FloresPortfolio .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4aaf7372",
   "metadata": {},
   "source": [
    "## Visualize Chatbot/Response Data to Inform Business Decisions\n",
    "#### Emily Flores\n",
    "\n",
    "Work on Customer Support Twitter dataset, modern corpus of tweets and replies. Check, analyze and understand the pattern and conversations, and for study of modern customer support practices and impact. Using the data, analyze and build interactive visualization to make business decision. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "530e03e9",
   "metadata": {},
   "source": [
    "####  Exploratory data analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3dda07d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import string\n",
    "from nltk.corpus import stopwords\n",
    "from collections import Counter \n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from bs4 import BeautifulSoup\n",
    "from spellchecker import SpellChecker \n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from textblob import TextBlob\n",
    "import plotly.figure_factory as ff\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e72a58af",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = 'twcs.csv'\n",
    "dataset = pd.read_csv('twcs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cdb2aaa4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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>tweet_id</th>\n",
       "      <th>author_id</th>\n",
       "      <th>inbound</th>\n",
       "      <th>created_at</th>\n",
       "      <th>text</th>\n",
       "      <th>response_tweet_id</th>\n",
       "      <th>in_response_to_tweet_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>sprintcare</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 22:10:47 +0000 2017</td>\n",
       "      <td>@115712 I understand. I would like to assist y...</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>115712</td>\n",
       "      <td>True</td>\n",
       "      <td>Tue Oct 31 22:11:45 +0000 2017</td>\n",
       "      <td>@sprintcare and how do you propose we do that</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>115712</td>\n",
       "      <td>True</td>\n",
       "      <td>Tue Oct 31 22:08:27 +0000 2017</td>\n",
       "      <td>@sprintcare I have sent several private messag...</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>sprintcare</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 21:54:49 +0000 2017</td>\n",
       "      <td>@115712 Please send us a Private Message so th...</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>115712</td>\n",
       "      <td>True</td>\n",
       "      <td>Tue Oct 31 21:49:35 +0000 2017</td>\n",
       "      <td>@sprintcare I did.</td>\n",
       "      <td>4</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tweet_id   author_id  inbound                      created_at  \\\n",
       "0         1  sprintcare    False  Tue Oct 31 22:10:47 +0000 2017   \n",
       "1         2      115712     True  Tue Oct 31 22:11:45 +0000 2017   \n",
       "2         3      115712     True  Tue Oct 31 22:08:27 +0000 2017   \n",
       "3         4  sprintcare    False  Tue Oct 31 21:54:49 +0000 2017   \n",
       "4         5      115712     True  Tue Oct 31 21:49:35 +0000 2017   \n",
       "\n",
       "                                                text response_tweet_id  \\\n",
       "0  @115712 I understand. I would like to assist y...                 2   \n",
       "1      @sprintcare and how do you propose we do that               NaN   \n",
       "2  @sprintcare I have sent several private messag...                 1   \n",
       "3  @115712 Please send us a Private Message so th...                 3   \n",
       "4                                 @sprintcare I did.                 4   \n",
       "\n",
       "   in_response_to_tweet_id  \n",
       "0                      3.0  \n",
       "1                      1.0  \n",
       "2                      4.0  \n",
       "3                      5.0  \n",
       "4                      6.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d07b40fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "rows, columns = dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0fa8ade3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Rows: 2811774\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of Rows:\", rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6e6379e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Columns: 7\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of Columns:\", columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "24fb60ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2811774 entries, 0 to 2811773\n",
      "Data columns (total 7 columns):\n",
      " #   Column                   Dtype  \n",
      "---  ------                   -----  \n",
      " 0   tweet_id                 int64  \n",
      " 1   author_id                object \n",
      " 2   inbound                  bool   \n",
      " 3   created_at               object \n",
      " 4   text                     object \n",
      " 5   response_tweet_id        object \n",
      " 6   in_response_to_tweet_id  float64\n",
      "dtypes: bool(1), float64(1), int64(1), object(4)\n",
      "memory usage: 131.4+ MB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "08d4be98",
   "metadata": {},
   "outputs": [],
   "source": [
    "description = dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "12d069b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           tweet_id  in_response_to_tweet_id\n",
      "count  2.811774e+06             2.017439e+06\n",
      "mean   1.504565e+06             1.463141e+06\n",
      "std    8.616450e+05             8.665730e+05\n",
      "min    1.000000e+00             1.000000e+00\n",
      "25%    7.601652e+05             7.155105e+05\n",
      "50%    1.507772e+06             1.439805e+06\n",
      "75%    2.253296e+06             2.220646e+06\n",
      "max    2.987950e+06             2.987950e+06\n"
     ]
    }
   ],
   "source": [
    "print(description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9440b0eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id                         0\n",
       "author_id                        0\n",
       "inbound                          0\n",
       "created_at                       0\n",
       "text                             0\n",
       "response_tweet_id          1040629\n",
       "in_response_to_tweet_id     794335\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "48b0106c",
   "metadata": {},
   "outputs": [],
   "source": [
    "duplicateValues = dataset.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2b4132fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "print(duplicateValues.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7cbe65a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>author_id</th>\n",
       "      <th>inbound</th>\n",
       "      <th>created_at</th>\n",
       "      <th>text</th>\n",
       "      <th>response_tweet_id</th>\n",
       "      <th>in_response_to_tweet_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [tweet_id, author_id, inbound, created_at, text, response_tweet_id, in_response_to_tweet_id]\n",
       "Index: []"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[duplicateValues]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65c7eb28",
   "metadata": {},
   "source": [
    "#### The data can be cleaned by reordering the 'created_at' attribute by date added.\n",
    "#### Most recent tweet at the top of data set * oldest at the end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a91c0d6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.sort_values(by='created_at', ascending=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b75a3c09",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>author_id</th>\n",
       "      <th>inbound</th>\n",
       "      <th>created_at</th>\n",
       "      <th>text</th>\n",
       "      <th>response_tweet_id</th>\n",
       "      <th>in_response_to_tweet_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2496505</th>\n",
       "      <td>2666295</td>\n",
       "      <td>751178</td>\n",
       "      <td>True</td>\n",
       "      <td>Fri Apr 01 17:37:48 +0000 2016</td>\n",
       "      <td>@AskSeagate Same issue Seagate. Is there a sol...</td>\n",
       "      <td>2666294</td>\n",
       "      <td>2666296.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2496504</th>\n",
       "      <td>2666294</td>\n",
       "      <td>AskSeagate</td>\n",
       "      <td>False</td>\n",
       "      <td>Fri Apr 01 18:22:40 +0000 2016</td>\n",
       "      <td>@751178 Pls give us more clarification on your...</td>\n",
       "      <td>2666293</td>\n",
       "      <td>2666295.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2496503</th>\n",
       "      <td>2666293</td>\n",
       "      <td>751178</td>\n",
       "      <td>True</td>\n",
       "      <td>Fri Apr 01 18:25:52 +0000 2016</td>\n",
       "      <td>@AskSeagate Bootstrap and media server daemon ...</td>\n",
       "      <td>2666291</td>\n",
       "      <td>2666294.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2496501</th>\n",
       "      <td>2666291</td>\n",
       "      <td>AskSeagate</td>\n",
       "      <td>False</td>\n",
       "      <td>Fri Apr 01 19:53:33 +0000 2016</td>\n",
       "      <td>@751178 Pls use the link bellow to download th...</td>\n",
       "      <td>2666292</td>\n",
       "      <td>2666293.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352823</th>\n",
       "      <td>402549</td>\n",
       "      <td>211231</td>\n",
       "      <td>True</td>\n",
       "      <td>Fri Apr 03 19:57:41 +0000 2015</td>\n",
       "      <td>@AsurionCares How do I get a receipt of my mon...</td>\n",
       "      <td>402547</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         tweet_id   author_id  inbound                      created_at  \\\n",
       "2496505   2666295      751178     True  Fri Apr 01 17:37:48 +0000 2016   \n",
       "2496504   2666294  AskSeagate    False  Fri Apr 01 18:22:40 +0000 2016   \n",
       "2496503   2666293      751178     True  Fri Apr 01 18:25:52 +0000 2016   \n",
       "2496501   2666291  AskSeagate    False  Fri Apr 01 19:53:33 +0000 2016   \n",
       "352823     402549      211231     True  Fri Apr 03 19:57:41 +0000 2015   \n",
       "\n",
       "                                                      text response_tweet_id  \\\n",
       "2496505  @AskSeagate Same issue Seagate. Is there a sol...           2666294   \n",
       "2496504  @751178 Pls give us more clarification on your...           2666293   \n",
       "2496503  @AskSeagate Bootstrap and media server daemon ...           2666291   \n",
       "2496501  @751178 Pls use the link bellow to download th...           2666292   \n",
       "352823   @AsurionCares How do I get a receipt of my mon...            402547   \n",
       "\n",
       "         in_response_to_tweet_id  \n",
       "2496505                2666296.0  \n",
       "2496504                2666295.0  \n",
       "2496503                2666294.0  \n",
       "2496501                2666293.0  \n",
       "352823                       NaN  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef63649e",
   "metadata": {},
   "source": [
    "#### The data can be cleaned by reorganizing the author_id by alphabetic order.\n",
    "#### Start with 'author_id' that starts with 'a' & ends with 'z'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6733a836",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.sort_values(by='author_id', ascending=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0ef4860a",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>author_id</th>\n",
       "      <th>inbound</th>\n",
       "      <th>created_at</th>\n",
       "      <th>text</th>\n",
       "      <th>response_tweet_id</th>\n",
       "      <th>in_response_to_tweet_id</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>576082</th>\n",
       "      <td>648216</td>\n",
       "      <td>10026</td>\n",
       "      <td>True</td>\n",
       "      <td>Thu Oct 05 00:08:01 +0000 2017</td>\n",
       "      <td>Screw you @116016 and your stupid Blueprint pr...</td>\n",
       "      <td>648215</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1543061</th>\n",
       "      <td>1693185</td>\n",
       "      <td>10026</td>\n",
       "      <td>True</td>\n",
       "      <td>Mon Nov 06 15:38:24 +0000 2017</td>\n",
       "      <td>Dear @ChaseSupport, it’s kinda hard to pay my ...</td>\n",
       "      <td>1693184</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1543060</th>\n",
       "      <td>1693184</td>\n",
       "      <td>10026</td>\n",
       "      <td>True</td>\n",
       "      <td>Mon Nov 06 15:45:29 +0000 2017</td>\n",
       "      <td>@ChaseSupport Actually it just doesn’t work in...</td>\n",
       "      <td>1693183</td>\n",
       "      <td>1693185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637146</th>\n",
       "      <td>713796</td>\n",
       "      <td>100363</td>\n",
       "      <td>True</td>\n",
       "      <td>Tue Oct 10 22:25:39 +0000 2017</td>\n",
       "      <td>Now the flight @Delta is sending our bag back ...</td>\n",
       "      <td>713795</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1752883</th>\n",
       "      <td>1906927</td>\n",
       "      <td>10103</td>\n",
       "      <td>True</td>\n",
       "      <td>Wed Oct 25 09:42:58 +0000 2017</td>\n",
       "      <td>@MOO Big thanks to Quentin for the exceptional...</td>\n",
       "      <td>1906926</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         tweet_id author_id  inbound                      created_at  \\\n",
       "576082     648216     10026     True  Thu Oct 05 00:08:01 +0000 2017   \n",
       "1543061   1693185     10026     True  Mon Nov 06 15:38:24 +0000 2017   \n",
       "1543060   1693184     10026     True  Mon Nov 06 15:45:29 +0000 2017   \n",
       "637146     713796    100363     True  Tue Oct 10 22:25:39 +0000 2017   \n",
       "1752883   1906927     10103     True  Wed Oct 25 09:42:58 +0000 2017   \n",
       "\n",
       "                                                      text response_tweet_id  \\\n",
       "576082   Screw you @116016 and your stupid Blueprint pr...            648215   \n",
       "1543061  Dear @ChaseSupport, it’s kinda hard to pay my ...           1693184   \n",
       "1543060  @ChaseSupport Actually it just doesn’t work in...           1693183   \n",
       "637146   Now the flight @Delta is sending our bag back ...            713795   \n",
       "1752883  @MOO Big thanks to Quentin for the exceptional...           1906926   \n",
       "\n",
       "         in_response_to_tweet_id  \n",
       "576082                       NaN  \n",
       "1543061                      NaN  \n",
       "1543060                1693185.0  \n",
       "637146                       NaN  \n",
       "1752883                      NaN  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48824021",
   "metadata": {},
   "source": [
    "#### I noticed there are repeated 'author_id' we can group them together. \n",
    "#### While maintaing the 'author_id' in alphabetic order.\n",
    "#### This will make it easier to access tweets from specific users. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7234a02b",
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_data = dataset.groupby('author_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "94ccf416",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>author_id</th>\n",
       "      <th>inbound</th>\n",
       "      <th>created_at</th>\n",
       "      <th>text</th>\n",
       "      <th>response_tweet_id</th>\n",
       "      <th>in_response_to_tweet_id</th>\n",
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       "  <tbody>\n",
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       "      <td>Screw you @116016 and your stupid Blueprint pr...</td>\n",
       "      <td>648215</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1543061</th>\n",
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       "      <td>10026</td>\n",
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       "      <td>Mon Nov 06 15:38:24 +0000 2017</td>\n",
       "      <td>Dear @ChaseSupport, it’s kinda hard to pay my ...</td>\n",
       "      <td>1693184</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>10026</td>\n",
       "      <td>True</td>\n",
       "      <td>Mon Nov 06 15:45:29 +0000 2017</td>\n",
       "      <td>@ChaseSupport Actually it just doesn’t work in...</td>\n",
       "      <td>1693183</td>\n",
       "      <td>1693185.0</td>\n",
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       "    <tr>\n",
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       "      <td>713796</td>\n",
       "      <td>100363</td>\n",
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       "      <td>Tue Oct 10 22:25:39 +0000 2017</td>\n",
       "      <td>Now the flight @Delta is sending our bag back ...</td>\n",
       "      <td>713795</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1752883</th>\n",
       "      <td>1906927</td>\n",
       "      <td>10103</td>\n",
       "      <td>True</td>\n",
       "      <td>Wed Oct 25 09:42:58 +0000 2017</td>\n",
       "      <td>@MOO Big thanks to Quentin for the exceptional...</td>\n",
       "      <td>1906926</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         tweet_id author_id  inbound                      created_at  \\\n",
       "576082     648216     10026     True  Thu Oct 05 00:08:01 +0000 2017   \n",
       "1543061   1693185     10026     True  Mon Nov 06 15:38:24 +0000 2017   \n",
       "1543060   1693184     10026     True  Mon Nov 06 15:45:29 +0000 2017   \n",
       "637146     713796    100363     True  Tue Oct 10 22:25:39 +0000 2017   \n",
       "1752883   1906927     10103     True  Wed Oct 25 09:42:58 +0000 2017   \n",
       "\n",
       "                                                      text response_tweet_id  \\\n",
       "576082   Screw you @116016 and your stupid Blueprint pr...            648215   \n",
       "1543061  Dear @ChaseSupport, it’s kinda hard to pay my ...           1693184   \n",
       "1543060  @ChaseSupport Actually it just doesn’t work in...           1693183   \n",
       "637146   Now the flight @Delta is sending our bag back ...            713795   \n",
       "1752883  @MOO Big thanks to Quentin for the exceptional...           1906926   \n",
       "\n",
       "         in_response_to_tweet_id  \n",
       "576082                       NaN  \n",
       "1543061                      NaN  \n",
       "1543060                1693185.0  \n",
       "637146                       NaN  \n",
       "1752883                      NaN  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b355013f",
   "metadata": {},
   "outputs": [],
   "source": [
    "inbound_count = dataset[dataset['inbound'] == True]['inbound'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "49741266",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of inbound responses: 1537843\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of inbound responses:\", inbound_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "013dbe67",
   "metadata": {},
   "outputs": [],
   "source": [
    "outbound_count = dataset[dataset['inbound'] == False]['inbound'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "79abc7c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outbound responses: 1273931\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of outbound responses:\", outbound_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dd2d402a",
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_tweets_count = dataset['tweet_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "de0bd306",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique tweets: 2811774\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of unique tweets:\", unique_tweets_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "24a73e2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "top_20_users = dataset['author_id'].value_counts().head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5f618a8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 20 Twitter users:\n",
      " AmazonHelp         169840\n",
      "AppleSupport       106860\n",
      "Uber_Support        56270\n",
      "SpotifyCares        43265\n",
      "Delta               42253\n",
      "Tesco               38573\n",
      "AmericanAir         36764\n",
      "TMobileHelp         34317\n",
      "comcastcares        33031\n",
      "British_Airways     29361\n",
      "SouthwestAir        28977\n",
      "VirginTrains        27817\n",
      "Ask_Spectrum        25860\n",
      "XboxSupport         24557\n",
      "sprintcare          22381\n",
      "hulu_support        21872\n",
      "sainsburys          19466\n",
      "GWRHelp             19364\n",
      "AskPlayStation      19098\n",
      "ChipotleTweets      18749\n",
      "Name: author_id, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Top 20 Twitter users:\\n\", top_20_users)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac3be714",
   "metadata": {},
   "source": [
    "### Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f98816ec",
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>author_id</th>\n",
       "      <th>inbound</th>\n",
       "      <th>created_at</th>\n",
       "      <th>text</th>\n",
       "      <th>response_tweet_id</th>\n",
       "      <th>in_response_to_tweet_id</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>576082</th>\n",
       "      <td>648216</td>\n",
       "      <td>10026</td>\n",
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       "      <td>Thu Oct 05 00:08:01 +0000 2017</td>\n",
       "      <td>Screw you @116016 and your stupid Blueprint pr...</td>\n",
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       "    </tr>\n",
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       "      <td>10026</td>\n",
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       "      <td>Dear @ChaseSupport, it’s kinda hard to pay my ...</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1543060</th>\n",
       "      <td>1693184</td>\n",
       "      <td>10026</td>\n",
       "      <td>True</td>\n",
       "      <td>Mon Nov 06 15:45:29 +0000 2017</td>\n",
       "      <td>@ChaseSupport Actually it just doesn’t work in...</td>\n",
       "      <td>1693183</td>\n",
       "      <td>1693185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637146</th>\n",
       "      <td>713796</td>\n",
       "      <td>100363</td>\n",
       "      <td>True</td>\n",
       "      <td>Tue Oct 10 22:25:39 +0000 2017</td>\n",
       "      <td>Now the flight @Delta is sending our bag back ...</td>\n",
       "      <td>713795</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1752883</th>\n",
       "      <td>1906927</td>\n",
       "      <td>10103</td>\n",
       "      <td>True</td>\n",
       "      <td>Wed Oct 25 09:42:58 +0000 2017</td>\n",
       "      <td>@MOO Big thanks to Quentin for the exceptional...</td>\n",
       "      <td>1906926</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         tweet_id author_id  inbound                      created_at  \\\n",
       "576082     648216     10026     True  Thu Oct 05 00:08:01 +0000 2017   \n",
       "1543061   1693185     10026     True  Mon Nov 06 15:38:24 +0000 2017   \n",
       "1543060   1693184     10026     True  Mon Nov 06 15:45:29 +0000 2017   \n",
       "637146     713796    100363     True  Tue Oct 10 22:25:39 +0000 2017   \n",
       "1752883   1906927     10103     True  Wed Oct 25 09:42:58 +0000 2017   \n",
       "\n",
       "                                                      text response_tweet_id  \\\n",
       "576082   Screw you @116016 and your stupid Blueprint pr...            648215   \n",
       "1543061  Dear @ChaseSupport, it’s kinda hard to pay my ...           1693184   \n",
       "1543060  @ChaseSupport Actually it just doesn’t work in...           1693183   \n",
       "637146   Now the flight @Delta is sending our bag back ...            713795   \n",
       "1752883  @MOO Big thanks to Quentin for the exceptional...           1906926   \n",
       "\n",
       "         in_response_to_tweet_id  \n",
       "576082                       NaN  \n",
       "1543061                      NaN  \n",
       "1543060                1693185.0  \n",
       "637146                       NaN  \n",
       "1752883                      NaN  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "664c1d90",
   "metadata": {},
   "outputs": [],
   "source": [
    "userTweetCount = dataset.groupby('author_id').size().reset_index(name='tweetCount')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "88e03fcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "topUsers = userTweetCount.sort_values(by='tweetCount', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ec402a88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           author_id  tweetCount\n",
      "702677    AmazonHelp      169840\n",
      "702679  AppleSupport      106860\n",
      "702754  Uber_Support       56270\n",
      "702746  SpotifyCares       43265\n",
      "702709         Delta       42253\n"
     ]
    }
   ],
   "source": [
    "print(topUsers.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dce1ad3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "authorResCount = dataset.groupby('author_id')['response_tweet_id'].count().reset_index(name='response_count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9110807f",
   "metadata": {},
   "outputs": [],
   "source": [
    "topAuthorsRes = authorResCount.sort_values(by='response_count', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "58c8dfb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           author_id  response_count\n",
      "702677    AmazonHelp           85274\n",
      "702679  AppleSupport           31564\n",
      "702754  Uber_Support           18036\n",
      "702759  VirginTrains           15235\n",
      "702678   AmericanAir           14556\n"
     ]
    }
   ],
   "source": [
    "print(topAuthorsRes.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "51f69d9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['created_at'] = pd.to_datetime(dataset['created_at'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f61e64b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "tweetsByDate = dataset.groupby(dataset['created_at'].dt.date).size().reset_index(name='tweet_count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "dd77502e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     created_at  tweet_count\n",
      "0    2008-05-08            2\n",
      "1    2010-02-16            1\n",
      "2    2010-03-31            2\n",
      "3    2010-07-05            2\n",
      "4    2010-08-30            1\n",
      "..          ...          ...\n",
      "979  2017-11-29        53610\n",
      "980  2017-11-30        53476\n",
      "981  2017-12-01        53857\n",
      "982  2017-12-02        46988\n",
      "983  2017-12-03        38164\n",
      "\n",
      "[984 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "print(tweetsByDate)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd1f44e0",
   "metadata": {},
   "source": [
    "### Data Cleaning Part 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4a582f02",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Now the flight @Delta is sending our bag back ...</td>\n",
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       "      <td>True</td>\n",
       "      <td>2017-10-25 09:42:58+00:00</td>\n",
       "      <td>@MOO Big thanks to Quentin for the exceptional...</td>\n",
       "      <td>1906926</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2071249</th>\n",
       "      <td>2230369</td>\n",
       "      <td>10103</td>\n",
       "      <td>True</td>\n",
       "      <td>2017-11-10 12:20:40+00:00</td>\n",
       "      <td>The ribotRainbow! New #businesscards thanks to...</td>\n",
       "      <td>2230368</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1871115</th>\n",
       "      <td>2027294</td>\n",
       "      <td>10221</td>\n",
       "      <td>True</td>\n",
       "      <td>2017-11-06 19:05:45+00:00</td>\n",
       "      <td>same. https://t.co/gxkJt8BNV6</td>\n",
       "      <td>2027293</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815955</th>\n",
       "      <td>909357</td>\n",
       "      <td>10221</td>\n",
       "      <td>True</td>\n",
       "      <td>2017-10-21 05:20:48+00:00</td>\n",
       "      <td>Yup https://t.co/GpkFa9MfHQ</td>\n",
       "      <td>909356</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2566140</th>\n",
       "      <td>2737084</td>\n",
       "      <td>10286</td>\n",
       "      <td>True</td>\n",
       "      <td>2017-11-18 00:40:34+00:00</td>\n",
       "      <td>@comcastcares Is it possible to get business c...</td>\n",
       "      <td>2737083</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082215</th>\n",
       "      <td>1197566</td>\n",
       "      <td>10297</td>\n",
       "      <td>True</td>\n",
       "      <td>2017-10-25 03:12:36+00:00</td>\n",
       "      <td>@Delta I will never fly your airline again</td>\n",
       "      <td>1197564</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         tweet_id author_id  inbound                created_at  \\\n",
       "576082     648216     10026     True 2017-10-05 00:08:01+00:00   \n",
       "1543061   1693185     10026     True 2017-11-06 15:38:24+00:00   \n",
       "1543060   1693184     10026     True 2017-11-06 15:45:29+00:00   \n",
       "637146     713796    100363     True 2017-10-10 22:25:39+00:00   \n",
       "1752883   1906927     10103     True 2017-10-25 09:42:58+00:00   \n",
       "2071249   2230369     10103     True 2017-11-10 12:20:40+00:00   \n",
       "1871115   2027294     10221     True 2017-11-06 19:05:45+00:00   \n",
       "815955     909357     10221     True 2017-10-21 05:20:48+00:00   \n",
       "2566140   2737084     10286     True 2017-11-18 00:40:34+00:00   \n",
       "1082215   1197566     10297     True 2017-10-25 03:12:36+00:00   \n",
       "\n",
       "                                                      text response_tweet_id  \\\n",
       "576082   Screw you @116016 and your stupid Blueprint pr...            648215   \n",
       "1543061  Dear @ChaseSupport, it’s kinda hard to pay my ...           1693184   \n",
       "1543060  @ChaseSupport Actually it just doesn’t work in...           1693183   \n",
       "637146   Now the flight @Delta is sending our bag back ...            713795   \n",
       "1752883  @MOO Big thanks to Quentin for the exceptional...           1906926   \n",
       "2071249  The ribotRainbow! New #businesscards thanks to...           2230368   \n",
       "1871115                      same. https://t.co/gxkJt8BNV6           2027293   \n",
       "815955                         Yup https://t.co/GpkFa9MfHQ            909356   \n",
       "2566140  @comcastcares Is it possible to get business c...           2737083   \n",
       "1082215         @Delta I will never fly your airline again           1197564   \n",
       "\n",
       "         in_response_to_tweet_id  \n",
       "576082                       NaN  \n",
       "1543061                      NaN  \n",
       "1543060                1693185.0  \n",
       "637146                       NaN  \n",
       "1752883                      NaN  \n",
       "2071249                      NaN  \n",
       "1871115                      NaN  \n",
       "815955                       NaN  \n",
       "2566140                      NaN  \n",
       "1082215                      NaN  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "aae23e48",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lower casing\n",
    "dataset['text_lower'] = dataset['text'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cacc63b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         tweet_id   author_id  inbound                created_at  \\\n",
      "576082     648216       10026     True 2017-10-05 00:08:01+00:00   \n",
      "1543061   1693185       10026     True 2017-11-06 15:38:24+00:00   \n",
      "1543060   1693184       10026     True 2017-11-06 15:45:29+00:00   \n",
      "637146     713796      100363     True 2017-10-10 22:25:39+00:00   \n",
      "1752883   1906927       10103     True 2017-10-25 09:42:58+00:00   \n",
      "...           ...         ...      ...                       ...   \n",
      "563523     634902  sprintcare    False 2017-11-22 16:50:06+00:00   \n",
      "1547778   1698065  sprintcare    False 2017-11-06 23:38:30+00:00   \n",
      "1444813   1589060  sprintcare    False 2017-11-04 23:20:15+00:00   \n",
      "2075585   2234492  sprintcare    False 2017-11-10 18:17:30+00:00   \n",
      "2325021   2491088  sprintcare    False 2017-11-15 17:13:42+00:00   \n",
      "\n",
      "                                                      text  \\\n",
      "576082   Screw you @116016 and your stupid Blueprint pr...   \n",
      "1543061  Dear @ChaseSupport, it’s kinda hard to pay my ...   \n",
      "1543060  @ChaseSupport Actually it just doesn’t work in...   \n",
      "637146   Now the flight @Delta is sending our bag back ...   \n",
      "1752883  @MOO Big thanks to Quentin for the exceptional...   \n",
      "...                                                    ...   \n",
      "563523   @270560 Let's see what's going on. Please send...   \n",
      "1547778  @515037 Let’s do some troubleshooting. Can you...   \n",
      "1444813  @488653 We apologize for the delay in helping ...   \n",
      "2075585  @196099 Please send us a Direct Message, so th...   \n",
      "2325021  @711261 Send me a DM so that I can take a look...   \n",
      "\n",
      "                       response_tweet_id  in_response_to_tweet_id  \\\n",
      "576082                            648215                      NaN   \n",
      "1543061                          1693184                      NaN   \n",
      "1543060                          1693183                1693185.0   \n",
      "637146                            713795                      NaN   \n",
      "1752883                          1906926                      NaN   \n",
      "...                                  ...                      ...   \n",
      "563523                               NaN                 634903.0   \n",
      "1547778                              NaN                1698066.0   \n",
      "1444813  1589061,1589062,1589063,1589064                1589065.0   \n",
      "2075585                              NaN                2234491.0   \n",
      "2325021                              NaN                2491089.0   \n",
      "\n",
      "                                                text_lower  \n",
      "576082   screw you @116016 and your stupid blueprint pr...  \n",
      "1543061  dear @chasesupport, it’s kinda hard to pay my ...  \n",
      "1543060  @chasesupport actually it just doesn’t work in...  \n",
      "637146   now the flight @delta is sending our bag back ...  \n",
      "1752883  @moo big thanks to quentin for the exceptional...  \n",
      "...                                                    ...  \n",
      "563523   @270560 let's see what's going on. please send...  \n",
      "1547778  @515037 let’s do some troubleshooting. can you...  \n",
      "1444813  @488653 we apologize for the delay in helping ...  \n",
      "2075585  @196099 please send us a direct message, so th...  \n",
      "2325021  @711261 send me a dm so that i can take a look...  \n",
      "\n",
      "[2811774 rows x 8 columns]\n"
     ]
    }
   ],
   "source": [
    "print(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d6f114d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removal of punctuation \n",
    "dataset['no_punct'] = dataset['text_lower'].apply(lambda text: text.translate(str.maketrans('', '', string.punctuation)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "228721c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         tweet_id   author_id  inbound                created_at  \\\n",
      "576082     648216       10026     True 2017-10-05 00:08:01+00:00   \n",
      "1543061   1693185       10026     True 2017-11-06 15:38:24+00:00   \n",
      "1543060   1693184       10026     True 2017-11-06 15:45:29+00:00   \n",
      "637146     713796      100363     True 2017-10-10 22:25:39+00:00   \n",
      "1752883   1906927       10103     True 2017-10-25 09:42:58+00:00   \n",
      "...           ...         ...      ...                       ...   \n",
      "563523     634902  sprintcare    False 2017-11-22 16:50:06+00:00   \n",
      "1547778   1698065  sprintcare    False 2017-11-06 23:38:30+00:00   \n",
      "1444813   1589060  sprintcare    False 2017-11-04 23:20:15+00:00   \n",
      "2075585   2234492  sprintcare    False 2017-11-10 18:17:30+00:00   \n",
      "2325021   2491088  sprintcare    False 2017-11-15 17:13:42+00:00   \n",
      "\n",
      "                                                      text  \\\n",
      "576082   Screw you @116016 and your stupid Blueprint pr...   \n",
      "1543061  Dear @ChaseSupport, it’s kinda hard to pay my ...   \n",
      "1543060  @ChaseSupport Actually it just doesn’t work in...   \n",
      "637146   Now the flight @Delta is sending our bag back ...   \n",
      "1752883  @MOO Big thanks to Quentin for the exceptional...   \n",
      "...                                                    ...   \n",
      "563523   @270560 Let's see what's going on. Please send...   \n",
      "1547778  @515037 Let’s do some troubleshooting. Can you...   \n",
      "1444813  @488653 We apologize for the delay in helping ...   \n",
      "2075585  @196099 Please send us a Direct Message, so th...   \n",
      "2325021  @711261 Send me a DM so that I can take a look...   \n",
      "\n",
      "                       response_tweet_id  in_response_to_tweet_id  \\\n",
      "576082                            648215                      NaN   \n",
      "1543061                          1693184                      NaN   \n",
      "1543060                          1693183                1693185.0   \n",
      "637146                            713795                      NaN   \n",
      "1752883                          1906926                      NaN   \n",
      "...                                  ...                      ...   \n",
      "563523                               NaN                 634903.0   \n",
      "1547778                              NaN                1698066.0   \n",
      "1444813  1589061,1589062,1589063,1589064                1589065.0   \n",
      "2075585                              NaN                2234491.0   \n",
      "2325021                              NaN                2491089.0   \n",
      "\n",
      "                                                text_lower  \\\n",
      "576082   screw you @116016 and your stupid blueprint pr...   \n",
      "1543061  dear @chasesupport, it’s kinda hard to pay my ...   \n",
      "1543060  @chasesupport actually it just doesn’t work in...   \n",
      "637146   now the flight @delta is sending our bag back ...   \n",
      "1752883  @moo big thanks to quentin for the exceptional...   \n",
      "...                                                    ...   \n",
      "563523   @270560 let's see what's going on. please send...   \n",
      "1547778  @515037 let’s do some troubleshooting. can you...   \n",
      "1444813  @488653 we apologize for the delay in helping ...   \n",
      "2075585  @196099 please send us a direct message, so th...   \n",
      "2325021  @711261 send me a dm so that i can take a look...   \n",
      "\n",
      "                                                  no_punct  \n",
      "576082   screw you 116016 and your stupid blueprint pro...  \n",
      "1543061  dear chasesupport it’s kinda hard to pay my bi...  \n",
      "1543060  chasesupport actually it just doesn’t work in ...  \n",
      "637146   now the flight delta is sending our bag back o...  \n",
      "1752883  moo big thanks to quentin for the exceptional ...  \n",
      "...                                                    ...  \n",
      "563523   270560 lets see whats going on please send us ...  \n",
      "1547778  515037 let’s do some troubleshooting can you f...  \n",
      "1444813  488653 we apologize for the delay in helping y...  \n",
      "2075585  196099 please send us a direct message so that...  \n",
      "2325021  711261 send me a dm so that i can take a look ...  \n",
      "\n",
      "[2811774 rows x 9 columns]\n"
     ]
    }
   ],
   "source": [
    "print(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a63e5dc8",
   "metadata": {},
   "source": [
    "#### Stemming (reduce words to their base form)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a48b7ca2",
   "metadata": {},
   "outputs": [],
   "source": [
    "stemmer = PorterStemmer()\n",
    "dataset['text'] = dataset['text'].apply(lambda x: ' '.join([stemmer.stem(word) for word in x.split()]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "122aee14",
   "metadata": {},
   "source": [
    "#### Lemmatization (combines similar meaning words as one word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f94e6cd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "lemmatizer = WordNetLemmatizer()\n",
    "dataset['text'] = dataset['text'].apply(lambda x: ' '.join([lemmatizer.lemmatize(word) for word in x.split()]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "df026493",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         tweet_id author_id  inbound                created_at  \\\n",
      "576082     648216     10026     True 2017-10-05 00:08:01+00:00   \n",
      "1543061   1693185     10026     True 2017-11-06 15:38:24+00:00   \n",
      "1543060   1693184     10026     True 2017-11-06 15:45:29+00:00   \n",
      "637146     713796    100363     True 2017-10-10 22:25:39+00:00   \n",
      "1752883   1906927     10103     True 2017-10-25 09:42:58+00:00   \n",
      "2071249   2230369     10103     True 2017-11-10 12:20:40+00:00   \n",
      "1871115   2027294     10221     True 2017-11-06 19:05:45+00:00   \n",
      "815955     909357     10221     True 2017-10-21 05:20:48+00:00   \n",
      "2566140   2737084     10286     True 2017-11-18 00:40:34+00:00   \n",
      "1082215   1197566     10297     True 2017-10-25 03:12:36+00:00   \n",
      "\n",
      "                                                      text response_tweet_id  \\\n",
      "576082   screw you @116016 and your stupid blueprint pr...            648215   \n",
      "1543061  dear @chasesupport, it’ kinda hard to pay my b...           1693184   \n",
      "1543060  @chasesupport actual it just doesn’t work in s...           1693183   \n",
      "637146   now the flight @delta is send our bag back on ...            713795   \n",
      "1752883  @moo big thank to quentin for the except servi...           1906926   \n",
      "2071249  the ribotrainbow! new #businesscard thank to @...           2230368   \n",
      "1871115                      same. https://t.co/gxkjt8bnv6           2027293   \n",
      "815955                         yup https://t.co/gpkfa9mfhq            909356   \n",
      "2566140  @comcastcar is it possibl to get busi class in...           2737083   \n",
      "1082215          @delta i will never fli your airlin again           1197564   \n",
      "\n",
      "         in_response_to_tweet_id  \\\n",
      "576082                       NaN   \n",
      "1543061                      NaN   \n",
      "1543060                1693185.0   \n",
      "637146                       NaN   \n",
      "1752883                      NaN   \n",
      "2071249                      NaN   \n",
      "1871115                      NaN   \n",
      "815955                       NaN   \n",
      "2566140                      NaN   \n",
      "1082215                      NaN   \n",
      "\n",
      "                                                text_lower  \\\n",
      "576082   screw you @116016 and your stupid blueprint pr...   \n",
      "1543061  dear @chasesupport, it’s kinda hard to pay my ...   \n",
      "1543060  @chasesupport actually it just doesn’t work in...   \n",
      "637146   now the flight @delta is sending our bag back ...   \n",
      "1752883  @moo big thanks to quentin for the exceptional...   \n",
      "2071249  the ribotrainbow! new #businesscards thanks to...   \n",
      "1871115                      same. https://t.co/gxkjt8bnv6   \n",
      "815955                         yup https://t.co/gpkfa9mfhq   \n",
      "2566140  @comcastcares is it possible to get business c...   \n",
      "1082215         @delta i will never fly your airline again   \n",
      "\n",
      "                                                  no_punct  \n",
      "576082   screw you 116016 and your stupid blueprint pro...  \n",
      "1543061  dear chasesupport it’s kinda hard to pay my bi...  \n",
      "1543060  chasesupport actually it just doesn’t work in ...  \n",
      "637146   now the flight delta is sending our bag back o...  \n",
      "1752883  moo big thanks to quentin for the exceptional ...  \n",
      "2071249  the ribotrainbow new businesscards thanks to m...  \n",
      "1871115                            same httpstcogxkjt8bnv6  \n",
      "815955                              yup httpstcogpkfa9mfhq  \n",
      "2566140  comcastcares is it possible to get business cl...  \n",
      "1082215          delta i will never fly your airline again  \n"
     ]
    }
   ],
   "source": [
    "print(dataset.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff55d811",
   "metadata": {},
   "source": [
    "#### Removal of Emoticons (punctuation marks, letters, and numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ce73dfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_emoticons(text):\n",
    "    emoticon_pattern = re.compile(u'(' + u'|'.join(k for k in EMOTICONS) + u')')\n",
    "    return emoticon_pattern.sub(r'', text)\n",
    "dataset['text'] = dataset['text'].apply(remove_emoticons)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49a0721a",
   "metadata": {},
   "source": [
    "#### Remove URLs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61b37b88",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['text'] = dataset['text'].str.replace(r'http\\S+|www\\S+|https\\S+', '', case=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "948bd9a1",
   "metadata": {},
   "source": [
    "#### Remove HTML Tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6be11f31",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['text'] = dataset['text'].apply(lambda x: BeautifulSoup(x, 'html.parser').get_text())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4749929a",
   "metadata": {},
   "source": [
    "#### Spelling Corrector "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "515175ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "spell = SpellChecker()\n",
    "dataset['text'] = dataset['text'].apply(lambda x: ' '.join([spell.correction(word) for word in x.split()]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51b32449",
   "metadata": {},
   "source": [
    "## Customer Support Twitter Analysis for Technology Companies\n",
    "\n",
    "##### Objective:\n",
    "This notebook explores customer interactions in a Customer Support Twitter dataset. We focus on understanding the relationship between response times and customer satisfaction for technology support services, including MicrosoftHelps, AppleSupport, DellCares, and HPSupport.\n",
    "\n",
    "##### Approach:\n",
    "Through meticulous analysis, we get into response times and customer sentiments in conversations. By examining these factors, we aim to uncover valuable patterns and trends, offering insights into effective customer support practices.\n",
    "\n",
    "##### Key Question: \n",
    "What is the relationship between average response time & customer satisfaction for each technology support service?\n",
    "\n",
    "##### Purpose impact:\n",
    "This analysis aims to help the analyzed companies by providing actionable insights derived from customer interactions. By understanding customer sentiments and response dynamics, these technology support companies can optimize their communication strategies, fostering improved customer satisfaction and engagement."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67012b4c",
   "metadata": {},
   "source": [
    "#### Load the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "cd5fd65f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>tweet_id</th>\n",
       "      <th>author_id</th>\n",
       "      <th>inbound</th>\n",
       "      <th>created_at</th>\n",
       "      <th>text</th>\n",
       "      <th>response_tweet_id</th>\n",
       "      <th>in_response_to_tweet_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>sprintcare</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 22:10:47 +0000 2017</td>\n",
       "      <td>@115712 I understand. I would like to assist y...</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>115712</td>\n",
       "      <td>True</td>\n",
       "      <td>Tue Oct 31 22:11:45 +0000 2017</td>\n",
       "      <td>@sprintcare and how do you propose we do that</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>115712</td>\n",
       "      <td>True</td>\n",
       "      <td>Tue Oct 31 22:08:27 +0000 2017</td>\n",
       "      <td>@sprintcare I have sent several private messag...</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>sprintcare</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 21:54:49 +0000 2017</td>\n",
       "      <td>@115712 Please send us a Private Message so th...</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>115712</td>\n",
       "      <td>True</td>\n",
       "      <td>Tue Oct 31 21:49:35 +0000 2017</td>\n",
       "      <td>@sprintcare I did.</td>\n",
       "      <td>4</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>sprintcare</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 21:46:24 +0000 2017</td>\n",
       "      <td>@115712 Can you please send us a private messa...</td>\n",
       "      <td>5,7</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>115712</td>\n",
       "      <td>True</td>\n",
       "      <td>Tue Oct 31 21:45:10 +0000 2017</td>\n",
       "      <td>@sprintcare is the worst customer service</td>\n",
       "      <td>9,6,10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11</td>\n",
       "      <td>sprintcare</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 22:10:35 +0000 2017</td>\n",
       "      <td>@115713 This is saddening to hear. Please shoo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12</td>\n",
       "      <td>115713</td>\n",
       "      <td>True</td>\n",
       "      <td>Tue Oct 31 22:04:47 +0000 2017</td>\n",
       "      <td>@sprintcare You gonna magically change your co...</td>\n",
       "      <td>11,13,14</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15</td>\n",
       "      <td>sprintcare</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 20:03:31 +0000 2017</td>\n",
       "      <td>@115713 We understand your concerns and we'd l...</td>\n",
       "      <td>12</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tweet_id   author_id  inbound                      created_at  \\\n",
       "0         1  sprintcare    False  Tue Oct 31 22:10:47 +0000 2017   \n",
       "1         2      115712     True  Tue Oct 31 22:11:45 +0000 2017   \n",
       "2         3      115712     True  Tue Oct 31 22:08:27 +0000 2017   \n",
       "3         4  sprintcare    False  Tue Oct 31 21:54:49 +0000 2017   \n",
       "4         5      115712     True  Tue Oct 31 21:49:35 +0000 2017   \n",
       "5         6  sprintcare    False  Tue Oct 31 21:46:24 +0000 2017   \n",
       "6         8      115712     True  Tue Oct 31 21:45:10 +0000 2017   \n",
       "7        11  sprintcare    False  Tue Oct 31 22:10:35 +0000 2017   \n",
       "8        12      115713     True  Tue Oct 31 22:04:47 +0000 2017   \n",
       "9        15  sprintcare    False  Tue Oct 31 20:03:31 +0000 2017   \n",
       "\n",
       "                                                text response_tweet_id  \\\n",
       "0  @115712 I understand. I would like to assist y...                 2   \n",
       "1      @sprintcare and how do you propose we do that               NaN   \n",
       "2  @sprintcare I have sent several private messag...                 1   \n",
       "3  @115712 Please send us a Private Message so th...                 3   \n",
       "4                                 @sprintcare I did.                 4   \n",
       "5  @115712 Can you please send us a private messa...               5,7   \n",
       "6          @sprintcare is the worst customer service            9,6,10   \n",
       "7  @115713 This is saddening to hear. Please shoo...               NaN   \n",
       "8  @sprintcare You gonna magically change your co...          11,13,14   \n",
       "9  @115713 We understand your concerns and we'd l...                12   \n",
       "\n",
       "   in_response_to_tweet_id  \n",
       "0                      3.0  \n",
       "1                      1.0  \n",
       "2                      4.0  \n",
       "3                      5.0  \n",
       "4                      6.0  \n",
       "5                      8.0  \n",
       "6                      NaN  \n",
       "7                     12.0  \n",
       "8                     15.0  \n",
       "9                     16.0  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = 'twcs.csv'\n",
    "data_set = pd.read_csv('twcs.csv')\n",
    "data_set.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "aae6616f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2811774, 7)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_set.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d2f5065",
   "metadata": {},
   "source": [
    "#### Exploring and cleaning the data to obtain specific companies "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "2e9200d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_data = data_set[data_set['author_id'].isin(['MicrosoftHelps', 'AppleSupport', 'DellCares', 'HPSupport'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b3242a02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>tweet_id</th>\n",
       "      <th>author_id</th>\n",
       "      <th>inbound</th>\n",
       "      <th>created_at</th>\n",
       "      <th>text</th>\n",
       "      <th>response_tweet_id</th>\n",
       "      <th>in_response_to_tweet_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>203</td>\n",
       "      <td>MicrosoftHelps</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 22:08:00 +0000 2017</td>\n",
       "      <td>@115751 Hello, Josh! To get better assistance ...</td>\n",
       "      <td>204</td>\n",
       "      <td>205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>207</td>\n",
       "      <td>MicrosoftHelps</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 22:04:21 +0000 2017</td>\n",
       "      <td>@115752 Help is here. To assist you in recover...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>208.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>696</td>\n",
       "      <td>AppleSupport</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 22:27:49 +0000 2017</td>\n",
       "      <td>@115854 We're here for you. Which version of t...</td>\n",
       "      <td>697</td>\n",
       "      <td>698.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>699</td>\n",
       "      <td>AppleSupport</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 22:36:27 +0000 2017</td>\n",
       "      <td>@115854 Lets take a closer look into this issu...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>697.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>701</td>\n",
       "      <td>AppleSupport</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 22:26:49 +0000 2017</td>\n",
       "      <td>@115855 Let's go to DM for the next steps. DM ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>702.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>703</td>\n",
       "      <td>AppleSupport</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 22:09:52 +0000 2017</td>\n",
       "      <td>@115855 Any steps tried since it started last ...</td>\n",
       "      <td>702</td>\n",
       "      <td>704.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>705</td>\n",
       "      <td>AppleSupport</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 21:57:00 +0000 2017</td>\n",
       "      <td>@115855 That's great it has iOS 11.1 as we can...</td>\n",
       "      <td>706,704</td>\n",
       "      <td>707.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>708</td>\n",
       "      <td>AppleSupport</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 21:43:15 +0000 2017</td>\n",
       "      <td>@115855 We'd like to look into this with you. ...</td>\n",
       "      <td>707</td>\n",
       "      <td>709.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>712</td>\n",
       "      <td>AppleSupport</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 22:26:29 +0000 2017</td>\n",
       "      <td>@115856 Hey, let's work together to figure out...</td>\n",
       "      <td>713</td>\n",
       "      <td>714.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>716</td>\n",
       "      <td>AppleSupport</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 22:24:54 +0000 2017</td>\n",
       "      <td>@115857 We'd like to investigate further with ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>717.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     tweet_id       author_id  inbound                      created_at  \\\n",
       "120       203  MicrosoftHelps    False  Tue Oct 31 22:08:00 +0000 2017   \n",
       "123       207  MicrosoftHelps    False  Tue Oct 31 22:04:21 +0000 2017   \n",
       "396       696    AppleSupport    False  Tue Oct 31 22:27:49 +0000 2017   \n",
       "398       699    AppleSupport    False  Tue Oct 31 22:36:27 +0000 2017   \n",
       "401       701    AppleSupport    False  Tue Oct 31 22:26:49 +0000 2017   \n",
       "403       703    AppleSupport    False  Tue Oct 31 22:09:52 +0000 2017   \n",
       "405       705    AppleSupport    False  Tue Oct 31 21:57:00 +0000 2017   \n",
       "407       708    AppleSupport    False  Tue Oct 31 21:43:15 +0000 2017   \n",
       "411       712    AppleSupport    False  Tue Oct 31 22:26:29 +0000 2017   \n",
       "414       716    AppleSupport    False  Tue Oct 31 22:24:54 +0000 2017   \n",
       "\n",
       "                                                  text response_tweet_id  \\\n",
       "120  @115751 Hello, Josh! To get better assistance ...               204   \n",
       "123  @115752 Help is here. To assist you in recover...               NaN   \n",
       "396  @115854 We're here for you. Which version of t...               697   \n",
       "398  @115854 Lets take a closer look into this issu...               NaN   \n",
       "401  @115855 Let's go to DM for the next steps. DM ...               NaN   \n",
       "403  @115855 Any steps tried since it started last ...               702   \n",
       "405  @115855 That's great it has iOS 11.1 as we can...           706,704   \n",
       "407  @115855 We'd like to look into this with you. ...               707   \n",
       "411  @115856 Hey, let's work together to figure out...               713   \n",
       "414  @115857 We'd like to investigate further with ...               NaN   \n",
       "\n",
       "     in_response_to_tweet_id  \n",
       "120                    205.0  \n",
       "123                    208.0  \n",
       "396                    698.0  \n",
       "398                    697.0  \n",
       "401                    702.0  \n",
       "403                    704.0  \n",
       "405                    707.0  \n",
       "407                    709.0  \n",
       "411                    714.0  \n",
       "414                    717.0  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "87026bb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>author_id</th>\n",
       "      <th>inbound</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AppleSupport</td>\n",
       "      <td>False</td>\n",
       "      <td>106860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DellCares</td>\n",
       "      <td>False</td>\n",
       "      <td>5336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HPSupport</td>\n",
       "      <td>False</td>\n",
       "      <td>7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MicrosoftHelps</td>\n",
       "      <td>False</td>\n",
       "      <td>11304</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        author_id  inbound   count\n",
       "0    AppleSupport    False  106860\n",
       "1       DellCares    False    5336\n",
       "2       HPSupport    False    7200\n",
       "3  MicrosoftHelps    False   11304"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group the filtered data and count the number of tweets in each company\n",
    "grouped_data = filtered_data.groupby(['author_id', 'inbound']).size().reset_index(name='count')\n",
    "grouped_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6516e729",
   "metadata": {},
   "source": [
    "#### How many null values does the filtered data contain?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6a45d202",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tweet_id                       0\n",
      "author_id                      0\n",
      "inbound                        0\n",
      "created_at                     0\n",
      "text                           0\n",
      "response_tweet_id          91724\n",
      "in_response_to_tweet_id      332\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(filtered_data.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ee7d4c52",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handle Missing Values\n",
    "filtered_data = data_set.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "4ec3f6b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tweet_id                   0\n",
      "author_id                  0\n",
      "inbound                    0\n",
      "created_at                 0\n",
      "text                       0\n",
      "response_tweet_id          0\n",
      "in_response_to_tweet_id    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(filtered_data.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aef56913",
   "metadata": {},
   "source": [
    "#### What company has the most and least amount of tweets?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "78edc9fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "companies = grouped_data['author_id']\n",
    "inbound_counts = grouped_data['count']\n",
    "colors = ['red', 'blue', 'green', 'purple']\n",
    "plt.figure(figsize=(7, 4))\n",
    "plt.bar(companies, inbound_counts, color=colors, edgecolor='black')\n",
    "plt.xlabel('Company')\n",
    "plt.ylabel('Number of Tweets')\n",
    "plt.title('Number of Inbound Tweets for Each Company')\n",
    "plt.xticks(rotation=45) \n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cbc7cdb",
   "metadata": {},
   "source": [
    "#### Now let's find the average response times for each company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "03b3d8e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_data = data_set[data_set['author_id'].str.contains('MicrosoftHelps|AppleSupport|DellCares|HPSupport', case=False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f420e058",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert 'created_at' and 'response_tweet_id' columns to datetime\n",
    "filtered_data['created_at'] = pd.to_datetime(filtered_data['created_at'], errors='coerce').dt.tz_localize(None)\n",
    "filtered_data['response_tweet_id'] = pd.to_datetime(filtered_data['response_tweet_id'], errors='coerce').dt.tz_localize(None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "aeb4151f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter out rows where 'response_tweet_id' or 'created_at' couldn't be parsed\n",
    "cleaned_filtered_data = filtered_data.dropna(subset=['created_at', 'response_tweet_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "09ce3ec7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate response time in seconds \n",
    "cleaned_filtered_data['response_time'] = (cleaned_filtered_data['response_tweet_id'] - cleaned_filtered_data['created_at']).dt.total_seconds()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e7b77fd",
   "metadata": {},
   "source": [
    "#### Results for average response times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "16a461a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "author_id\n",
      "AppleSupport      1.145536e+08\n",
      "DellCares         1.839929e+07\n",
      "HPSupport         7.365916e+08\n",
      "MicrosoftHelps    3.703211e+08\n",
      "Name: response_time, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "average_response_time = cleaned_filtered_data.groupby('author_id')['response_time'].mean()\n",
    "print(average_response_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b0678e1",
   "metadata": {},
   "source": [
    "#### What companies have the fastest and slowest response time?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f47853ee",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "companies = average_response_time.index\n",
    "response_times = average_response_time.values\n",
    "plt.figure(figsize=(8, 4))\n",
    "colors = ['red', 'blue', 'green', 'purple']\n",
    "plt.bar(companies, response_times, color=colors, edgecolor='black')\n",
    "plt.xlabel('Company')\n",
    "plt.ylabel('Average Response Time')\n",
    "plt.title('Average Response Time for Each Company')\n",
    "plt.xticks(rotation=45)  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32fa7d25",
   "metadata": {},
   "source": [
    "#### Let's calculate the final response times for each company based on the information found"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecc10496",
   "metadata": {},
   "source": [
    "#### AppleSupport"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a6d6cc19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Response Time for AppleSupport: 12241197696000.0 milliseconds\n"
     ]
    }
   ],
   "source": [
    "average_response_time_apple = 1.145536e+08  \n",
    "inbound_tweet_count_apple = 106860 \n",
    "final_response_time_apple = inbound_tweet_count_apple * average_response_time_apple\n",
    "print(f\"Final Response Time for AppleSupport: {final_response_time_apple} milliseconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "04e03b10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categories = ['Average Response Time', 'Total Inbound Tweets', 'Final Response Time']\n",
    "values = [average_response_time_apple, inbound_tweet_count_apple, final_response_time_apple]\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.bar(categories, values, color=['skyblue', 'hotpink', 'greenyellow'],edgecolor='black')\n",
    "\n",
    "for i, v in enumerate(values):\n",
    "    plt.text(i, v + 0.2 * v, f\"{v:.0f}\", ha='center', va='bottom', fontsize=10)\n",
    "\n",
    "plt.ylabel('Values (Log Scale)')\n",
    "plt.yscale('log') \n",
    "plt.title('Final Response Time Analysis for AppleSupport')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1875f448",
   "metadata": {},
   "source": [
    "#### DellCares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "db4de990",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Response Time for Dellcares: 98178611440.0 milliseconds\n"
     ]
    }
   ],
   "source": [
    "average_response_time_dell = 1.839929e+07\n",
    "inbound_tweet_count_dell = 5336\n",
    "final_response_time_dell = inbound_tweet_count_dell * average_response_time_dell\n",
    "print(f\"Final Response Time for Dellcares: {final_response_time_dell} milliseconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "b6ab3645",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categories = ['Average Response Time', 'Total Inbound Tweets', 'Final Response Time']\n",
    "values = [average_response_time_dell, inbound_tweet_count_dell, final_response_time_dell]\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.bar(categories, values, color=['palegreen', 'deeppink', 'plum'],edgecolor='black')\n",
    "\n",
    "for i, v in enumerate(values):\n",
    "    plt.text(i, v + 0.2 * v, f\"{v:.0f}\", ha='center', va='bottom', fontsize=10)\n",
    "\n",
    "plt.ylabel('Values (Log Scale)')\n",
    "plt.yscale('log') \n",
    "plt.title('Final Response Time Analysis for DellCares')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a33268d8",
   "metadata": {},
   "source": [
    "#### HPSupport"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "cc1c8904",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Response Time for HPSupport: 5303459520000.0 milliseconds\n"
     ]
    }
   ],
   "source": [
    "average_response_time_hp = 7.365916e+08\n",
    "inbound_tweet_count_hp = 7200\n",
    "final_response_time_hp = inbound_tweet_count_hp * average_response_time_hp\n",
    "print(f\"Final Response Time for HPSupport: {final_response_time_hp} milliseconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "471d5771",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categories = ['Average Response Time', 'Total Inbound Tweets', 'Final Response Time']\n",
    "values = [average_response_time_hp, inbound_tweet_count_hp, final_response_time_hp]\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.bar(categories, values, color=['plum', 'yellow', 'aquamarine'],edgecolor='black')\n",
    "\n",
    "for i, v in enumerate(values):\n",
    "    plt.text(i, v + 0.2 * v, f\"{v:.0f}\", ha='center', va='bottom', fontsize=10)\n",
    "\n",
    "plt.ylabel('Values (Log Scale)')\n",
    "plt.yscale('log') \n",
    "plt.title('Final Response Time Analysis for HPSupport')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "5d42de8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Response Time for MicrosoftHelps: 4186109714400.0 milliseconds\n"
     ]
    }
   ],
   "source": [
    "average_response_time_microsoft = 3.703211e+08\n",
    "inbound_tweet_count_microsoft = 11304\n",
    "final_response_time_microsoft = inbound_tweet_count_microsoft * average_response_time_microsoft\n",
    "print(f\"Final Response Time for MicrosoftHelps: {final_response_time_microsoft} milliseconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "3afc5456",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categories = ['Average Response Time', 'Total Inbound Tweets', 'Final Response Time']\n",
    "values = [average_response_time_microsoft, inbound_tweet_count_microsoft, final_response_time_microsoft]\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.bar(categories, values, color=['lightcyan', 'palegreen', 'hotpink'],edgecolor='black')\n",
    "\n",
    "for i, v in enumerate(values):\n",
    "    plt.text(i, v + 0.2 * v, f\"{v:.0f}\", ha='center', va='bottom', fontsize=10)\n",
    "\n",
    "plt.ylabel('Values (Log Scale)')\n",
    "plt.yscale('log') \n",
    "plt.title('Final Response Time Analysis for MicrosoftHelps')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd1bfb82",
   "metadata": {},
   "source": [
    "##### Final response times for all companies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "ef11e666",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categories = ['AppleSupport', 'DellCares', 'HPSupport', 'MicrosoftHelps']\n",
    "values = [final_response_time_apple, final_response_time_dell,final_response_time_hp, final_response_time_microsoft]\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.bar(categories, values, color=['lightblue', 'magenta', 'limegreen','yellow'],edgecolor='black')\n",
    "\n",
    "for i, v in enumerate(values):\n",
    "    plt.text(i, v + 0 * v, f\"{v:.0f}\", ha='center', va='bottom', fontsize=10)\n",
    "\n",
    "plt.ylabel('Values (Log Scale)')\n",
    "plt.yscale('log') \n",
    "plt.title('Final Response Time Analysis for Technology Companies')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6afc6e12",
   "metadata": {},
   "source": [
    "### Results\n",
    "\n",
    "'AppleSupport' has the highest average response time, while 'DellCares' has the lowest average response time.\n",
    "Shorter response times are often associated with higher customer satisfaction. Customers tend to be more satisfied when their issues are addressed promptly."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c210ae1e",
   "metadata": {},
   "source": [
    "## Customer Support Twitter Analysis for Airline Companies\n",
    "\n",
    "##### Objective:\n",
    "This notebook explores customer interactions in a Customer Support Twitter dataset. We focus on understanding the relationship between response times and customer satisfaction for Airline companies, American and Delta.\n",
    "\n",
    "##### Approach:\n",
    "Through meticulous analysis, we get into response times and customer sentiments in conversations. By examining these factors, we aim to uncover valuable patterns and trends, offering insights into effective customer support practices.\n",
    "\n",
    "##### Key Question: \n",
    "How does response time correlate to customer satisfaction when comparing American Airlines & Delta?\n",
    "\n",
    "##### Purpose impact:\n",
    "This analysis aims to help the analyzed companies by providing actionable insights derived from customer interactions. By understanding customer sentiments and response dynamics, these technology support companies can optimize their communication strategies, fostering improved customer satisfaction and engagement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "3aa5b7a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>author_id</th>\n",
       "      <th>inbound</th>\n",
       "      <th>created_at</th>\n",
       "      <th>text</th>\n",
       "      <th>response_tweet_id</th>\n",
       "      <th>in_response_to_tweet_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>sprintcare</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 22:10:47 +0000 2017</td>\n",
       "      <td>@115712 I understand. I would like to assist y...</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>115712</td>\n",
       "      <td>True</td>\n",
       "      <td>Tue Oct 31 22:11:45 +0000 2017</td>\n",
       "      <td>@sprintcare and how do you propose we do that</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>115712</td>\n",
       "      <td>True</td>\n",
       "      <td>Tue Oct 31 22:08:27 +0000 2017</td>\n",
       "      <td>@sprintcare I have sent several private messag...</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>sprintcare</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 21:54:49 +0000 2017</td>\n",
       "      <td>@115712 Please send us a Private Message so th...</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>115712</td>\n",
       "      <td>True</td>\n",
       "      <td>Tue Oct 31 21:49:35 +0000 2017</td>\n",
       "      <td>@sprintcare I did.</td>\n",
       "      <td>4</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>sprintcare</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 21:46:24 +0000 2017</td>\n",
       "      <td>@115712 Can you please send us a private messa...</td>\n",
       "      <td>5,7</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>115712</td>\n",
       "      <td>True</td>\n",
       "      <td>Tue Oct 31 21:45:10 +0000 2017</td>\n",
       "      <td>@sprintcare is the worst customer service</td>\n",
       "      <td>9,6,10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11</td>\n",
       "      <td>sprintcare</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 22:10:35 +0000 2017</td>\n",
       "      <td>@115713 This is saddening to hear. Please shoo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12</td>\n",
       "      <td>115713</td>\n",
       "      <td>True</td>\n",
       "      <td>Tue Oct 31 22:04:47 +0000 2017</td>\n",
       "      <td>@sprintcare You gonna magically change your co...</td>\n",
       "      <td>11,13,14</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15</td>\n",
       "      <td>sprintcare</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 20:03:31 +0000 2017</td>\n",
       "      <td>@115713 We understand your concerns and we'd l...</td>\n",
       "      <td>12</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tweet_id   author_id  inbound                      created_at  \\\n",
       "0         1  sprintcare    False  Tue Oct 31 22:10:47 +0000 2017   \n",
       "1         2      115712     True  Tue Oct 31 22:11:45 +0000 2017   \n",
       "2         3      115712     True  Tue Oct 31 22:08:27 +0000 2017   \n",
       "3         4  sprintcare    False  Tue Oct 31 21:54:49 +0000 2017   \n",
       "4         5      115712     True  Tue Oct 31 21:49:35 +0000 2017   \n",
       "5         6  sprintcare    False  Tue Oct 31 21:46:24 +0000 2017   \n",
       "6         8      115712     True  Tue Oct 31 21:45:10 +0000 2017   \n",
       "7        11  sprintcare    False  Tue Oct 31 22:10:35 +0000 2017   \n",
       "8        12      115713     True  Tue Oct 31 22:04:47 +0000 2017   \n",
       "9        15  sprintcare    False  Tue Oct 31 20:03:31 +0000 2017   \n",
       "\n",
       "                                                text response_tweet_id  \\\n",
       "0  @115712 I understand. I would like to assist y...                 2   \n",
       "1      @sprintcare and how do you propose we do that               NaN   \n",
       "2  @sprintcare I have sent several private messag...                 1   \n",
       "3  @115712 Please send us a Private Message so th...                 3   \n",
       "4                                 @sprintcare I did.                 4   \n",
       "5  @115712 Can you please send us a private messa...               5,7   \n",
       "6          @sprintcare is the worst customer service            9,6,10   \n",
       "7  @115713 This is saddening to hear. Please shoo...               NaN   \n",
       "8  @sprintcare You gonna magically change your co...          11,13,14   \n",
       "9  @115713 We understand your concerns and we'd l...                12   \n",
       "\n",
       "   in_response_to_tweet_id  \n",
       "0                      3.0  \n",
       "1                      1.0  \n",
       "2                      4.0  \n",
       "3                      5.0  \n",
       "4                      6.0  \n",
       "5                      8.0  \n",
       "6                      NaN  \n",
       "7                     12.0  \n",
       "8                     15.0  \n",
       "9                     16.0  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the Dataset\n",
    "file_path = 'twcs.csv'\n",
    "data_set = pd.read_csv('twcs.csv')\n",
    "data_set.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "18dd8de8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2811774, 7)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_set.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4429eb9",
   "metadata": {},
   "source": [
    "#### Exploring and cleaning the data to obtain specific companies "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "c48d8624",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handle missing values\n",
    "data_set['response_tweet_id'].fillna(-1, inplace=True)\n",
    "data_set['in_response_to_tweet_id'].fillna(-1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "a1f06a92",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_data = data_set[data_set['author_id'].str.contains('delta|american', case=False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "a8440c1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>tweet_id</th>\n",
       "      <th>author_id</th>\n",
       "      <th>inbound</th>\n",
       "      <th>created_at</th>\n",
       "      <th>text</th>\n",
       "      <th>response_tweet_id</th>\n",
       "      <th>in_response_to_tweet_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>609</td>\n",
       "      <td>Delta</td>\n",
       "      <td>False</td>\n",
       "      <td>Sat Aug 06 01:44:03 +0000 2016</td>\n",
       "      <td>@115818 Glad to check. Pls, DM your confirmati...</td>\n",
       "      <td>610</td>\n",
       "      <td>611.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>790</td>\n",
       "      <td>Delta</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 22:27:38 +0000 2017</td>\n",
       "      <td>@115882 I'm sorry. The earlier flight may not ...</td>\n",
       "      <td>791</td>\n",
       "      <td>792.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>793</td>\n",
       "      <td>Delta</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 22:52:37 +0000 2017</td>\n",
       "      <td>@115882 My apologies. If that flight never sho...</td>\n",
       "      <td>-1</td>\n",
       "      <td>791.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>794</td>\n",
       "      <td>Delta</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 22:25:31 +0000 2017</td>\n",
       "      <td>@115883 Please share additional details about ...</td>\n",
       "      <td>795</td>\n",
       "      <td>796.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>797</td>\n",
       "      <td>Delta</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 23:07:41 +0000 2017</td>\n",
       "      <td>@115883 Please do.  *AFC https://t.co/6iDGBJAc2m</td>\n",
       "      <td>798</td>\n",
       "      <td>795.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>799</td>\n",
       "      <td>Delta</td>\n",
       "      <td>False</td>\n",
       "      <td>Sun Oct 29 16:56:47 +0000 2017</td>\n",
       "      <td>@115883 I sincerely apologize, our IT team is ...</td>\n",
       "      <td>800,796</td>\n",
       "      <td>801.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>802</td>\n",
       "      <td>Delta</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 22:23:07 +0000 2017</td>\n",
       "      <td>@115884 Oh, no! Please speak to a member of th...</td>\n",
       "      <td>-1</td>\n",
       "      <td>803.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>804</td>\n",
       "      <td>Delta</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 22:22:01 +0000 2017</td>\n",
       "      <td>@115885 2/2 https://t.co/6iDGBJAc2m</td>\n",
       "      <td>805,806</td>\n",
       "      <td>807.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>809</td>\n",
       "      <td>Delta</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 19:21:13 +0000 2017</td>\n",
       "      <td>@115885 Hello, Richard! Please DM your confirm...</td>\n",
       "      <td>807</td>\n",
       "      <td>810.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>813</td>\n",
       "      <td>Delta</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 18:42:21 +0000 2017</td>\n",
       "      <td>@115885 Hi, Richard. Can you pls DM your confi...</td>\n",
       "      <td>811</td>\n",
       "      <td>814.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     tweet_id author_id  inbound                      created_at  \\\n",
       "315       609     Delta    False  Sat Aug 06 01:44:03 +0000 2016   \n",
       "484       790     Delta    False  Tue Oct 31 22:27:38 +0000 2017   \n",
       "486       793     Delta    False  Tue Oct 31 22:52:37 +0000 2017   \n",
       "488       794     Delta    False  Tue Oct 31 22:25:31 +0000 2017   \n",
       "490       797     Delta    False  Tue Oct 31 23:07:41 +0000 2017   \n",
       "493       799     Delta    False  Sun Oct 29 16:56:47 +0000 2017   \n",
       "495       802     Delta    False  Tue Oct 31 22:23:07 +0000 2017   \n",
       "497       804     Delta    False  Tue Oct 31 22:22:01 +0000 2017   \n",
       "501       809     Delta    False  Tue Oct 31 19:21:13 +0000 2017   \n",
       "504       813     Delta    False  Tue Oct 31 18:42:21 +0000 2017   \n",
       "\n",
       "                                                  text response_tweet_id  \\\n",
       "315  @115818 Glad to check. Pls, DM your confirmati...               610   \n",
       "484  @115882 I'm sorry. The earlier flight may not ...               791   \n",
       "486  @115882 My apologies. If that flight never sho...                -1   \n",
       "488  @115883 Please share additional details about ...               795   \n",
       "490   @115883 Please do.  *AFC https://t.co/6iDGBJAc2m               798   \n",
       "493  @115883 I sincerely apologize, our IT team is ...           800,796   \n",
       "495  @115884 Oh, no! Please speak to a member of th...                -1   \n",
       "497                @115885 2/2 https://t.co/6iDGBJAc2m           805,806   \n",
       "501  @115885 Hello, Richard! Please DM your confirm...               807   \n",
       "504  @115885 Hi, Richard. Can you pls DM your confi...               811   \n",
       "\n",
       "     in_response_to_tweet_id  \n",
       "315                    611.0  \n",
       "484                    792.0  \n",
       "486                    791.0  \n",
       "488                    796.0  \n",
       "490                    795.0  \n",
       "493                    801.0  \n",
       "495                    803.0  \n",
       "497                    807.0  \n",
       "501                    810.0  \n",
       "504                    814.0  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "7da6cbc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tweet_id                   0\n",
      "author_id                  0\n",
      "inbound                    0\n",
      "created_at                 0\n",
      "text                       0\n",
      "response_tweet_id          0\n",
      "in_response_to_tweet_id    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Handle Missing Values\n",
    "filtered_data = data_set[data_set['author_id'].str.contains('delta|american', case=False)]\n",
    "filtered_data = data_set.dropna()\n",
    "print(filtered_data.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "cacd3945",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>author_id</th>\n",
       "      <th>inbound</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AmericanAir</td>\n",
       "      <td>False</td>\n",
       "      <td>36764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Delta</td>\n",
       "      <td>False</td>\n",
       "      <td>42253</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     author_id  inbound  count\n",
       "0  AmericanAir    False  36764\n",
       "1        Delta    False  42253"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group the filtered data and count the number of tweets in each company\n",
    "filtered_data = data_set[data_set['author_id'].str.contains('delta|american', case=False)]\n",
    "grouped_data = filtered_data.groupby(['author_id', 'inbound']).size().reset_index(name='count')\n",
    "grouped_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62f7ab79",
   "metadata": {},
   "source": [
    "#### What company has the most and least amount of tweets?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "121f91e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "companies = ['AmericanAir', 'Delta']\n",
    "counts = [36764, 42253]\n",
    "\n",
    "plt.figure(figsize=(6, 4))\n",
    "\n",
    "plt.plot(range(len(companies)), counts, marker='o', markersize=10, linestyle='', color='orange')\n",
    "\n",
    "plt.xticks(range(len(companies)), companies)\n",
    "\n",
    "plt.xlabel('Company')\n",
    "plt.ylabel('Number of Tweets')\n",
    "plt.title('Number of Tweets for American Airlines and Delta based on Inbound Status')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "17d98958",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "companies = grouped_data['author_id']\n",
    "inbound_counts = grouped_data['count']\n",
    "colors = ['yellow', 'violet']\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.bar(companies, inbound_counts, color=colors, edgecolor='black')\n",
    "plt.xlabel('Company')\n",
    "plt.ylabel('Number of Tweets')\n",
    "plt.title('Number of Inbound Tweets for Each Company')\n",
    "plt.xticks(rotation=45) \n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "c0fb3a7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>author_id</th>\n",
       "      <th>inbound</th>\n",
       "      <th>created_at</th>\n",
       "      <th>text</th>\n",
       "      <th>response_tweet_id</th>\n",
       "      <th>in_response_to_tweet_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>609</td>\n",
       "      <td>Delta</td>\n",
       "      <td>False</td>\n",
       "      <td>Sat Aug 06 01:44:03 +0000 2016</td>\n",
       "      <td>@115818 Glad to check. Pls, DM your confirmati...</td>\n",
       "      <td>610.0</td>\n",
       "      <td>611.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>790</td>\n",
       "      <td>Delta</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 22:27:38 +0000 2017</td>\n",
       "      <td>@115882 I'm sorry. The earlier flight may not ...</td>\n",
       "      <td>791.0</td>\n",
       "      <td>792.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>793</td>\n",
       "      <td>Delta</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 22:52:37 +0000 2017</td>\n",
       "      <td>@115882 My apologies. If that flight never sho...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>791.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>794</td>\n",
       "      <td>Delta</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 22:25:31 +0000 2017</td>\n",
       "      <td>@115883 Please share additional details about ...</td>\n",
       "      <td>795.0</td>\n",
       "      <td>796.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>797</td>\n",
       "      <td>Delta</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 23:07:41 +0000 2017</td>\n",
       "      <td>@115883 Please do.  *AFC https://t.co/6iDGBJAc2m</td>\n",
       "      <td>798.0</td>\n",
       "      <td>795.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>799</td>\n",
       "      <td>Delta</td>\n",
       "      <td>False</td>\n",
       "      <td>Sun Oct 29 16:56:47 +0000 2017</td>\n",
       "      <td>@115883 I sincerely apologize, our IT team is ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>801.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>802</td>\n",
       "      <td>Delta</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 22:23:07 +0000 2017</td>\n",
       "      <td>@115884 Oh, no! Please speak to a member of th...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>803.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>804</td>\n",
       "      <td>Delta</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 22:22:01 +0000 2017</td>\n",
       "      <td>@115885 2/2 https://t.co/6iDGBJAc2m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>807.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>809</td>\n",
       "      <td>Delta</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 19:21:13 +0000 2017</td>\n",
       "      <td>@115885 Hello, Richard! Please DM your confirm...</td>\n",
       "      <td>807.0</td>\n",
       "      <td>810.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>813</td>\n",
       "      <td>Delta</td>\n",
       "      <td>False</td>\n",
       "      <td>Tue Oct 31 18:42:21 +0000 2017</td>\n",
       "      <td>@115885 Hi, Richard. Can you pls DM your confi...</td>\n",
       "      <td>811.0</td>\n",
       "      <td>814.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     tweet_id author_id  inbound                      created_at  \\\n",
       "315       609     Delta    False  Sat Aug 06 01:44:03 +0000 2016   \n",
       "484       790     Delta    False  Tue Oct 31 22:27:38 +0000 2017   \n",
       "486       793     Delta    False  Tue Oct 31 22:52:37 +0000 2017   \n",
       "488       794     Delta    False  Tue Oct 31 22:25:31 +0000 2017   \n",
       "490       797     Delta    False  Tue Oct 31 23:07:41 +0000 2017   \n",
       "493       799     Delta    False  Sun Oct 29 16:56:47 +0000 2017   \n",
       "495       802     Delta    False  Tue Oct 31 22:23:07 +0000 2017   \n",
       "497       804     Delta    False  Tue Oct 31 22:22:01 +0000 2017   \n",
       "501       809     Delta    False  Tue Oct 31 19:21:13 +0000 2017   \n",
       "504       813     Delta    False  Tue Oct 31 18:42:21 +0000 2017   \n",
       "\n",
       "                                                  text  response_tweet_id  \\\n",
       "315  @115818 Glad to check. Pls, DM your confirmati...              610.0   \n",
       "484  @115882 I'm sorry. The earlier flight may not ...              791.0   \n",
       "486  @115882 My apologies. If that flight never sho...               -1.0   \n",
       "488  @115883 Please share additional details about ...              795.0   \n",
       "490   @115883 Please do.  *AFC https://t.co/6iDGBJAc2m              798.0   \n",
       "493  @115883 I sincerely apologize, our IT team is ...                NaN   \n",
       "495  @115884 Oh, no! Please speak to a member of th...               -1.0   \n",
       "497                @115885 2/2 https://t.co/6iDGBJAc2m                NaN   \n",
       "501  @115885 Hello, Richard! Please DM your confirm...              807.0   \n",
       "504  @115885 Hi, Richard. Can you pls DM your confi...              811.0   \n",
       "\n",
       "     in_response_to_tweet_id  \n",
       "315                    611.0  \n",
       "484                    792.0  \n",
       "486                    791.0  \n",
       "488                    796.0  \n",
       "490                    795.0  \n",
       "493                    801.0  \n",
       "495                    803.0  \n",
       "497                    807.0  \n",
       "501                    810.0  \n",
       "504                    814.0  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert 'response_tweet_id' and 'in_response_to_tweet_id' to numeric \n",
    "filtered_data['response_tweet_id'] = pd.to_numeric(filtered_data['response_tweet_id'], errors='coerce')\n",
    "filtered_data['in_response_to_tweet_id'] = pd.to_numeric(filtered_data['in_response_to_tweet_id'], errors='coerce')\n",
    "filtered_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "586db545",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate response time in filtered data\n",
    "filtered_data['response_time'] = filtered_data['response_tweet_id'] - filtered_data['in_response_to_tweet_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "7750b925",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate average response time for each airline\n",
    "average_response_time = filtered_data.groupby('author_id')['response_time'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0e52cd7",
   "metadata": {},
   "source": [
    "#### Statistical Predicitions / Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "17cc8998",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "author_id\n",
      "AmericanAir   -9.832385e+05\n",
      "Delta         -1.069096e+06\n",
      "Name: response_time, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "correlation = filtered_data.corr(numeric_only=True)\n",
    "\n",
    "filtered_data['response_time'] = filtered_data['response_tweet_id'] - filtered_data['in_response_to_tweet_id']\n",
    "\n",
    "average_response_time = filtered_data.groupby('author_id')['response_time'].mean()\n",
    "\n",
    "print(average_response_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d366473c",
   "metadata": {},
   "source": [
    "##### Convert to positive numbers for better analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "603fb0fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "author_id\n",
      "AmericanAir    9.832385e+05\n",
      "Delta          1.069096e+06\n",
      "Name: response_time, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "filtered_data['response_time'] = filtered_data['response_tweet_id'] - filtered_data['in_response_to_tweet_id']\n",
    "\n",
    "average_response_time = filtered_data.groupby('author_id')['response_time'].mean().abs()\n",
    "\n",
    "print(average_response_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "220bb9b1",
   "metadata": {},
   "source": [
    "#### What company has the slowest and fastest response time?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "c2670a05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the data\n",
    "plt.figure(figsize=(8, 6))\n",
    "colors = ['red', 'blue'] \n",
    "average_response_time.plot(kind='bar', color=colors, edgecolor='black') \n",
    "plt.title('Average Response Time for American Airlines and Delta')\n",
    "plt.xlabel('Airline')\n",
    "plt.ylabel('Average Response Time')\n",
    "plt.xticks(range(len(average_response_time.index)), average_response_time.index, rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e778d458",
   "metadata": {},
   "source": [
    "#### Now let's compare the total inbound tweets and the repsonse time of both companies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7ed9406",
   "metadata": {},
   "source": [
    "#### American Airlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "c310d867",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Response Time for American Airline: 36147780214.0 milliseconds\n"
     ]
    }
   ],
   "source": [
    "average_response_time_american = 9.832385e+05\n",
    "inbound_tweet_count_american = 36764 \n",
    "final_response_time_american = inbound_tweet_count_american * average_response_time_american\n",
    "print(f\"Final Response Time for American Airline: {final_response_time_american} milliseconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "2c18c418",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8ZKpUqaLatWvb3O6kSJEiNssppxCl/KEYGxur+vXr6+jRo5o6dao2bdqkyMhIhYWF2bTLKmdnZ2sA/P7779WoUSN99dVX+vDDD61tUq69CggIsAaxlFt4eLji4uIk3fxj4ZdfflFQUJDGjx+vWrVqqVixYhowYIAuXrxos98SJUqkqSVlXcrpXGfOnFHJkiXTtPPy8rJpl+JOj2Fm6svssWYXDw8Pm2UHB4fbrr969apN3a+//nqauvv16ydJd6x97ty5cnJyUosWLXT+/HmdP39e1apVU5kyZbRgwQIlJyfbtE/9eEs3H/NbX4tLly5Vp06dVKpUKX3++efasmWLIiMj1atXL2vttzNw4ED9888/ioiIkHTz1M/AwEDVqlXL2ubjjz/Wm2++qeXLl6tx48by8PBQ+/bt9c8//2TYb/ny5bVmzRoVL15cISEhKl++vMqXL6+pU6fesab0nDlzRvb29ipWrJjNeovFohIlSqR5rab3us5IeHi4EhIS1KlTJ+vzcuHCBXXq1EmHDx+2Pja3ysrr6Nbn4eTJkzp//rwcHBzSvI5OnDiR5jWU2eM4ffr0HScU2bZtm5o3by5J+vTTT/Xbb78pMjJSw4cPl5T2My4zr7+MZPW1np6sPIfpKVCgQJrZHh0dHe/p+QAeFlxzBeCeLV++XJcuXdLSpUvl6+trXZ96Ioasypcvn034a9asmfz9/TVq1Ch169ZN3t7e1pnuvv32W5t9p8fX19d6LcPff/+tr7/+WiNHjtS1a9c0c+ZMa7sTJ06kuW/KupQ/mooUKaLjx4+naXfs2DFJSncGvju5U31ZOdbcJKXu0NBQdejQId02lSpVyvD+f//9t3799VdJshkNuNXq1avVsmXLLNX1+eefq2zZsgoPD7eZUCL1RfsZadKkiapWrapp06bJxcVFO3fu1Oeff27TpmDBgho1apRGjRqlkydPWkex2rRpYzNZQmr169dX/fr1lZycrO3bt+uTTz7RoEGD5Onpqf/9739ZOs4iRYro+vXrOn36tE3AMgxDJ06cUEBAgE37rPzmVsrrddCgQRo0aFC624OCgrJUb0aKFi2qIkWKWGe6TM3V1dVmObPHUaxYMR05cuS2bRYvXqz8+fPrhx9+sAkdy5cvz9Q+Msus1/r9+J26rD4fwMOCcAXgnqV8kd96UbxhGPr0009N3Y+jo6PCwsLUqFEjvf/++5o1a5aCgoJkb2+vAwcOqGPHjpnuq2LFinr77be1ZMkS66l+Kfbs2aPdu3fbnBr45ZdfytXV1Toq8dRTT2ns2LHauXOnzUjFwoULZbFY1Lhx43s61vTqu9tjzWmVKlXSI488ot27d+uDDz7I8v1T/oD/9NNPVaFCBZttV65cUbt27TRv3rwsh6uUH6e+9Q/REydO3HG2wFsNGDBAffv21YULF+Tp6alnn302w7aenp56/vnntXv3bk2ZMkWXL1/OcHKKFHZ2dnriiSdUuXJlffHFF9q5c2eWw9VTTz2l8ePH6/PPP9fgwYOt65csWaJLly7pqaeeylJ/KWJiYrRlyxZ17Ngx3anf33//fX333Xc6c+ZMuiM5WdW6dWstXrxYycnJeuKJJ+65vxTBwcF69913tXbtWjVp0iTdNhaLRfb29jan9l65ckWLFi0yrQ4p+17r2SG7ng8gryNcAbhnzZo1k4ODg7p06aI33nhDV69e1YwZM3Tu3DnT99WwYUO1bNlS8+fP17Bhw1S2bFmNHj1aw4cP17///qsWLVqocOHCOnnypLZt22YdOfjjjz/Uv39/Pfvss3rkkUfk4OCgtWvX6o8//tCwYcNs9uHl5aW2bdtq5MiRKlmypD7//HNFRERo3Lhx1j+GBw8erIULF6pVq1YaPXq0fH199eOPP2r69Ol65ZVXMn3NSorM1FemTJlMHWtuNGvWLAUHBysoKEjPP/+8SpUqpbNnzyomJkY7d+7UN998k+79rl+/roULF6pKlSrq06dPum3atGmjFStWpBmZuZOUqbr79eunZ555RocPH9Z7772nkiVL3va0vVs999xzCg0N1caNG/X2229bT3FL8cQTT6h169aqVq2aChcurJiYGC1atEiBgYEZBquZM2dq7dq1atWqlXx8fHT16lXrjJt3ui4oPc2aNVNQUJDefPNNxcfHq27dutbZAmvWrKnu3btnuU/p/4LAG2+8keb6NunmLHy//PKLPv/883v6odsU//vf//TFF1+oZcuWGjhwoB5//HHlz59fR44c0bp169SuXTs9/fTTWe530KBBCg8PV7t27TRs2DA9/vjjunLlijZs2KDWrVurcePGatWqlSZNmqSuXbvqpZde0pkzZ/TRRx/Z/IPSvcrO13p2yK7nA8jrCFcA7lnlypW1ZMkSvf322+rQoYOKFCmirl27asiQIQoODjZ9f+PGjdOqVav03nvvad68eQoNDZWfn5+mTp2qr776SomJiSpRooQCAgLUt29fSTevmSpfvrymT5+uw4cPy2KxqFy5cpo4caJeffVVm/5r1KihF154QSNGjNA///wjLy8vTZo0yeZf/YsVK6bNmzcrNDRUoaGhio+PV7ly5TR+/Hjr1NFZkdn6MnOsuVHjxo21bds2jRkzRoMGDdK5c+dUpEgR+fn5qVOnThne78cff9SJEyfSBOBbvfTSS1q6dKkWLVqUpcf+hRde0KlTpzRz5kzNmzdP5cqV07Bhw3TkyJFMh1RnZ2e1adNGn3/+ebqPf5MmTbRixQpNnjxZly9fVqlSpdSjRw/rtTrpqVGjhn7++WeNGDFCJ06ckIuLi6pWraoVK1ZYr/vJCovFouXLl2vkyJGaP3++xowZo6JFi6p79+764IMP7iogJCUladGiRapRo0a6wUq6+dMCpUuX1ty5c00JV3Z2dlqxYoWmTp2qRYsWaezYsbK3t1fp0qXVsGFDPfbYY3fVr6urq3799VeNHDlSs2fP1qhRo1S4cGEFBATopZdeknTzeZw3b57GjRunNm3aqFSpUnrxxRdVvHhx9e7d+56PTcre13p2yK7nA8jrLIaR6tfvAOAhVqZMGVWtWtX647jA7Vy7dk1lypRRvXr1svSDyACABxMjVwAAZNHp06e1b98+zZ8/XydPnrztaAMA4OFBuAIAIIt+/PFHvfDCCypZsqSmT59uM6kJAODhxWmBAAAAAGACfkQYAAAAAExAuAIAAAAAExCuAAAAAMAETGiRgRs3bujYsWNydXWVxWLJ6XIAAAAA5BDDMHTx4kV5eXkpX76Mx6cIVxk4duyYvL29c7oMAAAAALnE4cOHVbp06Qy3E65SCQsLU1hYmK5fvy7p5gPo5uaWw1UBAAAAyCnx8fHy9vaWq6vrbdsxFXsG4uPj5e7urgsXLhCuAAAAgIdYZrMBE1oAAAAAgAkIVwAAAABgAsIVAAAATDFjxgxVq1ZNbm5ucnNzU2BgoH766SebNjExMWrbtq3c3d3l6uqqJ598UrGxsdbts2fPVqNGjeTm5iaLxaLz589nuL/ExETVqFFDFotFUVFR1vULFiyQxWJJ93bq1ClJ0siRI9PdXrBgQZt9fPHFF6pevboKFCigkiVL6oUXXtCZM2ds2ixZskR+fn5ydHSUn5+fli1bZrN97NixCggIkKurq4oXL6727dtr3759Nm2WLl2qoKAgFS1aNM3xpGYYhoKDg2WxWLR8+fIM2+H+I1wBAADAFKVLl9aHH36o7du3a/v27WrSpInatWunPXv2SJIOHDigevXqqXLlylq/fr12796td955R05OTtY+Ll++rBYtWuitt9664/7eeOMNeXl5pVnfuXNnHT9+3OYWFBSkhg0bqnjx4pKk119/PU0bPz8/Pfvss9Z+fv31V/Xo0UO9e/fWnj179M033ygyMlJ9+vSxttmyZYs6d+6s7t27a/fu3erevbs6deqk33//3dpmw4YNCgkJ0datWxUREaHr16+refPmunTpkrXNpUuXVLduXX344Yd3PO4pU6bwU0G5lQEb06ZNM6pUqWJUrFjRkGRcuHAhp0sCAADIswoXLmzMmTPHMAzD6Ny5s/Hcc89l6n7r1q0zJBnnzp1Ld/vKlSuNypUrG3v27DEkGbt27cqwr1OnThn58+c3Fi5cmGGbqKgoQ5KxceNG67oJEyYY5cqVs2n38ccfG6VLl7Yud+rUyWjRooVNm6CgION///vfbeuRZGzYsCHNtoMHD972eKKioozSpUsbx48fNyQZy5Yty3A/MM+FCxcylQ0YuUolJCRE0dHRioyMzOlSAAAA8qzk5GQtXrxYly5dUmBgoG7cuKEff/xRFStWVFBQkIoXL64nnnjirk5rO3nypF588UUtWrRIBQoUuGP7hQsXqkCBAnrmmWcybDNnzhxVrFhR9evXt66rU6eOjhw5opUrV8owDJ08eVLffvutWrVqZW2zZcsWNW/e3KavoKAgbd68OcN9XbhwQZLk4eFxx9pvdfnyZXXp0kXTpk1TiRIlsnRf3B+EKwAAAJjmzz//lIuLixwdHdW3b18tW7ZMfn5+OnXqlBISEvThhx+qRYsW+vnnn/X000+rQ4cO2rBhQ6b7NwxDzz//vPr27avatWtn6j7z5s1T165d5ezsnO72xMREffHFF+rdu7fN+jp16uiLL75Q586d5eDgoBIlSqhQoUL65JNPrG1OnDghT09Pm/t5enrqxIkTGdY/ZMgQ1atXT1WrVs1U/SkGDx6sOnXqqF27dlm6H+4ffkQYAAAApqlUqZKioqJ0/vx5LVmyRD179tSGDRtUqFAhSVK7du00ePBgSVKNGjW0efNmzZw5Uw0bNsxU/5988oni4+MVGhqaqfZbtmxRdHS0Fi5cmGGbpUuX6uLFi+rRo4fN+ujoaA0YMEDvvvuugoKCdPz4cQ0dOlR9+/bV3Llzre1SX/9kGEaG10T1799ff/zxh3799ddM1Z9ixYoVWrt2rXbt2pWl++H+YuQKAAAApnFwcFCFChVUu3ZtjR07VtWrV9fUqVNVtGhR2dvby8/Pz6Z9lSpVbGYLvJO1a9dq69atcnR0lL29vSpUqCBJql27tnr27Jmm/Zw5c1SjRg35+/tn2OecOXPUunXrNKfajR07VnXr1tXQoUNVrVo1BQUFafr06Zo3b56OHz8uSSpRokSaUapTp06lGc2SpFdffVUrVqzQunXrVLp06Uwfc8pxHzhwQIUKFZK9vb3s7W+OkXTs2FGNGjXKUl/IPoxcAQAAINsYhqHExEQ5ODgoICAgzRTkf//9t3x9fTPd38cff6z333/funzs2DEFBQUpPDxcTzzxhE3bhIQEff311xo7dmyG/R08eFDr1q3TihUr0my7fPmyNcSksLOzsx6XJAUGBioiIsI6GidJP//8s+rUqWNdNgxDr776qpYtW6b169erbNmymT7eFMOGDbOZpVCSHnvsMU2ePFlt2rTJcn/IHoSrVMLCwhQWFqbk5OScLgUAACBPeeuttxQcHCxvb29dvHhRixcv1vr167Vq1SpJ0tChQ9W5c2c1aNBAjRs31qpVq/T9999r/fr11j5OnDihEydOaP/+/ZJuXsPl6uoqHx8feXh4yMfHx2afLi4ukqTy5cunGQ0KDw/X9evX1a1btwxrnjdvnkqWLKng4OA029q0aaMXX3xRM2bMsJ4WOGjQID3++OPWKeAHDhyoBg0aaNy4cWrXrp2+++47rVmzxua0v5CQEH355Zf67rvv5Orqah3pcnd3t14HdvbsWcXGxurYsWOSZA2hJUqUsLml5uPjc1dhDdkku6ctzKsyO90iAAAAburVq5fh6+trODg4GMWKFTOeeuop4+eff7ZpM3fuXKNChQqGk5OTUb16dWP58uU220eMGGFISnObP39+uvu83dTlgYGBRteuXTOsNzk52ShdurTx1ltvZdjm448/Nvz8/AxnZ2ejZMmSRrdu3YwjR47YtPnmm2+MSpUqGfnz5zcqV65sLFmyxGZ7eseT+pjmz5+fbpsRI0ZkWJuYiv2+yWw2sBjG/x/ThI34+Hi5u7vrwoULcnNzy+lyAAAAAOSQzGYDJrQAAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK5SCQsLk5+fnwICAnK6FAAAAAB5CFOxZ4Cp2AEAAABITMUOAAAAAPeVfU4XAAAA8DCKjY1VXFxcTpcB5GpFixaVj49PTpeRaYQrAACA+yw2NlaVq1TRlcuXc7oUIFdzLlBAe2Ni8kzAIlwBAADcZ3Fxcbpy+bKe/vxDFatSLqfLAXKl0zH/atlzwxQXF0e4AgAAwO0Vq1JOJWv55XQZAEzChBYAAAAAYALCFQAAAACYgHAFAAAAACYgXKUSFhYmPz8/BQQE5HQpAAAAAPIQwlUqISEhio6OVmRkZE6XAgAAACAPIVwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIV6mEhYXJz89PAQEBOV0KAAAAgDyEcJVKSEiIoqOjFRkZmdOlAAAAAMhDCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACR6IcPX000+rcOHCeuaZZ2zW//DDD6pUqZIeeeQRzZkzJ4eqAwAAAPAweCDC1YABA7Rw4UKbddevX9eQIUO0du1a7dy5U+PGjdPZs2dzqEIAAAAAD7oHIlw1btxYrq6uNuu2bdumRx99VKVKlZKrq6tatmyp1atX51CFAAAAAB50OR6uNm7cqDZt2sjLy0sWi0XLly9P02b69OkqW7asnJyc5O/vr02bNt2x32PHjqlUqVLW5dKlS+vo0aNmlg4AAAAAVjkeri5duqTq1atr2rRp6W4PDw/XoEGDNHz4cO3atUv169dXcHCwYmNjb9uvYRhp1lksFlNqBgAAAIDU7HO6gODgYAUHB2e4fdKkSerdu7f69OkjSZoyZYpWr16tGTNmaOzYsRner1SpUjYjVUeOHNETTzyRYfvExEQlJiZal+Pj47NyGAAAAAAecjk+cnU7165d044dO9S8eXOb9c2bN9fmzZtve9/HH39cf/31l44ePaqLFy9q5cqVCgoKyrD92LFj5e7ubr15e3ubcgwAAAAAHg65OlzFxcUpOTlZnp6eNus9PT114sQJ63JQUJCeffZZrVy5UqVLl1ZkZKTs7e01ceJENW7cWDVr1tTQoUNVpEiRDPcVGhqqCxcuWG+HDx/OtuMCAAAA8ODJ8dMCMyP1tVKGYdisy2gWwLZt26pt27aZ2oejo6McHR3vvkgAAAAAD7VcPXJVtGhR2dnZ2YxSSdKpU6fSjGYBAAAAQE7K1eHKwcFB/v7+ioiIsFkfERGhOnXqZMs+w8LC5Ofnp4CAgGzpHwAAAMCDKcdPC0xISND+/futywcPHlRUVJQ8PDzk4+OjIUOGqHv37qpdu7YCAwM1e/ZsxcbGqm/fvtlST0hIiEJCQhQfHy93d/ds2QcAAACAB0+Oh6vt27ercePG1uUhQ4ZIknr27KkFCxaoc+fOOnPmjEaPHq3jx4+ratWqWrlypXx9fXOqZAAAAABII8fDVaNGjdL9wd9b9evXT/369btPFQEAAABA1uXqa65yAtdcAQAAALgbhKtUQkJCFB0drcjIyJwuBQAAAEAeQrgCAAAAABMQrgAAAADABIQrAAAAADAB4SoVJrQAAAAAcDcIV6kwoQUAAACAu0G4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuEqF2QIBAAAA3A3CVSrMFggAAADgbhCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrlJhtkAAAAAAd4NwlQqzBQIAAAC4G4QrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuEqF37kCAAAAcDcIV6nwO1cAAAAA7gbhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEqlbCwMPn5+SkgICCnSwEAAACQhxCuUgkJCVF0dLQiIyNzuhQAAAAAeQjhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLhKJSwsTH5+fgoICMjpUgAAAADkIYSrVEJCQhQdHa3IyMicLgUAAABAHkK4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAEzwQIerjz76SI8++qiqVq2qzz//PKfLAQAAAPAAs8/pArLLn3/+qS+//FI7duyQJD311FNq3bq1ChUqlLOFAQAAAHggPbAjVzExMapTp46cnJzk5OSkGjVqaNWqVTldFgAAAIAHVK4NVxs3blSbNm3k5eUli8Wi5cuXp2kzffp0lS1bVk5OTvL399emTZus26pWrap169bp/PnzOn/+vNauXaujR4/exyMAAAAA8DDJteHq0qVLql69uqZNm5bu9vDwcA0aNEjDhw/Xrl27VL9+fQUHBys2NlaS5OfnpwEDBqhJkyZ6+umnFRAQIHv7B/YsSAAAAAA5LNeGq+DgYL3//vvq0KFDutsnTZqk3r17q0+fPqpSpYqmTJkib29vzZgxw9rm5Zdf1s6dO7Vu3To5ODioQoUKGe4vMTFR8fHxNjcAAAAAyKxcG65u59q1a9qxY4eaN29us7558+bavHmzdfnUqVOSpH379mnbtm0KCgrKsM+xY8fK3d3devP29s6e4gEAAAA8kPLkeXJxcXFKTk6Wp6enzXpPT0+dOHHCuty+fXudP39eBQsW1Pz58297WmBoaKiGDBliXY6PjydgAQAAAMi0PBmuUlgsFptlwzBs1t06inUnjo6OcnR0NK02AAAAAA+XPHlaYNGiRWVnZ2czSiXdPA0w9WgWAAAAANwPeTJcOTg4yN/fXxERETbrIyIiVKdOnXvqOywsTH5+fgoICLinfgAAAAA8XHLtaYEJCQnav3+/dfngwYOKioqSh4eHfHx8NGTIEHXv3l21a9dWYGCgZs+erdjYWPXt2/ee9hsSEqKQkBDFx8fL3d39Xg8DAAAAwEMi14ar7du3q3HjxtbllMkmevbsqQULFqhz5846c+aMRo8erePHj6tq1apauXKlfH19c6pkAAAAAA+xXBuuGjVqJMMwbtumX79+6tev332qCAAAAAAylievucpOXHMFAAAA4G4QrlIJCQlRdHS0IiMjc7oUAAAAAHkI4QoAAAAATEC4AgAAAAATEK4AAAAAwASEq1SY0AIAAADA3SBcpcKEFgAAAADuBuEKAAAAAExAuAIAAAAAExCuAAAAAMAEhKtUmNACAAAAwN0gXKXChBYAAAAA7gbhCgAAAABMQLgCAAAAABMQrgAAAADABIQrAAAAADAB4QoAAAAATEC4SoWp2AEAAADcDcJVKkzFDgAAAOBuEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhKhV+5woAAADA3SBcpcLvXAEAAAC4G4QrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhKtUwsLC5Ofnp4CAgJwuBQAAAEAeQrhKJSQkRNHR0YqMjMzpUgAAAADkIYQrAAAAADAB4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAIAAAAAExCuAAAAAMAEhCsAAAAAMAHhCgAAAABMQLgCAAAAABPYZ6Xxvn379NVXX2nTpk06dOiQLl++rGLFiqlmzZoKCgpSx44d5ejomF21AgAAAECulamRq127dqlZs2aqXr26Nm7cqICAAA0aNEjvvfeennvuORmGoeHDh8vLy0vjxo1TYmJidtedbcLCwuTn56eAgICcLgUAAABAHpKpkav27dtr6NChCg8Pl4eHR4bttmzZosmTJ2vixIl66623TCvyfgoJCVFISIji4+Pl7u6e0+UAAAAAyCMyFa7++ecfOTg43LFdYGCgAgMDde3atXsuDAAAAADykkydFphRsLp69WqW2gMAAADAgyrLswXeuHFD7733nkqVKiUXFxf9+++/kqR33nlHc+fONb1AAAAAAMgLshyu3n//fS1YsEDjx4+3GaF67LHHNGfOHFOLAwAAAIC8IsvhauHChZo9e7a6desmOzs76/pq1app7969phYHAAAAAHlFlsPV0aNHVaFChTTrb9y4oaSkJFOKAgAAAIC8Jsvh6tFHH9WmTZvSrP/mm29Us2ZNU4oCAAAAgLwmU1Ox32rEiBHq3r27jh49qhs3bmjp0qXat2+fFi5cqB9++CE7agQAAACAXC/LI1dt2rRReHi4Vq5cKYvFonfffVcxMTH6/vvv1axZs+yoEQAAAAByvSyPXElSUFCQgoKCzK4FAAAAAPKsLI9cAQAAAADSytTIVeHChWWxWDLV4dmzZ++pIAAAAADIizIVrqZMmZLNZQAAAABA3papcNWzZ8/srgMAAAAA8rS7mtAixZUrV9L8cLCbm9s9FQQAAAAAeVGWJ7S4dOmS+vfvr+LFi8vFxUWFCxe2uQEAAADAwyjL4eqNN97Q2rVrNX36dDk6OmrOnDkaNWqUvLy8tHDhwuyoEQAAAAByvSyHq++//17Tp0/XM888I3t7e9WvX19vv/22PvjgA33xxRfZUeNdmzx5sh599FH5+flpwIABMgwjp0sCAAAA8IDKcrg6e/asypYtK+nm9VUpU6/Xq1dPGzduNLe6e3D69GlNmzZNO3bs0J9//qkdO3Zo69atOV0WAAAAgAdUlsNVuXLldOjQIUmSn5+fvv76a0k3R7QKFSpkZm337Pr167p69aqSkpKUlJSk4sWL53RJAAAAAB5QWQ5XL7zwgnbv3i1JCg0NtV57NXjwYA0dOtS0wjZu3Kg2bdrIy8tLFotFy5cvT9Nm+vTpKlu2rJycnOTv769NmzZZtxUrVkyvv/66fHx85OXlpaZNm6p8+fKm1QcAAAAAt8ryVOyDBw+2/n/jxo21d+9ebd++XeXLl1f16tVNK+zSpUuqXr26XnjhBXXs2DHN9vDwcA0aNEjTp09X3bp1NWvWLAUHBys6Olo+Pj46d+6cfvjhBx06dEjOzs4KDg7Wxo0b1aBBA9NqBAAAAIAU9/Q7V5Lk4+MjHx8fM2qxERwcrODg4Ay3T5o0Sb1791afPn0kSVOmTNHq1as1Y8YMjR07VmvWrFGFChXk4eEhSWrVqpW2bt2aYbhKTExUYmKidTk+Pt7EowEAAADwoMvyaYEDBgzQxx9/nGb9tGnTNGjQIDNquqNr165px44dat68uc365s2ba/PmzZIkb29vbd68WVevXlVycrLWr1+vSpUqZdjn2LFj5e7ubr15e3tn6zEAAAAAeLBkOVwtWbJEdevWTbO+Tp06+vbbb00p6k7i4uKUnJwsT09Pm/Wenp46ceKEJOnJJ59Uy5YtVbNmTVWrVk3ly5dX27ZtM+wzNDRUFy5csN4OHz6crccAAAAA4MGS5dMCz5w5I3d39zTr3dzcFBcXZ0pRmWWxWGyWDcOwWTdmzBiNGTMmU305OjrK0dHR1PoAAAAAPDyyPHJVoUIFrVq1Ks36n376SeXKlTOlqDspWrSo7OzsrKNUKU6dOpVmNAsAAAAA7ocsj1wNGTJE/fv31+nTp9WkSRNJ0i+//KKJEydqypQpZteXLgcHB/n7+ysiIkJPP/20dX1ERITatWt3T32HhYUpLCxMycnJ91omAAAAgIdIlsNVr169lJiYqDFjxui9996TJJUpU0YzZsxQjx49TCssISFB+/fvty4fPHhQUVFR8vDwkI+Pj4YMGaLu3burdu3aCgwM1OzZsxUbG6u+ffve035DQkIUEhKi+Pj4dE9/BAAAAID03NVU7K+88opeeeUVnT59Ws7OznJxcTG7Lm3fvl2NGze2Lg8ZMkSS1LNnTy1YsECdO3fWmTNnNHr0aB0/flxVq1bVypUr5evra3otAAAAAHAn9/Q7V8WKFdOGDRt0+fJlPfnkkypcuLBZdalRo0YyDOO2bfr166d+/fqZtk8AAAAAuFuZntBiwoQJGjFihHXZMAy1aNFCjRs3VqtWrVSlShXt2bMnW4q8n8LCwuTn56eAgICcLgUAAABAHpLpcPXVV1/Jz8/Puvztt99q48aN2rRpk+Li4lS7dm2NGjUqW4q8n0JCQhQdHa3IyMicLgUAAABAHpLpcHXw4EFVq1bNurxy5Up17NhRdevWlYeHh95++21t2bIlW4oEAAAAgNwu0+EqKSnJ5kd2t2zZojp16liXvby87vuPCAMAAABAbpHpcFWhQgVt3LhRkhQbG6u///5bDRs2tG4/cuSIihQpYn6FAAAAAJAHZHq2wFdeeUX9+/fXpk2btHXrVgUGBtpcg7V27VrVrFkzW4q8n/gRYQAAAAB3I9MjVy+//LKmTp2qs2fPqkGDBlqyZInN9mPHjqlXr16mF3i/MaEFAAAAgLuRpd+56t27t3r37p3utunTp5tSEAAAAADkRZkeuQIAAAAAZIxwBQAAAAAmIFylEhYWJj8/PwUEBOR0KQAAAADyEMJVKkxoAQAAAOBuEK4AAAAAwARZmi1Qkp5++mlZLJY06y0Wi5ycnFShQgV17dpVlSpVMqVAAAAAAMgLsjxy5e7urrVr12rnzp3WkLVr1y6tXbtW169fV3h4uKpXr67ffvvN9GIBAAAAILfK8shViRIl1LVrV02bNk358t3MZjdu3NDAgQPl6uqqxYsXq2/fvnrzzTf166+/ml4wAAAAAORGWR65mjt3rgYNGmQNVpKUL18+vfrqq5o9e7YsFov69++vv/76y9RCAQAAACA3y3K4un79uvbu3Ztm/d69e5WcnCxJcnJySve6rLyAqdgBAAAA3I0snxbYvXt39e7dW2+99ZYCAgJksVi0bds2ffDBB+rRo4ckacOGDXr00UdNL/Z+CAkJUUhIiOLj4+Xu7p7T5QAAAADII7IcriZPnixPT0+NHz9eJ0+elCR5enpq8ODBevPNNyVJzZs3V4sWLcytFAAAAABysSyHKzs7Ow0fPlzDhw9XfHy8JMnNzc2mjY+PjznVAQAAAEAekeVwleL06dPat2+fLBaLKlWqpKJFi5pZFwAAAADkKVme0OLSpUvq1auXSpYsqQYNGqh+/foqWbKkevfurcuXL2dHjQAAAACQ62U5XA0ZMkQbNmzQ999/r/Pnz+v8+fP67rvvtGHDBr322mvZUSMAAAAA5HpZPi1wyZIl+vbbb9WoUSPrupYtW8rZ2VmdOnXSjBkzzKzvvgsLC1NYWJh1WnkAAAAAyIwsj1xdvnxZnp6eadYXL178gTgtMCQkRNHR0YqMjMzpUgAAAADkIVkOV4GBgRoxYoSuXr1qXXflyhWNGjVKgYGBphYHAAAAAHlFlk8LnDp1qlq0aKHSpUurevXqslgsioqKkqOjo37++efsqBEAAAAAcr0sh6uqVavqn3/+0eeff669e/fKMAz973//U7du3eTs7JwdNQIAAABArndXv3Pl7OysF1980WbdgQMH9OKLL2rt2rWmFAYAAAAAeUmWr7nKSEJCgjZs2GBWdwAAAACQp5gWrgAAAADgYUa4AgAAAAATEK4AAAAAwASZntCiZs2aslgsGW5/EH5AGAAAAADuVqbDVfv27bOxjNwjLCxMYWFhSk5OzulSAAAAAOQhmQ5XI0aMyM46co2QkBCFhIQoPj5e7u7uOV0OAAAAgDyCa64AAAAAwASZClctWrTQ5s2b79ju4sWLGjdunMLCwu65MAAAAADISzJ1WuCzzz6rTp06ydXVVW3btlXt2rXl5eUlJycnnTt3TtHR0fr111+1cuVKtW7dWhMmTMjuugEAAAAgV8lUuOrdu7e6d++ub7/9VuHh4fr00091/vx5SZLFYpGfn5+CgoK0Y8cOVapUKTvrBQAAAIBcKdMTWjg4OKhr167q2rWrJOnChQu6cuWKihQpovz582dbgQAAAACQF2Q6XKXm7u7ObHoAAAAA8P8xWyAAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABggiyHq8OHD+vIkSPW5W3btmnQoEGaPXu2qYUBAAAAQF6S5XDVtWtXrVu3TpJ04sQJNWvWTNu2bdNbb72l0aNHm14gAAAAAOQFWQ5Xf/31lx5//HFJ0tdff62qVatq8+bN+vLLL7VgwQKz67vvwsLC5Ofnp4CAgJwuBQAAAEAekuVwlZSUJEdHR0nSmjVr1LZtW0lS5cqVdfz4cXOrywEhISGKjo5WZGRkTpcCAAAAIA/Jcrh69NFHNXPmTG3atEkRERFq0aKFJOnYsWMqUqSI6QUCAAAAQF6Q5XA1btw4zZo1S40aNVKXLl1UvXp1SdKKFSuspwsCAAAAwMPGPqt3aNSokeLi4hQfH6/ChQtb17/00ksqUKCAqcUBAAAAQF5xV79zZRiGduzYoVmzZunixYuSJAcHB8IVAAAAgIdWlkeu/vvvP7Vo0UKxsbFKTExUs2bN5OrqqvHjx+vq1auaOXNmdtQJAAAAALlalkeuBg4cqNq1a+vcuXNydna2rn/66af1yy+/mFocAAAAAOQVWR65+vXXX/Xbb7/JwcHBZr2vr6+OHj1qWmEAAAAAkJdkeeTqxo0bSk5OTrP+yJEjcnV1NaUoAAAAAMhrshyumjVrpilTpliXLRaLEhISNGLECLVs2dLM2gAAAAAgz8jyaYGTJ09W48aN5efnp6tXr6pr1676559/VLRoUX311VfZUSMAAAAA5HpZDldeXl6KiorSV199pZ07d+rGjRvq3bu3unXrZjPBBQAAAAA8TLIcriTJ2dlZvXr1Uq9evcyuBwAAAADypCyHq4ULF952e48ePe66GAAAAADIq7IcrgYOHGiznJSUpMuXL8vBwUEFChQgXAEAAAB4KGV5tsBz587Z3BISErRv3z7Vq1ePCS0AAAAAPLSyHK7S88gjj+jDDz9MM6oFAAAAAA8LU8KVJNnZ2enYsWNmdQcAAAAAeUqWr7lasWKFzbJhGDp+/LimTZumunXrmlbYvdq3b586d+5ss/zVV1+pffv2OVcUAAAAgAdWlsNV6nBisVhUrFgxNWnSRBMnTjSrrntWqVIlRUVFSZISEhJUpkwZNWvWLGeLAgAAAPDAynK4unHjRnbUka1WrFihp556SgULFszpUgAAAAA8oEy75spsGzduVJs2beTl5SWLxaLly5enaTN9+nSVLVtWTk5O8vf316ZNm9Lt6+uvv7Y5RRAAAAAAzJapkashQ4ZkusNJkybddTG3unTpkqpXr64XXnhBHTt2TLM9PDxcgwYN0vTp01W3bl3NmjVLwcHBio6Olo+Pj7VdfHy8fvvtNy1evNiUugAAAAAgPZkKV7t27cpUZxaL5Z6KuVVwcLCCg4Mz3D5p0iT17t1bffr0kSRNmTJFq1ev1owZMzR27Fhru++++05BQUFycnK67f4SExOVmJhoXY6Pj7/HIwAAAADwMMlUuFq3bl1215El165d044dOzRs2DCb9c2bN9fmzZtt1n399dd66aWX7tjn2LFjNWrUKFPrBAAAAPDwyLXXXN1OXFyckpOT5enpabPe09NTJ06csC5fuHBB27ZtU1BQ0B37DA0N1YULF6y3w4cPm143AAAAgAdXlmcLlKTIyEh98803io2N1bVr12y2LV261JTCMiP1aYiGYdisc3d318mTJzPVl6OjoxwdHU2tDwAAAMDDI8sjV4sXL1bdunUVHR2tZcuWKSkpSdHR0Vq7dq3c3d2zo8Y0ihYtKjs7O5tRKkk6depUmtGsrAoLC5Ofn58CAgLuqR8AAAAAD5csh6sPPvhAkydP1g8//CAHBwdNnTpVMTEx6tSpk80sfdnJwcFB/v7+ioiIsFkfERGhOnXq3FPfISEhio6OVmRk5D31AwAAAODhkuVwdeDAAbVq1UrSzVPpLl26JIvFosGDB2v27NmmFZaQkKCoqChFRUVJkg4ePKioqCjFxsZKujk9/Jw5czRv3jzFxMRo8ODBio2NVd++fU2rAQAAAAAyK8vXXHl4eOjixYuSpFKlSumvv/7SY489pvPnz+vy5cumFbZ9+3Y1btzYupzyW1s9e/bUggUL1LlzZ505c0ajR4/W8ePHVbVqVa1cuVK+vr6m1QAAAAAAmZXlcFW/fn1FREToscceU6dOnTRw4ECtXbtWEREReuqpp0wrrFGjRjIM47Zt+vXrp379+pm2TwAAAAC4W5kOV1FRUapRo4amTZumq1evSro5fXn+/Pn166+/qkOHDnrnnXeyrdD7JSwsTGFhYUpOTs7pUgAAAADkIZm+5qpWrVry9/dXeHi4ChYsePPO+fLpjTfe0IoVKzRp0iQVLlw42wq9X5jQAgAAAMDdyHS4+u2331SrVi0NGzZMJUuW1HPPPad169ZlZ20AAAAAkGdkOlwFBgbq008/1YkTJzRjxgwdOXJETZs2Vfny5TVmzBgdOXIkO+sEAAAAgFwty1OxOzs7q2fPnlq/fr3+/vtvdenSRbNmzVLZsmXVsmXL7KjxvuJHhAEAAADcjSyHq1uVL19ew4YN0/Dhw+Xm5qbVq1ebVVeO4ZorAAAAAHcjy1Oxp9iwYYPmzZunJUuWyM7OTp06dVLv3r3NrA0AAAAA8owshavDhw9rwYIFWrBggQ4ePKg6derok08+UadOnawzCAIAAADAwyjT4apZs2Zat26dihUrph49eqhXr16qVKlSdtYGAAAAAHlGpsOVs7OzlixZotatW8vOzi47awIAAACAPCfT4WrFihXZWUeuERYWprCwMCUnJ+d0KQAAAADykHuaLfBBxGyBAAAAAO4G4QoAAAAATEC4AgAAAAATEK4AAAAAwASEKwAAAAAwAeEqlbCwMPn5+SkgICCnSwEAAACQhxCuUmG2QAAAAAB3g3AFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIV6nwO1cAAAAA7gbhKhV+5woAAADA3SBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFylEhYWJj8/PwUEBOR0KQAAAADyEMJVKiEhIYqOjlZkZGROlwIAAAAgDyFcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFephIWFyc/PTwEBATldCgAAAIA8hHCVSkhIiKKjoxUZGZnTpQAAAADIQwhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIV3ggXbx4UYMGDZKvr6+cnZ1Vp04dm0lKEhIS1L9/f5UuXVrOzs6qUqWKZsyYYdPHyy+/rPLly8vZ2VnFihVTu3bttHfvXuv2Q4cOqXfv3ipbtqycnZ1Vvnx5jRgxQteuXbO2OXPmjFq0aCEvLy85OjrK29tb/fv3V3x8vM2+Vq9erSeffFKurq4qVqyYOnbsqIMHD2bTowMAAIDsQLjCA6lPnz6KiIjQokWL9Oeff6p58+Zq2rSpjh49KkkaPHiwVq1apc8//1wxMTEaPHiwXn31VX333XfWPvz9/TV//nzFxMRo9erVMgxDzZs3V3JysiRp7969unHjhmbNmqU9e/Zo8uTJmjlzpt566y1rH/ny5VO7du20YsUK/f3331qwYIHWrFmjvn37Wtv8+++/ateunZo0aaKoqCitXr1acXFx6tChw316tAAAAGAGi2EYRk4XkRvFx8fL3d1dFy5ckJubW06Xgyy4cuWKXF1d9d1336lVq1bW9TVq1FDr1q31/vvvq2rVqurcubPeeecd63Z/f3+1bNlS7733Xrr9/vHHH6pevbr279+v8uXLp9tmwoQJmjFjhv79998M6/v44481YcIEHT58WJL07bffqkuXLkpMTFS+fDf/veP7779Xu3btlJiYqPz582f5MQAA5G47d+6Uv7+/XtrxtUrW8svpcoBc6fjOaM3276QdO3aoVq1aOVpLZrMBI1d44Fy/fl3JyclycnKyWe/s7Kxff/1VklSvXj2tWLFCR48elWEYWrdunf7++28FBQWl2+elS5c0f/58lS1bVt7e3hnu+8KFC/Lw8Mhw+7Fjx7R06VI1bNjQuq527dqys7PT/PnzlZycrAsXLmjRokVq3rw5wQoAACAPIVzhgePq6qrAwEC99957OnbsmJKTk/X555/r999/1/HjxyXdHD3y8/NT6dKl5eDgoBYtWmj69OmqV6+eTV/Tp0+Xi4uLXFxctGrVKkVERMjBwSHd/R44cECffPKJzSl/Kbp06aICBQqoVKlScnNz05w5c6zbypQpo59//llvvfWWHB0dVahQIR05ckSLFy828VEBAABAdiNc4YG0aNEiGYahUqVKydHRUR9//LG6du0qOzs7STfD1datW7VixQrt2LFDEydOVL9+/bRmzRqbfrp166Zdu3Zpw4YNeuSRR9SpUyddvXo1zf6OHTumFi1a6Nlnn1WfPn3SbJ88ebJ27typ5cuX68CBAxoyZIh124kTJ9SnTx/17NlTkZGR2rBhgxwcHPTMM8+Is3YBAADyDvucLgDIDuXLl9eGDRt06dIlxcfHq2TJkurcubPKli2rK1eu6K233tKyZcus12RVq1ZNUVFR+uijj9S0aVNrP+7u7nJ3d9cjjzyiJ598UoULF9ayZcvUpUsXa5tjx46pcePGCgwM1OzZs9Otp0SJEipRooQqV66sIkWKqH79+nrnnXdUsmRJhYWFyc3NTePHj7e2//zzz+Xt7a3ff/9dTz75ZDY9SgAAADATI1d4oBUsWFAlS5bUuXPntHr1arVr105JSUlKSkqyTh6Rws7OTjdu3Lhtf4ZhKDEx0bp89OhRNWrUSLVq1dL8+fPT9JlRH5Ks/Vy+fNk6onZrLZLuWA8AAAByD0au8EBKmTq9UqVK2r9/v4YOHapKlSrphRdeUP78+dWwYUMNHTpUzs7O8vX11YYNG7Rw4UJNmjRJ0s3p0cPDw9W8eXMVK1ZMR48e1bhx4+Ts7KyWLVtKujli1ahRI/n4+Oijjz7S6dOnrfsvUaKEJGnlypU6efKkAgIC5OLioujoaL3xxhuqW7euypQpI0lq1aqVJk+erNGjR6tLly66ePGi3nrrLfn6+qpmzZr394EDAADAXSNc4YF04cIFhYaG6siRI/Lw8FDHjh01ZswY6+x7ixcvVmhoqLp166azZ8/K19dXY8aMsU5G4eTkpE2bNmnKlCk6d+6cPD091aBBA23evFnFixeXJP3888/av3+/9u/fr9KlS9vsP2V0ytnZWZ9++qkGDx6sxMREeXt7q0OHDho2bJi1bZMmTfTll19q/PjxGj9+vAoUKKDAwECtWrVKzs7O9+PhAgAAgAn4nasM8DtXAAAgu/A7V8Cd8TtXAAAAAPCQeqDD1cGDB9W4cWP5+fnpscce06VLl3K6JAAAAAAPqAf6mqvnn39e77//vurXr6+zZ8/K0dExp0sCAAAA8IB6YMPVnj17lD9/ftWvX1+S5OHhkcMVAQAAAHiQ5drTAjdu3Kg2bdrIy8tLFotFy5cvT9Nm+vTpKlu2rJycnOTv769NmzZZt/3zzz9ycXFR27ZtVatWLX3wwQf3sXoAAAAAD5tcG64uXbqk6tWra9q0aeluDw8P16BBgzR8+HDt2rVL9evXV3BwsGJjYyVJSUlJ2rRpk8LCwrRlyxZFREQoIiLifh4CAAAAgIdIrg1XwcHBev/999WhQ4d0t0+aNEm9e/dWnz59VKVKFU2ZMkXe3t6aMWOGJKl06dIKCAiQt7e3HB0d1bJlS0VFRWW4v8TERMXHx9vcAAAAACCz8uQ1V9euXdOOHTtsfohVkpo3b67NmzdLkgICAnTy5EmdO3dO7u7u2rhxo15++eUM+xw7dqxGjRqVrXXfi9jYWMXFxeV0GUCuVbRoUfn4+OR0GQAA4CGWJ8NVXFyckpOT5enpabPe09NTJ06ckCTZ29vrgw8+UIMGDWQYhpo3b67WrVtn2GdoaKiGDBliXY6Pj5e3t3f2HEAWxcbGqnKVKrpy+XJOlwLkWs4FCmhvTAwBCwAA5Jg8Ga5SWCwWm2XDMGzWBQcHKzg4OFN9OTo65tqp2uPi4nTl8mV1en+Gipd9JKfLAXKdUwf/0ddvv6K4uDjCFQAAyDF5MlwVLVpUdnZ21lGqFKdOnUozmvUgKV72EZWqUj2nywAAAACQjlw7ocXtODg4yN/fP83sfxEREapTp8499R0WFiY/Pz8FBATcUz8AAAAAHi65duQqISFB+/fvty4fPHhQUVFR8vDwkI+Pj4YMGaLu3burdu3aCgwM1OzZsxUbG6u+ffve035DQkIUEhKi+Ph4ubu73+thAAAAAHhI5NpwtX37djVu3Ni6nDLZRM+ePbVgwQJ17txZZ86c0ejRo3X8+HFVrVpVK1eulK+vb06VDAAAAOAhlmvDVaNGjWQYxm3b9OvXT/369btPFQEAAABAxvLkNVfZiWuuAAAAANwNwlUqISEhio6OVmRkZE6XAgAAACAPIVwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcJUKE1oAAAAAuBuEq1SY0AIAAADA3SBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXKXCbIEAAAAA7gbhKhVmCwQAAABwNwhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIV6kwWyAAAACAu0G4SoXZAgEAAADcDcIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXKXC71wBAAAAuBuEq1T4nSsAAAAAd4NwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHCVSlhYmPz8/BQQEJDTpQAAAADIQwhXqYSEhCg6OlqRkZE5XQoAAACAPIRwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFylEhYWJj8/PwUEBOR0KQAAAADyEMJVKiEhIYqOjlZkZGROlwIAAAAgDyFcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACZ4oMOVvb29atSooRo1aqhPnz45XQ4AAACAB5h9TheQnQoVKqSoqKicLgMAAADAQ+CBHrkCAAAAgPsl14arjRs3qk2bNvLy8pLFYtHy5cvTtJk+fbrKli0rJycn+fv7a9OmTTbb4+Pj5e/vr3r16mnDhg33qXIAAAAAD6NcG64uXbqk6tWra9q0aeluDw8P16BBgzR8+HDt2rVL9evXV3BwsGJjY61tDh06pB07dmjmzJnq0aOH4uPj71f5AAAAAB4yuTZcBQcH6/3331eHDh3S3T5p0iT17t1bffr0UZUqVTRlyhR5e3trxowZ1jZeXl6SpKpVq8rPz09///13hvtLTExUfHy8zQ0AAAAAMivXhqvbuXbtmnbs2KHmzZvbrG/evLk2b94sSTp37pwSExMlSUeOHFF0dLTKlSuXYZ9jx46Vu7u79ebt7Z19BwAAAADggZMnw1VcXJySk5Pl6elps97T01MnTpyQJMXExKh27dqqXr26WrduralTp8rDwyPDPkNDQ3XhwgXr7fDhw9l6DAAAAAAeLHl6KnaLxWKzbBiGdV2dOnX0559/ZrovR0dHOTo6mlofAAAAgIdHnhy5Klq0qOzs7KyjVClOnTqVZjQLAAAAAO6HPBmuHBwc5O/vr4iICJv1ERERqlOnzj31HRYWJj8/PwUEBNxTPwAAAAAeLrn2tMCEhATt37/funzw4EFFRUXJw8NDPj4+GjJkiLp3767atWsrMDBQs2fPVmxsrPr27XtP+w0JCVFISIji4+Pl7u5+r4cBAAAA4CGRa8PV9u3b1bhxY+vykCFDJEk9e/bUggUL1LlzZ505c0ajR4/W8ePHVbVqVa1cuVK+vr45VTIAAACAh1iuDVeNGjWSYRi3bdOvXz/169fvPlUEAAAAABnLk9dcZSeuuQIAAABwNwhXqYSEhCg6OlqRkZE5XQoAAACAPIRwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsJVKkxoAQAAAOBuEK5SYUILAAAAAHeDcAUAAAAAJiBcAQAeGDNmzFC1atXk5uYmNzc3BQYG6qeffrJpExMTo7Zt28rd3V2urq568sknFRsbK0k6dOiQLBZLurdvvvnGpp8ff/xRTzzxhJydnVW0aFF16NAh3ZrOnDmj0qVLy2Kx6Pz589ly3ACA3ME+pwsAAMAspUuX1ocffqgKFSpIkj777DO1a9dOu3bt0qOPPqoDBw6oXr166t27t0aNGiV3d3fFxMTIyclJkuTt7a3jx4/b9Dl79myNHz9ewcHB1nVLlizRiy++qA8++EBNmjSRYRj6888/062pd+/eqlatmo4ePZpNRw0AyC0IV6mEhYUpLCxMycnJOV0KACCL2rRpY7M8ZswYzZgxQ1u3btWjjz6q4cOHq2XLlho/fry1Tbly5az/b2dnpxIlStj0sWzZMnXu3FkuLi6SpOvXr2vgwIGaMGGCevfubW1XqVKlNPXMmDFD58+f17vvvptmBA0A8ODhtMBUmNACAB4MycnJWrx4sS5duqTAwEDduHFDP/74oypWrKigoCAVL15cTzzxhJYvX55hHzt27FBUVJRNiNq5c6eOHj2qfPnyqWbNmipZsqSCg4O1Z88em/tGR0dr9OjRWrhwofLl4+sWAB4GfNoDAB4of/75p1xcXOTo6Ki+fftq2bJl8vPz06lTp5SQkKAPP/xQLVq00M8//6ynn35aHTp00IYNG9Lta+7cuapSpYrq1KljXffvv/9KkkaOHKm3335bP/zwgwoXLqyGDRvq7NmzkqTExER16dJFEyZMkI+PT/YfNAAgVyBcAQAeKJUqVVJUVJS2bt2qV155RT179lR0dLRu3LghSWrXrp0GDx6sGjVqaNiwYWrdurVmzpyZpp8rV67oyy+/tBm1kmTtZ/jw4erYsaP8/f01f/58m0kvQkNDVaVKFT333HPZfLQAgNyEcAUAeKA4ODioQoUKql27tsaOHavq1atr6tSpKlq0qOzt7eXn52fTvkqVKtbZAm/17bff6vLly+rRo4fN+pIlS0qSTT+Ojo4qV66ctZ+1a9fqm2++kb29vezt7fXUU09JkooWLaoRI0aYerwAgNyDCS0AAA80wzCUmJgoBwcHBQQEaN++fTbb//77b/n6+qa539y5c9W2bVsVK1bMZr2/v78cHR21b98+1atXT5KUlJSkQ4cOWftZsmSJrly5Yr1PZGSkevXqpU2bNql8+fJmHyIAIJcgXAEAHhhvvfWWgoOD5e3trYsXL2rx4sVav369Vq1aJUkaOnSoOnfurAYNGqhx48ZatWqVvv/+e61fv96mn/3792vjxo1auXJlmn24ubmpb9++GjFihLy9veXr66sJEyZIkp599llJShOg4uLiJN0cJStUqJDJRw0AyC0IV6kwFTsA5F0nT55U9+7ddfz4cbm7u6tatWpatWqVmjVrJkl6+umnNXPmTI0dO1YDBgxQpUqVtGTJEusIVIp58+apVKlSat68ebr7mTBhguzt7dW9e3dduXJFTzzxhNauXavChQtn+zECAHIvi2EYRk4XkRvFx8fL3d1dFy5ckJubW47WsnPnTvn7+6v/F2tUqkr1HK0FyI2OxuzWtG5NtWPHDtWqVSunywGAO0r5bn9px9cqWcvvzncAHkLHd0Zrtn+nXPH9ntlswIQWAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIVwAAAABgAsIVAAAAAJiAcJVKWFiY/Pz8FBAQkNOlAAAAAMhDCFephISEKDo6WpGRkTldCgAAAIA8xD6nCwAA2IqNjVVcXFxOlwHkWkWLFpWPj09OlwEAaRCuACAXiY2NVZXKVXT5yuWcLgXItQo4F1DM3hgCFoBch3AFALlIXFycLl+5rEkvTVJ5r/I5XQ6Q6xw4dkBDZg9RXFwc4QpArkO4AoBcqLxXeVUtUzWnywAAAFnAhBYAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXqYSFhcnPz08BAQE5XQoAAACAPIRwlUpISIiio6MVGRmZ06UAAAAAyEMIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACQhXAAAAAGACwhUAAAAAmIBwBQAAAAAmIFwBAAAAgAkIV6mEhYXJz89PAQEBOV0KAAAAgDyEcJVKSEiIoqOjFRkZmdOlAAAAAMhD7HO6gNzKMAxJUnx8fA5XIiUkJEiSrl2+pKsJF3O4GiD3uXb5kqSb75Xc8J69Fynv98tXL+viFd7vQGqXr16WlPff79bv9oTLSoxPyOFqgNzpWkLueb+n7D8lI2TEYtypxUPqyJEj8vb2zukyAAAAAOQShw8fVunSpTPcTrjKwI0bN3Ts2DG5urrKYrHkdDnIZeLj4+Xt7a3Dhw/Lzc0tp8sBkE14rwMPD97vuB3DMHTx4kV5eXkpX76Mr6zitMAM5MuX77apFJAkNzc3PoCBhwDvdeDhwfsdGXF3d79jGya0AAAAAAATEK4AAAAAwASEK+AuODo6asSIEXJ0dMzpUgBkI97rwMOD9zvMwIQWAAAAAGACRq4AAAAAwASEKwAAAAAwAeEKAAAAAExAuAKQoUOHDslisSgqKiqnS8EDqEyZMpoyZUqu79NMFotFy5cvz+kygNtq1KiRBg0aZGqfI0eOVI0aNUztEzfxXZ27EK4eUps3b5adnZ1atGiR06Vku5QPnZSbu7u7nnzySX3//fc5XVqOev75520el/Ru3t7eOn78uKpWrZrT5SIb3el18Pzzz9/x/tkRGB62P8bu9XnIjnoIgg+ujL4D9u/fr6VLl+q99967r/XwXZ0+vqvzHsLVQ2revHl69dVX9euvvyo2NjZb95WcnKwbN25k6z4yY82aNTp+/Lh+//13Pf744+rYsaP++uuvnC4rx0ydOlXHjx+33iRp/vz5Nuvs7OxUokQJ2dvb53C1yE63PudTpkyRm5ubzbqpU6fmdIkPBZ4H3G8tWrSweY0dP35cZcuWlYeHh1xdXXOkJr6rbfFdnfcQrh5Cly5d0tdff61XXnlFrVu31oIFC6zbAgMDNWzYMJv2p0+fVv78+bVu3TpJ0rVr1/TGG2+oVKlSKliwoJ544gmtX7/e2n7BggUqVKiQfvjhB/n5+cnR0VH//fefIiMj1axZMxUtWlTu7u5q2LChdu7cabOvvXv3ql69enJycpKfn5/WrFmT5l9Pjx49qs6dO6tw4cIqUqSI2rVrp0OHDt3xuIsUKaISJUqocuXKGjNmjJKSkqzHlJl+169fr8cff1wFCxZUoUKFVLduXf3333+S/u9f2GfNmiVvb28VKFBAzz77rM6fP2+9/40bNzR69GiVLl1ajo6OqlGjhlatWmXdnvKvdkuXLlXjxo1VoEABVa9eXVu2bLG2+e+//9SmTRsVLlxYBQsW1KOPPqqVK1dat0dHR6tly5ZycXGRp6enunfvrri4uHQfD3d3d5UoUcJ6k6RChQrZrEt9qsH69etlsVi0evVq1axZU87OzmrSpIlOnTqln376SVWqVJGbm5u6dOmiy5cvW/dlGIbGjx+vcuXKydnZWdWrV9e33357x+cM98etz7m7u7ssFovNui+//FLly5eXg4ODKlWqpEWLFlnvW6ZMGUnS008/LYvFYl0+cOCA2rVrJ09PT7m4uCggIEBr1qy5pzqff/55tW/fXh999JFKliypIkWKKCQkRElJSTbtLl68qK5du8rFxUVeXl765JNPbLbHxsaqXbt2cnFxkZubmzp16qSTJ0+m2c+tBg0apEaNGlmXGzVqpAEDBuiNN96Qh4eHSpQooZEjR9rc559//lGDBg2sn2cRERG3Pb6MngdPT0899thjNo9fjRo1VLx4cevyli1blD9/fiUkJEiSLly4oJdeeknFixeXm5ubmjRpot27d9vs7/vvv5e/v7+cnJxUrlw5jRo1StevX5eU8fO6e/duNW7cWK6urnJzc5O/v7+2b99+2+NC7uXo6GjzuitRooTs7OzSnBZYpkwZffDBB+rVq5dcXV3l4+Oj2bNn2/T15ptvqmLFiipQoIDKlSund955J817MzP4rrbFd3XeQ7h6CIWHh6tSpUqqVKmSnnvuOc2fP18pP3fWrVs3ffXVV7r158/Cw8Pl6emphg0bSpJeeOEF/fbbb1q8eLH++OMPPfvss2rRooX++ecf630uX76ssWPHas6cOdqzZ4+KFy+uixcvqmfPntq0aZO2bt2qRx55RC1bttTFixcl3fxAa9++vQoUKKDff/9ds2fP1vDhw21qv3z5sho3biwXFxdt3LhRv/76q1xcXNSiRQtdu3YtU8eflJSkTz/9VJKUP3/+TPV7/fp1tW/fXg0bNtQff/yhLVu26KWXXpLFYrH2u3//fn399df6/vvvtWrVKkVFRSkkJMS6ferUqZo4caI++ugj/fHHHwoKClLbtm1tHjdJGj58uF5//XVFRUWpYsWK6tKli/UPnpCQECUmJmrjxo36888/NW7cOLm4uEi6+a/eDRs2VI0aNbR9+3atWrVKJ0+eVKdOnTL1uGTFyJEjNW3aNG3evFmHDx9Wp06dNGXKFH355Zf68ccfFRERYfMH7dtvv6358+drxowZ2rNnjwYPHqznnntOGzZsML02mGvZsmUaOHCgXnvtNf311196+eWX9cILL1j/2ImMjJT0f/+SmrKckJCgli1bas2aNdq1a5eCgoLUpk2bex4pX7dunQ4cOKB169bps88+04IFC2z+gUiSJkyYoGrVqmnnzp0KDQ3V4MGDrcHGMAy1b99eZ8+e1YYNGxQREaEDBw6oc+fOWa7ls88+U8GCBfX7779r/PjxGj16tHU/N27cUIcOHWRnZ6etW7dq5syZevPNN+/qmC0Wixo0aGD9R6xz584pOjpaSUlJio6OlnTzjyl/f3+5uLjIMAy1atVKJ06c0MqVK7Vjxw7VqlVLTz31lM6ePStJWr16tZ577jkNGDBA0dHRmjVrlhYsWKAxY8ZIyvh57datm0qXLq3IyEjt2LFDw4YNs36O4sE2ceJE1a5dW7t27VK/fv30yiuvaO/evdbtrq6uWrBggaKjozV16lR9+umnmjx58l3vj+/qe8d3dQ4x8NCpU6eOMWXKFMMwDCMpKckoWrSoERERYRiGYZw6dcqwt7c3Nm7caG0fGBhoDB061DAMw9i/f79hsViMo0eP2vT51FNPGaGhoYZhGMb8+fMNSUZUVNRt67h+/brh6upqfP/994ZhGMZPP/1k2NvbG8ePH7e2iYiIMCQZy5YtMwzDMObOnWtUqlTJuHHjhrVNYmKi4ezsbKxevTrd/Rw8eNCQZDg7OxsFCxY08uXLZ0gyypQpY5w5cyZT/Z45c8aQZKxfvz7dfYwYMcKws7MzDh8+bF33008/Gfny5bMej5eXlzFmzBib+wUEBBj9+vWzqXPOnDnW7Xv27DEkGTExMYZhGMZjjz1mjBw5Mt0a3nnnHaN58+Y26w4fPmxIMvbt25fufW516+OcIqWmXbt2GYZhGOvWrTMkGWvWrLG2GTt2rCHJOHDggHXdyy+/bAQFBRmGYRgJCQmGk5OTsXnzZpu+e/fubXTp0uWOdeH+mj9/vuHu7m5drlOnjvHiiy/atHn22WeNli1bWpfTe+2kx8/Pz/jkk0+sy76+vsbkyZMzbD9ixAijevXq1uWePXsavr6+xvXr121q6dy5s02fLVq0sOmnc+fORnBwsGEYhvHzzz8bdnZ2RmxsrHV7yvts27Zt1v20a9fOpo+BAwcaDRs2tC43bNjQqFevnk2bgIAA48033zQMwzBWr16d7mdCZh+r1M/Dxx9/bFStWtUwDMNYvny5Ubt2baNDhw5GWFiYYRiG0bx5c+u+f/nlF8PNzc24evWqTZ/ly5c3Zs2aZRiGYdSvX9/44IMPbLYvWrTIKFmypHU5vVpdXV2NBQsW3LF+5H49e/Y07OzsjIIFC1pvzzzzjGEYN1/fAwcOtLb19fU1nnvuOevyjRs3jOLFixszZszIsP/x48cb/v7+1uXU7+fU+K7mu/pBwcjVQ2bfvn3atm2b/ve//0mS7O3t1blzZ82bN0+SVKxYMTVr1kxffPGFJOngwYPasmWLunXrJknauXOnDMNQxYoV5eLiYr1t2LBBBw4csO7HwcFB1apVs9n3qVOn1LdvX1WsWFHu7u5yd3dXQkKC9V+y9+3bJ29vb+uwtyQ9/vjjNn3s2LFD+/fvl6urq3XfHh4eunr1qs3+0xMeHq5du3ZpxYoVqlChgubMmSMPD49M9evh4aHnn3/e+q/vKedA38rHx0elS5e2LgcGBurGjRvat2+f4uPjdezYMdWtW9fmPnXr1lVMTIzNulsft5IlS1ofO0kaMGCA3n//fdWtW1cjRozQH3/8YfPYrFu3zuZ5qVy5siTd8bHJqltr9PT0tJ4Gcuu6lJqjo6N19epVNWvWzKa2hQsXml4XzBcTE5Op121qly5d0htvvCE/Pz8VKlRILi4u2rt37z2PXD366KOys7OzLpcsWdL6WksRGBiYZjml3piYGHl7e8vb29u6PaXGOx1Taqk/426tJSYmJt3PhLvVqFEj7dmzR3FxcdqwYYMaNWqkRo0aacOGDbp+/bo2b95sPbtgx44dSkhIUJEiRWzecwcPHrS+53bs2KHRo0fbbH/xxRd1/Phxm9OEUhsyZIj69Omjpk2b6sMPP+Q9nMc1btxYUVFR1tvHH3+cYdtbX+8pp6ze+t779ttvVa9ePZUoUUIuLi5655137ur9zne1efiuzhlc+faQmTt3rq5fv65SpUpZ1xmGofz58+vcuXMqXLiwunXrpoEDB+qTTz7Rl19+qUcffVTVq1eXdPNUFzs7O+3YscPmDxxJ1iFvSXJ2drYZhpduXsdw+vRpTZkyRb6+vnJ0dFRgYKD1dD7DMNLcJ7UbN27I39/fGv5uVaxYsdve19vbW4888ogeeeQRubi4qGPHjoqOjlbx4sUz1e/8+fM1YMAArVq1SuHh4Xr77bcVERGhJ598Mt39pRzLrceU+vjSO+ZbT7FJ2ZYyIUifPn0UFBSkH3/8UT///LPGjh2riRMn6tVXX9WNGzfUpk0bjRs3Lk0tKR/8ZkldY+rTgiwWi7XmlP/++OOPNq876eb5/sj9MvO6TW3o0KFavXq1PvroI1WoUEHOzs565plnMn36bkZu91q7nZR6M6r91vX58uWzOTVaUrrXjtyultT3v7WGu1G1alUVKVJEGzZs0IYNGzR69Gh5e3trzJgxioyM1JUrV1SvXj1JN99zJUuWtLkWNkWhQoWsbUaNGqUOHTqkaePk5JRhHSNHjlTXrl31448/6qefftKIESO0ePFiPf3003d9bMg5BQsWVIUKFTLV9nav961bt+p///ufRo0apaCgILm7u2vx4sWaOHFilmviu9o8fFfnDMLVQ+T69etauHChJk6cqObNm9ts69ixo7744gv1799f7du318svv6xVq1bpyy+/VPfu3a3tatasqeTkZJ06dUr169fP0v43bdqk6dOnq2XLlpKkw4cP21zAWblyZcXGxurkyZPy9PSU9H/n/aeoVauWwsPDrRdp362GDRuqatWqGjNmjKZOnZrpfmvWrKmaNWsqNDRUgYGB+vLLL60f2LGxsTp27Ji8vLwk3bzAPF++fKpYsaLc3Nzk5eWlX3/9VQ0aNLD2t3nz5jSjc3fi7e2tvn37qm/fvgoNDdWnn36qV199VbVq1dKSJUtUpkyZXDVjUMqkJrGxsdZ/WUfeUaVKFf3666/q0aOHdd3mzZtVpUoV63L+/PmVnJxsc79Nmzbp+eeft/7RnZCQkKmJZ8ywdevWNMsp/zLs5+en2NhYHT582Dp6FR0drQsXLliPqVixYmlmJ4uKisrStUUp+0n9mXC3Uq67+u677/TXX3+pfv36cnV1VVJSkmbOnKlatWpZZ3erVauWTpw4IXt7e+tEFKnVqlVL+/btu+0f1uk9r5JUsWJFVaxYUYMHD1aXLl00f/58wtVD7rfffpOvr6/NddIpk0jcC76r7x++q83DaYEPkR9++EHnzp1T7969VbVqVZvbM888o7lz50q6+S9Z7dq10zvvvKOYmBh17drV2kfFihXVrVs39ejRQ0uXLtXBgwcVGRmpcePG2cyEk54KFSpo0aJFiomJ0e+//65u3brJ2dnZur1Zs2YqX768evbsqT/++EO//fab9YM65V+FunXrpqJFi6pdu3batGmTDh48qA0bNmjgwIE6cuRIlh6P1157TbNmzdLRo0fv2O/BgwcVGhqqLVu26L///tPPP/+sv//+2+YPTCcnJ/Xs2VO7d+/Wpk2bNGDAAHXq1Ml6muPQoUM1btw4hYeHa9++fRo2bJiioqI0cODATNc8aNAgrV69WgcPHtTOnTu1du1aaw0hISE6e/asunTpom3btunff//Vzz//rF69eqX7B9L94urqqtdff12DBw/WZ599pgMHDmjXrl0KCwvTZ599lmN1IXOGDh2qBQsWaObMmfrnn380adIkLV26VK+//rq1TZkyZfTLL7/oxIkTOnfunKSb7/elS5cqKipKu3fvVteuXe/bTzL89ttvGj9+vP7++2+FhYXpm2++sb7PmjZtqmrVqqlbt27auXOntm3bph49eqhhw4aqXbu2JKlJkybavn27Fi5cqH/++UcjRozI8lTQTZs2VaVKldSjRw/rZ0LqCXqyqlGjRvryyy9VrVo1ubm5WQPXF198YTOTYdOmTRUYGKj27dtr9erVOnTokDZv3qy3337bOrPfu+++q4ULF2rkyJHas2ePYmJirP/KnyL183rlyhX1799f69ev13///afffvtNkZGRNp+DeDhVqFBBsbGxWrx4sQ4cOKCPP/5Yy5YtM6VvvqvvD76rzUO4eojMnTtXTZs2lbu7e5ptHTt2VFRUlHVq9G7dumn37t2qX7++fHx8bNrOnz9fPXr00GuvvaZKlSqpbdu2+v33322uYUjPvHnzdO7cOdWsWVPdu3fXgAEDbKYStrOz0/Lly5WQkKCAgAD16dPH+kWfcppKgQIFtHHjRvn4+KhDhw6qUqWKevXqpStXrmR5JKt169YqU6aMxowZc8d+CxQooL1796pjx46qWLGiXnrpJfXv318vv/yytb8KFSqoQ4cOatmypZo3b66qVatq+vTp1u0DBgzQa6+9ptdee02PPfaYVq1apRUrVuiRRx7JdM3JyckKCQlRlSpV1KJFC1WqVMm6Dy8vL/32229KTk5WUFCQqlatqoEDB8rd3V358uXsW/29997Tu+++q7Fjx6pKlSoKCgrS999/r7Jly+ZoXbiz9u3ba+rUqZowYYIeffRRzZo1S/Pnz7f5Y37ixImKiIiQt7e3atasKUmaPHmyChcurDp16qhNmzYKCgpSrVq17kvNr732mnbs2KGaNWvqvffe08SJExUUFCTp/34Yt3DhwmrQoIGaNm2qcuXKKTw83Hr/oKAgvfPOO3rjjTcUEBCgixcv2ozcZUa+fPm0bNkyJSYm6vHHH1efPn2sM/HdrcaNGys5OdnmsW/YsKGSk5Nt/qXZYrFo5cqVatCggXr16qWKFSvqf//7nw4dOmQ9KyAoKEg//PCDIiIiFBAQoCeffFKTJk2Sr6+vtZ/Uz6udnZ3OnDmjHj16qGLFiurUqZOCg4M1atSoezou5H3t2rXT4MGD1b9/f9WoUUObN2/WO++8Y0rffFffP3xXm8NipHdiOJBL/Pbbb6pXr57279+v8uXL53Q5GRo5cqSWL19u/Y0JAACQu/Bdjfsh95zsCejmb+q4uLjokUce0f79+zVw4EDVrVs3VwcrAAAAQCJcIZe5ePGi3njjDR0+fFhFixZV06ZN72q2IQAAAOB+47RAAAAAADABE1oAAAAAgAkIVwAAAABgAsIVAAAAAJiAcAUAAAAAJiBcAQAAAIAJCFcAAAAAYALCFQAAAACYgHAFAAAAACYgXAEAAACACf4f494V5m8ltW4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categories = ['Average Response Time', 'Total Inbound Tweets', 'Final Response Time']\n",
    "values = [average_response_time_american, inbound_tweet_count_american, final_response_time_american]\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.bar(categories, values, color=['skyblue', 'plum', 'aquamarine'],edgecolor='black')\n",
    "\n",
    "for i, v in enumerate(values):\n",
    "    plt.text(i, v + 0.2 * v, f\"{v:.0f}\", ha='center', va='bottom', fontsize=10)\n",
    "\n",
    "plt.ylabel('Values (Log Scale)')\n",
    "plt.yscale('log') \n",
    "plt.title('Final Response Time Analysis for American Airline')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffc8177b",
   "metadata": {},
   "source": [
    "#### Delta Airlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "d6fd0f66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Response Time for Delta Airlines: 45172513288.0 milliseconds\n"
     ]
    }
   ],
   "source": [
    "average_response_time_delta = 1.069096e+06\n",
    "inbound_tweet_count_delta = 42253\n",
    "final_response_time_delta = inbound_tweet_count_delta * average_response_time_delta\n",
    "print(f\"Final Response Time for Delta Airlines: {final_response_time_delta} milliseconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "9310018d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categories = ['Average Response Time', 'Total Inbound Tweets', 'Final Response Time']\n",
    "values = [average_response_time_delta, inbound_tweet_count_delta, final_response_time_delta]\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.bar(categories, values, color=['mediumpurple', 'cornflowerblue', 'greenyellow'],edgecolor='black')\n",
    "\n",
    "for i, v in enumerate(values):\n",
    "    plt.text(i, v + 0.2 * v, f\"{v:.0f}\", ha='center', va='bottom', fontsize=10)\n",
    "\n",
    "plt.ylabel('Values (Log Scale)')\n",
    "plt.yscale('log') \n",
    "plt.title('Final Response Time Analysis for Delta Airline')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "034bc948",
   "metadata": {},
   "source": [
    "#### Comparing final response times of American Airline and Delta Airline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9e2b4c9",
   "metadata": {},
   "source": [
    "#### What company has a faster final response time?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "98b55c5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categories = ['American Airline', 'Delta Airline']\n",
    "values = [final_response_time_american, final_response_time_delta]\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.bar(categories, values, color=['palegreen','aquamarine'],edgecolor='black')\n",
    "\n",
    "for i, v in enumerate(values):\n",
    "    plt.text(i, v + 0 * v, f\"{v:.0f}\", ha='center', va='bottom', fontsize=10)\n",
    "\n",
    "plt.ylabel('Values (Log Scale)')\n",
    "plt.yscale('log') \n",
    "plt.title('Final Response Time Analysis for Airline Companies')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46a3ef0e",
   "metadata": {},
   "source": [
    "## Result\n",
    "American Airlines with an average response time of approximately 36,147,780,214 units is faster than Delta with an average response time of approximately 45,172,513,288 units. This can correlate to customers being more satified with American Airlines customer support via the tweet data set."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}