Airline_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "30da79fb",
   "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": 126,
   "id": "05c66b58",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports used in this Python file\n",
    "import pandas as pd\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\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "aab3058d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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": 127,
     "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": 128,
   "id": "f1ea87d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2811774, 7)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_set.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27edf06b",
   "metadata": {},
   "source": [
    "#### Exploring and cleaning the data to obtain specific companies "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "6047c966",
   "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": 130,
   "id": "e97ab280",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_data = data_set[data_set['author_id'].str.contains('delta|american', case=False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "6a656b6b",
   "metadata": {},
   "outputs": [
    {
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       "<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",
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       "  <tbody>\n",
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       "      <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",
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       "      <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",
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       "      <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": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "b8143062",
   "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": 133,
   "id": "915572ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>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": 133,
     "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": "92cc3c62",
   "metadata": {},
   "source": [
    "#### What company has the most and least amount of tweets?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "bc1222e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grouped dot plot using Matplotlib\n",
    "plt.figure(figsize=(6, 4))\n",
    "\n",
    "plt.plot(range(len(american_data['inbound'])), american_data['count'], marker='o', linestyle='', markersize=10, label='American Airlines')\n",
    "plt.plot(range(len(delta_data['inbound'])), delta_data['count'], marker='o', linestyle='', markersize=10, label='Delta')\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.xticks(range(len(american_data['inbound'])), american_data['inbound'])\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "0810f976",
   "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": 135,
   "id": "6005428a",
   "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": 135,
     "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": 136,
   "id": "8bd0af07",
   "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": 137,
   "id": "9f3b4d24",
   "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": "e412d29a",
   "metadata": {},
   "source": [
    "#### Statistical Predicitions / Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "c0db84fe",
   "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": "49d5faf2",
   "metadata": {},
   "source": [
    "##### Convert to positive numbers for better analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "c5b940e3",
   "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": "f7cfb8bd",
   "metadata": {},
   "source": [
    "#### What company has the slowest and fastest response time?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "da44e325",
   "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": "f0caccc9",
   "metadata": {},
   "source": [
    "#### Now let's compare the total inbound tweets and the repsonse time of both companies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6530895e",
   "metadata": {},
   "source": [
    "#### American Airlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "35dd51c2",
   "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": 147,
   "id": "f832bf9d",
   "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_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": "698178cb",
   "metadata": {},
   "source": [
    "#### Delta Airlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "7f9d462f",
   "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": 149,
   "id": "41b6b8cf",
   "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": "eab87032",
   "metadata": {},
   "source": [
    "#### Comparing final response times of American Airline and Delta Airline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfbf32e7",
   "metadata": {},
   "source": [
    "#### What company has a faster final response time?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "b87a2138",
   "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": "e3955d3d",
   "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
}