subjective. See what they're interested in, strike up a conversation about it. Definitely ask them their thoughts.|||To dismiss my concept of it is to dismiss all history on the topic, dismiss what the apostles taught, and literally all sects of Christianity. But fine, whatever.|||Do cool shit, discuss ideas, have patience, don't try to judge personally too quickly or control them. ALSO, make the first move to show you're interested in talking with them.|||I used to feel about the same until I read the book Self-Made Man, where a lesbian 'butch' woman dressed like a man and tried to live as one. She noticed herself being considered a feminine man quite...|||I'm not sure what the hell is going on in this thread but I would like to say I know no ENTJs personally, I don't think. It'd be neat, though.|||All churches are forms of political institutions. Catholic and Orthodox ones have the benefit of being centralized. All sects have declared doctrine in one way or another.  You're speaking against...|||And here is the most boring post in the whole thread.|||What part? If it's the idea of a teaching authority, you have a problem with all church until the 13th century. If it's the idea of a group being guided by the Holy Spirit in their decisions on...|||For Catholics, the church has a dual meaning. The whole of the believers (the Body of Christ) and the political institution. For Catholicism, their teachings of Christ are said to be protected by...|||Priests were relocated to other parishes so they may quiet down the criticism of the time and handle thing themselves. Again, poor decision but that was their decision.  The church's first priority...|||Well sure then. Yes, the church did protect child offenders. The church's reasoning was the image of the church itself and the their leadership's own ignorance of the size of the problem. The church...|||Edit: Based double post|||No, it really isn't. The Bible itself is the canon collection of texts. The Bible was established once the theological and historical work was done to establish the canon and it was not modified...|||Yes, nevermind it because the two claims do not contradict each other at all. I could work through how there is no contradiction if you're struggling.    I wouldn't say all, no. That's a...|||? There's no way to tell? But many of them depend on logic and/or history that contradicts or ruins their claims? Granted there are a few cases of we're yet to be able to know but many of it can...|||The problem of induction is a grand way of humbling yourself about scientific findings when you're younger. It brings into question if what we know is what we could call true.|||Rubix cubes.|||Pantheism makes no sense. I'd advise otherwise. Atheism and Abrahamic Theism are the only two rational choices.|||You, motherfucker.|||What is the meaning of everything? The universe exists. We are to learn to love in it.|||Disappointed 24 year old reporting in.  Good luck, OP.|||This is exceedingly naive. The early church, before scripture, applied their teaching to better society. After scripture, it was held in high esteem as a basis to properly better society. Most work...|||From the Catholic Catechism (list of official teaching) here is how we are to read the Bible:  III. THE HOLY SPIRIT, INTERPRETER OF SCRIPTURE   109 In Sacred Scripture, God speaks to man in a...|||I never liked people pointing to the Scandinavian model for the success of their economic system and not their incredible oil and natural resource supply compared to their small populations.|||While shit just got stupid for a moment. Lets try to get back on track.|||You're pretty smart, you know that?  Look at this fucker, she thinks I'm smart. Pfft.|||Exceedingly poor.|||I've just been told I'm bright, but I'm sure most everyone else says the same to the kids.|||And what of you?|||The most efficient way to remove self-doubt is to stop doubting yourself so much. Faith and trust, nigga.|||You suspect wrongly. I like the chatter and I'm being friendly with you. I seriously do hope you have a good day too.|||Indeed you do. Feel free to ask/argue/chat/or bullshit with me if you want. Have a good one, man.|||I'd say that's a stretch to say. Catholics believe the bible was inspired by God but written by men. Orthodox do too. As they assembled the biblical canon I'd have to say they have the greatest say...|||Edit: Welp.|||I see the arguments for inconsistencies. Sometimes they aren't actually inconsistencies. Sometimes they are. I find the simplest example of a true one being the rabbit that is apparently chewing...|||Sorry to take so long to get back to you, my internet has been screwing up horribly.  As for the answer, there was a considerable amount of things that got me off the atheist train but if I were to...|||The believing portion was a long and arduous process for me. I researched culture as a hobby and eventually I rolled around to religion. It was an interesting topic for me and I eventually began to...|||To your first statement... just... what. Where did that even come from? Of course they can know love, though with God they can know it fully, I'd argue.  As for Hell, it's taken a variety of...|||I'm very mixed on him. His criticism of Christianity (and envy, now that I've seen the video) is, while popular, piss poor and Max Scheler did a wonderful job making an argument otherwise on his...|||People believing in something as incontrovertibly true is what gets people into a murderous rage? Why just the incontrovertibly true? Why not the empirically true? Why not just majority opinion? To...|||Sure thing. Sounds sorta similar to the Epicurean Paradox.  Suffering/free will exists to strengthen the good. If you create a people so they may know love they cannot sincerely know love if they...|||Yo. Christian INTP here.  Anything you want?|||It's not we can make healthy children! it's we only let the healthy babies live! which is the issue. It's a moral issue about the innate value of life.|||249210'\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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              "      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",
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IT IS DONE :D) This isn't a list of films with ENFPs IN them, but rather ones that you believe ENFPs would...|||I know that I liked UP. :D|||I choose feeding myself joy over beating myself up.\\u201d  --Buddy Wakefield|||[QUOTE=Pucca;2951774]I've never seen it.:shocked:QUOTE]  Me, neither.   Guess we should have a Movie Marathon of All Movies ENFPs Should Watch. :D|||That's hilarious. hahaha I used to pretend I had a bunch of dragon friends and a whole bunch of animals (like platypi and octopi and ocelots, and horses, etc.) I was always rescuing my dragons from...|||WOOO!  I'd love to be the TARDIS! :D|||This is from my signature (actually, just look at all the quotes in my signature)  I am a forest, and a night of dark trees: but he who is not afraid of my darkness, will find banks full of roses...|||I'm not quite that bad either (and counting sheep doesn't work for me because I get distracted and numbers are not my strong point).  It does take me a long time to fall asleep even when I'm tired. ...|||Rune: That is an awesome photo/e-card.  I love it!  It is so true! :D|||I'm an ENFP (and a 4w5).  I'm conflicted on the subject. :3  I like to say This Is Me, but at the same time, I don't want to be put in a box, or have a label or stereotype applied to me.  I like...|||I like how people can discuss things when they don't agree.  This begins to seem like people arguing rather than discussing and some of the comments are a bit mean-spirited and undiplomatic (No...|||Because I don't like editing--it's my least favorite part of the writing process, I sort of edited while I went.  Kind of.  And then read through it once to make sure everything made sense and there...|||Hey there! :D  Welcome and enjoy the forums!!! :D|||So I already posted about my conversation style and merely touched upon editing my writing without going into it.  This is going to be the third time/thread I've posted this, but here we go: I love...|||Yeah, I'd say my conversation/speaking style is meandering as well! :D hahaha I love writing and took a lot of English classes (I eventually decided that I wanted to be an English teacher and so I...|||I was thinking of being an English teacher for a while and so I have had many writing classes.  I think @ChanceyRose is correct about people's writing styles depending upon the teacher.  One of my...|||I think I (an ENFP) tend to just keep going when I'm trying to get a point across that I know I'm not verbalizing well.  (When I'm speaking....if it's an essay or something, then I edit and...|||Usually, I stick to one book at a time (not including what I used to have to read for school), but lately I've been reading several books at once.  Currently, I'm re-reading Tolkien's THE HOBBIT. ...|||Dear Anonymous(es)  Clearly you did not recieve my last letter.  Or you did not understand it.  I assume this because it has been a week and NO ONE HAS HIRED ME OR EVEN CALLED ME BACK.  Look, I...|||I don't think I was being sarcastic...sometimes it happens when I didn't intend it though. :)  Hufflepuff is from Harry Potter.  It's one of the four dormitories houses in the castle.  Each...|||agreeeeeeeed!!!!!! :d|||Fluffy can be strong, too.  :3  I know what you mean though.  We're champions and we inspire people.  But why can't we be silly and fun-loving and affectionate as well?  I'm not saying every ENFP...|||I also found it interesting, but what about if you are on a medication that changes the chemicals/hormones in your brain?  Wouldn't that mean that your personality would change as well (according to...|||I realize that being nice and being fair are not mutually exclusive.  I would hope that people try to be both!  However, someone posed the question to me where you HAD to pick one and I was curious...|||--Sincerity/being genuine/honesty:  don't try to be someone you're not because you think I'll like you better.  I'd much rather see/hear the real you.  I like differences in people and so I'd want to...|||Just because you're not the only one, doesn't mean you're not crazy. :P  You're just crazy in good company! :D <3|||No problem, darlin. :D  I love meeting/welcoming new people!|||Welcome and have fun posting!! :D I am also an ENFP.|||I've never been professionally diagnosed but I've had many people ask me if I have it.  *shrug*  I think it's because like so many ENFPs, I go off on tangents when I tell stories or explain something...|||Is it better to be Fair or to be Nice? Explain and discuss your answers. :D  Have fun!|||Is it better to be Fair or to be Nice?  Explain and discuss your responses. :D  Have fun!|||ENFP Hufflepuff! :D   And proud of both!   And I think people stereotype too much with this.  I think any type can be in any house.  There is value in every house! :D|||I hate shopping!  Unless it's at a bookstore, Renaissance Faire, or a Sci-Fi con dealer's room.  I get bored and frusterated when I'm shopping for clothes, shoes, or anything else because it's...|||Okay, I agree with the every-and-none personality type. Or borderline on everything...  But I thought it would be fun to type him/her/any-variation-there-upon if he/she exists (or existed or...|||Yeah, I'm thinking you're going to have to do some heavy lifting hahaha.  On the other hand, the reading might interest you and then it won't seem so heavy.   Also, we're more than just lables...|||Oh, wow!  Because I was totally guessing... couldn't tell you exactly WHY I thought that based on your reading.  :3  I mean, I couldn't cite specific instances (except for the two that I pointed out...|||Words: You are amazing/awesome/fascinating. I love how/when you ___. I love you. You're so interesting. You're inspirational You have such good ideas. You're an excellent writer....|||I LOVE the teddy bear metaphor!  Great analogy!  I'm stealing it. ;P|||Bienvenue!  :D  Have fun in the forums and good luck figuring out yourself and your direction/desires!|||A friend--or two--in my area (I recently moved halfway across the country).  And a fulfilling job.|||Okay...this is just a guess, but maybe ENTP?  I don't know enough about the cognitive functions to back it up that way, but here's the reasonign behind my guess: N because you talked about how you...|||I'm not very good with functions and picking out which function I'm using at which time. So do you think you could say which things were Ne in my responses?  Maybe then I can learn to pick up other...|||I knit or do some other craft (as long as the craft is simple and doesn't have a complicated pattern).  Or I'll listen to one while driving or doing chores that are physical only with no need for...|||I found this thread:  http://personalitycafe.com/whats-my-personality-type/99679-whats-my-type-questionnaire.html and decided to give it a go.  Here are my responses.  I'd be interested in hearing...|||[QUOTE=Kevinaswell;400994]When I say green and grape soda and you say huh?QUOTE]  Okay, I'm an N and I didn't get the reference or whatever, but instead of going huh I immediatly was...|||That's true...sort of! hahah, it goes in cycles for me.  Spurts of interest. :D|||Dear Anonymous(es),  You should hire me.  I'd be a great asset.  It IS possible to be fun-loving/lightheatrted AND professional.  And trust me, I am NOT over/underqualified.  I'm sorry you found my...|||My lovelife is nonexistant.   Recently (basically since I went on Vacation with my parents last week and non of my siblings could make it), I began thinking about how nice it would be to have somone...'\",\n          \"'It has been too long since I have been on personalitycafe - although it doesn't seem to have changed one bit - but I must say it is good to be back somewhere like this. Usually I turn to Doctor Who...|||http://www.youtube.com/watch?v=6EEW-9NDM5k|||Overwhelmed by the world around me.|||In one dream I have had I was being chased by a large shadowy creature, with someone else who I felt I had to save above all else. The dream ended after she reached safety, but as for what happened...|||Well now My Avatar is a Doctor Who Clockwork Creature. I always liked this monster because It is just a worker trying to do it's job, kind of :3|||1st - Thanks for your reply, I appreciate all the help I can get. 2nd - I think everyone has the right to their opinion :) (however too many people abuse that right :P )|||Yea, Not Doing the Iron Man Thing xD  Thanks for all the advice everyone :)|||Thanks :)  I think I needed some humour. I might show them this (maybe).   I know they won't do anything harsh like throw me out or beat me ( although I have places to go ) . But I really don't...|||I don't really know how to judge personality types, I can only say Mums Introverted, while dad is Extroverted :P|||Ok, So I understand what I want, and how I feel about liking men. That's all good. But the problem now Is how do I tell my parents?  I want to tell them, but I really don't know how to say to them...|||I'm Contempt, as always I suppose, I have spent this afternoon buried in my book and In my own world, only after spending this morning out with our animals. Suppose It's a good mix :) . I will have...|||Whats the hardest thing I'm going to face in life? . . I haven't got a clue. But from my current perspective, telling my parents I'm gay seems like a huge hurdle. I do suspect that it will seem...|||I do! MI6 too, and Torchwood.  ...oh that's fictional. oh well, still :)|||I just got the book two days ago and have just read it. I thought it was brilliant!|||This is true for me too. My family love me, and i them, but they never wanted to deeply understand who I am. I am contempt with this and just keeping the deeper me to my self. different people see...|||To look at, and understand yourself. knowing yourself is important; and there is so much to know!  also be happy - I have not let anything get me down lately, and I feel better for it -either that...|||I guess not, but I wasn't watching Comedy Central|||http://www.youtube.com/watch?v=i1GmxMTwUgs&amp;feature=plcp This is such an incredible song <3|||Thanks for your reply, and I can relate, in part at least, to you.|||This is not as big a deal as I may have made it to be, but the reply to pointing it out Well just do it more or something along those lines.|||I feel the same, although I have only ever 'entered' this state of mind in a time of 'need' or 'emotion' not just for the sake of it. sometimes I think I'm too young, other times I just live my life...|||I Find that my parents are never happy with any effort I give them with anything. I will say I used to be quite lazy but lately I have tried to do by best and do everything they ask without hassle...|||Hey there! 43579|||Those spoiler buttons still pre-load the images so it doesn't matter|||I'm not the best driver, and I currently hold only a restricted licence, but I drive as safely as I can, and (don't tell my parents) only speed on an open road with no one around.|||Don't let someone tell you your beliefs are wrong, they are yours. What you think is right and true. People may not agree with you, that does not make your beliefs wrong it just means they are...|||Midnight Blue, no question.  I can be so many things and I feel I'm very contrasting.  You may see a dark shade of blue where, in fact, there is a story behind every line of that magnificent...|||I'm fine with ads. I make websites so know how sometimes you need them. Just a reccomendation though, make them open in a new window when clicked ('target=_top')|||Yes I want people to Understand. Some people anyway. But I think it doesn't matter if they could understand me, because so many people don't care to. Not really and fully.|||Just interested to know what some other INFJs' ideas are on letting people in to our lives, or keeping a distance. Personaly there are very few people I let in to my world at all, and I let no one...|||I wouldn't be bothered personally. Actually I sometimes actually think that I don't realize how obvious I can be or how others might feel.|||If I don't see good in someone, I won't hang around. If it happens to be you chances are you won't notice me by accident. I can be good at just disappearing If I don't want to see you|||In a library or out on a walk somewhere remote alone|||I agree, and although i'm a very black and white person (on the outside anyway), this website needs some colour. It is too bright and the black doesn't help at all.|||I'm defiantly a night person, no doubt about it. I enjoy being out late and sleeping half the day away.|||When you say you have lost your balance I knew exactly how you feel, I can say I'm in a similar place right now and have lost the thing that really keeps me going (my balance) and my world i spinning...|||Looks good! I think the top navigation bar/news would look better with a darker colour, white seems to bright.|||I Would Be With The Tenth, all the way.  ... Actually Both of them ;) - and Capt. Jack|||I was going to say that (About Doctor Who), but I thought my second thing seemed more appropriate.     But apart from the TARDIS, I would get in my car and just drive, stopping only to enjoy a...|||Thanks @Solitude315. Your right and I do like constructive criticism because you have something to work on and improve, i guess it just upset me a little.|||How do you feel when people criticize your work or something you do?  Whenever someone tells me that something I have done is not good enough it makes me feel like I am not good enough.   Maybe...|||YES -  Owner of a Lonely Heart  http://www.youtube.com/watch?v=ELpmmeT69cE|||As a Doctor Who fan I have to say Repeated memory wipes by the Silence will eventually fry your brain - The Eleventh Doctor But I'm the same, and when I do remember it's after a while and people...|||Currently I am reading 'The White Wolf's Son', a good read. Not quite what I expected when I picked it up but I will finish it soon. and next I want to read 'The Wolf Gift'...|||Tonight I sit outside my window in the blackness of night, staring out into the wilderness we call space. Peace. Beauty. I could stay here till the morning light. The thousands of suns I stare upon...|||I was going to close my facebook a few months back, but as well as wanting to be able to message my family in ausse and school friends i found that i had connected a few other websites to it...|||30 Seconds to Mars - All of my collections. It seems to be fitting my mood right now.|||I have seen it, and i agree. I did actually think that the first time I watched the movie, and from the beginning (or when they got their powers) I kinda thought Andrew would never work right with...|||Ok so i have just watched Underworld 4 (Awakening) and must say it was a really good film, Compared to the other films out in the last few months anyway. I don't think it was as good as the first 3...|||I would never want to turn off my emotions. sometimes I hide them from the world, but I still need them for me.'\",\n          \"'So many questions when i do these things.  I would take the purple pill. Pick winning lottery numbers. Do whatever I want.|||I worked in a brewery for a couple years here. I dont know if you're familiar with the local micros/craft at all but Mill st. was the name of it. Their tankhouse is my favorite of anything local.|||I am going to summon the great and powerful mr cafebot and his song of destruction. The four horseman ride. Count your days people of earth.   Intro: Nick - the INTP male - from a basement...|||This thread is obviously about the textbook definition of manliness. Ruffin it, tuffin it, and going hard in the mother fking paint.|||Stand up for yourself. You asked for the time off and it was approved. When they say, oh shit we are short staffed, where is Badgers? They figure out you asked for it off, that's why you arn't there....|||Lol excellent thread @NT the DC. I love watching baseball, and playing it. Oh and I hate the yankees. I'm 19, I've had multiple partners, I consider myself a manly man, I lift, and I am intp.   No...|||The thing with relationships is that you spend very little time in the bedroom compared to everything else. So while a dumb blonde can make you happy for 20 minutes a night, its going to take more...|||Thinking: A man plants a fruit tree because it will give him food in a few years. Feeling: A few years later a squirrel is hungry so it eats the fruit from the tree. Intuitive: The man sees his...|||Well I think you can appreciate an Einstein quote.. Everyone is a genius, but if you judge a fish on it's abiility to climb a tree it will live it's whole life believing it is stupid.  Everybody...|||Vsauce|||Real men write poetry.|||19, 6'4|||Oh and: Mountain dew releases mountain flavored Doritos locos tacos skateboards. That is all.  Also  http://www.youtube.com/watch?v=VP9--tH_YRo&amp;feature=youtu.be check this out. (still...|||There's only two things I am certain of with Rusell Brand.  1. He is very heavilly extraverted. 2. He is a magical man.|||Here I am people.   https://www.youtube.com/watch?v=rKVJARVcuhc|||badgers  I'm gonna make a male version intp video today.|||The creative process of intps is getting around to doing the thing and not putting it off. I think we're always ready for work provided we have sleep, and food in our stomachs.|||I may look busy, but I hear everything.|||I'm a guy but I like making videos. I'd love to be a part of this.|||Hey man, it's your life. Do or do not, there is no try.|||Yes. You know you're an intp when you have more conversations in your head than you do with real people.|||tl;dr   Yes I posted this.|||spoken like a true intp.|||If you really are bothered (and I believe you are), I think you should tell your boyfriend what you just said there.|||Morality invariably gets me talking, because it has philosophy but with tangable answers.  Other good subjects include evolution, hunter gatherer societies, political systems, good movies,...|||Hey b3th. I read your post and my immediate reaction was that your boyfriend is flipping out over something insignificant. Having trust issues is nothing to wag a finger at, but I think your idea of...|||I enjoy 9/10 of DarkBarlow 's posts. So a crush, ya maybe.|||In self defense, but then it's not really a crime.  Would you kill somebody to save 1000 people? For millions of dollars?|||\\u201cThirty years later he could not come to any other conclusion: women  were indisputably better than men. They were gentler, more affectionate,  more loving and more compassionate, they were rarely...|||We really should make a clear-cut method of differentiating the two. Intp are lazy and useless. They do things for no reason, often with disastrous results. The basically lack the things that make...|||743177431874319|||>2013 >Not using adblocker  Ps: ask an analytical type they will tell you they want to see less advertising, not help you do it better.|||Hahahaha this.|||73701 This is now a mango thread.|||+ Chameleon skills. That thing where we can mirror the mood/personality of others.  - Chameleon skills. Makes me wonder if I'm being true to myself.|||Free for all.|||We wouldn't be human without either of them.|||Well I know 100% sure you are wrong, because I exist. But obviously I can't prove it.|||73121|||The problem is in the first word of your post, Watching the news. TV news is entertainment, not journalism.|||I resent the thought that humans are nothing but selfish creatures, when being selfless can be accomplished on a whim. Fulfillment in life often comes in the form of giving to others. And Solfonny,...|||yup basically this. lol.|||Suicide is a permanent solution to non-permanent problems.   Selfish?  -It will cause pain and burden to your family for the rest of their lives, so not thinking of them could be considered...|||Lol you're a women. That was easy.|||Seriously, a day where every intp is on adderal? We would live in utopia by noon.|||Repeat the mantra: I am self aware.|||So you reject ideas that don't come from gods?  Camus declares that humans are here, we exist, there is nothing that can change that. Now we have a choice.  1. Get pissed off, give up, or revolt,...|||What, in fact, is the Absurd Man? He who, without negating it, does nothing for the eternal. Not that nostalgia is foreign to him. But he prefers his courage and  his reasoning. The first teaches him...|||So they don't need to elbow past people in a hurry and potentially miss the train door closing. Otherwise, they want to be first off the train/bus and not get stuck behind a crowd of slow walkers. ...|||This is a loaded question.   As for me I'm 19 and still strongly believing in non-belief.      INTERNET POLICE:   RESTRAIN FROM TROLLING AT ONCE! INTP:                    I...I...CAN'T...'\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "dataset.tail()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QHl9zxfFfDL7"
      },
      "source": [
        "## Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-d69gU0SfGoJ"
      },
      "source": [
        "## Checking for missing values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0LjzYeIZfAKa",
        "outputId": "290d7987-cdbb-4913-ed92-d066a0e31f24"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "type     False\n",
              "posts    False\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "dataset.isnull().any()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HVi5XpFPfTSp"
      },
      "source": [
        "## Size of dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WbscJnTzfQo2"
      },
      "outputs": [],
      "source": [
        "nRow,nCol=dataset.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VfYUyL5xfYHI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ebf39d8c-d81e-4aaf-8c5f-fb21dd0381ab"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No of rows 8675\n",
            "No of columns 2\n"
          ]
        }
      ],
      "source": [
        "print(\"No of rows\", nRow)\n",
        "print(\"No of columns\", nCol)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-IAZ4DTOfgVm"
      },
      "source": [
        "## Dataset Information"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YsUHeRcDfa8j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2aacd2eb-adcb-4c6b-ec5b-001443b1ef51"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "type     object\n",
              "posts    object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "dataset.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4x-flO2OfnTQ",
        "outputId": "ed53dabe-2373-4e57-afae-599d3f7abfd8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8675 entries, 0 to 8674\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   type    8675 non-null   object\n",
            " 1   posts   8675 non-null   object\n",
            "dtypes: object(2)\n",
            "memory usage: 135.7+ KB\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "print(dataset.info())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C9rw0wOHfp65",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "49b86d33-b4ec-4a57-b5f4-d4be3ae09750"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        type                                              posts\n",
              "count   8675                                               8675\n",
              "unique    16                                               8675\n",
              "top     INFP  'http://www.youtube.com/watch?v=qsXHcwe3krw|||...\n",
              "freq    1832                                                  1"
            ],
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
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              "\n",
              "    <script>\n",
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              "        document.querySelector('#df-72954b52-aa96-486c-995a-df05b5a59c02 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-72954b52-aa96-486c-995a-df05b5a59c02');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "    </g>\n",
              "</svg>\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
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              "  }\n",
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              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d2136836-c91a-411b-bc00-78fd1ba0d127 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"dataset\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          16,\n          \"1832\",\n          \"8675\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"posts\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"8675\",\n          \"'http://www.youtube.com/watch?v=qsXHcwe3krw|||http://41.media.tumblr.com/tumblr_lfouy03PMA1qa1rooo1_500.jpg|||enfp and intj moments  https://www.youtube.com/watch?v=iz7lE1g4XM4  sportscenter not top ten plays  https://www.youtube.com/watch?v=uCdfze1etec  pranks|||What has been the most life-changing experience in your life?|||http://www.youtube.com/watch?v=vXZeYwwRDw8   http://www.youtube.com/watch?v=u8ejam5DP3E  On repeat for most of today.|||May the PerC Experience immerse you.|||The last thing my INFJ friend posted on his facebook before committing suicide the next day. Rest in peace~   http://vimeo.com/22842206|||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...|||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 ...|||Welcome and stuff.|||http://playeressence.com/wp-content/uploads/2013/08/RED-red-the-pokemon-master-32560474-450-338.jpg  Game. Set. Match.|||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...|||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...|||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...|||Dear ENFP:  What were your favorite video games growing up and what are your now, current favorite video games? :cool:|||https://www.youtube.com/watch?v=QyPqT8umzmY|||It appears to be too late. :sad:|||There's someone out there for everyone.|||Wait... I thought confidence was a good thing.|||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...|||Yo entp ladies... if you're into a complimentary personality,well, hey.|||... when your main social outlet is xbox live conversations and even then you verbally fatigue quickly.|||http://www.youtube.com/watch?v=gDhy7rdfm14  I really dig the part from 1:46 to 2:50|||http://www.youtube.com/watch?v=msqXffgh7b8|||Banned because this thread requires it of me.|||Get high in backyard, roast and eat marshmellows in backyard while conversing over something intellectual, followed by massages and kisses.|||http://www.youtube.com/watch?v=Mw7eoU3BMbE|||http://www.youtube.com/watch?v=4V2uYORhQOk|||http://www.youtube.com/watch?v=SlVmgFQQ0TI|||Banned for too many b's in that sentence. How could you! Think of the B!|||Banned for watching movies in the corner with the dunces.|||Banned because Health class clearly taught you nothing about peer pressure.|||Banned for a whole host of reasons!|||http://www.youtube.com/watch?v=IRcrv41hgz4|||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...|||a pokemon world  an infj society  everyone becomes an optimist|||49142|||http://www.youtube.com/watch?v=ZRCEq_JFeFM|||http://discovermagazine.com/2012/jul-aug/20-things-you-didnt-know-about-deserts/desert.jpg|||http://oyster.ignimgs.com/mediawiki/apis.ign.com/pokemon-silver-version/d/dd/Ditto.gif|||http://www.serebii.net/potw-dp/Scizor.jpg|||Not all artists are artists because they draw. It's the idea that counts in forming something of your own... like a signature.|||Welcome to the robot ranks, person who downed my self-esteem cuz I'm not an avid signature artist like herself. :proud:|||Banned for taking all the room under my bed. Ya gotta learn to share with the roaches.|||http://www.youtube.com/watch?v=w8IgImn57aQ|||Banned for being too much of a thundering, grumbling kind of storm... yep.|||Ahh... old high school music I haven't heard in ages.   http://www.youtube.com/watch?v=dcCRUPCdB1w|||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...|||I like this person's mentality. He's a confirmed INTJ by the way. http://www.youtube.com/watch?v=hGKLI-GEc6M|||Move to the Denver area and start a new life for myself.'\",\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "dataset.describe(include=['object'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l2KbHmZafu0d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d6c8ecd9-f784-4d3d-9e0c-15ce95f92255"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['ENFJ' 'ENFP' 'ENTJ' 'ENTP' 'ESFJ' 'ESFP' 'ESTJ' 'ESTP' 'INFJ' 'INFP'\n",
            " 'INTJ' 'INTP' 'ISFJ' 'ISFP' 'ISTJ' 'ISTP']\n"
          ]
        }
      ],
      "source": [
        "types=np.unique(np.array(dataset['type']))\n",
        "print(types)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wj_3x7Aif5nb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "outputId": "b346fb7d-0933-4cbc-ca89-1fb72b59c805"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      posts\n",
              "type       \n",
              "ENFJ    190\n",
              "ENFP    675\n",
              "ENTJ    231\n",
              "ENTP    685\n",
              "ESFJ     42\n",
              "ESFP     48\n",
              "ESTJ     39\n",
              "ESTP     89\n",
              "INFJ   1470\n",
              "INFP   1832\n",
              "INTJ   1091\n",
              "INTP   1304\n",
              "ISFJ    166\n",
              "ISFP    271\n",
              "ISTJ    205\n",
              "ISTP    337"
            ],
            "text/html": [
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "      <td>231</td>\n",
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              "      <td>42</td>\n",
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              "      <td>48</td>\n",
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              "      <td>1304</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>ISFP</th>\n",
              "      <td>271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ISTJ</th>\n",
              "      <td>205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ISTP</th>\n",
              "      <td>337</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      height: 32px;\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b6561680-b8a0-479a-95cc-0c66e5bc9c3b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b6561680-b8a0-479a-95cc-0c66e5bc9c3b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b2425772-47b7-4b85-be16-5e40c9e0e815\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b2425772-47b7-4b85-be16-5e40c9e0e815')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b2425772-47b7-4b85-be16-5e40c9e0e815 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_b4fd303e-2c9a-47fe-a345-a04690ae7c70\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('total')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_b4fd303e-2c9a-47fe-a345-a04690ae7c70 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('total');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "total",
              "summary": "{\n  \"name\": \"total\",\n  \"rows\": 16,\n  \"fields\": [\n    {\n      \"column\": \"type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"ENFJ\",\n          \"ENFP\",\n          \"ESFP\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"posts\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 576,\n        \"min\": 39,\n        \"max\": 1832,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          190,\n          675,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "total=dataset.groupby(['type']).count()\n",
        "total"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NW8M3g9IgBkt"
      },
      "source": [
        "## Data Visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        },
        "id": "WbuVrD63f9BN",
        "outputId": "684b9f7d-43ad-4408-bfdb-170552088f14"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Total posts for each personality type')"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "plt.bar(np.array(total.index),height=total['posts'],color='orange')\n",
        "plt.xlabel('Personality types',size=14)\n",
        "plt.ylabel('No of posts available',size=14)\n",
        "plt.title('Total posts for each personality type')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "R__sajizgJHu",
        "outputId": "d47998e4-435b-472a-9d4d-2951969adfe2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "cnt_srs=dataset['type'].value_counts()\n",
        "plt.figure(figsize=(15,5))\n",
        "sns.barplot(x=cnt_srs.index,y=cnt_srs.values,alpha=0.9, palette = 'Set2')\n",
        "plt.xlabel('Personality types',fontsize=14)\n",
        "plt.ylabel('No of posts available',fontsize=14)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r1f1mWnbgNW5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "outputId": "1d29c39a-39eb-4389-8c1f-9218440d8fa4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Distribution of Lengths of all 50 posts')"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "dataset[\"length_posts\"]=dataset[\"posts\"].apply(len)\n",
        "sns.distplot(dataset[\"length_posts\"]).set_title(\"Distribution of Lengths of all 50 posts\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xcJL3Ig4hgGk"
      },
      "source": [
        "## Finding most common words in all posts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q-cBiuTlhY6p",
        "outputId": "bfeb5af9-4447-4d1d-92cb-1f82de465ddf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('I', 387957),\n",
              " ('to', 290168),\n",
              " ('the', 270699),\n",
              " ('a', 230918),\n",
              " ('and', 219498),\n",
              " ('of', 177853),\n",
              " ('is', 128804),\n",
              " ('you', 128750),\n",
              " ('that', 127221),\n",
              " ('in', 117263),\n",
              " ('my', 104561),\n",
              " ('it', 93101),\n",
              " ('for', 83057),\n",
              " ('have', 79784),\n",
              " ('with', 77131),\n",
              " ('but', 74729),\n",
              " ('be', 69317),\n",
              " ('are', 65034),\n",
              " ('like', 61390),\n",
              " ('not', 59496),\n",
              " ('an', 59020),\n",
              " (\"I'm\", 57339),\n",
              " ('on', 57062),\n",
              " ('was', 56146),\n",
              " ('me', 55488),\n",
              " ('as', 53310),\n",
              " ('this', 52601),\n",
              " ('just', 48292),\n",
              " ('about', 46305),\n",
              " ('think', 46229),\n",
              " ('or', 45724),\n",
              " (\"don't\", 44821),\n",
              " ('so', 42935),\n",
              " ('your', 40918),\n",
              " ('do', 40867),\n",
              " ('what', 37746),\n",
              " ('at', 37566),\n",
              " ('can', 37535),\n",
              " ('if', 37042),\n",
              " ('people', 35546),\n",
              " ('when', 34389),\n",
              " ('would', 32605),\n",
              " ('really', 31369),\n",
              " ('all', 31344),\n",
              " ('more', 31188),\n",
              " ('know', 31130),\n",
              " ('they', 29386),\n",
              " ('get', 28948),\n",
              " ('because', 27438),\n",
              " ('am', 27109)]"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "words=list(dataset[\"posts\"].apply(lambda x:x.split()))\n",
        "words=[x for y in words for x in y]\n",
        "Counter(words).most_common(50)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UenIKjoBieH-"
      },
      "source": [
        "## Splitting the dataset into training and test data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AhY-1cEshjtz"
      },
      "outputs": [],
      "source": [
        "train_data,test_data=train_test_split(dataset,test_size=0.2,random_state=42,stratify=dataset.type)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8zeHJPcviqY6"
      },
      "source": [
        "## PreProcessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FC1_3dsSitAD"
      },
      "source": [
        "## Cleaning text data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NOkyUpZzijdf"
      },
      "outputs": [],
      "source": [
        "def clear_text(dataset):\n",
        "    data_length=[]\n",
        "    lemmatizer=WordNetLemmatizer()\n",
        "    cleaned_text=[]\n",
        "    for sentence in tqdm(dataset.posts):\n",
        "        sentence=sentence.lower()\n",
        "        sentence=re.sub('https?://[^\\s<>\"]+|www\\.[^\\s<>\"]+',' ',sentence)\n",
        "        sentence=re.sub('[^0-9a-z]',' ',sentence)\n",
        "        data_length.append(len(sentence.split()))\n",
        "        cleaned_text.append(sentence)\n",
        "    return cleaned_text,data_length"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GoYCOlrhixDA",
        "outputId": "7ff1e762-4c18-4b94-8859-784c5f27171f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 6940/6940 [00:05<00:00, 1227.42it/s]\n"
          ]
        }
      ],
      "source": [
        "train_data.posts,train_length=clear_text(train_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WnzNyCtrizeg",
        "outputId": "07608b77-19d5-4590-9dbb-e9ba54b7c60e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1735/1735 [00:01<00:00, 1211.55it/s]\n"
          ]
        }
      ],
      "source": [
        "test_data.posts,test_length=clear_text(test_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 861
        },
        "id": "sb9vmQ5Ei4XE",
        "outputId": "52c176dd-3d30-4b3f-bbe7-168a904a7f9b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(15,10))\n",
        "sns.distplot(train_length,label='train data word length')\n",
        "sns.distplot(test_length,label='test data word length')\n",
        "plt.title('Number of words in text',fontdict={'size':20,'style':'italic'})\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "cTXuf7Voi7Ey",
        "outputId": "91f90598-301f-4c14-f1a4-d20e909eee3b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        }
      ],
      "source": [
        "px.pie(train_data,names='type',title='Personality type',hole=0.3)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "\n",
        "# Create a dictionary with the data\n",
        "data = {\n",
        "    'ENFJ': 190, 'ENFP': 675, 'ENTJ': 231, 'ENTP': 685,\n",
        "    'ESFJ': 42, 'ESFP': 48, 'ESTJ': 39, 'ESTP': 89,\n",
        "    'INFJ': 1470, 'INFP': 1832, 'INTJ': 1091, 'INTP': 1304,\n",
        "    'ISFJ': 166, 'ISFP': 271, 'ISTJ': 205, 'ISTP': 337\n",
        "}\n",
        "\n",
        "# Convert the dictionary to a DataFrame\n",
        "df = pd.DataFrame(list(data.items()), columns=['type', 'posts'])\n",
        "\n",
        "# Create a pivot table\n",
        "heatmap_data = df.pivot_table(index=\"type\", columns=\"type\", values=\"posts\", aggfunc='first', fill_value=0)\n",
        "\n",
        "# Create the heatmap\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(heatmap_data, annot=True, fmt=\"d\", cmap=\"YlGnBu\")\n",
        "plt.title('Posts by Personality Type')\n",
        "plt.xlabel('Personality Type')\n",
        "plt.ylabel('Personality Type')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Uz_rlZ-rqEcf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "outputId": "63abdc13-2349-4a7c-e969-250a548fc6f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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O1uvr0KEDAGDKlCno3bt3qYGFIb+DgIAARERE4MCBAxq3BH5SYmIi+vfvj9deew2xsbFYu3Ythg8fDi8vLwAPBr2ffPIJZs2ahWvXrmHgwIGoU6cOEhMTsXnzZowfPx4ffPCB3rqJiIySVLfNIiJ5eXjL3Kfd5lYURbGoqEgMCwsT3dzcRDMzM7FRo0birFmzxPz8fPUyJ0+eFIcNGyY2btxYVCqVYt26dcXXX39dPH78uMa6jhw5Inbo0EE0NzdX3/Y0NTVVDA4OFlu2bClaWVmJNjY2YseOHcVffvmlwp9h9OjRopWVlXjlyhXx1VdfFWvVqiXWq1dPnDNnjlhSUlLmZx7eHnXdunWV2EsPuLq6VngrW1EUxbt374rBwcFio0aNRDMzM9HZ2Vns2bOn+N1336mXeXjL3F9//bXU5z/55BPxxRdfFG1tbUVLS0uxZcuW4vz588XCwkKN5fbu3Su+9NJLoqWlpWhtbS3269dPPHfunMYyD28P+/C2wQ89/N4TExPV87Zt2yZ6enqKFhYWYpMmTcRFixaJq1atKrVcZW6ZW1xcLE6ePFl0cnISBUEo8/a5unwHD5V36+WH2rRpIyoUCvHmzZul3nu4T86dOye++eabYp06dUQ7Oztx0qRJYl5eXqnlN27cKHbp0kW0srISraysxJYtW4rBwcHihQsXtO4mInrWCKL4xLFiIqJnyNSpU/HDDz8gKSlJfcoXVS9Dfgfe3t6wt7fHvn37Sr0XGhqKsLAwpKSkwNHRUa/bJSIiTbymg4ieWfn5+Vi7di0GDRrEAYdEDPkdHD9+HHFxcQgICNDreomISHu8poOInjnJycnYu3cvNmzYgLS0NLz33ntSJz1zDPkdnD17FidOnMBnn32G+vXr46233tLbuomISDccdBDRM+fcuXMYMWIE6tati4iICPWdtaj6GPI72LBhA+bOnYsWLVrgp59+goWFhd7WTUREuuE1HUREREREZFC8poOIiIiIiAyKgw4iIiIiIjIoDjqIiIiIiMigjPJC8nuFu6VOKJeNuZvUCUREREQy4CF1QLksGw+TbNt5N36SbNuGxCMdRERERERkUEZ5pIOIiIiISFeCwN/L6xv3KBERERERGRQHHUREREREZFA8vYqIiIiI6DECfy+vd9yjRERERERkUDzSQURERET0GF5Irn+SDjq8vb0hCMJTlzE1NYWzszNeeeUVTJgwAebm5tVUR0RERERE+iDpoGPgwIEVLqNSqZCcnIxPPvkE58+fx1dffWX4MCIiIiJ6ZvFIh/4JoiiKUkdUxsGDBzFkyBAkJSVVuCyfSE5EREQkd/J9Inkdt7GSbTs7MVKybRuSpMO4/fv3o7i4uFLLPvfccxg+fLiBi4iIiIiISN8kHXS88sorSE9PV7/28fHBrVu3yly2du3aWLp0aXWlEREREdEzShAEySZjJemg48kzuxISElBQUCBRDRERERERGcIzeZXMyeOXETLpW/j5foQX203BH/viNd5PS81C2H/Xws/3I3R9YRqmBH2FG9eTNZYpKCjC4k9+Qa8uM9H9xQ8wY+oPSEvNqrafITp6B3x9A9GunT8GD56G+PiL1bbtisi1Ta5dANt0Jdc2uXYBbNOVXNvk2gWwTVdybZNrl+EoJJyMk6Q/2ZOHkarrsFJ+XiGaezTA9P8OLvWeKIqY/t5K3LqZhk8j3sHaXz5E/fr2mPTOCuTlPjoKs2zxJhz6XwLCPxuHbyKnICX5HmZM/cHg7QCwc+chhIevRHDwMGzevBwtW7ohMHA20tIyq2X7NbFNrl1sM742uXaxzfja5NrFNuNrk2sX1SySn17Vs2dPPPfcc3juueeQm5uLfv36qV8/nPStc9fWeHfK6+jR06vUezeup+Bs/DXM+HgIWrd1hatbPcz4eAgKCoqw+/cTAICc7Dxs23QU708fiBc6eqBVm8aYPW8E4uMSceZ0ot57nxQZuQVDhvTGoEG94O7eGGFhE2FhocTGjTEG33ZNbZNrF9uMr02uXWwzvja5drHN+Nrk2kU1i6SDjjlz5mDQoEEYMGAABgwYgI8//hiDBw9Wv344Vaeiwgd301IqHz3CRKFQwMzMFKdPXgUAnD/3L4qLS/CiTwv1Mk2a1oNzfTucOX3NoH2FhUVISLiMzp0fDZgUCgU6d26PU6cuGHTbFZFrm1y7ALbpSq5tcu0C2KYrubbJtQtgm67k2ibXLkMTBIVkk7GS9OGAc+bMqfI6CgoKSl18XiAUQqnU7cnlTdweDB5WLN+OWbOHwrKWOdatOYDku5lI/f9rNtJSs2BmZoI61rU0PmvvUMfg13VkZGShpEQFBwc7jfkODra4evWmQbddEbm2ybULYJuu5Nom1y6AbbqSa5tcuwC26UqubXLtopqnxg+nwsPDYWNjozEtXfyzzuszNTPBomWBuHE9Bb26zES3Fz7Aib8voXOX1lAY8W3MiIiIiOgBHunQP0mPdPTo0aPCC8cFQcC+ffvKfX/WrFkICQnRmJcv/K9KXa3aNEb0hhnIyc5DUVEx7OzrYOzwz9CqdSMAgIOjNYqKSpCdlatxtCM9LRsOjtZV2nZF7OysYWKiQFpahsb8tLRMODralfOp6iHXNrl2AWzTlVzb5NoFsE1Xcm2TaxfANl3JtU2uXVTzSDqcat++Pby8vMqcmjZtiqNHj+KPP/546jqUSiWsra01Jl1PrXpS7TqWsLOvgxvXk3E+4Qa6+bYDALRq3Qimpib4+69Ht4u7nngXSXcy0M6riV62XR5zczO0aeOO2NhHt/lVqVSIjT0Nb+8WT/mk4cm1Ta5dANt0Jdc2uXYBbNOVXNvk2gWwTVdybZNrl6EJUEg2GStJj3QsW7as1Lzi4mKsWLEC8+fPR4MGDTBv3jy9bzc3twA3b6SoX9++lYaL/9yEtU0tONe3x97dp2BnXxvOzna4fOk2li7ahO6+nvDp3ArAg8FIf38fLF+yGdY2tWBlZYFPwzegnVcTtPNy03vvk8aOHYgZM5ahbVt3eHp6ICpqK/Ly8uHv38vg266pbXLtYpvxtcm1i23G1ybXLrYZX5tcu6hmkXTQ8aTo6GjMnj0beXl5CA0Nxfjx42Fqqv/E8wk38O64L9Svly/ZDADo2/9FzJk/EmmpWVi+ZDPS07Lh6GQNv34vIjCot8Y6pn7oD4UgYObUVSgsKoZP55b48KMhem8ti59fV6Sn30NERDRSUjLQqlVTrFwZJovDnHJtk2sX24yvTa5dbDO+Nrl2sc342uTaRcDBgwexZMkSnDhxAnfu3MHmzZsxcOBA9fvlXcawePFiTJ8+HQDQpEkTXL9+XeP98PBwzJw5U/06Pj4ewcHB+Pvvv+Hk5ITJkyfjww8/1KpVEEVR1OoTBrBr1y7MnDkTiYmJ+OCDDxASEgIrKyud13evcLce6/TLxtzwR0KIiIiI5M9D6oBy2TefJNm20y99Wellf//9dxw+fBgdOnSAv79/qUFHUlJSqeUDAwNx+fJlNG3aFMCDQUdgYCDeeecd9XJ16tRR/108KysLHh4e6NWrF2bNmoUzZ85g3LhxWL58OcaPH1/pVkmPdBw7dgwzZszA0aNHERQUhL1798LR0VHKJCIiIiKiGqFPnz7o06dPue87OztrvN66dSt69OihHnA8VKdOnVLLPhQdHY3CwkKsWrUK5ubmaNOmDeLi4rB06dKaM+jw8fGBpaUlgoKC4ObmhnXr1pW53JQpU6q5jIiIiIieVVLeurasZ9AplUoolcoqrffu3bvYsWMHoqKiSr23cOFCzJs3D40bN8bw4cMxdepU9SUOsbGx6NatG8zNH92oqXfv3li0aBEyMjJgZ1e50+wkHXQ0btwYgiBgy5Yt5S4jCAIHHURERET0TAgPD0dYWJjGvDlz5iA0NLRK642KikKdOnXg7++vMX/KlCl47rnnYG9vjyNHjmDWrFm4c+cOli5dCuDBKVpubpqXB9SrV0/9Xo0YdFy7dk3KzRMRERERyUpZz6Cr6lEOAFi1ahVGjBgBCwsLjfmPb8vT0xPm5uaYMGECwsPD9bLdhyS9GbCfnx/u3bunfr1w4UJkZmaqX6elpaF169YSlBERERHRs0rKJ5KX/Qy6qv3l/9ChQ7hw4QLefvvtCpft2LEjiouL1QcHnJ2dcffuXY1lHr4u7zqQskg66Ni1a5fGOWsLFixAenq6+nVxcTEuXLggRRoRERERkVH44Ycf0KFDB3h5eVW4bFxcHBQKBerWrQsA6NSpEw4ePIiioiL1MjExMWjRokWlT60CJB50PEkGd+8lIiIiomecIOH/aSMnJwdxcXGIi4sDACQmJiIuLg43btxQL5OVlYVff/21zKMcsbGxWL58OU6fPo2rV68iOjoaU6dOxciRI9UDiuHDh8Pc3ByBgYFISEjAzz//jM8//7zUKWAVkdXDAYmIiIiIqHKOHz+OHj16qF8/HAiMHj0aq1evBgCsX78eoihi2LBhpT6vVCqxfv16hIaGoqCgAG5ubpg6darGgMLGxgZ79uxBcHAwOnToAEdHR8yePVur2+UCEj8c0MTEBElJSXBycgLw4B7B8fHx6ivk7969CxcXF5SUlGi1Xj4ckIiIiEju5PtwwLotp0m27eR/PpNs24Yk6ZEOURQxZswY9cUx+fn5CAoKUj8B8cl7FBMRERERUc0j6aBj9OjRGq9HjhxZapmAgIDqyiEiIiIiIgOQdNARGRkp5eaJiIiIiEqR8onkxop7lIiIiIiIDMoo714l54u1Pzl1o+KFJPKRd2OpE4iIiIgkxyMd+sc9SkREREREBsVBBxERERERGZRRnl5FRERERKQ7/l5e37hHiYiIiIjIoHikg4iIiIjoMbyQXP8k3aP379/Hu+++iwYNGsDJyQlDhw5FSkqKlElERERERKRnkh7p+Pjjj/Hjjz9ixIgRsLS0xLp16zB+/Hhs3rxZyiwiIiIieobxSIf+STro2Lx5MyIjIzF48GAAwKhRo+Dj44Pi4mKYmvLMLyIiIiIiYyDpMO7mzZt46aWX1K87dOgAMzMz3L59W8IqIiIiIiLSJ0kPJ6hUKpiZmWnMMzU1RUlJiURFRERERPSsE3iDV72TdNAhiiJ69uypcSpVbm4u+vXrB3Nzc/W8kydPSpFHRERERER6IOmgY86cOaXmDRgwQIISIiIiIqIHeCG5/slu0EFERERERMZF0kHH/v370a1bN9neqSo6egd++GETUlIy0LKlGz7+eAI8PT0Mus3c9EycXLcFt+LOoaSgEHWcndA5aCQcmrkCAH4cGlzm554bMRBt+r0CANg06WPcT03XeN972AC0HfCqQdsfkmK/1eQugG26kmubXLsAtulKrm1y7QLYpiu5tsm1i2oOSY8dvfLKK0hPf/SXYx8fH9y6dUvCokd27jyE8PCVCA4ehs2bl6NlSzcEBs5GWlqmwbZZkJOLXbM/g8LEBD1nTkS/zz5Ch5H+MLeqpV7mzW8WaEydgkYCgoDGL3prrMtr8Osay7Xo3d1g3Y+TYr/V5C62GV+bXLvYZnxtcu1im/G1ybXLkARBkGwyVpIOOkRR1HidkJCAgoICiWo0RUZuwZAhvTFoUC+4uzdGWNhEWFgosXFjjMG2mbBtD6wc7ND53VFwdG+COnUd4eLVCnWcndTLWNraaEz/Ho+Hc+vmqFPPUWNdZpZKjeXMLJQG636cFPutJnexzfja5NrFNuNrk2sX24yvTa5dVLPwKpkyFBYWISHhMjp39lLPUygU6Ny5PU6dumCw7d48cQb2TRvjf8tW4pfxM/DbzHBc2ne43OXzMrNw69RZuPfoXOq9s1v34Oe3P8RvM8ORsD0Gqmq4DbFU+62mdgFs05Vc2+TaBbBNV3Jtk2sXwDZdybVNrl2GJggKySZjJenFFE8eRtLlsFJBQUGpoyNKZSGUSvNyPlGxjIwslJSo4OBgpzHfwcEWV6/e1Hm9FclOTkX23kNo7eeLdgN7I/XKdfy9+lcoTE3QrLtPqeWvHvwLZhYWaPxie435Lfu8DPsmjaCsbYWUi1dxav1W5GVk4fmAQQZrB6TbbxWRaxfANl3JtU2uXQDbdCXXNrl2AWzTlVzb5NpFNY+sntNR1jM6gKc/pyM8PBxhYWEa8+bMmYTQ0Mn6DzY0lQiHpo3hPezBbYPt3Roh8+ZtXNz7Z5mDjst/xMKtywswMdd8wGLrvj3Vf7ZzbQCFqQmOrvwJ3sP6w+SJhzESERERkSY+HFD/ZHXLXF2e0TFr1iyEhIRozFMqb1Spy87OGiYmCqSlZWjMT0vLhKOjXTmfqjpLO2vYNKyvMc/GxRk3/oortezd85eRdfsuur43rsL1Oro3gViiQk5KOmxc6ukrtxSp9ltF5NoFsE1Xcm2TaxfANl3JtU2uXQDbdCXXNrl2Uc0j6TBuzpw5lZqeRqlUwtraWmOqyqlVAGBuboY2bdwRGxuvnqdSqRAbexre3i2qtO6ncfJohqzbdzXmZd1JRm1H+1LLXj5wBPZNG8PetWGF602/fhOCIMDCuo7eWssi1X6rqV0A23Ql1za5dgFs05Vc2+TaBbBNV3Jtk2sX1TzyfECGDIwdOxAzZixD27bu8PT0QFTUVuTl5cPfv5fBttmqry92zf4UZzbvgmun55B2+Tou7T8Mn3eGaSxXmJuH63+dwvMj/UutI+XiVaRevoZ6bTxgZmGBlEtXcXzNRrh1fRHK2rVKLa9vUuy3mtzFNuNrk2sX24yvTa5dbDO+Nrl2GZIxX9AtFUkHHT169KjwwnFBELBv375qKnrEz68r0tPvISIiGikpGWjVqilWrgwz6KFEx2aueDlkPE6t34b4Tb+jtpMDXgh4E027vKix3LUjJwBRRJOXni+1DoWZKa4dOYHTG3ZCVVSM2nUd0MrPF637+hqs+3FS7Lea3MU242uTaxfbjK9Nrl1sM742uXZRzSKITz4soxpNnTq13Peys7Oxbt06FBQUoETr271erFqYAX1yqmrXmxjSR96NpU4gIiKiZ4Z8n2ju5r1Ysm0nnvpQsm0bkqRHOpYtW1ZqXnFxMVasWIH58+ejQYMGmDdvngRlRERERESkL7K6piM6OhqzZ89GXl4eQkNDMX78ePXtdImIiIiIqGaSxd/od+3ahZkzZyIxMREffPABQkJCYGVlJXUWERERET2D+JwO/ZN00HHs2DHMmDEDR48eRVBQEPbu3QtHR0cpk4iIiIiISM8kHXT4+PjA0tISQUFBcHNzw7p168pcbsqUKdVcRkRERETPLN4yV+8kHXQ0btwYgiBgy5Yt5S4jCAIHHURERERENZikg45r165JuXkiIiIiIqoGkh478vPzw71799SvFy5ciMzMTPXrtLQ0tG7dWoIyIiIiInpWCYJCsslYSfqT7dq1CwUFBerXCxYsQHp6uvp1cXExLly4IEUaERERERHpiSxumfuQhA9HJyIiIiIC8OCaYtIv4z2GQ0REREREsiDpkQ5BEEqNJDmyJCIiIiIp8eGA+ifpoEMURYwZMwZKpRIAkJ+fj6CgIPXTyB+/3oOIiIiIiGomSQcdo0eP1ng9cuTIUssEBARUVw4RERERERmAIBrl1dsXpQ6okdIL/pE6oVz2ypZSJxAREZFeeUgdUC6PF1ZItu2LfwdLtm1D4glrRERERERkULK6ZS4RERERkeR4YyO945EOIiIiIiIyKA46iIiIiIjIoHh6FRERERHR4/hreb3jLiUiIiIiIoOS9EjHtm3bKlzG1NQUzs7OaNu2LczNzauhioiIiIieabyQXO8kHXQMHDiw0ss6Ozvj559/RteuXQ0XREREREREeifp6VUqlarCqaSkBLdv34a/vz/ee+89KXOJiIiI6FkgCNJNRkrSQce4ceOQnZ391GUEQYCzszM++OADnDt3rprKiIiIiIhIXyQddERFRSEvL69SyzZp0gR37941cBEREREREembpNd0iKKo1fI2NjYGKiEiIiIi+n+8v6veSf6cjuzsbFhYWDx1GWtr62qqISIiIiIifZN8HOfh4QE7O7syJ1tbW9jZ2UnWFh29A76+gWjXzh+DB09DfPxFyVqeVN1tUSv3Y9ywz9HT5yP4dQ/FjPdW43pissYyC+duwJt+4ej+wiz06R6KD6dE4toTyyxduAVj3lqObh1mImDwUoM2P4nfp27Ypj25dgFs05Vc2+TaBbBNV3Jtk2uXoYiCINlkrCQfdGzYsAH79+8vczpw4AD2798vSdfOnYcQHr4SwcHDsHnzcrRs6YbAwNlIS8uUpEfqtlPHr2DQ0M74fu0kfP7deBQXl+D9oO+Rl1uoXqZl64b479y3sH7LdCz/+m2IIvD+hO9RUqLSWNfrb7yAnr29DNZaFn6fbHvWu9hmfG1y7WKb8bXJtYtqFkHU9sIKPVIoFEhKSkLdunX1vOaqj74HD56Gdu2aY/bsIAAPbu/bvftYjBr1OsaPH1zl9cuxLb3gn0ovm5GeA7+Xw/DVqnfh/XzTMpe5fPE2Rr25DL/umIGGjRw13lv51R4cPHAWa34NqdT27JUtK91Wlmfx+2Qbu9hm3G1y7WKb8bUZrstDP4EG0Lzrt5Jt+9KhCZJt25AkP9IhR4WFRUhIuIzOnR/9Nl6hUKBz5/Y4deqChGXyacvJyQcAWNvUKvP9vNxC/LblOFwa2KOes221dZVFLvusLGzTjVzb5NoFsE1Xcm2TaxfANl3JtU2uXQYnSDgZKUkHHa6urjAxManSOgoKCpCVlaUxFRQUVvzBp8jIyEJJiQoODprXkzg42CI1NaNK664qObSpVCosX7wNnt5N0Ky5s8Z7G9cfgW/H/8LX57+I/fMffP7dOzAzk/Z+BXLYZ+Vhm27k2ibXLoBtupJrm1y7ALbpSq5tcu2imkfSQUdiYiIcHByqtI7w8HDY2NhoTOHh0h0SexZ8On8zrl5OwrxFI0q917uvN6J+eR9frXoXjV2d8NEHa1FQUCRBJREREZGOFIJ0k5GS9FfQvr6+FS4jCAL27dtX7vuzZs1CSIjmdQFK5Y0qddnZWcPERIG0NM0RfFpaJhwdpbubFiB926cLNuPwwfP4OnIi6pZx2lTtOpaoXccSjVyd0NarMV59aTb+t+8sXvXzNnhbeaTeZ0/DNt3ItU2uXQDbdCXXNrl2AWzTlVzb5NpFNY+kRzq8vLzKnZo2bYqjR4/ijz/+eOo6lEolrK2tNSal0rxKXebmZmjTxh2xsfHqeSqVCrGxp+Ht3aJK664qqdpEUcSnCzbjf/vP4suVE+DS0L4SnwFEAEVFxQbrqgx+n7phm/F0AWzTlVzb5NoFsE1Xcm2Ta5fBCYJ0k5GS9EjHsmXLSs0rLi7GihUrMH/+fDRo0ADz5s2ToAwYO3YgZsxYhrZt3eHp6YGoqK3Iy8uHv38vSXqkbvt0/mbs+f0UFn0+BrWslEhLzQIAWNW2hIWFGW7dTMPeXafRsbMHbO2skHz3Hn784QCUSjN06tJKvZ5/b6QiL7cAaWnZKMgvxsV/bgEA3JrVM+i1H/w+2fasd7HN+Nrk2sU242uTaxfVLJI/kfxx0dHRmD17NvLy8hAaGorx48fD1FSaRD+/rkhPv4eIiGikpGSgVaumWLkyTBaHEqVo2/RLLAAgeNw3GvM/mjcEfQe8AHNzU5w+mYif1x5CdlYe7B1qo32HpvhuTTDsHWqrlw8P/RWnjl9Vvx49ZPmD9f8+C/UbVHz0RFf8Ptn2rHexzfja5NrFNuNrk2sX1SySPqfjoV27dmHmzJlITEzEBx98gJCQEFhZWVVhjcb9lExD0eY5HdWtqs/pICIiIrmR8XM6fL+XbNuX9r8j2bYNSdIjHceOHcOMGTNw9OhRBAUFYe/evXB0dKz4g0REREREVGNIOujw8fGBpaUlgoKC4ObmhnXr1pW53JQpU6q5jIiIiIieWUZ861qpSDroaNy4MQRBwJYtW8pdRhAEDjqIiIiIiGowSQcd165dk3LzRERERERUDSR9Toefnx/u3bunfr1w4UJkZmaqX6elpaF169YSlBERERHRM4vP6dA7SQcdu3btQkFBgfr1ggULkJ6ern5dXFyMCxcuSJFGRERERCRrBw8eRL9+/eDi4lLmJQtjxoyBIAga02uvvaaxTHp6OkaMGAFra2vY2toiMDAQOTk5GsvEx8eja9eusLCwQKNGjbB48WKtWyUddDxJBnfvJSIiIqJnnSDhpIX79+/Dy8sLK1asKHeZ1157DXfu3FFPP/30k8b7I0aMQEJCAmJiYvDbb7/h4MGDGD9+vPr9rKwsvPrqq3B1dcWJEyewZMkShIaG4rvvvtOqVVYPByQiIiIiosrp06cP+vTp89RllEolnJ2dy3zv/Pnz2LVrF/7++288//zzAIAvvvgCfn5++PTTT+Hi4oLo6GgUFhZi1apVMDc3R5s2bRAXF4elS5dqDE4qIumRjoeHeZ6cR0REREQkGYUg2VRQUICsrCyN6fHLEbT1xx9/oG7dumjRogXeffddpKWlqd+LjY2Fra2tesABAL169YJCocBff/2lXqZbt24wNzdXL9O7d29cuHABGRkZle6Q9EiHKIoYM2YMlEolACA/Px9BQUHqp5FXZQcTEREREdU04eHhCAsL05g3Z84chIaGar2u1157Df7+/nBzc8OVK1fwn//8B3369EFsbCxMTEyQlJSEunXranzG1NQU9vb2SEpKAgAkJSXBzc1NY5l69eqp37Ozs6tUi6SDjtGjR2u8HjlyZKllAgICqiuHiIiIiEhSs2bNQkhIiMa8h7+g19bQoUPVf27Xrh08PT3RrFkz/PHHH+jZs2eVOrUl6aAjMjJSys0TEREREZUm4dn+SqVS50FGRZo2bQpHR0dcvnwZPXv2hLOzM5KTkzWWKS4uRnp6uvo6EGdnZ9y9e1djmYevy7tWpCyyunsVEREREREZxs2bN5GWlob69esDADp16oTMzEycOHFCvcz+/fuhUqnQsWNH9TIHDx5EUVGRepmYmBi0aNGi0qdWAbx7FT3GXtlS6oRyhZ26IXVCmeZ4N5Y6gYiIiPRMrCE3NsrJycHly5fVrxMTExEXFwd7e3vY29sjLCwMgwYNgrOzM65cuYIPP/wQ7u7u6N27NwCgVatWeO211/DOO+/gm2++QVFRESZNmoShQ4fCxcUFADB8+HCEhYUhMDAQM2bMwNmzZ/H5559j2bJlWrXySAcRERERUQ10/PhxeHt7w9vbGwAQEhICb29vzJ49GyYmJoiPj0f//v3h4eGBwMBAdOjQAYcOHdI4fSs6OhotW7ZEz5494efnhy5dumg8g8PGxgZ79uxBYmIiOnTogGnTpmH27Nla3S4XAATRKJ/Id1HqANIzHukgIiIyNh5SB5TL3U+6644v7xwr2bYNiadXERERERE9TlEzTq+qSXh6FRERERERGZTkRzquXbuGmJgYFBYWonv37mjbtq3USURERET0LOOBDr2TdNBx4MABvP7668jLy3sQY2qKVatWlfmQQCIiIiIiqpkkPb3q448/xiuvvIJbt24hLS0N77zzDj788EMpk4iIiIjoWScI0k1GStJBx9mzZ7FgwQLUr18fdnZ2WLJkCZKTk5GWliZlFhERERER6ZGkg46srCw4OjqqX9eqVQuWlpa4d++ehFVERERERKRPkl9Ivnv3btjY2Khfq1Qq7Nu3D2fPnlXP69+/vxRpRERERPQs4i1z9U7yQcfo0aNLzZswYYL6z4IgoKSkpDqTiIiIiIhIjyQddKhUKik3T0RERERUGg906J2k13SMGzcO2dnZUiYQEREREZGBSTroiIqKUj+jQ46io3fA1zcQ7dr5Y/DgaYiPvyh1khrbNOWmZ+LIl6ux4e0P8fOo97Fj+nykXbmufr8oPx9/r/oZmyf+Fz+Peh+/TZuHSzGHNNaxN2w51g0N1piOrfzJ4O0Av09dybVNrl0A23Ql1za5dgFs05Vc2+TaRTWHpIMOURSl3PxT7dx5COHhKxEcPAybNy9Hy5ZuCAycjbS0TKnT2PaEwpxcxMz+DAoTE7w8cyL6fvYRnhvpD3OrWuplTq7ZhDunz6Fz8Gj0/exjtOzTA8cjf8HN4/Ea62rm+xLe+GaBevIePtBg3Q/x+zSuNrl2sc342uTaxTbja5Nrl0HxOR16J+mgAwCys7ORlZX11EkKkZFbMGRIbwwa1Avu7o0RFjYRFhZKbNwYI0kP28p3btse1HKwg8+7o+Do3gS16zqivlcr1HF2Ui+TevEq3Lr5oF4bD9Su6wD3Xl1g69pA42gIAJgqzWFpa6OezGpZGqz7IX6fxtUm1y62GV+bXLvYZnxtcu2imkXyQYeHhwfs7OzKnGxtbWFnZ1ftTYWFRUhIuIzOnb3U8xQKBTp3bo9Tpy5Ue8/j2FbazRNnYN+0MQ4tW4mN42fg95nhuLzvsMYyjh5NcetEPHLTMyGKIu4mXET2nWTU92ypsdy1P//Gxnc+xI4PPkHcT1tRXFBosG6A36eu5Nom1y6AbbqSa5tcuwC26UqubXLtMjge6dA7yW+Zu2HDBtjb2+v8+YKCAhQUFGjMUyoLoVSa67zOjIwslJSo4OCgOeBxcLDF1as3dV6vPrCttJzkVFzaewgt/XzRZmBvpF+5jhOrf4XC1ARNu/sAAJ4fOxjHvv8JWyb+F4KJAoKgwIvjh6Nuq+bq9TR56XnUcrJHLTsbZNy4hbh1W5F1+y66TRtvsHZ+n7qRa5tcuwC26UqubXLtAtimK7m2ybWLah7JBx0vvfQS6tatq/Pnw8PDERYWpjFvzpxJCA2dXNU0qilUIuybNkb7YQMAAPZujZB58zYu7f1TPei4uOt/SL2UiG7Tg2DlaI/k85dwfNXPqGVnA+d2D452uPfqol6lbeMGsLS1wf5PIpCdlKJxqhYREREZOcnPBTI+kg86qmrWrFkICQnRmKdU3qjSOu3srGFiokBaWobG/LS0TDg6Vv/pXo9jW2kWdtawaVhfY56NizP+/SsOAFBcWIjT67eh67TxaPBc2wetrg2Qef0Wzv+2Vz3oeJKjexMAQPZdww06+H3qRq5tcu0C2KYrubbJtQtgm67k2ibXLqp5JB3Hubq6wsTEpErrUCqVsLa21piqcmoVAJibm6FNG3fExj66s5FKpUJs7Gl4e7eo0rqrim2lOXk0Q9btuxrzsu4kw8rxwWl7YnEJVCUlEJ44T1JQCBBV5d9BLeP6g8PGlrY2ei5+hN+nbuTaJtcugG26kmubXLsAtulKrm1y7aKaR9IjHYmJiVJu/qnGjh2IGTOWoW1bd3h6eiAqaivy8vLh799L6jS2PaFlX1/smf0pEjbvQuNOzyHt8nVc3n8YL74zDABgVssSdVs1x6nozTAxN4OVkz2Sz11C4sFjeG6UPwAgOykF1w4fRwPvNjCvbYXMG7dwcs1G1G3lDjvXBgZrB/h9GlubXLvYZnxtcu1im/G1ybXLoIz4gm6pSDro8PX1rXAZQRCwb9++aqjR5OfXFenp9xAREY2UlAy0atUUK1eGyeJQIts0OTRzRbeQ8Yhbvw1nNv2O2k4O6BDwJty6vKhe5qX3xuL0T9tw5MvVKMzJhZWTPTyH9oP7K10BAApTU9w9+w8u/H4AxQUFqOVgh0Yd26PtG68ZrPshfp/G1SbXLrYZX5tcu9hmfG1y7aKaRRAlfELf1KlTy30vOzsb69atQ0FBAUpKSrRcM5+SaWzCTlXtOh1DmePdWOoEIiKiGspD6oByub8VLdm2L/88QrJtG5KkRzqWLVtWal5xcTFWrFiB+fPno0GDBpg3b54EZUREREREpC+yuntVdHQ0Zs+ejby8PISGhmL8+PEwNZVVIhERERERaUkWf6PftWsXZs6cicTERHzwwQcICQmBlZWV1FlERERE9AwSFbyQXN8kHXQcO3YMM2bMwNGjRxEUFIS9e/fC0dFRyiQiIiIiItIzSQcdPj4+sLS0RFBQENzc3LBu3boyl5syZUo1lxERERHRM4u3zNU7SQcdjRs3hiAI2LJlS7nLCILAQQcRERERUQ0m6aDj2rVrUm6eiIiIiIiqgULKjfv5+eHevXvq1wsXLkRmZqb6dVpaGlq3bi1BGRERERE9swQJJyMl6aBj165dKCgoUL9esGAB0tPT1a+Li4tx4cIFKdKIiIiIiEhPZHHL3IckfDg6EREREdEDvGWu3kl6pIOIiIiIiIyfpEc6BEGA8MQtyZ58TURERERUrfj3Ub2TdNAhiiLGjBkDpVIJAMjPz0dQUJD6aeSPX+9BREREREQ1k6SDjtGjR2u8HjlyZKllAgICqiuHiIiIiIgMQNJBR2RkpJSbpxpkjndjqRPKVCIWSp1QLhPBXOoEIiKimolnV+kdLyQnIiIiIiKDktUtc4mIiIiIJMdb5uodj3QQEREREZFBcdBBREREREQGxdOriIiIiIgex9Or9I5HOoiIiIiIyKB4pIOIiIiI6DEiD3TonaSDjvj4+AqXMTU1hbOzM+zt7auhiIiIiIiI9E3SQUf79u0hCAJEUXzqcoIgwMvLC2vWrEHbtm2rqY6IiIiInkm8pkPvJB10JCYmVriMSqXC3bt3sWTJErz77rs4dOhQNZQREREREZG+SDrocHV1rdRybm5uWLRoEby8vAxcRERERERE+ibp3asCAgKQnZ2tfn369GkUFRWVuaybmxuOHDlSXWlERERE9KwSBOkmIyXpoCM6Ohp5eXnq1127dsW///5b5rImJiY80kFEREREVANJOuh48gLyii4or27R0Tvg6xuIdu38MXjwNMTHX5Q6SY1t2pNj1/ffbULrloMQvmAVACAzMxufzFsJv9cmw9trGHx7TMD8T35AdvZ9yRrluN8ekmubXLsAtulKrm1y7QLYpiu5tsm1y2AUgnSTkeLDAcuxc+chhIevRHDwMGzevBwtW7ohMHA20tIypU5jm5F0nTlzGb/8HIMWLR5d25SSnIGU5HRM/zAAW7cvw4LwSfjz0Cl8/N+vJGmU436Te5tcu9hmfG1y7WKb8bXJtYtqFskHHefOnUN8fDzi4+MhiiL++ecf9euHkxQiI7dgyJDeGDSoF9zdGyMsbCIsLJTYuDFGkh62GVfX/ft5+PCD5QibFwRr69rq+c09GuPzLz5ED98X0LixM3x82uG9qcNx4MBxFBeXVHun3PZbTWiTaxfbjK9Nrl1sM742uXZRzSL5oKNnz55o37492rdvj9zcXLz++uto3749vL291f+/uhUWFiEh4TI6d350DYlCoUDnzu1x6tSFau95HNuMo+uTuSvR/eUOGk3lycnORe3atWBqalINZY/Icb89JNc2uXYBbNOVXNvk2gWwTVdybZNrl8EpJJyMlOyf01GRgoICFBQUaMxTKguhVJrrvM6MjCyUlKjg4GCnMd/BwRZXr97Ueb36wDbtya1r544/ce7cVfyyYVGFy2ZkZOHrr3/F4CG9qqGs9LbltN8eJ9c2uXYBbNOVXNvk2gWwTVdybZNrF9U8NeI5HU8THh6OsLAwjXlz5kxCaOjkKq+bSN/u3ElF+IJVWLlqdoUD45ycXARNWIBmzRoheNJb1VRIRERExnzrWqlIOuhITU3F/fv3NQYfCQkJ+PTTT3H//n0MHDgQw4cPf+o6Zs2ahZCQEI15SuWNKnXZ2VnDxESBtLQMjflpaZlwdLQr51PVg23ak1NXQsIVpKXdw5v+09XzSkpUOH78HNZF/464+PUwMTHB/Zw8jH/7E1hZWeCLLz+EmVn1/09VTvvtSXJtk2sXwDZdybVNrl0A23Ql1za5dlHNI+mZY5MnT0ZERIT6dXJyMrp27Yq///4bBQUFGDNmDH788cenrkOpVMLa2lpjqsqpVQBgbm6GNm3cERv76CJ2lUqF2NjT8PZuUaV1VxXbanZXJx9PbN22DJs2f6ae2rZthtf7dcWmzZ/BxMQEOTm5eDtwLszMTLHiq1lV/udZV3Lab0+Sa5tcuwC26UqubXLtAtimK7m2ybXL4HjLXL2T9EjH0aNHsXr1avXrNWvWwN7eHnFxcTA1NcWnn36KFStWYNSoUdXeNnbsQMyYsQxt27rD09MDUVFbkZeXD3//6j+3nm3G02VV2xLNPRprzLO0tICtbR0092isHnDk5xVg0ZL3kJOTi5ycXACAvb01TEyq92Jyuey3mtQm1y62GV+bXLvYZnxtcu2imkXSQUdSUhKaNGmifr1//374+/vD1PRBVv/+/REeHi5Jm59fV6Sn30NERDRSUjLQqlVTrFwZJotDiWwznq4nnUu4ivjTlwAAr70arPFezN6v0aBh3WrtkfN+k2ubXLvYZnxtcu1im/G1ybWLahZBlPAx4PXq1cOePXvg5fXgNmyOjo749ttvMWjQIADApUuX4O3tjZycHC3XbORPySTZKBELpU4ol4kgzWlZREREleMhdUC53N7bKtm2Ez8fINm2DUnSazp8fHwQEREBlUqFDRs2IDs7G76+vur3L168iEaNGklYSEREREREVSXp6VVz585Fr169sHbtWhQXF2PWrFmws3t0qG79+vXo1q2bhIVERERE9Mwx4of0SUXSQYeXlxfOnz+Pw4cPw9nZGR07dtR4f+jQoWjTpo1EdUREREREpA+SjuP8/PxgZmaGAQMGoGPHjli4cCEyMzPV7/v4+MDPz0+6QCIiIiIiqjJJBx27d+9GQUGB+vWCBQuQnp6ufl1cXIwLFy5IkUZEREREzyo+p0PvJB10PHnjLAlvpEVERERERAYi6TUdRERERESyIxjvEQepSHqkQxAECE98qU++JiIiIiKimk3SIx2iKGLMmDFQKpUAgPz8fAQFBcHKygoANK73ICIiIiKqFkZ8bYVUJB10jB49WuP1yJEjSy0TEBBQXTlERERERGQAkg46IiMjpdw8ERERERFVA15ITkRERET0OJ5dpXd8yDsRERERERkUBx1EVWAimMt2KhHzZTsRERHJmagQJJu0cfDgQfTr1w8uLi4QBAFbtmxRv1dUVIQZM2agXbt2sLKygouLCwICAnD79m2NdTRp0kR9R9mH08KFCzWWiY+PR9euXWFhYYFGjRph8eLFWu9TDjqIiIiIiGqg+/fvw8vLCytWrCj1Xm5uLk6ePImPP/4YJ0+exKZNm3DhwgX079+/1LJz587FnTt31NPkyZPV72VlZeHVV1+Fq6srTpw4gSVLliA0NBTfffedVq28poOIiIiISCYKCgpKPTZCqVSqHzHxuD59+qBPnz5lrsfGxgYxMTEa87788ku8+OKLuHHjBho3bqyeX6dOHTg7O5e5nujoaBQWFmLVqlUwNzdHmzZtEBcXh6VLl2L8+PGV/rl4pIOIiIiI6HEKQbIpPDwcNjY2GlN4eLhefqx79+5BEATY2tpqzF+4cCEcHBzg7e2NJUuWoLi4WP1ebGwsunXrBnNzc/W83r1748KFC8jIyKj0tnmkg4iIiIhIJmbNmoWQkBCNeWUd5dBWfn4+ZsyYgWHDhsHa2lo9f8qUKXjuuedgb2+PI0eOYNasWbhz5w6WLl0KAEhKSoKbm5vGuurVq6d+z87OrlLb56CDiIiIiOhxgnT3zC3vVKqqKCoqwpAhQyCKIr7++muN9x4f4Hh6esLc3BwTJkxAeHi4XjskP70qKysLMTEx2LFjB1JSUqTOISIiIiIyGg8HHNevX0dMTIzGUY6ydOzYEcXFxbh27RoAwNnZGXfv3tVY5uHr8q4DKYukg464uDi0bNkSr732Gvr16wd3d3fs3r1byiQiIiIietYpJJz06OGA49KlS9i7dy8cHBwq/ExcXBwUCgXq1q0LAOjUqRMOHjyIoqIi9TIxMTFo0aJFpU+tAiQedMyYMQNubm74888/ceLECfTs2ROTJk2SMomIiIiIqEbIyclBXFwc4uLiAACJiYmIi4vDjRs3UFRUhDfffBPHjx9HdHQ0SkpKkJSUhKSkJBQWFgJ4cJH48uXLcfr0aVy9ehXR0dGYOnUqRo4cqR5QDB8+HObm5ggMDERCQgJ+/vlnfP7556WuO6mIIIqiqNefXguOjo7Ys2cPnnvuOQBAZmYm7O3tkZmZWeGhn6e7qJ9AohpMzg/hMxEspE4gIiLJeUgdUK4ms3+XbNvX5pZ9C9yy/PHHH+jRo0ep+aNHj0ZoaGipC8AfOnDgAF5++WWcPHkSEydOxD///IOCggK4ublh1KhRCAkJ0bieIz4+HsHBwfj777/h6OiIyZMnY8aMGVr9XJIOOhQKBZKSktSHb4AH9wmOj48vdydVDgcdRBx0EBGRvMl40DFnl2Tbvhb2mmTbNiTJ71517tw5JCUlqV+Loojz588jOztbPc/T01OKNCIiIiIi0gPJBx09e/bEkwdbXn/9dQiCAFEUIQgCSkpKJKojIiIiomeOQrpb5horSQcdiYmJUm6eiIiIiIiqgaR3r3J1da3UJJXo6B3w9Q1Eu3b+GDx4GuLj5XOtCNu0J9cuQH5t33+3Ga1bDkb4gkj1vJSUDMz4MAJdu7yNDt4jMcj/Q+zZfVTCSvntt4fk2gWwTVdybZNrF8A2Xcm1Ta5dVHNIOugICAjQuHbj9OnTGvcAltLOnYcQHr4SwcHDsHnzcrRs6YbAwNlIS8uUOo1tRtQlx7YzZy7jl59j0KKF5oB/1owvcS3xNlZ8NQNbtn2GV17piJCpS3HunDRHLOW23+TexTbja5NrF9uMr02uXQalEKSbjJSkg47o6Gjk5eWpX3ft2hX//vuvhEWPREZuwZAhvTFoUC+4uzdGWNhEWFgosXFjjNRpbDOiLrm13b+fhw8/iEDYvCBYW1tpvHcq7gJGjOwDT8/maNSoHoLeHYQ6daxwLuFqtXcC8tpvNaGLbcbXJtcuthlfm1y7qGaRdNDx5AXkEt69V0NhYRESEi6jc2cv9TyFQoHOndvj1KkLEpaxzZi6APm1fTL3B3R/+Tl07lz6jnHe7Vvg951HkJmZDZVKhZ07DqOwsAgvvNi62jvltt/k3gWwTVdybZNrF8A2Xcm1Ta5dhiYKgmSTsZJ00KEPBQUFyMrK0pgKCgqrtM6MjCyUlKjg4KD5aHcHB1ukpmZUad1VxTbtybULkFfbzh2Hce7cVUwNGV7m+0uXh6C4uASdfcahvedwhM75FhFfTIera/1q7QTktd8eJ9cugG26kmubXLsAtulKrm1y7aKaR/Jb5j7+nA5RFPHPP/8gJydHY5mnPacjPDwcYWFhGvPmzJmE0NDJ+o8lMlJ37qQifEEkVq76GEqleZnLRHy+HlnZ9/FD5GzY2dXBvr1/I2TqUvy4di48nrj+g4iIqEar8b+Wlx/JBx1PPqfj9ddfB4BKP6dj1qxZCAkJ0ZinVN6oUpOdnTVMTBRIS9McwaelZcLR0a6cT1UPtmlPrl2AfNoSEq4iLe0e3vT/UD2vpESF48fPY130Luz4/XOsi96FrduXonnzRgCAli2b4MSJ81i3bjdCw8ZXWysgn/32JLl2AWzTlVzb5NoFsE1Xcm2TaxfVPJKO4xITE3H16lUkJiaWmh7Ov3r16RepKpVKWFtba0zl/aa2sszNzdCmjTtiY+PV81QqFWJjT8Pbu0WV1l1VbDOeLkA+bZ182mHrts+wafMS9dS2bTO83q8LNm1egvy8AgCA4om7apgoFBBVqmrrfEgu+62mdAFs05Vc2+TaBbBNV3Jtk2sX1TySHumQ8hkcFRk7diBmzFiGtm3d4enpgaiorcjLy4e/fy+p09hmRF1yabOqbYnmHo015llaKmFrWwfNPRqjqKgYjV2dETrnO0z/cBRsbR+cXnXkSDy++mZmtXU+Tg77rSZ1sc342uTaxTbja5Nrl0EZ8QXdUpF00JGamor79+9rDD4SEhLw6aef4v79+xg4cCCGDy/7olZD8/PrivT0e4iIiEZKSgZatWqKlSvDZHEokW3G0yX3tofMzEzxzbf/wbLPohH87iLk5uajcWNnhC8MRvfuz0nSJNf9Jtcuthlfm1y72GZ8bXLtoppFECW8T+2wYcPg4uKCzz77DACQnJyMli1bwsXFBc2aNcPvv/+OH374AaNGjdJyzXxKJlGJmC91QrlMBAupE4iISHIeUgeUy3XRPsm2fX1GT8m2bUiSXtNx9OhR9O/fX/16zZo1sLe3R1xcHLZu3YoFCxZgxYoVEhYSEREREVFVSTroSEpKQpMmTdSv9+/fD39/f5iaPjjrq3///rh06ZJEdUREREREpA+SDjqsra2RmZmpfn3s2DF07NhR/VoQBBQUFEhQRkRERETPLIUg3WSkJB10+Pj4ICIiAiqVChs2bEB2djZ8fX3V71+8eBGNGjWSsJCIiIiIiKpK0rtXzZ07F7169cLatWtRXFyMWbNmwc7u0Z0Q1q9fj27duklYSERERETPHOM94CAZSQcdXl5eOH/+PA4fPgxnZ2eNU6sAYOjQoWjTpo1EdUREREREpA+Snl7l5+cHMzMzDBgwAB07dsTChQs1rvHw8fGBn5+fdIFERERERFRlkg46du/erXGh+IIFC5Cenq5+XVxcjAsXLkiRRkRERETPKFEhSDYZK0kHHU8+l1DC5xQSEREREZGBSHpNBxERERGR7AjGe8RBKpIe6RAEAcITX+qTr4mIiIiIqGaT9EiHKIoYM2YMlEolACA/Px9BQUGwsrICAD4YkIiIiIiqnxFfWyEVSQcdo0eP1ng9cuTIUssEBARUVw4RERERERmApIOOyMhIKTdPRERERETVgBeSExkpE8FC6oRylYiFUieUy0QwlzqBiIikxrOr9E7SC8mJiIiIiMj48UgHEREREdFjFPy1vN5xlxIRERERkUFx0EFERERERAbF06uIiIiIiB7DZ1XrH490EBERERGRQfFIBxERERHRY3ikQ/94pIOIiIiIiAxK0iMd/v7+FS5jamoKZ2dnvPLKK+jXr181VBERERHRs0zgoQ69k/RIh7W1NWxsbJ46WVpa4tKlS3jrrbcwe/ZsKXOJiIiIiEgHgiiKotQRlfHbb79h4sSJuHHjRiWWvmjwHiLSXYlYKHVCuUwEc6kTiIieER5SB5Sr2dcHJdv2lXe7SbZtQ5L0SIeJiQmSk5MrtWyXLl3w/PPPG7iIiIiIiJ51giDdZKwkHXRoc5DF1tYWmzZtMmANEREREREZAu9e9RTR0Tvg6xuIdu38MXjwNMTHy+e0LbZpT65dANueZv1PuzCw/1S80GEkXugwEsPemoWDB0+q379xIwmTJy3CS53G4oUOIzH1/U+RmppZrY1PknqfPQ3bdCPXNrl2AWzTlVzb5NplKDzSoX+SDzpWrlyJiIiIp05S2LnzEMLDVyI4eBg2b16Oli3dEBg4G2lpmZL0sM04u9hWsXr1HDB12kj8unExft2wGB192mJS8CJcunQDubn5eCdwLgRBQOTqUESvm4+iomIEvxsOlUpVbY2Pk8M+Y9uz0SbXLrYZX5tcu6hmkfRCcoVCgYYNG8LExKTcZQRBwNWrV7Vcc9VH34MHT0O7ds0xe3YQAEClUqF797EYNep1jB8/uMrrZxu7nuW2ql5I7tNxNKZPHwVnZ0dMGD8fR49FoXbtWgCA7Oz78HlxNL7/4WN07uyl9bqreiH5s/h9so1dbDPuNsN1yfdC8ubfSnch+aUJvJDcII4fP47ExMRyJ+0HHFVXWFiEhITLGn9hUSgU6Ny5PU6dulDtPY9jm/F0AWzTVklJCXbu+BN5ufnwat8ChYVFEATA3NxMvYxSaQ6FQsDJE/9Ue58c99lDbNONXNvk2gWwTVdybZNrl6EJCukmYyXpj6aPB68UFBQgKytLYyooqNpvUTMyslBSooKDg53GfAcHW6SmZlRp3VXFNu3JtQtgW2VdvHAdHZ4bgfaeQxEW+i0ivvwQ7u6N4NXeA5aWFvjs0x+Rl1eA3Nx8LF4UhZISFVJSqn//yWmfPYltupFrm1y7ALbpSq5tcu2imqfG3L2qPOHh4aUeKBge/q0e6ohILpq4uWDT5k+x/ueFeGtob/xn5pe4fPlf2NvbYNnyafjjwHE8/9wIdHxhFLKz76N166ZQKIz4ajwiIjIoXkiuf6ZSbnzOnDmoXbt2ldYxa9YshISEaMxTKivzAMHy2dlZw8REgbQ0zRF8WlomHB3tyvlU9WCb9uTaBbCtsszNzeDqWh8A0KZtM5w9exk/rtmBsLlBeKlLe+yO+QoZGVkwMTGBtbUVunYJRJ9G9aq1EZDXPnsS23Qj1za5dgFs05Vc2+TaRTWPpEc6goODkZKSojEvISEBY8eOxZAhQ7Bu3boK16FUKmFtba0xKZVVuxDU3NwMbdq4IzY2Xj1PpVIhNvY0vL1bVGndVcU24+kC2KYrUSWiqLBIY56dnTWsra1w9OgZpKfdg2+PF6q9S877jG26kWubXLsAtulKrm1y7TI0hSDdZKx0PtJx+fJlXLlyBd26dYOlpSVEUdT6Go3JkyfDxcUFn332GQAgOTkZXbt2hYuLC5o1a4YxY8agpKQEo0aN0jVTZ2PHDsSMGcvQtq07PD09EBW1FXl5+fD371XtLWwz3i62VWzpZ2vRrZs36td3wv37efjtt0M4diwB36/8GACwaeN+NGvWEHb21oiLu4Dw+asQMPp1uDVtUG2Nj5PDPmPbs9Em1y62GV+bXLuoZtF60JGWloa33noL+/fvhyAIuHTpEpo2bYrAwEDY2dmpBxCVcfToUaxevVr9es2aNbC3t0dcXBxMTU3x6aefYsWKFZIMOvz8uiI9/R4iIqKRkpKBVq2aYuXKMFkcSmSb8XSxrWLp6fcwc8YXSEnJQJ06teDRwhXfr/wYnV96cCeVa9duYdmyaNy7l4MGLk6YEDQIo8f0q7a+J8lhn7Ht2WiTaxfbjK9Nrl1Us2j9nI6AgAAkJydj5cqVaNWqFU6fPo2mTZti9+7dCAkJQUJCQqXXZWlpiX/++Qeurq4AAD8/P7Rt2xaLFy8GAFy8eBGdOnVCWlqaNonQx3M6iMhwqvqcDkOq6nM6iIiosuT7nI7Wq6R7Tse5cXxOBwBgz549WLRoERo2bKgxv3nz5rh+/bpW67K2tkZmZqb69bFjx9CxY0f1a0EQUFBQoG0iERERERHJiNaDjvv376NWrVql5qenp0OpVGq1Lh8fH0REREClUmHDhg3Izs6Gr6+v+v2LFy+iUaNG2iYSEREREemMt8zVP60HHV27dsWaNWvUrwVBgEqlwuLFi9GjRw+t1jV37lxs27YNlpaWeOuttzB9+nTY2T06P3D9+vXo1s04DzERERERET0rtL6QfPHixejZsyeOHz+OwsJCfPjhh0hISEB6ejoOHz6s1bq8vLxw/vx5HD58GM7OzhqnVgHA0KFD0aZNG20TiYiIiIhIRrQ+0tG2bVtcvHgRXbp0wYABA3D//n34+/vj1KlTaNasmVbr8vPzg5mZGQYMGICOHTti4cKFGtd4+Pj4wM/PT9tEIiIiIiKdCYIg2WSstL57lT6ZmJjgzp07qFu3LoAHF5bHxcWhadOmAIC7d+/CxcUFJSUlWq6Zd68ikjPevYqIiOR896q2qw9Jtu2zY7pKtm1D0unhgBkZGfjhhx9w/vx5AEDr1q0xduxY2Nvba7WeJ8c7Eo5/iIiIiIgAAILW5wJRRbTepQcPHkSTJk0QERGBjIwMZGRkICIiAm5ubjh4ULp7GhMRERERkTxpfaQjODgYb731Fr7++muYmJgAAEpKSjBx4kQEBwfjzJkzlV5XWeeuGfO5bEREREQkf/zrqP5pPei4fPkyNmzYoB5wAA+uzQgJCdG4lW5liKKIMWPGqJ/vkZ+fj6CgIFhZWQEAHwxIRERERGQEtB50PPfcczh//jxatGihMf/8+fPw8vLSal2jR4/WeD1y5MhSywQEBGibSEREREREMqL1oGPKlCl47733cPnyZfj4+AAAjh49ihUrVmDhwoWIj49XL+vp6fnUdUVGRmq7eSIiIiIig+LpVfqn9S1zFYqnX3suCAJEUYQgCDrc6lZfeMtcIjnjLXOJiEjOt8z1WivdLXNPj+QtcwEAiYmJhuggomeInP9iX1ByT+qEMilNbKROICJ6ZvBIh/5pPehwdHRUX+hNRERERERUEa2f01GvXj2MGzcOf/75pyF6iIiIiIjIyGg96Fi7di3S09Ph6+sLDw8PLFy4ELdv3zZEGxERERFRtVMI0k3GSutBx8CBA7FlyxbcunULQUFBWLduHVxdXfH6669j06ZNKC4uNkQnERERERHVUFoPOh5ycnJCSEgI4uPjsXTpUuzduxdvvvkmXFxcMHv2bOTm5uqzk4iIiIioWgiCdJOx0nnQcffuXSxevBitW7fGzJkz8eabb2Lfvn347LPPsGnTJgwcOLDCdahUKixatAgvvfQSXnjhBcycORN5eXm6JhERERERkQxVetAxd+5c5ObmYtOmTejXrx8aNWqEdevWYeLEibh16xbWrl2LHj16YNSoUdi6dSv++OOPCtc5f/58/Oc//0Ht2rXRoEEDfP755wgODq7Kz0NEREREVCU15UjHwYMH0a9fP7i4uEAQBGzZskXjfVEUMXv2bNSvXx+Wlpbo1asXLl26pLFMeno6RowYAWtra9ja2iIwMBA5OTkay8THx6Nr166wsLBAo0aNsHjxYq33aaUHHWFhYcjJycHYsWPh4uKCw4cPIy4uDpMmTYKtra3Gsi4uLvjvf/9b4TrXrFmDr776Crt378aWLVuwfft2REdHQ6VSaf2DEBERERE9S+7fvw8vLy+sWLGizPcXL16MiIgIfPPNN/jrr79gZWWF3r17Iz8/X73MiBEjkJCQgJiYGPz22284ePAgxo8fr34/KysLr776KlxdXXHixAksWbIEoaGh+O6777RqrfQTyRUKBZKSklC7dm3UqlVLq42UR6lU4vLly2jUqJF6noWFBS5fvoyGDRtWYc18IjkR6YYPByQiqi7yfSL58+uleyL54TdeREFBgcY8pVIJpVL51M8JgoDNmzerL3EQRREuLi6YNm0aPvjgAwDAvXv3UK9ePaxevRpDhw7F+fPn0bp1a/z99994/vnnAQC7du2Cn58fbt68CRcXF3z99df473//i6SkJJibP3i478yZM7Flyxb8888/lf65tLqmQxAEvQ04AKC4uBgWFhYa88zMzFBUVKS3bRARERERaUNQCJJN4eHhsLGx0ZjCw8O1/hkSExORlJSEXr16qefZ2NigY8eOiI2NBQDExsbC1tZWPeAAgF69ekGhUOCvv/5SL9OtWzf1gAMAevfujQsXLiAjI6PSPVo9kdzDwwNCBSebpaenV3p9oihizJgxGiO3/Px8BAUFaTz1fNOmTdpkEhERERHVSLNmzUJISIjGvIqOcpQlKSkJwIMHez+uXr166veSkpJQt25djfdNTU1hb2+vsYybm1updTx8z87OrlI9Wg06wsLCYGOjv0P8AQEBpQYxI0eO1Nv6iYiIiIi0JeWtaytzKlVNpNWgY+jQoaVGQ1WxevVqva2LiIiIiIgecHZ2BvDgMRf169dXz7979y7at2+vXiY5OVnjc8XFxUhPT1d/3tnZGXfv3tVY5uHrh8tURqWv6ajotCpdmJiYlPpB5SQ6egd8fQPRrp0/Bg+ehvh4+VygzjbtybULYJuupG4rKVHhy4hf8dorU/GC91j49Q7Bt19vxuP350hLvYeP/vMtenafhBefG4eg8Ytw/VpStXY+Tup99jRs055cuwC26UqubXLtovK5ubnB2dkZ+/btU8/LysrCX3/9hU6dOgEAOnXqhMzMTJw4cUK9zP79+6FSqdCxY0f1MgcPHtS45jomJgYtWrSo9KlVgBaDjkre5EorhlinvuzceQjh4SsRHDwMmzcvR8uWbggMnI20tEyp09hmRF1sq9ltq1Zuxy/r9+E/HwVgy2+L8X7IUET+sAPr1u4B8ODfce9NXoab/ybj8y+n4ueNn8ClviPGB4YjNze/grXrnxz2GduMv4ttxtcm1y5DqinP6cjJyUFcXBzi4uIAPLh4PC4uDjdu3IAgCHj//ffxySefYNu2bThz5gwCAgLg4uKivsNVq1at8Nprr+Gdd97BsWPHcPjwYUyaNAlDhw6Fi4sLAGD48OEwNzdHYGAgEhIS8PPPP+Pzzz8vdd1JRSo96FCpVHo9tUruIiO3YMiQ3hg0qBfc3RsjLGwiLCyU2LgxRuo0thlRF9tqdtvpuEvo4dsB3bp7o0EDJ7za+0V0eqkdzp65AgC4fj0J8acv46PZY9G2XTO4ubngozljkV9QhN93xlZb50Ny2GdsM/4uthlfm1y7CDh+/Di8vb3h7e0NAAgJCYG3tzdmz54NAPjwww8xefJkjB8/Hi+88AJycnKwa9cujbvHRkdHo2XLlujZsyf8/PzQpUsXjWdw2NjYYM+ePUhMTESHDh0wbdo0zJ49W+NZHpWh1TUdhrBy5UrUrl37qctMmTKlmmoeKCwsQkLCZUyY8KZ6nkKhQOfO7XHq1IVqbXkS24ynC2CbruTS5tW+OTb+egDXrt1Bkyb1ceGf6zh18gKmfzji/zuLAQBKpZlGp7m5KU6dvIhBb/aotla57LOysM14ugC26UqubXLtMjQpLyTXxssvv/zUM4cEQcDcuXMxd+7ccpext7fHunXrnrodT09PHDpUtWeXSD7o+Oabb2BiYlLu+4IgPHXQUVBQUMYDVAqhVJqX84mKZWRkoaREBQcHzfPUHBxscfXqTZ3Xqw9s055cuwC26UoubYHv9MP9+3kY0PdDmJgoUFKiwuT3BqNvv5cAAG5u9VG/vgM+X/YzZocGwtJSiR/X/I67SelITcmstk5APvusLGzTnly7ALbpSq5tcu2imkfyQcfx48erdNpWeHg4wsLCNObNmTMJoaGTq5pGRPRUu3f9hR2/HcHCJRPRzL0hLvxzHYvD18Kpri0GDOwGMzNTLIt4H3M++h5dOk2AiYkCHTu1QZeuXrK+po2I6FmnqCFHOmoSrQcd9+/f13hwX1Xo445YZT9A5UaV1mlnZw0TEwXS0jSfspiWlglHx8pfpW8IbNOeXLsAtulKLm1LP/0JgW/3Qx+/B3cB8fBohDu3U/HD99sxYGA3AEDrNm74dfMCZGfnoqioGPb21hj+1hy0aev2tFXrnVz2WVnYpj25dgFs05Vc2+TaRTVPpS8kf6hevXoYN24c/vzzzypvXB+/6VMqlbC2ttaYqnJqFQCYm5uhTRt3xMbGq+epVCrExp6Gt3eLqiZXCduMpwtgm67k0pafVwjhiV+HKRQKiKrS/26rU6cW7O2tcf1aEs4lXEUP3w7VlQlAPvusLGwzni6AbbqSa5tcu6jm0fpIx9q1a7F69Wr4+vqiSZMmGDdunPr2W9qaM2dOhReRS2Xs2IGYMWMZ2rZ1h6enB6KitiIvLx/+/r2kTmObEXWxrWa3de/hje+/3Yr69R3QzL0h/jl/DT9G/Y6B/t3Vy+zZ9Rfs7Ougfn1HXLr4LxaF/4gePZ9H55faVVvnQ3LYZ2wz/i62GV+bXLsMqaZcSF6TaD3oGDhwIAYOHIiUlBT8+OOPWL16NT7++GP07t0b48aNQ//+/WFqWrnVBgcHIyUlBa6urup5CQkJ+PTTT3H//n0MHDgQw4cP1zZRL/z8uiI9/R4iIqKRkpKBVq2aYuXKMFkcSmSb8XSxrWa3zfpvAL6M2ID5c1cjPT0LTnXt8OYQXwS9+4Z6mZSUTCxZHI201HtwcrJFvwFdMCHojaes1XDksM/YZvxdbDO+Nrl2Uc0iiHo4x+mLL77A9OnTUVhYCEdHRwQFBWHmzJmoVavWUz83bNgwuLi44LPPPgMAJCcno2XLlnBxcUGzZs3w+++/44cffsCoUaO0LOJTMolINwUl96ROKJPSxEbqBCIiPfOQOqBcXbZW/TICXf05oItk2zYkra/peOju3btYvHgxWrdujZkzZ+LNN9/Evn378Nlnn2HTpk3qJx0+zdGjR9G/f3/16zVr1sDe3h5xcXHYunUrFixYgBUrVuiaSEREREREMqD16VWbNm1CZGQkdu/ejdatW2PixIkYOXIkbG1t1ct07twZrVq1qnBdSUlJaNKkifr1/v374e/vrz49q3///ggPD9c2kYiIiIiIZETrQcfYsWMxdOhQHD58GC+88EKZy7i4uOC///1vheuytrZGZmam+pqOY8eOITAwUP2+IAilHvxHRERERGRIvJBc/7QedNy5c6fCazUsLS0xZ86cCtfl4+ODiIgIfP/999i0aROys7Ph6+urfv/ixYto1KiRtolERERERCQjWl/TUadOHSQnJ5ean5aWBhMTE63WNXfuXGzbtg2WlpZ46623MH36dNjZPboTwvr169GtWzdtE4mIiIiIdCYIgmSTsdL6SEd5N7sqKCiAubl2D+Xz8vLC+fPncfjwYTg7O6Njx44a7w8dOhRt2rTRNpGIiIiIiGSk0oOOiIgIAA9GfitXrtR4qF9JSQkOHjyIli1barVxPz8//PTTTxgwYAAAYOHChQgKClJflO7j44OuXbvi3LlzWq2XiIiIiIjko9LP6XBzcwMAXL9+HQ0bNtQ4lcrc3BxNmjTB3LlzSx2teBoTExPcuXMHdevWBfDgwvK4uDg0bdoUwIPb8rq4uKCkpKTS63yAz+kgIt3wOR1ERNVFvs/p6P7bYcm2/b/XX5Js24ZU6SMdiYmJAIAePXpg06ZNGtde6OrJ8Y4enlNIREREREQyo/U1HQcOHDBEBxERERGRLBjx9dySqdSgIyQkBPPmzYOVlRVCQkKeuuzSpUsrvfGyrtI35qv2iYiIiIieRZUadJw6dQpFRUXqP5dH2wGDKIoYM2YMlEolACA/Px9BQUGwsrICAD4YkIiIiIiqHX8Hrn+VvpDcEMaOHVup5SIjI7VcMy8kJyLd8EJyIqLqIt8LyXvslO5C8gN+z/iF5Iag/WCCiIiIiIhqmkoNOvz9/Su9wk2bNukcQ0QkNbkeUXDvd0TqhHJd3t5Z6gQiIr1S8PQqvavUoMPGRp7/ESYiIiIiIvmr1KCDp0ERERER0bOCRzr0TyF1ABERERERGTedLiTfsGEDfvnlF9y4cQOFhYUa7508eVIvYUREREREZBy0PtIRERGBsWPHol69ejh16hRefPFFODg44OrVq+jTp48hGomIiIiIqo1CECWbjJXWg46vvvoK3333Hb744guYm5vjww8/RExMDKZMmYJ79+R5f3siIiIiIpKO1oOOGzduoHPnB7dHtLS0RHZ2NgBg1KhR+Omnn/RbR0RERERUzRSCdJOx0nrQ4ezsjPT0dABA48aNcfToUQBAYmIiJHy4ORERERERyZTWgw5fX19s27YNADB27FhMnToVr7zyCt566y288cYbeg8kIiIiIqpOCgknY6X13au+++47qFQqAEBwcDAcHBxw5MgR9O/fHxMmTNBqXVlZWRUHmpqiVq1a2mYSEREREZFMaD3oUCgUUCgejcOGDh2KoUOH6rRxW1tbCELFJ6/Vrl0bvXr1wueff46GDRvqtC0iIiIiIpKGTs/pyMzMxLFjx5CcnKw+6vFQQEBApdezf//+CgcdKpUKd+/exYoVKzB+/Hjs3LlTl2QiIiIiokox5lvXSkXrQcf27dsxYsQI5OTkwNraWmPQIAiCVoOOl19+udLLenp6wsfHR5tUIiIiIiKSAa2vV5k2bRrGjRuHnJwcZGZmIiMjQz09vKtVZc2ePRu5ubnq1xkZGeUu6+7ujh9//FHb3CqJjt4BX99AtGvnj8GDpyE+/mK1bv9p2KY9uXYBbNOVXNuqo+uFNvXw3cc9cXj1EFzePga9fBqXu+zciZ1wefsYjOnfWj2vY1tnXN4+psypXXMH9XItmtjhp4V9kLBxFA6tGox3/Nvq/Wd5SK7fJyDfNrl2AWzTlVzb5NplKLxlrv5pPei4desWpkyZopeLu+fPn4+cnBz1a1dXV1y9erXMZc3NzTFgwIAqb7Oydu48hPDwlQgOHobNm5ejZUs3BAbORlpaZrU1sM34u9hmfG3V1WVpYYrziekI/eboU5d7xacx2rdwQlLafY35J/9Jhs+onzWmn3dfxI2kbJy5lAYAqG1phtVzX8Xt5BwMnLodCyOPY8rw9nirt4defxZAvt+nnNvk2sU242uTaxfVLFoPOnr37o3jx4/rZeNPPtdDTs/5iIzcgiFDemPQoF5wd2+MsLCJsLBQYuPGGKnT2GZEXWwzvrbq6jp44haWrT2FmKM3yl2mnn0tzJnQEdM+O4jiYs1/vxYVq5CamaeeMrPz0atjI2zce0m9TP+Xm8LMVIGZEYdx6UYmdhxKxJrt5zFuYBu9/iyAfL9PObfJtYttxtcm1y6qWbQedPTt2xfTp09HaGgoNm7ciG3btmlMxqCwsAgJCZfRubOXep5CoUDnzu1x6tQFCcvYZkxdANt0Jdc2OXUJAvBpSFd8v+ksLt3IrHD5nh0bw7aOEhv3XlbP827phL8T7qKo+NENQw6dvIVmDW1gbWWut1Y57bcnybVNrl0A23Ql1za5dhkan9Ohf1pfSP7OO+8AAObOnVvqPUEQUFJSUul1CYKA7OxsWFhYQBRFCIKAnJycUs/vsLa2LncdBQUFKCgo0JinVBZCqdT9P4gZGVkoKVHBwcFOY76Dgy2uXr2p83r1gW3ak2sXwDZdybVNTl0TBrVDiUqFqO3nK7X84Fea49Cp20hKe3SdnZOdJf69m6OxXGpmnvq9rPuFemmV0357klzb5NoFsE1Xcm2TaxfVPFoPqFQqVbmTNgMO4MHpVB4eHrCzs4O9vT1ycnLg7e0NOzs72NnZwdbWFnZ2dk9dR3h4OGxsbDSm8PBvtf2xiIiMRptmDhjdvzU+XP5npZZ3dqiFrt4u+DXmUsULExE9A3ghuf7p9JwOfTlw4ECV1zFr1iyEhIRozFMqyz/HuTLs7KxhYqJAWprm3bTS0jLh6Pj0QZChsU17cu0C2KYrubbJpeuFNvXgYGOBg6sGq+eZmigwa9zzGNO/NV5+e4PG8oN6uSMzuwD7/tL8d2dKRh4cbS015j18nZKRp7deuey3ssi1Ta5dANt0Jdc2uXZRzaPTqWP/+9//0K9fP7i7u8Pd3R39+/fHoUOHtF5P9+7dKzU9jVKphLW1tcZUlVOrAMDc3Axt2rgjNjZePU+lUiE29jS8vVtUad1VxTbj6QLYpiu5tsmla8uBK+g7eSv6TdmmnpLS7mPl5gSMnbOn1PKDejXH5gNXUFyiebH5qX9S8EKbejA1efSrt5fau+DKzXt6O7UKkM9+K4tc2+TaBbBNV3Jtk2uXoQmCKNlkrLQ+0rF27VqMHTsW/v7+mDJlCgDg8OHD6NmzJ1avXo3hw4frPVIKY8cOxIwZy9C2rTs8PT0QFbUVeXn58PfvJXUa24yoi23G11ZdXbUsTOFa/9H1bo3q1UYrN3tk5hTgTsp9ZGZrXutWXCwiJSMPibc0r5nr5FkfjZ3r4Jc9pU+t2va/q5g8rD3Cp7yE7zaeRfPGthjdvxXmr/xbrz8LIN/vU85tcu1im/G1ybWLahatBx3z58/H4sWLMXXqVPW8KVOmYOnSpZg3b55Wgw6FQqHxRPOyCIKA4uJibTOrzM+vK9LT7yEiIhopKRlo1aopVq4Mk8WhRLYZTxfbjK+turrauTsiOvw19ev/vv0iAGDjvsuYUclrOQBg8KvNceLcXVy9ea/Uezm5RRgzew9Cg3ywZVk/ZGTl48v1p/Hzbv0/FEyu36ec2+TaxTbja5NrF9UsgqjlwzGUSiUSEhLg7u6uMf/y5cto27Yt8vPzK72urVu3lvtebGwsIiIioFKptFrnA8b9lEwieva49zsidUK5Lm/vLHUCEdVI+n/QqL4MOXBQsm3/0qObZNs2JK2PdDRq1Aj79u0rNejYu3cvGjVqpNW6ynrC+IULFzBz5kxs374dI0aMKPPWvEREREREVHNoPeiYNm0apkyZgri4OHTu/OC3W4cPH8bq1avx+eef6xxy+/ZtzJkzB1FRUejduzfi4uLQtm1bnddHRERERKQLY35In1S0HnS8++67cHZ2xmeffYZffvkFANCqVSv8/PPPZR65qMi9e/ewYMECfPHFF2jfvj327duHrl27ar0eIiIiIiKSJ52e0/HGG2/gjTfeqPLGFy9ejEWLFsHZ2Rk//fSTToMWIiIiIiKSN60HHf/++y8EQUDDhg0BAMeOHcO6devQunVrjB8/Xqt1zZw5E5aWlnB3d0dUVBSioqLKXG7Tpk3aZhIRERER6URhxM/LkIrWg47hw4dj/PjxGDVqFJKSktCrVy+0bdsW0dHRSEpKwuzZsyu9roCAgApvmUtERERERDWb1oOOs2fP4sUXH9wP/pdffkG7du1w+PBh7NmzB0FBQVoNOlavXq3t5omIiIiIDErB34nrndaDjqKiIiiVSgAPbpPbv39/AEDLli1x584drdbl7+9f4TKCIGDjxo3aZhIRERERkUxoPeho06YNvvnmG/Tt2xcxMTGYN28egAe3vHVwcNBqXTY2NtpunoiIiIjIoHjLXP3TetCxaNEivPHGG1iyZAlGjx4NLy8vAMC2bdvUp11VVmRkpLabJyIiIiKiGkarQYcoimjatClu3LiB4uJi2NnZqd8bP348atWqpfdAIiIiIiKq2bQedLi7uyMhIQHNmzfXeK9Jkyb67CIiIiIikgQvJNc/rU5ZUygUaN68OdLS0gzVQ0RERERERkbrazoWLlyI6dOn4+uvv0bbtm0N0URERE+4vL2z1Anlsmw8R+qEcuXdCJM6gYhqID4cUP+0HnQEBAQgNzcXXl5eMDc3h6Wlpcb76enpeosjIiIiIqKaT+tBx/Llyw2QQURERERExkrrQcfo0aMN0UFEREREJAu8kFz/dHr2yZUrV/DRRx9h2LBhSE5OBgD8/vvvSEhI0GscERERERHVfFoPOv73v/+hXbt2+Ouvv7Bp0ybk5OQAAE6fPo05c+R7MSERERERUWUoJJyMldY/28yZM/HJJ58gJiYG5ubm6vm+vr44evSoXuOIiIiIiKjm0/qajjNnzmDdunWl5tetWxepqalaB4iiiMuXL6OwsBAtWrSAqanWSUREREREesNb5uqf1kc6bG1tcefOnVLzT506hQYNGmi1rsTERHh6eqJly5bw9PREs2bNcPz4cW2TiIiIiIhIxrQedAwdOhQzZsxAUlISBEGASqXC4cOH8cEHHyAgIECrdU2fPh3FxcVYu3YtNmzYgIYNG2LChAnaJhERERERkYxpfS7TggULEBwcjEaNGqGkpAStW7dGSUkJhg8fjo8++kirdf3555/YsGEDunTpAgDw8fFBw4YNcf/+fVhZWWmbRkRERERUZbxlrv5pPegwNzfH999/j9mzZ+PMmTPIycmBt7c3mjdvrvXGk5OTNT5Xv359WFpaIjk5GW5ublqvj4iIiIiI5KfSgw6VSoUlS5Zg27ZtKCwsRM+ePTFnzhxYWlrqvHFBEJCTk6OxDoVCgezsbGRlZannWVtb67wNIiIiIiJt8EiH/lV60DF//nyEhoaiV69esLS0xOeff47k5GSsWrVK542LoggPD49S87y9vdV/FgQBJSUlOm+DiIiIiIikVekLydesWYOvvvoKu3fvxpYtW7B9+3ZER0dDpVLpvPEDBw5g//79GtPj8x7+WSrR0Tvg6xuIdu38MXjwNMTHX5Ss5Uls055cuwC26UqubXLtAgzf9tKLLbFh1Qe4+vdXyLvxE/q9+rzG+1a1lFg2dwwu//Ul0i9G4eS+JXh7ZC+NZb4ID0TCoeVIvxiFG6e+xS8rp8GjmYv6/XatGiPqi8m4dPTBOk7t+xTB417T68/xJLl+p3LtAtimK7m2ybXrWdekSRMIglBqCg4OBgC8/PLLpd4LCgrSWMeNGzfQt29f1KpVC3Xr1lXf6EnfKj3ouHHjBvz8/NSve/XqBUEQcPv2bZ033r1790pNUti58xDCw1ciOHgYNm9ejpYt3RAYOBtpaZmS9LDNOLvYZnxtcu2qrjarWkqcOXcD739U9lHwRbNH4ZWXvTD2vRVo7zsNX/7wO5bNHYO+r3RQL3PqTCLGT/sG7X2nof+ocAiCgN/WzoLi/8938G7XFClp9zD2vRV4rtd0LPpyC+bOGIqg0a/q7ed4nFy/U7l2sc342uTaZUg15Ynkf//9N+7cuaOeYmJiAACDBw9WL/POO+9oLLN48WL1eyUlJejbty8KCwtx5MgRREVFYfXq1Zg9e7aWJRWr9M9WXFwMCwsLjXlmZmYoKirSeeOzZ89Gbm6u+nVGRobO69K3yMgtGDKkNwYN6gV398YIC5sICwslNm6MkTqNbUbUxTbja5NrV3W17fnjNMI+/QXbdpf9zCWfDh5Yu+EgDh09jxs3U7Fq3X7En7+O572aqZdZtW4/Dh/7BzdupiLu7DWELfkFjRo4wrWREwBgzS9/4IPQNfjzr/O4diMZ6zf/iTW//A8DXntRbz/H4+T6ncq1i23G1ybXLgKcnJzg7Oysnn777Tc0a9ZM45f2tWrV0ljm8Wul9+zZg3PnzmHt2rVo3749+vTpg3nz5mHFihUoLCzUa2ulBx2iKGLMmDHw9/dXT/n5+QgKCtKYp4358+cjJydH/drV1RVXr17Vah2GUFhYhISEy+jc2Us9T6FQoHPn9jh16oKEZWwzpi6AbbqSa5tcuwD5tB09cRGvv9IBLvXsAADdOrVGc7f62Hswvszla1kqETCkOxJv3MXN22nlrtemTi1k3Msp931dyWW/1ZQugG26kmubXLsMTSGIkk0FBQXIysrSmAoKCipsLiwsxNq1azFu3DgIwqMr4aOjo+Ho6Ii2bdti1qxZGr/wj42NRbt27VCvXj31vN69eyMrKwsJCQn63aeVXXD06NGoW7cubGxs1NPIkSPh4uKiMU8boig+9XVllP3FVG1klpGRhZISFRwc7DTmOzjYIjVV2qMxbNOeXLsAtulKrm1y7QLk0xYyezXOX7qFK39/hawrP2Lbmpl4/+NIHD72j8Zy40e9gpTzkUi7sBqvvuyFviMWoKio7JuK+HRojjf7+eCH6H1675XLfnuSXLsAtulKrm1y7TJm4eHhGn+3trGxQXh4eIWf27JlCzIzMzFmzBj1vOHDh2Pt2rU4cOAAZs2ahR9//BEjR45Uv5+UlKQx4ACgfp2UlKSfH+j/VfruVZGRkXrdsL6Eh4cjLCxMY96cOZMQGjpZoiIiIirPxDG98aK3OwaNW4IbN1PRpWNLLJ83FnfuZuDAn2fVy63f8if2HToD57q2eH/C61j71Xvw9Q9FQYHmKb2tPRril5UfYP7yTdh36Ew1/zREZKykvGXurFmzEBISojFPqVRW+LkffvgBffr0gYvLoxtvjB8/Xv3ndu3aoX79+ujZsyeuXLmCZs2albUag9H64YD6JAgCsrOzYWFhob49bk5OjsYzOoCnP6ej7C/mRpW67OysYWKiQFqa5gg+LS0Tjo525XyqerBNe3LtAtimK7m2ybULkEebhdIMYR8OxVvjl2LX/lMAgLP/3IBna1e8P/51jUFHVnYesrLzcOVaEo6duoQ7Z1ZiQO8X8Mu2I+plWjZvgJ0/fYRV6/Zh0RebDdIsh/1WFrl2AWzTlVzb5NplzJRKZaUGGY+7fv069u7di02bNj11uY4dOwIALl++jGbNmsHZ2RnHjh3TWObu3bsAAGdnZ60aKqLtRfJ69fA5HXZ2drC3t1c/3dzOzg52dnawtbWFnd3T/4FWKpWwtrbWmJRK8yp1mZuboU0bd8TGPjrHWKVSITb2NLy9W1Rp3VXFNuPpAtimK7m2ybULkEebmZkpzM1NS91qvUSlUt+ZqiwPb/Nobv7o92StPBpi1/qPEb3xIEKX/GKwZjnst5rUBbBNV3Jtk2sXaYqMjETdunXRt2/fpy4XFxcHAKhfvz4AoFOnTjhz5gySk5PVy8TExMDa2hqtW7fWa6OkRzoOHDgg5eafauzYgZgxYxnatnWHp6cHoqK2Ii8vH/7+vSr+MNtk1ybXLrYZX5tcu6qrzaqWEs2aPPrtWJNGTvBs7YqMzBz8ezsNB2PPYcF/RyAvvxA3bqWia8dWGDGoG2bM/fHB8o3r4s1+nbDvYDxS07LQoL49pk0cgLz8Quw+EAfgwSlVv6//CHsPxiPi+x2o5/TgesKSEhVS07P19rM8JNfvVK5dbDO+Nrl2GZKkv5XXkkqlQmRkJEaPHg1T00d/tb9y5QrWrVsHPz8/ODg4ID4+HlOnTkW3bt3g6ekJAHj11VfRunVrjBo1CosXL0ZSUhI++ugjBAcHa320pSKSDjqkegZHZfj5dUV6+j1EREQjJSUDrVo1xcqVYbI4lMg24+lim/G1ybWrutqe82yKPb88ur/74jkBAIAff/0fxk/7BgGTIjB3xlCsjpgEO9vauHEzBaGLf8b3a/cCAAoKivDSCy0waVwf2NlYITn1Hv786zx6vDEHKWkPTr19o29H1HW0wXD/rhju31W9rev/pqDlS1P09rM8JNfvVK5dbDO+Nrl20QN79+7FjRs3MG7cOI355ubm2Lt3L5YvX4779++jUaNGGDRoED766CP1MiYmJvjtt9/w7rvvolOnTrCyssLo0aMxd+5cvXcKoi63jJI9PiWTiKi6WDaeI3VCufJuhFW8EBFJxEPqgHJ9eGy/ZNte/KKvZNs2JEmPdCgUCo37CJdFEASDPIqdiIiIiIiqh6SDjs2by7/bSGxsLCIiIkpdcEhERERERDWLpIOOAQMGlJp34cIFzJw5E9u3b8eIESMMck4ZEREREVF5BMEIrz6QmGwuzr99+zbeeecdtGvXDsXFxYiLi0NUVBRcXV2lTiMiIiIioiqQ9EgHANy7dw8LFizAF198gfbt22Pfvn3o2rVrxR8kIiIiIjIAKZ9IbqwkHXQsXrwYixYtgrOzM3766acyT7ciIiIiIqKaTdJBx8yZM2FpaQl3d3dERUUhKiqqzOUqeqQ7ERERERHJl6SDjoCAgApvmUtEREREVJ1kc9GzEZF00LF69WopN09ERERERNVA0kGHv79/hcsIgoCNGzdWQw0REREREaDgLXP1TtJBh42NjZSbJyIiIiKiaiDpoCMyMlLKzRMRERERlcJb5uofr5MhIiIiIiKD4qCDiIiIiIgMSvInkhMRUc2WdyNM6oRyeXyfJHVCuS6+4yx1AhGVg6dX6R+PdBARERERkUHxSAcRERER0WNMpA4wQjzSQUREREREBsVBBxERERERGRRPryIiIiIiegyfSK5/PNJBREREREQGxSMdRERERESP4S1z9Y9HOoiIiIiIyKB4pIOIiIiI6DE80qF/kg46QkJCKlzG1NQUzs7O6NmzJ7y8vKqhioiIiIiI9EnSQcfJkychCE8fSqpUKiQnJ2P69On44osvMHHixGqqIyIiIiIifZB00PHHH39UetmoqCjMnTuXgw4iIiIiMigTnl6ld5JeSN60aVOkpaVValk/Pz/Y2toaNoiIiIiIiPRO0kHHtWvXUFJSUqllnZyccOLECQMXaYqO3gFf30C0a+ePwYOnIT7+YrVu/2nYpj25dgFs05Vc2+TaBTzbbc872+CbV9vg0PCOuPhON/RydSi1zJQOrvhzREfEj30Jq/3awdXaQuP91g61EdmnHY4HdMZfozphXpfmqGWq+Z/Sjzo1w6aB3jg7rgu2+j+n15/hSc/y91kVbNOeXLsMRSFINxkr3jK3HDt3HkJ4+EoEBw/D5s3L0bKlGwIDZyMtLVPqNLYZURfbjK9Nrl1sA2qZKvBP+n3MPXK5zPff8WqIgDYNMOfPyxi8NQ65RSVY1acdzP//PIu6tcyx2q8drmflYfDWU3j79zNwt6uFhd1blFrXxotJ2Hk1RW/tZXnWv0+2sYtqFskHHbt378a2bdueOkkhMnILhgzpjUGDesHdvTHCwibCwkKJjRtjJOlhm3F2sc342uTaxTbg4M0MLD9+DTHXyj6td3TbBvjq1A3su56GC+n38eEfF1C3lhKvuDoCAHo0tkexSkTY4ctIvJeHM6k5mPPnJbzW1AmNHzsi8knsFUSfu4N/s/L11l6WZ/37ZBu7qGaRfNAxevRoDBw4sNzpjTfeqPamwsIiJCRcRufOj27Rq1Ao0Llze5w6daHaex7HNuPpAtimK7m2ybULYFtFGtWxQN1aSsTeylDPyykqwemULLSvZw0AMDdRoEglQnzsc/klKgBAh3o21dL5kBz2WXnYphu5tsm1y9AUgijZZKwkH3QkJSVBpVKVO1V0zUdBQQGysrI0poKCwio1ZWRkoaREBQcHO435Dg62SE3NKOdT1YNt2pNrF8A2Xcm1Ta5dANsq4mhpDgBIzSvSmJ+aVwQnSzMAQOztTDjWMkOgZ0OYKQRYm5vigxfcADw49ao6yWGflYdtupFrm1y7qOaRdNBR0TM6KiM8PBw2NjYaU3j4t3qoIyIieuRyRi5m/HEB49o1xOmxXXBkpA9uZucjJbcQKhjvbyeJnkW8kFz/JH1OhyhW/V/Ss2bNKvVkc6XyRpXWaWdnDRMTBdLSNEfwaWmZcHS0K+dT1YNt2pNrF8A2Xcm1Ta5dANsqkpr34Ai5o6UZUvIeHS13tDTD+bT76te/XUnBb1dS4GBphryiEogAxrZraPDrN54kh31WHrbpRq5tcu2imkfSIx2jR4+GpaVlldahVCphbW2tMSmVVTvMbW5uhjZt3BEbG6+ep1KpEBt7Gt7epe9SUp3YZjxdANt0Jdc2uXYBbKvIv9n5SM4tQKcGtup5VmYm8HKyRtzdrFLLp+UVIbdYBb+mTigoUeHwreo9zUQO+6w8bNONXNvk2mVoJhJOxkrSIx2RkZFSbv6pxo4diBkzlqFtW3d4enogKmor8vLy4e/fS+o0thlRF9uMr02uXWx7cMtcV+tHv+hqWMcCreytkFlQjDv3CxB19hbe9W6Ma/fycDM7H+8/3wTJuQWIuZ6q/szI1i44eTcLucUleKmBLT7s2BSfHktEduGj6w8bW1vAytQEjrXMoDRRoJW9FQDgcmYuilT6Ow3rWf8+2cYuqlkkHXQoFIoKr+sQBAHFxcXVVPSIn19XpKffQ0RENFJSMtCqVVOsXBkmi0OJbDOeLrYZX5tcu9gGtHWqg7WvP7oDz386NQMAbLqYhJn/u4jvT9+EpakJ5nX1gLW5KU7cvYfAXWdRWPJooOBZtw4md3CFlZkJrmbmYvahS9h6OVljO/O7eqCji6369dZBHQAAPX76C7dyCvT28zzr3yfb2EU1iyDq48IKHW3ZsqXcQUdsbCwiIiKgUqmQn6/tubLG/ZRMIiKqHI/vk6ROKNfFd5ylTiCSmIfUAeX65vweybYd1OpVybZtSJIe6Rg4cGCpeRcuXMDMmTOxfft2jBgxAnPnzq3+MCIiIiIi0hvJn9Px0O3bt/HOO++gXbt2KC4uRlxcHKKiouDq6ip1GhERERE9Q/hwQP2TfNBx7949zJgxA+7u7khISMC+ffuwfft2tG3bVuo0IiIiIiLSA0lPr1q8eDEWLVoEZ2dn/PTTTxgwYICUOUREREREZACSDjpmzpwJS0tLuLu7IyoqClFRUWUut2nTpmouIyIiIqJnlYkRPxlcKpIOOgICAiq8ZS4REREREdVskg46Vq9eLeXmiYiIiIhKUfB34non6aDD39+/wmUEQcDGjRuroYaIiIiIiAxB0kGHjY2NlJsnIiIiIiqFRzr0T9JBR2RkpJSbJyIiIiKiaiD5czqIiIiIiMi4SXqkg4iIiIhIbnh6lf7xSAcRERERERkUj3QQEZHRuviOs9QJ5Wo65ZzUCWW6GtFa6gQiyZkIotQJRodHOoiIiIiIyKA46CAiIiIiIoPi6VVERERERI/hb+X1j/uUiIiIiIgMikc6iIiIiIgew1vm6h+PdBARERERkUHxSAcRERER0WN4pEP/JB90/Pzzz9i2bRsKCwvRs2dPBAUFSZ1ERERERER6JOmg4+uvv0ZwcDCaN28OS0tLbNq0CVeuXMGSJUukzCIiIiIiIj2S9JqOL7/8EnPmzMGFCxcQFxeHqKgofPXVV1ImEREREdEzzkQQJZuMlaSDjqtXr2L06NHq18OHD0dxcTHu3LkjYRUREREREemTpKdXFRQUwMrKSv1aoVDA3NwceXl5ElYRERER0bOMF5Lrn+QXkn/88ceoVauW+nVhYSHmz58PGxsb9bylS5dKkUZERERERHog6elV3bp1w4ULF3Dq1Cn11LlzZ1y9elX9Oi4uTrK+6Ogd8PUNRLt2/hg8eBri4y9K1vIktmlPrl0A23Ql1za5dgFs05Wh215o5oDvx/sgdl5vXI0YiFfa1dd4/70+LRHz3544u+R1nFrohx+DO8PL1U5jGZtaZlgW0AGnF/dF3MK+WDjMG7XMTcrcnqujFeL/fzlDeZa/z6qQa5tcu6jmkHTQ8ccff+DAgQNPnfbv3y9J286dhxAevhLBwcOwefNytGzphsDA2UhLy5Skh23G2cU242uTaxfb5N1Wy9wE52/dw5xf48t8PzE5B6G/xqPPwv0YsvwQbqbnYs3EzrCvba5eZlnA82jubI2AFUfw9nexeLGZAxYMbV9qXaYKAZ+Pfh7Hr6Tprf9Jz/r3aWxtcu0yJIUg3WSsJB10NG3aFGlphvuXXlVERm7BkCG9MWhQL7i7N0ZY2ERYWCixcWOM1GlsM6Iuthlfm1y72Cbvtv+dT8bSHeexJ77sG6lsO3EThy+m4N+0XFxKysb8zWdRx9IMLV2sAQDN6tXGy63rYdZPp3D6egaOX01H6MZ4vP5cQ9S1ttBY17TXW+FKcg52nLqtt/4nPevfp7G1ybWLahZJBx3Xrl1DSUmJlAllKiwsQkLCZXTu7KWep1Ao0Llze5w6dUHCMrYZUxfANl3JtU2uXQDbdCXHNjMTAUM7N0FWbhHO38oCADznZo97uYU482+mernDF1KgEkW0b/LoNKxOzR3Rp30DzPn1tMH65LjPHmKb8XQZGo906J+kgw59KCgoQFZWlsZUUFBYpXVmZGShpEQFBwfN82UdHGyRmppRpXVXFdu0J9cugG26kmubXLsAtulKTm2+berhzJLXcf6z/hj3cjMEfHUYGfcf/PfOqY4F0rILNJYvUYnIzC2Ck7USAGBbywxLRjyH6dEnkZNfbLBOOe2zJ7FNe3LtoppH8rtX7d69W+NOVWXp379/ue+Fh4cjLCxMY96cOZMQGjpZL31ERERyEHspFa8vOgC72uYY2qkJvhj7Avw/+x/Scir3i7bwYd7YduIm/jbgtRxExsLEiI84SEXyQcfjDwcsiyAITz0Fa9asWQgJCdGYp1TeqFKTnZ01TEwUSEvTHMGnpWXC0dGunE9VD7ZpT65dANt0Jdc2uXYBbNOVnNryCktwPfU+rqfeR9y1DOz/qBeGdHLF1zGXkJKdD4c6So3lTRQCbGuZISXrwRGQTs2d0LOtM972dQfw4L+vJgoBF5f1x39/jsOvR6v2386H5LTPnsQ27cm1i2oeyU+vSkpKgkqlKneq6JoPpVIJa2trjUmpNH/qZypibm6GNm3cERv76C4iKpUKsbGn4e3dokrrriq2GU8XwDZdybVNrl0A23Ql5zZBIcDc9MEtcU8mpsOmljnaNnp05kAnD0coBAFx1x78ZXHQsv/h9cUH1NOyneeRnV+E1xcfwO7TZV/Args57zO2GU8XPRAaGgpBEDSmli1bqt/Pz89HcHAwHBwcULt2bQwaNAh3797VWMeNGzfQt29f1KpVC3Xr1sX06dNRXKz/UzAlPdIhCPI9djV27EDMmLEMbdu6w9PTA1FRW5GXlw9//15Sp7HNiLrYZnxtcu1im7zbapmbwNWptvp1I4daaNXABvdyC5FxvxDBr3pg79kkJN/Lh31tc4zq2hTONhbYeeoWAODK3Rz8ce4uFgz1xkc/x8HMRIGwN73w28mbSM7KVy/zuHaN7CCqgIt3svX2czz0rH+fxtYm1y5DUgii1AmV1qZNG+zdu1f92tT00V/vp06dih07duDXX3+FjY0NJk2aBH9/fxw+fBgAUFJSgr59+8LZ2RlHjhzBnTt3EBAQADMzMyxYsECvnZIOOkRRvl+on19XpKffQ0RENFJSMtCqVVOsXBkmi0OJbDOeLrYZX5tcu9gm77Z2je3w05Qu6tcf+bcDAGz46wY++jkOzerVgf+LjWFX2xyZ9wsRfyMTb31+CJeSHg0Ypq45jrA3PbF20ksQRWDX6dsI21D2cz8M7Vn/Po2tTa5d9ICpqSmcnZ1Lzb937x5++OEHrFu3Dr6+vgCAyMhItGrVCkePHoWPjw/27NmDc+fOYe/evahXrx7at/+/9u4+rub78f/483RxThenC0qdCEVKrtsQYZjLYh/JzMyMmKu1C8KSuUg+k6sPll3YtFUu9o1NDDNmRoyMIbmY5qJcjFwkhXJS5/z+8OvMWUWdzjnvd6fn/XN7327O+7zP+zx6n9qnV++rdpg/fz4iIiIQFRUFqbR6Rw89TaIW8Df/0NBQxMbGws7OTs9r5l0yiYhI3Jq8f1bohHJdim0hdALVGt5CB1Tol793CPbe3Zx7QanUvhqdTCaDTCYrs2xUVBSWLFkCBwcHWFlZoXPnzoiJiUGjRo3w66+/olevXsjNzYWjo6PmNY0bN8bkyZMxZcoUzJkzB1u3bkVaWprm+czMTDRp0gTHjx+Hn5+f3r4uQc/piI+PN8CAg4iIiIioZoqJiYGDg4PWFBMTU+6y/v7+SEhIwM6dO/HFF18gMzMT3bp1w/3795GdnQ2pVKo14AAAV1dXZGdnA3hybrWrq2uZ50uf0ydBD68yMzN77nkdEonEICezEBERERGJTflXZi27lwMAAgMDNf9u06YN/P390bhxY2zcuBHW1tYG7awqQQcdycnJFQ46UlNTERsbC5VKZeQqIiIiIqrNhLwzeEWHUlWGo6MjvL29ceHCBfTp0wdFRUW4d++e1t6Omzdvas4BUSgUOHLkiNY6Sq9uVd55ItUh6OFVwcHBGDRokNbUvHlzJCQkYOnSpRg6dCgyMjKETCQiIiIiqhEePHiAixcvws3NDS+++CIsLS2xZ88ezfMZGRm4cuUKOnfuDADo3LkzTp06hVu3bmmW2b17N+zt7dGihX7P7xL8Ph2lrl+/jnHjxqF169YoLi5GWloaEhMT0bhxY6HTiIiIiKgWMZcIN1XFtGnTkJKSgqysLBw6dAiDBw+Gubk5hg8fDgcHB4wdOxbh4eHYu3cvjh07htDQUHTu3BmdOnUCAPTt2xctWrTAyJEjcfLkSezatQuzZs1CWFiYzntbKiL4Hcnz8vKwYMECrFy5Eu3atcOePXvQrVs3obOIiIiIiETt2rVrGD58OHJyclCvXj107doVhw8fRr169QAAy5cvh5mZGYYMGQKlUol+/frh888/17ze3Nwc27dvx6RJk9C5c2fY2tpi1KhRiI6O1nuroJfMXbx4MRYtWgSFQoEFCxZg0KBBelozL5lLRETixkvmEon3krkpN4S7ZG53tyDB3tuQBB10mJmZwdraGr1794a5uXmFyyUnJ1dxzRx0EBGRuHHQQSTeQceB7B8Fe+9uigGCvbchCXp41VtvvfXcS+YSEREREVHNJuigIyEhQci3JyIiIiIqQ8hL5poqQQcdISEhz11GIpFg06ZNRqghIiIiIiJDEHTQ4eDgIOTbExERERGVwT0d+ifooCM+Pl7ItyciIiIiIiMQzc0BiYiIiIjINAl+c0AiIqLaSKyXpr3/+KrQCRWys2wodALVEvyrvP5xmxIRERERkUFxTwcRERER0VN4Gzn9454OIiIiIiIyKA46iIiIiIjIoHh4FRERERHRU3h0lf5xTwcRERERERkU93QQERERET2FJ5LrH/d0EBERERGRQXFPBxERERHRU/hXef3jNiUiIiIiIoMSdE9HbGzsc5exsLCAQqFA165d4eLiYoQqIiIiIiLSJ0EHHcuXL3/uMiqVCjk5OVCpVFi3bh1CQkKMUEZEREREtZVEohY6weQIOujIzMys1HIqlQoLFy7ERx99xEEHEREREVENUyPO6TAzM8OoUaNw584do77v+vU/4uWXx6J16xAMHToV6el/GfX9n4VtVSfWLoBtuhJrm1i7ALbpSqxtQnQd/+M8poR9jv49I9G+1TvYtyetzDKZF29gyrtfoHuncHTtMBlvDVuI7Bt3tZZJT7uEiWNWoGuHyejuH45xo5bh0aMig/cD4v08AfG2ibXLUCQCTqZK0EFHo0aNkJOTo3n86aefIj8/v9xlGzRogNu3bxsrDTt2HEBMTBzCwoZj8+YVaN7cE2PHzkFOzj2jNbDN9LvYZnptYu1im+m1CdVVWFiEZj7uiPhoWLnPX7tyG2+/tQwenq74Mn4KkjZ9hLETgyCVWmqWSU+7hPcmfopOAb5I/L8PkZgUgdeGd4eZmeF/5RLr5ynmNrF2Uc0i6KDj2rVrKCkp0TyeOXOm0fdmVCQ+fgtee60fhgzpDS+vRpg37x1YWcmwadNuodPYZkJdbDO9NrF2sc302oTq6tKtJd55/z/o2btduc9/FrsVAd1a4oOpIWju2xDujeqhe882qOtkp1lm2eLv8fqInhj9dj809aoPD09X9On/otbAxFDE+nmKuU2sXVSziOrwKrVaHCftFBU9xpkzFxAQ0FYzz8zMDAEB7XDiRIaAZWwzpS6AbboSa5tYuwC26UqsbWLtUqlUOLj/NBp7uODd8SvR56UPMWr4Yq1DsO7m3Mfp9CzUqSvHmBFL0PelCIwfvQxpxy8YvE+s2w0Qb5tYuwxNIhFuMlWiGnToQqlUIj8/X2tSKqt3TGhubj5KSlRwcqqjNd/JyRF37uRWa93VxbaqE2sXwDZdibVNrF0A23Ql1jaxdt29ex8FBUokfP0zOndtgU+/eg89e7XF9Mmrcezok3MA/r725IiG1Z/vQPCrXRH75bvw8W2ESWNjceXyLYP2iXW7AeJtE2sX1TyC35E8Li4OcrkcAFBcXIyEhAQ4OztrLfP+++9X+PqYmBjMmzdPa97cue8iKuo9/ccSERFRhdSqJ0csdO/ZBiPe6gUA8GneECfTLmHTxt/wYgdvqFQqAEDI0K74z+DOAIDmvg1x9PA5bE0+hHenBAvSTvQ0E97hIBhBBx2NGjXC6tWrNY8VCgXWrl2rtYxEInnmoCMyMhLh4eFa82SyK9XqqlPHHubmZsjJ0R7B5+Tcg7NznQpeZRxsqzqxdgFs05VY28TaBbBNV2JtE2uXYx05zC3M4NnUTWu+ZxMF0o5fBAA413N4Mq+poswy2dmG/cu5WLcbIN42sXZRzSPo4VVZWVnIzMx85nTp0qVnrkMmk8He3l5rksmk1eqSSi3RsqUXUlPTNfNUKhVSU0/Cz8+nWuuuLraZThfANl2JtU2sXQDbdCXWNrF2WVpaoGXLxriceVNr/pWsW3CrXxcAUL+BE+q5OOBylvahVJcv34KbW12D9ol1uwHibRNrl6GZSYSbTJXgh1eJVWhoMCIilqNVKy+0aeONxMQfUFj4CCEhvYVOY5sJdbHN9NrE2sU202sTqqug4BGuXvnnEvZ//52DjHNX4eBgC4VbXYwM7YPIaV/jhfZeaN/RG4d+O4sDKafwZfxkAE+OYBgZ2gdffrYdzXwawKe5O7b/8DsuZ97E4mXjDNoOiPfzFHObWLuoZhF00JGamoqcnBwMHDhQM2/NmjWYO3cuHj58iODgYKxcuRIymczobUFB3XD3bh5iY9fj9u1c+Po2QVzcPFHsSmSb6XSxzfTaxNrFNtNrE6rr7OkrmDhmhebx8sWbAAADB3VC1MdvoWfvdoicMxwJcbuwNOY7NPZwxaLl49DuBS/Na94Y+TKKlI+xfNH3yMsvgLd3A3y2+j24N6pn0HZAvJ+nmNvE2kU1i0Qt4HVqAwMD0aNHD0RERAAATp06hRdeeAGjR4+Gr68vlixZggkTJiAqKqqKazbtu2QSEREZyv3HV4VOqJCdZUOhE0ivvIUOqNCZ3O2CvXfLOgOfv1ANJOg5HWlpaejVq5fmcVJSEvz9/bF69WqEh4cjNjYWGzduFLCQiIiIiIiqS9DDq3Jzc+Hq6qp5nJKSgsDAQM3jDh064OpV8f7FhYiIiIhMjynfpE8ogu7pcHV1RWZmJgCgqKgIx48fR6dOnTTP379/H5aWlkLlERERERGRHgg66AgKCsKMGTNw4MABREZGwsbGBt26ddM8n56ejqZNmwpYSERERERE1SXo4VXz589HSEgIunfvDrlcjoSEBEil/9xj45tvvkHfvn0FLCQiIiKi2oZHV+mfoIMOZ2dn7N+/H3l5eZDL5TA3N9d6/rvvvoOdnZ1AdUREREREpA+CDjpCQkIqtVxycrKBS4iIiIiInuCeDv0TdNDh4OAg5NsTEREREZERCDroiI+PF/LtiYiIiIjKMOOuDr0T9OpVRERERERk+jjoICIiIiIigxL08CoiIiIiIrHh0VX6xz0dRERERERkUNzTQURERBp2lg2FTqhQduE5oRMqpLBuLnQC6ZFEohY6weRwTwcRERERERkUBx1ERERERGRQPLyKiIiIiOgpPJFc/7ing4iIiIiIDIp7OoiIiIiIniLhrg69454OIiIiIiIyKO7pICIiIiJ6Cv8qr3/cpkREREREZFCC7+k4fPgwtm3bhqKiIvTq1Qv9+/cXOomIiIiIiPRI0EHH999/j2HDhsHa2hqWlpZYtmwZFi1ahGnTpgmZRURERES1GE8k1z9BD6+KiYnBuHHjkJeXh9zcXPz3v//FggULhEwiIiIiIiI9k6jVarVQby6Xy5GWlgYvLy8AQFFREWxtbfH333/DxcWlGmv+Sz+BREREJBrZheeETqiQwrq50Ak1kLfQARW68mCbYO/dSP6KYO9tSILu6SgoKIC9vb3msVQqhZWVFR48eCBgFRERERER6ZPgJ5LHxcVBLpdrHhcXFyMhIQHOzs6aee+//74QaVi//kd8/XUybt/ORfPmnpg9ewLatBHHqJxtptMFsE1XYm0TaxfANl2JtU2sXYDx29Z9/Sv27zmFK1m3IZNZoFVbD0yYHIRGHk+OnLjx9128PiCm3NdGLX4TPfu2BQB8smgLTqdlIfNCNhp7uuDrjeEGay6PWD9TsXZRzSHono5GjRph9erVWL58uWZSKBRYu3at5vGKFSsEadux4wBiYuIQFjYcmzevQPPmnhg7dg5ycu4J0sM20+xim+m1ibWLbabXJtYuodpOHruIwcMC8MWad/G/VeNRXFyCaZNWo7CwCADgonBE8i+ztabQSX1hbSODf1ftQ6OCBnVAz35tDdZaEbF+pmLtMiSJRLjJVAk66MjKykJmZuYzp0uXLgnSFh+/Ba+91g9DhvSGl1cjzJv3DqysZNi0abcgPWwzzS62mV6bWLvYZnptYu0Sqm3J5+MQOKgDPL0U8PKpj8joYbh54x7+OnsNAGBubgYnZ3ut6cCvp9GzbxvY2Mg06/kgIhiDX++C+g2cDNZaEbF+pmLtopqFNwcsR1HRY5w5cwEBAf/8lcPMzAwBAe1w4kSGgGVsM6UugG26EmubWLsAtulKrG1i7QLE0/bgwSMAgJ2DTbnPZ5y9hgsZ1zEguKPRmp5FLNutpnQZmkTAyVQJfnhVTk6O5vGnn36K/Pz8Kq1DqVQiPz9fa1Iqi6rVlZubj5ISFZyc6mjNd3JyxJ07udVad3WxrerE2gWwTVdibRNrF8A2XYm1TaxdgDjaVCoVPl2yFa3beaCJl6LcZX7cfASNm7igVTsPozQ9jxi2W3nE2kU1j6CDjmvXrqGkpETzeObMmbhz506V1hETEwMHBwetKSbmS32nEhERUQ2xPGYzMi9kY86iEeU+r3z0GHt+OiGavRwkPmYS4SZTJfjVq56myy1DIiMjER6ufWUJmexKtTrq1LGHubkZcnK0R/A5Offg7FynglcZB9uqTqxdANt0JdY2sXYBbNOVWNvE2gUI37YiZjNS9/+Jld+8AxdXx3KX2fdLOh49eox+A180eE9lCb3dKiLWLqp5avw5HTKZDPb29lqTTCat1jqlUku0bOmF1NR0zTyVSoXU1JPw8/OpbnK1sM10ugC26UqsbWLtAtimK7G2ibULEK5NrVZjRcxmHPj1NFZ8NQFuDepWuOyOzUfQpUcLONaVV7iMsYn1MxVrF9U8gu/pePo+HeXdowMQ5j4doaHBiIhYjlatvNCmjTcSE39AYeEjhIT0NnoL20y3i22m1ybWLraZXptYu4RqW75gM/b8dAIfrxgNa1sZcu48OUdULreGzMpSs9y1K3dw8ngmFn06ptz1XLtyB4UFStzNuQ+lshjnz/0NAPBo6gpLS8P+2iTWz1SsXYZkwkc5CUbQQUfpfTpKld6j42kSiUSQQUdQUDfcvZuH2Nj1uH07F76+TRAXN08UuxLZZjpdbDO9NrF2sc302sTaJVTbD9+lAgA+eHuV1vwZ815D4KAOmsc7thxFPVcHdOhc/o3tlsz7DmnH/rlc/9uvrwAAJP0Y+cy9J/og1s9UrF1Us0jUupxIIXp/CR1AREREepZdeE7ohAoprJs/fyH6F/He0Ty7cKtg762w/o9g721INf6cDiIiIiIiEjdBBx2pqanYvn271rw1a9bA09MTLi4uGD9+PJRKpUB1RERERESkD4IOOqKjo3HmzBnN41OnTmHs2LHo3bs3ZsyYgW3btiEmJkbAQiIiIiKqbXhHcv0TdNCRlpaGXr16aR4nJSXB398fq1evRnh4OGJjY7Fx40YBC4mIiIiIxCkmJgYdOnSAnZ0dXFxcEBwcjIyMDK1levToAYlEojVNnDhRa5krV65gwIABsLGxgYuLC6ZPn47i4mK9tgp69arc3Fy4urpqHqekpCAwMFDzuEOHDrh69aoQaURERERUS0lqyC6HlJQUhIWFoUOHDiguLsbMmTPRt29fnD17Fra2tprlxo0bh+joaM1jGxsbzb9LSkowYMAAKBQKHDp0CDdu3MBbb70FS0tLLFiwQG+tgg46XF1dkZmZiYYNG6KoqAjHjx/HvHnzNM/fv38flpaWz1gDEREREVHttHPnTq3HCQkJcHFxwbFjx/DSSy9p5tvY2EChUJS7jp9//hlnz57FL7/8AldXV7Rr1w7z589HREQEoqKiIJVW76bbpQQ9vCooKAgzZszAgQMHEBkZCRsbG3Tr1k3zfHp6Opo2bSpgIRERERGR8SiVSuTn52tNlb2wUl5eHgCgbl3te8qsX78ezs7OaNWqFSIjI1FQUKB5LjU1Fa1bt9Y6+qhfv37Iz8/XOve6ugQddMyfPx8WFhbo3r07Vq9eja+++kprNPXNN9+gb9++AhYSERERUW0j5InkMTExcHBw0Joqc2EllUqFyZMno0uXLmjVqpVm/htvvIF169Zh7969iIyMxNq1a/Hmm29qns/OztYacADQPM7Ozq7sJnsuQQ+vcnZ2xv79+5GXlwe5XA5zc3Ot57/77jvY2dkJVEdEREREZFyRkZEIDw/XmieTyZ77urCwMJw+fRq//fab1vzx48dr/t26dWu4ubmhV69euHjxolGPKBJ00BESElKp5ZKTkw1cQkRERET0hJCHAslkskoNMp727rvvYvv27di/fz/c3d2fuay/vz8A4MKFC2jatCkUCgWOHDmitczNmzcBoMLzQHQh6KDDwcFByLcnIiIiIqqx1Go13nvvPWzevBn79u2Dp6fnc1+TlpYGAHBzcwMAdO7cGR9//DFu3boFFxcXAMDu3bthb2+PFi1a6K1Volar1Xpbm2j8JXQAERER6Vl24TmhEyqksG4udEIN5C10QIXuKrcK9t51Zf+p9LLvvPMOvv32W/zwww/w8fHRzHdwcIC1tTUuXryIb7/9FkFBQXByckJ6ejqmTJkCd3d3pKSkAHhyydx27dqhfv36WLx4MbKzszFy5Ei8/fbber1kLgcdREREVCNw0GFqOOgoT1UGHZIKbigSHx+P0aNH4+rVq3jzzTdx+vRpPHz4EA0bNsTgwYMxa9Ys2Nvba5a/fPkyJk2ahH379sHW1hajRo3CwoULYWGhv4OiOOggIiKiGoGDDlPDQUd5qjLoqEkEPaeDiIiIqLLE/Iv9vaKLQidUyFHKe55VXQ25JXkNIuh9OoiIiIiIyPRxTwcRERER0VMk3NOhd9zTQUREREREBsVBBxERERERGRQPryIiIiIieopEwr/L6xu3KBERERERGRT3dBARERERaeGJ5PrGPR1ERERERGRQ3NNBRERERPQUXjJX/7ing4iIiIiIDErQPR116tSBRPLskaSFhQUUCgX69OmD2bNnw9HR0ThxRERERESkF4IOOlasWPHcZVQqFW7duoX4+Hhcv34d//d//2f4MCIiIiKqxXh4lb4JOujw9PREQEAALCyen9GnTx/06dPHCFVERERERKRPgp7T0bNnT9y9e7dSy/r6+mLOnDkGLtK2fv2PePnlsWjdOgRDh05FevpfRn3/Z2Fb1Ym1C2CbrsTaJtYugG26EmubWLsAtj0tIW43Rr/+P/T0j0D/7rMw/f04XM68qbWMUvkYi//7Pfp0nYkeHT9ExJRvkHPnvtYy/q0nl5l+/um4QdtLifnzNASJxEywyVQJ+pWp1epKL2ttbY0PPvjAgDXaduw4gJiYOISFDcfmzSvQvLknxo6dg5yce0ZrYJvpd7HN9NrE2sU202sTaxfbyjrxx0W8+npXfL1+MmK/moTiYhXen7AKhQVKzTIrFm/GbymnEfO/0fgi/j3cuZWHGVO+KbOu2fOHY8feaM3U/eXWBusuJebPk2oOwYdTzzuRXCjx8Vvw2mv9MGRIb3h5NcK8ee/AykqGTZt2C53GNhPqYpvptYm1i22m1ybWLraV9cmqiRgY7I8mXm7w9mmAOf99A9k3cnHu7DUAwIP7hdia/Ds+mB6M9v7e8G3ZELPnv4H0tEycOpmltS47O2s4OdtrJpnM0mDdpcT8eVLNIfigY/To0QgJCXnmZGxFRY9x5swFBAS01cwzMzNDQEA7nDiRYfSep7HNdLoAtulKrG1i7QLYpiuxtom1C2BbZTx4UAgAsHewAQCcO3sVxcUl6NjJW7OMRxNXKNzq4PS/Bh1LFmxC324fIXT4MmzdfLhKR43oQizbzPgkAk6mSfCbA9rZ2cHa2lrn1yuVSiiVSq15MlkRZDKpzuvMzc1HSYkKTk51tOY7OTni0qVrOq9XH9hWdWLtAtimK7G2ibULYJuuxNom1i6Abc+jUqmwfNFmtPHzRNNmbgCAnDv3YWlpDjt7G61l6zrZIedOvubx+LBAtPdvBisrKX4/dA5L/vs9CguUGDaiu8F6xbDNyDQIPuiIjY2Fi4uLzq+PiYnBvHnztObNnfsuoqLeq24aERERkV4t+fh7XLpwA18mVv081bET+2n+7ePrjsLCIqyL32vQQUdtxTuS65+gh1fp43yOyMhI5OXlaU2RkROqtc46dexhbm6GnJxcrfk5Offg7FynglcZB9uqTqxdANt0JdY2sXYBbNOVWNvE2gWw7VmWfPw9fks5i8+/fheuCkfNfCdnOzx+XIL7+QVay9/NuQ8nZ/sK19eyTWPcunkPRUXFhkoWfJuR6agxV6+qiEwmg729vdZUnUOrAEAqtUTLll5ITU3XzFOpVEhNPQk/P5/qJlcL20ynC2CbrsTaJtYugG26EmubWLsAtpVHrVZjycffI+XXU/js6zDUd3fSer55i4awsDDH0d/Pa+ZdzryJ7Bu5aNXWo8L1nj/3N+ztbSCVGu7AFTF/noYkEfB/pkrQw6v27t2LunXrCplQodDQYERELEerVl5o08YbiYk/oLDwEUJCegudxjYT6mKb6bWJtYttptcm1i62lbXk4++xa8cxLPnkbdjayjTnadjKrWBlJYXczhr/CfHHJ0u2wN7BBra2VvhfzCa0buuB1v9/0HFg32nczbmPVm08IJVZ4EhqBhLifsGIUT0N1l1KzJ8n1RyCDjqkUil27tyJgQMHauatWbMGc+fOxcOHDxEcHIyVK1dCJpMZvS0oqBvu3s1DbOx63L6dC1/fJoiLmyeKXYlsM50utplem1i72GZ6bWLtYltZmzYcBABMGvOp1vzZ84djYLA/AGDyh4MhkZghcko8ih4Xo1NAc3w461XNshYW5vg+6TesWLwFarUa7o2c8cG0QQh+tbPBukuJ+fOkmkOiNvS11p4hMDAQPXr0QEREBADg1KlTeOGFFzB69Gj4+vpiyZIlmDBhAqKioqq4ZtO+SyYRERGJy72ii0InVMhR2lTohAp4P38RgTx4vE+w95Zb9hDsvQ1J0HM60tLS0KtXL83jpKQk+Pv7Y/Xq1QgPD0dsbCw2btwoYCEREREREVWXoIdX5ebmwtXVVfM4JSUFgYGBmscdOnTA1atXhUgjIiIiolpKH1dYJW2C7ulwdXVFZmYmAKCoqAjHjx9Hp06dNM/fv38flpaWQuUREREREZEeCDroCAoKwowZM3DgwAFERkbCxsYG3bp10zyfnp6Opk3FehwiERERERFVhqCHV82fPx8hISHo3r075HI5EhISIJX+c4+Nb775Bn379hWwkIiIiIhqHx5epW+CXr2qVF5eHuRyOczNzbXm3717F3Z2djocYsWrVxEREZHx8OpVuhDv1aseFu8X7L1tLV4S7L0NSdA9HSEhIZVaLjk52cAlRERERERPmPKdwYUi6KDDwcFByLcnIiIiIiIjEHTQER8fL+TbExERERGVQ9BrLZkkblEiIiIiIjIoDjqIiIiIiMigBD28ioiIiIhIbHgiuf5xTwcRERERERkU93QQERERVZN474UBXMjPEDqhXF724r1Ph0TCPR36xj0dRERERERkUBx0EBERERGRQfHwKiIiIiIiLTy8St+4p4OIiIiIiAyKezqIiIiIiJ4i4d/l9Y5blIiIiIiIDIp7OoiIiIiItPCcDn0TxZ4OpVKJhw8fCp1BREREREQGIOig4/bt2wgMDIRcLoe9vT06deqECxcuCJlERERERER6JuigIyIiAmlpaYiOjsbSpUtx7949jBs3TsgkIiIiIqrlJBKJYJOpEvScjt27dyMhIQH9+vUDAAwcOBC+vr5QKpWQyWRCphERERERkZ4Iuqfj+vXraNu2reZxs2bNIJPJcOPGDQGriIiIiKh2kwg4mSbBTyQ3Nzcv81itVgtUQ0RERERE+ibo4VVqtRre3t5ax689ePAAfn5+MDP7Zzx09+5dIfKwfv2P+PrrZNy+nYvmzT0xe/YEtGnjLUjLv7HNdLoAtulKrG1i7QLYpiuxtom1C2Cbrozd9uP3h7BjUypu3njyu1bjJgoMH9sb7bv4AgBWLvgeaUfO4+6dPFhZy+DbxgOh7w1AQw8XAMDubUexInpD+V/LrrlwrGtnsHaqWSRqAXcrJCYmVmq5UaNGVXHNf1U95l927DiADz9chnnzwtC2rTcSE7di587fsHPnKjg5OVZ7/WxjF9tMs02sXWwzvTaxdrFNfG0X8jMqfO73/WdgZm6G+g2dATXwy49/IHntPsSum4LGTRX4KfkwGnrUQz1FHdzPL8D6r35G5l/X8fUPM2Fubgblo8d4+KBQa53L523A46LHWPjlO8/s8rJ/ReevydAeq04I9t6WZn6CvbchCTro2L9/PwICAmBhoe8dLtUfdAwdOhWtWzfDnDkTAQAqlQrdu4di5MiBGD9+aLXXzzZ2sc0028TaxTbTaxNrF9vE1/asQUd5hvWajTHvD0S/Qf5lnss8fx3vvrEMcZtnwM3duczzebkP8FbQfHww+zW8HPTiM9+Hg47ymeqgQ9BzOnr27CnYoVPPUlT0GGfOXEBAwD8nuZuZmSEgoB1OnKjaD66+sc10ugC26UqsbWLtAtimK7G2ibULYJuuxNBWUqJCys8n8KiwCL6tG5d5/lGhEru3HYVr/bpwdnUsdx17fvwDMitLdHm5jYFrDY0nkuub4Od0VJdSqYRSqdSaJ5MVQSaT6rzO3Nx8lJSo4ORUR2u+k5MjLl26pvN69YFtVSfWLoBtuhJrm1i7ALbpSqxtYu0C2KYrIduyLtzA1DErUVRUDGtrKWYtGY1GTRSa57d/dxDxK3/Eo8IiuDeuh48/Gw9Ly/J/hfx56xF07+cHmZWlQZup5hH86lXVvQlKTEwMHBwctKaYmC/1VEdERERk2ho0roeV68OxLP59BA0JwLKoJFy5lK15vmfgC4hdNwWLvpyE+o3qISZyLYqUj8us58/0LFzNvIW+5RyWVdNIBPyfqRJ0TwcAjB49+rk3AkxOTq7wucjISISHh2vNk8muVKupTh17mJubIScnV2t+Ts49ODvXqeBVxsG2qhNrF8A2XYm1TaxdANt0JdY2sXYBbNOVkG2WlhZPTiQH0MzXHX+dvYofkn7DezNfBQDYyq1hK7dGg0b14NO6MYa9PBuH9p1Gj37a5x7s+uEImnjXRzNfd4P2Us0k+J4OOzu7Mnsq/j09i0wmg729vdZUnUOrAEAqtUTLll5ITU3XzFOpVEhNPQk/P59qrbu62GY6XQDbdCXWNrF2AWzTlVjbxNoFsE1XYmpTq1V4XFRcwZNPpn8/X1igxG+/nETfQR0NH0g1kuB7OmJjY+Hi4iJ0RhmhocGIiFiOVq280KaNNxITf0Bh4SOEhPQWOo1tJtTFNtNrE2sX20yvTaxdbKtZbQmf7kD7AB/UU9RBYYES+3aewKljlzB/5TjcuJaDA7vT4NfJBw51bHHnZh6+S/wVUitLdOjSXGs9+3enoaSkBD0Dn33Fqpqiuof/U1mCDjrE/IEGBXXD3bt5iI1dj9u3c+Hr2wRxcfME3/3KNtPqYpvptYm1i22m1ybWLrbVrLZ7uQ/wv6gk3L2TD1u5FTy86mP+ynHw8/dGzu08nEnLxA9JB/AgvxCOdeVo5dcES+PeLXPTv59/OIKAHq0ht7M2WCvVbILep8PMzAzZ2dkG2NNR/ft0EBEREZmCqt6nw1jEfJ+OEvVpwd7bXNJKsPc2JEHP6di7dy/q1q0rZAIRERERERmYoIMOqVSKnTt3as1bs2YNPD094eLigvHjx5e5BwcREREREdUsgg46oqOjcebMGc3jU6dOYezYsejduzdmzJiBbdu2ISYmRsBCIiIiIqpteJ8O/RN00JGWloZevXppHiclJcHf3x+rV69GeHg4YmNjsXHjRgELiYiIiIiougS9elVubi5cXV01j1NSUhAYGKh53KFDB1y9elWINCIiIiKqtUx3j4NQBN3T4erqiszMTABAUVERjh8/jk6dOmmev3//PiwtLYXKIyIiIiIiPRB00BEUFIQZM2bgwIEDiIyMhI2NDbp166Z5Pj09HU2bNhWwkIiIiIiIqkvQw6vmz5+PkJAQdO/eHXK5HAkJCZBKpZrnv/nmG/Tt21fAQiIiIiKqbcR8A+uaStCbA5bKy8uDXC6Hubm51vy7d+/Czs5Oh0OseHNAIiIiIoA3B9SFGn8K9t4S+Ar23oYk6J6OkJCQSi2XnJxs4BIiIiIiolKCnoFgkgTdog4ODpWaiIiIiIiorM8++wweHh6wsrKCv78/jhw5InRSuQTd0xEfHy/k2xMRERERlVFTbtK3YcMGhIeHY9WqVfD398eKFSvQr18/ZGRkwMXFReg8Ldx3RERERERUAy1btgzjxo1DaGgoWrRogVWrVsHGxgbffPON0GllcNBBRERERCQSSqUS+fn5WpNSqSyzXFFREY4dO4bevXtr5pmZmaF3795ITU01ZnLlqOmZHj16pJ47d6760aNHQqdoEWuXWs02XYm1TaxdajXbdCXWNrF2qdVs05VY28TapVazjdTquXPnqgFoTXPnzi2z3N9//60GoD506JDW/OnTp6s7duxopNrKE8Ulc8UsPz8fDg4OyMvLg729vdA5GmLtAtimK7G2ibULYJuuxNom1i6AbboSa5tYuwC20ZM9Hf/esyGTySCTybTmXb9+HQ0aNMChQ4fQuXNnzfwPP/wQKSkp+P33343SW1mCnkhORERERET/KG+AUR5nZ2eYm5vj5s2bWvNv3rwJhUJhqDyd8ZwOIiIiIqIaRiqV4sUXX8SePXs081QqFfbs2aO150MsuKeDiIiIiKgGCg8Px6hRo9C+fXt07NgRK1aswMOHDxEaGip0WhkcdDyHTCbD3LlzK7Wby5jE2gWwTVdibRNrF8A2XYm1TaxdANt0JdY2sXYBbKOqGTZsGG7fvo05c+YgOzsb7dq1w86dO+Hq6ip0Whk8kZyIiIiIiAyK53QQEREREZFBcdBBREREREQGxUEHEREREREZFAcdRERERERkULVm0DF69GhIJJIyU//+/QEAHh4ekEgkOHz4sNbrJk+ejB49emgeR0VFlbueX375RfN8u3btRNdUOs/CwgIeHh6YMmUKHjx4YJTG0mUqmkaPHg0AkEgk2LJli6h6SicHBwd06dIFv/7663O3WWU6T548if/85z9wcXGBlZUVPDw8MGzYMNy6dQsAkJWVVe7r33zzTa3n09LSKtVjzKbSycnJCX379sWJEyeM0ljRz8HTU+l7BAcHi6qn9LFUKoWXlxeio6NRXFxc6e3276+rdJ0LFy7UWmbLli2a9wWAffv2lds1a9Ysrefv3btXpRZD95ROrq6uGDJkCC5dulTlPl0aK/p+KJ08PDwAAD169MDkyZNF11Q6z8rKCi1atMDnn3+uU+O/O2/fvo1JkyahUaNGkMlkUCgU6NevHw4ePKhZvrz/7rq7u2s9v2LFClH1lM63tbXFCy+8gO+++07nvqo0VvRz8PS0b98+JCQkwNHRUVQ9pY/NzMzg7u6O0NBQzf+HENWqS+b2798f8fHxWvOevuyblZUVIiIikJKS8sz1tGzZUvMLfam6deuKuqn0+eLiYhw8eBBjxoxBQUEBvvzyS4M3Hj16FCUlJQCAQ4cOYciQIcjIyIC9vT0AwNra+rkNQvXEx8ejf//+uHPnDj766CMMHDgQp0+fRpMmTXTuvH37Nnr16oWBAwdi165dcHR0RFZWFrZu3YqHDx9qLf/LL7+gZcuWmsdV3VZCNJU+f+3aNbz//vsIDAzEuXPnKv1/jro2Tps2DRMnTtS8pkOHDhg/fjzGjRtXqfcVsqf0PZRKJXbs2IGwsDBYWloiMjJS524rKyssWrQIEyZMQJ06dZ657NPf/wAgl8t1fl9j9GRkZMDOzg7nz5/H+PHj8corryA9PR3m5uYGbfzkk0+0BgBubm6a/0YAqPb7G7pp3LhxiI6ORkFBAdasWYOwsDDUqVMHw4cPr1bjkCFDUFRUhMTERDRp0gQ3b97Enj17kJOTo7VcdHS01ve/IbaXPntKn8/Pz8f//vc/DBs2DA0aNEBAQIBBG/v3748bN25olv3ggw+Qn5+v9d+hunXrIisrq9od+u6xt7dHRkYGVCoVTp48idDQUFy/fh27du3SWyvVXLVq0FE6eq/I+PHjsWrVKuzYsQNBQUEVLmdhYaG328sbq+np54cNG4Y9e/Zg69atlRp0VLexXr16mn+XDoRcXFx0/guNMXscHR2hUCigUCjwxRdfoEGDBti9ezcmTJigc+eWLVuQl5eHuLg4WFg8+RH09PREz549yyzr5OSkt+81YzWVPq9QKLB06VJ06dIFv//+O/r162fwxqd/OTU3N4ednV21t58xep5+j0mTJmHz5s3YunVrtQYdvXv3xoULFxATE4PFixc/c9nq/DwK0VP6vJubG+bMmYMRI0bgwoUL8PHxMWijg4MDHBwctOaV/jfCUPTZZGNjo5kfFRWFb7/9Flu3bq3WoOPevXs4cOAA9u3bh+7duwMAGjdujI4dO5ZZVh8/j8bsKX1eoVDgs88+w7p167Bt27ZqDzoq0/h0l7W1NZRKpcG2nT57JBKJZn79+vXx/vvvY/bs2SgsLKz2H82o5qs1h1dVhqenJyZOnIjIyEioVCqhcwAYrsna2hpFRUV6WZfYtpshtxmAam83hUKB4uJibN68GWK5TY6hmvS1zQDxbTdD9ujj59Pc3BwLFizAypUrce3aNT2Via9Hn99jYttmgGGb9PF9JpfLIZfLsWXLFiiVSj2Via/HwsIClpaWevk+qy3bDHjyPaZSqap8uCiZplo16Ni+fbvmh6t0WrBggdYys2bNQmZmJtavX1/hek6dOqW1jvL+giLmpmPHjuHbb7/Fyy+/bNRGfRGip6CgALNmzYK5ubnmL0G6dnbq1AkzZ87EG2+8AWdnZwQGBmLJkiW4efNmmXUEBARovb4q50cI3XTv3j3Mnz+/yj8j+mjUJ2P2qNVq/PLLL9i1a1elfz6fZfDgwWjXrh3mzp37zOXc3d21vr5/H4aiL/ruuXHjBpYuXYoGDRpUey9HVRuNSd9NJSUlWLduHdLT06v9fWZhYYGEhAQkJibC0dERXbp0wcyZM5Genl5m2YiICK3PNTY2tlrvbayeoqIixMTEIC8vTy8/l1VpNAZD9Zw/fx6rVq1C+/btYWdnp6daqslq1eFVPXv2xBdffKE179/nYtSrVw/Tpk3DnDlzMGzYsHLX4+Pjg61bt2oeP30+gVibSgclJSUlKCoqwoABA/Dpp58atVFfjNkzfPhwmJubo7CwEPXq1cPXX3+NNm3aVLvz448/Rnh4OH799Vf8/vvvWLVqFRYsWID9+/ejdevWmuU3bNgAX19fzeOGDRvq/LUYqykgIABmZmZ4+PAhmjRpgg0bNsDV1dWojfpkjJ7Sgc3jx4+hUqnwxhtvICoqSi/9ixYtwssvv4xp06ZVuMyBAwe0fil43jkXQve4u7tDrVajoKAAbdu2xaZNmyCVSo3aaGz6aPr8888RFxeHoqIimJubY8qUKZg0aVK124YMGYIBAwbgwIEDOHz4MH766ScsXrwYcXFxmgtzAMD06dO1Hjs7O1f7vQ3ZExERgVmzZuHRo0eQy+VYuHAhBgwYYNRGY9FXT15eHuRyOVQqFR49eoSuXbsiLi7OcOFUo9SqQYetrS28vLyeu1x4eDg+//zzCq/sUXqFmZrUVDoosbCwQP369av0f9D6atQXY/YsX74cvXv3hoODg9a5IJXxvE4nJycMHToUQ4cOxYIFC+Dn54elS5ciMTFRs0zDhg319r1mrKYNGzagRYsWcHJy0uk8AX006pMxekoHNlKpFPXr19ecI6IPL730Evr164fIyMgKf3nw9PQ0+Dkd+uw5cOAA7O3t4eLiYpC/oFam0dj00TRixAh89NFHsLa2hpubG8zM9Hewg5WVFfr06YM+ffpg9uzZePvttzF37twyv9Tr879nhu4pHZTI5XK4urpqXWnNWI3GpI8eOzs7HD9+HGZmZnBzc+N5HKSlVh1eVVlyuRyzZ8/Gxx9/jPv37wudA6D6TaWDEg8PD73+RfBpYttu+uhRKBTw8vKq8oCjqqRSKZo2bVrmSlFC0rWpYcOGaNq0qVF+iRXbdtO1p3Rg06hRI70OOEotXLgQ27ZtQ2pqqt7XrYvq9nh6eqJp06YGPWRDbNsMqH6Tg4MDvLy80KBBA70OOMrTokUL0fxcArr1lA5KFAqF3gcc5TGFbWZmZgYvLy80adKEAw4qo1bt6VAqlcjOztaaZ2FhUe4u3vHjx2P58uX49ttv4e/vX6ua/k1sjWLrqUhFnYcPH0ZSUhJef/11eHt7Q61WY9u2bdixY0eZS7PWhiaxN4qtRxetW7fGiBEjDHIMvS7E1lMeMTaKsSknJwdDhw7FmDFj0KZNG9jZ2eGPP/7A4sWLMWjQoFrfUx6xNYqth0xXrRp07Ny5E25ublrzfHx8cO7cuTLLWlpaYv78+XjjjTeq9B4qlapKf6k0RlN1GWu7AajUtqsJ2wyouHPHjh2wsbHB1KlTcfXqVchkMjRr1gxxcXEYOXJkpdZdle1lrCZ9MXSjvn5GxbTNKiM6OhobNmyo0mt0/T4zVI+x6dpY1e+xqhDbdpPL5fD398fy5ctx8eJFPH78GA0bNsS4ceMwc+bMSq9HX9tMXz2GxG1GtZVELYZrT5qQiRMn4tq1a9i+fbvQKTVKdnY23NzccPToUbRv317oHNE7fPgwOnfujNu3bxvsZExT1b9/f3h5eVX6Qgq1WVJSEsaNGyeKwyVrkubNm+Ptt98W1YnoYlZSUgJ7e3skJibi1VdfFTqnxli4cCHWrVuH06dPC51CVCm1ak+HId2/fx8nTpxAcnIy/zJQBWq1GpcvX8bSpUvh6uqKVq1aCZ0kasXFxcjKysKSJUvQtm1bDjiqIDc3FwcPHsS+ffu07hROZSmVSly8eBGffvopevXqJXROjXHr1i389NNPyMjI4HarpGvXrmHNmjUoKSlB165dhc6pEQoKCnDu3DnEx8cjMDBQ6ByiSuOgQ0/mzJmD9evXY/DgwfyFpgry8vLg4+MDX19fJCUlwcrKSugkUTt9+jQCAgLQrl07rFmzRuicGmXMmDE4evQopk6dyuOUn+Onn37CyJEjERAQIKrzB8Suf//+yM3NRWxsLPz8/ITOqRHatWsHJycnrF271uB3KzcVX331FaKjo9G7d2/MmTNH6ByiSuPhVUREREREZFC8ZC4RERERERkUBx1ERERERGRQHHQQEREREZFBcdBBREREREQGxUEHEREREREZFAcdREQ1wOjRoxEcHKx53KNHD0yePFmwHiIioqrgoIOITMbo0aMhkUggkUgglUrh5eWF6OhoFBcXC52md8nJyZg/f77msYeHB1asWFGtdZZuu4qmqKio6kUTEVGtxZsDEpFJ6d+/P+Lj46FUKrFjxw6EhYXB0tISkZGRVV5XSUkJJBIJzMzE9/eZunXr6n2dN27c0Px7w4YNmDNnDjIyMjTz5HK53t+TiIhqB/H9PykRUTXIZDIoFAo0btwYkyZNQu/evbF161YAgFKpxLRp09CgQQPY2trC398f+/bt07w2ISEBjo6O2Lp1K1q0aAGZTIYrV65g37596NixI2xtbeHo6IguXbrg8uXLmtd98cUXaNq0KaRSKXx8fLB27VqtJolEgri4OAwePBg2NjZo1qyZpgl4MrgZO3YsPD09YW1tDR8fH3zyySfP/DqfPryqR48euHz5MqZMmaLZK/Hw4UPY29vj+++/13rdli1bYGtri/v375dZp0Kh0EwODg6QSCRQKBSws7ODt7c3du7cWeG6srKyIJFIkJSUhICAAFhZWaFVq1ZISUnRes3p06cRGBgIuVwOV1dXjBw5Enfu3Hnm10pERDUfBx1EZNKsra1RVFQEAHj33XeRmpqKpKQkpKenY+jQoejfvz/Onz+vWb6goACLFi1CXFwczpw5g7p16yI4OBjdu3dHeno6UlNTMX78eEgkEgDA5s2b8cEHH2Dq1Kk4ffo0JkyYgNDQUOzdu1erY968eXjttdeQnp6OoKAgjBgxAnfv3gUAqFQquLu747vvvsPZs2cxZ84czJw5Exs3bqzU15icnAx3d3dER0fjxo0buHHjBmxtbfH6668jPj5ea9n4+Hi8+uqrsLOzq/Q2rMq6pk+fjqlTp+LEiRPo3LkzXnnlFeTk5AAA7t27h5dffhl+fn74448/sHPnTty8eROvvfZapVuIiKiGUhMRmYhRo0apBw0apFar1WqVSqXevXu3WiaTqadNm6a+fPmy2tzcXP33339rvaZXr17qyMhItVqtVsfHx6sBqNPS0jTP5+TkqAGo9+3bV+57BgQEqMeNG6c1b+jQoeqgoCDNYwDqWbNmaR4/ePBADUD9008/Vfi1hIWFqYcMGVLu16ZWq9Xdu3dXf/DBB5rHjRs3Vi9fvlxrHb///rva3Nxcff36dbVarVbfvHlTbWFhUeHX8rT4+Hi1g4NDpdeVmZmpBqBeuHCh5jWPHz9Wu7u7qxctWqRWq9Xq+fPnq/v27av1PlevXlUDUGdkZDy3iYiIai7u6SAik7J9+3bI5XJYWVkhMDAQw4YNQ1RUFE6dOoWSkhJ4e3tDLpdrppSUFFy8eFHzeqlUijZt2mge161bF6NHj0a/fv3wyiuv4JNPPtE69+HPP/9Ely5dtBq6dOmCP//8U2ve0+u0tbWFvb09bt26pZn32Wef4cUXX0S9evUgl8vx1Vdf4cqVK9XaFh07dkTLli2RmJgIAFi3bh0aN26Ml156yWDr6ty5s+bfFhYWaN++vWZbnDx5Env37tXa/s2bNwcArc+AiIhMDwcdRGRSevbsibS0NJw/fx6FhYVITEyEra0tHjx4AHNzcxw7dgxpaWma6c8//9Q6f8La2lpz6FSp+Ph4pKamIiAgABs2bIC3tzcOHz5cpS5LS0utxxKJBCqVCgCQlJSEadOmYezYsfj555+RlpaG0NBQzWFh1fH2228jISFB83WEhoaW+fqMta4HDx7glVde0dr+pZ+VLgMhIiKqOTjoICKTYmtrCy8vLzRq1AgWFv9coM/Pzw8lJSW4desWvLy8tCaFQvHc9fr5+SEyMhKHDh1Cq1at8O233wIAfH19cfDgQa1lDx48iBYtWlS6+eDBgwgICMA777wDPz8/eHl5Vfkv/1KpFCUlJWXmv/nmm7h8+TJiY2Nx9uxZjBo1qkrrreq6nh6MFRcX49ixY/D19QUAvPDCCzhz5gw8PDzKfAa2trY6dxERkfhx0EFEtYK3tzdGjBiBt956C8nJycjMzMSRI0cQExODH3/8scLXZWZmIjIyEqmpqbh8+TJ+/vlnnD9/XvOL9PTp05GQkIAvvvgC58+fx7Jly5CcnIxp06ZVuq1Zs2b4448/sGvXLvz111+YPXs2jh49WqWvz8PDA/v378fff/+tdTWoOnXqICQkBNOnT0ffvn3h7u5epfU+rTLr+uyzz7B582acO3cOYWFhyM3NxZgxYwAAYWFhuHv3LoYPH46jR4/i4sWL2LVrF0JDQ8sdMBERkengoIOIao34+Hi89dZbmDp1Knx8fBAcHIyjR4+iUaNGFb7GxsYG586dw5AhQ+Dt7Y3x48cjLCwMEyZMAAAEBwfjk08+wdKlS9GyZUt8+eWXiI+PR48ePSrdNWHCBISEhGDYsGHw9/dHTk4O3nnnnSp9bdHR0cjKykLTpk1Rr149refGjh2LoqIizS//1fG8dS1cuBALFy5E27Zt8dtvv2Hr1q1wdnYGANSvXx8HDx5ESUkJ+vbti9atW2Py5MlwdHQU5b1QiIhIfyRqtVotdAQRERnO2rVrMWXKFFy/fh1SqdQg68rKyoKnpydOnDiBdu3aVbOYiIhMDe9ITkRkogoKCnDjxg0sXLgQEyZMqNaAQ5/rIiKi2of7s4mITNTixYvRvHlzKBQKREZGimZdRERU+/DwKiIiIiIiMiju6SAiIiIiIoPioIOIiIiIiAyKgw4iIiIiIjIoDjqIiIiIiMigOOggIiIiIiKD4qCDiIiIiIgMioMOIiIiIiIyKA46iIiIiIjIoP4f9CiSh01C/JoAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sxtYQg5hjA6u"
      },
      "source": [
        "## Tokenizing Words"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WAAOwfRNjT_j",
        "outputId": "c621ab63-3058-4f24-b4ce-229ef5c4c954"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (3.8.1)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk) (8.1.7)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk) (1.3.2)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk) (2023.12.25)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from nltk) (4.66.2)\n"
          ]
        }
      ],
      "source": [
        "!pip install nltk"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eoqWmKfoi-zz",
        "outputId": "f9a4e460-5107-443f-8cc9-843fd6362dff"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "import nltk\n",
        "nltk.download('wordnet')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "14Q-WEkUjEKS",
        "outputId": "b0ec8a94-2513-475a-abda-6acb5cced19f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TfidfVectorizer(max_features=5000, stop_words='english',\n",
              "                tokenizer=<function lemmatize_text at 0x7c322f921b40>)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TfidfVectorizer(max_features=5000, stop_words=&#x27;english&#x27;,\n",
              "                tokenizer=&lt;function lemmatize_text at 0x7c322f921b40&gt;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer(max_features=5000, stop_words=&#x27;english&#x27;,\n",
              "                tokenizer=&lt;function lemmatize_text at 0x7c322f921b40&gt;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "import nltk\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "def lemmatize_text(text):\n",
        "    lemmatizer = WordNetLemmatizer()\n",
        "    return [lemmatizer.lemmatize(w) for w in text.split()]\n",
        "vectorizer = TfidfVectorizer(max_features=5000,stop_words='english',tokenizer=lemmatize_text)\n",
        "vectorizer.fit(train_data.posts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 829
        },
        "id": "To7DAvdTjjw8",
        "outputId": "aa218a04-d61c-409b-9364-7fc115b19120"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7c322fadebc0>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1500 with 1 Axes>"
            ],
            "image/png": 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ASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "feature_names=vectorizer.get_feature_names_out()\n",
        "wc=WordCloud(max_words=400)\n",
        "wc.generate(' '.join(word for word in feature_names[500:3500]))\n",
        "plt.figure(figsize=(20,15))\n",
        "plt.axis('off')\n",
        "plt.imshow(wc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CbZRmQMTjnCp"
      },
      "outputs": [],
      "source": [
        "train_post=vectorizer.transform(train_data.posts).toarray()\n",
        "test_post=vectorizer.transform(test_data.posts).toarray()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lO90PSEekDGG",
        "outputId": "b6f20b31-9344-43fe-f1d9-42e4396cd23c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6940, 5000)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "train_post.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FwePa-jTkGy9"
      },
      "outputs": [],
      "source": [
        "target_encoder=LabelEncoder()\n",
        "train_target=target_encoder.fit_transform(train_data.type)\n",
        "test_target=target_encoder.fit_transform(test_data.type)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wh0FanlnkL4O"
      },
      "source": [
        "## Model Selection"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "V85DnJK8kJQS"
      },
      "outputs": [],
      "source": [
        "models_accuracy={}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CFcq4w2EkZQm"
      },
      "source": [
        "## Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "IEinUdvvkRGl",
        "outputId": "eaddd889-f745-40bd-85aa-66e6c4fded72"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=0.5, max_iter=3000, n_jobs=-1)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=0.5, max_iter=3000, n_jobs=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=0.5, max_iter=3000, n_jobs=-1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "model_log=LogisticRegression(max_iter=3000,C=0.5,n_jobs=-1)\n",
        "model_log.fit(train_post,train_target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o5HYcKNDkVaA",
        "outputId": "b086ece9-9008-47fa-e961-d0c757f4885a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train classification report \n",
            "                precision    recall  f1-score   support\n",
            "\n",
            "        ENFJ       0.86      0.16      0.27       152\n",
            "        ENFP       0.81      0.64      0.72       540\n",
            "        ENTJ       0.94      0.26      0.41       185\n",
            "        ENTP       0.82      0.67      0.73       548\n",
            "        ESFJ       0.00      0.00      0.00        33\n",
            "        ESFP       0.00      0.00      0.00        38\n",
            "        ESTJ       0.00      0.00      0.00        31\n",
            "        ESTP       1.00      0.04      0.08        71\n",
            "        INFJ       0.73      0.81      0.77      1176\n",
            "        INFP       0.64      0.93      0.76      1466\n",
            "        INTJ       0.74      0.81      0.77       873\n",
            "        INTP       0.69      0.87      0.77      1043\n",
            "        ISFJ       0.91      0.23      0.37       133\n",
            "        ISFP       0.85      0.20      0.33       217\n",
            "        ISTJ       0.84      0.22      0.35       164\n",
            "        ISTP       0.88      0.48      0.62       270\n",
            "\n",
            "    accuracy                           0.72      6940\n",
            "   macro avg       0.67      0.40      0.43      6940\n",
            "weighted avg       0.74      0.72      0.69      6940\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print('train classification report \\n ',classification_report(train_target,model_log.predict(train_post),target_names=target_encoder.inverse_transform([i for i in range(16)])))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tE4dHxf4kogu",
        "outputId": "25fc6700-efd5-4988-b4d4-b6d0345c9e24"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test classification report \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "        ENFJ       1.00      0.08      0.15        38\n",
            "        ENFP       0.75      0.55      0.63       135\n",
            "        ENTJ       0.83      0.11      0.19        46\n",
            "        ENTP       0.67      0.51      0.58       137\n",
            "        ESFJ       0.00      0.00      0.00         9\n",
            "        ESFP       0.00      0.00      0.00        10\n",
            "        ESTJ       0.00      0.00      0.00         8\n",
            "        ESTP       0.00      0.00      0.00        18\n",
            "        INFJ       0.65      0.71      0.68       294\n",
            "        INFP       0.55      0.89      0.68       366\n",
            "        INTJ       0.63      0.65      0.64       218\n",
            "        INTP       0.65      0.82      0.72       261\n",
            "        ISFJ       0.67      0.06      0.11        33\n",
            "        ISFP       0.88      0.13      0.23        54\n",
            "        ISTJ       0.50      0.05      0.09        41\n",
            "        ISTP       0.76      0.43      0.55        67\n",
            "\n",
            "    accuracy                           0.62      1735\n",
            "   macro avg       0.53      0.31      0.33      1735\n",
            "weighted avg       0.64      0.62      0.58      1735\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print('test classification report \\n',classification_report(test_target,model_log.predict(test_post),target_names=target_encoder.inverse_transform([i for i in range(16)])))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KE2FfP4_kr2Q"
      },
      "outputs": [],
      "source": [
        "models_accuracy['logistic regression']=accuracy_score(test_target,model_log.predict(test_post))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g8DbYtuBkzcm"
      },
      "source": [
        "## Linear Support Vector Classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "l82kYL2EkwCo",
        "outputId": "5b1cc577-09cd-4f8f-dd72-3dc00bb9e90f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearSVC(C=0.1)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVC(C=0.1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC(C=0.1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "model_linear_svc=LinearSVC(C=0.1)\n",
        "model_linear_svc.fit(train_post,train_target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RvMNder-k-RZ",
        "outputId": "14e2f3a9-5843-4fc5-a40b-2f958435080c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train classification report \n",
            "                precision    recall  f1-score   support\n",
            "\n",
            "        ENFJ       0.90      0.40      0.55       152\n",
            "        ENFP       0.84      0.76      0.80       540\n",
            "        ENTJ       0.91      0.63      0.74       185\n",
            "        ENTP       0.84      0.81      0.83       548\n",
            "        ESFJ       0.90      0.27      0.42        33\n",
            "        ESFP       1.00      0.11      0.19        38\n",
            "        ESTJ       1.00      0.23      0.37        31\n",
            "        ESTP       0.94      0.44      0.60        71\n",
            "        INFJ       0.81      0.85      0.83      1176\n",
            "        INFP       0.76      0.93      0.84      1466\n",
            "        INTJ       0.83      0.85      0.84       873\n",
            "        INTP       0.80      0.89      0.84      1043\n",
            "        ISFJ       0.91      0.65      0.76       133\n",
            "        ISFP       0.89      0.56      0.69       217\n",
            "        ISTJ       0.88      0.65      0.75       164\n",
            "        ISTP       0.90      0.80      0.85       270\n",
            "\n",
            "    accuracy                           0.82      6940\n",
            "   macro avg       0.88      0.61      0.68      6940\n",
            "weighted avg       0.83      0.82      0.81      6940\n",
            "\n",
            "test classification report \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "        ENFJ       0.67      0.21      0.32        38\n",
            "        ENFP       0.75      0.61      0.67       135\n",
            "        ENTJ       0.68      0.33      0.44        46\n",
            "        ENTP       0.63      0.55      0.59       137\n",
            "        ESFJ       1.00      0.22      0.36         9\n",
            "        ESFP       0.00      0.00      0.00        10\n",
            "        ESTJ       1.00      0.12      0.22         8\n",
            "        ESTP       0.75      0.17      0.27        18\n",
            "        INFJ       0.69      0.71      0.70       294\n",
            "        INFP       0.61      0.87      0.72       366\n",
            "        INTJ       0.63      0.66      0.65       218\n",
            "        INTP       0.70      0.82      0.76       261\n",
            "        ISFJ       0.60      0.27      0.37        33\n",
            "        ISFP       0.85      0.31      0.46        54\n",
            "        ISTJ       0.80      0.29      0.43        41\n",
            "        ISTP       0.69      0.55      0.61        67\n",
            "\n",
            "    accuracy                           0.66      1735\n",
            "   macro avg       0.69      0.42      0.47      1735\n",
            "weighted avg       0.67      0.66      0.64      1735\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print('train classification report \\n ',classification_report(train_target,model_linear_svc.predict(train_post),target_names=target_encoder.inverse_transform([i for i in range(16)])))\n",
        "print('test classification report \\n',classification_report(test_target,model_linear_svc.predict(test_post),target_names=target_encoder.inverse_transform([i for i in range(16)])))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nMygPfbxlBq0"
      },
      "outputs": [],
      "source": [
        "models_accuracy['Linear Support Vector classifier']=accuracy_score(test_target,model_linear_svc.predict(test_post))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j0t6185DlJtD"
      },
      "source": [
        "## Support Vector Classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "Y3H6F6rXlHSl",
        "outputId": "abf8e85c-9f23-4538-cb05-9407307d14d3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "model_svc=SVC()\n",
        "model_svc.fit(train_post,train_target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZawCba_slNDD",
        "outputId": "3d1de4c9-1036-417b-d793-baffb4dc87b7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train classification report \n",
            "                precision    recall  f1-score   support\n",
            "\n",
            "        ENFJ       0.95      0.83      0.89       152\n",
            "        ENFP       0.95      0.95      0.95       540\n",
            "        ENTJ       0.98      0.88      0.93       185\n",
            "        ENTP       0.95      0.95      0.95       548\n",
            "        ESFJ       1.00      0.58      0.73        33\n",
            "        ESFP       1.00      0.29      0.45        38\n",
            "        ESTJ       1.00      0.45      0.62        31\n",
            "        ESTP       1.00      0.79      0.88        71\n",
            "        INFJ       0.95      0.97      0.96      1176\n",
            "        INFP       0.91      0.98      0.95      1466\n",
            "        INTJ       0.95      0.96      0.96       873\n",
            "        INTP       0.94      0.97      0.95      1043\n",
            "        ISFJ       1.00      0.85      0.92       133\n",
            "        ISFP       0.97      0.86      0.91       217\n",
            "        ISTJ       0.94      0.90      0.92       164\n",
            "        ISTP       0.98      0.92      0.95       270\n",
            "\n",
            "    accuracy                           0.94      6940\n",
            "   macro avg       0.97      0.82      0.87      6940\n",
            "weighted avg       0.94      0.94      0.94      6940\n",
            "\n",
            "test classification report \n",
            "                precision    recall  f1-score   support\n",
            "\n",
            "        ENFJ       0.62      0.26      0.37        38\n",
            "        ENFP       0.76      0.57      0.65       135\n",
            "        ENTJ       0.75      0.26      0.39        46\n",
            "        ENTP       0.62      0.53      0.57       137\n",
            "        ESFJ       0.33      0.11      0.17         9\n",
            "        ESFP       0.00      0.00      0.00        10\n",
            "        ESTJ       0.00      0.00      0.00         8\n",
            "        ESTP       0.83      0.28      0.42        18\n",
            "        INFJ       0.68      0.68      0.68       294\n",
            "        INFP       0.58      0.86      0.69       366\n",
            "        INTJ       0.65      0.64      0.64       218\n",
            "        INTP       0.66      0.82      0.73       261\n",
            "        ISFJ       0.83      0.30      0.44        33\n",
            "        ISFP       0.78      0.33      0.47        54\n",
            "        ISTJ       0.79      0.27      0.40        41\n",
            "        ISTP       0.73      0.54      0.62        67\n",
            "\n",
            "    accuracy                           0.64      1735\n",
            "   macro avg       0.60      0.40      0.45      1735\n",
            "weighted avg       0.66      0.64      0.63      1735\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print('train classification report \\n ',classification_report(train_target,model_svc.predict(train_post),target_names=target_encoder.inverse_transform([i for i in range(16)])))\n",
        "print('test classification report \\n ',classification_report(test_target,model_svc.predict(test_post),target_names=target_encoder.inverse_transform([i for i in range(16)])))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wMB_wlamlQKC"
      },
      "outputs": [],
      "source": [
        "models_accuracy['Support Vector classifier']=accuracy_score(test_target,model_svc.predict(test_post))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kNHb-xDJlY-M"
      },
      "source": [
        "## MultiNomial Naive Bayes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "TblWRXAilWi4",
        "outputId": "04173e7f-9482-4a66-a1e8-4b6dce8abec0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ],
      "source": [
        "model_multinomial_nb=MultinomialNB()\n",
        "model_multinomial_nb.fit(train_post,train_target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ysUWBnmolcu7",
        "outputId": "35aad0cc-7c76-4735-8392-f81c411b32e9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train classification report \n",
            "                precision    recall  f1-score   support\n",
            "\n",
            "        ENFJ       0.00      0.00      0.00       152\n",
            "        ENFP       1.00      0.01      0.03       540\n",
            "        ENTJ       0.00      0.00      0.00       185\n",
            "        ENTP       0.91      0.05      0.10       548\n",
            "        ESFJ       0.00      0.00      0.00        33\n",
            "        ESFP       0.00      0.00      0.00        38\n",
            "        ESTJ       0.00      0.00      0.00        31\n",
            "        ESTP       0.00      0.00      0.00        71\n",
            "        INFJ       0.51      0.60      0.55      1176\n",
            "        INFP       0.35      0.94      0.50      1466\n",
            "        INTJ       0.79      0.39      0.52       873\n",
            "        INTP       0.59      0.62      0.60      1043\n",
            "        ISFJ       0.00      0.00      0.00       133\n",
            "        ISFP       0.00      0.00      0.00       217\n",
            "        ISTJ       0.00      0.00      0.00       164\n",
            "        ISTP       0.00      0.00      0.00       270\n",
            "\n",
            "    accuracy                           0.45      6940\n",
            "   macro avg       0.26      0.16      0.14      6940\n",
            "weighted avg       0.50      0.45      0.37      6940\n",
            "\n",
            "test classification report \n",
            "                precision    recall  f1-score   support\n",
            "\n",
            "        ENFJ       0.00      0.00      0.00        38\n",
            "        ENFP       1.00      0.04      0.07       135\n",
            "        ENTJ       0.00      0.00      0.00        46\n",
            "        ENTP       0.43      0.02      0.04       137\n",
            "        ESFJ       0.00      0.00      0.00         9\n",
            "        ESFP       0.00      0.00      0.00        10\n",
            "        ESTJ       0.00      0.00      0.00         8\n",
            "        ESTP       0.00      0.00      0.00        18\n",
            "        INFJ       0.38      0.43      0.40       294\n",
            "        INFP       0.31      0.91      0.46       366\n",
            "        INTJ       0.74      0.17      0.28       218\n",
            "        INTP       0.53      0.54      0.53       261\n",
            "        ISFJ       0.00      0.00      0.00        33\n",
            "        ISFP       0.00      0.00      0.00        54\n",
            "        ISTJ       0.00      0.00      0.00        41\n",
            "        ISTP       0.00      0.00      0.00        67\n",
            "\n",
            "    accuracy                           0.37      1735\n",
            "   macro avg       0.21      0.13      0.11      1735\n",
            "weighted avg       0.41      0.37      0.29      1735\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print('train classification report \\n ',classification_report(train_target,model_multinomial_nb.predict(train_post),target_names=target_encoder.inverse_transform([i for i in range(16)])))\n",
        "print('test classification report \\n ',classification_report(test_target,model_multinomial_nb.predict(test_post),target_names=target_encoder.inverse_transform([i for i in range(16)])))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LEDeWfuDlfph"
      },
      "outputs": [],
      "source": [
        "models_accuracy['Multinomial Naive Bayes']=accuracy_score(test_target,model_multinomial_nb.predict(test_post))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EkLDoruRlo_o"
      },
      "source": [
        "## Decision Tree Classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "o4xOrIQblib1",
        "outputId": "30cbc980-5356-40f8-f90d-751ffd40ca06"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=14)"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=14)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=14)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "model_tree=DecisionTreeClassifier(max_depth=14)\n",
        "model_tree.fit(train_post,train_target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FAr6trQBlw4G",
        "outputId": "d279bcea-0279-49ba-9e34-d34f44970b78"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train classification report \n",
            "                precision    recall  f1-score   support\n",
            "\n",
            "        ENFJ       0.87      0.56      0.68       152\n",
            "        ENFP       0.90      0.84      0.87       540\n",
            "        ENTJ       0.78      0.71      0.74       185\n",
            "        ENTP       0.93      0.75      0.83       548\n",
            "        ESFJ       0.57      0.39      0.46        33\n",
            "        ESFP       1.00      0.29      0.45        38\n",
            "        ESTJ       0.72      0.42      0.53        31\n",
            "        ESTP       0.81      0.42      0.56        71\n",
            "        INFJ       0.80      0.86      0.83      1176\n",
            "        INFP       0.67      0.93      0.78      1466\n",
            "        INTJ       0.85      0.82      0.83       873\n",
            "        INTP       0.86      0.82      0.84      1043\n",
            "        ISFJ       1.00      0.50      0.67       133\n",
            "        ISFP       0.97      0.66      0.78       217\n",
            "        ISTJ       0.87      0.55      0.68       164\n",
            "        ISTP       0.94      0.66      0.77       270\n",
            "\n",
            "    accuracy                           0.80      6940\n",
            "   macro avg       0.85      0.64      0.71      6940\n",
            "weighted avg       0.82      0.80      0.80      6940\n",
            "\n",
            "test classification report \n",
            "                precision    recall  f1-score   support\n",
            "\n",
            "        ENFJ       0.18      0.18      0.18        38\n",
            "        ENFP       0.50      0.44      0.46       135\n",
            "        ENTJ       0.21      0.17      0.19        46\n",
            "        ENTP       0.44      0.36      0.39       137\n",
            "        ESFJ       0.00      0.00      0.00         9\n",
            "        ESFP       0.00      0.00      0.00        10\n",
            "        ESTJ       0.00      0.00      0.00         8\n",
            "        ESTP       0.33      0.11      0.17        18\n",
            "        INFJ       0.59      0.58      0.59       294\n",
            "        INFP       0.52      0.73      0.61       366\n",
            "        INTJ       0.57      0.56      0.57       218\n",
            "        INTP       0.67      0.66      0.66       261\n",
            "        ISFJ       0.44      0.24      0.31        33\n",
            "        ISFP       0.50      0.22      0.31        54\n",
            "        ISTJ       0.35      0.27      0.31        41\n",
            "        ISTP       0.49      0.42      0.45        67\n",
            "\n",
            "    accuracy                           0.53      1735\n",
            "   macro avg       0.36      0.31      0.33      1735\n",
            "weighted avg       0.52      0.53      0.52      1735\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print('train classification report \\n ',classification_report(train_target,model_tree.predict(train_post),target_names=target_encoder.inverse_transform([i for i in range(16)])))\n",
        "print('test classification report \\n ',classification_report(test_target,model_tree.predict(test_post),target_names=target_encoder.inverse_transform([i for i in range(16)])))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-ARC1LEwlzM2"
      },
      "outputs": [],
      "source": [
        "models_accuracy['Decision Tree classifier']=accuracy_score(test_target,model_tree.predict(test_post))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sshOn3I2l6ex"
      },
      "source": [
        "## Random Forest Classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "pXaqFRDyl3ox",
        "outputId": "c546d5eb-aaa8-4446-9439-d0065fa751a2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=10)"
            ],
            "text/html": [
              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=10)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ],
      "source": [
        "model_forest=RandomForestClassifier(max_depth=10)\n",
        "model_forest.fit(train_post,train_target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "De2Rq9a4l-wu",
        "outputId": "522a2740-2734-4dd8-cb68-71e0d5ffcaf9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train classification report \n",
            "                precision    recall  f1-score   support\n",
            "\n",
            "        ENFJ       1.00      0.04      0.08       152\n",
            "        ENFP       1.00      0.41      0.58       540\n",
            "        ENTJ       1.00      0.03      0.06       185\n",
            "        ENTP       0.99      0.54      0.70       548\n",
            "        ESFJ       0.00      0.00      0.00        33\n",
            "        ESFP       0.00      0.00      0.00        38\n",
            "        ESTJ       0.00      0.00      0.00        31\n",
            "        ESTP       1.00      0.01      0.03        71\n",
            "        INFJ       0.78      0.82      0.80      1176\n",
            "        INFP       0.44      0.99      0.61      1466\n",
            "        INTJ       0.93      0.74      0.82       873\n",
            "        INTP       0.80      0.85      0.83      1043\n",
            "        ISFJ       1.00      0.10      0.18       133\n",
            "        ISFP       1.00      0.03      0.05       217\n",
            "        ISTJ       1.00      0.02      0.04       164\n",
            "        ISTP       1.00      0.15      0.26       270\n",
            "\n",
            "    accuracy                           0.66      6940\n",
            "   macro avg       0.75      0.30      0.31      6940\n",
            "weighted avg       0.79      0.66      0.61      6940\n",
            "\n",
            "test classification report \n",
            "                precision    recall  f1-score   support\n",
            "\n",
            "        ENFJ       0.00      0.00      0.00        38\n",
            "        ENFP       0.77      0.13      0.22       135\n",
            "        ENTJ       0.00      0.00      0.00        46\n",
            "        ENTP       0.71      0.15      0.24       137\n",
            "        ESFJ       0.00      0.00      0.00         9\n",
            "        ESFP       0.00      0.00      0.00        10\n",
            "        ESTJ       0.00      0.00      0.00         8\n",
            "        ESTP       0.00      0.00      0.00        18\n",
            "        INFJ       0.54      0.57      0.56       294\n",
            "        INFP       0.34      0.93      0.50       366\n",
            "        INTJ       0.80      0.35      0.49       218\n",
            "        INTP       0.62      0.66      0.64       261\n",
            "        ISFJ       0.00      0.00      0.00        33\n",
            "        ISFP       0.00      0.00      0.00        54\n",
            "        ISTJ       0.00      0.00      0.00        41\n",
            "        ISTP       1.00      0.01      0.03        67\n",
            "\n",
            "    accuracy                           0.46      1735\n",
            "   macro avg       0.30      0.17      0.17      1735\n",
            "weighted avg       0.51      0.46      0.39      1735\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print('train classification report \\n ',classification_report(train_target,model_forest.predict(train_post),target_names=target_encoder.inverse_transform([i for i in range(16)])))\n",
        "print('test classification report \\n ',classification_report(test_target,model_forest.predict(test_post),target_names=target_encoder.inverse_transform([i for i in range(16)])))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EpFu_RcdmByL"
      },
      "outputs": [],
      "source": [
        "models_accuracy['Random Forest Classifier']=accuracy_score(test_target,model_forest.predict(test_post))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Iw6aECSmm_Fs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "77980011-8407-41d0-aed4-ff5ea803e7e8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'logistic regression': 0.624207492795389,\n",
              " 'Linear Support Vector classifier': 0.661671469740634,\n",
              " 'Support Vector classifier': 0.6443804034582132,\n",
              " 'Multinomial Naive Bayes': 0.37060518731988473,\n",
              " 'Decision Tree classifier': 0.5285302593659942,\n",
              " 'Random Forest Classifier': 0.4564841498559078}"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ],
      "source": [
        "models_accuracy"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "models_accuracy.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OM7UXcMbAH4w",
        "outputId": "23e9a4de-54d5-4660-fdae-cfb3a7ab15af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['logistic regression', 'Linear Support Vector classifier', 'Support Vector classifier', 'Multinomial Naive Bayes', 'Decision Tree classifier', 'Random Forest Classifier'])"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuarcy=pd.DataFrame(models_accuracy.items(),columns=['Models','Test accuracy'])"
      ],
      "metadata": {
        "id": "hV8lYt_0AJxc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuarcy.sort_values(by='Test accuracy',ascending=False,ignore_index=True).style.background_gradient(cmap='Blues')"
      ],
      "metadata": {
        "id": "3SiRH5jNAMYY",
        "outputId": "11f77c98-c8f9-494e-924a-d09ed6fb0224",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7c322fc69a20>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_05512_row0_col1 {\n",
              "  background-color: #08306b;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_05512_row1_col1 {\n",
              "  background-color: #084082;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_05512_row2_col1 {\n",
              "  background-color: #08519c;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_05512_row3_col1 {\n",
              "  background-color: #5da5d1;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_05512_row4_col1 {\n",
              "  background-color: #b8d5ea;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_05512_row5_col1 {\n",
              "  background-color: #f7fbff;\n",
              "  color: #000000;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_05512\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_05512_level0_col0\" class=\"col_heading level0 col0\" >Models</th>\n",
              "      <th id=\"T_05512_level0_col1\" class=\"col_heading level0 col1\" >Test accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_05512_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_05512_row0_col0\" class=\"data row0 col0\" >Linear Support Vector classifier</td>\n",
              "      <td id=\"T_05512_row0_col1\" class=\"data row0 col1\" >0.661671</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_05512_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_05512_row1_col0\" class=\"data row1 col0\" >Support Vector classifier</td>\n",
              "      <td id=\"T_05512_row1_col1\" class=\"data row1 col1\" >0.644380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_05512_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_05512_row2_col0\" class=\"data row2 col0\" >logistic regression</td>\n",
              "      <td id=\"T_05512_row2_col1\" class=\"data row2 col1\" >0.624207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_05512_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_05512_row3_col0\" class=\"data row3 col0\" >Decision Tree classifier</td>\n",
              "      <td id=\"T_05512_row3_col1\" class=\"data row3 col1\" >0.528530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_05512_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_05512_row4_col0\" class=\"data row4 col0\" >Random Forest Classifier</td>\n",
              "      <td id=\"T_05512_row4_col1\" class=\"data row4 col1\" >0.456484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_05512_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_05512_row5_col0\" class=\"data row5 col0\" >Multinomial Naive Bayes</td>\n",
              "      <td id=\"T_05512_row5_col1\" class=\"data row5 col1\" >0.370605</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNLoySl2cyj4aRiZjvr/DWv",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}