Analysis_of_the_Chat_Transcript.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Analysis of the Chat Transcript.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "32qeJmS5Jy4H",
        "_uSH-YfpgW2J",
        "OW9F1VRHw2Zx"
      ],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jainakki16/Chat-Transcript-Analysis/blob/main/Analysis_of_the_Chat_Transcript.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XGennQOEMHjT"
      },
      "source": [
        "#Analysis of the Chat transcript \n",
        "\n",
        "Data will be collected from the chat transcripts of different courses to extract the different information \n",
        "regarding interaction of the students, types of question, innovative ideas etc."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZJ_uhQ5Yrcjb",
        "outputId": "e07fc457-f2ff-4d84-b74d-0bb9ec11e471"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive', force_remount=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dlswQZBZMKtq"
      },
      "source": [
        "# Importing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AZtJd0-sDfZk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a311bba-8cb5-40e0-b4eb-4a979a6c6492"
      },
      "source": [
        "!pip install python-docx --quiet\n",
        "!pip install pyspellchecker --quiet"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 5.6 MB 4.2 MB/s \n",
            "\u001b[?25h  Building wheel for python-docx (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[K     |████████████████████████████████| 2.7 MB 4.1 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qJ4pt0JUDbkN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2f62c83c-5580-4eeb-89ae-e3f12305c225"
      },
      "source": [
        "import os\n",
        "import re\n",
        "import docx\n",
        "import operator\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from collections import defaultdict\n",
        "\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "import scipy\n",
        "from scipy.spatial import distance\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "sns.set_style('darkgrid')\n",
        "nltk.download(\"punkt\")\n",
        "nltk.download('stopwords')\n",
        "stopwords = stopwords.words('english')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nQawZ4BsMN5K"
      },
      "source": [
        "# Reading Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k-b2PSoQQ5Oh"
      },
      "source": [
        "def read_data(path):\n",
        "  time = []\n",
        "  name = []\n",
        "  chat = []\n",
        "\n",
        "  document = docx.Document(path)\n",
        "\n",
        "  for para in document.paragraphs:\n",
        "    line = para.text.split()\n",
        "    if len(line) > 1:\n",
        "      time.append(line[0])\n",
        "      name.append(line[1] + ' ' + line[2][:-1])\n",
        "      chat.append(' '.join(line[3:]))\n",
        "\n",
        "  data = pd.DataFrame({'Time': time, 'Name': name, 'Chat': chat})\n",
        "\n",
        "  return data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQF5iZ2Jx2mH"
      },
      "source": [
        "def create_dataset():\n",
        "  dir_path = '/content/gdrive/MyDrive/R&D Project'\n",
        "  dir_all_path = sorted(os.listdir(dir_path))\n",
        "  data = {}\n",
        "\n",
        "  student_number = 1\n",
        "  replace_name = {}\n",
        "  student_number = 1\n",
        "  teacher_number = 1\n",
        "\n",
        "  for i in range(len(dir_all_path)):\n",
        "    path = dir_path + '/' + dir_all_path[i]\n",
        "    if path.endswith('.docx'):\n",
        "      data[f'data_{i + 1}'] = read_data(path)\n",
        "      new_name = []\n",
        "      for name in data[f'data_{i + 1}']['Name']:\n",
        "        if name == 'Ratna Sanyal':\n",
        "          replace_name[name] = 'teacher_' + str(teacher_number)\n",
        "        elif name not in replace_name:\n",
        "          replace_name[name] = 'student_' + str(student_number)\n",
        "          student_number += 1\n",
        "        new_name.append(replace_name[name])\n",
        "\n",
        "      data[f'data_{i + 1}']['Name'] = new_name\n",
        "  \n",
        "  return data, replace_name"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JcnirYmhySW4"
      },
      "source": [
        "dataset, student_names = create_dataset()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z2QclLEEmZAR"
      },
      "source": [
        "# Number of questions asked by faculty\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iJsypbWpVQ5Z"
      },
      "source": [
        "def get_professor_question(data, professor_name):\n",
        "  return data[data['Name'] == professor_name]\n",
        "\n",
        "def get_marks(marks_lists):\n",
        "  return np.random.choice(marks_lists, p=[0.1, 0.1, 0.2, 0.3, 0.3])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "46BzlYHAmVoT"
      },
      "source": [
        "j = 0\n",
        "all_ques_count = []\n",
        "\n",
        "for i in dataset:\n",
        "  j += 1\n",
        "  ques_count = get_professor_question(dataset[i], 'teacher_1').shape[0]\n",
        "  all_ques_count.append(ques_count)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "yc2pHnyL1iDA",
        "outputId": "6dd42f48-c616-4be1-bc69-125704b359b0"
      },
      "source": [
        "sns.lineplot(x=list(range(1, len(all_ques_count) + 1)), y=all_ques_count, marker = 'o')\n",
        "plt.title('Questions Asked in Each Session', weight = 'bold')\n",
        "plt.xlabel('Session Number')\n",
        "plt.ylabel('Questions')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XpzbtuWEmghC"
      },
      "source": [
        "# Questions asked by faculty with their timestamp"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n-edA9W3mhYJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b026194e-46e7-41bd-c422-05d606399691"
      },
      "source": [
        "j = 0\n",
        "for i in dataset:\n",
        "  j += 1\n",
        "  if get_professor_question(dataset[i], 'teacher_1')[['Time', 'Chat']].shape[0] != 0:\n",
        "      print(f\"Questions asked by faculty with their timestamp in session-{j} : \\n\\n \\\n",
        "     {get_professor_question(dataset[i], 'teacher_1')[['Time', 'Chat']]}\\n\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Questions asked by faculty with their timestamp in session-1 : \n",
            "\n",
            "              Time                                      Chat\n",
            "0   00:16:01             give examples of prepositions\n",
            "8   00:20:50  change position of prepositional phrases\n",
            "21  00:54:02     In SRS document, how can we apply NLP\n",
            "\n",
            "Questions asked by faculty with their timestamp in session-2 : \n",
            "\n",
            "               Time                                 Chat\n",
            "0    00:12:59        Give the example for Homonyms\n",
            "39   00:30:10         Can you example for antonyms\n",
            "78   00:38:44  Write example for Hyponymy/Hypernym\n",
            "110  00:45:48            Write example for Meronym\n",
            "\n",
            "Questions asked by faculty with their timestamp in session-3 : \n",
            "\n",
            "              Time                         Chat\n",
            "0   00:29:14  I'd like to fly from A to B\n",
            "2   00:31:05                           S-\n",
            "7   00:33:35                     I'd like\n",
            "8   00:33:40                       to fly\n",
            "9   00:33:56                      S-NP PP\n",
            "10  00:34:14                          I'd\n",
            "11  00:34:21                  Like to fly\n",
            "12  00:34:33                      S-NP VP\n",
            "13  00:40:57                   The flight\n",
            "14  00:41:07                       DET NN\n",
            "15  00:41:17                 The * Flight\n",
            "16  00:41:28               DET NOM Flight\n",
            "17  00:41:51                   DET NOM NN\n",
            "27  00:50:49   I prefer, a morning flight\n",
            "29  00:53:12     Chunker / Shallow parser\n",
            "\n",
            "Questions asked by faculty with their timestamp in session-4 : \n",
            "\n",
            "             Time           Chat\n",
            "0  00:24:09  NP - NP VP PP\n",
            "3  00:25:49           V NP\n",
            "\n",
            "Questions asked by faculty with their timestamp in session-5 : \n",
            "\n",
            "              Time                                               Chat\n",
            "0   00:12:59                               What are stop words?\n",
            "34  00:18:45  Correct answer is Stop words are said to be us...\n",
            "35  00:19:09                            What is parsing in NLP?\n",
            "43  00:20:23                             Where can NER be used?\n",
            "50  00:22:19    What are some popular Python libraries for NLP?\n",
            "\n",
            "Questions asked by faculty with their timestamp in session-7 : \n",
            "\n",
            "             Time                           Chat\n",
            "3  00:26:15  Correct answer pine-1, cone-3\n",
            "\n",
            "Questions asked by faculty with their timestamp in session-8 : \n",
            "\n",
            "             Time                                   Chat\n",
            "0  00:12:30  Give some example for Misspelled word\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BmjjlJwsupSM"
      },
      "source": [
        "# Most frequent words in question"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VAx3LiBu1Wjn"
      },
      "source": [
        "def get_top_five(dic):\n",
        "  sorted_dict = dict(sorted(dic.items(), key=operator.itemgetter(1), reverse=True))\n",
        "  top_five = {k: sorted_dict[k] for k in list(sorted_dict)[:5]}\n",
        "  return top_five"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c2zFbAUYeqZK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "381aa8e0-9520-488b-9518-75723a6fbde7"
      },
      "source": [
        "j = 0\n",
        "for i in dataset:\n",
        "  j += 1\n",
        "\n",
        "  session = ' '.join(get_professor_question(dataset[i], 'teacher_1')['Chat'].tolist())\n",
        "  tokenizer = nltk.RegexpTokenizer(r\"\\w+\")\n",
        "  session = tokenizer.tokenize(session)\n",
        "\n",
        "  if session != []:\n",
        "\n",
        "    word_count = {}\n",
        "    for word in session:\n",
        "      if word in word_count:\n",
        "        word_count[word] += 1\n",
        "      else:\n",
        "        word_count[word] = 1\n",
        "    \n",
        "    print(f'\\nSession - {j}\\n')\n",
        "    plt.figure(figsize=(10, 5))\n",
        "\n",
        "    word_count = get_top_five(word_count)\n",
        "    word_count_keys = list(word_count.keys())\n",
        "\n",
        "    sns.barplot(x = word_count_keys, y = [word_count[k] for k in word_count_keys], edgecolor = 'black')\n",
        "    plt.xticks(rotation=30, weight = 'bold')\n",
        "    plt.yticks(weight = 'bold')\n",
        "    plt.title(f'Session - {j}', weight = 'bold')\n",
        "    plt.ylabel('Frequency', weight = 'bold')\n",
        "    plt.xlabel('Words', weight = 'bold')\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Session - 1\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Session - 2\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Session - 3\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Session - 4\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Session - 5\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Session - 7\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Session - 8\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cmpoDWISz-CQ"
      },
      "source": [
        "# Total number of responses in each lecture"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VtukU-d6V9vh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "409b6dce-d59e-4f02-8165-c6a27a44845a"
      },
      "source": [
        "def get_ans_count(data):\n",
        "  return data[data['Name'] != 'teacher_1']['Name'].value_counts()\n",
        "\n",
        "j = 0\n",
        "total_student_response = {}\n",
        "for i in dataset:\n",
        "  j += 1\n",
        "  print(f'Total number of responses by students in session-{j} : {get_ans_count(dataset[i]).sum()}')\n",
        "  total_student_response[f'Session-{j}'] = get_ans_count(dataset[i]).sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of responses by students in session-1 : 26\n",
            "Total number of responses by students in session-2 : 140\n",
            "Total number of responses by students in session-3 : 15\n",
            "Total number of responses by students in session-4 : 8\n",
            "Total number of responses by students in session-5 : 52\n",
            "Total number of responses by students in session-6 : 19\n",
            "Total number of responses by students in session-7 : 3\n",
            "Total number of responses by students in session-8 : 16\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "5Nr_f9hv1Lnr",
        "outputId": "3959d7bd-594b-4444-c0d4-27ec33a80266"
      },
      "source": [
        "plt.figure(figsize=(10, 5))\n",
        "\n",
        "total_student_response_keys = list(total_student_response.keys())\n",
        "sns.lineplot(x = total_student_response_keys, \n",
        "             y = [total_student_response[k] for k in total_student_response_keys], \n",
        "             marker = \"o\")\n",
        "\n",
        "plt.title('Total Responses by students', weight = 'bold')\n",
        "plt.ylabel('Count')\n",
        "plt.xlabel('Sessions')\n",
        "plt.show()  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_8lpiKcA5Wh3"
      },
      "source": [
        "# Number of responses by each student"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n8sE_KIK8-Ac"
      },
      "source": [
        "def get_response_data(data):\n",
        "  j = 1\n",
        "  all_num_responses = {}\n",
        "\n",
        "  for i in data:\n",
        "    session_data = get_ans_count(data[i])\n",
        "    for index, value in session_data.items():\n",
        "      if j == 1:\n",
        "        all_num_responses[index] = [value]\n",
        "      else:\n",
        "        if index not in all_num_responses:\n",
        "          all_num_responses[index] = [0]\n",
        "          while len(all_num_responses[index]) != j - 1: \n",
        "            all_num_responses[index].append(0)\n",
        "        all_num_responses[index].append(value)\n",
        "\n",
        "    for key, value in all_num_responses.items():\n",
        "      if len(value) != j:\n",
        "        while len(all_num_responses[key]) != j: \n",
        "            all_num_responses[key].append(0)\n",
        "    j += 1\n",
        "\n",
        "\n",
        "  all_session = []\n",
        "\n",
        "  for i in range(len(data)):\n",
        "    each_session = []\n",
        "    for value in all_num_responses.values():\n",
        "        each_session.append(value[i])\n",
        "    all_session.append(each_session)\n",
        "\n",
        "  total_session = []\n",
        "\n",
        "  for i in range(len(all_session[0])):\n",
        "    each_total = 0\n",
        "    for j in range(len(all_session)):\n",
        "      each_total += all_session[j][i]\n",
        "    total_session.append(each_total)\n",
        "\n",
        "  response_dic = {'student_name': list(all_num_responses.keys()), 'total_response': total_session}\n",
        "  for i in range(len(data)):\n",
        "    response_dic[f'session-{i + 1}'] = all_session[i]\n",
        "\n",
        "  response_data = pd.DataFrame(response_dic).sort_values('total_response', ascending = False).head(n = 15).reset_index(drop=True)\n",
        "  return response_data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3wHZp2NF5WMh"
      },
      "source": [
        "response_data = get_response_data(dataset)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "WeE-AX6yX5Cg",
        "outputId": "cc60912f-2369-456d-f7f7-bd90a5790bf9"
      },
      "source": [
        "plt.figure(figsize=(6, 9))\n",
        "color = list(sns.color_palette())\n",
        "alpha = 1\n",
        "for i in range(len(dataset), 0, -1):\n",
        "  kwargs = {'alpha':alpha}\n",
        "  sns.barplot(x=f\"session-{i}\", y=\"student_name\", data=response_data, label=f\"S - {i}\", color=color[i-1], **kwargs)\n",
        "  alpha -= 0.1\n",
        "\n",
        "plt.xlabel('No. of Responses')\n",
        "plt.ylabel('Student Name')\n",
        "plt.title('Number of responses by each student')\n",
        "plt.legend(ncol = 2)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x648 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4JlW4IhWl0xs"
      },
      "source": [
        "# Responses For Each Question"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nMpzCpNFYcaC"
      },
      "source": [
        "def response_after_question(data):\n",
        "  data_copy = data.copy(deep = True)\n",
        "  data_copy.loc[len(data_copy.index)] = [' ', 'teacher_1', ' ']\n",
        "  ques_index = data_copy.index[data_copy['Name'] == 'teacher_1'].tolist()\n",
        "  \n",
        "  all_responses = []\n",
        "  for i in range(len(ques_index) - 1):\n",
        "    all_responses.append(get_ans_count(data_copy[ques_index[i] + 1 : ques_index[i + 1]]).sum())\n",
        "  return all_responses"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZXfCV51bQdnr"
      },
      "source": [
        "def get_all_responses(data):\n",
        "  j = 0\n",
        "  all_responses = []\n",
        "  for i in data:\n",
        "    j += 1\n",
        "    all_responses.append(response_after_question(data[i]))\n",
        "\n",
        "  max_length = len(max(all_responses, key=len))\n",
        "  for i in range(len(data)):\n",
        "    if len(all_responses[i]) != max_length:\n",
        "      diff = max_length - len(all_responses[i])\n",
        "      all_responses[i].extend([0]*diff)\n",
        "\n",
        "  return all_responses, max_length"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "fL1knaCjIaZz",
        "outputId": "4f436271-e0a9-4f7d-f140-6a58646cee0c"
      },
      "source": [
        "all_responses, max_length = get_all_responses(dataset)\n",
        "\n",
        "plt.figure(figsize=(12, 5))\n",
        "for i in range(len(all_responses)):\n",
        "  sns.lineplot(x = range(1, max_length + 1), y = all_responses[i], marker = 'o', label = f'Session {i + 1}')\n",
        "\n",
        "plt.xlabel('Question Number')\n",
        "plt.ylabel('No. of Responses')\n",
        "plt.title('Responses For Each Question')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "De3d9mbm51NW"
      },
      "source": [
        "# Most Frequent Words in Responses"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hwlYeQF350J3"
      },
      "source": [
        "def get_student_responses(data, professor_name):\n",
        "  return data[data['Name'] != professor_name]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "bxG3EoJDatAE",
        "outputId": "c738f4eb-398c-4246-b607-c235ec5112f6"
      },
      "source": [
        "j = 0\n",
        "for i in dataset:\n",
        "  j += 1\n",
        "\n",
        "  responses = ' '.join(get_student_responses(dataset[i], 'teacher_1')['Chat'].tolist())\n",
        "  tokenizer = nltk.RegexpTokenizer(r\"\\w+\")\n",
        "  responses = tokenizer.tokenize(responses)\n",
        "\n",
        "  if responses == []:\n",
        "    continue\n",
        "\n",
        "  word_count_1 = {}\n",
        "  for word in responses:\n",
        "    if word in word_count_1:\n",
        "      word_count_1[word] += 1\n",
        "    else:\n",
        "      word_count_1[word] = 1\n",
        "\n",
        "  # ---- dictionary without stopword -----\n",
        "  dict_without_stop = word_count_1.copy()\n",
        "  for k in stopwords:\n",
        "    if k in word_count_1:\n",
        "      dict_without_stop.pop(k)\n",
        "\n",
        "  word_count_1 = get_top_five(word_count_1)\n",
        "  word_count_keys_1 = list(word_count_1.keys())\n",
        "\n",
        "  dict_without_stop = get_top_five(dict_without_stop)\n",
        "  dict_without_stop_keys = list(dict_without_stop.keys())\n",
        "\n",
        "  print(f'\\nSession - {j}\\n')\n",
        "  f, axes = plt.subplots(nrows = 1, ncols = 2, figsize=(12,5), edgecolor='k')\n",
        "  f.subplots_adjust(hspace=0.5, wspace=0.3, bottom = 0.1)\n",
        "  sns.barplot(x = word_count_keys_1, y = [word_count_1[k] for k in word_count_keys_1], edgecolor = 'black', ax=axes[0])\n",
        "  sns.barplot(x = dict_without_stop_keys, y = [dict_without_stop[k] for k in dict_without_stop_keys], edgecolor = 'black', ax=axes[1])\n",
        "\n",
        "  axes[0].set_title('With Stopwords')\n",
        "  axes[0].set_xlabel('Word')\n",
        "  axes[0].set_ylabel('Frequency')\n",
        "  axes[1].set_title('Without Stopwords')\n",
        "  axes[1].set_xlabel('Word')\n",
        "  axes[1].set_ylabel('Frequency')\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Session - 1\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Session - 2\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AALDBqdMzwsPDVbVqVVWoUEFubm5KTExUVlaWnn32WaWlpalu3bqaOnWqatSo4cwyAAAAgBJx+pnm+fPnKzk5WYmJiZKkhIQEhYaGavXq1QoNDVVCQoKzSwAAAABKpNSnZ6xbt069e/eWJPXu3Vtr164t7RIAAACAYnF6aH7ssccUExOjRYsWSZIyMzPl5+cnSapdu7YyMzOdXQIAAABQIk6d07xw4UKZzWZlZmYqNjZWDRs2LLDeZDLJZDLZ3I+bm0ne3lWcVSYAAABwXU4NzWazWZLk6+uriIgI7d27V76+vsrIyJCfn58yMjLk4+Njcz9cvxPAzYDrNAPArctp0zNyc3OVnZ1tfbx582Y1atRI4eHhSkpKkiQlJSWpU6dOzioBAAAAcAinnWnOzMxUfHy8JMlisSgyMlLt27dX8+bNNWLECH3xxReqU6eOpk6d6qwSAAAAAIcwGYZhuLoIWy5dsjA9A4DLMT3DeejzAG4G1+vzTp3T7GzNW96r9BNpri6j2My31dW+7/a7ugwAuOmV1T4v0euB8qZMh+b0E2lq/fx8V5dRbLsnPerqEgCgTCirfV6i1wPlTanf3AQAAAAoawjNAAAAgA1lenoGAMA5xowZo2+++Ua+vr5asWKFJGnEiBE6fPiwJOn8+fOqVq2akpOTC702PDxcVatWVYUKFeTm5qbExMRSrR0AnIHQDAAoJCYmRgMHDtSLL75oXfbnS4ROnDhRXl5e13z9/Pnz7bp5FQCUFUzPAAAUEhwcrBo1ahS5zjAMffnll4qMjCzlqgDAdQjNAIBi2blzp3x9fdWgQYNrbvPYY48pJiZGixYtKr3CAMCJmJ4BACiWFStWXPcs88KFC2U2m5WZmanY2Fg1bNhQwcHB192nm5tJ3t5VHF2qy5XHMQG3KkIzAMBu+fn5WrNmzXW/3Gc2myVJvr6+ioiI0N69e22GZovFKJd3BCyPYwLKs+vdEZDpGQAAu23ZskUNGzaUv79/ketzc3OVnZ1tfbx582Y1atSoNEsEAKcgNAMAChk5cqT69++vw4cPq3379lq8eLEkadWqVerRo0eBbdPT0/XEE09IkjIzM/WPf/xDvXr1Ur9+/dShQwe1b9++1OsHAEdjegYAoJDJkycXuXzixImFlpnNZn3wwQeSpPr162vZsmVOrQ0AXIEzzQAAAIANhGYAAADABkIzAAAAYAOhGQAAALCB0AwAAADYQGgGAAAAbCA0AwAAADYQmgEAAAAbCM0AAACADYRmAAAAwAZCMwAAAGADoRkAAACwgdAMAAAA2EBoBgAAAGwgNAMAAAA2EJoBAAAAGwjNAAAAgA2EZgAAAMAGQjMAAABgA6EZAAAAsIHQDAAAANhAaAYAAABsIDQDAAAANhCaAQAAABsIzQAAAIANhGYAAADABkIzAAAAYAOhGQAAALCB0AwAKGTMmDEKDQ1VZGSkddn06dP1wAMPKCoqSlFRUdqwYUORr924caO6du2qiIgIJSQklFbJAOBUhGYAQCExMTGaM2dOoeWDBw9WcnKykpOT1aFDh0LrLRaLxo8frzlz5mjlypVasWKFfvnll9IoGQCcitAMACgkODhYNWrUKPbr9u7dqzvuuEP169eXh4eHevTooXXr1jmhQgAoXYRmAIDdPvnkE/Xs2VNjxozR2bNnC61PT0+Xv7+/9bnZbFZ6enpplggATuHu6gIAAGXDgAEDNHToUJlMJk2bNk0TJ07UW2+95ZB9u7mZ5O1dxSH7upnYO6a776yn39JOOrka57i9rr9+OXzM1WUATuf00GyxWNSnTx+ZzWbNnj1bqampGjlypLKyshQQEKD//Oc/8vDwcHYZAIASqlWrlvVxv3799NRTTxXaxmw26+TJ/4W/9PR0mc1mm/u2WAxlZeU6ptCbiL1j+i3tpI6Oa+bkapzjjn99Xy4/O9yaateuds11Tp+e8dFHH+muu+6yPp80aZIGDx6sNWvWqHr16vriiy+cXQIAwAEyMjKsj9euXatGjRoV2qZ58+Y6cuSIUlNTdfHiRa1cuVLh4eGlWSYAOIVTQ/PJkyf1zTffqG/fvpIkwzC0detWde3aVZIUHR3NF0QA4CY0cuRI9e/fX4cPH1b79u21ePFivf322+rZs6d69uyprVu3asyYMZKunE1+4oknJEnu7u569dVX9fjjj+vBBx9U9+7diwzXAFDWOHV6xoQJEzRq1Cjl5ORIks6cOaPq1avL3f3KYf39/e36gkh5nOtW3sYDoHyZPHlyoWX9+vUrcluz2awPPvjA+rxDhw5FXo4OAMoyp4Xmr7/+Wj4+PmrWrJm2bdtWon2Vx7lu5W08wK3genPdAADlm9NC8+7du7V+/Xpt3LhReXl5ys7O1ptvvqlz584pPz9f7u7uOnnypF1fEAEAAABcyWlzmp977jlt3LhR69ev1+TJk3XffffpnXfeUUhIiL766itJ0tKlS/mCCAAAAG56pX5zk1GjRmnevHmKiIhQVlbWNefIAQAAADeLUrm5SUhIiEJCQiRJ9evX5zJzAAAAKFO4jTYAAABgA6EZAAAAsIHQDAAAANhAaAYAAABsIDQDAAAANpTK1TNQMq1bNtKxE7ZvN36zqXebWbu/O+jqMgAAAEqM0FwGHDuRrqPjmrm6jGK741/fu7oEAAAAh2B6BgAAAGADoRkAAACwgdAMAAAA2EBoBgAAAGwgNAMAAAA2EJoBAAAAGwjNAAAAgA2EZgAAAMAGQjMAAABgA6EZAAAAsIHQDAAAANhAaAYAAABsIDQDAAAANri7ugAAAHDraN6qsdKPn3R1GcVmruOvfXsOuLoMuBChGQAAlJr04yd1/7v3u7qMYtsyfIurS4CLMT0DAAAAsIEzzQCAQsaMGaNvvvlGvr6+WrFihSTp3//+t77++mtVrFhRt99+u9566y1Vr1690GvDw8NVtWpVVahQQW5ubkpMTCzt8gHA4TjTDAAoJCYmRnPmzCmwrF27dlqxYoWWL1+uBg0aaPbs2dd8/fz585WcnExgBlBuEJoBAIUEBwerRo0aBZaFhYXJ3f3KHyhbtWqlkyfL3pe5AOBGEZoBAMW2ZMkStW/f/prrH3vsMcXExGjRokWlWBUAOA9zmgEAxTJz5ky5ubmpV69eRa5fuHChzGazMjMzFRsbq4YNGyo4OPi6+3RzM8nbu4ozynWp8jimojBO3AoIzQAAuyUmJuqbb77Rhx9+KJPJVOQ2ZrNZkuTr66uIiAjt3bvXZmi2WAxlZeU6vF5XK49jKgrjRHlRu3a1a65jegYAwC4bN27UnDlzNHPmTHl6eha5TW5urrKzs62PN2/erEaNGpVmmQDgFJxpBgAUMnLkSG3fvl1nzpxR+/bt9fTTTyshIUEXL15UbGysJKlly5YaP3680tPT9fLLL+uDDz5QZmam4uPjJUkWi0WRkZHXnfsMAGUFoRkAUMjkyZMLLevXr1+R25rNZn3wwQeSpPr162vZsmVOrQ0AXIHpGQAAAIANhGYAAADABkIzAAAAYAOhGQAAALCB0AwAAADYQGgGAAAAbCA0AwAAADYQmgEAAAAbCM0AAACADYRmAAAAwAZCMwAAAGADoRkAAACwgdAMAAAA2ODurB3n5eXp4Ycf1sWLF2WxWNS1a1cNHz5cqampGjlypLKyshQQEKD//Oc/8vDwcFYZAAAAQIk57Uyzh4eH5s+fr2XLlikpKUnffvut9uzZo0mTJmnw4MFas2aNqlevri+++MJZJQAAAAAO4bTQbDKZVLVqVUlSfn6+8vPzZTKZtHXrVnXt2lWSFB0drXXr1jmrBAAAAMAh7ArNP//88w3t3GKxKCoqSvfff7/uv/9+1a9fX9WrV5e7+5VZIf7+/kpPT7+hfQMA7HOjPRwA8D92zWkeN26cLl68qOjoaPXq1UvVqlWza+dubm5KTk7WuXPnFB8fr0OHDt1QkW5uJnl7V7mh196sytt4ruVWGSdwM7vRHg4A+B+7QvOnn36qI0eOaMmSJYqJiVGLFi0UExOjdu3a2XWQ6tWrKyQkRHv27NG5c+eUn58vd3d3nTx5Umaz2ebrLRZDWVm5dh2rrChv47mWW2WcuDXUrl02w2ZJezgAoBhzmhs0aKARI0bo+eef1/bt2/XGG2+oW7duWr16dZHbnz59WufOnZMk/fHHH9qyZYvuuusuhYSE6KuvvpIkLV26VOHh4Q4YBgDgeorbwwEABdl1pvmnn35SYmKiNmzYoPvvv1+zZs1SQECA0tPT1b9/f3Xp0qXQazIyMjR69GhZLBYZhqFu3bqpY8eOuvvuu/Xss89q6tSpuvfee9WvXz+HDwoA8D830sMBAAXZFZrfeOMN9e3bVyNHjlTlypWty81ms5555pkiX9OkSRMlJSUVWl6/fn0uMwcApehGejgAoCC7QvPs2bNVuXJlubm5SZIuX76svLw8eXp6qnfv3k4tEABQMvRwACg5u+Y0x8bG6o8//rA+v3DhgmJjY51WFADAcejhAFBydoXmvLw8641KJKlq1aq6cOGC04oCADgOPRwASs6u0Ozp6akffvjB+vz7778vMC8OAHDzoocDQMnZNaf5pZde0jPPPCM/Pz8ZhqHff/9dU6ZMcXZtAAAHoIcDQMnZFZpbtGihL7/8UocPH5Yk3XnnnapYsaJTCwMAOAY9HABKzq7QLEn79u1TWlqaLBaLfvzxR0niW9cAUEYUt4ePGTNG33zzjXx9fbVixQpJUlZWlp599lmlpaWpbt26mjp1qmrUqFHotUuXLtXMmTMlSUOGDFF0dLQTRgQApcuu0Dxq1CilpqaqSZMm1ksWmUwmQjMAlAE30sNjYmI0cOBAvfjii9ZlCQkJCg0NVVxcnBISEpSQkKBRo0YVeF1WVpbee+89LVmyRCaTSTExMQoPDy8yXANAWWJXaP7++++1atUqmUwmZ9cDAHCwG+nhwcHBOnbsWIFl69at04IFCyRdOUs9aNCgQqF506ZNateunby9vSVJ7dq107fffqvIyMgSjgIAXMuuq2c0atRIp06dcnYtAAAncFQPz8zMlJ+fnySpdu3ayszMLLRNenq6/P39rc/NZrPS09NLfGwAcDW7zjSfOXNGPXr0UIsWLQp8eWTWrFlOKwwA4BjO6OEmk8mhf310czPJ27uKw/Z3syiPYyoK48StwK7Q/PTTTzu7DgCAkziqh/v6+iojI0N+fn7KyMiQj49PoW3MZrO2b99ufZ6enq62bdva3LfFYigrK9chdd5MyuOYisI4UV7Url3tmuvsmp7Rtm1b1a1bV/n5+Wrbtq2aN2+upk2bOqxAAIDzOKqHh4eHKykpSZKUlJSkTp06FdomLCxMmzZt0tmzZ3X27Flt2rRJYWFhJR4DALiaXaH5888/1/Dhw/Xqq69KunLmID4+3qmFAQAc40Z6+MiRI9W/f38dPnxY7du31+LFixUXF6fNmzerS5cu2rJli+Li4iRduZzd2LFjJUne3t4aOnSo+vbtq759+yo+Pt76pUAAKMvsmp7xySefaPHixXrooYckSQ0aNNDp06edWhgAwDFupIdPnjy5yOXz588vtKx58+Zq3ry59fnVwAwA5YldZ5o9PDzk4eFhfZ6fn++0ggAAjkUPB4CSs+tMc3BwsGbNmqU//vhDmzdv1qeffqrw8HBn1wYAcAB6OACUnF1nmp9//nn5+PiocePGWrRokTp06KARI0Y4uzYAgAPQwwGg5Ow601yhQgU99NBD1vlwAICygx4OACVnV2gODw8v8iL269atc3hBAADHoocDQMnZFZqXLFlifXzx4kV9+eWXOnv2rNOKAgA4Dj0cAErOrjnNNWvWtP5nNps1ePBgbdiwwdm1AQAcgB4OACVn15nmH374wfr48uXL+v7777lkEQCUEfRwACg5u0LzxIkT//cCd3fVrVtXU6dOdVpRAADHoYcDQMnZFZoXLFjg7DoAAE5CDweAkrMrNM+bN++662NjYx1SDADA8ejhAFBydoXm77//Xvv27bPeQerrr79W8+bN1aBBA2fWBgBwAHo4AJScXaH55MmTSkxMlJeXlyRp2LBhevLJJzVp0iSnFgcAKDl6OACUnF2XnPv999/l4eFhfe7h4aHff//daZBx6gQAABznSURBVEUBAByHHg4AJWfXmebevXurb9++ioiIkCStXbtW0dHRTi0MAOAY9HAAKDm7QvOQIUPUvn177dy5U5L01ltvqWnTpk4tDADgGPRwACg5u6ZnSNKFCxfk5eWlRx99VP7+/kpNTXVmXQAAB6KHA0DJ2BWa33vvPc2ZM0cJCQmSpEuXLmnUqFFOLQwA4Bj0cAAoObtC85o1azRz5kx5enpKksxms3JycpxaGADAMejhAFBydoXmihUrymQyyWQySZJyc3OdWhQAwHHo4QBQcnZ9EbB79+569dVXde7cOX3++edasmSJHnroIWfXBgBwAHo4AJSczdBsGIYefPBBHTp0SFWrVtXhw4c1fPhwtWvXrjTqAwCUAD0cABzDZmg2mUyKi4vT8uXLabIAUMbQwwHAMeya09y0aVPt3bvX2bUAAJyAHg4AJWfXnObvvvtOy5YtU926da3fvpak5cuXO60wAIBj0MMBoOSuG5qPHz+uOnXqaO7cuaVVDwDAQejhAOA4152eER8fL0mqW7euJk6cqLp16xb4DwBw86KHA4DjXDc0G4ZhfcwtVwGgbKGHA4DjXDc0X70Q/l8fAwBufvRwAHCc685p/umnn9S6dWsZhqG8vDy1bt1a0pWzFyaTSbt37y6VInFraN6qsdKPn3R1GTfEXMdf+/YccHUZQAHO6OGHDh3Ss88+a32empqq4cOHa/DgwdZl27Zt09ChQ1WvXj1JUkREhIYNG1aywQCAi103NO/fv7+06gCUfvyk7n/3fleXcUO2DN/i6hKAQpzRwxs2bKjk5GRJksViUfv27RUREVFou6CgIM2ePdvhxwcAV7HrknM34sSJE3rhhReUmZkpk8mkhx56SI8++qiysrL07LPPKi0tTXXr1tXUqVNVo0YNZ5UBAHCS//73v6pfvz5fKgRwS3BaaHZzc9Po0aMVEBCg7Oxs9enTR+3atVNiYqJCQ0MVFxenhIQEJSQkaNSoUc4qAwDgJCtXrlRkZGSR6/bs2aNevXrJz89PL774oho1anTdfbm5meTtXcUZZbpUeRxTURgnbgVOC81+fn7y8/OTJHl5ealhw4ZKT0/XunXrtGDBAklS7969NWjQIEIzAJQxFy9e1Pr16/Xcc88VWhcQEKD169eratWq2rBhg+Lj47V69err7s9iMZSVleuscl2mPI6pKIwT5UXt2tWuuc6u22iX1LFjx7R//361bNlSmZmZ1jBdu3ZtZWZmlkYJAAAH2rhxowICAlSrVq1C67y8vFS1alVJUocOHZSfn6/Tp0+XdokA4FBOO9N8VU5OjoYPH66XXnpJXl5eBdaZTCa7LoNUHv9sV97Gcy23yjilW2uswMqVK9WjR48i1506dUq1atWSyWTS3r17dfnyZdWsWbOUKwQAx3JqaL506ZKGDx+unj17qkuXLpIkX19fZWRkyM/PTxkZGfLx8bG5n/L4Z7vyNp5ruVXGKd1aY71VXe/PdreS3NxcbdmyRePHj7cuW7hwoSRpwIAB+uqrr7Rw4UK5ubmpcuXKmjx5MteJBlDmOS00G4ahsWPHqmHDhoqNjbUuDw8PV1JSkuLi4pSUlKROnTo5qwQAgBNUqVJF27ZtK7BswIAB1scDBw7UwIEDS7ssAHAqp4XmXbt2KTk5WY0bN1ZUVJQkaeTIkYqLi9OIESP0xRdfqE6dOpo6daqzSgAAAAAcwmmhOSgoSD///HOR6+bPn++swwIAAAAOVypXzwAAAADKMkIzAAAAYAOhGQAAALCB0AwAAADYQGgGAAAAbCA0AwAAADY4/TbaAAoKbN5YaeknXV3GDalr9lfKvgOuLgMAgFJHaAZKWVr6SX3zQHtXl3FD/vbtRleXAACASzA9AwAAALCB0AwAAADYQGgGAAAAbCA0AwAAADYQmgEAAAAbCM0AAACADYRmAAAAwAau0wwAAOBgZfVGVtzE6toIzQAAAA5WVm9kxU2sro3pGQAAAIANhGYAAADABkIzAAAAYAOhGQAAALCB0AwAAADYwNUzADhNq5ZNdfzEMVeXUWx1bqunPd/96OoyAAA3EUIzAKc5fuKYpo9c5uoyiu3pyb1cXQIA4CbD9AwAAADABkIzAAAAYAPTMwAAxRIeHq6qVauqQoUKcnNzU2JiYoH1hmHozTff1IYNG1S5cmVNnDhRAQEBLqoWAByD0AwAKLb58+fLx8enyHUbN27UkSNHtHr1an333Xd67bXXtHjx4lKuEAAci+kZAACHWrdunXr37i2TyaRWrVrp3LlzysjIcHVZAFAihGYAQLE99thjiomJ0aJFiwqtS09Pl7+/v/W5v7+/0tPTS7M8AHA4pmcAAIpl4cKFMpvNyszMVGxsrBo2bKjg4OAS7dPNzSRv7yoOqvDmUR7HVBTGWb4UZ5x3NrhTacdTnViN89StU1+Hjxy2e3tCMwCgWMxmsyTJ19dXERER2rt3b4HQbDabdfLkSevzkydPWl9zLRaLoaysXOcU7ELlcUxFYZzlS3HGmXY8tUxej1+6ck3+v461du1q19ye6RkAALvl5uYqOzvb+njz5s1q1KhRgW3Cw8OVlJQkwzC0Z88eVatWTX5+fq4oFwAchjPNAAC7ZWZmKj4+XpJksVgUGRmp9u3ba+HChZKkAQMGqEOHDtqwYYMiIiLk6empCRMmuLJkAHAIQjMAwG7169fXsmWF/xQ7YMAA62OTyaR//etfpVkWADgd0zMAAAAAGwjNAAAAgA2EZgAAAMAGQjMAAABgA6EZAAAAsIHQDAAAANhAaAYAAABsIDQDAAAANhCaAQAAABsIzQAAAIANTgvNY8aMUWhoqCIjI63LsrKyFBsbqy5duig2NlZnz5511uEBAAAAh3FaaI6JidGcOXMKLEtISFBoaKhWr16t0NBQJSQkOOvwAAAAgMM4LTQHBwerRo0aBZatW7dOvXv3liT17t1ba9euddbhAQAAAIcp1TnNmZmZ8vPzkyTVrl1bmZmZpXl4AAAA4Ia4u+rAJpNJJpPJrm3d3Ezy9q7i5IpKV3kbz7XcKuOUbp2xMk4AwK2oVEOzr6+vMjIy5Ofnp4yMDPn4+Nj1OovFUFZWrpOrK13lbTzXcquMU7p1xnorj7N27WouqAQAcDMo1ekZ4eHhSkpKkiQlJSWpU6dOpXl4AAAA4IY4LTSPHDlS/fv31+HDh9W+fXstXrxYcXFx2rx5s7p06aItW7YoLi7OWYcHAAAAHMZp0zMmT55c5PL58+c765AAAACAU3BHQAAAAMAGQjMAAABgA6EZAAAAsIHQDAAAANhAaAYAAABsIDQDAAAANhCaAQAAABsIzQAAAIANhGYAAADABqfdERAAUP6cOHFCL7zwgjIzM2UymfTQQw/p0UcfLbDNtm3bNHToUNWrV0+SFBERoWHDhrmiXABwGEIzAMBubm5uGj16tAICApSdna0+ffqoXbt2uvvuuwtsFxQUpNmzZ7uoSgBwPKZnAADs5ufnp4CAAEmSl5eXGjZsqPT0dBdXBQDOR2gGANyQY8eOaf/+/WrZsmWhdXv27FGvXr30+OOP6+DBgy6oDgAci+kZAIBiy8nJ0fDhw/XSSy/Jy8urwLqAgACtX79eVatW1YYNGxQfH6/Vq1dfd39ubiZ5e1dxZskuUR7HVBTGWb7cKuOUijdWQjMAoFguXbqk4cOHq2fPnurSpUuh9X8O0R06dNC4ceN0+vRp+fj4XHOfFouhrKxcp9TrSuVxTEVhnOXLrTJOqfBYa9euds1tmZ4BALCbYRgaO3asGjZsqNjY2CK3OXXqlAzDkCTt3btXly9fVs2aNUuzTABwOM40AwDstmvXLiUnJ6tx48aKioqSJI0cOVLHjx+XJA0YMEBfffWVFi5cKDc3N1WuXFmTJ0+WyWRyZdkAUGKEZgCA3YKCgvTzzz9fd5uBAwdq4MCBpVQRAJQOpmcAAAAANhCaAQAAABsIzQAAAIANhGYAAADABkIzAAAAYAOhGQAAALCB0AwAAADYQGgGAAAAbCA0AwAAADYQmgEAAAAbCM0AAACADYRmAAAAwAZCMwAAAGADoRkAAACwgdAMAAAA2EBoBgAAAGwgNAMAAAA2EJoBAAAAGwjNAAAAgA2EZgAAAMAGQjMAAABgA6EZAAAAsIHQDAAAANhAaAYAAABsIDQDAAAANhCaAQAAABsIzQAAAIANhGYAAADABpeE5o0bN6pr166KiIhQQkKCK0oAANwgWz384sWLGjFihCIiItSvXz8dO3bMBVUCgGOVemi2WCwaP3685syZo5UrV2rFihX65ZdfSrsMAMANsKeHL168WNWrV9eaNWs0ePBgTZo0yUXVAoDjlHpo3rt3r+644w7Vr19fHh4e6tGjh9atW1faZQAAboA9PXz9+vWKjo6WJHXt2lX//e9/ZRiGK8oFAIcp9dCcnp4uf39/63Oz2az09PTSLgMAcAPs6eHp6em67bbbJEnu7u6qVq2azpw5U6p1AoCjubu6AHtUrOim2rWrFVpeZs9cvP1IsTYvq+M0Xi3m9mV0nJKkp+3ftCyPs7iVl9WxDnunbNZdlpW7Pi8Vq9eX5XHeMr2+GH1eKrvjvFX6vFT8Xl/qZ5rNZrNOnjxpfZ6eni6z2VzaZQAAboA9PdxsNuvEiROSpPz8fJ0/f141a9Ys1ToBwNFKPTQ3b95cR44cUWpqqi5evKiVK1cqPDy8tMsAANwAe3p4eHi4li5dKkn66quvdN9998lkMrmiXABwGJPhgvPqGzZs0IQJE2SxWNSnTx8NGTKktEsAANygonr4tGnT1KxZM3Xq1El5eXkaNWqU9u/frxo1amjKlCmqX7++q8sGgBJxSWgGAAAAyhLuCAgAAADYQGgGAAAAbCA0/0n//v1dXYLLfPTRR+revbuee+45V5fiEoGBgaV6vGPHjikyMrJUjwmUVfy+wJnOnTunTz75xKH75GfWNme8785GaP6Tzz77zNUluMynn36qefPm6Z133nF1KQCAEsjPz3d1CaXqr+Mt7vjPnTunhQsXOrKkUlVWP+9rve8383jKxM1NSktgYKBSUlKUkZGhZ599VtnZ2bJYLHrttdcUFBTk6vIcZt68eVqyZIkkqW/fvjp06JCOHTumJ554Qn369NHgwYNdW+ANGjp0qE6ePKm8vDw98sgj+vvf/67AwEA98sgj+vrrr1W5cmXNmDFDtWrVUmpqqp5//nnl5ua67JKHFotFL7/8slJSUmQ2mzVmzBi98MIL1kt1HTlyRM8++6yWLl2q8PBwdevWTd9++60qVaqkd955R3fccYeOHTuml156SWfOnJGPj4/eeust1alTR6NHj5aXl5e+//57nTp1SqNGjVK3bt30wgsvqEuXLurcubMk6bnnnlP37t117tw5rV27VhcuXNDRo0f1z3/+U5cuXVJycrI8PDyUkJAgb29vl7xPf1XU54zy71b4fTl27Jgef/xxtWrVSikpKWrWrJn69Omjd999V6dPn9akSZN099136/XXX9fBgweVn5+vYcOGqXPnzkpMTNTq1auVm5ury5cv67333tNLL72k1NRUeXp6avz48WrSpImmT5+u48eP69ixYzp+/LgeffRRPfLII5o2bZpq1Khh7f9TpkyRj4+P9TXVqlXTgQMH1L17dzVu3FgfffSR8vLy9P777+v222932OeclJSkuXPnymQy6Z577tEzzzxzzc/Mw8ND+/fvV+vWrXX27NkCzx9++GGNGzdOZ86cUeXKlfX666/rrrvu0u+//65//etfSk1NlSS99tprWrBggX777TdFRUXp/vvv14svvuiQsTjiZ/b06dP617/+pePHj0uSXnrpJbVp00bTp0/Xb7/9ptTUVNWpU0d33nnnTf25FuWdd96xvu/u7u6qVKmSqlevrsOHD2vVqlWaNGmStm/frosXL+rhhx+2zgaYM2eOvvzyS128eFEREREaPny4U+sswIBVq1atDMMwjLlz5xozZswwDMMw8vPzjfPnz7uyLIfat2+fERkZaeTk5BjZ2dnGgw8+aPzwww9Gx44djczMTFeXVyJnzpwxDMMwLly4YPTo0cM4ffq00bhxY2PdunWGYRjGv//9b+P99983DMMwnnzySWPp0qWGYRjGxx9/bP3sS0tqaqpx7733Gj/++KNhGIYxfPhwIykpyRg4cKB12TvvvGN89NFHhmEYRseOHa0/k0uXLjXi4uKs40hMTDQMwzAWL15sDBkyxDAMw3jxxReNp59+2rBYLMbBgweNzp07G4ZhGNu2bbNuc+7cOaNjx47GpUuXjCVLlhidO3c2zp8/b2RmZhqtW7c2Pv30U8MwDOPNN9805s2bVwrvin2K+pxRvt0qvy9Xx/nTTz8ZFovFiI6ONkaPHm1cvnzZWLNmjTFkyBDjnXfeMZKSkgzDMIyzZ88aXbp0MXJycowlS5YYDzzwgPX3Y/z48cb06dMNwzCMLVu2GL169TIMwzDeffdd4+9//7uRl5dnZGZmGm3btjUuXrxopKamGr179zYMwzAsFovRqVMn4/Tp08bWrVuNNm3aGOnp6UZeXp4RFhZmTJs2zTAMw/jwww+NN95444bGWpQDBw4YXbp0sf5bdObMmet+ZnFxcUZ+fn6Rzx955BHj8OHDhmEYxp49e4xBgwYZhmEYzzzzjPXzyc/PN86dO2ekpqYaPXr0cNg4DMNxP7MjR440duzYYRiGYaSlpRndunUzDOPK5xgdHW1cuHDB+vxm/Vyv9x5dfd+3bt1qtGzZ0vjtt98MwzCMzz77zPrvdV5enhEdHW389ttvxrfffmu8/PLLxuXLlw2LxWLExcUZ27dvd3qtVzE9owjNmzdXYmKipk+frgMHDsjLy8vVJTnMrl271LlzZ1WpUkVVq1ZVRESEdu7c6eqyHGLBggXq1auXHnroIZ04cUJHjx5VxYoV1bFjR0lSs2bNlJaWJklKSUlRjx49JElRUVEuqbdevXq69957JUkBAQFKS0tTv379tGTJElksFq1atarAnLirj3v06KE9e/ZIujKOq8ujoqK0a9cu6/adO3dWhQoVdPfdd+v333+XJLVt21ZHjx7V6dOntWLFCnXt2lXu7lf+4BQSEiIvLy/5+PioWrVq1jPwjRs3tr5vN4OiPmeUf7fK70u9evV0zz33WGsJDQ21nnVNS0vTpk2b9MEHHygqKkqDBg1SXl6e9e6L7dq1s57h3rVrl7W3hYaGKisrS9nZ2ZKkDh06yMPDQz4+PvLx8VFmZqbq1asnb29v/fjjj9q0aZOaNm1qvYtj8+bN5efnJw8PD91+++1q166dQ8b6V1u3blW3bt3k4+MjSfL29r7uZ9atWze5ubkVep6Tk6OUlBQ988wzioqK0quvvqpTp05Zj/GPf/xDkuTm5qZq1Qrfut1RHPEzu2XLFr3++uuKiorSkCFDlJ2drZycHElXbiJUuXJl6+tv1s/VXs2bN7dez33z5s1KTk5WVFSU+vXrp6ysLB09elSbN2/W5s2b1bt3b0VHR+vQoUM6cuRIqdXI9IwiBAcH6+OPP9aGDRs0evRoxcbGqnfv3q4uC9exbds2bdmyRYsWLZKnp6f1H5OKFSta70RWoUIFWSwW62tcfYcyDw8P62M3Nzfl5eWpa9euev/993XfffcpICCgRLce/vP+/ywqKkrLli3TypUr9dZbbxW5fYUKFVSxYkXr4z+/b650rc8Z5d+t8vvy1/1efW4ymWSxWOTm5qZ3331XDRs2LPC67777Tp6ensU+hpubm3UOab9+/ZSYmKjff/9dffr0sVmTq3vDX8d79blhGKpevbqSk5NdUZaVI35mL1++rM8//1yVKlUqtO6v4y/rn2uVKlWsjw3D0Msvv6wHHnigwDabNm1SXFycyy7cwJnmIqSlpalWrVp66KGH1K9fP/3www+uLslhgoKCrHPxcnNztXbt2nIxX/v8+fOqUaOGPD099euvv1r/L/1aAgMDtXLlSknSsmXLSqNEu1SqVElhYWF67bXXFBMTU2Ddl19+KUlatWqV9Woffx7H8uXL7fosY2JiNH/+fEnS3Xff7cjyna64nzPKt1vx9yUsLEwff/yxjP9/X7Iff/yxyO2CgoKsvW3btm2qWbOmzb+adu7cWd9++6327dunsLAwxxZuh/vuu0//93//pzNnzkiSsrKybugz8/LyUr169aw/A4Zh6KeffpJ05az7p59+KunKnOPz58+ratWq1rO3zlbcn9mwsDAtWLDAus3+/fuLfUxXf67Xcr33PSwsTAsXLtSlS5ckSYcPH1Zubq7CwsK0ZMkS6+vS09OVmZlZajVzprkI27dv19y5c+Xu7q4qVaro3//+t6tLcpiAgADFxMSoX79+kq58EbBp06Yurqrk2rdvr88++0zdu3fXnXfeqVatWl13+7Fjx+r555/XnDlzXPZFwGvp2bOn1qxZU6i5nT17Vj179pSHh4cmT54sSXrllVc0ZswYzZ071/olGVtq1aqlhg0bWr/cVJYU93NG+Xer/b4MHTpUEyZMUK9evXT58mXVq1dPs2fPLrTdsGHD9NJLL6lnz57y9PTUxIkTbe7bw8NDISEhql69eoFpD6WlUaNGeuqppzRo0CBVqFBBTZs2vaHPTJLefvttvfbaa5o5c6by8/P14IMPqkmTJho7dqxeeeUVLVmyRBUqVNBrr72mwMBAtW7dWpGRkXrggQcc9kXAaynOz+zYsWM1fvx49ezZUxaLRUFBQRo/fnyxjufqz/VaatasaX3fK1WqpFq1alnX9evXT2lpaYqJiZFhGKpZs6ZmzJihsLAw/frrr9YzzVWqVNHbb78tX1/fUqmZ22gDN5m5c+fq/PnzGjFihHVZeHi4vvjiC+tcv5K4cOGCevbsqaVLlzp1Ph9QGvh9cZzLly8rOjpa06ZNU4MGDVxdTrnl7J/Zv+JzdRymZwA3kfj4eCUlJemRRx5xyv63bNmiBx98UAMHDiz3AQDlH78vjvPLL78oIiJCoaGhBCsncvbP7F/xuToWZ5oBAAAAGzjTDAAAANhAaAYAAABsIDQDAAAANhCaUSZNmDBBH374ofX5Y489prFjx1qfT5w4UfPmzSv2frdt26Ynn3zSESUCAEqAPo+bDaEZZVLr1q2VkpIi6crldM6cOaNffvnFuj4lJcV6cfjruVnudAcAKIg+j5sNNzdBmRQYGGi9yP3BgwfVqFEjnTp1SmfPnrXeLe78+fPq3bu3LBaLmjVrpnHjxsnDw0Ph4eHq3r27tmzZoscff1zVqlXThAkT5OnpqTZt2rh4ZAAAiT6Pmw9nmlEmmc1mubm56fjx40pJSVGrVq3UokUL7dmzR/v27dMdd9yhl19+WVOmTNHy5ctlsVist06VJG9vby1dulSdO3fWK6+8olmzZikxMVGnTp1y4agAAFfR53GzITSjzAoMDFRKSor1T3SBgYHavXu3UlJS5O/vr3r16unOO++UJEVHR2vnzp3W1z744IOSpEOHDqlevXpq0KCBTCaTevXq5ZKxAAAKo8/jZkJoRpl1db7bgQMH1KhRI7Vs2VJ79uxRSkqK2rZte93Xenp6llKVAIAbRZ/HzYTQjDKrdevW+vrrr1WjRg25ubnJ29tb58+f1549e9S1a1elpaXp6NGjkqTk5GQFBwcX2kfDhg2Vlpam3377TZK0cuXKUh0DAODa6PO4mfBFQJRZjRs31pkzZxQZGVlgWU5Ojvz9/fXWW2/pmWeesX5BZMCAAYX2UalSJY0fP15xcXHWL4jk5OSU5jAAANdAn8fNxGQYhuHqIgAAAICbGdMzAAAAABsIzQAAAIANhGYAAADABkIzAAAAYAOhGQAAALCB0AwAAADYQGgGAAAAbCA0AwAAADb8P51HBkVfkemxAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Session - 3\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Session - 4\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Session - 5\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Session - 6\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Session - 7\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AAAAAJlGiAQAAAJMo0QAAAIBJlGgAAADAJEo0AAAAYBIlGgAAADCJEg0AAACYRIkGAAAATLLsw1YuXLigv/zlLyouLpbdblefPn2UmprqtE9xcbEee+wx7d27V4GBgUpLS1OTJk2sGgkAUMHIegDeyrIz0X5+flq8eLE+/vhjffTRR/riiy+UmZnptM/SpUtVt25dff755xo1apReeuklq8YBAFiArAfgrSwr0TabTbVr15YklZSUqKSkRDabzWmfDRs2aPDgwZKkPn366KuvvpJhGFaNBACoYGQ9AG9l2eUckmS325WcnKwjR47o7rvvVocOHZy25+fnq2HDhpcG8fVVnTp1VFhYqKCgoKse08fHpsDAWlfc/4fmzXU0N7tiF+AmoY2a6MihQ54eAwB+F3dlPTkP4EZiaYn28fHRihUrdOrUKY0bN0779+9XWFjYdR3Tbjd08uTZK+4/mputyEcXX9exPWXXSyPLXBOAG1eDBnU8PcINw11ZT84DcLfyst4t785Rt25dderUSV988YXT/SEhIcrLy5N06deAp0+fVv369d0xEgCggpH1ALyJZSX6xIkTOnXqlCTp/Pnz2rp1q1q2bOm0T1xcnD788ENJ0po1a9S5c+crrqUDANy4yHoA3sqyyzkKCgo0ZcoU2e12GYahvn37qkePHpozZ47atWunnj17asiQIZo8ebLi4+NVr149paWlWTUOAMACZD0Ab2UzKtlLpC9etJd5XVlwcN1Kfa1cQcEpT48BwASuibZWWVlPzgNwN49fEw0AAABUJZRoAAAAwCRKNAAAAGASJRoAAAAwiRINAAAAmESJBgAAAEyiRAMAAAAmUaIBAAAAkyjRAAAAgEmUaAAAAMAkSjQAAABgEiUaAAAAMIkSDQAAAJhEiQYAAABMokQDAAAAJlGiAQAAAJMo0QAAAIBJlGgAAADAJEo0AAAAYBIlGgAAADCJEg0AAACYRIkGAAAATKJEAwAAACZRogEAAACTKNEAAACASZRoAAAAwCRKNAAAAGASJRoAAAAwiRINAAAAmESJBgAAAEyiRAMAAAAmUaIBAAAAkyjRAAAAgEmUaAAAAMAkSjQAAABgkq9VB87Ly9Njjz2m48ePy2azaejQoRo5cqTTPtu2bdNDDz2kJk2aSJLi4+M1fvx4q0YCAFQgch6AN7OsRPv4+GjKlCkKDw/XmTNnlJKSoq5du6pVq1ZO+0VHR+uNN96wagwAgEXIeQDezLLLOYKDgxUeHi5JCggIUMuWLZWfn2/V0wEA3IycB+DN3HJNdHZ2tvbt26cOHTpcsS0zM1NJSUm6//77deDAAXeMAwCoYOQ8AG9j2eUclxUVFSk1NVVPPvmkAgICnLaFh4drw4YNql27tjZt2qRx48Zp7dq15R7Px8emwMBaVo7sEVVxTQC8Q0XnvFQ1s76qrQfwdpaW6IsXLyo1NVWJiYnq3bv3Fdt/HbaxsbGaNm2aTpw4oaCgoKse0243dPLkWUvm9aSquCagKmvQoI6nR7ghWJHzUtXM+qq2HsAblJf1ll3OYRiGpk6dqpYtW2r06NFl7nPs2DEZhiFJ2r17t0pLS1W/fn2rRgIAVCByHoA3s+xM9M6dO7VixQqFhYVp4MCBkqRJkyYpNzdXkjRs2DCtWbNG77//vnx8fFSzZk3Nnj1bNpvNqpEAABWInAfgzWzG5VMElcTFi/YyfyUWHFxXkY8u9sBE12/XSyNVUHDK02MAMIHLOaxVVtaT8wDczSOXcwAAAABVFSUaAAAAMIkSDQAAAJhEiQYAAABMokQDAAAAJlGiAQAAAJMo0QAAAIBJlGgAAADAJEo0AAAAYBIlGgAAADCJEg0AAACYRIkGAAAATKJEAwAAACZRogEAAACTKNEAAACASZRoAAAAwCRKNAAAAGASJRoAAAAwiRINAAAAmESJBgAAAEyiRAMAAAAmUaIBAAAAkyjRAAAAgEmUaAAAAMAkSjQAAABgEiUaAAAAMIkSDQAAAJhEiQYAAABMokQDAAAAJlGiAQAAAJMo0QAAAIBJlGgAAADAJEo0AAAAYBIlGgAAADCJEg0AAACYRIkGAAAATLKsROfl5WnEiBHq37+/BgwYoMWLF1+xj2EYmjFjhuLj45WYmKi9e/daNQ4AwAJkPQBv5WvVgX18fDRlyhSFh4frzJkzSklJUdeuXdWqVSvHPps3b9ahQ4e0du1affPNN3r22We1dOlSq0YCAFQwsh6At7LsTHRwcLDCw8MlSQEBAWrZsqXy8/Od9lm/fr0GDRokm82m22+/XadOnVJBQYFVIwEAKhhZD8BbueWa6OzsbO3bt08dOnRwuj8/P1+hoaGO26GhoVeELwCgciDrAXgTyy7nuKyoqEipqal68sknFRAQcN3H8/GxKTCwVgVMdmNxdU2tWjTRkZyjFk9jjT80DtUPP2W7tG+zFk2Vl5Nn8UTWaNi4oQ7/lOXpMQC3Iuuvzcx6KmvWm8l5iaxH5WZpib548aJSU1OVmJio3r17X7E9JCRER4/+JySOHj2qkJCQco9ptxs6efJshc/qaa6u6UjOUR2e1s7iaazR7G/furzOvJw83fHKHRZPZI2tqVur5P+jcNagQR1Pj3DDIOtdY2Y9lTXrzeS8RNbjxlde1lt2OYdhGJo6dapatmyp0aNHl7lPXFycPvroIxmGoczMTNWpU0fBwcFWjQQAqGBkPQBvZdmZ6J07d2rFihUKCwvTwIEDJUmTJk1Sbm6uJGnYsGGKjY3Vpk2bFB8fL39/f82cOdOqcQAAFiDrAXgry0p0dHS0/v3vf5e7j81m09/+9jerRgAAWIysB+Ct+MRCAAAAwCRKNAAAAGASJRoAAAAwiRINAAAAmESJBgAAAEyiRAMAAAAmUaIBAAAAkyjRAAAAgEkulehrvZE+AKDyI+sBwHUufWLhtGnTVFxcrMGDByspKUl16tSxei4AgJuR9QDgOpdK9HvvvadDhw5p2bJlSk5OVvv27ZWcnKyuXbtaPR8AwE3IegBwnUslWpKaN2+uiRMnql27dpoxY4a+++47GYahSZMmqXfv3lbOCABwE7IeAFzjUon+/vvvtXz5cm3atEl33HGH5s+fr/DwcOXn5+uuu+4iWAGgCiDrAcB1LpXoGTNmaMiQIZo0aZJq1qzpuD8kJEQPP/ywZcMBANyHrAcA17lUot944w3VrFlTPj4+kqTS0lJduHBB/v7+GjRokKUDAgDcg6wHANe59BZ3o0eP1vnz5x23z507p9GjR1s2FADA/ch6AHCdSyX6woULql27tuN27dq1de7cOcuGAgC4H1kPAK5zqUT7+/tr7969jtvffvut0/VyAIDKj6wHANe5dE30k08+qYcffljBwcEyDEM///yz0tLSrJ4NAOBGZD0AuM6lEt2+fXt9+umn+umnnyRJLVq0UPXq1S0dDADgXmQ9ALjO5Q9b2bNnj3JycmS32/Xdd99JEq/WBoAqhqwHANe4VKInT56srKwstWnTxvHWRzabjWAFgCqErAcA17lUor/99lutXr1aNpvN6nkAAB5C1gOA61x6d47WrVvr2LFjVs8CAPAgsh4AXOfSmejCwkINGDBA7du3d3qRyfz58y0bDADgXmQ9ALjOpRI9YcIEq+cAAHgYWQ8ArnOpRHfs2FE5OTk6fPiw7rjjDp07d052u93q2QAAbkTWA4DrXLomesmSJUpNTdUzzzwjScrPz9e4ceMsHQwA4F5kPQC4zqUS/e677+r9999XQECAJKl58+Y6ceKEpYMBANyLrAcA17lUov38/OTn5+e4XVJSYtlAAADPIOsBwHUuXRMdExOj+fPn6/z589qyZYvee+89xcXFWT0bAMCNyHoAcJ1LZ6IfffRRBQUFKSwsTB988IFiY2M1ceJEq2cDALgRWQ8ArnPpTHS1atU0dOhQDR061Op5AAAeQtYDgOtcKtFxcXFlfgzs+vXrK3wgAIBnkPUA4DqXSvSyZcscXxcXF+vTTz/VL7/8YtlQAAD3I+sBwHUuXRNdv359x38hISEaNWqUNm3aVO5jnnjiCXXp0kUJCQllbt+2bZuioqI0cOBADRw4UK+++qr56QEAFYasBwDXuXQmeu/evY6vS0tL9e23317zrY+Sk5M1fPhwPf7441fdJzo6Wm+88YaLowIArETWA4DrXCrRL7744n8e4Ourxo0b6+WXXy73MTExMcrOzr6+6QAAbkPWA4DrXCrR77zzjiVPnpmZqaSkJAUHB+vxxx9X69atLXkeAMC1kfUA4DqXSvSiRYvK3T569GjTTxweHq4NGzaodu3a2rRpk8aNG6e1a9de83E+PjYFBtYy/Xw3uqq4prKwTuDGRdZbq6qt52q8ZZ2Sd60VV3KpRH/77bfas2eP45OrNm7cqNtuu03Nmzf/3U8cEBDg+Do2NlbTpk3TiRMnFBQUVO7j7HZDJ0+e/d3Pe6OqimsqC+tEVdGgQR1Pj1DhyHprVbX1XI23rFPyrrV6q/Ky3qUSffToUS1fvtwRhuPHj9eYMWP00ksv/e6hjh07pptvvlk2m027d+9WaWmp6tev/7uPBwC4PmQ9ALjOpRL9888/y8/Pz3Hbz89PP//8c7mPmTRpkrZv367CwkJ1795dEyZMcLzKe9iwYVqzZo3ef/99+fj4qGbNmpo9e3aZb/IPAHAPsh4AXOdSiR40aJCGDBmi+Ph4SdK6des0ePDgch8ze/bscrcPHz5cw4cPd3FMAIDVyHoAcJ1LJfrBBx9U9+7dlZ6eLkl64YUX1LZtW0sHAwC4F1kPAK5z6RMLJencuXMKCAjQyJEjFRoaqqysLCvnAgB4AFkPAK5xqUS/+uqrWrBggd58801J0sWLFzV58mRLBwMAuBdZDwCuc6lEf/7555o3b578/f0lSSEhISoqKrJ0MACAe5H1AOA6l0p09erVZbPZHK+oPnuW90UEgKqGrAcA17n0wsJ+/frpmWee0alTp7RkyRItW7ZMQ4cOtXo2AIAbkfUA4LprlmjDMNS/f3/9+OOPql27tn766Selpqaqa9eu7pgPAOAGZD0AmHPNEm2z2fTAAw9o5cqVhCkAVFFkPQCY49I10W3bttXu3butngUA4EFkPQC4zqVror/55ht9/PHHaty4seNV25K0cuVKywYDALgXWQ8Ariu3ROfm5qpRo0ZauHChu+YBALgZWQ8A5pV7Oce4ceMkSY0bN9aLL76oxo0bO/0HAKj8yHoAMK/cEm0YhuNrPvoVAKomsh4AzCu3RF9+w/3ffg0AqDrIegAwr9xror///ntFRkbKMAxduHBBkZGRki6dtbDZbNq1a5dbhgQAWIesBwDzyi3R+/btc9ccAAAPIesBwDyX3icaAAAAwH9QogEAAACTKNEAAACASZRoAAAAwCRKNAAAAGASJRoAAAAwiRINAAAAmESJBgAAAEyiRAMAAAAmUaIBAAAAkyjRAAAAgEmUaAAAAMAkSjQAAABgEiUaAAAAMIkSDQAAAJhEiQYAAABMokQDAAAAJlGiAQAAAJMo0QAAAIBJlGgAAADAJMtK9BNPPKEuXbooISGhzO2GYWjGjBmKj49XYmKi9u7da9UoAACLkPUAvJVlJTo5OVkLFiy46vbNmzfr0KFDWrt2rZ577jk9++yzVo0CALAIWQ/AW1lWomNiYlSvXr2rbl+/fr0GDRokm82m22+/XadOnVJBQYFV4wAALEDWA/BWHrsmOj8/X6GhoY7boaGhys/P99Q4AAALkPUAqipfTw9glo+PTYGBtTw9RoWrimsqC+u80h+bNVVWXp6F01ijacOGOng4y+X9WzRvoZxc1/e/kTRu1FQ/HfrJ02N4laqY9VVtPVfjLeuUXF9rZc15yVzWe1vOe6xEh4SE6OjRo47bR48eVUhIyDUfZ7cbOnnyrJWjeURVXFNZWOeVsvLy9K//6m7hNNb40xebTa0zJzdLcyd9bOFE1pkwO+mKtTZoUMdD01QuZP1/VLX1XI23rFNyfa2VNeclc1lf1XJeKj/rPXY5R1xcnD766CMZhqHMzEzVqVNHwcHBnhoHAGABsh5AVWXZmehJkyZp+/btKiwsVPfu3TVhwgSVlJRIkoYNG6bY2Fht2rRJ8fHx8skpcboAAA9dSURBVPf318yZM60aBQBgEbIegLeyrETPnj273O02m01/+9vfrHp6AIAbkPUAvBWfWAgAAACYRIkGAAAATKJEAwAAACZRogEAAACTKNEAAACASZRoAAAAwCRKNAAAAGASJRoAAAAwiRINAAAAmESJBgAAAEyiRAMAAAAmUaIBAAAAkyjRAAAAgEmUaAAAAMAkSjQAAABgEiUaAAAAMIkSDQAAAJhEiQYAAABMokQDAAAAJlGiAQAAAJMo0QAAAIBJlGgAAADAJEo0AAAAYBIlGgAAADCJEg0AAACYRIkGAAAATKJEAwAAACZRogEAAACTKNEAAACASZRoAAAAwCRKNAAAAGASJRoAAAAwiRINAAAAmESJBgAAAEyiRAMAAAAmWVqiN2/erD59+ig+Pl5vvvnmFduXL1+uzp07a+DAgRo4cKCWLl1q5TgAgApGzgPwVr5WHdhut2v69OlatGiRQkJCNGTIEMXFxalVq1ZO+/Xv31/PPPOMVWMAACxCzgPwZpadid69e7eaNWumpk2bys/PTwMGDND69eutejoAgJuR8wC8mWUlOj8/X6GhoY7bISEhys/Pv2K/tWvXKjExUampqcrLy7NqHABABSPnAXgzyy7ncEWPHj2UkJAgPz8//eMf/9Djjz+ut99+u9zH+PjYFBhYy00Tuk9VXFNZWGfV4i3rlLxrrRXp9+S8VDWzvqqt52q8ZZ2S96yVdZbNshIdEhKio0ePOm7n5+crJCTEaZ/69es7vr7zzjs1a9asax7Xbjd08uTZihv0BlEV11QW1lm1eMs6pSvX2qBBHQ9NcuOwKuelqpn1VW09V+Mt65S8Z63evM7yst6yyzluu+02HTp0SFlZWSouLtaqVasUFxfntE9BQYHj6w0bNuiPf/yjVeMAACoYOQ/Am1l2JtrX11fPPPOM7r//ftntdqWkpKh169aaM2eO2rVrp549e+qdd97Rhg0b5OPjo3r16umFF16wahwAQAUj5wF4M0uviY6NjVVsbKzTfQ8//LDj60ceeUSPPPKIlSMAACxEzgPwVnxiIQAAAGASJRoAAAAwiRINAAAAmESJBgAAAEyiRAMAAAAmUaIBAAAAkyjRAAAAgEmUaAAAAMAkSjQAAABgEiUaAAAAMIkSDQAAAJhEiQYAAABMokQDAAAAJlGiAQAAAJMo0QAAAIBJlGgAAADAJEo0AAAAYBIlGgAAADCJEg0AAACYRIkGAAAATKJEAwAAACZRogEAAACTKNEAAACASZRoAAAAwCRKNAAAAGASJRoAAAAwiRINAAAAmESJBgAAAEyiRAMAAAAmUaIBAAAAkyjRAAAAgEmUaAAAAMAkSjQAAABgEiUaAAAAMIkSDQAAAJhEiQYAAABMsrREb968WX369FF8fLzefPPNK7YXFxdr4sSJio+P15133qns7GwrxwEAWICsB+CNLCvRdrtd06dP14IFC7Rq1Sp98skn+uGHH5z2Wbp0qerWravPP/9co0aN0ksvvWTVOAAAC5D1ALyVZSV69+7datasmZo2bSo/Pz8NGDBA69evd9pnw4YNGjx4sCSpT58++uqrr2QYhlUjAQAqGFkPwFtZVqLz8/MVGhrquB0SEqL8/Pwr9mnYsKEkydfXV3Xq1FFhYaFVIwEAKhhZD8Bb+Xp6ALOqV/dRgwZ1rri/Up/VmHWPy7tW5nUaz5jYtxKvUxPM7V5Z12p26sq6Tkka/z+Vd/bKqqysr8z/D5nJeanyrtVMzkuVd52STGV9ZV6nmckr8zp/T85bdiY6JCRER48eddzOz89XSEjIFfvk5eVJkkpKSnT69GnVr1/fqpEAABWMrAfgrSwr0bfddpsOHTqkrKwsFRcXa9WqVYqLi3PaJy4uTh9++KEkac2aNercubNsNptVIwEAKhhZD8Bb2QwLz71v2rRJM2fOlN1uV0pKih588EHNmTNH7dq1U8+ePXXhwgVNnjxZ+/btU7169ZSWlqamTZtaNQ4AwAJkPQBvZGmJBgAAAKoiPrEQAAAAMIkSDQAAAJhEiS7HE088oS5duighIcHTo1hm6tSpV3y6GG4sM2fO1FtvveW4fd9992nq1KmO2y+++KIWLVqkMWPGlPn4a/2M586dq4ULF1bYvEBlU9Wznpy/8ZHzlRMluhzJyclasGCBp8ew1PPPP69WrVp5egyUIzIyUhkZGZKk0tJSFRYWOoVlRkaGLl68eNXH8zMGylfVs54MuPGR85UTJbocMTExqlevnqfHqBDZ2dnq27evHnnkEfXr10+pqak6d+6cRowYoT179kiSIiIilJaWpqSkJA0dOlQ///yzJOnEiROaMGGCUlJSlJKSop07d3pyKVf10UcfKTExUUlJSZo8ebKys7N1zz33KDExUSNHjlRubq4kacqUKZoxY4buuusu9ezZU5999pnjGAsWLFBKSooSExP1yiuveGopTiIiIpSZmSlJOnDggFq3bq3atWvrl19+UXFxsQ4ePKi2bdvq7NmzSk1NdfycL79m+Nc/482bN2vw4MFKSkrSyJEjHc/xww8/aMSIEerZs6fefvtt9y/yGhYtWqSEhAQlJCTorbfeUnZ2tvr166ennnpKAwYM0L333qvz589Lko4cOaL77rtPycnJuvvuu3Xw4EEPT48bXVXJem/IealqZj05f0mly3oD5crKyjIGDBjg6TGuW1ZWlhEWFmakp6cbhmEYU6ZMMRYsWGAMHz7c2L17t2EYhhEWFmasX7/eMAzD+O///m/jtddeMwzDMCZNmmTs2LHDMAzDyMnJMfr27euBFZRv//79Ru/evY3jx48bhmEYhYWFxpgxY4zly5cbhmEYS5cuNR588EHDMAzj8ccfNyZMmGDY7XbjwIEDRq9evQzDMIwvvvjCeOqpp4zS0lLDbrcbDzzwgLF9+3bPLOg3evToYeTk5Bjvv/++8d577xlpaWnGv/71LyM9Pd0YNmyY8fXXXxuRkZFGXl6eYbfbjaFDhzp+Zpd/xsePHze6d+9uHDlyxDCMS98jwzCMV155xfjzn/9sXLhwwTh+/LjRsWNHo7i42GNr/a09e/YYCQkJRlFRkXHmzBmjf//+xt69e41bb73V+O677wzDMIzU1FTjo48+MgzDMO655x7jp59+MgzDMDIzM40RI0Z4anRUIlUh66t6zhtG1c56b855w6icWV/pPvYbv1/Dhg0VFRUlSUpKStI777zjtL169erq0aOHJKldu3basmWLJGnr1q1Ov1Y6c+aMioqKVLt2bTdNfm1ff/21+vbtq6CgIElSYGCgMjIyNHfuXEnSwIEDNWvWLMf+vXr1UrVq1dSqVSvHmZgtW7Zoy5YtGjRokCTp7NmzOnTokGJiYty8mitFREQoIyNDGRkZGj16tPLz87Vr1y7VqVNHkZGRkqT27dsrNDRUktSmTRvl5OQoOjracYzMzExFR0c73p83MDDQsS02NlZ+fn4KCgpSUFCQjh8/7jiWp+3cuVO9evVSrVq1JEnx8fFKT09XkyZNdOutt0qSwsPDlZOTo6KiImVkZOjhhx92PL64uNgjcwOeUJVzXqraWe/NOS9VzqynRHuR335C2G9vV69e3XFftWrVZLfbJV26PmvJkiWqUaOGewZ1Az8/vyvuMwxDDzzwgO666y4PTFS+y9fL7d+/X61bt1ZoaKj+93//VwEBAUpOTpbkvCYfHx/Hz88Vv31sSUlJxQ1vkd/OfOHCBRmGobp162rFihUenAzwHHLeWWXKenK+bDdy1nNNtBfJzc11vHDhk08+cZytuJZu3bo5nc3Yt2+fJfNdj86dO+uzzz5TYWGhJOnkyZOKiIjQqlWrJEkrV650+td6Wbp166Zly5apqKhIkpSfn6/jx49bO7iLIiMjtXHjRtWrV08+Pj4KDAzU6dOnlZmZqYiICJeOcfvttys9PV1ZWVmSLn2PKoPo6GitW7dO586d09mzZ7Vu3bqr/iwDAgLUpEkTffrpp5Iu/WX5/fffu3NcwKOqcs5LVTvrvTnnpcqZ9ZyJLsekSZO0fft2FRYWqnv37powYYLuvPNOT4/1u7Vo0ULvvvuunnzySbVq1UrDhg3Txo0br/m4qVOnavr06UpMTJTdbld0dLSmT5/uhold17p1a40dO1YjRoxQtWrV1LZtWz399NN64okntHDhQgUFBemFF14o9xjdunXTwYMHHWcnatWqpVmzZummm25yxxLKFRYWpsLCQqe34AoLC1NRUZHj15rXEhQUpOnTp2vChAkqLS3VTTfdpEWLFlk1coUJDw9XcnKy48/ekCFDVLdu3avuP2vWLD377LOaN2+eSkpK1L9/f7Vp08Zd46ISqkpZX5VzXqraWe/NOS9VzqznY7+9RHZ2tsaOHatPPvnE06MAACxAzgPuxeUcAAAAgEmciQYAAABM4kw0AAAAYBIlGgAAADCJEg0AAACYRIlGpTdz5ky99dZbjtv33Xefpk6d6rj94osv/q63+Nm2bZvGjBlTESMCAK4TWY8bDSUald7lT3mSLn3qVmFhodPH12ZkZLj0RvVmPvkJAOBeZD1uNHzYCiq9iIgIx5vrHzhwQK1bt9axY8f0yy+/yN/fXwcPHtTp06c1aNAg2e12tWvXTtOmTZOfn5/i4uLUr18/bd26Vffff7/q1KmjmTNnyt/f3+VP+gIAWI+sx42GM9Go9EJCQuTj4+P4uNvbb79d7du3V2Zmpvbs2aNmzZrpqaeeUlpamlauXCm73a733nvP8fjAwEB9+OGH6tWrl55++mnNnz9fy5cv17Fjxzy4KgDAr5H1uNFQolElREREKCMjw/HrvIiICO3atUsZGRkKDQ1VkyZN1KJFC0nS4MGDlZ6e7nhs//79JUk//vijmjRpoubNm8tmsykpKckjawEAlI2sx42EEo0q4fK1cvv371fr1q3VoUMHZWZmKiMjQx07diz3sf7+/m6aEgBwPch63Ego0agSIiMjtXHjRtWrV08+Pj4KDAzU6dOnlZmZqT59+ignJ0eHDx+WJK1YsUIxMTFXHKNly5bKycnRkSNHJEmrVq1y6xoAAOUj63Ej4YWFqBLCwsJUWFiohIQEp/uKiooUGhqqF154QQ8//LDjxSbDhg274hg1atTQ9OnT9cADDzhebFJUVOTOZQAAykHW40ZiMwzD8PQQAAAAQGXC5RwAAACASZRoAAAAwCRKNAAAAGASJRoAAAAwiRINAAAAmESJBgAAAEyiRAMAAAAmUaIBAAAAk/4fMn8Q0Njz3mcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Session - 8\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dhET31EbyjqV"
      },
      "source": [
        "# Abbreviations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M-nwI_ozyg8-"
      },
      "source": [
        "def clean(chat_string):\n",
        "    chat_string = re.sub(r\"w/e\", \"whatever\", chat_string)\n",
        "    chat_string = re.sub(r\"w/\", \"with\", chat_string)\n",
        "    chat_string = re.sub(r\"USAgov\", \"USA government\", chat_string)\n",
        "    chat_string = re.sub(r\"recentlu\", \"recently\", chat_string)\n",
        "    chat_string = re.sub(r\"Ph0tos\", \"Photos\", chat_string)\n",
        "    chat_string = re.sub(r\"amirite\", \"am I right\", chat_string)\n",
        "    chat_string = re.sub(r\"exp0sed\", \"exposed\", chat_string)\n",
        "    chat_string = re.sub(r\"<3\", \"love\", chat_string)\n",
        "    chat_string = re.sub(r\"amageddon\", \"armageddon\", chat_string)\n",
        "    chat_string = re.sub(r\"Trfc\", \"Traffic\", chat_string)\n",
        "    chat_string = re.sub(r\"8/5/2015\", \"2015-08-05\", chat_string)\n",
        "    chat_string = re.sub(r\"WindStorm\", \"Wind Storm\", chat_string)\n",
        "    chat_string = re.sub(r\"8/6/2015\", \"2015-08-06\", chat_string)\n",
        "    chat_string = re.sub(r\"10:38PM\", \"10:38 PM\", chat_string)\n",
        "    chat_string = re.sub(r\"10:30pm\", \"10:30 PM\", chat_string)\n",
        "    chat_string = re.sub(r\"16yr\", \"16 year\", chat_string)\n",
        "    chat_string = re.sub(r\"lmao\", \"laughing my ass off\", chat_string)   \n",
        "    chat_string = re.sub(r\"TRAUMATISED\", \"traumatized\", chat_string)\n",
        "    return chat_string"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PO5Otf2alkef"
      },
      "source": [
        "# Tableau Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "m_lg7aKezXx6",
        "outputId": "d610db79-8bcf-490a-b2c8-9e07f7620980"
      },
      "source": [
        "dic = {}\n",
        "\n",
        "all_data = pd.DataFrame()\n",
        "\n",
        "for count, name in enumerate(dataset.keys()):\n",
        "  a = dataset[name]\n",
        "\n",
        "  chat_len = []\n",
        "  for chat in a['Chat']:\n",
        "    chat_len.append(len(chat))\n",
        "\n",
        "  a['Session Number'] = [f'Session-{count + 1}' ]* len(dataset[name])\n",
        "  a['Length of chat'] = chat_len\n",
        "\n",
        "  all_data = pd.concat([all_data, a])\n",
        "  all_data.reset_index(drop=True, inplace=True)\n",
        "\n",
        "all_data.sample(n = 10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Time</th>\n",
              "      <th>Name</th>\n",
              "      <th>Chat</th>\n",
              "      <th>Session Number</th>\n",
              "      <th>Length of chat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>00:47:07</td>\n",
              "      <td>student_9</td>\n",
              "      <td>Feather is meronym is Bird</td>\n",
              "      <td>Session-2</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>00:31:12</td>\n",
              "      <td>student_21</td>\n",
              "      <td>arrogant/humble</td>\n",
              "      <td>Session-2</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>00:39:52</td>\n",
              "      <td>student_3</td>\n",
              "      <td>NIIT University is a hyponym of University</td>\n",
              "      <td>Session-2</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>00:49:14</td>\n",
              "      <td>student_2</td>\n",
              "      <td>There are two meanings</td>\n",
              "      <td>Session-1</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>00:16:41</td>\n",
              "      <td>student_7</td>\n",
              "      <td>Caret and Carrot</td>\n",
              "      <td>Session-2</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>00:30:59</td>\n",
              "      <td>student_28</td>\n",
              "      <td>Few /many</td>\n",
              "      <td>Session-2</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>00:40:12</td>\n",
              "      <td>student_22</td>\n",
              "      <td>hypernym gemstone-hyponym diamond</td>\n",
              "      <td>Session-2</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>240</th>\n",
              "      <td>00:16:59</td>\n",
              "      <td>student_13</td>\n",
              "      <td>\"stop words\" usually refers to the most common words in a language</td>\n",
              "      <td>Session-5</td>\n",
              "      <td>66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>00:13:55</td>\n",
              "      <td>student_11</td>\n",
              "      <td>Accept and except</td>\n",
              "      <td>Session-2</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>00:46:41</td>\n",
              "      <td>student_22</td>\n",
              "      <td>both are correct</td>\n",
              "      <td>Session-2</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Time        Name  ... Session Number Length of chat\n",
              "158  00:47:07   student_9  ...      Session-2             26\n",
              "85   00:31:12  student_21  ...      Session-2             15\n",
              "116  00:39:52   student_3  ...      Session-2             42\n",
              "20   00:49:14   student_2  ...      Session-1             22\n",
              "55   00:16:41   student_7  ...      Session-2             16\n",
              "80   00:30:59  student_28  ...      Session-2              9\n",
              "119  00:40:12  student_22  ...      Session-2             33\n",
              "240  00:16:59  student_13  ...      Session-5             66\n",
              "30   00:13:55  student_11  ...      Session-2             17\n",
              "152  00:46:41  student_22  ...      Session-2             16\n",
              "\n",
              "[10 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wnchVrXOQK-r"
      },
      "source": [
        "compression_opts = dict(method='zip',\n",
        "                        archive_name='out.csv')  \n",
        "\n",
        "all_data.to_csv('out.zip', index=False,\n",
        "          compression=compression_opts)  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "32qeJmS5Jy4H"
      },
      "source": [
        "# Incorrect Word"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ufGRFjXJxf8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "61f366cc-20ce-4d8e-dea7-dbdb6be7b398"
      },
      "source": [
        "from spellchecker import SpellChecker\n",
        "spell = SpellChecker()\n",
        "\n",
        "j = 0\n",
        "for i in dataset:\n",
        "  j += 1\n",
        "  all_data =  ' '.join(dataset[i]['Chat'].tolist())\n",
        "  tokenizer = nltk.RegexpTokenizer(r\"\\w+\")\n",
        "  all_data = tokenizer.tokenize(all_data)\n",
        "  misspelled = spell.unknown(all_data)\n",
        "  print('Session:',j,'\\n')\n",
        "  matrix = []\n",
        "  matrix.append(['Mispelled Word','Correct Word'])\n",
        "  for word in misspelled:\n",
        "    correct_word = spell.correction(word)\n",
        "    arr = [None]*2\n",
        "    if correct_word not in misspelled:\n",
        "      arr[0],arr[1] = word,correct_word\n",
        "      matrix.append(arr)\n",
        "  s = [[str(e) for e in row] for row in matrix]\n",
        "  lens = [max(map(len, col)) for col in zip(*s)]\n",
        "  fmt = '\\t'.join('{{:{}}}'.format(x) for x in lens)\n",
        "  table = [fmt.format(*row) for row in s]\n",
        "  print('\\n'.join(table),'\\n')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Session: 1 \n",
            "\n",
            "Mispelled Word\tCorrect Word\n",
            "nlp           \tnap         \n",
            "srs           \tsis         \n",
            "m             \ti           \n",
            "11am          \tam          \n",
            "sundar        \tsunday       \n",
            "\n",
            "Session: 2 \n",
            "\n",
            "Mispelled Word\tCorrect Word\n",
            "hyponym       \tsynonym     \n",
            "darsh         \tharsh       \n",
            "oneplus       \tonerous     \n",
            "niit          \tnit         \n",
            "shresth       \tsurest      \n",
            "gg            \tgo          \n",
            "homophones    \thomophobes  \n",
            "meronym       \tacronym     \n",
            "0steering     \tsteering    \n",
            "1st           \test         \n",
            "m             \ti           \n",
            "s             \ti           \n",
            "hyponyms      \tsynonyms    \n",
            "antonyms      \tantony's    \n",
            "t             \ti           \n",
            "meronum       \tmeconium    \n",
            "touchpad      \ttouched      \n",
            "\n",
            "Session: 3 \n",
            "\n",
            "Mispelled Word\tCorrect Word\n",
            "b             \ti           \n",
            "parser        \tparker      \n",
            "chunker       \thunker      \n",
            "pp            \tup          \n",
            "nn            \tin          \n",
            "s             \ti           \n",
            "nltk          \tnot         \n",
            "vp            \tup          \n",
            "d             \ti           \n",
            "np            \tno          \n",
            "pos_tag       \tpostal       \n",
            "\n",
            "Session: 4 \n",
            "\n",
            "Mispelled Word\tCorrect Word\n",
            "pp            \tup          \n",
            "vp            \tup          \n",
            "v             \ti           \n",
            "np            \tno           \n",
            "\n",
            "Session: 5 \n",
            "\n",
            "Mispelled Word\tCorrect Word\n",
            "nlp           \tnap         \n",
            "t             \ti           \n",
            "chatbots      \tchariots    \n",
            "s             \ti           \n",
            "corenlp       \tcornell     \n",
            "textblob      \ttextbook    \n",
            "spacie        \tspace       \n",
            "pyplot        \tpilot        \n",
            "\n",
            "Session: 6 \n",
            "\n",
            "Mispelled Word\tCorrect Word\n",
            "p             \ti           \n",
            "s             \ti           \n",
            "nvm           \tnom          \n",
            "\n",
            "Session: 7 \n",
            "\n",
            "Mispelled Word\tCorrect Word \n",
            "\n",
            "Session: 8 \n",
            "\n",
            "Mispelled Word\tCorrect Word\n",
            "helol         \thell        \n",
            "v             \ti           \n",
            "istead        \tinstead     \n",
            "unigram       \tanagram     \n",
            "anmol         \tamol        \n",
            "bigram        \tbagram      \n",
            "covvee        \tcover       \n",
            "accidentantly \taccidentally\n",
            "f             \ti            \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "hIXr09EOhH4B",
        "outputId": "3f9dfef9-0cae-4db9-8415-3c256e402c5a"
      },
      "source": [
        "pd.set_option('display.max_colwidth', None)\n",
        "dataset['data_2'].head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Time</th>\n",
              "      <th>Name</th>\n",
              "      <th>Chat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>00:12:59</td>\n",
              "      <td>teacher_1</td>\n",
              "      <td>Give the example for Homonyms</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>00:13:55</td>\n",
              "      <td>student_11</td>\n",
              "      <td>Accept and except</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>00:14:14</td>\n",
              "      <td>student_1</td>\n",
              "      <td>are and our - homophones</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>00:14:16</td>\n",
              "      <td>student_3</td>\n",
              "      <td>piece and peace</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>00:14:39</td>\n",
              "      <td>student_13</td>\n",
              "      <td>break and brake</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>00:14:44</td>\n",
              "      <td>student_8</td>\n",
              "      <td>bow</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>00:14:48</td>\n",
              "      <td>student_11</td>\n",
              "      <td>Know and no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>00:14:49</td>\n",
              "      <td>student_14</td>\n",
              "      <td>reed and read</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>00:15:22</td>\n",
              "      <td>student_15</td>\n",
              "      <td>piece and peace</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>00:15:23</td>\n",
              "      <td>student_16</td>\n",
              "      <td>cell and sell</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Time        Name                           Chat\n",
              "0  00:12:59   teacher_1  Give the example for Homonyms\n",
              "1  00:13:55  student_11              Accept and except\n",
              "2  00:14:14   student_1       are and our - homophones\n",
              "3  00:14:16   student_3                piece and peace\n",
              "4  00:14:39  student_13                break and brake\n",
              "5  00:14:44   student_8                            bow\n",
              "6  00:14:48  student_11                    Know and no\n",
              "7  00:14:49  student_14                  reed and read\n",
              "8  00:15:22  student_15                piece and peace\n",
              "9  00:15:23  student_16                  cell and sell"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_uSH-YfpgW2J"
      },
      "source": [
        "# Class Participation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "biYvsBDAK2a2"
      },
      "source": [
        "def loadGloveModel(gloveFile):\n",
        "  with open(gloveFile, encoding=\"utf8\" ) as f:\n",
        "      content = f.readlines()\n",
        "  model = {}\n",
        "  for line in content:\n",
        "      splitLine = line.split()\n",
        "      word = splitLine[0]\n",
        "      embedding = np.array([float(val) for val in splitLine[1:]])\n",
        "      model[word] = embedding\n",
        "  return model\n",
        "\n",
        "# Glove Model path\n",
        "# gloveFile = \"/content/drive/MyDrive/glove.6B/glove.6B.50d.txt\"\n",
        "\n",
        "gloveFile = \"/content/gdrive/MyDrive/R&D Project/glove.6B.50d.txt\" \n",
        "model = loadGloveModel(gloveFile)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vj2BlJSsEXsv"
      },
      "source": [
        "def preprocess(raw_text):\n",
        "  letters_only_text = re.sub(\"[^a-zA-Z]\", \" \", raw_text)\n",
        "  words = letters_only_text.lower().split()\n",
        "  stopword_set = set(stopwords)\n",
        "  cleaned_words = list(set([w for w in words if w not in stopword_set]))\n",
        "  if 'dtype' in cleaned_words:\n",
        "    cleaned_words.remove('dtype')\n",
        "  return cleaned_words\n",
        "\n",
        "\n",
        "def cosine_distance_wordembedding_method(s1, s2):\n",
        "  vector_1 = np.mean([model[word] for word in preprocess(s1)],axis=0)\n",
        "  vector_2 = np.mean([model[word] for word in preprocess(s2)],axis=0)\n",
        "  cosine = scipy.spatial.distance.cosine(vector_1, vector_2)\n",
        "  return round((1-cosine)*100,2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wq0yXfKG5Ka2"
      },
      "source": [
        "def get_professor_ques_ans(data):\n",
        "  start_string = 'Correct answer is '\n",
        "  teacher_text = data[data['Name'] == 'teacher_1']\n",
        "  teacher_answers = data[data['Chat'].str.startswith(start_string)]\n",
        "\n",
        "  for index in teacher_answers.index:\n",
        "    if index in teacher_text.index:\n",
        "      teacher_text = teacher_text.drop(index)\n",
        "\n",
        "  teacher_questions = teacher_text\n",
        "  return teacher_questions, teacher_answers\n",
        "\n",
        "def set_student_marks(data, teacher_questions, teacher_answers, student_marks):\n",
        "  for i in range(len(teacher_questions.index)):\n",
        "    question_index = teacher_questions.index[i]\n",
        "    answer_index = teacher_answers.index[i]\n",
        "    student_answers = data[question_index + 1 : answer_index]\n",
        "\n",
        "    for tea_ans in teacher_answers['Chat']:\n",
        "      for name, answer in zip(student_answers['Name'], student_answers['Chat']):\n",
        "        similarity = cosine_distance_wordembedding_method(answer, tea_ans)\n",
        "\n",
        "        if name in student_marks:\n",
        "          input_dict = student_marks[name]\n",
        "        else:\n",
        "          input_dict = defaultdict(float)\n",
        "        \n",
        "        if 0 < similarity < 70 :\n",
        "          input_dict['marks'] += 0.25\n",
        "        elif 71 < similarity < 80 :\n",
        "          input_dict['marks'] += 0.5\n",
        "        elif 81 < similarity < 90 :\n",
        "          input_dict['marks'] += 0.75\n",
        "        else:\n",
        "          input_dict['marks'] += 1\n",
        "        \n",
        "        input_dict['count'] += 1\n",
        "\n",
        "        student_marks[name] = input_dict\n",
        "    \n",
        "    return student_marks"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NuoebtW9CnO"
      },
      "source": [
        "def get_participation_marks(student_marks):\n",
        "  pd_student_name =  student_marks.keys()\n",
        "  pd_student_count = []\n",
        "  pd_student_marks = []\n",
        "  for name in pd_student_name:\n",
        "    pd_student_count.append(student_marks[name]['count'])\n",
        "    pd_student_marks.append(student_marks[name]['marks'] / student_marks[name]['count'])\n",
        "\n",
        "  df = pd.DataFrame({'Name': pd_student_name, 'Count': pd_student_count, 'Marks': pd_student_marks})\n",
        "  return df.sort_values(by = ['Marks'], ascending = False).reset_index(drop = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "A34F9OapIWHF",
        "outputId": "25df04c9-dede-49be-edd4-f4d016d6b6b3"
      },
      "source": [
        "student_marks = {}\n",
        "teacher_questions, teacher_answers = get_professor_ques_ans(dataset['data_5'])\n",
        "student_marks = set_student_marks(dataset['data_5'], teacher_questions, teacher_answers, student_marks)\n",
        "get_participation_marks(student_marks).sample(n = 10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py:3373: RuntimeWarning: Mean of empty slice.\n",
            "  out=out, **kwargs)\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  ret = ret.dtype.type(ret / rcount)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "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>Name</th>\n",
              "      <th>Count</th>\n",
              "      <th>Marks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>student_9</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>student_4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>student_21</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>student_16</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.833333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>student_18</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.875000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>student_19</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>student_14</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>student_25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>student_3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>student_5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.750000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Name  Count     Marks\n",
              "15   student_9    2.0  0.750000\n",
              "2    student_4    1.0  1.000000\n",
              "8   student_21    1.0  0.750000\n",
              "7   student_16    3.0  0.833333\n",
              "4   student_18    2.0  0.875000\n",
              "20  student_19    1.0  0.500000\n",
              "1   student_14    1.0  1.000000\n",
              "9   student_25    1.0  0.750000\n",
              "17   student_3    2.0  0.750000\n",
              "18   student_5    1.0  0.750000"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ECtn1UHcTzdr"
      },
      "source": [
        "def heat_map_matrix_between_two_sentences(s1,s2):\n",
        "    df = calculate_heat_matrix_for_two_sentences(s1,s2)\n",
        "    fig, ax = plt.subplots(figsize=(5,5)) \n",
        "    ax_blue = sns.heatmap(df, cmap=\"YlGnBu\")\n",
        "    print(cosine_distance_wordembedding_method(s1, s2))\n",
        "    return ax_blue"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Marks"
      ],
      "metadata": {
        "id": "OW9F1VRHw2Zx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "all_marks = []\n",
        "marks_lists = [0, 0.25, 0.5, 0.75, 1]\n",
        "\n",
        "for i in all_data.index:\n",
        "  if all_data.loc[i, 'Name'] == 'teacher_1':\n",
        "    all_marks.append(None)\n",
        "  else:\n",
        "    all_marks.append(get_marks(marks_lists))"
      ],
      "metadata": {
        "id": "Q8vdsbp6xPZB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "marks_data = all_data.copy(deep=True)\n",
        "marks_data['Marks'] = all_marks\n",
        "marks_data.sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "V0HBNK8oxgrB",
        "outputId": "369ae629-67ef-417c-890b-f0165d303b91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Time</th>\n",
              "      <th>Name</th>\n",
              "      <th>Chat</th>\n",
              "      <th>Session Number</th>\n",
              "      <th>Length of chat</th>\n",
              "      <th>Marks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>194</th>\n",
              "      <td>00:42:24</td>\n",
              "      <td>student_22</td>\n",
              "      <td>an*</td>\n",
              "      <td>Session-3</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>294</th>\n",
              "      <td>00:12:33</td>\n",
              "      <td>student_44</td>\n",
              "      <td>say i want to write hello, i may accidentantly misspell it by helol</td>\n",
              "      <td>Session-8</td>\n",
              "      <td>67</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>265</th>\n",
              "      <td>00:22:51</td>\n",
              "      <td>student_21</td>\n",
              "      <td>I think we can use TextBLob and SpaCy</td>\n",
              "      <td>Session-5</td>\n",
              "      <td>37</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>00:14:49</td>\n",
              "      <td>student_14</td>\n",
              "      <td>reed and read</td>\n",
              "      <td>Session-2</td>\n",
              "      <td>13</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>253</th>\n",
              "      <td>00:19:40</td>\n",
              "      <td>student_8</td>\n",
              "      <td>Parsing means passing a sentence through a checkpoint.</td>\n",
              "      <td>Session-5</td>\n",
              "      <td>54</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Time        Name  ... Length of chat Marks\n",
              "194  00:42:24  student_22  ...              3   1.0\n",
              "294  00:12:33  student_44  ...             67   0.5\n",
              "265  00:22:51  student_21  ...             37   0.5\n",
              "36   00:14:49  student_14  ...             13   0.5\n",
              "253  00:19:40   student_8  ...             54   0.0\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    }
  ]
}