PyTorch Depression detection _NLP analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7a87aeb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#basic import \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.nn.utils.rnn import pad_sequence\n",
    "from torch.utils.data import DataLoader,Dataset\n",
    "from sklearn.metrics import classification_report\n",
    "import spacy\n",
    "import warnings\n",
    "from collections import defaultdict\n",
    "from sklearn.model_selection import train_test_split\n",
    "warnings.filterwarnings('ignore')\n",
    "device = torch.device(\"mps\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c18aed8",
   "metadata": {},
   "source": [
    "### Reading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8fe59a39",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('/Users/aadityajoshi/Downloads/depression_dataset_reddit_cleaned.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b91b4c57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clean_text</th>\n",
       "      <th>is_depression</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>we understand that most people who reply immed...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>welcome to r depression s check in post a plac...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>anyone else instead of sleeping more when depr...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>i ve kind of stuffed around a lot in my life d...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sleep is my greatest and most comforting escap...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          clean_text  is_depression\n",
       "0  we understand that most people who reply immed...              1\n",
       "1  welcome to r depression s check in post a plac...              1\n",
       "2  anyone else instead of sleeping more when depr...              1\n",
       "3  i ve kind of stuffed around a lot in my life d...              1\n",
       "4  sleep is my greatest and most comforting escap...              1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7c017dff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7731 entries, 0 to 7730\n",
      "Data columns (total 2 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   clean_text     7731 non-null   object\n",
      " 1   is_depression  7731 non-null   int64 \n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 120.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a8a07c96",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['text'] = df['clean_text']\n",
    "df['label'] = df['is_depression']\n",
    "df.drop(['clean_text','is_depression'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80460312",
   "metadata": {},
   "source": [
    "### removing blanks nan and duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "25546335",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "source": [
    "df.dropna(inplace=True)\n",
    "df.drop_duplicates(inplace=True)\n",
    "blanks = []\n",
    "for i,txt,lb in df.itertuples():\n",
    "    if type(txt) == str:\n",
    "        if txt.isspace() == True:\n",
    "            blanks.append(i)\n",
    "print(blanks)\n",
    "df.drop(blanks,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c5d9ca16",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['len'] = df['text'].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f6521e7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load('en_core_web_sm')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9989c62",
   "metadata": {},
   "source": [
    "### Cleaning text using spacy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a33210d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#clean and check again\n",
    "def clean_lemm(text):\n",
    "    doc = nlp(text.lower())\n",
    "    clean_text = [token.lemma_ for token in doc if not token.is_punct and not token.is_stop and not token.like_url]\n",
    "    return ' '.join(clean_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ce9a5d85",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['text'] = df['text'].apply(clean_lemm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fc127546",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x340ff3350>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['len'] = df['text'].apply(len)\n",
    "sns.displot(df['len'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a0898570",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "750.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['len'].quantile(0.95)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d6e05da",
   "metadata": {},
   "source": [
    "### truncating lengths of sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6a59a359",
   "metadata": {},
   "outputs": [],
   "source": [
    "#tuncating to 750 chars\n",
    "len_ind = list(df[df['len']>=750].index)\n",
    "for i in len_ind:\n",
    "    df['text'].loc[i] = df['text'].loc[i][:750]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "835a0501",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('len',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "edacb5c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='label', ylabel='count'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VNDY2yufzyTAMGYYhn8+npqammz1EAOj2IhoUlZWVWrhwoaqqqlRRUaGvv/5a2dnZOnfuXFjd1KlTVV9fby1vvvlm2PaCggLt2LFD5eXl2rNnj1paWpSbm6sLFy5YNfn5+aqpqZHf75ff71dNTY18Pl+njBMAurOoSJ7c7/eHrf/2t7+Vy+VSdXW17r33Xqvd6XTK4/Fc9hjBYFBbtmzR1q1bNWnSJElSWVmZvF6vdu3apSlTpujYsWPy+/2qqqpSZmamJGnz5s3KysrS8ePHNXTo0Js0QgDo/rrUNYpgMChJio+PD2vfvXu3XC6X7rzzTs2dO1cNDQ3Wturqap0/f17Z2dlWW3JystLS0rR3715J0r59+2QYhhUSkjR69GgZhmHVXCoUCqm5uTlsAYDeqMsEhWmaWrp0qX74wx8qLS3Nas/JydG2bdv09ttv67nnntPBgwc1YcIEhUIhSVIgEFB0dLQGDhwYdjy3261AIGDVuFyudud0uVxWzaWKi4ut6xmGYcjr9d6ooQJAtxLRj56+bdGiRfrwww+1Z8+esPaHHnrI+jktLU2jRo1SSkqK3njjDc2YMeOKxzNNUw6Hw1r/9s9Xqvm2wsJCLV261Fpvbm4mLAD0Sl1iRrF48WK99tpreueddzR48GDb2qSkJKWkpOjEiROSJI/Ho7a2NjU2NobVNTQ0yO12WzWnTp1qd6zTp09bNZdyOp2Ki4sLWwCgN4poUJimqUWLFunll1/W22+/rSFDhnznPmfOnFFdXZ2SkpIkSRkZGerbt68qKiqsmvr6eh05ckRjxoyRJGVlZSkYDOrAgQNWzf79+xUMBq0aAMDlRfSjp4ULF+qll17Sq6++qtjYWOt6gWEYiomJUUtLi1atWqUHHnhASUlJ+vTTT/X4448rMTFR999/v1U7Z84cLVu2TAkJCYqPj9fy5cuVnp5u3QU1bNgwTZ06VXPnztWmTZskSfPmzVNubi53PAHAd4hoUGzcuFGSNG7cuLD23/72t5o9e7b69Omjw4cP68UXX1RTU5OSkpI0fvx4bd++XbGxsVb9unXrFBUVpZkzZ6q1tVUTJ05UaWmp+vTpY9Vs27ZNS5Ysse6OysvLU0lJyc0fJAB0cxENCtM0bbfHxMTorbfe+s7j9OvXTxs2bNCGDRuuWBMfH6+ysrJr7iMA9HZd4mI2AKDrIigAALYICgCALYICAGCLoAAA2CIoAAC2CAoAgC2CAgBgi6AAANgiKAAAtggKAICtDgXFhAkT1NTU1K69ublZEyZMuN4+AQC6kA4Fxe7du9XW1tau/auvvtJ777133Z0CAHQd1/T22A8//ND6+aOPPgr7vukLFy7I7/frb//2b29c7wAAEXdNQfF3f/d3cjgccjgcl/2IKSYmxvZV3wCA7ueagqK2tlamaer222/XgQMHNGjQIGtbdHS0XC5X2JcFAQC6v2sKipSUFEnSxYsXb0pnAABdT4e/4e4Pf/iDdu/erYaGhnbB8e///u/X3TEAQNfQoaDYvHmzfvrTnyoxMVEej0cOh8Pa5nA4CAoA6EE6FBTPPPOMnn32WT322GM3uj8AgC6mQ89RNDY26sEHH7zRfQEAdEEdCooHH3xQO3fuvNF9AQB0QR366OmOO+7QypUrVVVVpfT0dPXt2zds+5IlS25I5wAAkdehoHj++ed16623qrKyUpWVlWHbHA4HQQEAPUiHgqK2tvZG9wMA0EXxmnEAgK0OzSj+5V/+xXb7b37zmw51BgDQ9XT49thvLw0NDXr77bf18ssvX/Z7Kq6kuLhYd999t2JjY+VyuTR9+nQdP348rMY0Ta1atUrJycmKiYnRuHHjdPTo0bCaUCikxYsXKzExUQMGDFBeXp5OnjzZrs8+n0+GYcgwDPl8vmvqKwD0Vh2aUezYsaNd28WLF7VgwQLdfvvtV32cyspKLVy4UHfffbe+/vprPfHEE8rOztZHH32kAQMGSJLWrFmjtWvXqrS0VHfeeaeeeeYZTZ48WcePH1dsbKwkqaCgQK+//rrKy8uVkJCgZcuWKTc3V9XV1dZLCvPz83Xy5En5/X5J0rx58+Tz+fT666935D8BAPQaDtM0zRt1sOPHj2vcuHGqr6/v0P6nT5+Wy+VSZWWl7r33XpmmqeTkZBUUFFhPgYdCIbndbv385z/X/PnzFQwGNWjQIG3dulUPPfSQJOnzzz+X1+vVm2++qSlTpujYsWMaPny4qqqqlJmZKUmqqqpSVlaWPv74Yw0dOvQ7+9bc3CzDMBQMBhUXF3fNY7tn/tPXvA+6r/c2rYzYubPLCyN2bnS+nQ8Xd3jfq/27dkMvZv/f//2fvv766w7vHwwGJUnx8fGS/np3VSAQUHZ2tlXjdDo1duxY7d27V5JUXV2t8+fPh9UkJycrLS3Nqtm3b58Mw7BCQpJGjx4twzCsmkuFQiE1NzeHLQDQG3Xoo6elS5eGrZumqfr6er3xxhuaNWtWhzpimqaWLl2qH/7wh0pLS5Mk6xv03G53WK3b7daf/vQnqyY6OloDBw5sV/PN/oFAQC6Xq905XS5X2Lf0fVtxcbGeeuqpDo0FAHqSDgXFBx98ELZ+yy23aNCgQXruuee+846oK1m0aJE+/PBD7dmzp922b7+dVvprqFzadqlLay5Xb3ecwsLCsEBsbm6W1+u1PScA9EQdCop33nnnhnZi8eLFeu211/Tuu+9q8ODBVrvH45H01xlBUlKS1d7Q0GDNMjwej9ra2tTY2Bg2q2hoaNCYMWOsmlOnTrU77+nTp9vNVr7hdDrldDqvf3AA0M1d1zWK06dPa8+ePXr//fd1+vTpa97fNE0tWrRIL7/8st5++20NGTIkbPuQIUPk8XhUUVFhtbW1tamystIKgYyMDPXt2zespr6+XkeOHLFqsrKyFAwGdeDAAatm//79CgaDVg0A4PI6NKM4d+6cFi9erBdffNH6drs+ffron//5n7Vhwwb179//qo6zcOFCvfTSS3r11VcVGxtrXS8wDEMxMTFyOBwqKChQUVGRUlNTlZqaqqKiIvXv31/5+flW7Zw5c7Rs2TIlJCQoPj5ey5cvV3p6uiZNmiRJGjZsmKZOnaq5c+dq06ZNkv56e2xubu5V3fEEAL1Zh2YUS5cuVWVlpV5//XU1NTWpqalJr776qiorK7Vs2bKrPs7GjRsVDAY1btw4JSUlWcv27dutmhUrVqigoEALFizQqFGj9Oc//1k7d+60nqGQpHXr1mn69OmaOXOm/uEf/kH9+/fX66+/bj1DIUnbtm1Tenq6srOzlZ2drREjRmjr1q0dGT4A9Codeo4iMTFR//Vf/6Vx48aFtb/zzjuaOXNmhz6G6up4jgLXguco0Fm67HMUf/nLXy57Edjlcukvf/lLRw4JAOiiOhQUWVlZevLJJ/XVV19Zba2trXrqqaeUlZV1wzoHAIi8Dl3MXr9+vXJycjR48GCNHDlSDodDNTU1cjqdfEUqAPQwHQqK9PR0nThxQmVlZfr4449lmqYefvhh/ehHP1JMTMyN7iMAIII6FBTFxcVyu92aO3duWPtvfvMbnT592nqBHwCg++vQNYpNmzbp+9//frv2u+66S7/+9a+vu1MAgK6jQ0Fx6Ss1vjFo0KAOv2IcANA1dSgovF6v3n///Xbt77//vpKTk6+7UwCArqND1yh+8pOfqKCgQOfPn9eECRMkSb///e+1YsWKa3oyGwDQ9XUoKFasWKEvv/xSCxYsUFtbmySpX79+euyxx1RYyFOhANCTdCgoHA6Hfv7zn2vlypU6duyYYmJilJqaymu5AaAH6lBQfOPWW2/V3XfffaP6AgDogm7od2YDAHoeggIAYIugAADYIigAALYICgCALYICAGCLoAAA2CIoAAC2CAoAgC2CAgBgi6AAANgiKAAAtggKAIAtggIAYCuiQfHuu+9q2rRpSk5OlsPh0CuvvBK2ffbs2XI4HGHL6NGjw2pCoZAWL16sxMREDRgwQHl5eTp58mRYTWNjo3w+nwzDkGEY8vl8ampqusmjA4CeIaJBce7cOY0cOVIlJSVXrJk6darq6+ut5c033wzbXlBQoB07dqi8vFx79uxRS0uLcnNzdeHCBasmPz9fNTU18vv98vv9qqmpkc/nu2njAoCe5Lq+uOh65eTkKCcnx7bG6XTK4/FcdlswGNSWLVu0detWTZo0SZJUVlYmr9erXbt2acqUKTp27Jj8fr+qqqqUmZkpSdq8ebOysrJ0/PhxDR069MYOCgB6mC5/jWL37t1yuVy68847NXfuXDU0NFjbqqurdf78eWVnZ1ttycnJSktL0969eyVJ+/btk2EYVkhI0ujRo2UYhlUDALiyiM4ovktOTo4efPBBpaSkqLa2VitXrtSECRNUXV0tp9OpQCCg6OhoDRw4MGw/t9utQCAgSQoEAnK5XO2O7XK5rJrLCYVCCoVC1npzc/MNGhUAdC9dOigeeugh6+e0tDSNGjVKKSkpeuONNzRjxowr7meaphwOh7X+7Z+vVHOp4uJiPfXUUx3sOQD0HF3+o6dvS0pKUkpKik6cOCFJ8ng8amtrU2NjY1hdQ0OD3G63VXPq1Kl2xzp9+rRVczmFhYUKBoPWUldXdwNHAgDdR7cKijNnzqiurk5JSUmSpIyMDPXt21cVFRVWTX19vY4cOaIxY8ZIkrKyshQMBnXgwAGrZv/+/QoGg1bN5TidTsXFxYUtANAbRfSjp5aWFn3yySfWem1trWpqahQfH6/4+HitWrVKDzzwgJKSkvTpp5/q8ccfV2Jiou6//35JkmEYmjNnjpYtW6aEhATFx8dr+fLlSk9Pt+6CGjZsmKZOnaq5c+dq06ZNkqR58+YpNzeXO54A4CpENCgOHTqk8ePHW+tLly6VJM2aNUsbN27U4cOH9eKLL6qpqUlJSUkaP368tm/frtjYWGufdevWKSoqSjNnzlRra6smTpyo0tJS9enTx6rZtm2blixZYt0dlZeXZ/vsBgDg/4toUIwbN06maV5x+1tvvfWdx+jXr582bNigDRs2XLEmPj5eZWVlHeojAPR23eoaBQCg8xEUAABbBAUAwBZBAQCwRVAAAGwRFAAAWwQFAMAWQQEAsEVQAABsERQAAFsEBQDAFkEBALBFUAAAbBEUAABbBAUAwBZBAQCwRVAAAGwRFAAAWwQFAMAWQQEAsEVQAABsERQAAFsEBQDAFkEBALBFUAAAbBEUAABbBAUAwFZEg+Ldd9/VtGnTlJycLIfDoVdeeSVsu2maWrVqlZKTkxUTE6Nx48bp6NGjYTWhUEiLFy9WYmKiBgwYoLy8PJ08eTKsprGxUT6fT4ZhyDAM+Xw+NTU13eTRAUDPENGgOHfunEaOHKmSkpLLbl+zZo3Wrl2rkpISHTx4UB6PR5MnT9bZs2etmoKCAu3YsUPl5eXas2ePWlpalJubqwsXLlg1+fn5qqmpkd/vl9/vV01NjXw+300fHwD0BFGRPHlOTo5ycnIuu800Ta1fv15PPPGEZsyYIUl64YUX5Ha79dJLL2n+/PkKBoPasmWLtm7dqkmTJkmSysrK5PV6tWvXLk2ZMkXHjh2T3+9XVVWVMjMzJUmbN29WVlaWjh8/rqFDh3bOYAGgm+qy1yhqa2sVCASUnZ1ttTmdTo0dO1Z79+6VJFVXV+v8+fNhNcnJyUpLS7Nq9u3bJ8MwrJCQpNGjR8swDKvmckKhkJqbm8MWAOiNumxQBAIBSZLb7Q5rd7vd1rZAIKDo6GgNHDjQtsblcrU7vsvlsmoup7i42LqmYRiGvF7vdY0HALqrLhsU33A4HGHrpmm2a7vUpTWXq/+u4xQWFioYDFpLXV3dNfYcAHqGLhsUHo9Hktr9q7+hocGaZXg8HrW1tamxsdG25tSpU+2Of/r06XazlW9zOp2Ki4sLWwCgN+qyQTFkyBB5PB5VVFRYbW1tbaqsrNSYMWMkSRkZGerbt29YTX19vY4cOWLVZGVlKRgM6sCBA1bN/v37FQwGrRoAwJVF9K6nlpYWffLJJ9Z6bW2tampqFB8fr9tuu00FBQUqKipSamqqUlNTVVRUpP79+ys/P1+SZBiG5syZo2XLlikhIUHx8fFavny50tPTrbughg0bpqlTp2ru3LnatGmTJGnevHnKzc3ljicAuAoRDYpDhw5p/Pjx1vrSpUslSbNmzVJpaalWrFih1tZWLViwQI2NjcrMzNTOnTsVGxtr7bNu3TpFRUVp5syZam1t1cSJE1VaWqo+ffpYNdu2bdOSJUusu6Py8vKu+OwGACCcwzRNM9Kd6A6am5tlGIaCwWCHrlfcM//pm9ArdFXvbVoZsXNnlxdG7NzofDsfLu7wvlf7d63LXqMAAHQNBAUAwBZBAQCwRVAAAGwRFAAAWwQFAMAWQQEAsEVQAABsERQAAFsEBQDAFkEBALBFUAAAbBEUAABbBAUAwBZBAQCwRVAAAGwRFAAAWwQFAMAWQQEAsEVQAABsERQAAFsEBQDAFkEBALBFUAAAbBEUAABbBAUAwBZBAQCw1aWDYtWqVXI4HGGLx+OxtpumqVWrVik5OVkxMTEaN26cjh49GnaMUCikxYsXKzExUQMGDFBeXp5OnjzZ2UMBgG6rSweFJN11112qr6+3lsOHD1vb1qxZo7Vr16qkpEQHDx6Ux+PR5MmTdfbsWaumoKBAO3bsUHl5ufbs2aOWlhbl5ubqwoULkRgOAHQ7UZHuwHeJiooKm0V8wzRNrV+/Xk888YRmzJghSXrhhRfkdrv10ksvaf78+QoGg9qyZYu2bt2qSZMmSZLKysrk9Xq1a9cuTZkypVPHAgDdUZefUZw4cULJyckaMmSIHn74Yf3xj3+UJNXW1ioQCCg7O9uqdTqdGjt2rPbu3StJqq6u1vnz58NqkpOTlZaWZtVcSSgUUnNzc9gCAL1Rlw6KzMxMvfjii3rrrbe0efNmBQIBjRkzRmfOnFEgEJAkud3usH3cbre1LRAIKDo6WgMHDrxizZUUFxfLMAxr8Xq9N3BkANB9dOmgyMnJ0QMPPKD09HRNmjRJb7zxhqS/fsT0DYfDEbaPaZrt2i51NTWFhYUKBoPWUldX18FRAED31qWD4lIDBgxQenq6Tpw4YV23uHRm0NDQYM0yPB6P2tra1NjYeMWaK3E6nYqLiwtbAKA36lZBEQqFdOzYMSUlJWnIkCHyeDyqqKiwtre1tamyslJjxoyRJGVkZKhv375hNfX19Tpy5IhVAwCw16Xvelq+fLmmTZum2267TQ0NDXrmmWfU3NysWbNmyeFwqKCgQEVFRUpNTVVqaqqKiorUv39/5efnS5IMw9CcOXO0bNkyJSQkKD4+XsuXL7c+ygIAfLcuHRQnT57UP/3TP+mLL77QoEGDNHr0aFVVVSklJUWStGLFCrW2tmrBggVqbGxUZmamdu7cqdjYWOsY69atU1RUlGbOnKnW1lZNnDhRpaWl6tOnT6SGBQDdisM0TTPSnegOmpubZRiGgsFgh65X3DP/6ZvQK3RV721aGbFzZ5cXRuzc6Hw7Hy7u8L5X+3etW12jAAB0PoICAGCLoAAA2CIoAAC2CAoAgC2CAgBgi6AAANgiKAAAtggKAIAtggIAYIugAADYIigAALYICgCALYICAGCLoAAA2CIoAAC2CAoAgC2CAgBgi6AAANgiKAAAtggKAIAtggIAYIugAADYIigAALYICgCALYICAGCLoAAA2OpVQfGrX/1KQ4YMUb9+/ZSRkaH33nsv0l0CgC6v1wTF9u3bVVBQoCeeeEIffPCB7rnnHuXk5Oizzz6LdNcAoEvrNUGxdu1azZkzRz/5yU80bNgwrV+/Xl6vVxs3box01wCgS4uKdAc6Q1tbm6qrq/Vv//ZvYe3Z2dnau3fvZfcJhUIKhULWejAYlCQ1Nzd3qA9ft33Vof3QPXX09+RG+Povoe8uQo9xPb9r3+xrmqZtXa8Iii+++EIXLlyQ2+0Oa3e73QoEApfdp7i4WE899VS7dq/Xe1P6iJ7FKC2KdBfQSxhz1l33Mc6ePSvDMK64vVcExTccDkfYumma7dq+UVhYqKVLl1rrFy9e1JdffqmEhIQr7oNwzc3N8nq9qqurU1xcXKS7gx6M37WOMU1TZ8+eVXJysm1drwiKxMRE9enTp93soaGhod0s4xtOp1NOpzOs7W/+5m9uVhd7tLi4OP7nRafgd+3a2c0kvtErLmZHR0crIyNDFRUVYe0VFRUaM2ZMhHoFAN1Dr5hRSNLSpUvl8/k0atQoZWVl6fnnn9dnn32mRx55JNJdA4AurdcExUMPPaQzZ87oZz/7merr65WWlqY333xTKSkpke5aj+V0OvXkk0+2+wgPuNH4Xbu5HOZ33RcFAOjVesU1CgBAxxEUAABbBAUAwBZBAQCwRVDgpuG17rjZ3n33XU2bNk3JyclyOBx65ZVXIt2lHomgwE3Ba93RGc6dO6eRI0eqpKQk0l3p0bg9FjdFZmamfvCDH4S9xn3YsGGaPn26iouLI9gz9FQOh0M7duzQ9OnTI92VHocZBW64b17rnp2dHdZu91p3AF0XQYEbriOvdQfQdREUuGmu5bXuALouggI3XEde6w6g6yIocMPxWnegZ+k1b49F5+K17ugMLS0t+uSTT6z12tpa1dTUKD4+XrfddlsEe9azcHssbppf/epXWrNmjfVa93Xr1unee++NdLfQg+zevVvjx49v1z5r1iyVlpZ2fod6KIICAGCLaxQAAFsEBQDAFkEBALBFUAAAbBEUAABbBAUAwBZBAQCwRVAAN8G4ceNUUFBwVbW7d++Ww+FQU1PTdZ3ze9/7ntavX39dxwAuh6AAANgiKAAAtggK4CYrKyvTqFGjFBsbK4/Ho/z8fDU0NLSre//99zVy5Ej169dPmZmZOnz4cNj2vXv36t5771VMTIy8Xq+WLFmic+fOddYw0IsRFMBN1tbWpqefflr/+7//q1deeUW1tbWaPXt2u7p//dd/1S9+8QsdPHhQLpdLeXl5On/+vCTp8OHDmjJlimbMmKEPP/xQ27dv1549e7Ro0aJOHg16JRPADTd27Fjz0Ucfvey2AwcOmJLMs2fPmqZpmu+8844pySwvL7dqzpw5Y8bExJjbt283TdM0fT6fOW/evLDjvPfee+Ytt9xitra2mqZpmikpKea6detu/GDQ6zGjAG6yDz74QPfdd59SUlIUGxurcePGSZI+++yzsLqsrCzr5/j4eA0dOlTHjh2TJFVXV6u0tFS33nqrtUyZMkUXL15UbW1tp40FvRNfXATcROfOnVN2drays7NVVlamQYMG6bPPPtOUKVPU1tb2nft/8x3jFy9e1Pz587VkyZJ2NXxBD242ggK4iT7++GN98cUXWr16tbxeryTp0KFDl62tqqqy/ug3NjbqD3/4g77//e9Lkn7wgx/o6NGjuuOOOzqn48C38NETcBPddtttio6O1oYNG/THP/5Rr732mp5++unL1v7sZz/T73//ex05ckSzZ89WYmKipk+fLkl67LHHtG/fPi1cuFA1NTU6ceKEXnvtNS1evLgTR4PeiqAAbqJBgwaptLRU//mf/6nhw4dr9erV+sUvfnHZ2tWrV+vRRx9VRkaG6uvr9dprryk6OlqSNGLECFVWVurEiRO655579Pd///dauXKlkpKSOnM46KX4KlQAgC1mFAAAWwQFAMAWQQEAsEVQAABsERQAAFsEBQDAFkEBALBFUAAAbBEUAABbBAUAwBZBAQCwRVAAAGz9P+ehYg0DvbRxAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,4))\n",
    "sns.countplot(x='label',data=df,palette='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2354e647",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "887a3d10dbcf467196edb6e39ed6831f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cc1ca8b59b124a16ab5c1880f5ddc984",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5b6c02e31ff1449099a82dfde955811b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<iframe width=\"100%\" height=\"800px\" srcdoc=\"&lt;!doctype html&gt;&lt;html lang=en&gt;&lt;head&gt;&lt;meta charset=utf-8&gt;&lt;meta name=viewport content=&quot;width=device-width, initial-scale=1, shrink-to-fit=no&quot;&gt;&lt;meta name=description content=&quot;Profile report generated by YData! Visit us at https://ydata.ai&quot;&gt;&lt;meta name=author content=&quot;YData and the open source community.&quot;&gt;&lt;meta name=generator content=&quot;Pandas Profiling vv4.6.4&quot;&gt;&lt;meta name=url content=https://github.com/ydataai/ydata-profiling&gt;&lt;meta name=date content=&quot;2024-10-17 14:27:11.329338&quot;&gt;&lt;title&gt;Pandas Profiling Report&lt;/title&gt;&lt;style&gt;\n",
       "/*!\n",
       " * Bootstrap v3.3.7 (http://getbootstrap.com)\n",
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GradientType=0);background-repeat:repeat-x;border-color:#dcdcdc;-webkit-box-shadow:inset 0 1px 3px rgba(0,0,0,.05),0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 3px rgba(0,0,0,.05),0 1px 0 rgba(255,255,255,.1)}\n",
       "/*# sourceMappingURL=bootstrap-theme.min.css.map */        &lt;/style&gt;&lt;style&gt;body {\n",
       "  padding-top: 80px;\n",
       "}\n",
       ".content .container-fluid{\n",
       "    margin-left: 20px !important;\n",
       "    margin-right: 20px !important;\n",
       "    margin-bottom: 20px;\n",
       "}\n",
       "\n",
       ".page-header{\n",
       "    border:0 !important;\n",
       "}\n",
       "\n",
       ".row.variable, .section-items &gt; .row {\n",
       "    border: 1px solid #e1e1e8;\n",
       "    border-top: hidden;\n",
       "}\n",
       "\n",
       ".row.spacing {\n",
       "    padding: 2em 1em;\n",
       "}\n",
       "\n",
       ".row.header {\n",
       "    border-bottom: 1px solid #e1e1e8;\n",
       "    /*background-color: #f5f5f5;*/\n",
       "    padding-left: 2em;\n",
       "}\n",
       "\n",
       "\n",
       ".dl-horizontal dt {\n",
       "    text-align: left;\n",
       "    padding-right: 1em;\n",
       "    white-space: normal;\n",
       "}\n",
       "\n",
       ".dl-horizontal dd {\n",
       "    margin-left: 0;\n",
       "}\n",
       "\n",
       ".col-md-12 {\n",
       "    padding-left: 2em;\n",
       "}\n",
       "\n",
       ".indent {\n",
       "    margin-left: 1em;\n",
       "}\n",
       "\n",
       ".center-img {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    display: block;\n",
       "}\n",
       "\n",
       "/* Table example_values */\n",
       "table.example_values {\n",
       "    border: 0;\n",
       "}\n",
       "\n",
       ".example_values th {\n",
       "    border: 0;\n",
       "    padding: 0;\n",
       "    color: #555;\n",
       "    font-weight: 600;\n",
       "}\n",
       "\n",
       ".example_values tr, .example_values td {\n",
       "    border: 0;\n",
       "    padding: 0;\n",
       "    color: #555;\n",
       "}\n",
       "\n",
       "table.correlation-table {\n",
       "    width: 98%  /* To avoid an unnecessary overflow. */\n",
       "}\n",
       "\n",
       "/* STATS */\n",
       "table.stats, table.sample, table.duplicate, table.correlation-table{\n",
       "    border: 0;\n",
       "}\n",
       "\n",
       ".stats tr, .sample tr, .duplicate tr, .correlation-table tr {\n",
       "    border: 0;\n",
       "}\n",
       "\n",
       ".stats th, .stats td{\n",
       "    color: #555;\n",
       "    border: 0;\n",
       "}\n",
       "\n",
       ".stats th {\n",
       "    padding: 0 2em 0 0;\n",
       "    font-weight: 600;\n",
       "}\n",
       "\n",
       ".stats td {\n",
       "    padding: 1px;\n",
       "}\n",
       "\n",
       "\n",
       "/* Sample table */\n",
       "table.sample, table.duplicate, table.correlation-table {\n",
       "    margin-bottom: 2em;\n",
       "    margin-left: 1em;\n",
       "}\n",
       "\n",
       ".sample td, .sample th, .duplicate td, .duplicate th, .correlation-table td, .correlation-table th {\n",
       "    padding: 0.5em;\n",
       "    white-space: nowrap;\n",
       "    border: 0;\n",
       "\n",
       "}\n",
       "\n",
       ".sample thead, .duplicate thead, .correlation-table thead {\n",
       "    border-top: 0;\n",
       "    border-bottom: 2px solid #ddd;\n",
       "}\n",
       "\n",
       ".sample td, .duplicate td {\n",
       "    width: 100%;\n",
       "} \n",
       "\n",
       "/* There is no good solution available to make the divs equal height and then center ... */\n",
       ".histogram {\n",
       "    margin-top: 3em;\n",
       "}\n",
       "\n",
       "/* Freq table */\n",
       "table.freq {\n",
       "    margin-bottom: 2em;\n",
       "    border: 0;\n",
       "}\n",
       "\n",
       "table.freq th, table.freq tr, table.freq td {\n",
       "    border: 0;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".freq thead {\n",
       "    font-weight: 600;\n",
       "    white-space: nowrap;\n",
       "    overflow: hidden;\n",
       "    text-overflow: ellipsis;\n",
       "\n",
       "}\n",
       "\n",
       "/* Freq mini */\n",
       ".freq.mini td {\n",
       "    width: 50%;\n",
       "    padding: 1px;\n",
       "    font-size: 12px;\n",
       "\n",
       "}\n",
       "\n",
       "table.freq.mini {\n",
       "    width: 100%;\n",
       "}\n",
       "\n",
       ".freq.mini th {\n",
       "    overflow: hidden;\n",
       "    text-overflow: ellipsis;\n",
       "    white-space: nowrap;\n",
       "    max-width: 5em;\n",
       "    font-weight: 400;\n",
       "    text-align: right;\n",
       "    padding-right: 0.5em;\n",
       "}\n",
       "\n",
       "/* Message classes */\n",
       ".missing {\n",
       "    color: #a94442;\n",
       "}\n",
       "\n",
       ".alert, .alert &gt; th, .alert &gt; td {\n",
       "    color: #a94442;\n",
       "}\n",
       "\n",
       ".ignore {\n",
       "    opacity: 0.4;\n",
       "}\n",
       "\n",
       "/* Bars in tables */\n",
       ".freq.table{\n",
       "    table-layout: fixed;\n",
       "}\n",
       "\n",
       ".freq:not(.mini) tr td:nth-child(1), .freq:not(.mini) tr th:nth-child(1){\n",
       "    width: auto;\n",
       "    max-width: none;\n",
       "\n",
       "    white-space: nowrap;\n",
       "    overflow: hidden;\n",
       "    text-overflow: ellipsis;\n",
       "}\n",
       "\n",
       ".freq:not(.mini) tr td:nth-child(2), .freq:not(.mini) tr td:nth-child(3), .freq:not(.mini) tr th:nth-child(2), .freq:not(.mini) tr th:nth-child(3){\n",
       "    width: 100px;\n",
       "    text-align: right;\n",
       "}\n",
       ".freq:not(.mini) tr td:nth-child(4), .freq:not(.mini) tr th:nth-child(4){\n",
       "    width:200px;\n",
       "}\n",
       "\n",
       ".freq .bar {\n",
       "    float: left;\n",
       "    width: 0;\n",
       "    height: 100%;\n",
       "    line-height: 20px;\n",
       "    color: #fff;\n",
       "    text-align: center;\n",
       "    border-radius: 3px;\n",
       "    margin-right: 4px;\n",
       "}\n",
       "\n",
       ".other .bar {\n",
       "    background-color: #999;\n",
       "}\n",
       "\n",
       ".missing .bar {\n",
       "    background-color: #a94442;\n",
       "}\n",
       "\n",
       ".tooltip-inner {\n",
       "    width: 100%;\n",
       "    white-space: nowrap;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".extrapadding {\n",
       "    padding: 2em;\n",
       "}\n",
       "\n",
       ".variable .h4 {\n",
       "    text-overflow: ellipsis;\n",
       "    display:inline-block;\n",
       "    width: calc(90%); /* The trick is here! */\n",
       "    overflow:hidden;\n",
       "}\n",
       "\n",
       ".variable.ignore .h4{\n",
       "    text-decoration: line-through;\n",
       "}\n",
       "\n",
       ".table-responsive{\n",
       "    overflow: scroll;\n",
       "    width: 100%;\n",
       "    overflow-y: hidden;\n",
       "}\n",
       ".img-responsive{\n",
       "    max-width: 99%;\n",
       "    min-width: 99%;\n",
       "}\n",
       ".footer-text{\n",
       "    padding:20px;\n",
       "}\n",
       "\n",
       "table.list-warnings td{\n",
       "    padding-right:10px;\n",
       "}\n",
       "\n",
       "a.anchor-pos {\n",
       "    display: block;\n",
       "    position: relative;\n",
       "    top: -70px;\n",
       "    visibility: hidden;\n",
       "}\n",
       "\n",
       "a.anchor-pos-variable{\n",
       "    /*top: -70px;*/\n",
       "}\n",
       "\n",
       "#sample-container, #duplicate-container {\n",
       "    overflow: auto;\n",
       "    width: 100%;\n",
       "    overflow-y: hidden;\n",
       "}\n",
       "\n",
       "#correlation-table-container {\n",
       "    max-height: 400px;\n",
       "    overflow: auto;\n",
       "}\n",
       "\n",
       "#overview-content td, #overview-content th{\n",
       "    border-top: 0;\n",
       "    line-height: 1;\n",
       "}\n",
       "\n",
       ".variable-description{\n",
       "    color: #777;\n",
       "    font-size: 10pt;\n",
       "    margin-top: 10px;\n",
       "    font-style: italic;\n",
       "}\n",
       "\n",
       "select.multiple{\n",
       "    width: 180px;\n",
       "    height: 500px;\n",
       "    margin: 10px 0;\n",
       "}\n",
       "\n",
       ".named-list-item{\n",
       "    padding: 1em;\n",
       "}\n",
       "\n",
       "/* not printing tabs */\n",
       "@media print {\n",
       "    .tab-content &gt; .tab-pane, .collapse {\n",
       "        display: block !important;\n",
       "        opacity: 1 !important;\n",
       "        visibility: visible !important;\n",
       "        /*page-break-after: always;*/\n",
       "        page-break-after: right;\n",
       "        page-break-before: avoid;\n",
       "    }\n",
       "\n",
       "    .nav-pills, .nav-tabs, button[data-toggle=&quot;collapse&quot;], .mini, .col-sm-3 img {\n",
       "        display:none !important;\n",
       "    }\n",
       "\n",
       "    a[download=&quot;config.yml&quot;]:after {\n",
       "        content: none !important;\n",
       "    }\n",
       "\n",
       "    .row {\n",
       "        border: 0 !important;\n",
       "    }\n",
       "}\n",
       "\n",
       ".text-placeholder {\n",
       "    display: inline-block;\n",
       "    background-color: #444;\n",
       "    height: 12px;\n",
       "    border-radius: 100px;\n",
       "    margin: 5px 0;\n",
       "    min-width: 200px;\n",
       "    opacity: .1;\n",
       "}\n",
       "\n",
       "    .freq.table-0 .bar {\n",
       "        background-color: #377eb8;\n",
       "    }\n",
       "    .freq.table-1 .bar {\n",
       "        background-color: #e41a1c;\n",
       "    }\n",
       "&lt;/style&gt;&lt;/head&gt;&lt;body&gt;&lt;a class=anchor-pos id=top&gt;&lt;/a&gt;&lt;nav class=&quot;navbar navbar-default navbar-fixed-top&quot;&gt;&lt;div class=container-fluid&gt;&lt;div class=navbar-header&gt;&lt;button type=button class=&quot;navbar-toggle collapsed&quot; data-toggle=collapse data-target=#navbar aria-expanded=false aria-controls=navbar&gt;&lt;span class=sr-only&gt;Toggle navigation&lt;/span&gt;&lt;span class=icon-bar&gt;&lt;/span&gt;&lt;span class=icon-bar&gt;&lt;/span&gt;&lt;span class=icon-bar&gt;&lt;/span&gt;&lt;/button&gt;&lt;a class=&quot;navbar-brand anchor&quot; href=#top&gt;Pandas Profiling Report&lt;/a&gt;&lt;/div&gt;&lt;div id=navbar class=&quot;navbar-collapse collapse&quot;&gt;&lt;ul class=&quot;nav navbar-nav navbar-right&quot;&gt;&lt;li&gt;&lt;a href=#overview&gt;Overview&lt;/a&gt;&lt;/li&gt;&lt;li&gt;&lt;a href=#variables-dropdown&gt;Variables&lt;/a&gt;&lt;/li&gt;&lt;li&gt;&lt;a href=#missing&gt;Missing values&lt;/a&gt;&lt;/li&gt;&lt;li&gt;&lt;a href=#sample&gt;Sample&lt;/a&gt;&lt;/li&gt;&lt;li&gt;&lt;a href=#duplicate&gt;Duplicate rows&lt;/a&gt;&lt;/li&gt;&lt;/ul&gt;&lt;/div&gt;&lt;/div&gt;&lt;/nav&gt;&lt;div class=content&gt;&lt;div class=container&gt;&lt;div class=&quot;row header&quot;&gt;&lt;a class=anchor-pos id=overview&gt;&lt;/a&gt;&lt;h1 class=page-header&gt;Overview&lt;/h1&gt;&lt;/div&gt;&lt;div class=section-items&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;ul class=&quot;nav nav-pills&quot; role=tablist&gt;&lt;li role=presentation class=active&gt;&lt;a href=#overview-dataset_overview aria-controls=overview-dataset_overview role=tab data-toggle=tab&gt;Overview&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#overview-alerts aria-controls=overview-alerts role=tab data-toggle=tab&gt;Alerts &lt;span class=badge&gt;1&lt;/span&gt;&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#overview-reproduction aria-controls=overview-reproduction role=tab data-toggle=tab&gt;Reproduction&lt;/a&gt;&lt;/li&gt;&lt;/ul&gt;&lt;div class=tab-content style=&quot;padding-top: 10px;&quot;&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12 active&quot; id=overview-dataset_overview&gt;&lt;div class=col-sm-6&gt;&lt;p class=h4&gt;Dataset statistics&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Number of variables&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;2&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Number of observations&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;7650&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing cells&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing cells (%)&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;0.0%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Duplicate rows&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;23&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Duplicate rows (%)&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;0.3%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Total size in memory&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;437.3 KiB&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Average record size in memory&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;58.5 B&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=col-sm-6&gt;&lt;p class=h4&gt;Variable types&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Text&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;1&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Categorical&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;1&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=overview-alerts&gt;&lt;div class=col-sm-12&gt;&lt;table class=&quot;table table-condensed list-warnings&quot;&gt;&lt;p class=h4&gt;Alerts&lt;/p&gt;&lt;tr style=border-top:0&gt;&lt;td&gt;Dataset has 23 (0.3%) &lt;a href=#duplicate&gt;duplicate rows&lt;/a&gt;&lt;/td&gt;&lt;td&gt;&lt;span class=&quot;label label-default&quot;&gt;Duplicates&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=overview-reproduction&gt;&lt;div class=col-sm-12&gt;&lt;p class=h4&gt;Reproduction&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Analysis started&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;2024-10-17 14:27:11.329338&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Analysis finished&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;2024-10-17 14:27:11.966918&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Duration&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;0.64 seconds&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Software version&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;&lt;a href=https://github.com/ydataai/ydata-profiling&gt;ydata-profiling vv4.6.4&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Download configuration&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;&lt;a download=config.json href=&quot;data:text/plain;charset=utf-8,%7B%22title%22%3A%20%22Pandas%20Profiling%20Report%22%2C%20%22dataset%22%3A%20%7B%22description%22%3A%20%22%22%2C%20%22creator%22%3A%20%22%22%2C%20%22author%22%3A%20%22%22%2C%20%22copyright_holder%22%3A%20%22%22%2C%20%22copyright_year%22%3A%20%22%22%2C%20%22url%22%3A%20%22%22%7D%2C%20%22variables%22%3A%20%7B%22descriptions%22%3A%20%7B%7D%7D%2C%20%22infer_dtypes%22%3A%20true%2C%20%22show_variable_description%22%3A%20true%2C%20%22pool_size%22%3A%200%2C%20%22progress_bar%22%3A%20true%2C%20%22vars%22%3A%20%7B%22num%22%3A%20%7B%22quantiles%22%3A%20%5B0.05%2C%200.25%2C%200.5%2C%200.75%2C%200.95%5D%2C%20%22skewness_threshold%22%3A%2020%2C%20%22low_categorical_threshold%22%3A%205%2C%20%22chi_squared_threshold%22%3A%200.999%7D%2C%20%22text%22%3A%20%7B%22length%22%3A%20true%2C%20%22words%22%3A%20true%2C%20%22characters%22%3A%20true%2C%20%22redact%22%3A%20false%7D%2C%20%22cat%22%3A%20%7B%22length%22%3A%20true%2C%20%22characters%22%3A%20true%2C%20%22words%22%3A%20true%2C%20%22cardinality_threshold%22%3A%2050%2C%20%22percentage_cat_threshold%22%3A%200.5%2C%20%22imbalance_threshold%22%3A%200.5%2C%20%22n_obs%22%3A%205%2C%20%22chi_squared_threshold%22%3A%200.999%2C%20%22coerce_str_to_date%22%3A%20false%2C%20%22redact%22%3A%20false%2C%20%22histogram_largest%22%3A%2050%2C%20%22stop_words%22%3A%20%5B%5D%7D%2C%20%22image%22%3A%20%7B%22active%22%3A%20false%2C%20%22exif%22%3A%20true%2C%20%22hash%22%3A%20true%7D%2C%20%22bool%22%3A%20%7B%22n_obs%22%3A%203%2C%20%22imbalance_threshold%22%3A%200.5%2C%20%22mappings%22%3A%20%7B%22t%22%3A%20true%2C%20%22f%22%3A%20false%2C%20%22yes%22%3A%20true%2C%20%22no%22%3A%20false%2C%20%22y%22%3A%20true%2C%20%22n%22%3A%20false%2C%20%22true%22%3A%20true%2C%20%22false%22%3A%20false%7D%7D%2C%20%22path%22%3A%20%7B%22active%22%3A%20false%7D%2C%20%22file%22%3A%20%7B%22active%22%3A%20false%7D%2C%20%22url%22%3A%20%7B%22active%22%3A%20false%7D%2C%20%22timeseries%22%3A%20%7B%22active%22%3A%20false%2C%20%22sortby%22%3A%20null%2C%20%22autocorrelation%22%3A%200.7%2C%20%22lags%22%3A%20%5B1%2C%207%2C%2012%2C%2024%2C%2030%5D%2C%20%22significance%22%3A%200.05%2C%20%22pacf_acf_lag%22%3A%20100%7D%7D%2C%20%22sort%22%3A%20null%2C%20%22missing_diagrams%22%3A%20%7B%22bar%22%3A%20true%2C%20%22matrix%22%3A%20true%2C%20%22heatmap%22%3A%20true%7D%2C%20%22correlation_table%22%3A%20true%2C%20%22correlations%22%3A%20%7B%22auto%22%3A%20%7B%22key%22%3A%20%22auto%22%2C%20%22calculate%22%3A%20true%2C%20%22warn_high_correlations%22%3A%2010%2C%20%22threshold%22%3A%200.5%2C%20%22n_bins%22%3A%2010%7D%2C%20%22spearman%22%3A%20%7B%22key%22%3A%20%22spearman%22%2C%20%22calculate%22%3A%20false%2C%20%22warn_high_correlations%22%3A%2010%2C%20%22threshold%22%3A%200.5%2C%20%22n_bins%22%3A%2010%7D%2C%20%22pearson%22%3A%20%7B%22key%22%3A%20%22pearson%22%2C%20%22calculate%22%3A%20false%2C%20%22warn_high_correlations%22%3A%2010%2C%20%22threshold%22%3A%200.5%2C%20%22n_bins%22%3A%2010%7D%2C%20%22phi_k%22%3A%20%7B%22key%22%3A%20%22phi_k%22%2C%20%22calculate%22%3A%20false%2C%20%22warn_high_correlations%22%3A%2010%2C%20%22threshold%22%3A%200.5%2C%20%22n_bins%22%3A%2010%7D%2C%20%22cramers%22%3A%20%7B%22key%22%3A%20%22cramers%22%2C%20%22calculate%22%3A%20false%2C%20%22warn_high_correlations%22%3A%2010%2C%20%22threshold%22%3A%200.5%2C%20%22n_bins%22%3A%2010%7D%2C%20%22kendall%22%3A%20%7B%22key%22%3A%20%22kendall%22%2C%20%22calculate%22%3A%20false%2C%20%22warn_high_correlations%22%3A%2010%2C%20%22threshold%22%3A%200.5%2C%20%22n_bins%22%3A%2010%7D%7D%2C%20%22interactions%22%3A%20%7B%22continuous%22%3A%20true%2C%20%22targets%22%3A%20%5B%5D%7D%2C%20%22categorical_maximum_correlation_distinct%22%3A%20100%2C%20%22memory_deep%22%3A%20false%2C%20%22plot%22%3A%20%7B%22missing%22%3A%20%7B%22force_labels%22%3A%20true%2C%20%22cmap%22%3A%20%22RdBu%22%7D%2C%20%22image_format%22%3A%20%22svg%22%2C%20%22correlation%22%3A%20%7B%22cmap%22%3A%20%22RdBu%22%2C%20%22bad%22%3A%20%22%23000000%22%7D%2C%20%22dpi%22%3A%20800%2C%20%22histogram%22%3A%20%7B%22bins%22%3A%2050%2C%20%22max_bins%22%3A%20250%2C%20%22x_axis_labels%22%3A%20true%2C%20%22density%22%3A%20false%7D%2C%20%22scatter_threshold%22%3A%201000%2C%20%22cat_freq%22%3A%20%7B%22show%22%3A%20true%2C%20%22type%22%3A%20%22bar%22%2C%20%22max_unique%22%3A%2010%2C%20%22colors%22%3A%20null%7D%7D%2C%20%22duplicates%22%3A%20%7B%22head%22%3A%2010%2C%20%22key%22%3A%20%22%23%20duplicates%22%7D%2C%20%22samples%22%3A%20%7B%22head%22%3A%2010%2C%20%22tail%22%3A%2010%2C%20%22random%22%3A%200%7D%2C%20%22reject_variables%22%3A%20true%2C%20%22n_obs_unique%22%3A%2010%2C%20%22n_freq_table_max%22%3A%2010%2C%20%22n_extreme_obs%22%3A%2010%2C%20%22report%22%3A%20%7B%22precision%22%3A%208%7D%2C%20%22html%22%3A%20%7B%22style%22%3A%20%7B%22primary_colors%22%3A%20%5B%22%23377eb8%22%2C%20%22%23e41a1c%22%2C%20%22%234daf4a%22%5D%2C%20%22logo%22%3A%20%22%22%2C%20%22theme%22%3A%20null%7D%2C%20%22navbar_show%22%3A%20true%2C%20%22minify_html%22%3A%20true%2C%20%22use_local_assets%22%3A%20true%2C%20%22inline%22%3A%20true%2C%20%22assets_prefix%22%3A%20null%2C%20%22assets_path%22%3A%20null%2C%20%22full_width%22%3A%20false%7D%2C%20%22notebook%22%3A%20%7B%22iframe%22%3A%20%7B%22height%22%3A%20%22800px%22%2C%20%22width%22%3A%20%22100%25%22%2C%20%22attribute%22%3A%20%22srcdoc%22%7D%7D%7D&quot;&gt;config.json&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=&quot;row header&quot;&gt;&lt;a class=anchor-pos id=variables-dropdown&gt;&lt;/a&gt;&lt;h1 class=page-header&gt;Variables&lt;/h1&gt;&lt;/div&gt;&lt;div class=section-items&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;select name=Variables id=variables-dropdown class=dropdown-toggle&gt;&lt;option value&gt;Select Columns&lt;/option&gt;&lt;option value=text&gt;text&lt;/option&gt;&lt;option value=label&gt;label&lt;/option&gt;&lt;/select&gt;&lt;/div&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;a class=&quot;anchor-pos anchor-pos-variable&quot; id=pp_var_2077041933236717579&gt;&lt;/a&gt;&lt;div class=variable&gt;&lt;div class=col-sm-12&gt;&lt;p class=h4 title=text&gt;&lt;a href=#pp_var_2077041933236717579&gt;text&lt;/a&gt;&lt;br&gt;&lt;small&gt;Text&lt;/small&gt;&lt;/p&gt;&lt;p class=variable-description&gt;&lt;/p&gt;&lt;/div&gt;&lt;div class=col-sm-6&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Distinct&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;7616&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct (%)&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;99.6%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing (%)&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;0.0%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Memory size&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;377.6 KiB&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=col-sm-6&gt;&lt;?xml version=&quot;1.0&quot; encoding=&quot;utf-8&quot; standalone=&quot;no&quot;?&gt;&lt;!DOCTYPE svg class=&quot;img-responsive center-img&quot;PUBLIC &quot;-//W3C//DTD SVG 1.1//EN&quot;\n",
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&quot; id=image58040681d7 x=55.08 y=-34.56 width=332.64 height=221.76 transform=&quot;scale(1 -1) translate(0 -221.76)&quot;/&gt;&lt;/g&gt;&lt;/g&gt;&lt;/g&gt;&lt;defs&gt;&lt;clippath id=pce56a9bb60&gt;&lt;rect x=55.08 y=34.56 width=332.64 height=221.76 /&gt;&lt;/clippath&gt;&lt;/defs&gt;&lt;/svg&gt;&lt;/div&gt;&lt;div class=&quot;col-sm-12 text-right&quot;&gt;&lt;button class=&quot;btn btn-default btn-sm&quot; data-toggle=collapse data-target=&quot;#bottom-2077041933236717579, #minifreqtable2077041933236717579&quot; aria-expanded=true aria-controls=collapseExample&gt; More details &lt;/button&gt;&lt;/div&gt;&lt;div id=bottom-2077041933236717579 class=collapse&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;ul class=&quot;nav nav-tabs&quot; role=tablist&gt;&lt;li role=presentation class=active&gt;&lt;a href=#2077041933236717579bottom-2077041933236717579overview aria-controls=2077041933236717579bottom-2077041933236717579overview role=tab data-toggle=tab&gt;Overview&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#2077041933236717579bottom-2077041933236717579word aria-controls=2077041933236717579bottom-2077041933236717579word role=tab data-toggle=tab&gt;Words&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#2077041933236717579bottom-2077041933236717579characters aria-controls=2077041933236717579bottom-2077041933236717579characters role=tab data-toggle=tab&gt;Characters&lt;/a&gt;&lt;/li&gt;&lt;/ul&gt;&lt;div class=tab-content&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12 active&quot; id=2077041933236717579bottom-2077041933236717579overview&gt;&lt;div class=row&gt;&lt;div class=col-sm-3&gt;&lt;p class=h4&gt;Length&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Max length&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;750&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Median length&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;675&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Mean length&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;159.67346&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Min length&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=col-sm-3&gt;&lt;p class=h4&gt;Characters and Unicode&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Total characters&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;1221502&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct characters&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;29&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct categories&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;3 &lt;a title=&quot;Unicode categories (click for more information)&quot; href=https://en.wikipedia.org/wiki/Unicode_character_property#General_Category&gt;&lt;span class=&quot;badge pull-right&quot; style=color:#fff;background-color:#337ab7; title=&quot;Unicode categories (click for more information)&quot;&gt;?&lt;/span&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct scripts&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;2 &lt;a title=&quot;Unicode scripts (click for more information)&quot; href=https://en.wikipedia.org/wiki/Script_(Unicode)#List_of_scripts_in_Unicode&gt;&lt;span class=&quot;badge pull-right&quot; style=color:#fff;background-color:#337ab7; title=&quot;Unicode scripts (click for more information)&quot;&gt;?&lt;/span&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct blocks&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;1 &lt;a title=&quot;Unicode blocks (click for more information)&quot; href=https://en.wikipedia.org/wiki/Unicode_block&gt;&lt;span class=&quot;badge pull-right&quot; style=color:#fff;background-color:#337ab7; title=&quot;Unicode blocks (click for more information)&quot;&gt;?&lt;/span&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;div class=&quot;caption text-center text-muted&quot;&gt; The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables. &lt;/div&gt;&lt;/div&gt;&lt;div class=col-sm-3&gt;&lt;p class=h4&gt;Unique&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Unique&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;7592 &lt;span class=&quot;badge pull-right&quot; style=color:#fff;background-color:#337ab7; title=&quot;The number of unique values (all values that occur exactly once in the dataset).&quot;&gt;?&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Unique (%)&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;99.2%&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=col-sm-3&gt;&lt;p class=h4&gt;Sample&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;1st row&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;understand people reply immediately op invitation talk privately mean help type response usually lead disappointment disaster usually work differently pm anytime casual social context huge admiration appreciation goodwill good citizenship support flag inappropriate content know struggle hard work scene information resource easy quality help small start new wiki page explain detail s well respond public comment ve get know maintain r depression wiki private contact text current version summary act helper invite accept private contact e pm chat kind offsite communication early conversion show bad intention bad judgement way s unwise trust pm anytime like kind generous offer perfectly meaning solid rapport ha establish s wise idea point consid&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;2nd row&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;welcome r depression s check post place moment share go accomplishment want talk shouldn t standalone post sub violate role model rule welcome have tough time prefer post place share subreddit rule locate sidebar access r depression rule exist important safety reason ask read follow click report harmful content want know deal soon wiki help find give support r depression wiki depression provide guidance isn t depressive disorder guidance complex nature illness usually group depression label redirect information common topic issue r depression wiki give help offer information nature value peer support mental health issue general lot guidance learn isn t usually helpful give peer support ysk type rule violation frequently interfere people get&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;3rd row&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;instead sleep depress stay night avoid day come soon social anxiety life peaceful asleep expect thing&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;4th row&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;ve kind stuff lot life delay inevitable have work job responsible adult m long ve hold job wa 9 month wasn t m lazy wa thing enjoy know unemployment ha cause depression recently feel utterly hopeless think soon ll shitty house work job couldn t care le like perfect recipe depression&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;5th row&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;sleep great comforting escape wake day literal emotion feel misery remind problem t single second s like wake everyday welcome hell&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=2077041933236717579bottom-2077041933236717579word&gt;&lt;div class=col-sm-6&gt;&lt;table class=&quot;freq table table-hover table-striped table-0&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=t&gt; t &lt;/td&gt;&lt;td&gt;6414&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:3.6%&gt; &amp;nbsp; &lt;/div&gt; 3.0% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=m&gt; m &lt;/td&gt;&lt;td&gt;5758&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:3.2%&gt; &amp;nbsp; &lt;/div&gt; 2.7% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=feel&gt; feel &lt;/td&gt;&lt;td&gt;3586&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:2.0%&gt; &amp;nbsp; &lt;/div&gt; 1.7% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=s&gt; s &lt;/td&gt;&lt;td&gt;3540&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:2.0%&gt; &amp;nbsp; &lt;/div&gt; 1.7% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=like&gt; like &lt;/td&gt;&lt;td&gt;3074&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:1.7%&gt; &amp;nbsp; &lt;/div&gt; 1.4% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=wa&gt; wa &lt;/td&gt;&lt;td&gt;3054&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:1.7%&gt; &amp;nbsp; &lt;/div&gt; 1.4% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=don&gt; don &lt;/td&gt;&lt;td&gt;2533&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:1.4%&gt; &amp;nbsp; &lt;/div&gt; 1.2% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=want&gt; want &lt;/td&gt;&lt;td&gt;2227&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:1.2%&gt; &amp;nbsp; &lt;/div&gt; 1.0% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=know&gt; know &lt;/td&gt;&lt;td&gt;2059&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:1.2%&gt; &amp;nbsp; &lt;/div&gt; 1.0% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=ve&gt; ve 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&quot; id=image92ffcdbbb5 x=55.08 y=-34.56 width=332.64 height=221.76 transform=&quot;scale(1 -1) translate(0 -221.76)&quot;/&gt;&lt;/g&gt;&lt;/g&gt;&lt;/g&gt;&lt;defs&gt;&lt;clippath id=p472e95ad87&gt;&lt;rect x=55.08 y=34.56 width=332.64 height=221.76 /&gt;&lt;/clippath&gt;&lt;/defs&gt;&lt;/svg&gt;&lt;/div&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=2077041933236717579bottom-2077041933236717579characters&gt;&lt;div class=col-sm-12&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;ul class=&quot;nav nav-tabs&quot; role=tablist&gt;&lt;li role=presentation class=active&gt;&lt;a href=#2077041933236717579unicode-2077041933236717579characters aria-controls=2077041933236717579unicode-2077041933236717579characters role=tab data-toggle=tab&gt;Characters&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#2077041933236717579unicode-2077041933236717579categories aria-controls=2077041933236717579unicode-2077041933236717579categories role=tab data-toggle=tab&gt;Categories&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#2077041933236717579unicode-2077041933236717579scripts aria-controls=2077041933236717579unicode-2077041933236717579scripts role=tab data-toggle=tab&gt;Scripts&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#2077041933236717579unicode-2077041933236717579blocks aria-controls=2077041933236717579unicode-2077041933236717579blocks role=tab data-toggle=tab&gt;Blocks&lt;/a&gt;&lt;/li&gt;&lt;/ul&gt;&lt;div class=tab-content&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12 active&quot; id=2077041933236717579unicode-2077041933236717579characters&gt;&lt;div class=&quot;row named-list-item&quot;&gt;&lt;h4&gt;Most occurring characters&lt;/h4&gt;&lt;table class=&quot;freq table table-hover table-striped table-0&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=&quot; &quot;&gt;&lt;/td&gt;&lt;td&gt;204859&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:59.2%&gt; 16.8% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=e&gt; e &lt;/td&gt;&lt;td&gt;129282&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:37.3%&gt; &amp;nbsp; &lt;/div&gt; 10.6% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=a&gt; a &lt;/td&gt;&lt;td&gt;80977&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:23.4%&gt; &amp;nbsp; &lt;/div&gt; 6.6% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=t&gt; t &lt;/td&gt;&lt;td&gt;79393&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:22.9%&gt; &amp;nbsp; &lt;/div&gt; 6.5% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=i&gt; i &lt;/td&gt;&lt;td&gt;69188&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:20.0%&gt; &amp;nbsp; &lt;/div&gt; 5.7% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=o&gt; o &lt;/td&gt;&lt;td&gt;67659&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:19.5%&gt; &amp;nbsp; &lt;/div&gt; 5.5% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=n&gt; n &lt;/td&gt;&lt;td&gt;65895&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:19.0%&gt; &amp;nbsp; &lt;/div&gt; 5.4% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=l&gt; l &lt;/td&gt;&lt;td&gt;62622&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:18.1%&gt; &amp;nbsp; &lt;/div&gt; 5.1% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=r&gt; r &lt;/td&gt;&lt;td&gt;60062&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:17.3%&gt; &amp;nbsp; &lt;/div&gt; 4.9% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=s&gt; s &lt;/td&gt;&lt;td&gt;55293&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:16.0%&gt; &amp;nbsp; &lt;/div&gt; 4.5% &lt;/td&gt;&lt;/tr&gt;&lt;tr class=other&gt;&lt;td title=&quot;Other values (19)&quot;&gt; Other values (19) &lt;/td&gt;&lt;td&gt;346272&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 28.3% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=2077041933236717579unicode-2077041933236717579categories&gt;&lt;div class=&quot;row named-list-item&quot;&gt;&lt;h4&gt;Most occurring categories&lt;/h4&gt;&lt;table class=&quot;freq table table-hover table-striped table-0&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=&quot;Lowercase Letter&quot;&gt; Lowercase Letter &lt;/td&gt;&lt;td&gt;1014740&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 83.1% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=&quot;Space Separator&quot;&gt; Space Separator &lt;/td&gt;&lt;td&gt;204859&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:20.2%&gt; &amp;nbsp; &lt;/div&gt; 16.8% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=&quot;Decimal Number&quot;&gt; Decimal Number &lt;/td&gt;&lt;td&gt;1903&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.2%&gt; &amp;nbsp; &lt;/div&gt; 0.2% &lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=&quot;row named-list-item&quot;&gt;&lt;h4&gt;Most frequent character per category&lt;/h4&gt;&lt;div class=row&gt;&lt;div class=col-sm-12&gt;&lt;h5&gt;&lt;em&gt;Lowercase Letter&lt;/em&gt;&lt;/h5&gt;&lt;table class=&quot;freq table table-hover table-striped table-0&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=e&gt; e &lt;/td&gt;&lt;td&gt;129282&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:42.8%&gt; 12.7% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=a&gt; a &lt;/td&gt;&lt;td&gt;80977&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:26.8%&gt; &amp;nbsp; &lt;/div&gt; 8.0% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=t&gt; t &lt;/td&gt;&lt;td&gt;79393&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:26.3%&gt; &amp;nbsp; &lt;/div&gt; 7.8% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=i&gt; i &lt;/td&gt;&lt;td&gt;69188&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:22.9%&gt; &amp;nbsp; &lt;/div&gt; 6.8% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=o&gt; o &lt;/td&gt;&lt;td&gt;67659&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:22.4%&gt; &amp;nbsp; &lt;/div&gt; 6.7% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=n&gt; n &lt;/td&gt;&lt;td&gt;65895&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:21.8%&gt; &amp;nbsp; &lt;/div&gt; 6.5% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=l&gt; l &lt;/td&gt;&lt;td&gt;62622&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:20.7%&gt; &amp;nbsp; &lt;/div&gt; 6.2% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=r&gt; r &lt;/td&gt;&lt;td&gt;60062&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:19.9%&gt; &amp;nbsp; &lt;/div&gt; 5.9% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=s&gt; s &lt;/td&gt;&lt;td&gt;55293&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:18.3%&gt; &amp;nbsp; &lt;/div&gt; 5.4% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=d&gt; d &lt;/td&gt;&lt;td&gt;42055&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:13.9%&gt; &amp;nbsp; &lt;/div&gt; 4.1% &lt;/td&gt;&lt;/tr&gt;&lt;tr class=other&gt;&lt;td title=&quot;Other values (16)&quot;&gt; Other values (16) &lt;/td&gt;&lt;td&gt;302314&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 29.8% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=row&gt;&lt;div class=col-sm-12&gt;&lt;h5&gt;&lt;em&gt;Decimal Number&lt;/em&gt;&lt;/h5&gt;&lt;table class=&quot;freq table table-hover table-striped table-0&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=0&gt; 0 &lt;/td&gt;&lt;td&gt;1531&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 80.5% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=9&gt; 9 &lt;/td&gt;&lt;td&gt;372&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:24.3%&gt; &amp;nbsp; &lt;/div&gt; 19.5% &lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=row&gt;&lt;div class=col-sm-12&gt;&lt;h5&gt;&lt;em&gt;Space Separator&lt;/em&gt;&lt;/h5&gt;&lt;table class=&quot;freq table table-hover table-striped table-0&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=&quot; &quot;&gt;&lt;/td&gt;&lt;td&gt;204859&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 100.0% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=2077041933236717579unicode-2077041933236717579scripts&gt;&lt;div class=&quot;row named-list-item&quot;&gt;&lt;h4&gt;Most occurring scripts&lt;/h4&gt;&lt;table class=&quot;freq table table-hover table-striped table-0&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=Latin&gt; Latin &lt;/td&gt;&lt;td&gt;1014740&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 83.1% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=Common&gt; Common &lt;/td&gt;&lt;td&gt;206762&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:20.4%&gt; &amp;nbsp; &lt;/div&gt; 16.9% &lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=&quot;row named-list-item&quot;&gt;&lt;h4&gt;Most frequent character per script&lt;/h4&gt;&lt;div class=row&gt;&lt;div class=col-sm-12&gt;&lt;h5&gt;&lt;em&gt;Latin&lt;/em&gt;&lt;/h5&gt;&lt;table class=&quot;freq table table-hover table-striped table-0&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=e&gt; e &lt;/td&gt;&lt;td&gt;129282&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:42.8%&gt; 12.7% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=a&gt; a &lt;/td&gt;&lt;td&gt;80977&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:26.8%&gt; &amp;nbsp; &lt;/div&gt; 8.0% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=t&gt; t &lt;/td&gt;&lt;td&gt;79393&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:26.3%&gt; &amp;nbsp; &lt;/div&gt; 7.8% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=i&gt; i &lt;/td&gt;&lt;td&gt;69188&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:22.9%&gt; &amp;nbsp; &lt;/div&gt; 6.8% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=o&gt; o &lt;/td&gt;&lt;td&gt;67659&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:22.4%&gt; &amp;nbsp; &lt;/div&gt; 6.7% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=n&gt; n &lt;/td&gt;&lt;td&gt;65895&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:21.8%&gt; &amp;nbsp; &lt;/div&gt; 6.5% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=l&gt; l &lt;/td&gt;&lt;td&gt;62622&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:20.7%&gt; &amp;nbsp; &lt;/div&gt; 6.2% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=r&gt; r &lt;/td&gt;&lt;td&gt;60062&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:19.9%&gt; &amp;nbsp; &lt;/div&gt; 5.9% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=s&gt; s &lt;/td&gt;&lt;td&gt;55293&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:18.3%&gt; &amp;nbsp; &lt;/div&gt; 5.4% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=d&gt; d &lt;/td&gt;&lt;td&gt;42055&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:13.9%&gt; &amp;nbsp; &lt;/div&gt; 4.1% &lt;/td&gt;&lt;/tr&gt;&lt;tr class=other&gt;&lt;td title=&quot;Other values (16)&quot;&gt; Other values (16) &lt;/td&gt;&lt;td&gt;302314&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 29.8% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=row&gt;&lt;div class=col-sm-12&gt;&lt;h5&gt;&lt;em&gt;Common&lt;/em&gt;&lt;/h5&gt;&lt;table class=&quot;freq table table-hover table-striped table-0&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=&quot; &quot;&gt;&lt;/td&gt;&lt;td&gt;204859&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 99.1% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=0&gt; 0 &lt;/td&gt;&lt;td&gt;1531&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.7%&gt; &amp;nbsp; &lt;/div&gt; 0.7% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=9&gt; 9 &lt;/td&gt;&lt;td&gt;372&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.2%&gt; &amp;nbsp; &lt;/div&gt; 0.2% &lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=2077041933236717579unicode-2077041933236717579blocks&gt;&lt;div class=&quot;row named-list-item&quot;&gt;&lt;h4&gt;Most occurring blocks&lt;/h4&gt;&lt;table class=&quot;freq table table-hover table-striped table-0&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=ASCII&gt; ASCII &lt;/td&gt;&lt;td&gt;1221502&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 100.0% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=&quot;row named-list-item&quot;&gt;&lt;h4&gt;Most frequent character per block&lt;/h4&gt;&lt;div class=row&gt;&lt;div class=col-sm-12&gt;&lt;h5&gt;&lt;em&gt;ASCII&lt;/em&gt;&lt;/h5&gt;&lt;table class=&quot;freq table table-hover table-striped table-0&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=&quot; &quot;&gt;&lt;/td&gt;&lt;td&gt;204859&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:59.2%&gt; 16.8% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=e&gt; e &lt;/td&gt;&lt;td&gt;129282&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:37.3%&gt; &amp;nbsp; &lt;/div&gt; 10.6% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=a&gt; a &lt;/td&gt;&lt;td&gt;80977&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:23.4%&gt; &amp;nbsp; &lt;/div&gt; 6.6% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=t&gt; t &lt;/td&gt;&lt;td&gt;79393&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:22.9%&gt; &amp;nbsp; &lt;/div&gt; 6.5% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=i&gt; i &lt;/td&gt;&lt;td&gt;69188&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:20.0%&gt; &amp;nbsp; &lt;/div&gt; 5.7% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=o&gt; o &lt;/td&gt;&lt;td&gt;67659&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:19.5%&gt; &amp;nbsp; &lt;/div&gt; 5.5% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=n&gt; n &lt;/td&gt;&lt;td&gt;65895&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:19.0%&gt; &amp;nbsp; &lt;/div&gt; 5.4% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=l&gt; l &lt;/td&gt;&lt;td&gt;62622&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:18.1%&gt; &amp;nbsp; &lt;/div&gt; 5.1% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=r&gt; r &lt;/td&gt;&lt;td&gt;60062&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:17.3%&gt; &amp;nbsp; &lt;/div&gt; 4.9% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=s&gt; s &lt;/td&gt;&lt;td&gt;55293&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:16.0%&gt; &amp;nbsp; &lt;/div&gt; 4.5% &lt;/td&gt;&lt;/tr&gt;&lt;tr class=other&gt;&lt;td title=&quot;Other values (19)&quot;&gt; Other values (19) &lt;/td&gt;&lt;td&gt;346272&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 28.3% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;a class=&quot;anchor-pos anchor-pos-variable&quot; id=pp_var_-6929448776245444169&gt;&lt;/a&gt;&lt;div class=variable&gt;&lt;div class=col-sm-12&gt;&lt;p class=h4 title=label&gt;&lt;a href=#pp_var_-6929448776245444169&gt;label&lt;/a&gt;&lt;br&gt;&lt;small&gt;Categorical&lt;/small&gt;&lt;/p&gt;&lt;p class=variable-description&gt;&lt;/p&gt;&lt;/div&gt;&lt;div class=col-sm-6&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Distinct&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;2&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct (%)&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;&lt; 0.1%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing (%)&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;0.0%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Memory size&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;377.6 KiB&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=col-sm-6&gt;&lt;div class=row id=minifreqtable&gt;&lt;table class=&quot;mini freq table-0&quot;&gt;&lt;tr class&gt;&lt;th width=10%&gt; 0 &lt;/th&gt;&lt;td width=10%&gt;&lt;div class=bar style=width:100.0% data-toggle=tooltip data-placement=center data-html=true data-delay=500&gt; 3889&amp;nbsp; &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;th width=10%&gt; 1 &lt;/th&gt;&lt;td width=10%&gt;&lt;div class=bar style=width:96.7% data-toggle=tooltip data-placement=center data-html=true data-delay=500&gt; 3761&amp;nbsp; &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=&quot;col-sm-12 text-right&quot;&gt;&lt;button class=&quot;btn btn-default btn-sm&quot; data-toggle=collapse data-target=&quot;#bottom--6929448776245444169, #minifreqtable-6929448776245444169&quot; aria-expanded=true aria-controls=collapseExample&gt; More details &lt;/button&gt;&lt;/div&gt;&lt;div id=bottom--6929448776245444169 class=collapse&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;ul class=&quot;nav nav-tabs&quot; role=tablist&gt;&lt;li role=presentation class=active&gt;&lt;a href=#-6929448776245444169bottom--6929448776245444169overview aria-controls=-6929448776245444169bottom--6929448776245444169overview role=tab data-toggle=tab&gt;Overview&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#-6929448776245444169bottom--6929448776245444169string aria-controls=-6929448776245444169bottom--6929448776245444169string role=tab data-toggle=tab&gt;Categories&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#-6929448776245444169bottom--6929448776245444169word aria-controls=-6929448776245444169bottom--6929448776245444169word role=tab data-toggle=tab&gt;Words&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#-6929448776245444169bottom--6929448776245444169characters aria-controls=-6929448776245444169bottom--6929448776245444169characters role=tab data-toggle=tab&gt;Characters&lt;/a&gt;&lt;/li&gt;&lt;/ul&gt;&lt;div class=tab-content&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12 active&quot; id=-6929448776245444169bottom--6929448776245444169overview&gt;&lt;div class=row&gt;&lt;div class=col-sm-3&gt;&lt;p class=h4&gt;Length&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Max length&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;1&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Median length&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;1&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Mean length&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;1&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Min length&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;1&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=col-sm-3&gt;&lt;p class=h4&gt;Characters and Unicode&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Total characters&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;7650&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct characters&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;2&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct categories&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;1 &lt;a title=&quot;Unicode categories (click for more information)&quot; href=https://en.wikipedia.org/wiki/Unicode_character_property#General_Category&gt;&lt;span class=&quot;badge pull-right&quot; style=color:#fff;background-color:#337ab7; title=&quot;Unicode categories (click for more information)&quot;&gt;?&lt;/span&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct scripts&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;1 &lt;a title=&quot;Unicode scripts (click for more information)&quot; href=https://en.wikipedia.org/wiki/Script_(Unicode)#List_of_scripts_in_Unicode&gt;&lt;span class=&quot;badge pull-right&quot; style=color:#fff;background-color:#337ab7; title=&quot;Unicode scripts (click for more information)&quot;&gt;?&lt;/span&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct blocks&lt;/th&gt;&lt;td style=&quot;white-space:pre white-space:nowrap&quot;&gt;1 &lt;a title=&quot;Unicode blocks (click for more information)&quot; href=https://en.wikipedia.org/wiki/Unicode_block&gt;&lt;span class=&quot;badge pull-right&quot; style=color:#fff;background-color:#337ab7; title=&quot;Unicode blocks (click for more information)&quot;&gt;?&lt;/span&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;div class=&quot;caption text-center text-muted&quot;&gt; The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables. &lt;/div&gt;&lt;/div&gt;&lt;div class=col-sm-3&gt;&lt;p class=h4&gt;Unique&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Unique&lt;/th&gt;&lt;td 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       "&quot; clip-path=url(#p11e800cf3e) style=&quot;fill: none; stroke: #ffffff; stroke-width: 1.75; stroke-linecap: round&quot;/&gt;&lt;/g&gt;&lt;/g&gt;&lt;/g&gt;&lt;defs&gt;&lt;clippath id=p11e800cf3e&gt;&lt;rect x=72 y=86.4 width=576 height=144 /&gt;&lt;/clippath&gt;&lt;/defs&gt;&lt;/svg&gt;&lt;div class=&quot;caption text-center text-muted&quot;&gt; Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion. &lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=&quot;row header&quot;&gt;&lt;a class=anchor-pos id=sample&gt;&lt;/a&gt;&lt;h1 class=page-header&gt;Sample&lt;/h1&gt;&lt;/div&gt;&lt;div class=section-items&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;ul class=&quot;nav nav-tabs&quot; role=tablist&gt;&lt;li role=presentation class=active&gt;&lt;a href=#sample-head aria-controls=sample-head role=tab data-toggle=tab&gt;First rows&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#sample-tail aria-controls=sample-tail role=tab data-toggle=tab&gt;Last rows&lt;/a&gt;&lt;/li&gt;&lt;/ul&gt;&lt;div class=tab-content&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12 active&quot; id=sample-head&gt;&lt;div id=sample-container class=col-sm-12&gt;&lt;table border=1 class=&quot;dataframe sample table table-striped&quot;&gt;&lt;thead&gt;&lt;tr style=&quot;text-align: right;&quot;&gt;&lt;th&gt;&lt;/th&gt;&lt;th&gt;text&lt;/th&gt;&lt;th&gt;label&lt;/th&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;0&lt;/th&gt;&lt;td&gt;understand people reply immediately op invitation talk privately mean help type response usually lead disappointment disaster usually work differently pm anytime casual social context huge admiration appreciation goodwill good citizenship support flag inappropriate content know struggle hard work scene information resource easy quality help small start new wiki page explain detail s well respond public comment ve get know maintain r depression wiki private contact text current version summary act helper invite accept private contact e pm chat kind offsite communication early conversion show bad intention bad judgement way s unwise trust pm anytime like kind generous offer perfectly meaning solid rapport ha establish s wise idea point consid&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;1&lt;/th&gt;&lt;td&gt;welcome r depression s check post place moment share go accomplishment want talk shouldn t standalone post sub violate role model rule welcome have tough time prefer post place share subreddit rule locate sidebar access r depression rule exist important safety reason ask read follow click report harmful content want know deal soon wiki help find give support r depression wiki depression provide guidance isn t depressive disorder guidance complex nature illness usually group depression label redirect information common topic issue r depression wiki give help offer information nature value peer support mental health issue general lot guidance learn isn t usually helpful give peer support ysk type rule violation frequently interfere people get&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;2&lt;/th&gt;&lt;td&gt;instead sleep depress stay night avoid day come soon social anxiety life peaceful asleep expect thing&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;3&lt;/th&gt;&lt;td&gt;ve kind stuff lot life delay inevitable have work job responsible adult m long ve hold job wa 9 month wasn t m lazy wa thing enjoy know unemployment ha cause depression recently feel utterly hopeless think soon ll shitty house work job couldn t care le like perfect recipe depression&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;4&lt;/th&gt;&lt;td&gt;sleep great comforting escape wake day literal emotion feel misery remind problem t single second s like wake everyday welcome hell&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;5&lt;/th&gt;&lt;td&gt;m year old turning soon month live constant dread passion goal special achievement life relationship don t know want m call life feel exhaust cry sleep everyday feel extremely disconnected don t fit try hard decent human try treat kindness respect good listener help people need help work hard exercise daily basis good care appearance matter hard try present good feel don t fit feel unworthy unwanted want proud matter feel m good crave meaningful connection want share spend day laugh cry share handmade gift smile face feel like home ve feel homeless time want feel love care want s like s idea meaningful connection head barely hold conversation try talk feel extremely drain m stuck rabbit hole feel extremely lonely have energy talk time don t&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;6&lt;/th&gt;&lt;td&gt;live despite prone loneliness find emotionally needy urge cut important people life depressed friend girlfriend feel easily annoy judgmental wrong t explain maybe post find people relate sort like feeling well doe find constantly have urge push people away feel like consume depression&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;7&lt;/th&gt;&lt;td&gt;m look sympathy simply state m try survive m sit dark cry eye th birthday know s birthday ll m tired fight life single day sense wa ve fight t anymore m exhausted illness ha break relationship people t mend understand hope understand ve decision husband magically find love m sorry leave way meet squishy&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;8&lt;/th&gt;&lt;td&gt;don t know communicate thought stay inside instead tell people like mind simply win t let try try lose mind endless nightmare thought despair hatred feel numb emotional time not know feel like horrible waste human space strip bone past seemingly dim future&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;9&lt;/th&gt;&lt;td&gt;mom m sad hurt heart feeling fall stomach t stop tear start mom m sad achieve fall apart t morning t focus sadness t clean clothe away don t energy try sleep day time fast s hard try care dog mean get thought race day mom m sad don t know don t know go don t want break dad heart think heart ha break long time fight inside start slow turn black don t know way light anymore&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=sample-tail&gt;&lt;div id=sample-container class=col-sm-12&gt;&lt;table border=1 class=&quot;dataframe sample table table-striped&quot;&gt;&lt;thead&gt;&lt;tr style=&quot;text-align: right;&quot;&gt;&lt;th&gt;&lt;/th&gt;&lt;th&gt;text&lt;/th&gt;&lt;th&gt;label&lt;/th&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;7721&lt;/th&gt;&lt;td&gt;nz place miss bed bit&lt;/td&gt;&lt;td&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;7722&lt;/th&gt;&lt;td&gt;read r s amazing blog tired don t want school tomorrow hmmph&lt;/td&gt;&lt;td&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;7723&lt;/th&gt;&lt;td&gt;hate awake go school western civ presentation andn awesome lunch&lt;/td&gt;&lt;td&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;7724&lt;/th&gt;&lt;td&gt;ha discover downside go away weekend food shopping need&lt;/td&gt;&lt;td&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;7725&lt;/th&gt;&lt;td&gt;ophelia haha sorreh try spek normalz k&lt;/td&gt;&lt;td&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;7726&lt;/th&gt;&lt;td&gt;snow&lt;/td&gt;&lt;td&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;7727&lt;/th&gt;&lt;td&gt;moulin rouge mad cry&lt;/td&gt;&lt;td&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;7728&lt;/th&gt;&lt;td&gt;try shout t find people list&lt;/td&gt;&lt;td&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;7729&lt;/th&gt;&lt;td&gt;ughh t find red sox hat get to wear creepy nick pirro version&lt;/td&gt;&lt;td&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;7730&lt;/th&gt;&lt;td&gt;sleep wonderfully finally try swatching new project classic line cardi stash don t like color wait&lt;/td&gt;&lt;td&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=&quot;row header&quot;&gt;&lt;a class=anchor-pos id=duplicate&gt;&lt;/a&gt;&lt;h1 class=page-header&gt;Duplicate rows&lt;/h1&gt;&lt;/div&gt;&lt;div class=section-items&gt;&lt;div class=row&gt;&lt;div class=col-sm-12&gt;&lt;h4 class=indent&gt;Most frequently occurring&lt;/h4&gt;&lt;div id=duplicate-container class=col-sm-12&gt;&lt;table border=1 class=&quot;dataframe duplicate table table-striped&quot;&gt;&lt;thead&gt;&lt;tr style=&quot;text-align: right;&quot;&gt;&lt;th&gt;&lt;/th&gt;&lt;th&gt;text&lt;/th&gt;&lt;th&gt;label&lt;/th&gt;&lt;th&gt;# duplicates&lt;/th&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;8&lt;/th&gt;&lt;td&gt;homework&lt;/td&gt;&lt;td&gt;0&lt;/td&gt;&lt;td&gt;4&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;22&lt;/th&gt;&lt;td&gt;work&lt;/td&gt;&lt;td&gt;0&lt;/td&gt;&lt;td&gt;4&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;0&lt;/th&gt;&lt;td&gt;&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;3&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;1&lt;/th&gt;&lt;td&gt;cold&lt;/td&gt;&lt;td&gt;0&lt;/td&gt;&lt;td&gt;3&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;11&lt;/th&gt;&lt;td&gt;need hug&lt;/td&gt;&lt;td&gt;0&lt;/td&gt;&lt;td&gt;3&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;2&lt;/th&gt;&lt;td&gt;depression fr&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;2&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;3&lt;/th&gt;&lt;td&gt;flu&lt;/td&gt;&lt;td&gt;0&lt;/td&gt;&lt;td&gt;2&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;4&lt;/th&gt;&lt;td&gt;go work&lt;/td&gt;&lt;td&gt;0&lt;/td&gt;&lt;td&gt;2&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;5&lt;/th&gt;&lt;td&gt;ha oh god don t know m laugh think feel scared m laugh m cry don t know think friend suicidal scrap know problem m kind suicidal point life ve sort stuck rut repressed emotion feel desire let kill m worried say tomorrow don t think go tonight say want point fail don t know not ask help promise confidentiality know pain trust break t option know safe tonight problem don t know feel well m terrible comfort people good time m half asleep think m little trigger context dad tell wa go kill leave detail worthless wa way kill sh attempt suicidal multiple occasion s touchy subject don t need people tell police tell need advice feel well t bear thought friend feel feel ve feel s fucking torture deserve shit say t talk feel like attention seeker try&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;2&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;6&lt;/th&gt;&lt;td&gt;hate&lt;/td&gt;&lt;td&gt;0&lt;/td&gt;&lt;td&gt;2&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;footer&gt;&lt;div class=container-fluid&gt;&lt;div class=&quot;row center-block footer-text&quot;&gt;&lt;p class=&quot;text-muted text-center&quot;&gt;Report generated by &lt;a href=&quot;https://ydata.ai/?utm_source=opensource&amp;utm_medium=pandasprofiling&amp;utm_campaign=report&quot;&gt;YData&lt;/a&gt;.&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/footer&gt;&lt;script&gt;\n",
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clearTimeout(c.timeout),c.hoverState=&quot;out&quot;,c.options.delay&amp;&amp;c.options.delay.hide?void(c.timeout=setTimeout(function(){&quot;out&quot;==c.hoverState&amp;&amp;c.hide()},c.options.delay.hide)):c.hide()},c.prototype.show=function(){var b=a.Event(&quot;show.bs.&quot;+this.type);if(this.hasContent()&amp;&amp;this.enabled){this.$element.trigger(b);var d=a.contains(this.$element[0].ownerDocument.documentElement,this.$element[0]);if(b.isDefaultPrevented()||!d)return;var e=this,f=this.tip(),g=this.getUID(this.type);this.setContent(),f.attr(&quot;id&quot;,g),this.$element.attr(&quot;aria-describedby&quot;,g),this.options.animation&amp;&amp;f.addClass(&quot;fade&quot;);var h=&quot;function&quot;==typeof this.options.placement?this.options.placement.call(this,f[0],this.$element[0]):this.options.placement,i=/\\s?auto?\\s?/i,j=i.test(h);j&amp;&amp;(h=h.replace(i,&quot;&quot;)||&quot;top&quot;),f.detach().css({top:0,left:0,display:&quot;block&quot;}).addClass(h).data(&quot;bs.&quot;+this.type,this),this.options.container?f.appendTo(this.options.container):f.insertAfter(this.$element),this.$element.trigger(&quot;inserted.bs.&quot;+this.type);var k=this.getPosition(),l=f[0].offsetWidth,m=f[0].offsetHeight;if(j){var n=h,o=this.getPosition(this.$viewport);h=&quot;bottom&quot;==h&amp;&amp;k.bottom+m&gt;o.bottom?&quot;top&quot;:&quot;top&quot;==h&amp;&amp;k.top-m&lt;o.top?&quot;bottom&quot;:&quot;right&quot;==h&amp;&amp;k.right+l&gt;o.width?&quot;left&quot;:&quot;left&quot;==h&amp;&amp;k.left-l&lt;o.left?&quot;right&quot;:h,f.removeClass(n).addClass(h)}var p=this.getCalculatedOffset(h,k,l,m);this.applyPlacement(p,h);var q=function(){var a=e.hoverState;e.$element.trigger(&quot;shown.bs.&quot;+e.type),e.hoverState=null,&quot;out&quot;==a&amp;&amp;e.leave(e)};a.support.transition&amp;&amp;this.$tip.hasClass(&quot;fade&quot;)?f.one(&quot;bsTransitionEnd&quot;,q).emulateTransitionEnd(c.TRANSITION_DURATION):q()}},c.prototype.applyPlacement=function(b,c){var d=this.tip(),e=d[0].offsetWidth,f=d[0].offsetHeight,g=parseInt(d.css(&quot;margin-top&quot;),10),h=parseInt(d.css(&quot;margin-left&quot;),10);isNaN(g)&amp;&amp;(g=0),isNaN(h)&amp;&amp;(h=0),b.top+=g,b.left+=h,a.offset.setOffset(d[0],a.extend({using:function(a){d.css({top:Math.round(a.top),left:Math.round(a.left)})}},b),0),d.addClass(&quot;in&quot;);var i=d[0].offsetWidth,j=d[0].offsetHeight;&quot;top&quot;==c&amp;&amp;j!=f&amp;&amp;(b.top=b.top+f-j);var k=this.getViewportAdjustedDelta(c,b,i,j);k.left?b.left+=k.left:b.top+=k.top;var l=/top|bottom/.test(c),m=l?2*k.left-e+i:2*k.top-f+j,n=l?&quot;offsetWidth&quot;:&quot;offsetHeight&quot;;d.offset(b),this.replaceArrow(m,d[0][n],l)},c.prototype.replaceArrow=function(a,b,c){this.arrow().css(c?&quot;left&quot;:&quot;top&quot;,50*(1-a/b)+&quot;%&quot;).css(c?&quot;top&quot;:&quot;left&quot;,&quot;&quot;)},c.prototype.setContent=function(){var a=this.tip(),b=this.getTitle();a.find(&quot;.tooltip-inner&quot;)[this.options.html?&quot;html&quot;:&quot;text&quot;](b),a.removeClass(&quot;fade in top bottom left right&quot;)},c.prototype.hide=function(b){function d(){&quot;in&quot;!=e.hoverState&amp;&amp;f.detach(),e.$element&amp;&amp;e.$element.removeAttr(&quot;aria-describedby&quot;).trigger(&quot;hidden.bs.&quot;+e.type),b&amp;&amp;b()}var e=this,f=a(this.$tip),g=a.Event(&quot;hide.bs.&quot;+this.type);if(this.$element.trigger(g),!g.isDefaultPrevented())return f.removeClass(&quot;in&quot;),a.support.transition&amp;&amp;f.hasClass(&quot;fade&quot;)?f.one(&quot;bsTransitionEnd&quot;,d).emulateTransitionEnd(c.TRANSITION_DURATION):d(),this.hoverState=null,this},c.prototype.fixTitle=function(){var a=this.$element;(a.attr(&quot;title&quot;)||&quot;string&quot;!=typeof a.attr(&quot;data-original-title&quot;))&amp;&amp;a.attr(&quot;data-original-title&quot;,a.attr(&quot;title&quot;)||&quot;&quot;).attr(&quot;title&quot;,&quot;&quot;)},c.prototype.hasContent=function(){return this.getTitle()},c.prototype.getPosition=function(b){b=b||this.$element;var c=b[0],d=&quot;BODY&quot;==c.tagName,e=c.getBoundingClientRect();null==e.width&amp;&amp;(e=a.extend({},e,{width:e.right-e.left,height:e.bottom-e.top}));var f=window.SVGElement&amp;&amp;c instanceof window.SVGElement,g=d?{top:0,left:0}:f?null:b.offset(),h={scroll:d?document.documentElement.scrollTop||document.body.scrollTop:b.scrollTop()},i=d?{width:a(window).width(),height:a(window).height()}:null;return a.extend({},e,h,i,g)},c.prototype.getCalculatedOffset=function(a,b,c,d){return&quot;bottom&quot;==a?{top:b.top+b.height,left:b.left+b.width/2-c/2}:&quot;top&quot;==a?{top:b.top-d,left:b.left+b.width/2-c/2}:&quot;left&quot;==a?{top:b.top+b.height/2-d/2,left:b.left-c}:{top:b.top+b.height/2-d/2,left:b.left+b.width}},c.prototype.getViewportAdjustedDelta=function(a,b,c,d){var e={top:0,left:0};if(!this.$viewport)return e;var f=this.options.viewport&amp;&amp;this.options.viewport.padding||0,g=this.getPosition(this.$viewport);if(/right|left/.test(a)){var h=b.top-f-g.scroll,i=b.top+f-g.scroll+d;h&lt;g.top?e.top=g.top-h:i&gt;g.top+g.height&amp;&amp;(e.top=g.top+g.height-i)}else{var j=b.left-f,k=b.left+f+c;j&lt;g.left?e.left=g.left-j:k&gt;g.right&amp;&amp;(e.left=g.left+g.width-k)}return e},c.prototype.getTitle=function(){var a,b=this.$element,c=this.options;return a=b.attr(&quot;data-original-title&quot;)||(&quot;function&quot;==typeof c.title?c.title.call(b[0]):c.title)},c.prototype.getUID=function(a){do a+=~~(1e6*Math.random());while(document.getElementById(a));return a},c.prototype.tip=function(){if(!this.$tip&amp;&amp;(this.$tip=a(this.options.template),1!=this.$tip.length))throw new Error(this.type+&quot; `template` option must consist of exactly 1 top-level element!&quot;);return this.$tip},c.prototype.arrow=function(){return this.$arrow=this.$arrow||this.tip().find(&quot;.tooltip-arrow&quot;)},c.prototype.enable=function(){this.enabled=!0},c.prototype.disable=function(){this.enabled=!1},c.prototype.toggleEnabled=function(){this.enabled=!this.enabled},c.prototype.toggle=function(b){var c=this;b&amp;&amp;(c=a(b.currentTarget).data(&quot;bs.&quot;+this.type),c||(c=new 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a&quot;,this.offsets=[],this.targets=[],this.activeTarget=null,this.scrollHeight=0,this.$scrollElement.on(&quot;scroll.bs.scrollspy&quot;,a.proxy(this.process,this)),this.refresh(),this.process()}function c(c){return this.each(function(){var d=a(this),e=d.data(&quot;bs.scrollspy&quot;),f=&quot;object&quot;==typeof c&amp;&amp;c;e||d.data(&quot;bs.scrollspy&quot;,e=new b(this,f)),&quot;string&quot;==typeof c&amp;&amp;e[c]()})}b.VERSION=&quot;3.3.7&quot;,b.DEFAULTS={offset:10},b.prototype.getScrollHeight=function(){return this.$scrollElement[0].scrollHeight||Math.max(this.$body[0].scrollHeight,document.documentElement.scrollHeight)},b.prototype.refresh=function(){var b=this,c=&quot;offset&quot;,d=0;this.offsets=[],this.targets=[],this.scrollHeight=this.getScrollHeight(),a.isWindow(this.$scrollElement[0])||(c=&quot;position&quot;,d=this.$scrollElement.scrollTop()),this.$body.find(this.selector).map(function(){var b=a(this),e=b.data(&quot;target&quot;)||b.attr(&quot;href&quot;),f=/^#./.test(e)&amp;&amp;a(e);return f&amp;&amp;f.length&amp;&amp;f.is(&quot;:visible&quot;)&amp;&amp;[[f[c]().top+d,e]]||null}).sort(function(a,b){return a[0]-b[0]}).each(function(){b.offsets.push(this[0]),b.targets.push(this[1])})},b.prototype.process=function(){var a,b=this.$scrollElement.scrollTop()+this.options.offset,c=this.getScrollHeight(),d=this.options.offset+c-this.$scrollElement.height(),e=this.offsets,f=this.targets,g=this.activeTarget;if(this.scrollHeight!=c&amp;&amp;this.refresh(),b&gt;=d)return g!=(a=f[f.length-1])&amp;&amp;this.activate(a);if(g&amp;&amp;b&lt;e[0])return this.activeTarget=null,this.clear();for(a=e.length;a--;)g!=f[a]&amp;&amp;b&gt;=e[a]&amp;&amp;(void 0===e[a+1]||b&lt;e[a+1])&amp;&amp;this.activate(f[a])},b.prototype.activate=function(b){\n",
       "this.activeTarget=b,this.clear();var c=this.selector+&#x27;[data-target=&quot;&#x27;+b+&#x27;&quot;],&#x27;+this.selector+&#x27;[href=&quot;&#x27;+b+&#x27;&quot;]&#x27;,d=a(c).parents(&quot;li&quot;).addClass(&quot;active&quot;);d.parent(&quot;.dropdown-menu&quot;).length&amp;&amp;(d=d.closest(&quot;li.dropdown&quot;).addClass(&quot;active&quot;)),d.trigger(&quot;activate.bs.scrollspy&quot;)},b.prototype.clear=function(){a(this.selector).parentsUntil(this.options.target,&quot;.active&quot;).removeClass(&quot;active&quot;)};var d=a.fn.scrollspy;a.fn.scrollspy=c,a.fn.scrollspy.Constructor=b,a.fn.scrollspy.noConflict=function(){return a.fn.scrollspy=d,this},a(window).on(&quot;load.bs.scrollspy.data-api&quot;,function(){a(&#x27;[data-spy=&quot;scroll&quot;]&#x27;).each(function(){var b=a(this);c.call(b,b.data())})})}(jQuery),+function(a){&quot;use strict&quot;;function b(b){return this.each(function(){var d=a(this),e=d.data(&quot;bs.tab&quot;);e||d.data(&quot;bs.tab&quot;,e=new c(this)),&quot;string&quot;==typeof b&amp;&amp;e[b]()})}var c=function(b){this.element=a(b)};c.VERSION=&quot;3.3.7&quot;,c.TRANSITION_DURATION=150,c.prototype.show=function(){var b=this.element,c=b.closest(&quot;ul:not(.dropdown-menu)&quot;),d=b.data(&quot;target&quot;);if(d||(d=b.attr(&quot;href&quot;),d=d&amp;&amp;d.replace(/.*(?=#[^\\s]*$)/,&quot;&quot;)),!b.parent(&quot;li&quot;).hasClass(&quot;active&quot;)){var e=c.find(&quot;.active:last a&quot;),f=a.Event(&quot;hide.bs.tab&quot;,{relatedTarget:b[0]}),g=a.Event(&quot;show.bs.tab&quot;,{relatedTarget:e[0]});if(e.trigger(f),b.trigger(g),!g.isDefaultPrevented()&amp;&amp;!f.isDefaultPrevented()){var h=a(d);this.activate(b.closest(&quot;li&quot;),c),this.activate(h,h.parent(),function(){e.trigger({type:&quot;hidden.bs.tab&quot;,relatedTarget:b[0]}),b.trigger({type:&quot;shown.bs.tab&quot;,relatedTarget:e[0]})})}}},c.prototype.activate=function(b,d,e){function f(){g.removeClass(&quot;active&quot;).find(&quot;&gt; 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       &lt;/script&gt;&lt;script&gt;\n",
       "$(function () {\n",
       "    $(&#x27;[data-toggle=&quot;tooltip&quot;]&#x27;).tooltip();\n",
       "});\n",
       "\n",
       "$(&quot;a&quot;).not(&quot;[href^=&#x27;#&#x27;]&quot;).attr(&#x27;target&#x27;, &#x27;_blank&#x27;);\n",
       "\n",
       "$(&quot;a[href^=&#x27;#&#x27;]&quot;).not(&quot;[role=&#x27;tab&#x27;]&quot;).on(&#x27;click&#x27;, function (e) {\n",
       "\n",
       "    // prevent default anchor click behavior\n",
       "    e.preventDefault();\n",
       "\n",
       "    // store hash\n",
       "    var hash = this.hash;\n",
       "\n",
       "    // animate\n",
       "    $(&#x27;html, body&#x27;).animate({\n",
       "        scrollTop: $(hash).offset().top\n",
       "    }, 300, function () {\n",
       "\n",
       "        // when done, add hash to url\n",
       "        // (default click behaviour)\n",
       "        window.location.hash = hash;\n",
       "    });\n",
       "\n",
       "});\n",
       "\n",
       "$(&quot;select#variables-dropdown&quot;).on(&quot;change&quot;, function (e) {\n",
       "    var searchText = $(&quot;select#variables-dropdown&quot;).val().toLowerCase();\n",
       "    var variables = $(&quot;.variable&quot;);\n",
       "    variables.each(function (index) {\n",
       "        var isMatch = $(this.firstChild.firstChild).attr(&quot;title&quot;).toLowerCase() == (searchText);\n",
       "        if(searchText == &quot;&quot;){isMatch = true};\n",
       "        $(this).parent().toggle(isMatch);\n",
       "    });\n",
       "});        &lt;/script&gt;&lt;/body&gt;&lt;/html&gt;\" frameborder=\"0\" allowfullscreen></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ydata_profiling import ProfileReport\n",
    "report = ProfileReport(df)\n",
    "report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "fc7f8067",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidf = TfidfVectorizer()\n",
    "X = df['text']\n",
    "y = df['label']\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2)\n",
    "X_train_tfidf = tfidf.fit_transform(X_train).toarray()\n",
    "X_test_tfidf = tfidf.transform(X_test).toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1840d82",
   "metadata": {},
   "source": [
    "### Dtree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "7710c8d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.93      0.92       772\n",
      "           1       0.93      0.91      0.92       752\n",
      "\n",
      "    accuracy                           0.92      1524\n",
      "   macro avg       0.92      0.92      0.92      1524\n",
      "weighted avg       0.92      0.92      0.92      1524\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtree = DecisionTreeClassifier()\n",
    "dtree.fit(X_train_tfidf,y_train)\n",
    "pred_dtree = dtree.predict(X_test_tfidf)\n",
    "print(classification_reportication_report(y_test,pred_dtree))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0462f471",
   "metadata": {},
   "source": [
    "### RFC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "3420cef2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.99      0.96       772\n",
      "           1       0.99      0.92      0.95       752\n",
      "\n",
      "    accuracy                           0.96      1524\n",
      "   macro avg       0.96      0.96      0.96      1524\n",
      "weighted avg       0.96      0.96      0.96      1524\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfc = RandomForestClassifier()\n",
    "rfc.fit(X_train_tfidf,y_train)\n",
    "pred_rfc = rfc.predict(X_test_tfidf)\n",
    "print(classification_report(y_test,pred_rfc))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2488f867",
   "metadata": {},
   "source": [
    "### Naive bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "e74755fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.68      0.81       772\n",
      "           1       0.75      0.99      0.86       752\n",
      "\n",
      "    accuracy                           0.84      1524\n",
      "   macro avg       0.87      0.84      0.83      1524\n",
      "weighted avg       0.87      0.84      0.83      1524\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "nb = MultinomialNB()\n",
    "nb.fit(X_train_tfidf,y_train)\n",
    "pred_nb = nb.predict(X_test_tfidf)\n",
    "print(classification_report(y_test,pred_nb))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14aa8ef5",
   "metadata": {},
   "source": [
    "### SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "0408bb4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.98      0.96       772\n",
      "           1       0.98      0.94      0.96       752\n",
      "\n",
      "    accuracy                           0.96      1524\n",
      "   macro avg       0.96      0.96      0.96      1524\n",
      "weighted avg       0.96      0.96      0.96      1524\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svm = SVC()\n",
    "svm.fit(X_train_tfidf,y_train)\n",
    "pred_svm = svm.predict(X_test_tfidf)\n",
    "print(classification_report(y_test,pred_svm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3b4e315d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a09e0fc",
   "metadata": {},
   "source": [
    "### text pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "78cac0c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#reducing length\n",
    "def spacy_tokenizer(text):\n",
    "    clean_text = clean_lemm(text)\n",
    "    return clean_text.split()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b32b6170",
   "metadata": {},
   "source": [
    "### building vocab from scratch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "de5b7003",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_vocab(text_iterator,specials=('<unk>','<pad>'),min_freq=2):\n",
    "    token_count = defaultdict(int)\n",
    "    for text in text_iterator:\n",
    "        for token in spacy_tokenizer(text):\n",
    "            token_count[token] += 1\n",
    "    vocab = {token:idx for idx,(token,count) in enumerate(token_count.items()) if count >= min_freq}\n",
    "    for special in specials:\n",
    "        if special not in vocab:\n",
    "            vocab[special] = len(vocab)\n",
    "    return vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "cc8fe167",
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab = build_vocab(df['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "abc8c34f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vocab_size : 6760\n"
     ]
    }
   ],
   "source": [
    "vocab_size = len(vocab)\n",
    "print(f\"vocab_size : {vocab_size}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "73961dcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#text pipeline\n",
    "text_pipeline = lambda x : [vocab.get(token,vocab['<unk>'] )for token in spacy_tokenizer(x)]\n",
    "label_pipeline = lambda x : int(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80532be9",
   "metadata": {},
   "source": [
    "### Custom Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ffe3a3cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#datasets\n",
    "class TextDataset(Dataset):\n",
    "     \n",
    "    def __init__(self,df,text_pipeline,label_pipeline):\n",
    "        self.texts = df['text'].tolist()\n",
    "        self.labels = df['label'].tolist()\n",
    "        self.text_pipeline = text_pipeline\n",
    "        self.label_pipeline = label_pipeline\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.texts)\n",
    "    \n",
    "    def __getitem__(self,idx):\n",
    "        text = self.texts[idx]\n",
    "        label = self.labels[idx]\n",
    "        return self.text_pipeline(text),self.label_pipeline(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2a5f1b07",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df,test_df = train_test_split(df,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "48521120",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = TextDataset(train_df,text_pipeline,label_pipeline)\n",
    "test_dataset = TextDataset(test_df,text_pipeline,label_pipeline)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20e349fd",
   "metadata": {},
   "source": [
    "### custom collate function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "e19ce585",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pad collate\n",
    "def pad_collate(batch):\n",
    "    text_list = []\n",
    "    label_list = []\n",
    "    for text,label in batch:\n",
    "        label_list.append(label)\n",
    "        text_list.append(torch.LongTensor(text))\n",
    "    label_list = torch.LongTensor(label_list)\n",
    "    text_list = pad_sequence(text_list,batch_first=True)\n",
    "    return text_list.to(device),label_list.to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29e36119",
   "metadata": {},
   "source": [
    "### dataloader objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "c69a8a31",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_batch_size = 64\n",
    "test_batch_size = len(test_dataset)\n",
    "train_loader = DataLoader(train_dataset,train_batch_size,shuffle=True,collate_fn=pad_collate)\n",
    "test_loader = DataLoader(test_dataset,test_batch_size,shuffle=False,collate_fn=pad_collate)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f13ebeae",
   "metadata": {},
   "source": [
    "### LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "aedb3599",
   "metadata": {},
   "outputs": [],
   "source": [
    "class LSTM(nn.Module):\n",
    "    \n",
    "    def __init__(self,vocab_size,embedding_dim,hidden_size,out_size=2,num_layers=2,bidirectional=False,p=0.4):\n",
    "        super(LSTM,self).__init__()\n",
    "        self.num_layers = num_layers\n",
    "        self.hidden_size = hidden_size\n",
    "        self.num_dir = 2 if bidirectional else 1\n",
    "        \n",
    "        self.embedding = nn.Embedding(vocab_size,embedding_dim)\n",
    "        self.dropout = nn.Dropout(p)\n",
    "        \n",
    "        self.lstm = nn.LSTM(embedding_dim,hidden_size,num_layers=num_layers,bidirectional=bidirectional,batch_first=True)\n",
    "        self.fc = nn.Linear(self.num_dir * hidden_size,out_size)\n",
    "        \n",
    "    def forward(self,x):\n",
    "        hidden = self.init_hidden(x.size(0))\n",
    "        embeddings = self.embedding(x)\n",
    "        lstm_out,hidden = self.lstm(embeddings,hidden)\n",
    "        lstm_out = self.dropout(lstm_out)\n",
    "        pred = self.fc(lstm_out[:,-1,:])\n",
    "        return pred\n",
    "    \n",
    "    def init_hidden(self,batch_size):\n",
    "        hidden = (torch.zeros(self.num_dir*self.num_layers,batch_size,self.hidden_size).to(device),\n",
    "                 torch.zeros(self.num_dir*self.num_layers,batch_size,self.hidden_size).to(device))\n",
    "        return hidden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "299a2264",
   "metadata": {},
   "outputs": [],
   "source": [
    "lstm = LSTM(vocab_size,32,16,out_size=2,num_layers=2,bidirectional=False,p=0.4).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "1c4b92a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LSTM(\n",
       "  (embedding): Embedding(6760, 32)\n",
       "  (dropout): Dropout(p=0.4, inplace=False)\n",
       "  (lstm): LSTM(32, 16, num_layers=2, batch_first=True)\n",
       "  (fc): Linear(in_features=16, out_features=2, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lstm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "084d7689",
   "metadata": {},
   "source": [
    "### training params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "64c6ccdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_epochs = 10\n",
    "optimizer = torch.optim.Adam(lstm.parameters(),lr=0.005)\n",
    "criterion = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "ac8db2c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1/10: 100%|████████████████| 191/191 [00:33<00:00,  5.70it/s, loss=0.7034]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10, Average Loss: 0.7053\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 2/10: 100%|████████████████| 191/191 [00:33<00:00,  5.74it/s, loss=0.3507]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/10, Average Loss: 0.6231\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 3/10: 100%|████████████████| 191/191 [00:32<00:00,  5.81it/s, loss=0.1047]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/10, Average Loss: 0.3145\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 4/10: 100%|████████████████| 191/191 [00:33<00:00,  5.76it/s, loss=0.0427]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/10, Average Loss: 0.1625\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 5/10: 100%|████████████████| 191/191 [00:33<00:00,  5.77it/s, loss=0.1400]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/10, Average Loss: 0.1284\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 6/10: 100%|████████████████| 191/191 [00:33<00:00,  5.75it/s, loss=0.0192]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/10, Average Loss: 0.1057\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 7/10: 100%|████████████████| 191/191 [00:32<00:00,  5.79it/s, loss=0.0857]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/10, Average Loss: 0.0878\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 8/10: 100%|████████████████| 191/191 [00:32<00:00,  5.81it/s, loss=0.0138]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/10, Average Loss: 0.0941\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 9/10: 100%|████████████████| 191/191 [00:32<00:00,  5.92it/s, loss=0.0153]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/10, Average Loss: 0.0772\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 10/10: 100%|███████████████| 191/191 [00:32<00:00,  5.81it/s, loss=0.0097]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/10, Average Loss: 0.0658\n",
      "Total training time: 5.50 minutes\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "from tqdm import tqdm\n",
    "\n",
    "start = time.time()\n",
    "for epoch in range(num_epochs):\n",
    "    lstm.train()\n",
    "    # Wrap your train_loader with tqdm\n",
    "    pbar = tqdm(train_loader, desc=f\"Epoch {epoch+1}/{num_epochs}\")\n",
    "    total_loss = 0\n",
    "    for text, label in pbar:\n",
    "        optimizer.zero_grad()\n",
    "        y_pred = lstm(text)\n",
    "        loss = criterion(y_pred, label)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        # Update total loss and progress bar\n",
    "        total_loss += loss.item()\n",
    "        pbar.set_postfix({'loss': f'{loss.item():.4f}'})\n",
    "    \n",
    "    # Calculate and print average loss for the epoch\n",
    "    avg_loss = total_loss / len(train_loader)\n",
    "    print(f\"Epoch {epoch+1}/{num_epochs}, Average Loss: {avg_loss:.4f}\")\n",
    "\n",
    "print(f\"Total training time: {(time.time() - start) / 60:.2f} minutes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "04322835",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.97      0.95       753\n",
      "           1       0.97      0.92      0.95       771\n",
      "\n",
      "    accuracy                           0.95      1524\n",
      "   macro avg       0.95      0.95      0.95      1524\n",
      "weighted avg       0.95      0.95      0.95      1524\n",
      "\n"
     ]
    }
   ],
   "source": [
    "all_preds = []\n",
    "all_labels = []\n",
    "\n",
    "lstm.eval()\n",
    "with torch.no_grad():\n",
    "    for text, label in test_loader:\n",
    "        pred = lstm(text)\n",
    "        pred = torch.argmax(pred, dim=1)\n",
    "        \n",
    "        # Collect predictions and labels\n",
    "        all_preds.extend(pred.cpu().numpy())\n",
    "        all_labels.extend(label.cpu().numpy())\n",
    "\n",
    "# Now, use classification_report with the collected predictions and labels\n",
    "print(classification_report(all_labels, all_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f97e06ac",
   "metadata": {},
   "source": [
    "### BiLSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "329a6c98",
   "metadata": {},
   "outputs": [],
   "source": [
    "class BILSTM(nn.Module):\n",
    "    \n",
    "    def __init__(self,vocab_size,embedding_dim,hidden_size,out_size=2,num_layers=2,bidirectional=False,p=0.4):\n",
    "        super(BILSTM,self).__init__()\n",
    "        self.num_layers = num_layers\n",
    "        self.hidden_size = hidden_size\n",
    "        self.num_dir = 2 if bidirectional else 1\n",
    "        \n",
    "        self.embedding = nn.Embedding(vocab_size,embedding_dim)\n",
    "        self.dropout = nn.Dropout(p)\n",
    "        \n",
    "        self.lstm = nn.LSTM(embedding_dim,hidden_size,num_layers=num_layers,bidirectional=bidirectional,batch_first=True)\n",
    "        self.fc = nn.Linear(self.num_dir * hidden_size,out_size)\n",
    "        \n",
    "    def forward(self,x):\n",
    "        hidden = self.init_hidden(x.size(0))\n",
    "        embeddings = self.embedding(x)\n",
    "        lstm_out,hidden = self.lstm(embeddings,hidden)\n",
    "        lstm_out = self.dropout(lstm_out)\n",
    "        pred = self.fc(lstm_out[:,-1,:])\n",
    "        return pred\n",
    "    \n",
    "    def init_hidden(self,batch_size):\n",
    "        hidden = (torch.zeros(self.num_dir*self.num_layers,batch_size,self.hidden_size).to(device),\n",
    "                 torch.zeros(self.num_dir*self.num_layers,batch_size,self.hidden_size).to(device))\n",
    "        return hidden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "a9928d4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "bilstm = BILSTM(vocab_size,200,32,out_size=2,num_layers=2,bidirectional=True,p=0.4).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "ed550c48",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1/5: 100%|███████████████████| 96/96 [00:33<00:00,  2.86it/s, loss=0.0403]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5, Average Loss: 0.0148\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 2/5: 100%|███████████████████| 96/96 [00:32<00:00,  3.00it/s, loss=0.0000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/5, Average Loss: 0.0084\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 3/5: 100%|███████████████████| 96/96 [00:31<00:00,  3.01it/s, loss=0.0002]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/5, Average Loss: 0.0069\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 4/5: 100%|███████████████████| 96/96 [00:32<00:00,  2.98it/s, loss=0.0015]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/5, Average Loss: 0.0075\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 5/5: 100%|███████████████████| 96/96 [00:32<00:00,  2.96it/s, loss=0.0000]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/5, Average Loss: 0.0055\n",
      "Total training time: 2.70 minutes\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "from tqdm import tqdm\n",
    "num_epochs = 5\n",
    "optimizer = torch.optim.Adam(bilstm.parameters(),lr=0.0025)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "start = time.time()\n",
    "for epoch in range(num_epochs):\n",
    "    bilstm.train()\n",
    "    # Wrap your train_loader with tqdm\n",
    "    pbar = tqdm(train_loader, desc=f\"Epoch {epoch+1}/{num_epochs}\")\n",
    "    total_loss = 0\n",
    "    for text, label in pbar:\n",
    "        optimizer.zero_grad()\n",
    "        y_pred = bilstm(text)\n",
    "        loss = criterion(y_pred, label)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        # Update total loss and progress bar\n",
    "        total_loss += loss.item()\n",
    "        pbar.set_postfix({'loss': f'{loss.item():.4f}'})\n",
    "    \n",
    "    # Calculate and print average loss for the epoch\n",
    "    avg_loss = total_loss / len(train_loader)\n",
    "    print(f\"Epoch {epoch+1}/{num_epochs}, Average Loss: {avg_loss:.4f}\")\n",
    "\n",
    "print(f\"Total training time: {(time.time() - start) / 60:.2f} minutes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "27b8260e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.95      0.95       753\n",
      "           1       0.95      0.95      0.95       771\n",
      "\n",
      "    accuracy                           0.95      1524\n",
      "   macro avg       0.95      0.95      0.95      1524\n",
      "weighted avg       0.95      0.95      0.95      1524\n",
      "\n"
     ]
    }
   ],
   "source": [
    "all_preds = []\n",
    "all_labels = []\n",
    "\n",
    "bilstm.eval()\n",
    "with torch.no_grad():\n",
    "    for text, label in test_loader:\n",
    "        pred = bilstm(text)\n",
    "        pred = torch.argmax(pred, dim=1)\n",
    "        \n",
    "        # Collect predictions and labels\n",
    "        all_preds.extend(pred.cpu().numpy())\n",
    "        all_labels.extend(label.cpu().numpy())\n",
    "\n",
    "# Now, use classification_report with the collected predictions and labels\n",
    "print(classification_report(all_labels, all_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "906866f6",
   "metadata": {},
   "source": [
    "### GRU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "5fc36d56",
   "metadata": {},
   "outputs": [],
   "source": [
    "class GRU(nn.Module):\n",
    "    \n",
    "    def __init__(self,vocab_size,embedding_dim,hidden_size,out_size=2,num_layers=2,bidirectional=False,p=0.4):\n",
    "        super(GRU,self).__init__()\n",
    "        self.num_layers = num_layers\n",
    "        self.hidden_size = hidden_size\n",
    "        self.num_dir = 2 if bidirectional else 1\n",
    "        \n",
    "        self.embedding = nn.Embedding(vocab_size,embedding_dim)\n",
    "        self.dropout = nn.Dropout(p)\n",
    "        \n",
    "        self.gru = nn.GRU(embedding_dim,hidden_size,num_layers=num_layers,bidirectional=bidirectional,batch_first=True)\n",
    "        self.fc = nn.Linear(self.num_dir * hidden_size,out_size)\n",
    "        \n",
    "    def forward(self,x):\n",
    "        hidden = self.init_hidden(x.size(0))\n",
    "        embeddings = self.embedding(x)\n",
    "        gru_out,hidden = self.gru(embeddings,hidden)\n",
    "        gru_out = self.dropout(gru_out)\n",
    "        pred = self.fc(gru_out[:,-1,:])\n",
    "        return pred\n",
    "    \n",
    "    def init_hidden(self,batch_size):\n",
    "        hidden = (torch.zeros(self.num_dir*self.num_layers,batch_size,self.hidden_size).to(device))\n",
    "        return hidden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "8ec20216",
   "metadata": {},
   "outputs": [],
   "source": [
    "gru = GRU(vocab_size,64,16,out_size=2,num_layers=2,bidirectional=False,p=0.4).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "34a6dab5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1/10: 100%|████████████████| 191/191 [01:03<00:00,  3.00it/s, loss=0.2133]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10, Average Loss: 0.3562\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 2/10: 100%|████████████████| 191/191 [01:00<00:00,  3.18it/s, loss=0.2067]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/10, Average Loss: 0.1141\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 3/10: 100%|████████████████| 191/191 [01:00<00:00,  3.16it/s, loss=0.0377]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/10, Average Loss: 0.0700\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 4/10: 100%|████████████████| 191/191 [01:00<00:00,  3.15it/s, loss=0.0424]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/10, Average Loss: 0.0453\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 5/10: 100%|████████████████| 191/191 [01:02<00:00,  3.08it/s, loss=0.1318]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/10, Average Loss: 0.0344\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 6/10: 100%|████████████████| 191/191 [01:01<00:00,  3.09it/s, loss=0.2076]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/10, Average Loss: 0.0312\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 7/10: 100%|████████████████| 191/191 [01:00<00:00,  3.13it/s, loss=0.0015]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/10, Average Loss: 0.0340\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 8/10: 100%|████████████████| 191/191 [01:00<00:00,  3.16it/s, loss=0.0143]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/10, Average Loss: 0.0202\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 9/10: 100%|████████████████| 191/191 [01:00<00:00,  3.16it/s, loss=0.0016]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/10, Average Loss: 0.0141\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 10/10: 100%|███████████████| 191/191 [01:00<00:00,  3.15it/s, loss=0.0032]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/10, Average Loss: 0.0121\n",
      "Total training time: 10.18 minutes\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "from tqdm import tqdm\n",
    "num_epochs = 10\n",
    "optimizer = torch.optim.Adam(gru.parameters(),lr=0.005)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "start = time.time()\n",
    "for epoch in range(num_epochs):\n",
    "    gru.train()\n",
    "    # Wrap your train_loader with tqdm\n",
    "    pbar = tqdm(train_loader, desc=f\"Epoch {epoch+1}/{num_epochs}\")\n",
    "    total_loss = 0\n",
    "    for text, label in pbar:\n",
    "        optimizer.zero_grad()\n",
    "        y_pred = gru(text)\n",
    "        loss = criterion(y_pred, label)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        # Update total loss and progress bar\n",
    "        total_loss += loss.item()\n",
    "        pbar.set_postfix({'loss': f'{loss.item():.4f}'})\n",
    "    \n",
    "    # Calculate and print average loss for the epoch\n",
    "    avg_loss = total_loss / len(train_loader)\n",
    "    print(f\"Epoch {epoch+1}/{num_epochs}, Average Loss: {avg_loss:.4f}\")\n",
    "\n",
    "print(f\"Total training time: {(time.time() - start) / 60:.2f} minutes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "ead59c30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.89      0.93       753\n",
      "           1       0.90      0.98      0.94       771\n",
      "\n",
      "    accuracy                           0.93      1524\n",
      "   macro avg       0.94      0.93      0.93      1524\n",
      "weighted avg       0.94      0.93      0.93      1524\n",
      "\n"
     ]
    }
   ],
   "source": [
    "all_preds = []\n",
    "all_labels = []\n",
    "\n",
    "gru.eval()\n",
    "with torch.no_grad():\n",
    "    for text, label in test_loader:\n",
    "        pred =gru(text)\n",
    "        pred = torch.argmax(pred, dim=1)\n",
    "        \n",
    "        # Collect predictions and labels\n",
    "        all_preds.extend(pred.cpu().numpy())\n",
    "        all_labels.extend(label.cpu().numpy())\n",
    "\n",
    "# Now, use classification_report with the collected predictions and labels\n",
    "print(classification_report(all_labels, all_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "e78cced0",
   "metadata": {},
   "outputs": [],
   "source": [
    "class BIGRU(nn.Module):\n",
    "    \n",
    "    def __init__(self,vocab_size,embedding_dim,hidden_size,out_size=2,num_layers=2,bidirectional=False,p=0.4):\n",
    "        super(BIGRU,self).__init__()\n",
    "        self.num_layers = num_layers\n",
    "        self.hidden_size = hidden_size\n",
    "        self.num_dir = 2 if bidirectional else 1\n",
    "        \n",
    "        self.embedding = nn.Embedding(vocab_size,embedding_dim)\n",
    "        self.dropout = nn.Dropout(p)\n",
    "        \n",
    "        self.gru = nn.GRU(embedding_dim,hidden_size,num_layers=num_layers,bidirectional=bidirectional,batch_first=True)\n",
    "        self.fc = nn.Linear(self.num_dir * hidden_size,out_size)\n",
    "        \n",
    "    def forward(self,x):\n",
    "        hidden = self.init_hidden(x.size(0))\n",
    "        embeddings = self.embedding(x)\n",
    "        gru_out,hidden = self.gru(embeddings,hidden)\n",
    "        gru_out = self.dropout(gru_out)\n",
    "        pred = self.fc(gru_out[:,-1,:])\n",
    "        return pred\n",
    "    \n",
    "    def init_hidden(self,batch_size):\n",
    "        hidden = (torch.zeros(self.num_dir*self.num_layers,batch_size,self.hidden_size).to(device))\n",
    "        return hidden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "2335a9e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "bigru = BIGRU(vocab_size,64,32,out_size=2,num_layers=2,bidirectional=True,p=0.6).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "8d95efad",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1/1: 100%|█████████████████| 191/191 [01:32<00:00,  2.06it/s, loss=0.0005]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1, Average Loss: 0.0081\n",
      "Total training time: 1.54 minutes\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "from tqdm import tqdm\n",
    "num_epochs = 1\n",
    "optimizer = torch.optim.Adam(bigru.parameters(),lr=0.001)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "start = time.time()\n",
    "for epoch in range(num_epochs):\n",
    "    bigru.train()\n",
    "    # Wrap your train_loader with tqdm\n",
    "    pbar = tqdm(train_loader, desc=f\"Epoch {epoch+1}/{num_epochs}\")\n",
    "    total_loss = 0\n",
    "    for text, label in pbar:\n",
    "        optimizer.zero_grad()\n",
    "        y_pred = bigru(text)\n",
    "        loss = criterion(y_pred, label)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        # Update total loss and progress bar\n",
    "        total_loss += loss.item()\n",
    "        pbar.set_postfix({'loss': f'{loss.item():.4f}'})\n",
    "    \n",
    "    # Calculate and print average loss for the epoch\n",
    "    avg_loss = total_loss / len(train_loader)\n",
    "    print(f\"Epoch {epoch+1}/{num_epochs}, Average Loss: {avg_loss:.4f}\")\n",
    "\n",
    "print(f\"Total training time: {(time.time() - start) / 60:.2f} minutes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "f9b9bbbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.97      0.95       753\n",
      "           1       0.97      0.94      0.95       771\n",
      "\n",
      "    accuracy                           0.95      1524\n",
      "   macro avg       0.95      0.95      0.95      1524\n",
      "weighted avg       0.95      0.95      0.95      1524\n",
      "\n"
     ]
    }
   ],
   "source": [
    "all_preds = []\n",
    "all_labels = []\n",
    "\n",
    "bigru.eval()\n",
    "with torch.no_grad():\n",
    "    for text, label in test_loader:\n",
    "        pred =bigru(text)\n",
    "        pred = torch.argmax(pred, dim=1)\n",
    "        \n",
    "        # Collect predictions and labels\n",
    "        all_preds.extend(pred.cpu().numpy())\n",
    "        all_labels.extend(label.cpu().numpy())\n",
    "\n",
    "# Now, use classification_report with the collected predictions and labels\n",
    "print(classification_report(all_labels, all_preds))"
   ]
  }
 ],
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