B20CS004_BONUSPROJECT (1).ipynb

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      "name": "B20CS004_BONUSPROJECT.ipynb",
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        "id": "38W0QcCBIjKc"
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      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "E9Fp7atUIoFp"
      },
      "execution_count": null,
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    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# Import text stuffs\n",
        "import re\n",
        "import string\n",
        "\n",
        "# Import visualisation tools\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "\n",
        "# Import NLTK stuffs\n",
        "from nltk.tokenize import word_tokenize, TreebankWordTokenizer\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import SnowballStemmer\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "\n",
        "# Import Vectorizers\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
        "\n",
        "# Import Models and Metrics\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "# from sklearn.neighbors.nearest_centroid import NearestCentroid\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.svm import SVC"
      ],
      "metadata": {
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      "outputs": [
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_original = pd.read_csv(\"/content/drive/MyDrive/mbti_1.csv\")\n",
        "# train1=pd.read_csv()\n",
        "# test_original = pd.read_csv(\"../input/test.csv\")"
      ],
      "metadata": {
        "id": "PO1tlzG485y_"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "train = train_original.copy()\n",
        "# test = test_original.copy()\n",
        "full = pd.concat([train], ignore_index=True, sort=False)"
      ],
      "metadata": {
        "id": "upScaOhk9I7u"
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      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "train = train_original.copy()\n",
        "# test = test_original.copy()\n",
        "full = pd.concat([train])"
      ],
      "metadata": {
        "id": "j9HBFJTP8_Jy"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "train_original.head()"
      ],
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              "   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",
              "4  ENTJ  'You're fired.|||That's another silly misconce..."
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              "      <td>INFJ</td>\n",
              "      <td>'http://www.youtube.com/watch?v=qsXHcwe3krw|||...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>'I'm finding the lack of me in these posts ver...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>INTP</td>\n",
              "      <td>'Good one  _____   https://www.youtube.com/wat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>INTJ</td>\n",
              "      <td>'Dear INTP,   I enjoyed our conversation the o...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ENTJ</td>\n",
              "      <td>'You're fired.|||That's another silly misconce...</td>\n",
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        "trainshape = train_original.shape\n",
        "# testshape = test_original.shape\n",
        "fullshape = full.shape\n",
        "print(trainshape)\n",
        "# print(testshape)\n",
        "print(fullshape)"
      ],
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        "colab": {
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          "text": [
            "(8675, 2)\n",
            "(8675, 2)\n"
          ]
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    {
      "cell_type": "code",
      "source": [
        "# Checking for personality types\n",
        "train_original['type'].unique()"
      ],
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        },
        "id": "nc90NGZr9REw",
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          "data": {
            "text/plain": [
              "array(['INFJ', 'ENTP', 'INTP', 'INTJ', 'ENTJ', 'ENFJ', 'INFP', 'ENFP',\n",
              "       'ISFP', 'ISTP', 'ISFJ', 'ISTJ', 'ESTP', 'ESFP', 'ESTJ', 'ESFJ'],\n",
              "      dtype=object)"
            ]
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          "metadata": {},
          "execution_count": 75
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    {
      "cell_type": "code",
      "source": [
        "train_original.iloc[1163 ,1]"
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          "data": {
            "text/plain": [
              "\"It's Not My Fault That I'm Not Popular, anyone? I recently began reading it and I just want to give Tomoko a big hug.|||oh that's nice i guess i could be an intj instead./strokes one of those white fluffy cats with the mashed up faces and laughs at globe maniacally  I'm guessing xSFJ. Lots and lots of Si and Fe.|||Heh, that's me!|||http://www.youtube.com/watch?v=N8F5YSA1Oz0&amp;feature=relmfu /One-ups everyone.|||I didn't even notice it, but now that you mention it it's annoying the shit out of me. It's pointing up at something I'd rather not mention {I'm sure you can deduce} in another tab.|||Link it if it's worth watching.  http://www.youtube.com/watch?v=9C_HReR_McQ|||wow rude. :[|||Donnetout cechic etromantique. Alors faisons-le comme ils le font sur u200bu200bdécouverte canal Je tiens à vous dire comment se plonger dans ta chatte caverneux Nous allons tenir la main sur un tas de...|||http://24.media.tumblr.com/tumblr_ltx26s9wFT1qbtkcmo1_500.jpg Scientists, too. Y'all mothafuckas lyin, it's gettin me pissed.|||http://desmond.imageshack.us/Himg402/scaled.php?server=402&filename=smallt.gif&res=landing http://static.fjcdn.com/comments/that+_7ea989a253bf6350e91a4ee8f6cc91ec.jpg Welp that's enough internet...|||Okay. Yeah. Thanks for that. It's not like I was planning on sleeping again, anyway.  Fucking clowns, man. ಠ_ಠ|||HaHAHhaHAHa pissing my fucking pants I CAN'T BREATHE rofl http://24.media.tumblr.com/tumblr_l6x5biTNkm1qas7b2o1_500.jpg...|||japanese girls exchanging bodily fluids|||Most of my friends are my age or older than me by a few years.|||what if one day you woke up and your nipples were gone like no scars or anything just flat skin and once you leave your room you find out your dad died and several days later you found out that every...|||Stupid people: Mild annoyance. Jokingly insult them and sit back in dissonant serenity. Being challenged: Prove them wrong. Being insulted: I don't usually have problems with this, but at first...|||ENTP, Slytherin.|||I'm thinking IxTx, he isn't the best at communicating with people. Being totally typist here and going by stereotypes.|||I'm the kind of person who downs a metric fuckton of coffee at their friend's house in hopes of looking mature then stays up all night with no pants giggling hysterically at stupid shit on the...|||Boku no Pico.|||Whatever Loki is, I'm going to stalk the hell out of that type. (ﾉﾟ▽ﾟ)ﾉ・。*。✧・゜゜・。✧。*・゜゜・✧。・゜゜・。*。・゜*✧|||http://www.youtube.com/watch?v=9VIVSHOQboo&feature=related|||I'm just here to observe and learn because I have trouble relating to ISFJs. Funny how many of you claim to use seriousness as a security blanket. I use humor that way because it isn't socially...|||Paranoid|||||||||||||||||| 74% Schizoid||||||||||||50% Schizotypal||||||||||||||54% Antisocial||||||||||||||||||||86% Borderline||||||||||34% Histrionic||||||26%...|||ENTP, Slytherin.|||Your score was 86 of 100.   This puts you in the category of the high Machs, people who do not belive in the goodness of the world and that because that it must be manipulated, people who...|||What's your type and what do you mistype as a lot? If you had to be a different type, what would your runner-up be? Are you on the middle of any axis? Why?  If not an ENTP, I think I'd be an ENFP...|||The Introspector You scored 15Artist, 65 Philosopher, 20 Scientist!  You are an abstract thinker and probably spend a good deal of time by yourself, or at least, isolated mentally. Much of the way...|||Meh, knew it would be an experiment. It's on an ENTP forum after all. I was hoping there would be some link to a shock site or something interesting like that.|||http://www.youtube.com/watch?v=k_LP4IU6XD4|||Sounds a little supervillainy, but that pretty much sums it up. Often toys to play until they break.|||Personal ~  * Name - What do you preferred to be called?  - Any nicknames? Hmm...Vulpes Inculta. ;D * Male/Female/Trans? Female. Location - Where were you originally born? Where do you live...|||I'm an ENTP on the verge of ENFP. Well, it's evil manifested as something other than actions, so the most heinous thing they can imagine.  (This guy:...|||http://www.youtube.com/watch?v=e17aGdM_Jwc Yeah, I'm immature.|||People try to pull this crap on me a lot. I'm nice to them at first but if they keep doing it I'll sarcastically congratulate them on the keen observation as if it's hard to see then tell them to...|||I like them.|||did i hear someone say .gif spam thread or am i tripping over a massive majestical pile of nutsacs WE ARE SEX BOMB OMB AND WE'RE HERE TO SLOW YOUR INTERNET DOWN AND STUFF....|||I'm an ENTP and I'd have to say the funniest show I've watched is South Park. I love the principle of it, that nothing is sacred and no one is exempt from parody. I like Dan Vs., Regular Show, and...|||Country of origin: The inviting and freefalling but firm bosom of Lady America.  Country lived in the longest: Same.  Nationality: Russian/American  Urban/suburban/rural (When you were growing...|||D: please type me because i must be defined or i will exploooode!!!1111  1) What aspect of your personality made you unsure of your type? Pretty much everything. I'm a popular perpetual loner, a...|||31217 Eeeyup. asdkakfdjlas why is it tiny.|||http://img292.imagevenue.com/loc82/th_192859181_Untitled_123_82lo.jpg ..Yeeaaaahh. WHERE IS YOUR GOD NOW?|||http://www.lowbird.com/data/images/2011/12/explosm-donatephysics3.gif http://pandyland.net/comics/2011-10-26-vinnie.png|||Doth thou accept thy challenge? Then onward..taketh! Enough with the pseudo-ye-olde-English. I'm really bored and curious about correlation between personality types and other factors. So yes....|||Stealing this from another forum.  Say something reeeeaaally un-ENTP. I figured out I was an ENTP recently and want to see how far the results go and how much we're alike. Let's start off simple....|||The creator of this meme read my mind.|||Baby: I cried and shat myself like any other baby. Toddler: I had a nasty biting problem and an impressive vocabulary. Talked like Stewie. Little kid {preschool}: Got along well with boys but not...|||I'm an ENTP and I have yellowy green snake eyes.|||Oh, the future hands down. Pardon my French, but the past can smd. You've got textbooks to learn about the past. Stepping on a bug on the past could result in Hitler having three nipples and the...|||Your type: ENTP Mom's type: ISFJ Dad's type: Never knew him. Siblings' type(s): I'm an only child. Best friends' type(s): To put it simply, all of my bonds are shallow and volatile, but I do have...\""
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      "source": [
        "# Creating a data frame for us to play around with safely\n",
        "EDAdf = train_original.copy()\n",
        "EDAdf['post_length']=EDAdf['posts'].apply(lambda x :len(x.split('|||')))\n",
        "len(EDAdf.iloc[0,1].split('|||'))"
      ],
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            "text/plain": [
              "50"
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          "metadata": {},
          "execution_count": 77
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      "cell_type": "code",
      "source": [
        "EDAdf[EDAdf['post_length']>50].head()\n",
        "EDAdf[EDAdf['post_length']>50].head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
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              "     type                                              posts  post_length\n",
              "55   INFP  'ideal you |||||| real you ||||||      Introve...           58\n",
              "167  INTJ  'Hi, are you really manic?|||greetings.|||Yes,...           59\n",
              "200  ESFP  'Edit: I forgot what board this was on.|||I am...           53\n",
              "207  INTP  'http://chzragecomics.files.wordpress.com/2012...           56\n",
              "293  ENTP  'liberal arts is stupid.  Get an engineering o...           63"
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              "      <th>55</th>\n",
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              "      <th>167</th>\n",
              "      <td>INTJ</td>\n",
              "      <td>'Hi, are you really manic?|||greetings.|||Yes,...</td>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>ESFP</td>\n",
              "      <td>'Edit: I forgot what board this was on.|||I am...</td>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>207</th>\n",
              "      <td>INTP</td>\n",
              "      <td>'http://chzragecomics.files.wordpress.com/2012...</td>\n",
              "      <td>56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>293</th>\n",
              "      <td>ENTP</td>\n",
              "      <td>'liberal arts is stupid.  Get an engineering o...</td>\n",
              "      <td>63</td>\n",
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    {
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      "source": [
        "pattern = \"[\\\\|\\\\|\\\\|]+\"\n",
        "split_post = re.split(pattern, train_original.iloc[1163 ,1])\n",
        "split_post"
      ],
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            "text/plain": [
              "[\"It's Not My Fault That I'm Not Popular, anyone? I recently began reading it and I just want to give Tomoko a big hug.\",\n",
              " \"oh that's nice i guess i could be an intj instead./strokes one of those white fluffy cats with the mashed up faces and laughs at globe maniacally  I'm guessing xSFJ. Lots and lots of Si and Fe.\",\n",
              " \"Heh, that's me!\",\n",
              " 'http://www.youtube.com/watch?v=N8F5YSA1Oz0&amp;feature=relmfu /One-ups everyone.',\n",
              " \"I didn't even notice it, but now that you mention it it's annoying the shit out of me. It's pointing up at something I'd rather not mention {I'm sure you can deduce} in another tab.\",\n",
              " \"Link it if it's worth watching.  http://www.youtube.com/watch?v=9C_HReR_McQ\",\n",
              " 'wow rude. :[',\n",
              " 'Donnetout cechic etromantique. Alors faisons-le comme ils le font sur u200bu200bdécouverte canal Je tiens à vous dire comment se plonger dans ta chatte caverneux Nous allons tenir la main sur un tas de...',\n",
              " \"http://24.media.tumblr.com/tumblr_ltx26s9wFT1qbtkcmo1_500.jpg Scientists, too. Y'all mothafuckas lyin, it's gettin me pissed.\",\n",
              " \"http://desmond.imageshack.us/Himg402/scaled.php?server=402&filename=smallt.gif&res=landing http://static.fjcdn.com/comments/that+_7ea989a253bf6350e91a4ee8f6cc91ec.jpg Welp that's enough internet...\",\n",
              " \"Okay. Yeah. Thanks for that. It's not like I was planning on sleeping again, anyway.  Fucking clowns, man. ಠ_ಠ\",\n",
              " \"HaHAHhaHAHa pissing my fucking pants I CAN'T BREATHE rofl http://24.media.tumblr.com/tumblr_l6x5biTNkm1qas7b2o1_500.jpg...\",\n",
              " 'japanese girls exchanging bodily fluids',\n",
              " 'Most of my friends are my age or older than me by a few years.',\n",
              " 'what if one day you woke up and your nipples were gone like no scars or anything just flat skin and once you leave your room you find out your dad died and several days later you found out that every...',\n",
              " \"Stupid people: Mild annoyance. Jokingly insult them and sit back in dissonant serenity. Being challenged: Prove them wrong. Being insulted: I don't usually have problems with this, but at first...\",\n",
              " 'ENTP, Slytherin.',\n",
              " \"I'm thinking IxTx, he isn't the best at communicating with people. Being totally typist here and going by stereotypes.\",\n",
              " \"I'm the kind of person who downs a metric fuckton of coffee at their friend's house in hopes of looking mature then stays up all night with no pants giggling hysterically at stupid shit on the...\",\n",
              " 'Boku no Pico.',\n",
              " \"Whatever Loki is, I'm going to stalk the hell out of that type. (ﾉﾟ▽ﾟ)ﾉ・。*。✧・゜゜・。✧。*・゜゜・✧。・゜゜・。*。・゜*✧\",\n",
              " 'http://www.youtube.com/watch?v=9VIVSHOQboo&feature=related',\n",
              " \"I'm just here to observe and learn because I have trouble relating to ISFJs. Funny how many of you claim to use seriousness as a security blanket. I use humor that way because it isn't socially...\",\n",
              " 'Paranoid',\n",
              " ' 74% Schizoid',\n",
              " '50% Schizotypal',\n",
              " '54% Antisocial',\n",
              " '86% Borderline',\n",
              " '34% Histrionic',\n",
              " '26%...',\n",
              " 'ENTP, Slytherin.',\n",
              " 'Your score was 86 of 100.   This puts you in the category of the high Machs, people who do not belive in the goodness of the world and that because that it must be manipulated, people who...',\n",
              " \"What's your type and what do you mistype as a lot? If you had to be a different type, what would your runner-up be? Are you on the middle of any axis? Why?  If not an ENTP, I think I'd be an ENFP...\",\n",
              " 'The Introspector You scored 15Artist, 65 Philosopher, 20 Scientist!  You are an abstract thinker and probably spend a good deal of time by yourself, or at least, isolated mentally. Much of the way...',\n",
              " \"Meh, knew it would be an experiment. It's on an ENTP forum after all. I was hoping there would be some link to a shock site or something interesting like that.\",\n",
              " 'http://www.youtube.com/watch?v=k_LP4IU6XD4',\n",
              " 'Sounds a little supervillainy, but that pretty much sums it up. Often toys to play until they break.',\n",
              " 'Personal ~  * Name - What do you preferred to be called?  - Any nicknames? Hmm...Vulpes Inculta. ;D * Male/Female/Trans? Female. Location - Where were you originally born? Where do you live...',\n",
              " \"I'm an ENTP on the verge of ENFP. Well, it's evil manifested as something other than actions, so the most heinous thing they can imagine.  (This guy:...\",\n",
              " \"http://www.youtube.com/watch?v=e17aGdM_Jwc Yeah, I'm immature.\",\n",
              " \"People try to pull this crap on me a lot. I'm nice to them at first but if they keep doing it I'll sarcastically congratulate them on the keen observation as if it's hard to see then tell them to...\",\n",
              " 'I like them.',\n",
              " \"did i hear someone say .gif spam thread or am i tripping over a massive majestical pile of nutsacs WE ARE SEX BOMB OMB AND WE'RE HERE TO SLOW YOUR INTERNET DOWN AND STUFF....\",\n",
              " \"I'm an ENTP and I'd have to say the funniest show I've watched is South Park. I love the principle of it, that nothing is sacred and no one is exempt from parody. I like Dan Vs., Regular Show, and...\",\n",
              " 'Country of origin: The inviting and freefalling but firm bosom of Lady America.  Country lived in the longest: Same.  Nationality: Russian/American  Urban/suburban/rural (When you were growing...',\n",
              " \"D: please type me because i must be defined or i will exploooode!!!1111  1) What aspect of your personality made you unsure of your type? Pretty much everything. I'm a popular perpetual loner, a...\",\n",
              " '31217 Eeeyup. asdkakfdjlas why is it tiny.',\n",
              " 'http://img292.imagevenue.com/loc82/th_192859181_Untitled_123_82lo.jpg ..Yeeaaaahh. WHERE IS YOUR GOD NOW?',\n",
              " 'http://www.lowbird.com/data/images/2011/12/explosm-donatephysics3.gif http://pandyland.net/comics/2011-10-26-vinnie.png',\n",
              " \"Doth thou accept thy challenge? Then onward..taketh! Enough with the pseudo-ye-olde-English. I'm really bored and curious about correlation between personality types and other factors. So yes....\",\n",
              " \"Stealing this from another forum.  Say something reeeeaaally un-ENTP. I figured out I was an ENTP recently and want to see how far the results go and how much we're alike. Let's start off simple....\",\n",
              " 'The creator of this meme read my mind.',\n",
              " 'Baby: I cried and shat myself like any other baby. Toddler: I had a nasty biting problem and an impressive vocabulary. Talked like Stewie. Little kid {preschool}: Got along well with boys but not...',\n",
              " \"I'm an ENTP and I have yellowy green snake eyes.\",\n",
              " \"Oh, the future hands down. Pardon my French, but the past can smd. You've got textbooks to learn about the past. Stepping on a bug on the past could result in Hitler having three nipples and the...\",\n",
              " \"Your type: ENTP Mom's type: ISFJ Dad's type: Never knew him. Siblings' type(s): I'm an only child. Best friends' type(s): To put it simply, all of my bonds are shallow and volatile, but I do have...\"]"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
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        "EDAdf[EDAdf['post_length']>50].head()"
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              "     type                                              posts  post_length\n",
              "55   INFP  'ideal you |||||| real you ||||||      Introve...           58\n",
              "167  INTJ  'Hi, are you really manic?|||greetings.|||Yes,...           59\n",
              "200  ESFP  'Edit: I forgot what board this was on.|||I am...           53\n",
              "207  INTP  'http://chzragecomics.files.wordpress.com/2012...           56\n",
              "293  ENTP  'liberal arts is stupid.  Get an engineering o...           63"
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    {
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      "source": [
        "# create new column with revised posts text\n",
        "EDAdf['posts_r'] = EDAdf['posts'].copy()\n",
        "\n",
        "# replaces post separators with empty space\n",
        "EDAdf['posts_r'] = EDAdf['posts_r'].apply(lambda x: x.replace('|||', ' '))\n",
        "\n",
        "\n",
        "# replace hyperlinks with 'URL'\n",
        "EDAdf['posts_r'] = EDAdf['posts_r'].apply(lambda x: re.sub\\\n",
        "                                                  (r'\\bhttps?:\\/\\/.*?[\\r\\n]*? ', 'URL ', x, flags=re.MULTILINE))\n",
        "\n",
        "EDAdf.head()"
      ],
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   type                                              posts  post_length  \\\n",
              "0  INFJ  'http://www.youtube.com/watch?v=qsXHcwe3krw|||...           50   \n",
              "1  ENTP  'I'm finding the lack of me in these posts ver...           50   \n",
              "2  INTP  'Good one  _____   https://www.youtube.com/wat...           50   \n",
              "3  INTJ  'Dear INTP,   I enjoyed our conversation the o...           50   \n",
              "4  ENTJ  'You're fired.|||That's another silly misconce...           50   \n",
              "\n",
              "                                             posts_r  \n",
              "0  'URL URL enfp and intj moments  URL  sportscen...  \n",
              "1  'I'm finding the lack of me in these posts ver...  \n",
              "2  'Good one  _____   URL Of course, to which I s...  \n",
              "3  'Dear INTP,   I enjoyed our conversation the o...  \n",
              "4  'You're fired. That's another silly misconcept...  "
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              "      <th></th>\n",
              "      <th>type</th>\n",
              "      <th>posts</th>\n",
              "      <th>post_length</th>\n",
              "      <th>posts_r</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>INFJ</td>\n",
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    {
      "cell_type": "code",
      "source": [
        "# Add columns for the total number of words (across 50 posts), and average words per post\n",
        "EDAdf['total_words'] = EDAdf['posts_r'].apply(lambda x: len(re.findall(r'\\w+', x)))\n",
        "EDAdf['avg_words_per_post'] = EDAdf['total_words'] / 50\n",
        "EDAdf['total_ellipsis'] = EDAdf['posts_r'].apply(lambda x: len(re.findall(\"[/././.]+\", x)))\n",
        "EDAdf['avg_ellipsis_per_post'] = EDAdf['total_ellipsis'] / 50\n",
        "EDAdf.head()"
      ],
      "metadata": {
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        },
        "id": "t_n4YhDaAkeE",
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            "text/plain": [
              "   type                                              posts  post_length  \\\n",
              "0  INFJ  'http://www.youtube.com/watch?v=qsXHcwe3krw|||...           50   \n",
              "1  ENTP  'I'm finding the lack of me in these posts ver...           50   \n",
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              "3  INTJ  'Dear INTP,   I enjoyed our conversation the o...           50   \n",
              "4  ENTJ  'You're fired.|||That's another silly misconce...           50   \n",
              "\n",
              "                                             posts_r  total_words  \\\n",
              "0  'URL URL enfp and intj moments  URL  sportscen...          624   \n",
              "1  'I'm finding the lack of me in these posts ver...         1268   \n",
              "2  'Good one  _____   URL Of course, to which I s...          913   \n",
              "3  'Dear INTP,   I enjoyed our conversation the o...         1164   \n",
              "4  'You're fired. That's another silly misconcept...         1049   \n",
              "\n",
              "   avg_words_per_post  total_ellipsis  avg_ellipsis_per_post  \n",
              "0               12.48              52                   1.04  \n",
              "1               25.36             125                   2.50  \n",
              "2               18.26              65                   1.30  \n",
              "3               23.28             104                   2.08  \n",
              "4               20.98             101                   2.02  "
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    {
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      "source": [
        "EDAdf['avg_words_per_post'].mean()"
      ],
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      "execution_count": null,
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        {
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          "data": {
            "text/plain": [
              "26.424853025936567"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "EDAdf['avg_words_per_post'].groupby(EDAdf['type']).mean()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wvmcwGs8Ap2I",
        "outputId": "72e773bf-ce85-4524-b92b-5480763eea62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "type\n",
              "ENFJ    27.650632\n",
              "ENFP    27.116948\n",
              "ENTJ    26.211515\n",
              "ENTP    26.042336\n",
              "ESFJ    27.770952\n",
              "ESFP    22.192083\n",
              "ESTJ    26.428205\n",
              "ESTP    25.071910\n",
              "INFJ    27.495510\n",
              "INFP    26.801070\n",
              "INTJ    25.722970\n",
              "INTP    25.860966\n",
              "ISFJ    26.789036\n",
              "ISFP    24.605166\n",
              "ISTJ    26.185463\n",
              "ISTP    25.231632\n",
              "Name: avg_words_per_post, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "split_post"
      ],
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        "id": "LEkd2g1-Asg0",
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[\"It's Not My Fault That I'm Not Popular, anyone? I recently began reading it and I just want to give Tomoko a big hug.\",\n",
              " \"oh that's nice i guess i could be an intj instead./strokes one of those white fluffy cats with the mashed up faces and laughs at globe maniacally  I'm guessing xSFJ. Lots and lots of Si and Fe.\",\n",
              " \"Heh, that's me!\",\n",
              " 'http://www.youtube.com/watch?v=N8F5YSA1Oz0&amp;feature=relmfu /One-ups everyone.',\n",
              " \"I didn't even notice it, but now that you mention it it's annoying the shit out of me. It's pointing up at something I'd rather not mention {I'm sure you can deduce} in another tab.\",\n",
              " \"Link it if it's worth watching.  http://www.youtube.com/watch?v=9C_HReR_McQ\",\n",
              " 'wow rude. :[',\n",
              " 'Donnetout cechic etromantique. Alors faisons-le comme ils le font sur u200bu200bdécouverte canal Je tiens à vous dire comment se plonger dans ta chatte caverneux Nous allons tenir la main sur un tas de...',\n",
              " \"http://24.media.tumblr.com/tumblr_ltx26s9wFT1qbtkcmo1_500.jpg Scientists, too. Y'all mothafuckas lyin, it's gettin me pissed.\",\n",
              " \"http://desmond.imageshack.us/Himg402/scaled.php?server=402&filename=smallt.gif&res=landing http://static.fjcdn.com/comments/that+_7ea989a253bf6350e91a4ee8f6cc91ec.jpg Welp that's enough internet...\",\n",
              " \"Okay. Yeah. Thanks for that. It's not like I was planning on sleeping again, anyway.  Fucking clowns, man. ಠ_ಠ\",\n",
              " \"HaHAHhaHAHa pissing my fucking pants I CAN'T BREATHE rofl http://24.media.tumblr.com/tumblr_l6x5biTNkm1qas7b2o1_500.jpg...\",\n",
              " 'japanese girls exchanging bodily fluids',\n",
              " 'Most of my friends are my age or older than me by a few years.',\n",
              " 'what if one day you woke up and your nipples were gone like no scars or anything just flat skin and once you leave your room you find out your dad died and several days later you found out that every...',\n",
              " \"Stupid people: Mild annoyance. Jokingly insult them and sit back in dissonant serenity. Being challenged: Prove them wrong. Being insulted: I don't usually have problems with this, but at first...\",\n",
              " 'ENTP, Slytherin.',\n",
              " \"I'm thinking IxTx, he isn't the best at communicating with people. Being totally typist here and going by stereotypes.\",\n",
              " \"I'm the kind of person who downs a metric fuckton of coffee at their friend's house in hopes of looking mature then stays up all night with no pants giggling hysterically at stupid shit on the...\",\n",
              " 'Boku no Pico.',\n",
              " \"Whatever Loki is, I'm going to stalk the hell out of that type. (ﾉﾟ▽ﾟ)ﾉ・。*。✧・゜゜・。✧。*・゜゜・✧。・゜゜・。*。・゜*✧\",\n",
              " 'http://www.youtube.com/watch?v=9VIVSHOQboo&feature=related',\n",
              " \"I'm just here to observe and learn because I have trouble relating to ISFJs. Funny how many of you claim to use seriousness as a security blanket. I use humor that way because it isn't socially...\",\n",
              " 'Paranoid',\n",
              " ' 74% Schizoid',\n",
              " '50% Schizotypal',\n",
              " '54% Antisocial',\n",
              " '86% Borderline',\n",
              " '34% Histrionic',\n",
              " '26%...',\n",
              " 'ENTP, Slytherin.',\n",
              " 'Your score was 86 of 100.   This puts you in the category of the high Machs, people who do not belive in the goodness of the world and that because that it must be manipulated, people who...',\n",
              " \"What's your type and what do you mistype as a lot? If you had to be a different type, what would your runner-up be? Are you on the middle of any axis? Why?  If not an ENTP, I think I'd be an ENFP...\",\n",
              " 'The Introspector You scored 15Artist, 65 Philosopher, 20 Scientist!  You are an abstract thinker and probably spend a good deal of time by yourself, or at least, isolated mentally. Much of the way...',\n",
              " \"Meh, knew it would be an experiment. It's on an ENTP forum after all. I was hoping there would be some link to a shock site or something interesting like that.\",\n",
              " 'http://www.youtube.com/watch?v=k_LP4IU6XD4',\n",
              " 'Sounds a little supervillainy, but that pretty much sums it up. Often toys to play until they break.',\n",
              " 'Personal ~  * Name - What do you preferred to be called?  - Any nicknames? Hmm...Vulpes Inculta. ;D * Male/Female/Trans? Female. Location - Where were you originally born? Where do you live...',\n",
              " \"I'm an ENTP on the verge of ENFP. Well, it's evil manifested as something other than actions, so the most heinous thing they can imagine.  (This guy:...\",\n",
              " \"http://www.youtube.com/watch?v=e17aGdM_Jwc Yeah, I'm immature.\",\n",
              " \"People try to pull this crap on me a lot. I'm nice to them at first but if they keep doing it I'll sarcastically congratulate them on the keen observation as if it's hard to see then tell them to...\",\n",
              " 'I like them.',\n",
              " \"did i hear someone say .gif spam thread or am i tripping over a massive majestical pile of nutsacs WE ARE SEX BOMB OMB AND WE'RE HERE TO SLOW YOUR INTERNET DOWN AND STUFF....\",\n",
              " \"I'm an ENTP and I'd have to say the funniest show I've watched is South Park. I love the principle of it, that nothing is sacred and no one is exempt from parody. I like Dan Vs., Regular Show, and...\",\n",
              " 'Country of origin: The inviting and freefalling but firm bosom of Lady America.  Country lived in the longest: Same.  Nationality: Russian/American  Urban/suburban/rural (When you were growing...',\n",
              " \"D: please type me because i must be defined or i will exploooode!!!1111  1) What aspect of your personality made you unsure of your type? Pretty much everything. I'm a popular perpetual loner, a...\",\n",
              " '31217 Eeeyup. asdkakfdjlas why is it tiny.',\n",
              " 'http://img292.imagevenue.com/loc82/th_192859181_Untitled_123_82lo.jpg ..Yeeaaaahh. WHERE IS YOUR GOD NOW?',\n",
              " 'http://www.lowbird.com/data/images/2011/12/explosm-donatephysics3.gif http://pandyland.net/comics/2011-10-26-vinnie.png',\n",
              " \"Doth thou accept thy challenge? Then onward..taketh! Enough with the pseudo-ye-olde-English. I'm really bored and curious about correlation between personality types and other factors. So yes....\",\n",
              " \"Stealing this from another forum.  Say something reeeeaaally un-ENTP. I figured out I was an ENTP recently and want to see how far the results go and how much we're alike. Let's start off simple....\",\n",
              " 'The creator of this meme read my mind.',\n",
              " 'Baby: I cried and shat myself like any other baby. Toddler: I had a nasty biting problem and an impressive vocabulary. Talked like Stewie. Little kid {preschool}: Got along well with boys but not...',\n",
              " \"I'm an ENTP and I have yellowy green snake eyes.\",\n",
              " \"Oh, the future hands down. Pardon my French, but the past can smd. You've got textbooks to learn about the past. Stepping on a bug on the past could result in Hitler having three nipples and the...\",\n",
              " \"Your type: ENTP Mom's type: ISFJ Dad's type: Never knew him. Siblings' type(s): I'm an only child. Best friends' type(s): To put it simply, all of my bonds are shallow and volatile, but I do have...\"]"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train = train_original.copy()\n",
        "# test = test_original.copy()\n",
        "full = pd.concat([train], ignore_index=True)\n",
        "train_visual = train_original.copy()"
      ],
      "metadata": {
        "id": "Oun50z4Z9z91"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_original['type'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oqUAJz4b9z3q",
        "outputId": "eb0d51de-2928-4d66-bea3-5828c6f4bb8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "INFP    1832\n",
              "INFJ    1470\n",
              "INTP    1304\n",
              "INTJ    1091\n",
              "ENTP     685\n",
              "ENFP     675\n",
              "ISTP     337\n",
              "ISFP     271\n",
              "ENTJ     231\n",
              "ISTJ     205\n",
              "ENFJ     190\n",
              "ISFJ     166\n",
              "ESTP      89\n",
              "ESFP      48\n",
              "ESFJ      42\n",
              "ESTJ      39\n",
              "Name: type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize= (15.0, 4.0))\n",
        "sns.set_palette(sns.color_palette(\"GnBu_d\", 16))\n",
        "sns.countplot(x = \"type\", data = train_visual, order=[\"INTJ\", \"INTP\", \"ENTJ\", \"ENTP\", \n",
        "                                               \"INFJ\", \"INFP\", \"ENFJ\", \"ENFP\", \n",
        "                                               \"ISTJ\", \"ISFJ\", \"ESTJ\", \"ESFJ\", \n",
        "                                               \"ISTP\", \"ISFP\", \"ESTP\", \"ESFP\"])\n",
        "plt.title(\"Distribution of poster's personality type\")\n",
        "plt.plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "BzFzYX1i-xN-",
        "outputId": "487fdcdf-83e1-418b-f98d-3eaf4c85dd08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.plot>"
            ]
          },
          "metadata": {},
          "execution_count": 88
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,10))\n",
        "sns.violinplot(x='type', y=EDAdf['posts'].apply(lambda x: len(x.split())/50), data=EDAdf, inner=None, color='lightgray')\n",
        "sns.stripplot(x='type', y=EDAdf['posts'].apply(lambda x: len(x.split())/50), data=EDAdf, size=4, jitter=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "id": "KDj7tJRK-zv8",
        "outputId": "fd3b5758-70e8-4e84-9aed-7c9298681c91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6997c029d0>"
            ]
          },
          "metadata": {},
          "execution_count": 89
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating mapping for the different criterion\n",
        "train_visual[\"Mind\"] = train_visual[\"type\"].map(lambda x: 'Introversion' if x[0] == 'I' else 'Extroversion')\n",
        "train_visual[\"Energy\"] = train_visual[\"type\"].map(lambda x: 'Intuition' if x[1] == 'N' else 'Sensing')\n",
        "train_visual[\"Nature\"] = train_visual[\"type\"].map(lambda x: 'Thinking' if x[2] == 'T' else 'Feeling')\n",
        "train_visual[\"Tactics\"] = train_visual[\"type\"].map(lambda x: 'Judging' if x[3] == 'J' else 'Perceiving')"
      ],
      "metadata": {
        "id": "f2AiKCE1-14K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_visual.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Qde15BWn-5CE",
        "outputId": "6430a9b2-4850-4a68-e421-2ba2f068eef1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   type                                              posts          Mind  \\\n",
              "0  INFJ  'http://www.youtube.com/watch?v=qsXHcwe3krw|||...  Introversion   \n",
              "1  ENTP  'I'm finding the lack of me in these posts ver...  Extroversion   \n",
              "2  INTP  'Good one  _____   https://www.youtube.com/wat...  Introversion   \n",
              "3  INTJ  'Dear INTP,   I enjoyed our conversation the o...  Introversion   \n",
              "4  ENTJ  'You're fired.|||That's another silly misconce...  Extroversion   \n",
              "\n",
              "      Energy    Nature     Tactics  \n",
              "0  Intuition   Feeling     Judging  \n",
              "1  Intuition  Thinking  Perceiving  \n",
              "2  Intuition  Thinking  Perceiving  \n",
              "3  Intuition  Thinking     Judging  \n",
              "4  Intuition  Thinking     Judging  "
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>type</th>\n",
              "      <th>posts</th>\n",
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              "      <th>0</th>\n",
              "      <td>INFJ</td>\n",
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              "      <td>Introversion</td>\n",
              "      <td>Intuition</td>\n",
              "      <td>Feeling</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ENTP</td>\n",
              "      <td>'I'm finding the lack of me in these posts ver...</td>\n",
              "      <td>Extroversion</td>\n",
              "      <td>Intuition</td>\n",
              "      <td>Thinking</td>\n",
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              "      <td>Introversion</td>\n",
              "      <td>Intuition</td>\n",
              "      <td>Thinking</td>\n",
              "      <td>Perceiving</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>INTJ</td>\n",
              "      <td>'Dear INTP,   I enjoyed our conversation the o...</td>\n",
              "      <td>Introversion</td>\n",
              "      <td>Intuition</td>\n",
              "      <td>Thinking</td>\n",
              "      <td>Judging</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ENTJ</td>\n",
              "      <td>'You're fired.|||That's another silly misconce...</td>\n",
              "      <td>Extroversion</td>\n",
              "      <td>Intuition</td>\n",
              "      <td>Thinking</td>\n",
              "      <td>Judging</td>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\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-convert: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-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-c804c3e0-36e2-4685-b752-bbef19a35976 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-c804c3e0-36e2-4685-b752-bbef19a35976');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Explore the counts for each axis of the types\n",
        "print('Introversion (I) – Extroversion (E)', '\\n', train_visual['Mind'].value_counts(), '\\n')\n",
        "print('Intuition (N) – Sensing (S)', '\\n', train_visual['Energy'].value_counts(), '\\n')\n",
        "print('Thinking (T) – Feeling (F)', '\\n', train_visual['Nature'].value_counts(), '\\n')\n",
        "print('Judging (J) – Perceiving (P)', '\\n', train_visual['Tactics'].value_counts(), '\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uKGaN62V-7qm",
        "outputId": "d0c92362-cf8c-488d-dc0c-ac39a3cff957"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Introversion (I) – Extroversion (E) \n",
            " Introversion    6676\n",
            "Extroversion    1999\n",
            "Name: Mind, dtype: int64 \n",
            "\n",
            "Intuition (N) – Sensing (S) \n",
            " Intuition    7478\n",
            "Sensing      1197\n",
            "Name: Energy, dtype: int64 \n",
            "\n",
            "Thinking (T) – Feeling (F) \n",
            " Feeling     4694\n",
            "Thinking    3981\n",
            "Name: Nature, dtype: int64 \n",
            "\n",
            "Judging (J) – Perceiving (P) \n",
            " Perceiving    5241\n",
            "Judging       3434\n",
            "Name: Tactics, dtype: int64 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set_palette(sns.color_palette(\"GnBu_d\", 2))\n",
        "sns.countplot(x=\"Mind\", data=train_visual, order=[\"Introversion\", \"Extroversion\"])\n",
        "plt.ylim(0,6000)\n",
        "plt.title(\"Mind Distribution\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "eXTt5ks1--NU",
        "outputId": "241389d3-2fa5-4459-e908-ffd299fd5d1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Mind Distribution')"
            ]
          },
          "metadata": {},
          "execution_count": 93
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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xhttiJUmbpmHD4klJZo8ttDOLYc9KJEnT3LD/w/9r4BtJPtOWX4sPyUnSjDHsE9xnJllK97coAH63qq4fXbckSRuToYeSWjgYEJI0A63zK8olSTOPYSFJ6mVYSJJ6GRaSpF6GhSSpl2EhSeplWEiSehkWkqRehoUkqZdhIUnqZVhIknoZFpKkXoaFJKmXYSFJ6mVYSJJ6GRaSpF4jC4skOyS5OMn1Sa5L8rZWvk2SJUlubJ+zW3mSnJRkWZKrk+w+0NaCVv/GJAtG1WdJ0pqN8sziQeBPq2o3YC/gmCS7Ae8ELqqqXYCL2jLAgcAubVoInAJduADHAnsCewDHjgWMJGlqjCwsquq2qvpWm/8RcAMwFzgYWNSqLQIOafMHA2dW51Jg6yTbA/sDS6pqVVXdBSwBDhhVvyVJjzf03+B+IpLsBPwS8E1gu6q6ra26Hdiuzc8Fbh3YbHkrm6h8/D4W0p2RsOOOO05e56WNzEHv/vCG7oI2Que//09G2v7IL3An2QL4HPD2qrp3cF1VFVCTsZ+qOrWq5lfV/Dlz5kxGk5KkZqRhkeTJdEHxqar6fCu+ow0v0T7vbOUrgB0GNp/XyiYqlyRNkVHeDRXgNOCGqvqbgVWLgbE7mhYA5w2UH9nuitoLuKcNV10I7JdkdruwvV8rkyRNkVFes/g14I3ANUmuamV/AXwAOCfJ0cAtwGFt3fnAQcAy4H7gKICqWpXkfcDlrd5xVbVqhP2WJI0zsrCoqq8DmWD1PmuoX8AxE7R1OnD65PVOkrQufIJbktTLsJAk9TIsJEm9DAtJUi/DQpLUy7CQJPUyLCRJvQwLSVIvw0KS1MuwkCT1MiwkSb0MC0lSL8NCktTLsJAk9TIsJEm9DAtJUi/DQpLUy7CQJPUyLCRJvQwLSVIvw0KS1MuwkCT1MiwkSb0MC0lSL8NCktTLsJAk9TIsJEm9RhYWSU5PcmeSawfKtkmyJMmN7XN2K0+Sk5IsS3J1kt0HtlnQ6t+YZMGo+itJmtgozyzOAA4YV/ZO4KKq2gW4qC0DHAjs0qaFwCnQhQtwLLAnsAdw7FjASJKmzsjCoqq+CqwaV3wwsKjNLwIOGSg/szqXAlsn2R7YH1hSVauq6i5gCY8PIEnSiE31NYvtquq2Nn87sF2bnwvcOlBveSubqFySNIU22AXuqiqgJqu9JAuTLE2ydOXKlZPVrCSJqQ+LO9rwEu3zzla+AthhoN68VjZR+eNU1alVNb+q5s+ZM2fSOy5JM9lUh8ViYOyOpgXAeQPlR7a7ovYC7mnDVRcC+yWZ3S5s79fKJElTaNaoGk7yaWBvYNsky+nuavoAcE6So4FbgMNa9fOBg4BlwP3AUQBVtSrJ+4DLW73jqmr8RXNJ0oiNLCyq6ogJVu2zhroFHDNBO6cDp09i1yRJ68gnuCVJvQwLSVIvw0KS1MuwkCT1MiwkSb0MC0lSL8NCktTLsJAk9TIsJEm9DAtJUi/DQpLUy7CQJPUyLCRJvQwLSVIvw0KS1MuwkCT1MiwkSb0MC0lSL8NCktTLsJAk9TIsJEm9DAtJUi/DQpLUy7CQJPUyLCRJvQwLSVIvw0KS1MuwkCT1mjZhkeSAJN9NsizJOzd0fyRpJpkWYZFkM+CjwIHAbsARSXbbsL2SpJljWoQFsAewrKpuqqqfAmcBB2/gPknSjDFrQ3dgSHOBWweWlwN7DlZIshBY2BZXJ/nuFPVtJtgW+MGG7sTG4EMbugMaz2OzyfHvmIxmnjfRiukSFr2q6lTg1A3dj01RkqVVNX9D90Maz2Nz6kyXYagVwA4Dy/NamSRpCkyXsLgc2CXJzkmeAhwOLN7AfZKkGWNaDENV1YNJ3gJcCGwGnF5V123gbs0kDu9pY+WxOUVSVRu6D5Kkjdx0GYaSJG1AhoUkqZdhMc0kWT1EnbcnecZU9KdPkuOS/MaG7odGK8lDSa4amNb6Sp4kfzFVfeuT5GO+EaKf1yymmSSrq2qLnjo3A/Or6nEPKyXZrKoemuQ+he5Yengy29X0McxxOUz9UR1LSWZV1YOT2eZM45nFNJVk7ySXJPlsku8k+VQ6/x14LnBxkotb3dVJ/jrJt4GXJXlHkmvb9PZW5wNJjhlo/z1J/keb/7Mklye5Osl7W9lO7cWOZwLXAjskOaO1eU2SP2n1zkhyaJvfJ8mVbf3pSZ7aym9O8t4k32rrdp2yH6RGJslW7Rh5YVv+dJL/muQDwNPbGcinJjiWPjhwLL2ubX9Wkt8caP+MJIcm2azVHztG/7Ct3zvJ15IsBq5PsnmSLyb5dmt7rN1Lksxv80e0fV6b5ISBfa1Ocnzb9tIk203Vz3GjUVVO02gCVrfPvYF76B5QfBLwDeDlbd3NwLYD2xRwWJv/ZeAaYHNgC+A64Jfa9JWBba6nexByP7rbE9P28wXglcBOwMPAXgPtLhnYfuv2eQZwKPA0ule2vKCVnwm8faC/b23z/w342Ib+OTut83H5EHDVwPS6Vr5vOzYPBy4Yfxy3+fHH0u8BS+huk98O+D6wPfAaYFGr85R2PD2d7jU/727lTwWWAju3fyP3ATsPtPsPA/vdqn1eAsyn+yXr+8AcuscKvgwc0uoU8Ntt/q/G9jeTJs8sprfLqmp5dafsV9H9o1uTh4DPtfmXA+dW1X1VtRr4PPCKqroSeHaS5yZ5CXBXVd1KFxb7AVcC3wJ2BXZpbd1SVZe2+ZuAn0vykSQHAPeO68MLge9V1b+15UV0oTPm8+3zirV8D228flxVLx2YzgaoqiV0v5x8FPiDtWw/eCy9HPh0VT1UVXcAXwF+Bfhn4FXtjPRA4KtV9WO64/PIJFcB3wSexaPH6GVV9b02fw2wb5ITkryiqu4Z14dfAS6pqpXVDVl9ikeP0Z/S/aIEM/QYnRYP5WlCPxmYf4iJ/3s+UMNdp/gM3VnAc4CzW1mA/1NV/3ewYpKd6H5rA6Cq7mohsz/wR8BhwJuH2OeYse+ytu+haSbJk4AXAfcDs+leArom901Q/oiqeiDJJXTH2Ovo3j4N3TH61qq6cNy+9+axx+i/JdkdOAh4f5KLquq4Ib/Kz6qdVjBDj1HPLDZNPwKeOcG6rwGHJHlGks3pTu2/1tadTTdccChdcED31Pybk2wBkGRukmePbzTJtsCTqupzwLuB3cdV+S6wU5Lnt+U30v3GqE3bnwA3AK8HPp7kya38ZwPz430NeF27FjGH7rf7y9q6s4GjgFcAF7SyC4E/HmsvyQvasf0YSZ4L3F9VnwQ+yOOP0cuAX0+ybbq/oXMEHqOPmHHpOEOcClyQ5D+q6lWDK6rqW0nO4NF/fB9rQ1BU1XVJngmsqKrbWtmXkrwI+EZ3owqrgTfQ/XY1aC7d/wzGfgF517j9PpDkKOAzSWbRve/r7yfn62oj8PQ2DDTmAuDjdENPe1TVj5J8le4XiWPpjtGrk3wL+MtxbZ0LvAz4Nt21gj+vqtvbui8BnwDOq+5v2wB8jG5Y6FvpDtKVwCFr6OMvAB9M8jDwM+CPB1dW1W3pbvm9mO5s5YtVdd66/Rg2Xd46K0nq5TCUJKmXYSFJ6mVYSJJ6GRaSpF6GhSSpl2EhTYIkleSTA8uzkqxM8oW2/DvpeRPrGtp85L1a0obmcxbS5LgP+C9Jnt5eQbEvsGJsZVUtxr8br2nMMwtp8pwPjL0V9Qjg02Mrkrwpyclt/owkJyX5/0luGngrb5Kc3N7A+i/A456UlzYUw0KaPGcBhyd5GvCLdC+1m8j2dC/M+y3gA63sNXQvXNwNOBL41dF1VVo3hoU0SarqarrXThxBd5axNv9UVQ9X1fV0r+GG7h1IY29b/Q+6V2RLGwWvWUiTazHwIbq/pfCstdQbfGNwRtkhaTJ4ZiFNrtOB91bVNeux7Vd59G2r2wOv6ttAmiqeWUiTqKqWAyet5+bnAq+m+yuF36f7C3PSRsG3zkqSejkMJUnqZVhIknoZFpKkXoaFJKmXYSFJ6mVYSJJ6GRaSpF7/CZKC6nDtaTQrAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set_palette(sns.color_palette(\"BuGn_r\", 2))\n",
        "sns.countplot(x=\"Energy\", data=train_visual, order=[\"Intuition\", \"Sensing\"])\n",
        "plt.ylim(0,6000)\n",
        "plt.title(\"Energy Distribution\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "l2hbGb0S_AND",
        "outputId": "e4eaeba8-b3fa-4de2-ac58-4daf3f3880cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Energy Distribution')"
            ]
          },
          "metadata": {},
          "execution_count": 94
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set_palette(sns.light_palette(\"purple\", reverse=True))\n",
        "sns.countplot(x=\"Nature\", data=train_visual, order=[\"Thinking\", \"Feeling\"])\n",
        "plt.ylim(0,6000)\n",
        "plt.title(\"Nature Distribution\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "Z0SmJ-yy_CYk",
        "outputId": "cbae0652-85d2-487f-aea5-573359208e5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Nature Distribution')"
            ]
          },
          "metadata": {},
          "execution_count": 95
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set_palette(sns.color_palette(\"RdBu\"))\n",
        "sns.countplot(x=\"Tactics\", data=train_visual, order=[\"Judging\", \"Perceiving\"])\n",
        "plt.ylim(0,6000)\n",
        "plt.title(\"Tactics Distribution\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "lwHZP5mG_EtI",
        "outputId": "284e2d84-08d4-40d6-a6d9-0734cacceb61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Tactics Distribution')"
            ]
          },
          "metadata": {},
          "execution_count": 96
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Splitting posts\n",
        "full['posts'] = full['posts'].apply(lambda x: x.split('|||'))\n",
        "full['posts'] = full['posts'].apply(lambda x: ' '.join(x))"
      ],
      "metadata": {
        "id": "kmk5z9Ft_HTn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "b3dbu7OnBPRT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "ZseOlwI3BE4B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Mapping binary according to competition standard\n",
        "full['mind'] = full['type'].apply(lambda s: s[0])\n",
        "full['mind'] = full['mind'].map({'I':0,'E':1})\n",
        "\n",
        "full['energy'] = full['type'].apply(lambda s: s[1])\n",
        "full['energy'] = full['energy'].map({'S':0,'N':1})\n",
        "\n",
        "full['nature'] = full['type'].apply(lambda s: s[2])\n",
        "full['nature'] = full['nature'].map({'F':0,'T':1})\n",
        "\n",
        "full['tactics'] = full['type'].apply(lambda s: s[3])\n",
        "full['tactics'] = full['tactics'].map({'P':0,'J':1})"
      ],
      "metadata": {
        "id": "Oex0KEk-_JsQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing the website addresses and replacing it with URL\n",
        "url_pattern = r'https?:\\/\\/(www\\.)?[-a-zA-Z0-9@:%._\\+~#=]{2,256}\\.[a-z]{2,6}\\b([-a-zA-Z0-9@:%_\\+.~#?&//=]*)'\n",
        "url_replace = r'URL'\n",
        "full['posts'] = full['posts'].replace(to_replace = url_pattern, value = url_replace, regex = True)"
      ],
      "metadata": {
        "id": "nJU0UNru_Lwb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "full['posts'] = full['posts'].str.lower()"
      ],
      "metadata": {
        "id": "qz3vclo8_N-R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def remove_punctuation(post):\n",
        "    return ''.join([letter for letter in post if letter not in string.punctuation])\n",
        "\n",
        "full['posts'] = full['posts'].apply(remove_punctuation)"
      ],
      "metadata": {
        "id": "7j18MaHw_QuH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "full['tokened'] = full['posts'].apply(TreebankWordTokenizer().tokenize)"
      ],
      "metadata": {
        "id": "3Mil9Ps4_TGK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Stemming the posts\n",
        "stemmer = SnowballStemmer('english')\n",
        "\n",
        "def stemmerizer(words, stemmer):\n",
        "    return [stemmer.stem(word) for word in words]\n",
        "\n",
        "full['stemmed'] = full['tokened'].apply(stemmerizer, args=(stemmer, ))"
      ],
      "metadata": {
        "id": "y1VacBTy_Va9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing stop words\n",
        "def remove_stop_words(tokens):    \n",
        "    return [t for t in tokens if t not in stopwords.words('english')]\n",
        "full['stopped'] = full['stemmed'].apply(remove_stop_words)"
      ],
      "metadata": {
        "id": "rDOnw7nW_YCJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Cleaned posts\n",
        "full['clean'] = full['stopped'].apply(lambda x: ' '.join(x))"
      ],
      "metadata": {
        "id": "vuv0vtCa_aJu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# =pd.dataframe()"
      ],
      "metadata": {
        "id": "vwkm1hkuDVSC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "full['id']=[x for x in range(len(full))]\n"
      ],
      "metadata": {
        "id": "x64qrD2qGv0K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "full"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "zC7JyFC8G-TD",
        "outputId": "1cdec6dc-ad7e-4182-a52a-ecc6803199e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      type                                              posts  \\\n",
              "0     INFJ  url url enfp and intj moments  url  sportscent...   \n",
              "1     ENTP  im finding the lack of me in these posts very ...   \n",
              "2     INTP  good one     url of course to which i say i kn...   \n",
              "3     INTJ  dear intp   i enjoyed our conversation the oth...   \n",
              "4     ENTJ  youre fired thats another silly misconception ...   \n",
              "...    ...                                                ...   \n",
              "8670  ISFP  url ixfp just because i always think of cats a...   \n",
              "8671  ENFP  soif this thread already exists someplace else...   \n",
              "8672  INTP  so many questions when i do these things  i wo...   \n",
              "8673  INFP  i am very conflicted right now when it comes t...   \n",
              "8674  INFP  it has been too long since i have been on pers...   \n",
              "\n",
              "                                                tokened  \\\n",
              "0     [url, url, enfp, and, intj, moments, url, spor...   \n",
              "1     [im, finding, the, lack, of, me, in, these, po...   \n",
              "2     [good, one, url, of, course, to, which, i, say...   \n",
              "3     [dear, intp, i, enjoyed, our, conversation, th...   \n",
              "4     [youre, fired, thats, another, silly, misconce...   \n",
              "...                                                 ...   \n",
              "8670  [url, ixfp, just, because, i, always, think, o...   \n",
              "8671  [soif, this, thread, already, exists, someplac...   \n",
              "8672  [so, many, questions, when, i, do, these, thin...   \n",
              "8673  [i, am, very, conflicted, right, now, when, it...   \n",
              "8674  [it, has, been, too, long, since, i, have, bee...   \n",
              "\n",
              "                                                stemmed  \\\n",
              "0     [url, url, enfp, and, intj, moment, url, sport...   \n",
              "1     [im, find, the, lack, of, me, in, these, post,...   \n",
              "2     [good, one, url, of, cours, to, which, i, say,...   \n",
              "3     [dear, intp, i, enjoy, our, convers, the, othe...   \n",
              "4     [your, fire, that, anoth, silli, misconcept, t...   \n",
              "...                                                 ...   \n",
              "8670  [url, ixfp, just, becaus, i, alway, think, of,...   \n",
              "8671  [soif, this, thread, alreadi, exist, someplac,...   \n",
              "8672  [so, mani, question, when, i, do, these, thing...   \n",
              "8673  [i, am, veri, conflict, right, now, when, it, ...   \n",
              "8674  [it, has, been, too, long, sinc, i, have, been...   \n",
              "\n",
              "                                                stopped  \\\n",
              "0     [url, url, enfp, intj, moment, url, sportscent...   \n",
              "1     [im, find, lack, post, veri, alarm, sex, bore,...   \n",
              "2     [good, one, url, cours, say, know, bless, curs...   \n",
              "3     [dear, intp, enjoy, convers, day, esoter, gab,...   \n",
              "4     [fire, anoth, silli, misconcept, approach, log...   \n",
              "...                                                 ...   \n",
              "8670  [url, ixfp, becaus, alway, think, cat, fi, dom...   \n",
              "8671  [soif, thread, alreadi, exist, someplac, els, ...   \n",
              "8672  [mani, question, thing, would, take, purpl, pi...   \n",
              "8673  [veri, conflict, right, come, want, children, ...   \n",
              "8674  [long, sinc, personalitycaf, although, doesnt,...   \n",
              "\n",
              "                                                  clean    id  \n",
              "0     url url enfp intj moment url sportscent top te...     0  \n",
              "1     im find lack post veri alarm sex bore posit of...     1  \n",
              "2     good one url cours say know bless curs doe abs...     2  \n",
              "3     dear intp enjoy convers day esoter gab natur u...     3  \n",
              "4     fire anoth silli misconcept approach logic go ...     4  \n",
              "...                                                 ...   ...  \n",
              "8670  url ixfp becaus alway think cat fi dom reason ...  8670  \n",
              "8671  soif thread alreadi exist someplac els doe url...  8671  \n",
              "8672  mani question thing would take purpl pill pick...  8672  \n",
              "8673  veri conflict right come want children honest ...  8673  \n",
              "8674  long sinc personalitycaf although doesnt seem ...  8674  \n",
              "\n",
              "[8675 rows x 7 columns]"
            ],
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              "      <th></th>\n",
              "      <th>type</th>\n",
              "      <th>posts</th>\n",
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              "      <th>0</th>\n",
              "      <td>INFJ</td>\n",
              "      <td>url url enfp and intj moments  url  sportscent...</td>\n",
              "      <td>[url, url, enfp, and, intj, moments, url, spor...</td>\n",
              "      <td>[url, url, enfp, and, intj, moment, url, sport...</td>\n",
              "      <td>[url, url, enfp, intj, moment, url, sportscent...</td>\n",
              "      <td>url url enfp intj moment url sportscent top te...</td>\n",
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              "      <th>1</th>\n",
              "      <td>ENTP</td>\n",
              "      <td>im finding the lack of me in these posts very ...</td>\n",
              "      <td>[im, finding, the, lack, of, me, in, these, po...</td>\n",
              "      <td>[im, find, the, lack, of, me, in, these, post,...</td>\n",
              "      <td>[im, find, lack, post, veri, alarm, sex, bore,...</td>\n",
              "      <td>im find lack post veri alarm sex bore posit of...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>INTP</td>\n",
              "      <td>good one     url of course to which i say i kn...</td>\n",
              "      <td>[good, one, url, of, course, to, which, i, say...</td>\n",
              "      <td>[good, one, url, of, cours, to, which, i, say,...</td>\n",
              "      <td>[good, one, url, cours, say, know, bless, curs...</td>\n",
              "      <td>good one url cours say know bless curs doe abs...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>INTJ</td>\n",
              "      <td>dear intp   i enjoyed our conversation the oth...</td>\n",
              "      <td>[dear, intp, i, enjoyed, our, conversation, th...</td>\n",
              "      <td>[dear, intp, i, enjoy, our, convers, the, othe...</td>\n",
              "      <td>[dear, intp, enjoy, convers, day, esoter, gab,...</td>\n",
              "      <td>dear intp enjoy convers day esoter gab natur u...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ENTJ</td>\n",
              "      <td>youre fired thats another silly misconception ...</td>\n",
              "      <td>[youre, fired, thats, another, silly, misconce...</td>\n",
              "      <td>[your, fire, that, anoth, silli, misconcept, t...</td>\n",
              "      <td>[fire, anoth, silli, misconcept, approach, log...</td>\n",
              "      <td>fire anoth silli misconcept approach logic go ...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8670</th>\n",
              "      <td>ISFP</td>\n",
              "      <td>url ixfp just because i always think of cats a...</td>\n",
              "      <td>[url, ixfp, just, because, i, always, think, o...</td>\n",
              "      <td>[url, ixfp, just, becaus, i, alway, think, of,...</td>\n",
              "      <td>[url, ixfp, becaus, alway, think, cat, fi, dom...</td>\n",
              "      <td>url ixfp becaus alway think cat fi dom reason ...</td>\n",
              "      <td>8670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8671</th>\n",
              "      <td>ENFP</td>\n",
              "      <td>soif this thread already exists someplace else...</td>\n",
              "      <td>[soif, this, thread, already, exists, someplac...</td>\n",
              "      <td>[soif, this, thread, alreadi, exist, someplac,...</td>\n",
              "      <td>[soif, thread, alreadi, exist, someplac, els, ...</td>\n",
              "      <td>soif thread alreadi exist someplac els doe url...</td>\n",
              "      <td>8671</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8672</th>\n",
              "      <td>INTP</td>\n",
              "      <td>so many questions when i do these things  i wo...</td>\n",
              "      <td>[so, many, questions, when, i, do, these, thin...</td>\n",
              "      <td>[so, mani, question, when, i, do, these, thing...</td>\n",
              "      <td>[mani, question, thing, would, take, purpl, pi...</td>\n",
              "      <td>mani question thing would take purpl pill pick...</td>\n",
              "      <td>8672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8673</th>\n",
              "      <td>INFP</td>\n",
              "      <td>i am very conflicted right now when it comes t...</td>\n",
              "      <td>[i, am, very, conflicted, right, now, when, it...</td>\n",
              "      <td>[i, am, veri, conflict, right, now, when, it, ...</td>\n",
              "      <td>[veri, conflict, right, come, want, children, ...</td>\n",
              "      <td>veri conflict right come want children honest ...</td>\n",
              "      <td>8673</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8674</th>\n",
              "      <td>INFP</td>\n",
              "      <td>it has been too long since i have been on pers...</td>\n",
              "      <td>[it, has, been, too, long, since, i, have, bee...</td>\n",
              "      <td>[it, has, been, too, long, sinc, i, have, been...</td>\n",
              "      <td>[long, sinc, personalitycaf, although, doesnt,...</td>\n",
              "      <td>long sinc personalitycaf although doesnt seem ...</td>\n",
              "      <td>8674</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8675 rows × 7 columns</p>\n",
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              "    .colab-df-container {\n",
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              "    .colab-df-convert {\n",
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              "        buttonEl.style.display =\n",
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              "          const element = document.querySelector('#df-5b7ac9ae-e0a8-42b0-b577-c23d8652a2b6');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vectorizer = TfidfVectorizer()\n",
        "X = vectorizer.fit_transform(full['clean'])\n",
        "testX = vectorizer.transform(full['clean'])"
      ],
      "metadata": {
        "id": "GNpsq7_X_c4E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Train shape is:', X.shape)\n",
        "print('Test shape is:', testX.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yOYjOULi_ieI",
        "outputId": "60e42617-9db5-4ec5-9c88-4127c12cf501"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train shape is: (8675, 127501)\n",
            "Test shape is: (8675, 127501)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train Test Split\n",
        "y = full['type'].values\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)\n",
        "print('X_train ', X_train.shape, '\\n', 'X_test ', X_test.shape, '\\n', 'y_train ', y_train.shape, '\\n', \n",
        "      'y_test', y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LuMIM6HE_k9t",
        "outputId": "c6acca37-02f6-4ee5-ab02-4f369f218ab7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train  (6940, 127501) \n",
            " X_test  (1735, 127501) \n",
            " y_train  (6940,) \n",
            " y_test (1735,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lgr = LogisticRegression()\n",
        "lgr1=lgr.fit(X_train, y_train)\n",
        "print(\"TRAINING SET\")\n",
        "print(\"Accuracy: \", lgr1.score(X_train, y_train))\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_matrix(y_train, lgr1.predict(X_train)))\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_train, lgr1.predict(X_train)))\n",
        "print(\"\")\n",
        "\n",
        "print(\"TEST SET\")\n",
        "print(\"Accuracy: \", lgr1.score(X_test, y_test))\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_matrix(y_test, lgr1.predict(X_test)))\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_test, lgr1.predict(X_test)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xgMrc8O__oF2",
        "outputId": "aa7ea63a-df13-4255-af31-ecef76bd53b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TRAINING SET\n",
            "Accuracy:  0.8093659942363113\n",
            "Confusion Matrix:\n",
            "[[  49    8    0    1    0    0    0    0   34   36    8   12    0    0\n",
            "     0    1]\n",
            " [   1  414    2    9    0    0    0    0   29   60   21    8    1    0\n",
            "     2    3]\n",
            " [   1    6   91    7    0    0    0    0    5   19   29   24    1    2\n",
            "     0    2]\n",
            " [   0   11    0  432    0    0    0    0   22   24   17   42    0    1\n",
            "     1    0]\n",
            " [   0    2    0    0    4    0    0    0    6    9    1   10    1    2\n",
            "     0    0]\n",
            " [   0    3    0    1    0    0    0    0    8   16    5    6    0    1\n",
            "     0    0]\n",
            " [   0    3    1    4    0    0    1    0    4    9    5    4    0    0\n",
            "     0    1]\n",
            " [   0    1    2   11    0    0    0   13   12    8    9   12    0    1\n",
            "     1    4]\n",
            " [   1    9    0    9    0    0    0    0 1069   60   14   17    0    1\n",
            "     0    2]\n",
            " [   2    6    1    6    0    0    0    1   18 1391   13   16    1    5\n",
            "     0    2]\n",
            " [   0    4    0    8    0    0    0    0   26   35  800   21    0    0\n",
            "     2    2]\n",
            " [   0    4    0   11    0    0    0    0   19   34   17  923    0    1\n",
            "     1    1]\n",
            " [   0    4    0    0    0    0    0    0   16   34    5   12   45    3\n",
            "     1    1]\n",
            " [   0    4    1    5    0    0    0    1   19   59    7   13    0  104\n",
            "     0    5]\n",
            " [   0    6    0    4    0    0    0    0   13   20   24   21    0    0\n",
            "    73    0]\n",
            " [   0    0    0    3    0    0    0    0    5   22   12   20    0    0\n",
            "     0  208]]\n",
            "Classification Report:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "        ENFJ       0.91      0.33      0.48       149\n",
            "        ENFP       0.85      0.75      0.80       550\n",
            "        ENTJ       0.93      0.49      0.64       187\n",
            "        ENTP       0.85      0.79      0.81       550\n",
            "        ESFJ       1.00      0.11      0.21        35\n",
            "        ESFP       0.00      0.00      0.00        40\n",
            "        ESTJ       1.00      0.03      0.06        32\n",
            "        ESTP       0.87      0.18      0.29        74\n",
            "        INFJ       0.82      0.90      0.86      1182\n",
            "        INFP       0.76      0.95      0.84      1462\n",
            "        INTJ       0.81      0.89      0.85       898\n",
            "        INTP       0.80      0.91      0.85      1011\n",
            "        ISFJ       0.92      0.37      0.53       121\n",
            "        ISFP       0.86      0.48      0.61       218\n",
            "        ISTJ       0.90      0.45      0.60       161\n",
            "        ISTP       0.90      0.77      0.83       270\n",
            "\n",
            "    accuracy                           0.81      6940\n",
            "   macro avg       0.82      0.53      0.58      6940\n",
            "weighted avg       0.82      0.81      0.79      6940\n",
            "\n",
            "\n",
            "TEST SET\n",
            "Accuracy:  0.6403458213256484\n",
            "Confusion Matrix:\n",
            "[[  6   3   0   0   0   0   0   0  11  15   2   3   0   0   1   0]\n",
            " [  0  71   0   4   0   0   0   0  10  25   9   5   0   0   0   1]\n",
            " [  2   2  12   4   0   0   0   0   2   4  12   6   0   0   0   0]\n",
            " [  0   5   0  74   0   0   0   0  14  14  13  14   0   1   0   0]\n",
            " [  0   0   0   1   0   0   0   0   0   1   2   2   0   0   1   0]\n",
            " [  0   2   1   2   0   0   0   0   0   1   1   1   0   0   0   0]\n",
            " [  0   0   1   1   0   0   0   0   2   2   1   0   0   0   0   0]\n",
            " [  0   0   0   1   0   0   0   2   1   3   1   5   0   0   0   2]\n",
            " [  0   6   0   8   0   0   0   0 195  49  14  16   0   0   0   0]\n",
            " [  2   4   0   4   0   0   0   0  15 322   7  13   0   1   1   1]\n",
            " [  0   2   0   2   0   0   0   0  18  16 135  20   0   0   0   0]\n",
            " [  0   2   0   4   0   0   0   0  11  30   8 236   0   0   0   2]\n",
            " [  1   1   1   0   0   0   0   0   9  16   2   4  10   0   0   1]\n",
            " [  0   1   0   1   0   0   0   0   6  29   1   3   0  10   1   1]\n",
            " [  1   2   0   2   0   0   0   0   1  11  10   6   1   1   8   1]\n",
            " [  0   1   0   2   0   0   0   0   4   8   6  16   0   0   0  30]]\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "        ENFJ       0.50      0.15      0.23        41\n",
            "        ENFP       0.70      0.57      0.63       125\n",
            "        ENTJ       0.80      0.27      0.41        44\n",
            "        ENTP       0.67      0.55      0.60       135\n",
            "        ESFJ       0.00      0.00      0.00         7\n",
            "        ESFP       0.00      0.00      0.00         8\n",
            "        ESTJ       0.00      0.00      0.00         7\n",
            "        ESTP       1.00      0.13      0.24        15\n",
            "        INFJ       0.65      0.68      0.66       288\n",
            "        INFP       0.59      0.87      0.70       370\n",
            "        INTJ       0.60      0.70      0.65       193\n",
            "        INTP       0.67      0.81      0.73       293\n",
            "        ISFJ       0.91      0.22      0.36        45\n",
            "        ISFP       0.77      0.19      0.30        53\n",
            "        ISTJ       0.67      0.18      0.29        44\n",
            "        ISTP       0.77      0.45      0.57        67\n",
            "\n",
            "    accuracy                           0.64      1735\n",
            "   macro avg       0.58      0.36      0.40      1735\n",
            "weighted avg       0.65      0.64      0.61      1735\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KC2p4xaaeeoZ",
        "outputId": "dba64bb4-ed06-425d-eefa-e4c0579f7a53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['INFJ', 'ENTP', 'INTP', ..., 'INTP', 'INFP', 'INFP'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "pickle.dump(lgr1,open('iri.pkl','wb'))"
      ],
      "metadata": {
        "id": "NalRF_02K76P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cm_lgr = np.array(confusion_matrix(y_test, lgr.predict(X_test)))\n",
        "\n",
        "cm_logreg = pd.DataFrame(cm_lgr, index=['ENFJ', 'ENFP', 'ENTJ', 'ENTP', 'ESFJ', 'ESFP',\n",
        "                                       'ESTJ', 'ESTP', 'INFJ', 'INFP', 'INTJ', 'INTP',\n",
        "                                       'ISFJ', 'ISFP', 'ISTJ', 'ISTP'], \n",
        "                            columns=['predict_ENFJ','predict_ENFP','predict_ENTJ',\n",
        "                                     'predict_ENTP','predict_ESFJ','predict_ESFP',\n",
        "                                     'predict_ESTJ','predict_ESTP','predict_INFJ',\n",
        "                                     'predict_INFP','predict_INTJ','predict_INTP',\n",
        "                                     'predict_ISFJ','predict_ISFP','predict_ISTJ',\n",
        "                                     'predict_ISTP'])\n",
        "cm_logreg"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        },
        "id": "a1a55gFZHnoF",
        "outputId": "d04e2957-cc33-4695-f8ec-4827d41e0628"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      predict_ENFJ  predict_ENFP  predict_ENTJ  predict_ENTP  predict_ESFJ  \\\n",
              "ENFJ             6             3             0             0             0   \n",
              "ENFP             0            71             0             4             0   \n",
              "ENTJ             2             2            12             4             0   \n",
              "ENTP             0             5             0            74             0   \n",
              "ESFJ             0             0             0             1             0   \n",
              "ESFP             0             2             1             2             0   \n",
              "ESTJ             0             0             1             1             0   \n",
              "ESTP             0             0             0             1             0   \n",
              "INFJ             0             6             0             8             0   \n",
              "INFP             2             4             0             4             0   \n",
              "INTJ             0             2             0             2             0   \n",
              "INTP             0             2             0             4             0   \n",
              "ISFJ             1             1             1             0             0   \n",
              "ISFP             0             1             0             1             0   \n",
              "ISTJ             1             2             0             2             0   \n",
              "ISTP             0             1             0             2             0   \n",
              "\n",
              "      predict_ESFP  predict_ESTJ  predict_ESTP  predict_INFJ  predict_INFP  \\\n",
              "ENFJ             0             0             0            11            15   \n",
              "ENFP             0             0             0            10            25   \n",
              "ENTJ             0             0             0             2             4   \n",
              "ENTP             0             0             0            14            14   \n",
              "ESFJ             0             0             0             0             1   \n",
              "ESFP             0             0             0             0             1   \n",
              "ESTJ             0             0             0             2             2   \n",
              "ESTP             0             0             2             1             3   \n",
              "INFJ             0             0             0           195            49   \n",
              "INFP             0             0             0            15           322   \n",
              "INTJ             0             0             0            18            16   \n",
              "INTP             0             0             0            11            30   \n",
              "ISFJ             0             0             0             9            16   \n",
              "ISFP             0             0             0             6            29   \n",
              "ISTJ             0             0             0             1            11   \n",
              "ISTP             0             0             0             4             8   \n",
              "\n",
              "      predict_INTJ  predict_INTP  predict_ISFJ  predict_ISFP  predict_ISTJ  \\\n",
              "ENFJ             2             3             0             0             1   \n",
              "ENFP             9             5             0             0             0   \n",
              "ENTJ            12             6             0             0             0   \n",
              "ENTP            13            14             0             1             0   \n",
              "ESFJ             2             2             0             0             1   \n",
              "ESFP             1             1             0             0             0   \n",
              "ESTJ             1             0             0             0             0   \n",
              "ESTP             1             5             0             0             0   \n",
              "INFJ            14            16             0             0             0   \n",
              "INFP             7            13             0             1             1   \n",
              "INTJ           135            20             0             0             0   \n",
              "INTP             8           236             0             0             0   \n",
              "ISFJ             2             4            10             0             0   \n",
              "ISFP             1             3             0            10             1   \n",
              "ISTJ            10             6             1             1             8   \n",
              "ISTP             6            16             0             0             0   \n",
              "\n",
              "      predict_ISTP  \n",
              "ENFJ             0  \n",
              "ENFP             1  \n",
              "ENTJ             0  \n",
              "ENTP             0  \n",
              "ESFJ             0  \n",
              "ESFP             0  \n",
              "ESTJ             0  \n",
              "ESTP             2  \n",
              "INFJ             0  \n",
              "INFP             1  \n",
              "INTJ             0  \n",
              "INTP             2  \n",
              "ISFJ             1  \n",
              "ISFP             1  \n",
              "ISTJ             1  \n",
              "ISTP            30  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9d2608c0-ba8d-42ea-8653-980f84134467\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>predict_ENFJ</th>\n",
              "      <th>predict_ENFP</th>\n",
              "      <th>predict_ENTJ</th>\n",
              "      <th>predict_ENTP</th>\n",
              "      <th>predict_ESFJ</th>\n",
              "      <th>predict_ESFP</th>\n",
              "      <th>predict_ESTJ</th>\n",
              "      <th>predict_ESTP</th>\n",
              "      <th>predict_INFJ</th>\n",
              "      <th>predict_INFP</th>\n",
              "      <th>predict_INTJ</th>\n",
              "      <th>predict_INTP</th>\n",
              "      <th>predict_ISFJ</th>\n",
              "      <th>predict_ISFP</th>\n",
              "      <th>predict_ISTJ</th>\n",
              "      <th>predict_ISTP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ENFJ</th>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ENFP</th>\n",
              "      <td>0</td>\n",
              "      <td>71</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>25</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ENTJ</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ENTP</th>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>14</td>\n",
              "      <td>13</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ESFJ</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ESFP</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ESTJ</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ESTP</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>INFJ</th>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>195</td>\n",
              "      <td>49</td>\n",
              "      <td>14</td>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>INFP</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>322</td>\n",
              "      <td>7</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>INTJ</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "      <td>16</td>\n",
              "      <td>135</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>INTP</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>30</td>\n",
              "      <td>8</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ISFJ</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>16</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ISFP</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ISTJ</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>10</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ISTP</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9d2608c0-ba8d-42ea-8653-980f84134467')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\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-convert: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-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-9d2608c0-ba8d-42ea-8653-980f84134467 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-9d2608c0-ba8d-42ea-8653-980f84134467');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create confusion matrix heatmap of Logistic Regression model \n",
        "fig, ax = plt.subplots(figsize=(14,10)) \n",
        "plt.title('Confusion Matrix for Logistic Regression', fontsize=16,\n",
        "          fontweight='bold', y=1.02)\n",
        "sns.heatmap(cm_logreg, robust=True, annot=True, linewidth=0.5, \n",
        "            fmt='', cmap='RdBu_r', vmax=303, ax=ax)\n",
        "plt.xticks(fontsize=12)\n",
        "plt.yticks(rotation=0, fontsize=12);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689
        },
        "id": "17KwrxX9ISC-",
        "outputId": "4459aa8e-34d2-4c65-fd34-9ce6e1c5b51f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rfc = RandomForestClassifier(n_estimators=30, min_samples_leaf=50, oob_score=True, n_jobs= -1, random_state=123)\n",
        "rfc.fit(X_train, y_train)\n",
        "print(\"TRAINING SET\")\n",
        "print(\"Accuracy: \", rfc.score(X_train, y_train))\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_matrix(y_train, rfc.predict(X_train)))\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_train, rfc.predict(X_train)))\n",
        "print(\"\")\n",
        "\n",
        "print(\"TEST SET\")\n",
        "print(\"Accuracy: \", rfc.score(X_test, y_test))\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_matrix(y_test, rfc.predict(X_test)))\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_test, rfc.predict(X_test)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PBaFIqcWIdDh",
        "outputId": "c97dbf3d-9ac0-4efc-88ab-278cc8551c6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TRAINING SET\n",
            "Accuracy:  0.36023054755043227\n",
            "Confusion Matrix:\n",
            "[[   0    0    0    0    0    0    0    0   19  127    1    2    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0   60  486    2    2    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0   21  148    8   10    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0   71  436    5   38    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0    4   31    0    0    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0    4   32    2    2    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0    2   30    0    0    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0   10   61    2    1    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0  638  539    1    4    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0   30 1430    1    1    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0   82  608  162   46    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0   61  670   10  270    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0   15  104    1    1    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0   18  198    0    2    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0    9  144    4    4    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0   16  244    1    9    0    0\n",
            "     0    0]]\n",
            "Classification Report:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "        ENFJ       0.00      0.00      0.00       149\n",
            "        ENFP       0.00      0.00      0.00       550\n",
            "        ENTJ       0.00      0.00      0.00       187\n",
            "        ENTP       0.00      0.00      0.00       550\n",
            "        ESFJ       0.00      0.00      0.00        35\n",
            "        ESFP       0.00      0.00      0.00        40\n",
            "        ESTJ       0.00      0.00      0.00        32\n",
            "        ESTP       0.00      0.00      0.00        74\n",
            "        INFJ       0.60      0.54      0.57      1182\n",
            "        INFP       0.27      0.98      0.42      1462\n",
            "        INTJ       0.81      0.18      0.30       898\n",
            "        INTP       0.69      0.27      0.38      1011\n",
            "        ISFJ       0.00      0.00      0.00       121\n",
            "        ISFP       0.00      0.00      0.00       218\n",
            "        ISTJ       0.00      0.00      0.00       161\n",
            "        ISTP       0.00      0.00      0.00       270\n",
            "\n",
            "    accuracy                           0.36      6940\n",
            "   macro avg       0.15      0.12      0.10      6940\n",
            "weighted avg       0.36      0.36      0.28      6940\n",
            "\n",
            "\n",
            "TEST SET\n",
            "Accuracy:  0.2962536023054755\n",
            "Confusion Matrix:\n",
            "[[  0   0   0   0   0   0   0   0   6  35   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   8 116   0   1   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   5  37   1   1   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0  20 101   1  13   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   1   6   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   7   1   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   7   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   1  13   0   1   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0 107 176   0   5   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0  15 352   1   2   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0  23 148  17   5   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0  13 240   2  38   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   4  41   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   3  50   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   5  39   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   6  57   0   4   0   0   0   0]]\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "        ENFJ       0.00      0.00      0.00        41\n",
            "        ENFP       0.00      0.00      0.00       125\n",
            "        ENTJ       0.00      0.00      0.00        44\n",
            "        ENTP       0.00      0.00      0.00       135\n",
            "        ESFJ       0.00      0.00      0.00         7\n",
            "        ESFP       0.00      0.00      0.00         8\n",
            "        ESTJ       0.00      0.00      0.00         7\n",
            "        ESTP       0.00      0.00      0.00        15\n",
            "        INFJ       0.49      0.37      0.42       288\n",
            "        INFP       0.25      0.95      0.39       370\n",
            "        INTJ       0.74      0.09      0.16       193\n",
            "        INTP       0.54      0.13      0.21       293\n",
            "        ISFJ       0.00      0.00      0.00        45\n",
            "        ISFP       0.00      0.00      0.00        53\n",
            "        ISTJ       0.00      0.00      0.00        44\n",
            "        ISTP       0.00      0.00      0.00        67\n",
            "\n",
            "    accuracy                           0.30      1735\n",
            "   macro avg       0.13      0.10      0.07      1735\n",
            "weighted avg       0.31      0.30      0.21      1735\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert confusion matrix to a dataframe to prepare it for heatmapping\n",
        "cm_rfc = np.array(confusion_matrix(y_test, rfc.predict(X_test)))\n",
        "\n",
        "cm_randomforest = pd.DataFrame(cm_rfc, index=['ENFJ', 'ENFP', 'ENTJ', 'ENTP', 'ESFJ', 'ESFP',\n",
        "                                       'ESTJ', 'ESTP', 'INFJ', 'INFP', 'INTJ', 'INTP',\n",
        "                                       'ISFJ', 'ISFP', 'ISTJ', 'ISTP'], \n",
        "                            columns=['predict_ENFJ','predict_ENFP','predict_ENTJ',\n",
        "                                     'predict_ENTP','predict_ESFJ','predict_ESFP',\n",
        "                                     'predict_ESTJ','predict_ESTP','predict_INFJ',\n",
        "                                     'predict_INFP','predict_INTJ','predict_INTP',\n",
        "                                     'predict_ISFJ','predict_ISFP','predict_ISTJ',\n",
        "                                     'predict_ISTP'])\n",
        "cm_randomforest"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        },
        "id": "QoP2Cgj-Igp1",
        "outputId": "b05cbd71-4f5e-460e-a1a1-3d076dd957b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      predict_ENFJ  predict_ENFP  predict_ENTJ  predict_ENTP  predict_ESFJ  \\\n",
              "ENFJ             0             0             0             0             0   \n",
              "ENFP             0             0             0             0             0   \n",
              "ENTJ             0             0             0             0             0   \n",
              "ENTP             0             0             0             0             0   \n",
              "ESFJ             0             0             0             0             0   \n",
              "ESFP             0             0             0             0             0   \n",
              "ESTJ             0             0             0             0             0   \n",
              "ESTP             0             0             0             0             0   \n",
              "INFJ             0             0             0             0             0   \n",
              "INFP             0             0             0             0             0   \n",
              "INTJ             0             0             0             0             0   \n",
              "INTP             0             0             0             0             0   \n",
              "ISFJ             0             0             0             0             0   \n",
              "ISFP             0             0             0             0             0   \n",
              "ISTJ             0             0             0             0             0   \n",
              "ISTP             0             0             0             0             0   \n",
              "\n",
              "      predict_ESFP  predict_ESTJ  predict_ESTP  predict_INFJ  predict_INFP  \\\n",
              "ENFJ             0             0             0             6            35   \n",
              "ENFP             0             0             0             8           116   \n",
              "ENTJ             0             0             0             5            37   \n",
              "ENTP             0             0             0            20           101   \n",
              "ESFJ             0             0             0             1             6   \n",
              "ESFP             0             0             0             0             7   \n",
              "ESTJ             0             0             0             0             7   \n",
              "ESTP             0             0             0             1            13   \n",
              "INFJ             0             0             0           107           176   \n",
              "INFP             0             0             0            15           352   \n",
              "INTJ             0             0             0            23           148   \n",
              "INTP             0             0             0            13           240   \n",
              "ISFJ             0             0             0             4            41   \n",
              "ISFP             0             0             0             3            50   \n",
              "ISTJ             0             0             0             5            39   \n",
              "ISTP             0             0             0             6            57   \n",
              "\n",
              "      predict_INTJ  predict_INTP  predict_ISFJ  predict_ISFP  predict_ISTJ  \\\n",
              "ENFJ             0             0             0             0             0   \n",
              "ENFP             0             1             0             0             0   \n",
              "ENTJ             1             1             0             0             0   \n",
              "ENTP             1            13             0             0             0   \n",
              "ESFJ             0             0             0             0             0   \n",
              "ESFP             1             0             0             0             0   \n",
              "ESTJ             0             0             0             0             0   \n",
              "ESTP             0             1             0             0             0   \n",
              "INFJ             0             5             0             0             0   \n",
              "INFP             1             2             0             0             0   \n",
              "INTJ            17             5             0             0             0   \n",
              "INTP             2            38             0             0             0   \n",
              "ISFJ             0             0             0             0             0   \n",
              "ISFP             0             0             0             0             0   \n",
              "ISTJ             0             0             0             0             0   \n",
              "ISTP             0             4             0             0             0   \n",
              "\n",
              "      predict_ISTP  \n",
              "ENFJ             0  \n",
              "ENFP             0  \n",
              "ENTJ             0  \n",
              "ENTP             0  \n",
              "ESFJ             0  \n",
              "ESFP             0  \n",
              "ESTJ             0  \n",
              "ESTP             0  \n",
              "INFJ             0  \n",
              "INFP             0  \n",
              "INTJ             0  \n",
              "INTP             0  \n",
              "ISFJ             0  \n",
              "ISFP             0  \n",
              "ISTJ             0  \n",
              "ISTP             0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2606334f-14d3-47f9-8d7e-b9b7cb543224\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>predict_ENFJ</th>\n",
              "      <th>predict_ENFP</th>\n",
              "      <th>predict_ENTJ</th>\n",
              "      <th>predict_ENTP</th>\n",
              "      <th>predict_ESFJ</th>\n",
              "      <th>predict_ESFP</th>\n",
              "      <th>predict_ESTJ</th>\n",
              "      <th>predict_ESTP</th>\n",
              "      <th>predict_INFJ</th>\n",
              "      <th>predict_INFP</th>\n",
              "      <th>predict_INTJ</th>\n",
              "      <th>predict_INTP</th>\n",
              "      <th>predict_ISFJ</th>\n",
              "      <th>predict_ISFP</th>\n",
              "      <th>predict_ISTJ</th>\n",
              "      <th>predict_ISTP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ENFJ</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ENFP</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>116</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ENTJ</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ENTP</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>101</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ESFJ</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ESFP</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ESTJ</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ESTP</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>INFJ</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>107</td>\n",
              "      <td>176</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>INFP</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>352</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>INTJ</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23</td>\n",
              "      <td>148</td>\n",
              "      <td>17</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>INTP</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>240</td>\n",
              "      <td>2</td>\n",
              "      <td>38</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ISFJ</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ISFP</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>50</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ISTJ</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ISTP</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2606334f-14d3-47f9-8d7e-b9b7cb543224')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\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-convert: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-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-2606334f-14d3-47f9-8d7e-b9b7cb543224 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-2606334f-14d3-47f9-8d7e-b9b7cb543224');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create confusion matrix heatmap of Random Forest Classifier model \n",
        "fig, ax = plt.subplots(figsize=(14,10)) \n",
        "plt.title('Confusion Matrix for Random Forest Classifier', fontsize=16,\n",
        "          fontweight='bold', y=1.02)\n",
        "sns.heatmap(cm_randomforest, robust=True, annot=True, linewidth=0.5, \n",
        "            fmt='', cmap='RdBu_r', vmax=303, ax=ax)\n",
        "plt.xticks(fontsize=12)\n",
        "plt.yticks(rotation=0, fontsize=12);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689
        },
        "id": "JaAjo9KKItbG",
        "outputId": "55b73b78-26f8-47ab-aa0f-b5a9e0f2389e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train-test splits, using type variables as target and posts variable as predictor\n",
        "yIE = full['mind'].values\n",
        "yNS = full['energy'].values\n",
        "yTF = full['nature'].values\n",
        "yJP = full['tactics'].values\n",
        "\n",
        "# Introversion - Extroversion\n",
        "X_train_IE, X_test_IE, y_train_IE, y_test_IE = train_test_split(X, yIE, test_size=0.2, random_state = 42)\n",
        "# Intuition - Sensing\n",
        "X_train_NS, X_test_NS, y_train_NS, y_test_NS = train_test_split(X, yNS, test_size=0.2, random_state = 42)\n",
        "# Thinking - Feeling\n",
        "X_train_TF, X_test_TF, y_train_TF, y_test_TF = train_test_split(X, yTF, test_size=0.2, random_state = 42)\n",
        "# Judging - Perceiving\n",
        "X_train_JP, X_test_JP, y_train_JP, y_test_JP = train_test_split(X, yJP, test_size=0.2, random_state = 42)"
      ],
      "metadata": {
        "id": "F3LSVA1ZJN2q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rfc = RandomForestClassifier(random_state = 42)\n",
        "param_grid = { \n",
        "    'n_estimators': [10, 20, 50, 85],\n",
        "    'max_features': ['auto', 'sqrt', 'log2'],\n",
        "    'max_depth' : [4,5,6,7,8],\n",
        "    'criterion' :['gini', 'entropy'],\n",
        "    'class_weight' : ['balanced', 'balanced_subsample']\n",
        "}\n",
        "\n",
        "rfcclf = GridSearchCV(rfc, param_grid, cv=5)\n",
        "gridrfcm = rfcclf.fit(X_train_IE, y_train_IE)\n",
        "print('Best Mind n_estimators:', gridrfcm.best_estimator_.get_params()['n_estimators'])\n",
        "print('Best Mind max_features:', gridrfcm.best_estimator_.get_params()['max_features'])\n",
        "print('Best Mind max_depth:', gridrfcm.best_estimator_.get_params()['max_depth'])\n",
        "print('Best Mind criterion:', gridrfcm.best_estimator_.get_params()['criterion'])\n",
        "print('Best Mind class_weight:', gridrfcm.best_estimator_.get_params()['class_weight'])\n",
        "print(\" \")\n",
        "gridrfce = rfcclf.fit(X_train_NS, y_train_NS)\n",
        "print('Best Energy n_estimators:', gridrfce.best_estimator_.get_params()['n_estimators'])\n",
        "print('Best Energy max_features:', gridrfce.best_estimator_.get_params()['max_features'])\n",
        "print('Best Energy max_depth:', gridrfce.best_estimator_.get_params()['max_depth'])\n",
        "print('Best Energy criterion:', gridrfce.best_estimator_.get_params()['criterion'])\n",
        "print('Best Energy class_weight:', gridrfce.best_estimator_.get_params()['class_weight'])\n",
        "print(\" \")\n",
        "gridrfcn = rfcclf.fit(X_train_TF, y_train_TF)\n",
        "print('Best Nature n_estimators:', gridrfcn.best_estimator_.get_params()['n_estimators'])\n",
        "print('Best Nature max_features:', gridrfcn.best_estimator_.get_params()['max_features'])\n",
        "print('Best Nature max_depth:', gridrfcn.best_estimator_.get_params()['max_depth'])\n",
        "print('Best Nature criterion:', gridrfcn.best_estimator_.get_params()['criterion'])\n",
        "print('Best Nature class_weight:', gridrfcn.best_estimator_.get_params()['class_weight'])\n",
        "print(\" \")\n",
        "gridrfct = rfcclf.fit(X_train_JP, y_train_JP)\n",
        "print('Best Tactics n_estimators:', gridrfct.best_estimator_.get_params()['n_estimators'])\n",
        "print('Best Tactics max_features:', gridrfct.best_estimator_.get_params()['max_features'])\n",
        "print('Best Tactics max_depth:', gridrfct.best_estimator_.get_params()['max_depth'])\n",
        "print('Best Tactics criterion:', gridrfct.best_estimator_.get_params()['criterion'])\n",
        "print('Best Tactics class_weight:', gridrfct.best_estimator_.get_params()['class_weight'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ESjmUqj7NgOG",
        "outputId": "6c7854f9-0f60-4bf2-b76e-895f79f0438b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Mind n_estimators: 85\n",
            "Best Mind max_features: auto\n",
            "Best Mind max_depth: 8\n",
            "Best Mind criterion: entropy\n",
            "Best Mind class_weight: balanced\n",
            " \n",
            "Best Energy n_estimators: 85\n",
            "Best Energy max_features: auto\n",
            "Best Energy max_depth: 8\n",
            "Best Energy criterion: entropy\n",
            "Best Energy class_weight: balanced\n",
            " \n",
            "Best Nature n_estimators: 85\n",
            "Best Nature max_features: auto\n",
            "Best Nature max_depth: 8\n",
            "Best Nature criterion: entropy\n",
            "Best Nature class_weight: balanced_subsample\n",
            " \n",
            "Best Tactics n_estimators: 85\n",
            "Best Tactics max_features: auto\n",
            "Best Tactics max_depth: 8\n",
            "Best Tactics criterion: entropy\n",
            "Best Tactics class_weight: balanced_subsample\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit and score a Random Forest Classifier using the parameters identified by the grid search\n",
        "rfc = RandomForestClassifier(n_estimators = 85, max_features = 'auto', max_depth = 8, \n",
        "                             criterion = 'gini', class_weight = 'balanced', random_state = 42)\n",
        "rfc.fit(X_train_IE, y_train_IE)\n",
        "print(\"TRAINING SET\")\n",
        "print(\"Accuracy: \", rfc.score(X_train_IE, y_train_IE))\n",
        "print(\"Confusion Matrix (counts):\")\n",
        "print(confusion_matrix(y_train_IE, rfc.predict(X_train_IE)))\n",
        "print(\"Confusion Matrix (percentages):\")\n",
        "print(pd.DataFrame(confusion_matrix(y_train_IE, rfc.predict(X_train_IE))).apply(lambda x: x/x.sum(),axis=1))\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_train_IE, rfc.predict(X_train_IE)))\n",
        "print(\"\")\n",
        "\n",
        "print(\"TEST SET\")\n",
        "print(\"Accuracy: \", rfc.score(X_test_IE, y_test_IE))\n",
        "print(\"Confusion Matrix (counts):\")\n",
        "print(confusion_matrix(y_test_IE, rfc.predict(X_test_IE)))\n",
        "print(\"Confusion Matrix (percentages):\")\n",
        "print(pd.DataFrame(confusion_matrix(y_test_IE, rfc.predict(X_test_IE))).apply(lambda x: x/x.sum(),axis=1))\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_test_IE, rfc.predict(X_test_IE)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AglGy-upN4KY",
        "outputId": "cf26c491-2e36-48f3-984d-ff05247ee426"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TRAINING SET\n",
            "Accuracy:  0.9210374639769452\n",
            "Confusion Matrix (counts):\n",
            "[[4996  327]\n",
            " [ 221 1396]]\n",
            "Confusion Matrix (percentages):\n",
            "          0         1\n",
            "0  0.938568  0.061432\n",
            "1  0.136673  0.863327\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.94      0.95      5323\n",
            "           1       0.81      0.86      0.84      1617\n",
            "\n",
            "    accuracy                           0.92      6940\n",
            "   macro avg       0.88      0.90      0.89      6940\n",
            "weighted avg       0.92      0.92      0.92      6940\n",
            "\n",
            "\n",
            "TEST SET\n",
            "Accuracy:  0.768299711815562\n",
            "Confusion Matrix (counts):\n",
            "[[1189  164]\n",
            " [ 238  144]]\n",
            "Confusion Matrix (percentages):\n",
            "          0         1\n",
            "0  0.878788  0.121212\n",
            "1  0.623037  0.376963\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.88      0.86      1353\n",
            "           1       0.47      0.38      0.42       382\n",
            "\n",
            "    accuracy                           0.77      1735\n",
            "   macro avg       0.65      0.63      0.64      1735\n",
            "weighted avg       0.75      0.77      0.76      1735\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l6KaloMBdNOg",
        "outputId": "7b9de790-f290-4bd5-9afb-8e9ddcbb7fff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  (0, 78398)\t0.020256411037713983\n",
            "  (0, 105833)\t0.017466548675258146\n",
            "  (0, 16196)\t0.037213277946294365\n",
            "  (0, 33844)\t0.08344035042898479\n",
            "  (0, 121114)\t0.013745507326470351\n",
            "  (0, 29288)\t0.04582577195869324\n",
            "  (0, 54107)\t0.024363289014377582\n",
            "  (0, 72740)\t0.032108957985915156\n",
            "  (0, 83924)\t0.06452396260455856\n",
            "  (0, 43085)\t0.0519972435672692\n",
            "  (0, 20710)\t0.024610844827411354\n",
            "  (0, 88575)\t0.030001907420112925\n",
            "  (0, 20480)\t0.01866987514353595\n",
            "  (0, 104243)\t0.02593187332860386\n",
            "  (0, 61795)\t0.01431083106773352\n",
            "  (0, 12736)\t0.024606875075872493\n",
            "  (0, 125566)\t0.016554113264891448\n",
            "  (0, 104744)\t0.02600303085443124\n",
            "  (0, 90772)\t0.03731622443339121\n",
            "  (0, 43066)\t0.036125357694751545\n",
            "  (0, 12592)\t0.028782489669992584\n",
            "  (0, 53222)\t0.029730803300468518\n",
            "  (0, 52929)\t0.024496253692707137\n",
            "  (0, 76465)\t0.024646632015417856\n",
            "  (0, 97905)\t0.02200918829246474\n",
            "  :\t:\n",
            "  (8674, 124099)\t0.058557436558337575\n",
            "  (8674, 120726)\t0.0974895827729282\n",
            "  (8674, 115716)\t0.013414848569516209\n",
            "  (8674, 71610)\t0.03345083587362324\n",
            "  (8674, 101975)\t0.02818450102648356\n",
            "  (8674, 71974)\t0.01448848112746293\n",
            "  (8674, 36177)\t0.1118037358864911\n",
            "  (8674, 66605)\t0.019917000562437767\n",
            "  (8674, 114559)\t0.025678633306278644\n",
            "  (8674, 41611)\t0.018197916012069285\n",
            "  (8674, 112747)\t0.0344192974118169\n",
            "  (8674, 82456)\t0.0771080697843217\n",
            "  (8674, 85396)\t0.03647597032690623\n",
            "  (8674, 32815)\t0.031368538901390075\n",
            "  (8674, 78504)\t0.026043659684174485\n",
            "  (8674, 42900)\t0.03656963422131563\n",
            "  (8674, 46758)\t0.01361154307515267\n",
            "  (8674, 58486)\t0.01930222307464043\n",
            "  (8674, 111506)\t0.05827381826654293\n",
            "  (8674, 66113)\t0.01874118345086466\n",
            "  (8674, 71604)\t0.056138150885316776\n",
            "  (8674, 94277)\t0.03826847233225326\n",
            "  (8674, 67333)\t0.028233922613615237\n",
            "  (8674, 113775)\t0.030620084299487364\n",
            "  (8674, 118147)\t0.03162802604357526\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# y = full['type'].values\n",
        "# X_train1, X_test1, y_train1, y_test1 = train_test_split(X, y, test_size = 0.2, random_state = 42)\n",
        "# print('X_train ', X_train.shape, '\\n', 'X_test ', X_test.shape, '\\n', 'y_train ', y_train.shape, '\\n', \n",
        "#       'y_test', y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2_WCyC80cIce",
        "outputId": "3a39f0e3-40b0-441b-e89e-579cff519579"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train  (6940, 127501) \n",
            " X_test  (1735, 127501) \n",
            " y_train  (6940,) \n",
            " y_test (1735,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "53kYx-pscfSG",
        "outputId": "6c750a81-b66e-4b88-a95b-fe83839bda35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['INFJ', 'ENFP', 'ENTP', ..., 'INFJ', 'INFP', 'INTJ'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import lightgbm as lgb\n",
        "model_lgb=lgb.LGBMClassifier(random_state=39)\n",
        "model_lgb.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SYX25Q-YfEZl",
        "outputId": "d8c01bcc-d083-42f7-b61a-7f2e15d950b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMClassifier(random_state=39)"
            ]
          },
          "metadata": {},
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"TRAINING SET\")\n",
        "print(\"Accuracy: \", model_lgb.score(X_train, y_train))\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_matrix(y_train, model_lgb.predict(X_train)))\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_train, model_lgb.predict(X_train)))\n",
        "print(\"\")\n",
        "\n",
        "print(\"TEST SET\")\n",
        "print(\"Accuracy: \", model_lgb.score(X_test, y_test))\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_matrix(y_test, model_lgb.predict(X_test)))\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_test, model_lgb.predict(X_test)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PqKI10zEhya3",
        "outputId": "cc6938ce-929c-429a-8f74-1bbf1903cb8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TRAINING SET\n",
            "Accuracy:  1.0\n",
            "Confusion Matrix:\n",
            "[[ 149    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "     0    0]\n",
            " [   0  550    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "     0    0]\n",
            " [   0    0  187    0    0    0    0    0    0    0    0    0    0    0\n",
            "     0    0]\n",
            " [   0    0    0  550    0    0    0    0    0    0    0    0    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0   35    0    0    0    0    0    0    0    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0   40    0    0    0    0    0    0    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0   32    0    0    0    0    0    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0   74    0    0    0    0    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0 1182    0    0    0    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0    0 1462    0    0    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0    0    0  898    0    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0    0    0    0 1011    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0    0    0    0    0  121    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0    0    0    0    0    0  218\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "   161    0]\n",
            " [   0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "     0  270]]\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "        ENFJ       1.00      1.00      1.00       149\n",
            "        ENFP       1.00      1.00      1.00       550\n",
            "        ENTJ       1.00      1.00      1.00       187\n",
            "        ENTP       1.00      1.00      1.00       550\n",
            "        ESFJ       1.00      1.00      1.00        35\n",
            "        ESFP       1.00      1.00      1.00        40\n",
            "        ESTJ       1.00      1.00      1.00        32\n",
            "        ESTP       1.00      1.00      1.00        74\n",
            "        INFJ       1.00      1.00      1.00      1182\n",
            "        INFP       1.00      1.00      1.00      1462\n",
            "        INTJ       1.00      1.00      1.00       898\n",
            "        INTP       1.00      1.00      1.00      1011\n",
            "        ISFJ       1.00      1.00      1.00       121\n",
            "        ISFP       1.00      1.00      1.00       218\n",
            "        ISTJ       1.00      1.00      1.00       161\n",
            "        ISTP       1.00      1.00      1.00       270\n",
            "\n",
            "    accuracy                           1.00      6940\n",
            "   macro avg       1.00      1.00      1.00      6940\n",
            "weighted avg       1.00      1.00      1.00      6940\n",
            "\n",
            "\n",
            "TEST SET\n",
            "Accuracy:  0.6639769452449568\n",
            "Confusion Matrix:\n",
            "[[ 10   3   0   1   0   0   0   0   9  10   3   3   0   0   1   1]\n",
            " [  0  83   0   5   0   0   1   0   8  15  10   3   0   0   0   0]\n",
            " [  2   2  20   5   0   0   0   0   1   4   8   2   0   0   0   0]\n",
            " [  0   8   0  85   0   0   0   0   9   7   9  16   0   1   0   0]\n",
            " [  0   0   0   3   0   0   0   0   1   0   0   2   0   0   1   0]\n",
            " [  0   2   1   1   0   0   0   0   1   1   1   0   0   1   0   0]\n",
            " [  0   0   1   1   0   0   2   0   1   1   1   0   0   0   0   0]\n",
            " [  0   0   0   1   0   0   0   4   2   3   0   3   0   0   0   2]\n",
            " [  0   7   2  12   0   1   0   0 195  37  11  16   0   2   1   4]\n",
            " [  2   9   0   7   0   0   1   0  25 296   8  14   0   4   1   3]\n",
            " [  0   2   3   2   0   0   0   0  17  17 137  13   0   1   1   0]\n",
            " [  0   1   2  11   0   0   0   0  18  28  10 217   1   0   0   5]\n",
            " [  0   2   1   1   0   0   0   0   8   5   0   4  23   0   0   1]\n",
            " [  0   2   0   0   0   0   1   0   3  12   6   3   0  23   1   2]\n",
            " [  1   2   0   0   0   0   0   0   4   8   4   3   0   2  19   1]\n",
            " [  0   1   0   2   0   0   0   0   4  10   6   5   1   0   0  38]]\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "        ENFJ       0.67      0.24      0.36        41\n",
            "        ENFP       0.67      0.66      0.67       125\n",
            "        ENTJ       0.67      0.45      0.54        44\n",
            "        ENTP       0.62      0.63      0.62       135\n",
            "        ESFJ       0.00      0.00      0.00         7\n",
            "        ESFP       0.00      0.00      0.00         8\n",
            "        ESTJ       0.40      0.29      0.33         7\n",
            "        ESTP       1.00      0.27      0.42        15\n",
            "        INFJ       0.64      0.68      0.66       288\n",
            "        INFP       0.65      0.80      0.72       370\n",
            "        INTJ       0.64      0.71      0.67       193\n",
            "        INTP       0.71      0.74      0.73       293\n",
            "        ISFJ       0.92      0.51      0.66        45\n",
            "        ISFP       0.68      0.43      0.53        53\n",
            "        ISTJ       0.76      0.43      0.55        44\n",
            "        ISTP       0.67      0.57      0.61        67\n",
            "\n",
            "    accuracy                           0.66      1735\n",
            "   macro avg       0.61      0.46      0.50      1735\n",
            "weighted avg       0.67      0.66      0.65      1735\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "model_multinomial_nb=MultinomialNB()\n",
        "model_multinomial_nb.fit(X_train, y_train)\n",
        "\n",
        "print(\"TRAINING SET\")\n",
        "print(\"Accuracy: \", model_multinomial_nb.score(X_train, y_train))\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_matrix(y_train, model_multinomial_nb.predict(X_train)))\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_train, model_multinomial_nb.predict(X_train)))\n",
        "print(\"\")\n",
        "\n",
        "print(\"TEST SET\")\n",
        "print(\"Accuracy: \", model_multinomial_nb.score(X_test, y_test))\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_matrix(y_test, model_multinomial_nb.predict(X_test)))\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_test, model_multinomial_nb.predict(X_test)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K40XQyK-icwv",
        "outputId": "72bfc62b-03e1-454e-98a5-304c77aa1d6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TRAINING SET\n",
            "Accuracy:  0.21253602305475505\n",
            "Confusion Matrix:\n",
            "[[   0    0    0    0    0    0    0    0    0  149    0    0    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0    0  550    0    0    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0    0  187    0    0    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0    0  550    0    0    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0    0   35    0    0    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0    0   40    0    0    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0    0   32    0    0    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0    0   74    0    0    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0   13 1169    0    0    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0    0 1462    0    0    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0    0  897    0    1    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0    0 1011    0    0    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0    0  121    0    0    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0    0  218    0    0    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0    1  160    0    0    0    0\n",
            "     0    0]\n",
            " [   0    0    0    0    0    0    0    0    0  270    0    0    0    0\n",
            "     0    0]]\n",
            "Classification Report:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "        ENFJ       0.00      0.00      0.00       149\n",
            "        ENFP       0.00      0.00      0.00       550\n",
            "        ENTJ       0.00      0.00      0.00       187\n",
            "        ENTP       0.00      0.00      0.00       550\n",
            "        ESFJ       0.00      0.00      0.00        35\n",
            "        ESFP       0.00      0.00      0.00        40\n",
            "        ESTJ       0.00      0.00      0.00        32\n",
            "        ESTP       0.00      0.00      0.00        74\n",
            "        INFJ       0.93      0.01      0.02      1182\n",
            "        INFP       0.21      1.00      0.35      1462\n",
            "        INTJ       0.00      0.00      0.00       898\n",
            "        INTP       0.00      0.00      0.00      1011\n",
            "        ISFJ       0.00      0.00      0.00       121\n",
            "        ISFP       0.00      0.00      0.00       218\n",
            "        ISTJ       0.00      0.00      0.00       161\n",
            "        ISTP       0.00      0.00      0.00       270\n",
            "\n",
            "    accuracy                           0.21      6940\n",
            "   macro avg       0.07      0.06      0.02      6940\n",
            "weighted avg       0.20      0.21      0.08      6940\n",
            "\n",
            "\n",
            "TEST SET\n",
            "Accuracy:  0.2132564841498559\n",
            "Confusion Matrix:\n",
            "[[  0   0   0   0   0   0   0   0   0  41   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0 125   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0  44   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0 135   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   7   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   8   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   7   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0  15   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0 288   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0 370   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   1 192   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0 293   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0  45   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0  53   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0  44   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0  67   0   0   0   0   0   0]]\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "        ENFJ       0.00      0.00      0.00        41\n",
            "        ENFP       0.00      0.00      0.00       125\n",
            "        ENTJ       0.00      0.00      0.00        44\n",
            "        ENTP       0.00      0.00      0.00       135\n",
            "        ESFJ       0.00      0.00      0.00         7\n",
            "        ESFP       0.00      0.00      0.00         8\n",
            "        ESTJ       0.00      0.00      0.00         7\n",
            "        ESTP       0.00      0.00      0.00        15\n",
            "        INFJ       0.00      0.00      0.00       288\n",
            "        INFP       0.21      1.00      0.35       370\n",
            "        INTJ       0.00      0.00      0.00       193\n",
            "        INTP       0.00      0.00      0.00       293\n",
            "        ISFJ       0.00      0.00      0.00        45\n",
            "        ISFP       0.00      0.00      0.00        53\n",
            "        ISTJ       0.00      0.00      0.00        44\n",
            "        ISTP       0.00      0.00      0.00        67\n",
            "\n",
            "    accuracy                           0.21      1735\n",
            "   macro avg       0.01      0.06      0.02      1735\n",
            "weighted avg       0.05      0.21      0.08      1735\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    }
  ]
}