CustomerSatisfaction_RegressionAnalysis.ipynb

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   "source": [
    "# Airline Customer Satisfaction: EFA, Logistic Regression, & Visualizations\n",
    "#### _Author: Titas Saha_\n",
    "##### _2022-04-21_\n"
   ]
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    "## Table of Contents\n",
    "\n",
    "[1. Goals](#1)\n",
    "\n",
    "[2. Dataset](#2)\n",
    "\n",
    "[3. Cleaning the dataset](#3)\n",
    "\n",
    "[4. Correlation Matrix](#4)\n",
    "\n",
    "[5. Exploratory Factor Analysis](#5)\n",
    "\n",
    "[6. Exploratory Factor Analysis Interpretation](#6)\n",
    "\n",
    "[7. Logistic Regression](#7)\n",
    "\n",
    "[8. Prediction on Test Data](#8)\n",
    "\n",
    "[9. Results of Logistic Regression](#9)\n",
    "\n",
    "[10. Visualizations](#10)"
   ]
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    "<a id=\"1\"></a> <br>\n",
    "### Goals\n",
    "\n",
    "1. Conduct an exploratory factor analysis to find out latent variables and understand which customer satisfaction factors group together \n",
    "\n",
    "2. Which factors affect satisfaction the most? Conduct logistic regression to find this out\n",
    "\n",
    "3.  Explore the significant variables and visualize them.\n",
    "\n",
    "### Packages and libraries"
   ]
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    "knitr::opts_chunk$set(warning = FALSE, message = FALSE) "
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      "Installing packages into ‘/usr/local/lib/R/site-library’\n",
      "(as ‘lib’ is unspecified)\n",
      "\n",
      "Warning message:\n",
      "“dependencies ‘graph’, ‘Rgraphviz’ are not available”\n",
      "also installing the dependencies ‘nloptr’, ‘psychTools’, ‘lme4’, ‘Rcsdp’\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: ‘dplyr’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n",
      "\n",
      "── \u001b[1mAttaching packages\u001b[22m ─────────────────────────────────────── tidyverse 1.3.1 ──\n",
      "\n",
      "\u001b[32m✔\u001b[39m \u001b[34mggplot2\u001b[39m 3.3.5     \u001b[32m✔\u001b[39m \u001b[34mpurrr  \u001b[39m 0.3.4\n",
      "\u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 3.1.6     \u001b[32m✔\u001b[39m \u001b[34mstringr\u001b[39m 1.4.0\n",
      "\u001b[32m✔\u001b[39m \u001b[34mreadr  \u001b[39m 2.1.2     \u001b[32m✔\u001b[39m \u001b[34mforcats\u001b[39m 0.5.1\n",
      "\n",
      "── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m    masks \u001b[34mstats\u001b[39m::lag()\n",
      "\n",
      "corrplot 0.92 loaded\n",
      "\n",
      "\n",
      "Attaching package: ‘psych’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:ggplot2’:\n",
      "\n",
      "    %+%, alpha\n",
      "\n",
      "\n"
     ]
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    "install.packages(c(\"psych\",\"GPArotation\"), dependencies = TRUE)\n",
    "library(tidyr)\n",
    "library(dplyr)\n",
    "library(tidyverse)\n",
    "library(corrplot)\n",
    "library(psych)\n",
    "library(GPArotation)"
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    "<a id=\"2\"></a> <br>\n",
    "### Dataset"
   ]
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       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 25</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>X</th><th scope=col>id</th><th scope=col>Gender</th><th scope=col>Customer.Type</th><th scope=col>Age</th><th scope=col>Type.of.Travel</th><th scope=col>Class</th><th scope=col>Flight.Distance</th><th scope=col>Inflight.wifi.service</th><th scope=col>Departure.Arrival.time.convenient</th><th scope=col>⋯</th><th scope=col>Inflight.entertainment</th><th scope=col>On.board.service</th><th scope=col>Leg.room.service</th><th scope=col>Baggage.handling</th><th scope=col>Checkin.service</th><th scope=col>Inflight.service</th><th scope=col>Cleanliness</th><th scope=col>Departure.Delay.in.Minutes</th><th scope=col>Arrival.Delay.in.Minutes</th><th scope=col>satisfaction</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>⋯</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>0</td><td> 70172</td><td>Male  </td><td>Loyal Customer   </td><td>13</td><td>Personal Travel</td><td>Eco Plus</td><td> 460</td><td>3</td><td>4</td><td>⋯</td><td>5</td><td>4</td><td>3</td><td>4</td><td>4</td><td>5</td><td>5</td><td>25</td><td>18</td><td>neutral or dissatisfied</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>1</td><td>  5047</td><td>Male  </td><td>disloyal Customer</td><td>25</td><td>Business travel</td><td>Business</td><td> 235</td><td>3</td><td>2</td><td>⋯</td><td>1</td><td>1</td><td>5</td><td>3</td><td>1</td><td>4</td><td>1</td><td> 1</td><td> 6</td><td>neutral or dissatisfied</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>2</td><td>110028</td><td>Female</td><td>Loyal Customer   </td><td>26</td><td>Business travel</td><td>Business</td><td>1142</td><td>2</td><td>2</td><td>⋯</td><td>5</td><td>4</td><td>3</td><td>4</td><td>4</td><td>4</td><td>5</td><td> 0</td><td> 0</td><td>satisfied              </td></tr>\n",
       "\t<tr><th scope=row>4</th><td>3</td><td> 24026</td><td>Female</td><td>Loyal Customer   </td><td>25</td><td>Business travel</td><td>Business</td><td> 562</td><td>2</td><td>5</td><td>⋯</td><td>2</td><td>2</td><td>5</td><td>3</td><td>1</td><td>4</td><td>2</td><td>11</td><td> 9</td><td>neutral or dissatisfied</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>4</td><td>119299</td><td>Male  </td><td>Loyal Customer   </td><td>61</td><td>Business travel</td><td>Business</td><td> 214</td><td>3</td><td>3</td><td>⋯</td><td>3</td><td>3</td><td>4</td><td>4</td><td>3</td><td>3</td><td>3</td><td> 0</td><td> 0</td><td>satisfied              </td></tr>\n",
       "\t<tr><th scope=row>6</th><td>5</td><td>111157</td><td>Female</td><td>Loyal Customer   </td><td>26</td><td>Personal Travel</td><td>Eco     </td><td>1180</td><td>3</td><td>4</td><td>⋯</td><td>1</td><td>3</td><td>4</td><td>4</td><td>4</td><td>4</td><td>1</td><td> 0</td><td> 0</td><td>neutral or dissatisfied</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 25\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & X & id & Gender & Customer.Type & Age & Type.of.Travel & Class & Flight.Distance & Inflight.wifi.service & Departure.Arrival.time.convenient & ⋯ & Inflight.entertainment & On.board.service & Leg.room.service & Baggage.handling & Checkin.service & Inflight.service & Cleanliness & Departure.Delay.in.Minutes & Arrival.Delay.in.Minutes & satisfaction\\\\\n",
       "  & <int> & <int> & <chr> & <chr> & <int> & <chr> & <chr> & <int> & <int> & <int> & ⋯ & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t1 & 0 &  70172 & Male   & Loyal Customer    & 13 & Personal Travel & Eco Plus &  460 & 3 & 4 & ⋯ & 5 & 4 & 3 & 4 & 4 & 5 & 5 & 25 & 18 & neutral or dissatisfied\\\\\n",
       "\t2 & 1 &   5047 & Male   & disloyal Customer & 25 & Business travel & Business &  235 & 3 & 2 & ⋯ & 1 & 1 & 5 & 3 & 1 & 4 & 1 &  1 &  6 & neutral or dissatisfied\\\\\n",
       "\t3 & 2 & 110028 & Female & Loyal Customer    & 26 & Business travel & Business & 1142 & 2 & 2 & ⋯ & 5 & 4 & 3 & 4 & 4 & 4 & 5 &  0 &  0 & satisfied              \\\\\n",
       "\t4 & 3 &  24026 & Female & Loyal Customer    & 25 & Business travel & Business &  562 & 2 & 5 & ⋯ & 2 & 2 & 5 & 3 & 1 & 4 & 2 & 11 &  9 & neutral or dissatisfied\\\\\n",
       "\t5 & 4 & 119299 & Male   & Loyal Customer    & 61 & Business travel & Business &  214 & 3 & 3 & ⋯ & 3 & 3 & 4 & 4 & 3 & 3 & 3 &  0 &  0 & satisfied              \\\\\n",
       "\t6 & 5 & 111157 & Female & Loyal Customer    & 26 & Personal Travel & Eco      & 1180 & 3 & 4 & ⋯ & 1 & 3 & 4 & 4 & 4 & 4 & 1 &  0 &  0 & neutral or dissatisfied\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 25\n",
       "\n",
       "| <!--/--> | X &lt;int&gt; | id &lt;int&gt; | Gender &lt;chr&gt; | Customer.Type &lt;chr&gt; | Age &lt;int&gt; | Type.of.Travel &lt;chr&gt; | Class &lt;chr&gt; | Flight.Distance &lt;int&gt; | Inflight.wifi.service &lt;int&gt; | Departure.Arrival.time.convenient &lt;int&gt; | ⋯ ⋯ | Inflight.entertainment &lt;int&gt; | On.board.service &lt;int&gt; | Leg.room.service &lt;int&gt; | Baggage.handling &lt;int&gt; | Checkin.service &lt;int&gt; | Inflight.service &lt;int&gt; | Cleanliness &lt;int&gt; | Departure.Delay.in.Minutes &lt;int&gt; | Arrival.Delay.in.Minutes &lt;dbl&gt; | satisfaction &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 0 |  70172 | Male   | Loyal Customer    | 13 | Personal Travel | Eco Plus |  460 | 3 | 4 | ⋯ | 5 | 4 | 3 | 4 | 4 | 5 | 5 | 25 | 18 | neutral or dissatisfied |\n",
       "| 2 | 1 |   5047 | Male   | disloyal Customer | 25 | Business travel | Business |  235 | 3 | 2 | ⋯ | 1 | 1 | 5 | 3 | 1 | 4 | 1 |  1 |  6 | neutral or dissatisfied |\n",
       "| 3 | 2 | 110028 | Female | Loyal Customer    | 26 | Business travel | Business | 1142 | 2 | 2 | ⋯ | 5 | 4 | 3 | 4 | 4 | 4 | 5 |  0 |  0 | satisfied               |\n",
       "| 4 | 3 |  24026 | Female | Loyal Customer    | 25 | Business travel | Business |  562 | 2 | 5 | ⋯ | 2 | 2 | 5 | 3 | 1 | 4 | 2 | 11 |  9 | neutral or dissatisfied |\n",
       "| 5 | 4 | 119299 | Male   | Loyal Customer    | 61 | Business travel | Business |  214 | 3 | 3 | ⋯ | 3 | 3 | 4 | 4 | 3 | 3 | 3 |  0 |  0 | satisfied               |\n",
       "| 6 | 5 | 111157 | Female | Loyal Customer    | 26 | Personal Travel | Eco      | 1180 | 3 | 4 | ⋯ | 1 | 3 | 4 | 4 | 4 | 4 | 1 |  0 |  0 | neutral or dissatisfied |\n",
       "\n"
      ],
      "text/plain": [
       "  X id     Gender Customer.Type     Age Type.of.Travel  Class   \n",
       "1 0  70172 Male   Loyal Customer    13  Personal Travel Eco Plus\n",
       "2 1   5047 Male   disloyal Customer 25  Business travel Business\n",
       "3 2 110028 Female Loyal Customer    26  Business travel Business\n",
       "4 3  24026 Female Loyal Customer    25  Business travel Business\n",
       "5 4 119299 Male   Loyal Customer    61  Business travel Business\n",
       "6 5 111157 Female Loyal Customer    26  Personal Travel Eco     \n",
       "  Flight.Distance Inflight.wifi.service Departure.Arrival.time.convenient ⋯\n",
       "1  460            3                     4                                 ⋯\n",
       "2  235            3                     2                                 ⋯\n",
       "3 1142            2                     2                                 ⋯\n",
       "4  562            2                     5                                 ⋯\n",
       "5  214            3                     3                                 ⋯\n",
       "6 1180            3                     4                                 ⋯\n",
       "  Inflight.entertainment On.board.service Leg.room.service Baggage.handling\n",
       "1 5                      4                3                4               \n",
       "2 1                      1                5                3               \n",
       "3 5                      4                3                4               \n",
       "4 2                      2                5                3               \n",
       "5 3                      3                4                4               \n",
       "6 1                      3                4                4               \n",
       "  Checkin.service Inflight.service Cleanliness Departure.Delay.in.Minutes\n",
       "1 4               5                5           25                        \n",
       "2 1               4                1            1                        \n",
       "3 4               4                5            0                        \n",
       "4 1               4                2           11                        \n",
       "5 3               3                3            0                        \n",
       "6 4               4                1            0                        \n",
       "  Arrival.Delay.in.Minutes satisfaction           \n",
       "1 18                       neutral or dissatisfied\n",
       "2  6                       neutral or dissatisfied\n",
       "3  0                       satisfied              \n",
       "4  9                       neutral or dissatisfied\n",
       "5  0                       satisfied              \n",
       "6  0                       neutral or dissatisfied"
      ]
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       "<ol class=list-inline><li>103904</li><li>25</li></ol>\n"
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       "\\begin{enumerate*}\n",
       "\\item 103904\n",
       "\\item 25\n",
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       "1. 103904\n",
       "2. 25\n",
       "\n",
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       "[1] 103904     25"
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     "text": [
      "'data.frame':\t103904 obs. of  25 variables:\n",
      " $ X                                : int  0 1 2 3 4 5 6 7 8 9 ...\n",
      " $ id                               : int  70172 5047 110028 24026 119299 111157 82113 96462 79485 65725 ...\n",
      " $ Gender                           : chr  \"Male\" \"Male\" \"Female\" \"Female\" ...\n",
      " $ Customer.Type                    : chr  \"Loyal Customer\" \"disloyal Customer\" \"Loyal Customer\" \"Loyal Customer\" ...\n",
      " $ Age                              : int  13 25 26 25 61 26 47 52 41 20 ...\n",
      " $ Type.of.Travel                   : chr  \"Personal Travel\" \"Business travel\" \"Business travel\" \"Business travel\" ...\n",
      " $ Class                            : chr  \"Eco Plus\" \"Business\" \"Business\" \"Business\" ...\n",
      " $ Flight.Distance                  : int  460 235 1142 562 214 1180 1276 2035 853 1061 ...\n",
      " $ Inflight.wifi.service            : int  3 3 2 2 3 3 2 4 1 3 ...\n",
      " $ Departure.Arrival.time.convenient: int  4 2 2 5 3 4 4 3 2 3 ...\n",
      " $ Ease.of.Online.booking           : int  3 3 2 5 3 2 2 4 2 3 ...\n",
      " $ Gate.location                    : int  1 3 2 5 3 1 3 4 2 4 ...\n",
      " $ Food.and.drink                   : int  5 1 5 2 4 1 2 5 4 2 ...\n",
      " $ Online.boarding                  : int  3 3 5 2 5 2 2 5 3 3 ...\n",
      " $ Seat.comfort                     : int  5 1 5 2 5 1 2 5 3 3 ...\n",
      " $ Inflight.entertainment           : int  5 1 5 2 3 1 2 5 1 2 ...\n",
      " $ On.board.service                 : int  4 1 4 2 3 3 3 5 1 2 ...\n",
      " $ Leg.room.service                 : int  3 5 3 5 4 4 3 5 2 3 ...\n",
      " $ Baggage.handling                 : int  4 3 4 3 4 4 4 5 1 4 ...\n",
      " $ Checkin.service                  : int  4 1 4 1 3 4 3 4 4 4 ...\n",
      " $ Inflight.service                 : int  5 4 4 4 3 4 5 5 1 3 ...\n",
      " $ Cleanliness                      : int  5 1 5 2 3 1 2 4 2 2 ...\n",
      " $ Departure.Delay.in.Minutes       : int  25 1 0 11 0 0 9 4 0 0 ...\n",
      " $ Arrival.Delay.in.Minutes         : num  18 6 0 9 0 0 23 0 0 0 ...\n",
      " $ satisfaction                     : chr  \"neutral or dissatisfied\" \"neutral or dissatisfied\" \"satisfied\" \"neutral or dissatisfied\" ...\n"
     ]
    }
   ],
   "source": [
    "ds <- read.csv(\"/kaggle/input/airline-passenger-satisfaction/train.csv\")\n",
    "head(ds)\n",
    "dim(ds)\n",
    "str(ds)"
   ]
  },
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   "source": [
    "**Observations**\n",
    "\n",
    "- The dataset contains 25 columns and 103904 entries from customers.\n",
    "- Personal information on customers include gender and age.\n",
    "- Flight habits of customers are recorded as customer type, the type of travel they use, class of flight, and the distance they have travelled.\n",
    "- Customer satisfaction is registered over several variables \n",
    "\n",
    "<a id=\"3\"></a> <br>\n",
    "### Cleaning the dataset\n",
    "\n",
    "- Let's check the presence of NA's , outliers, and variables that are not useful"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "id": "dce5a09d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T06:20:14.918508Z",
     "iopub.status.busy": "2022-04-29T06:20:14.916900Z",
     "iopub.status.idle": "2022-04-29T06:20:15.099630Z",
     "shell.execute_reply": "2022-04-29T06:20:15.097695Z"
    },
    "papermill": {
     "duration": 0.203625,
     "end_time": "2022-04-29T06:20:15.102553",
     "exception": false,
     "start_time": "2022-04-29T06:20:14.898928",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "TRUE"
      ],
      "text/latex": [
       "TRUE"
      ],
      "text/markdown": [
       "TRUE"
      ],
      "text/plain": [
       "[1] TRUE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "       X                id            Gender          Customer.Type     \n",
       " Min.   :     0   Min.   :     1   Length:103904      Length:103904     \n",
       " 1st Qu.: 25976   1st Qu.: 32534   Class :character   Class :character  \n",
       " Median : 51952   Median : 64856   Mode  :character   Mode  :character  \n",
       " Mean   : 51952   Mean   : 64924                                        \n",
       " 3rd Qu.: 77927   3rd Qu.: 97368                                        \n",
       " Max.   :103903   Max.   :129880                                        \n",
       "                                                                        \n",
       "      Age        Type.of.Travel        Class           Flight.Distance\n",
       " Min.   : 7.00   Length:103904      Length:103904      Min.   :  31   \n",
       " 1st Qu.:27.00   Class :character   Class :character   1st Qu.: 414   \n",
       " Median :40.00   Mode  :character   Mode  :character   Median : 843   \n",
       " Mean   :39.38                                         Mean   :1189   \n",
       " 3rd Qu.:51.00                                         3rd Qu.:1743   \n",
       " Max.   :85.00                                         Max.   :4983   \n",
       "                                                                      \n",
       " Inflight.wifi.service Departure.Arrival.time.convenient Ease.of.Online.booking\n",
       " Min.   :0.00          Min.   :0.00                      Min.   :0.000         \n",
       " 1st Qu.:2.00          1st Qu.:2.00                      1st Qu.:2.000         \n",
       " Median :3.00          Median :3.00                      Median :3.000         \n",
       " Mean   :2.73          Mean   :3.06                      Mean   :2.757         \n",
       " 3rd Qu.:4.00          3rd Qu.:4.00                      3rd Qu.:4.000         \n",
       " Max.   :5.00          Max.   :5.00                      Max.   :5.000         \n",
       "                                                                               \n",
       " Gate.location   Food.and.drink  Online.boarding  Seat.comfort  \n",
       " Min.   :0.000   Min.   :0.000   Min.   :0.00    Min.   :0.000  \n",
       " 1st Qu.:2.000   1st Qu.:2.000   1st Qu.:2.00    1st Qu.:2.000  \n",
       " Median :3.000   Median :3.000   Median :3.00    Median :4.000  \n",
       " Mean   :2.977   Mean   :3.202   Mean   :3.25    Mean   :3.439  \n",
       " 3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:4.00    3rd Qu.:5.000  \n",
       " Max.   :5.000   Max.   :5.000   Max.   :5.00    Max.   :5.000  \n",
       "                                                                \n",
       " Inflight.entertainment On.board.service Leg.room.service Baggage.handling\n",
       " Min.   :0.000          Min.   :0.000    Min.   :0.000    Min.   :1.000   \n",
       " 1st Qu.:2.000          1st Qu.:2.000    1st Qu.:2.000    1st Qu.:3.000   \n",
       " Median :4.000          Median :4.000    Median :4.000    Median :4.000   \n",
       " Mean   :3.358          Mean   :3.382    Mean   :3.351    Mean   :3.632   \n",
       " 3rd Qu.:4.000          3rd Qu.:4.000    3rd Qu.:4.000    3rd Qu.:5.000   \n",
       " Max.   :5.000          Max.   :5.000    Max.   :5.000    Max.   :5.000   \n",
       "                                                                          \n",
       " Checkin.service Inflight.service  Cleanliness    Departure.Delay.in.Minutes\n",
       " Min.   :0.000   Min.   :0.00     Min.   :0.000   Min.   :   0.00           \n",
       " 1st Qu.:3.000   1st Qu.:3.00     1st Qu.:2.000   1st Qu.:   0.00           \n",
       " Median :3.000   Median :4.00     Median :3.000   Median :   0.00           \n",
       " Mean   :3.304   Mean   :3.64     Mean   :3.286   Mean   :  14.82           \n",
       " 3rd Qu.:4.000   3rd Qu.:5.00     3rd Qu.:4.000   3rd Qu.:  12.00           \n",
       " Max.   :5.000   Max.   :5.00     Max.   :5.000   Max.   :1592.00           \n",
       "                                                                            \n",
       " Arrival.Delay.in.Minutes satisfaction      \n",
       " Min.   :   0.00          Length:103904     \n",
       " 1st Qu.:   0.00          Class :character  \n",
       " Median :   0.00          Mode  :character  \n",
       " Mean   :  15.18                            \n",
       " 3rd Qu.:  13.00                            \n",
       " Max.   :1584.00                            \n",
       " NA's   :310                                "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "any(is.na(ds))\n",
    "summary(ds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1ccabeb",
   "metadata": {
    "papermill": {
     "duration": 0.016247,
     "end_time": "2022-04-29T06:20:15.135184",
     "exception": false,
     "start_time": "2022-04-29T06:20:15.118937",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Observations**\n",
    "- There are 310 missing values in \"Arrival.Delay.in.Minutes\". I will replace the NA's with its median=0 to normalize it.\n",
    "- I will remove column X which is the serial number as it's not needed\n",
    "- Arrival.Delay.in.Minutes is in num datatype, let's change that to int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c7a8c0df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T06:20:15.170887Z",
     "iopub.status.busy": "2022-04-29T06:20:15.169311Z",
     "iopub.status.idle": "2022-04-29T06:20:15.419457Z",
     "shell.execute_reply": "2022-04-29T06:20:15.417547Z"
    },
    "papermill": {
     "duration": 0.270886,
     "end_time": "2022-04-29T06:20:15.422317",
     "exception": false,
     "start_time": "2022-04-29T06:20:15.151431",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "FALSE"
      ],
      "text/latex": [
       "FALSE"
      ],
      "text/markdown": [
       "FALSE"
      ],
      "text/plain": [
       "[1] FALSE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds <- subset(ds,select = -X)\n",
    "ds$Departure.Delay.in.Minutes = as.numeric(ds$Departure.Delay.in.Minutes)\n",
    "ds$Arrival.Delay.in.Minutes[is.na(ds$Arrival.Delay.in.Minutes)] <-0\n",
    "any(is.na(ds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86767673",
   "metadata": {
    "papermill": {
     "duration": 0.016506,
     "end_time": "2022-04-29T06:20:15.455267",
     "exception": false,
     "start_time": "2022-04-29T06:20:15.438761",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Now that the NA's are replaced, let's look at the plot of outliers in numerical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "347884d1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T06:20:15.491870Z",
     "iopub.status.busy": "2022-04-29T06:20:15.490069Z",
     "iopub.status.idle": "2022-04-29T06:20:23.532503Z",
     "shell.execute_reply": "2022-04-29T06:20:23.344195Z"
    },
    "papermill": {
     "duration": 8.064482,
     "end_time": "2022-04-29T06:20:23.536147",
     "exception": false,
     "start_time": "2022-04-29T06:20:15.471665",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 840
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_num<-select_if(ds,is.numeric)%>%select(-id)\n",
    "ds_num_p<-ds_num %>% gather(variable,values,1:18 )\n",
    "options(repr.plot.width = 14, repr.plot.height = 8)\n",
    "ggplot(ds_num_p)+\n",
    "  geom_boxplot(aes(x=variable,y=values),fill=\"cadetblue\") + \n",
    "  facet_wrap(~variable,ncol=6,scales=\"free\") + \n",
    "  theme(strip.text.x = element_blank(),\n",
    "        text = element_text(size=14))  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "507d37b5",
   "metadata": {
    "papermill": {
     "duration": 0.017685,
     "end_time": "2022-04-29T06:20:23.572075",
     "exception": false,
     "start_time": "2022-04-29T06:20:23.554390",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Observation**\n",
    "\n",
    "- The variables with outliers are departure, arrival, flight distance, and rating on check-in service. None of these variables can be modified as they contain subjective data. \n",
    "\n",
    "**Outcome Variable**\n",
    "\n",
    "- This is the dependent variable in the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "201e6c84",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T06:20:23.610975Z",
     "iopub.status.busy": "2022-04-29T06:20:23.609245Z",
     "iopub.status.idle": "2022-04-29T06:20:24.121404Z",
     "shell.execute_reply": "2022-04-29T06:20:24.118470Z"
    },
    "papermill": {
     "duration": 0.534629,
     "end_time": "2022-04-29T06:20:24.124395",
     "exception": false,
     "start_time": "2022-04-29T06:20:23.589766",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 840
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 14, repr.plot.height = 8)\n",
    "ggplot(ds, aes(x=`satisfaction`))+\n",
    "  geom_bar(fill=\"cadetblue\", alpha=0.6)+\n",
    "  geom_text(aes(label=scales::percent((..count..)/sum(..count..))), stat=\"count\", vjust = -0.8, size=6)+\n",
    "  stat_count(aes(y=..count..,label=paste0(\"n=\",..count..)),geom=\"text\",vjust=1.2,size=5.8,color=\"gray35\")+\n",
    "  labs(x=\"Satisfaction\", title = \"Distribution of Customer Satisfaction\")+\n",
    "  theme(text = element_text(size=16)) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80a09161",
   "metadata": {
    "papermill": {
     "duration": 0.019027,
     "end_time": "2022-04-29T06:20:24.162803",
     "exception": false,
     "start_time": "2022-04-29T06:20:24.143776",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id=\"4\"></a> <br>\n",
    "### Correlation Matrix\n",
    "\n",
    "Let's take a look at the correlation matrix of the dataset. This will be done in 2 parts:\n",
    "\n",
    "1) Numerical variables\n",
    "\n",
    "2) Categorical variables\n",
    "\n",
    "**Numerical variable Correlation Plot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "06938357",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T06:20:24.204774Z",
     "iopub.status.busy": "2022-04-29T06:20:24.203047Z",
     "iopub.status.idle": "2022-04-29T06:20:24.391577Z",
     "shell.execute_reply": "2022-04-29T06:20:24.389780Z"
    },
    "papermill": {
     "duration": 0.211894,
     "end_time": "2022-04-29T06:20:24.393762",
     "exception": false,
     "start_time": "2022-04-29T06:20:24.181868",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      Age           Flight.Distance     Departure.Delay.in.Minutes\n",
       " Min.   :-0.01210   Min.   :-0.002470   Min.   :-0.0101522        \n",
       " 1st Qu.:-0.01064   1st Qu.: 0.001001   1st Qu.:-0.0009195        \n",
       " Median : 0.04465   Median : 0.050809   Median : 0.4812026        \n",
       " Mean   : 0.26930   Mean   : 0.274787   Mean   : 0.4880632        \n",
       " 3rd Qu.: 0.32460   3rd Qu.: 0.324596   3rd Qu.: 0.9701853        \n",
       " Max.   : 1.00000   Max.   : 1.000000   Max.   : 1.0000000        \n",
       " Arrival.Delay.in.Minutes\n",
       " Min.   :-0.012105       \n",
       " 1st Qu.:-0.004879       \n",
       " Median : 0.478888       \n",
       " Mean   : 0.486418       \n",
       " 3rd Qu.: 0.970185       \n",
       " Max.   : 1.000000       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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ACADj+/GvDEwpOWNh49A7s62+iobxISEgyfCvoia9Sr501/f9m6M5kF1SO2zX1H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\nDAi6ePiDyKfvG5r1+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     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 840
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_cor1 <- cor(subset(ds,select = c(Age, Flight.Distance,Departure.Delay.in.Minutes,Arrival.Delay.in.Minutes)))\n",
    "summary(ds_cor1)\n",
    "options(repr.plot.width = 14, repr.plot.height = 8)\n",
    "corrplot(ds_cor1, na.label = \" \", method=\"color\", tl.col = \"black\", tl.cex = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b46a7256",
   "metadata": {
    "papermill": {
     "duration": 0.019769,
     "end_time": "2022-04-29T06:20:24.433381",
     "exception": false,
     "start_time": "2022-04-29T06:20:24.413612",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Categorical variable Correlation Plot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e745113f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T06:20:24.476737Z",
     "iopub.status.busy": "2022-04-29T06:20:24.475092Z",
     "iopub.status.idle": "2022-04-29T06:20:24.742690Z",
     "shell.execute_reply": "2022-04-29T06:20:24.740824Z"
    },
    "papermill": {
     "duration": 0.291719,
     "end_time": "2022-04-29T06:20:24.745202",
     "exception": false,
     "start_time": "2022-04-29T06:20:24.453483",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       " Inflight.wifi.service Departure.Arrival.time.convenient Ease.of.Online.booking\n",
       " Min.   :0.04319       Min.   :-0.004861                 Min.   :0.01108       \n",
       " 1st Qu.:0.12179       1st Qu.: 0.012904                 1st Qu.:0.03272       \n",
       " Median :0.14760       Median : 0.071122                 Median :0.04293       \n",
       " Mean   :0.28635       Mean   : 0.188676                 Mean   :0.24087       \n",
       " 3rd Qu.:0.34195       3rd Qu.: 0.281217                 3rd Qu.:0.42874       \n",
       " Max.   :1.00000       Max.   : 1.000000                 Max.   :1.00000       \n",
       " Gate.location       Food.and.drink      Online.boarding     Seat.comfort     \n",
       " Min.   :-0.035428   Min.   :-0.001159   Min.   :0.001688   Min.   :0.003669  \n",
       " 1st Qu.:-0.003162   1st Qu.: 0.032871   1st Qu.:0.093448   1st Qu.:0.070549  \n",
       " Median : 0.002001   Median : 0.073186   Median :0.219465   Median :0.127314  \n",
       " Mean   : 0.155562   Mean   : 0.250517   Mean   :0.274701   Mean   :0.287480  \n",
       " 3rd Qu.: 0.253103   3rd Qu.: 0.489534   3rd Qu.:0.385935   3rd Qu.:0.535970  \n",
       " Max.   : 1.000000   Max.   : 1.000000   Max.   :1.000000   Max.   :1.000000  \n",
       " Inflight.entertainment On.board.service   Leg.room.service   \n",
       " Min.   :-0.004861      Min.   :-0.02837   Min.   :-0.005873  \n",
       " 1st Qu.: 0.142981      1st Qu.: 0.08204   1st Qu.: 0.098668  \n",
       " Median : 0.338951      Median : 0.14371   Median : 0.138544  \n",
       " Mean   : 0.363484      Mean   : 0.26857   Mean   : 0.227110  \n",
       " 3rd Qu.: 0.562981      3rd Qu.: 0.40399   3rd Qu.: 0.341544  \n",
       " Max.   : 1.000000      Max.   : 1.00000   Max.   : 1.000000  \n",
       " Baggage.handling   Checkin.service    Inflight.service    Cleanliness      \n",
       " Min.   :0.002313   Min.   :-0.03543   Min.   :0.001681   Min.   :-0.00383  \n",
       " 1st Qu.:0.072730   1st Qu.: 0.08881   1st Qu.:0.070243   1st Qu.: 0.09053  \n",
       " Median :0.108358   Median : 0.16636   Median :0.099610   Median : 0.12796  \n",
       " Mean   :0.260790   Mean   : 0.19740   Mean   :0.262666   Mean   : 0.29305  \n",
       " 3rd Qu.:0.376043   3rd Qu.: 0.22596   3rd Qu.:0.395805   3rd Qu.: 0.57620  \n",
       " Max.   :1.000000   Max.   : 1.00000   Max.   :1.000000   Max.   : 1.00000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nNp6fDW1cd/3bPYd/jS5iFbr+97VqbSaDgam5s1GfB1xrfDlW6koSjI2OLbdpJ5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\nD0FEekLIRfw/Pz7AZDJnZpJAOJm5wzJEdNGvbMsTFWUR+woot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R0AAAAAAAAUWXZmEiJiGJ22xDJMWgt5pJK/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\nd7uXySHoq1v3zhjTS05tDpv3QcThiy0mfBa1J3z5xJ4+tlO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ZNabXPMJMRQAAIABJREFUAWBJLADgAXhi4S//Nvd89v2NI9pWGRdaq2H1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\nnMMYkqK/ymWU/o2Cnbo7AAA8j84xH19RFBdOkVCU2z0AgBuRsAMAFHSpq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C1o4FeLg02KiLz17eyFFdoMa115VZOWtapEHdq66uuNh8Pv7DL/0fJXRrvt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\nAIE1AAAFQ0lEQVTavXfHl3G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     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 840
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_cor2 <- cor(select_if(subset(ds, select=-c(id, Age, Flight.Distance,Departure.Delay.in.Minutes,Arrival.Delay.in.Minutes)), is.numeric))\n",
    "summary(ds_cor2)\n",
    "options(repr.plot.width = 14, repr.plot.height = 8)\n",
    "corrplot(ds_cor2, na.label=\" \", tl.cex=1, tl.col=\"black\", method=\"color\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "251f752a",
   "metadata": {
    "papermill": {
     "duration": 0.023118,
     "end_time": "2022-04-29T06:20:24.791211",
     "exception": false,
     "start_time": "2022-04-29T06:20:24.768093",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Observations**\n",
    "\n",
    "- There is a high positive correlation between arrival delay and departure delay. If a flight departs late, it is likely to arrive late as well. \n",
    "\n",
    "- Variables such as seat comfort, food and drink, and in-flight entertainment tend to have a positive correlation.\n",
    "- Variables such as WiFi service, online booking, and gate location tend to have a positive correlation.\n",
    "- Cleanliness is correlated with gate location, food and drink, seat comfort, and in-flight entertainment. \n",
    "\n",
    "<a id=\"5\"></a> <br>\n",
    "### Exploratory Factor Analysis\n",
    "\n",
    "#### Step 1: Check factorability\n",
    "\n",
    "Let's check whether the dataset is factorable using :\n",
    "\n",
    "- The Kaiser-Meyer-Olkin test \n",
    "- Barlett test\n",
    "\n",
    "**Kaiser-Meyer-Olkin test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ce0c5cec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T06:20:24.839463Z",
     "iopub.status.busy": "2022-04-29T06:20:24.837867Z",
     "iopub.status.idle": "2022-04-29T06:20:24.896666Z",
     "shell.execute_reply": "2022-04-29T06:20:24.892973Z"
    },
    "papermill": {
     "duration": 0.086872,
     "end_time": "2022-04-29T06:20:24.900599",
     "exception": false,
     "start_time": "2022-04-29T06:20:24.813727",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Kaiser-Meyer-Olkin factor adequacy\n",
       "Call: KMO(r = ds_cor2)\n",
       "Overall MSA =  0.78\n",
       "MSA for each item = \n",
       "            Inflight.wifi.service Departure.Arrival.time.convenient \n",
       "                             0.74                              0.75 \n",
       "           Ease.of.Online.booking                     Gate.location \n",
       "                             0.68                              0.71 \n",
       "                   Food.and.drink                   Online.boarding \n",
       "                             0.84                              0.74 \n",
       "                     Seat.comfort            Inflight.entertainment \n",
       "                             0.83                              0.77 \n",
       "                 On.board.service                  Leg.room.service \n",
       "                             0.83                              0.89 \n",
       "                 Baggage.handling                   Checkin.service \n",
       "                             0.81                              0.70 \n",
       "                 Inflight.service                       Cleanliness \n",
       "                             0.78                              0.82 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "KMO(ds_cor2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c45630a5",
   "metadata": {
    "papermill": {
     "duration": 0.039727,
     "end_time": "2022-04-29T06:20:24.980524",
     "exception": false,
     "start_time": "2022-04-29T06:20:24.940797",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Observation**\n",
    "\n",
    "- The KMO test indicates that the dataset is fit for factor analysis as the overall MSA is 0.78 (Overall MSA should be 0-1.0 & >.60).\n",
    "- The individual MSA of each variable is > 0.60, which suggests none of them have to be removed for EFA\n",
    "\n",
    "**Barlett Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2b87e9b7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T06:20:25.030211Z",
     "iopub.status.busy": "2022-04-29T06:20:25.028625Z",
     "iopub.status.idle": "2022-04-29T06:20:25.051939Z",
     "shell.execute_reply": "2022-04-29T06:20:25.050101Z"
    },
    "papermill": {
     "duration": 0.051365,
     "end_time": "2022-04-29T06:20:25.054829",
     "exception": false,
     "start_time": "2022-04-29T06:20:25.003464",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in cortest.bartlett(ds_cor2):\n",
      "“n not specified, 100 used”\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$chisq</dt>\n",
       "\t\t<dd>541.464323738045</dd>\n",
       "\t<dt>$p.value</dt>\n",
       "\t\t<dd>3.1488512140558e-65</dd>\n",
       "\t<dt>$df</dt>\n",
       "\t\t<dd>91</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$chisq] 541.464323738045\n",
       "\\item[\\$p.value] 3.1488512140558e-65\n",
       "\\item[\\$df] 91\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$chisq\n",
       ":   541.464323738045\n",
       "$p.value\n",
       ":   3.1488512140558e-65\n",
       "$df\n",
       ":   91\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$chisq\n",
       "[1] 541.4643\n",
       "\n",
       "$p.value\n",
       "[1] 3.148851e-65\n",
       "\n",
       "$df\n",
       "[1] 91\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cortest.bartlett(ds_cor2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3ed11dd",
   "metadata": {
    "papermill": {
     "duration": 0.022445,
     "end_time": "2022-04-29T06:20:25.099876",
     "exception": false,
     "start_time": "2022-04-29T06:20:25.077431",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Observation**\n",
    "\n",
    "- Barlett test have a p-value < 0.05, indicating a go-ahead for EFA\n",
    "\n",
    "#### Step 2: Identify the number of factors\n",
    "\n",
    "I will use the following methods to determine the number of factors:\n",
    "\n",
    "- Eigen value method\n",
    "- Scree plot\n",
    "\n",
    "**Eigen value method**\n",
    "\n",
    "Eigen values is a measure that ethat says how much varianceof the variables can a factor explain. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "655feaf3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T06:20:25.148176Z",
     "iopub.status.busy": "2022-04-29T06:20:25.146710Z",
     "iopub.status.idle": "2022-04-29T06:20:25.534972Z",
     "shell.execute_reply": "2022-04-29T06:20:25.532230Z"
    },
    "papermill": {
     "duration": 0.415457,
     "end_time": "2022-04-29T06:20:25.537832",
     "exception": false,
     "start_time": "2022-04-29T06:20:25.122375",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Eigen values of variables with ev>=1:  \n",
      " \n",
      " 3.800117 2.361986 2.165892 1.063274 \n",
      " \n",
      " Sum of eigen values where ev >1 : \n",
      " 9.391269 \n",
      " "
     ]
    },
    {
     "data": {
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Kcff8px95+zROn3Mtdy25928YZLhRBeP2ts8yWTLhg1N91U0X3be/686Q8+fcCw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\nfgf0P+CQEELNZ1fnORYAAAAAtE/ZBbvP6tIhhHU6ls3vgJKqVUM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     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 840
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.eigen <- data.frame(eigen(ds_cor2)$values)\n",
    "colnames(ds.eigen)<-\"eigen_value\"\n",
    "ds.eigen$type <- ifelse(ds.eigen$`eigen_value`>=1,\">=1\",\"<1\")\n",
    "\n",
    "options(repr.plot.width = 14, repr.plot.height = 8)\n",
    "ggplot(ds.eigen, aes(x= reorder(rownames(ds.eigen),-`eigen_value`) ,y=`eigen_value`, fill=`type`))+\n",
    "  geom_col()+\n",
    "  labs(x=\"Variables\", y=\"Eigen Value\")+\n",
    "  scale_fill_manual(values=c(\"darkslategray2\", \"cadetblue3\"))+\n",
    "  ylim(0,4.5)+\n",
    "theme(text=element_text(size=16))\n",
    "\n",
    "cat(\"\\n Eigen values of variables with ev>=1: \" , \"\\n \\n\", \n",
    "    ds.eigen$eigen_value[c(1,2,3,4)] , \"\\n \\n\",\n",
    "    \"Sum of eigen values where ev >1 : \\n\" , sum(ds.eigen$eigen_value[c(1,2,3,4)]), \"\\n \")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac29db21",
   "metadata": {
    "papermill": {
     "duration": 0.02373,
     "end_time": "2022-04-29T06:20:25.585613",
     "exception": false,
     "start_time": "2022-04-29T06:20:25.561883",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Observation**\n",
    "\n",
    "- Eigen value method indicates a possiblity of 4 factors (or latent variables)\n",
    "- The sum of eigen values (where eigen value >=1) explains the variance of 9.3 variables.\n",
    "\n",
    "**Scree plot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0239d5f7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T06:20:25.635859Z",
     "iopub.status.busy": "2022-04-29T06:20:25.634177Z",
     "iopub.status.idle": "2022-04-29T06:20:25.850131Z",
     "shell.execute_reply": "2022-04-29T06:20:25.848111Z"
    },
    "papermill": {
     "duration": 0.243463,
     "end_time": "2022-04-29T06:20:25.852343",
     "exception": false,
     "start_time": "2022-04-29T06:20:25.608880",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QSDBg3Kycl5/PHHww4BAAAAwGBHEPTq1SslJeWRRx4pLi4OuwUAAACgpjPYESQn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\nzz77hN0SBEEQiUTS09OvvPLKCRMm9OvXL+wcAAAAgJBVpRHqN+O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qL4R5kG5Ux15eppAyQNDY2dO3dmZWWJDoFqaGhohISE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\nJyckJCQlJT25ce7cuaKiojLnmpu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     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 840
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 14, repr.plot.height = 8)\n",
    "scree(ds_cor2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "413c74c8",
   "metadata": {
    "papermill": {
     "duration": 0.024688,
     "end_time": "2022-04-29T06:20:25.902524",
     "exception": false,
     "start_time": "2022-04-29T06:20:25.877836",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Observation**\n",
    "\n",
    "- Scree plot suggests 3 factors."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3031217",
   "metadata": {
    "papermill": {
     "duration": 0.02444,
     "end_time": "2022-04-29T06:20:25.951644",
     "exception": false,
     "start_time": "2022-04-29T06:20:25.927204",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Step 3: Determining rotation (orthogonal or oblique)\n",
    "\n",
    "- Orthogonal rotation (eg. varimax) should be used when the correlation between the factors is at least <0.3 (a general rule of thumb). Statistically, it is easier to interpret the results of orthogonally rotated factors because the factors are assumed to be uncorrelated. \n",
    "\n",
    "- Oblique rotation (eg. promax) should be used when the correlation between the factors is >0.3. Oblique rotation is less restrictive, i.e., they don't force factors to correlate but they allow it. In cases where factors are not distinct and load common variables, although it becomes complex to explain, may give a better explanation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9429c176",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T06:20:26.004265Z",
     "iopub.status.busy": "2022-04-29T06:20:26.002736Z",
     "iopub.status.idle": "2022-04-29T06:20:26.226400Z",
     "shell.execute_reply": "2022-04-29T06:20:26.224520Z"
    },
    "papermill": {
     "duration": 0.255575,
     "end_time": "2022-04-29T06:20:26.231561",
     "exception": false,
     "start_time": "2022-04-29T06:20:25.975986",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Factor Analysis using method =  minres\n",
       "Call: fa(r = ds_cor2, nfactors = 3, rotate = \"none\")\n",
       "Standardized loadings (pattern matrix) based upon correlation matrix\n",
       "                                   MR1   MR2   MR3   h2   u2 com\n",
       "Inflight.wifi.service             0.43  0.65 -0.10 0.62 0.38 1.8\n",
       "Departure.Arrival.time.convenient 0.17  0.48 -0.01 0.26 0.74 1.2\n",
       "Ease.of.Online.booking            0.32  0.87 -0.13 0.87 0.13 1.3\n",
       "Gate.location                     0.11  0.49 -0.08 0.26 0.74 1.2\n",
       "Food.and.drink                    0.58 -0.24 -0.42 0.57 0.43 2.2\n",
       "Online.boarding                   0.47  0.20 -0.18 0.30 0.70 1.7\n",
       "Seat.comfort                      0.65 -0.24 -0.39 0.63 0.37 1.9\n",
       "Inflight.entertainment            0.84 -0.27 -0.05 0.77 0.23 1.2\n",
       "On.board.service                  0.49 -0.05  0.51 0.50 0.50 2.0\n",
       "Leg.room.service                  0.36  0.02  0.34 0.24 0.76 2.0\n",
       "Baggage.handling                  0.47 -0.03  0.60 0.59 0.41 1.9\n",
       "Checkin.service                   0.28 -0.04  0.15 0.10 0.90 1.5\n",
       "Inflight.service                  0.48 -0.04  0.64 0.64 0.36 1.9\n",
       "Cleanliness                       0.70 -0.29 -0.42 0.74 0.26 2.0\n",
       "\n",
       "                       MR1  MR2  MR3\n",
       "SS loadings           3.40 1.96 1.73\n",
       "Proportion Var        0.24 0.14 0.12\n",
       "Cumulative Var        0.24 0.38 0.51\n",
       "Proportion Explained  0.48 0.28 0.24\n",
       "Cumulative Proportion 0.48 0.76 1.00\n",
       "\n",
       "Mean item complexity =  1.7\n",
       "Test of the hypothesis that 3 factors are sufficient.\n",
       "\n",
       "The degrees of freedom for the null model are  91  and the objective function was  5.79\n",
       "The degrees of freedom for the model are 52  and the objective function was  0.5 \n",
       "\n",
       "The root mean square of the residuals (RMSR) is  0.04 \n",
       "The df corrected root mean square of the residuals is  0.06 \n",
       "\n",
       "Fit based upon off diagonal values = 0.98\n",
       "Measures of factor score adequacy             \n",
       "                                                   MR1  MR2  MR3\n",
       "Correlation of (regression) scores with factors   0.96 0.95 0.90\n",
       "Multiple R square of scores with factors          0.91 0.90 0.81\n",
       "Minimum correlation of possible factor scores     0.82 0.80 0.63"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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/hQEeQoiSi9quncdmlclC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zqubm5vr6+p54ZSrDYrFYJiYmhBAOh0Pt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\nZ61cf7ytmVwIoVFlX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50hOG1t7Uat5Y/aPbFY/OrVq5rtuc8Tnaul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\nxGJxSEjIpk2bLly4IJFIbG1tv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/vkj07r+dAAAAAGqEJbGtRnH6Tg+PcOa1uLIk9UncjXsvWCzWiKVHd44xlnZzWHf20/OW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\nhOIAAAAgAElEQVRafX092yQ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     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 840
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# No rotation \n",
    "ds.fa1 <- fa(ds_cor2, 3,rotate= \"none\")\n",
    "ds.fa1\n",
    "fa.diagram(ds.fa1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "174bec90",
   "metadata": {
    "papermill": {
     "duration": 0.033782,
     "end_time": "2022-04-29T06:20:26.300339",
     "exception": false,
     "start_time": "2022-04-29T06:20:26.266557",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Observation: no rotation**\n",
    "\n",
    "- with no rotation, you can see that the variables load onto more than 1 factor. For eg. , Inflight.service has loadings 0.48 and 0.64 for factors 1 and 3, respectively. \n",
    "\n",
    "- each factor loads at least 3 variables. \n",
    "\n",
    "- Cross loading is present\n",
    "\n",
    "- To get a simple structure, i.e., variables loading distinctly onto 1 factor, I'd like to see the results after applying a rotation technique. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e407b2c8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T06:20:26.358313Z",
     "iopub.status.busy": "2022-04-29T06:20:26.356787Z",
     "iopub.status.idle": "2022-04-29T06:20:26.577021Z",
     "shell.execute_reply": "2022-04-29T06:20:26.574377Z"
    },
    "papermill": {
     "duration": 0.253042,
     "end_time": "2022-04-29T06:20:26.580810",
     "exception": false,
     "start_time": "2022-04-29T06:20:26.327768",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Factor Analysis using method =  minres\n",
       "Call: fa(r = ds_cor2, nfactors = 3, rotate = \"promax\")\n",
       "Standardized loadings (pattern matrix) based upon correlation matrix\n",
       "                                    MR1   MR3   MR2   h2   u2 com\n",
       "Inflight.wifi.service              0.10  0.06  0.75 0.62 0.38 1.0\n",
       "Departure.Arrival.time.convenient -0.08  0.04  0.51 0.26 0.74 1.1\n",
       "Ease.of.Online.booking            -0.04 -0.04  0.95 0.87 0.13 1.0\n",
       "Gate.location                     -0.07 -0.06  0.52 0.26 0.74 1.1\n",
       "Food.and.drink                     0.80 -0.13 -0.06 0.57 0.43 1.1\n",
       "Online.boarding                    0.37  0.02  0.33 0.30 0.70 2.0\n",
       "Seat.comfort                       0.82 -0.07 -0.04 0.63 0.37 1.0\n",
       "Inflight.entertainment             0.71  0.35 -0.08 0.77 0.23 1.5\n",
       "On.board.service                  -0.03  0.72 -0.02 0.50 0.50 1.0\n",
       "Leg.room.service                  -0.01  0.49  0.05 0.24 0.76 1.0\n",
       "Baggage.handling                  -0.11  0.80 -0.02 0.59 0.41 1.0\n",
       "Checkin.service                    0.10  0.27  0.00 0.10 0.90 1.3\n",
       "Inflight.service                  -0.12  0.84 -0.03 0.64 0.36 1.0\n",
       "Cleanliness                        0.90 -0.07 -0.08 0.74 0.26 1.0\n",
       "\n",
       "                       MR1  MR3  MR2\n",
       "SS loadings           2.74 2.27 2.08\n",
       "Proportion Var        0.20 0.16 0.15\n",
       "Cumulative Var        0.20 0.36 0.51\n",
       "Proportion Explained  0.39 0.32 0.29\n",
       "Cumulative Proportion 0.39 0.71 1.00\n",
       "\n",
       " With factor correlations of \n",
       "     MR1  MR3  MR2\n",
       "MR1 1.00 0.35 0.19\n",
       "MR3 0.35 1.00 0.16\n",
       "MR2 0.19 0.16 1.00\n",
       "\n",
       "Mean item complexity =  1.2\n",
       "Test of the hypothesis that 3 factors are sufficient.\n",
       "\n",
       "The degrees of freedom for the null model are  91  and the objective function was  5.79\n",
       "The degrees of freedom for the model are 52  and the objective function was  0.5 \n",
       "\n",
       "The root mean square of the residuals (RMSR) is  0.04 \n",
       "The df corrected root mean square of the residuals is  0.06 \n",
       "\n",
       "Fit based upon off diagonal values = 0.98\n",
       "Measures of factor score adequacy             \n",
       "                                                   MR1  MR3  MR2\n",
       "Correlation of (regression) scores with factors   0.95 0.92 0.95\n",
       "Multiple R square of scores with factors          0.90 0.85 0.91\n",
       "Minimum correlation of possible factor scores     0.80 0.70 0.82"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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uf90VKJNZefgMzS6rNo5p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\ng058mq37gKPT/tSBgWKx6NeJZyQt10c3ZzAYwoL4eQPbqapyl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AjUR8fPzgwYOVlJROnTplYGCg6HAAAACgcpCwA0XSsune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\nmPSV/3fb+w/+Mv1PauU1iir31oDBexlM3tJtr90B1hiqvCt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JRGL8hrW1tZWVlbGGzp07Z2RkODs75+XllfH/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\ns9bhAAAP8UlEQVTCaxOf69d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     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 840
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Oblique (promax) rotation \n",
    "options(repr.plot.width = 14, repr.plot.height = 8)\n",
    "ds.fa2 <- fa(ds_cor2, 3,rotate=\"promax\")\n",
    "ds.fa2\n",
    "fa.diagram(ds.fa2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ea2dcb2",
   "metadata": {
    "papermill": {
     "duration": 0.029572,
     "end_time": "2022-04-29T06:20:26.640378",
     "exception": false,
     "start_time": "2022-04-29T06:20:26.610806",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Observation: Oblique (promax) rotation**\n",
    "\n",
    "- Factors 1 and 3 have a correlation of 0.35 \n",
    "\n",
    "- Factor 2 does not have high correlation with factors 1 and 3\n",
    "\n",
    "- Since the factors are not highly correlated to one another, promax is not the ideal choice of rotation. Thus, we can move to orthogonal rotation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "22030e29",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T06:20:26.702189Z",
     "iopub.status.busy": "2022-04-29T06:20:26.700620Z",
     "iopub.status.idle": "2022-04-29T06:20:26.953756Z",
     "shell.execute_reply": "2022-04-29T06:20:26.951892Z"
    },
    "papermill": {
     "duration": 0.286934,
     "end_time": "2022-04-29T06:20:26.956542",
     "exception": false,
     "start_time": "2022-04-29T06:20:26.669608",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Factor Analysis using method =  minres\n",
       "Call: fa(r = ds_cor2, nfactors = 3, rotate = \"varimax\")\n",
       "Standardized loadings (pattern matrix) based upon correlation matrix\n",
       "                                    MR1   MR3  MR2   h2   u2 com\n",
       "Inflight.wifi.service              0.17  0.13 0.76 0.62 0.38 1.2\n",
       "Departure.Arrival.time.convenient -0.03  0.06 0.50 0.26 0.74 1.0\n",
       "Ease.of.Online.booking             0.03  0.02 0.93 0.87 0.13 1.0\n",
       "Gate.location                     -0.03 -0.03 0.50 0.26 0.74 1.0\n",
       "Food.and.drink                     0.75  0.01 0.01 0.57 0.43 1.0\n",
       "Online.boarding                    0.40  0.11 0.36 0.30 0.70 2.2\n",
       "Seat.comfort                       0.79  0.08 0.03 0.63 0.37 1.0\n",
       "Inflight.entertainment             0.75  0.47 0.01 0.77 0.23 1.7\n",
       "On.board.service                   0.09  0.70 0.03 0.50 0.50 1.0\n",
       "Leg.room.service                   0.07  0.48 0.08 0.24 0.76 1.1\n",
       "Baggage.handling                   0.02  0.76 0.03 0.59 0.41 1.0\n",
       "Checkin.service                    0.14  0.28 0.03 0.10 0.90 1.5\n",
       "Inflight.service                   0.02  0.80 0.02 0.64 0.36 1.0\n",
       "Cleanliness                        0.86  0.09 0.00 0.74 0.26 1.0\n",
       "\n",
       "                       MR1  MR3  MR2\n",
       "SS loadings           2.71 2.30 2.09\n",
       "Proportion Var        0.19 0.16 0.15\n",
       "Cumulative Var        0.19 0.36 0.51\n",
       "Proportion Explained  0.38 0.32 0.29\n",
       "Cumulative Proportion 0.38 0.71 1.00\n",
       "\n",
       "Mean item complexity =  1.2\n",
       "Test of the hypothesis that 3 factors are sufficient.\n",
       "\n",
       "The degrees of freedom for the null model are  91  and the objective function was  5.79\n",
       "The degrees of freedom for the model are 52  and the objective function was  0.5 \n",
       "\n",
       "The root mean square of the residuals (RMSR) is  0.04 \n",
       "The df corrected root mean square of the residuals is  0.06 \n",
       "\n",
       "Fit based upon off diagonal values = 0.98\n",
       "Measures of factor score adequacy             \n",
       "                                                   MR1  MR3  MR2\n",
       "Correlation of (regression) scores with factors   0.94 0.91 0.95\n",
       "Multiple R square of scores with factors          0.89 0.83 0.91\n",
       "Minimum correlation of possible factor scores     0.77 0.67 0.81"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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uf90VKJNZefgMzS6rNo5p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\nbcdf66b15XbPWu5nVU+fjw6pVKqdO3fOmDEjJye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RdwXl3ThVrBk0MmGRZJKYXzbG8M2XWVH+Kkcs1WLZ1Gcm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\nKZsLCAhwcHCo2ukcfN5YNfSLfp9P92t6ZmCH8JsJOxet3ac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neqivhzy6f/l6JvPWm8eek/9he7CCHEWLQkaOpJv93XOjj3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\n4Jjwji3rXzq8b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     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 840
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Orthogonal (varimax) rotation\n",
    "options(repr.plot.width = 14, repr.plot.height = 8)\n",
    "ds.fa3 <- fa(ds_cor2, 3, rotate = \"varimax\")\n",
    "ds.fa3\n",
    "fa.diagram(ds.fa3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cbc0d4e",
   "metadata": {
    "papermill": {
     "duration": 0.032072,
     "end_time": "2022-04-29T06:20:27.021491",
     "exception": false,
     "start_time": "2022-04-29T06:20:26.989419",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Observation: Orthogonal (varimax) rotation**\n",
    "\n",
    "- Each factor loads at least 4 variables- this is a positive indication for retaining the factors\n",
    "\n",
    "- Only check-in service did not load onto any factor\n",
    "\n",
    "- The loading for each variable onto the factor that it loads is atleast 0.3 (cutoff). \n",
    "\n",
    "Now let's take a closer look at the loadings. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e6f79e08",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T06:20:27.087330Z",
     "iopub.status.busy": "2022-04-29T06:20:27.085659Z",
     "iopub.status.idle": "2022-04-29T06:20:27.101731Z",
     "shell.execute_reply": "2022-04-29T06:20:27.099596Z"
    },
    "papermill": {
     "duration": 0.051202,
     "end_time": "2022-04-29T06:20:27.103923",
     "exception": false,
     "start_time": "2022-04-29T06:20:27.052721",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Loadings:\n",
      "                                  MR1    MR3    MR2   \n",
      "Inflight.wifi.service                            0.758\n",
      "Departure.Arrival.time.convenient                0.501\n",
      "Ease.of.Online.booking                           0.933\n",
      "Gate.location                                    0.504\n",
      "Food.and.drink                     0.753              \n",
      "Online.boarding                    0.395         0.359\n",
      "Seat.comfort                       0.790              \n",
      "Inflight.entertainment             0.746  0.467       \n",
      "On.board.service                          0.701       \n",
      "Leg.room.service                          0.481       \n",
      "Baggage.handling                          0.765       \n",
      "Checkin.service                                       \n",
      "Inflight.service                          0.800       \n",
      "Cleanliness                        0.858              \n",
      "\n",
      "                 MR1   MR3   MR2\n",
      "SS loadings    2.707 2.295 2.089\n",
      "Proportion Var 0.193 0.164 0.149\n",
      "Cumulative Var 0.193 0.357 0.507\n"
     ]
    }
   ],
   "source": [
    "print(ds.fa3$loadings, cutoff = 0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09cdfa94",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Observation**\n",
    "\n",
    "- Factor 1: food and drink,seat comfort, inflight entertainment, cleanliness\n",
    "\n",
    "- Factor 2: inflight wifi, departure arrival time convenience, online booking ease, gate location\n",
    "\n",
    "- Factor 3: on board service, leg room service, baggage handling, inflight services\n",
    "- online boarding is a low-contribution variable so I chose to leave it out of the factors\n",
    "\n",
    "Let's check how reliable the factors are using Cronbach's alpha.\n",
    "\n",
    "#### Reliability of factors using Cronbach's alpha\n",
    "\n",
    "- to do this, first the factors have to be grouped together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "37e29516",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Installing package into ‘/usr/local/lib/R/site-library’\n",
      "(as ‘lib’ is unspecified)\n",
      "\n",
      "Loading required package: MASS\n",
      "\n",
      "\n",
      "Attaching package: ‘MASS’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:dplyr’:\n",
      "\n",
      "    select\n",
      "\n",
      "\n",
      "Loading required package: msm\n",
      "\n",
      "Loading required package: polycor\n",
      "\n",
      "\n",
      "Attaching package: ‘polycor’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:psych’:\n",
      "\n",
      "    polyserial\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: ‘ltm’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:psych’:\n",
      "\n",
      "    factor.scores\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "Cronbach's alpha for the 'factor1' data-set\n",
       "\n",
       "Items: 4\n",
       "Sample units: 14\n",
       "alpha: 0.954\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "Cronbach's alpha for the 'factor2' data-set\n",
       "\n",
       "Items: 4\n",
       "Sample units: 14\n",
       "alpha: 0.879\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "Cronbach's alpha for the 'factor3' data-set\n",
       "\n",
       "Items: 5\n",
       "Sample units: 14\n",
       "alpha: 0.828\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "install.packages(\"ltm\")\n",
    "library(ltm)\n",
    "factor1 <- ds_cor2[, c(\"Food.and.drink\", \"Seat.comfort\", \"Inflight.entertainment\", \"Cleanliness\")]\n",
    "factor2 <- ds_cor2[,c(\"Inflight.wifi.service\",\"Departure.Arrival.time.convenient\", \"Ease.of.Online.booking\",\"Gate.location\")]\n",
    "factor3 <- ds_cor2[,c(\"Inflight.entertainment\", \"On.board.service\",\"Leg.room.service\",\"Baggage.handling\",\"Inflight.service\" )]\n",
    "cronbach.alpha(factor1)\n",
    "cronbach.alpha(factor2)\n",
    "cronbach.alpha(factor3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4619506",
   "metadata": {
    "papermill": {
     "duration": 0.032941,
     "end_time": "2022-04-29T06:20:46.882492",
     "exception": false,
     "start_time": "2022-04-29T06:20:46.849551",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Observation**\n",
    "\n",
    "- the Cronbach's alpha for each factor is more than 0.80. This indicates a good level of factor reliability.\n",
    "\n",
    "_Caution: Cronbach's alpha > 0.95 could hint to redundancy_ \n",
    "\n",
    "<a id=\"6\"></a> <br>\n",
    "### Exploratory Factor Analysis Interpretation\n",
    "\n",
    "Based on the variables in the the factors, I interpret the latent variables as:\n",
    "\n",
    "- Factor 1 - Comfort (food and drink, seat comfort, inflight entertainment, and cleanliness) \n",
    "\n",
    "- Factor 2 - Convenience (inflight wifi service,Departure and arrival time, ease of online booking, gate location)\n",
    "\n",
    "- Factor 3 - Services (entertainment, on board service, leg room service, baggage handling, inflight service)\n",
    "\n",
    "<a id=\"7\"></a> <br>\n",
    "### Logistic Regression\n",
    "\n",
    "The dependent variable in the dataset is binary (\"neutral or dissatisfied\" and \"satisfied\"). Therefore, it is fitting to use logistic regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "df66e656",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Installing packages into ‘/usr/local/lib/R/site-library’\n",
      "(as ‘lib’ is unspecified)\n",
      "\n",
      "Loading required package: lattice\n",
      "\n",
      "\n",
      "Attaching package: ‘caret’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:purrr’:\n",
      "\n",
      "    lift\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:httr’:\n",
      "\n",
      "    progress\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "install.packages(c(\"caret\",\"broom\",\"MASS\",\"ROCR\"))\n",
    "library(MASS)\n",
    "library(ROCR)\n",
    "library(broom)\n",
    "library(caret)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec280d7e",
   "metadata": {
    "papermill": {
     "duration": 0.034813,
     "end_time": "2022-04-29T06:21:58.878598",
     "exception": false,
     "start_time": "2022-04-29T06:21:58.843785",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Before building the logistic regression model, we have to make sure the dataset is ready. To do that:\n",
    "\n",
    "- First, check if there is a satisfaction imbalance in the DV \n",
    "\n",
    "- Next, the DV which should be categotical "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "46687cd7",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "neutral or dissatisfied               satisfied \n",
       "                  58879                   45025 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t103904 obs. of  24 variables:\n",
      " $ id                               : int  70172 5047 110028 24026 119299 111157 82113 96462 79485 65725 ...\n",
      " $ Gender                           : chr  \"Male\" \"Male\" \"Female\" \"Female\" ...\n",
      " $ Customer.Type                    : chr  \"Loyal Customer\" \"disloyal Customer\" \"Loyal Customer\" \"Loyal Customer\" ...\n",
      " $ Age                              : int  13 25 26 25 61 26 47 52 41 20 ...\n",
      " $ Type.of.Travel                   : chr  \"Personal Travel\" \"Business travel\" \"Business travel\" \"Business travel\" ...\n",
      " $ Class                            : chr  \"Eco Plus\" \"Business\" \"Business\" \"Business\" ...\n",
      " $ Flight.Distance                  : int  460 235 1142 562 214 1180 1276 2035 853 1061 ...\n",
      " $ Inflight.wifi.service            : int  3 3 2 2 3 3 2 4 1 3 ...\n",
      " $ Departure.Arrival.time.convenient: int  4 2 2 5 3 4 4 3 2 3 ...\n",
      " $ Ease.of.Online.booking           : int  3 3 2 5 3 2 2 4 2 3 ...\n",
      " $ Gate.location                    : int  1 3 2 5 3 1 3 4 2 4 ...\n",
      " $ Food.and.drink                   : int  5 1 5 2 4 1 2 5 4 2 ...\n",
      " $ Online.boarding                  : int  3 3 5 2 5 2 2 5 3 3 ...\n",
      " $ Seat.comfort                     : int  5 1 5 2 5 1 2 5 3 3 ...\n",
      " $ Inflight.entertainment           : int  5 1 5 2 3 1 2 5 1 2 ...\n",
      " $ On.board.service                 : int  4 1 4 2 3 3 3 5 1 2 ...\n",
      " $ Leg.room.service                 : int  3 5 3 5 4 4 3 5 2 3 ...\n",
      " $ Baggage.handling                 : int  4 3 4 3 4 4 4 5 1 4 ...\n",
      " $ Checkin.service                  : int  4 1 4 1 3 4 3 4 4 4 ...\n",
      " $ Inflight.service                 : int  5 4 4 4 3 4 5 5 1 3 ...\n",
      " $ Cleanliness                      : int  5 1 5 2 3 1 2 4 2 2 ...\n",
      " $ Departure.Delay.in.Minutes       : num  25 1 0 11 0 0 9 4 0 0 ...\n",
      " $ Arrival.Delay.in.Minutes         : num  18 6 0 9 0 0 23 0 0 0 ...\n",
      " $ satisfaction                     : chr  \"neutral or dissatisfied\" \"neutral or dissatisfied\" \"satisfied\" \"neutral or dissatisfied\" ...\n"
     ]
    }
   ],
   "source": [
    "table(ds$satisfaction)\n",
    "str(ds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6db334f0",
   "metadata": {
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     "duration": 0.034194,
     "end_time": "2022-04-29T06:21:59.065384",
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     "start_time": "2022-04-29T06:21:59.031190",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- The ratio is not 1:2 so we can work with the current balance.\n",
    "\n",
    "- I decided to not only convert the datatype of satisfaction from character to factor but also to change the column to binary.\n",
    "Hence, 1 corresponds to satisfied and 0 corresponds to neutral or dissatisfied. \n",
    "- I will change all ordinal variables to factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "397a02a9",
   "metadata": {
    "execution": {
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    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t103904 obs. of  23 variables:\n",
      " $ Gender                           : Factor w/ 2 levels \"Female\",\"Male\": 2 2 1 1 2 1 2 1 1 2 ...\n",
      " $ Customer.Type                    : Factor w/ 2 levels \"disloyal Customer\",..: 2 1 2 2 2 2 2 2 2 1 ...\n",
      " $ Age                              : int  13 25 26 25 61 26 47 52 41 20 ...\n",
      " $ Type.of.Travel                   : Factor w/ 2 levels \"Business travel\",..: 2 1 1 1 1 2 2 1 1 1 ...\n",
      " $ Class                            : Factor w/ 3 levels \"Business\",\"Eco\",..: 3 1 1 1 1 2 2 1 1 2 ...\n",
      " $ Flight.Distance                  : int  460 235 1142 562 214 1180 1276 2035 853 1061 ...\n",
      " $ Inflight.wifi.service            : Factor w/ 6 levels \"0\",\"1\",\"2\",\"3\",..: 4 4 3 3 4 4 3 5 2 4 ...\n",
      " $ Departure.Arrival.time.convenient: Factor w/ 6 levels \"0\",\"1\",\"2\",\"3\",..: 5 3 3 6 4 5 5 4 3 4 ...\n",
      " $ Ease.of.Online.booking           : Factor w/ 6 levels \"0\",\"1\",\"2\",\"3\",..: 4 4 3 6 4 3 3 5 3 4 ...\n",
      " $ Gate.location                    : Factor w/ 6 levels \"0\",\"1\",\"2\",\"3\",..: 2 4 3 6 4 2 4 5 3 5 ...\n",
      " $ Food.and.drink                   : Factor w/ 6 levels \"0\",\"1\",\"2\",\"3\",..: 6 2 6 3 5 2 3 6 5 3 ...\n",
      " $ Online.boarding                  : Factor w/ 6 levels \"0\",\"1\",\"2\",\"3\",..: 4 4 6 3 6 3 3 6 4 4 ...\n",
      " $ Seat.comfort                     : Factor w/ 6 levels \"0\",\"1\",\"2\",\"3\",..: 6 2 6 3 6 2 3 6 4 4 ...\n",
      " $ Inflight.entertainment           : Factor w/ 6 levels \"0\",\"1\",\"2\",\"3\",..: 6 2 6 3 4 2 3 6 2 3 ...\n",
      " $ On.board.service                 : Factor w/ 6 levels \"0\",\"1\",\"2\",\"3\",..: 5 2 5 3 4 4 4 6 2 3 ...\n",
      " $ Leg.room.service                 : Factor w/ 6 levels \"0\",\"1\",\"2\",\"3\",..: 4 6 4 6 5 5 4 6 3 4 ...\n",
      " $ Baggage.handling                 : Factor w/ 5 levels \"1\",\"2\",\"3\",\"4\",..: 4 3 4 3 4 4 4 5 1 4 ...\n",
      " $ Checkin.service                  : Factor w/ 6 levels \"0\",\"1\",\"2\",\"3\",..: 5 2 5 2 4 5 4 5 5 5 ...\n",
      " $ Inflight.service                 : Factor w/ 6 levels \"0\",\"1\",\"2\",\"3\",..: 6 5 5 5 4 5 6 6 2 4 ...\n",
      " $ Cleanliness                      : Factor w/ 6 levels \"0\",\"1\",\"2\",\"3\",..: 6 2 6 3 4 2 3 5 3 3 ...\n",
      " $ Departure.Delay.in.Minutes       : num  25 1 0 11 0 0 9 4 0 0 ...\n",
      " $ Arrival.Delay.in.Minutes         : num  18 6 0 9 0 0 23 0 0 0 ...\n",
      " $ satisfaction                     : Factor w/ 2 levels \"0\",\"1\": 1 1 2 1 2 1 1 2 1 1 ...\n"
     ]
    }
   ],
   "source": [
    "ds <- read.csv(\"/kaggle/input/airline-passenger-satisfaction/train.csv\")\n",
    "ds <- subset(ds, select=-c(id,X))\n",
    "ds$Departure.Delay.in.Minutes = as.numeric(ds$Departure.Delay.in.Minutes)\n",
    "ds$Arrival.Delay.in.Minutes[is.na(ds$Arrival.Delay.in.Minutes)] <-0\n",
    "\n",
    "ds$satisfaction <- ifelse(ds$satisfaction==\"satisfied\",1,0)\n",
    "ds$satisfaction <- factor(ds$satisfaction, levels = c(0, 1))\n",
    "\n",
    "ds$Gender <- as.factor(ds$Gender)\n",
    "ds$Customer.Type <- as.factor(ds$Customer.Type)\n",
    "ds$Type.of.Travel <- as.factor(ds$Type.of.Travel)\n",
    "ds$Class <- as.factor(ds$Class)\n",
    "ds$Inflight.wifi.service <- as.factor(ds$Inflight.wifi.service)\n",
    "ds$Departure.Arrival.time.convenient <- as.factor(ds$Departure.Arrival.time.convenient)\n",
    "ds$Ease.of.Online.booking <- as.factor(ds$Ease.of.Online.booking)\n",
    "ds$Gate.location <- as.factor(ds$Gate.location)\n",
    "ds$Food.and.drink <- as.factor(ds$Food.and.drink)\n",
    "ds$Online.boarding <- as.factor(ds$Online.boarding)\n",
    "ds$Seat.comfort <- as.factor(ds$Seat.comfort)\n",
    "ds$Leg.room.service <- as.factor(ds$Leg.room.service)\n",
    "ds$Inflight.entertainment <- as.factor(ds$Inflight.entertainment)\n",
    "ds$On.board.service <- as.factor(ds$On.board.service)\n",
    "ds$Checkin.service <- as.factor(ds$Checkin.service)\n",
    "ds$Inflight.service <- as.factor(ds$Inflight.service)\n",
    "ds$Cleanliness <- as.factor(ds$Cleanliness)\n",
    "ds$Baggage.handling <- as.factor(ds$Baggage.handling)\n",
    "  \n",
    "str(ds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74a0a9d1",
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    },
    "tags": []
   },
   "source": [
    "Let's build the logistic regression model with all variables (Model 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "db1cc205",
   "metadata": {
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    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“glm.fit: fitted probabilities numerically 0 or 1 occurred”\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "glm(formula = satisfaction ~ Gender + Customer.Type + Age + Type.of.Travel + \n",
       "    Class + Flight.Distance + Inflight.wifi.service + Departure.Arrival.time.convenient + \n",
       "    Ease.of.Online.booking + Gate.location + Food.and.drink + \n",
       "    Online.boarding + Seat.comfort + Inflight.entertainment + \n",
       "    On.board.service + Leg.room.service + Baggage.handling + \n",
       "    Checkin.service + Inflight.service + Cleanliness + Departure.Delay.in.Minutes + \n",
       "    Arrival.Delay.in.Minutes, family = \"binomial\", data = ds)\n",
       "\n",
       "Deviance Residuals: \n",
       "    Min       1Q   Median       3Q      Max  \n",
       "-4.6962  -0.2131  -0.0471   0.1327   4.4049  \n",
       "\n",
       "Coefficients: (3 not defined because of singularities)\n",
       "                                     Estimate Std. Error z value Pr(>|z|)    \n",
       "(Intercept)                         6.153e+00  9.961e+03   0.001 0.999507    \n",
       "GenderMale                          4.650e-02  2.730e-02   1.703 0.088498 .  \n",
       "Customer.TypeLoyal Customer         3.354e+00  4.953e-02  67.718  < 2e-16 ***\n",
       "Age                                -2.311e-03  1.017e-03  -2.273 0.023034 *  \n",
       "Type.of.TravelPersonal Travel      -4.273e+00  5.507e-02 -77.587  < 2e-16 ***\n",
       "ClassEco                           -6.297e-01  3.720e-02 -16.927  < 2e-16 ***\n",
       "ClassEco Plus                      -8.369e-01  6.048e-02 -13.836  < 2e-16 ***\n",
       "Flight.Distance                     7.201e-06  1.535e-05   0.469 0.639016    \n",
       "Inflight.wifi.service1             -2.402e+01  8.868e+01  -0.271 0.786537    \n",
       "Inflight.wifi.service2             -2.427e+01  8.868e+01  -0.274 0.784328    \n",
       "Inflight.wifi.service3             -2.432e+01  8.868e+01  -0.274 0.783935    \n",
       "Inflight.wifi.service4             -2.276e+01  8.868e+01  -0.257 0.797414    \n",
       "Inflight.wifi.service5             -1.720e+01  8.868e+01  -0.194 0.846240    \n",
       "Departure.Arrival.time.convenient1  3.142e-01  9.296e-02   3.380 0.000724 ***\n",
       "Departure.Arrival.time.convenient2  4.301e-01  8.959e-02   4.801 1.58e-06 ***\n",
       "Departure.Arrival.time.convenient3  2.414e-01  8.631e-02   2.797 0.005158 ** \n",
       "Departure.Arrival.time.convenient4 -6.775e-01  7.733e-02  -8.761  < 2e-16 ***\n",
       "Departure.Arrival.time.convenient5 -9.129e-01  8.491e-02 -10.751  < 2e-16 ***\n",
       "Ease.of.Online.booking1             3.063e+00  9.138e-01   3.352 0.000802 ***\n",
       "Ease.of.Online.booking2             2.995e+00  9.138e-01   3.277 0.001049 ** \n",
       "Ease.of.Online.booking3             3.494e+00  9.136e-01   3.825 0.000131 ***\n",
       "Ease.of.Online.booking4             4.340e+00  9.133e-01   4.752 2.02e-06 ***\n",
       "Ease.of.Online.booking5             3.709e+00  9.137e-01   4.059 4.93e-05 ***\n",
       "Gate.location1                     -1.876e+01  6.523e+03  -0.003 0.997705    \n",
       "Gate.location2                     -1.868e+01  6.523e+03  -0.003 0.997715    \n",
       "Gate.location3                     -1.885e+01  6.523e+03  -0.003 0.997694    \n",
       "Gate.location4                     -1.910e+01  6.523e+03  -0.003 0.997663    \n",
       "Gate.location5                     -1.931e+01  6.523e+03  -0.003 0.997638    \n",
       "Food.and.drink1                    -3.285e-01  1.744e+00  -0.188 0.850614    \n",
       "Food.and.drink2                    -4.686e-02  1.744e+00  -0.027 0.978566    \n",
       "Food.and.drink3                    -1.766e-01  1.744e+00  -0.101 0.919355    \n",
       "Food.and.drink4                    -1.325e-01  1.744e+00  -0.076 0.939458    \n",
       "Food.and.drink5                    -2.872e-01  1.744e+00  -0.165 0.869221    \n",
       "Online.boarding1                   -3.623e+00  9.174e-01  -3.949 7.84e-05 ***\n",
       "Online.boarding2                   -3.543e+00  9.173e-01  -3.863 0.000112 ***\n",
       "Online.boarding3                   -3.774e+00  9.170e-01  -4.116 3.86e-05 ***\n",
       "Online.boarding4                   -2.127e+00  9.167e-01  -2.321 0.020290 *  \n",
       "Online.boarding5                   -8.783e-01  9.169e-01  -0.958 0.338106    \n",
       "Seat.comfort1                       2.048e+01  6.523e+03   0.003 0.997495    \n",
       "Seat.comfort2                       1.995e+01  6.523e+03   0.003 0.997560    \n",
       "Seat.comfort3                       1.889e+01  6.523e+03   0.003 0.997689    \n",
       "Seat.comfort4                       1.960e+01  6.523e+03   0.003 0.997603    \n",
       "Seat.comfort5                       2.044e+01  6.523e+03   0.003 0.997500    \n",
       "Inflight.entertainment1             3.970e+01  1.515e+03   0.026 0.979101    \n",
       "Inflight.entertainment2             4.045e+01  1.515e+03   0.027 0.978704    \n",
       "Inflight.entertainment3             4.129e+01  1.515e+03   0.027 0.978265    \n",
       "Inflight.entertainment4             4.096e+01  1.515e+03   0.027 0.978438    \n",
       "Inflight.entertainment5             4.020e+01  1.515e+03   0.027 0.978839    \n",
       "On.board.service1                  -2.335e+01  4.051e+03  -0.006 0.995402    \n",
       "On.board.service2                  -2.320e+01  4.051e+03  -0.006 0.995431    \n",
       "On.board.service3                  -2.267e+01  4.051e+03  -0.006 0.995536    \n",
       "On.board.service4                  -2.258e+01  4.051e+03  -0.006 0.995553    \n",
       "On.board.service5                  -2.205e+01  4.051e+03  -0.005 0.995658    \n",
       "Leg.room.service1                  -2.398e+00  9.579e-01  -2.503 0.012310 *  \n",
       "Leg.room.service2                  -2.124e+00  9.574e-01  -2.219 0.026479 *  \n",
       "Leg.room.service3                  -2.241e+00  9.572e-01  -2.341 0.019209 *  \n",
       "Leg.room.service4                  -1.543e+00  9.573e-01  -1.611 0.107091    \n",
       "Leg.room.service5                  -1.381e+00  9.571e-01  -1.443 0.149060    \n",
       "Baggage.handling2                  -2.194e-01  7.601e-02  -2.887 0.003892 ** \n",
       "Baggage.handling3                  -8.445e-01  7.099e-02 -11.895  < 2e-16 ***\n",
       "Baggage.handling4                  -2.463e-01  6.902e-02  -3.569 0.000358 ***\n",
       "Baggage.handling5                   5.151e-01  7.336e-02   7.021 2.20e-12 ***\n",
       "Checkin.service1                   -1.425e+00  5.429e-02 -26.258  < 2e-16 ***\n",
       "Checkin.service2                   -1.235e+00  5.401e-02 -22.857  < 2e-16 ***\n",
       "Checkin.service3                   -7.263e-01  4.346e-02 -16.713  < 2e-16 ***\n",
       "Checkin.service4                   -7.455e-01  4.324e-02 -17.241  < 2e-16 ***\n",
       "Checkin.service5                           NA         NA      NA       NA    \n",
       "Inflight.service1                  -4.822e-01  7.645e-02  -6.307 2.84e-10 ***\n",
       "Inflight.service2                  -7.016e-01  6.933e-02 -10.120  < 2e-16 ***\n",
       "Inflight.service3                  -1.394e+00  5.729e-02 -24.331  < 2e-16 ***\n",
       "Inflight.service4                  -6.946e-01  4.493e-02 -15.458  < 2e-16 ***\n",
       "Inflight.service5                          NA         NA      NA       NA    \n",
       "Cleanliness1                       -9.970e-01  7.512e-02 -13.272  < 2e-16 ***\n",
       "Cleanliness2                       -9.541e-01  7.303e-02 -13.064  < 2e-16 ***\n",
       "Cleanliness3                       -4.689e-01  6.144e-02  -7.632 2.31e-14 ***\n",
       "Cleanliness4                       -6.023e-01  6.021e-02 -10.004  < 2e-16 ***\n",
       "Cleanliness5                               NA         NA      NA       NA    \n",
       "Departure.Delay.in.Minutes          4.345e-03  1.249e-03   3.478 0.000505 ***\n",
       "Arrival.Delay.in.Minutes           -8.231e-03  1.235e-03  -6.666 2.62e-11 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "(Dispersion parameter for binomial family taken to be 1)\n",
       "\n",
       "    Null deviance: 142189  on 103903  degrees of freedom\n",
       "Residual deviance:  37004  on 103828  degrees of freedom\n",
       "AIC: 37156\n",
       "\n",
       "Number of Fisher Scoring iterations: 17\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "set.seed(123)\n",
    "model1 <- glm(satisfaction ~ Gender + Customer.Type + Age + \n",
    "                 \n",
    "                 Type.of.Travel + Class + Flight.Distance + Inflight.wifi.service +\n",
    "                \n",
    "                 Departure.Arrival.time.convenient + Ease.of.Online.booking + \n",
    "                 Gate.location + Food.and.drink + Online.boarding + Seat.comfort +\n",
    "                 Inflight.entertainment + On.board.service + Leg.room.service +\n",
    "                 Baggage.handling + Checkin.service + Inflight.service +\n",
    "                 Cleanliness \n",
    "               + Departure.Delay.in.Minutes + Arrival.Delay.in.Minutes , \n",
    "               data = ds, family = \"binomial\")\n",
    "summary(model1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7cd6aae",
   "metadata": {
    "papermill": {
     "duration": 0.034572,
     "end_time": "2022-04-29T06:22:11.158411",
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     "start_time": "2022-04-29T06:22:11.123839",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Observations**\n",
    "\n",
    "- The AIC of Model 1 is 37156\n",
    "\n",
    "- Variables like gender, flight distance, inflight wifi service, gate location, food and drinks, seat comfort, inflight entertainment, onboard services, legroom service are not significant. \n",
    "\n",
    "Let's create a model with significant variables only (Model 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8217e085",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T06:22:11.229962Z",
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     "shell.execute_reply": "2022-04-29T06:22:15.952164Z"
    },
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     "duration": 4.768307,
     "end_time": "2022-04-29T06:22:15.960908",
     "exception": false,
     "start_time": "2022-04-29T06:22:11.192601",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "glm(formula = satisfaction ~ Customer.Type + Age + Type.of.Travel + \n",
       "    Class + Departure.Arrival.time.convenient + Ease.of.Online.booking + \n",
       "    Online.boarding + Seat.comfort + Inflight.entertainment + \n",
       "    On.board.service + Leg.room.service + Baggage.handling + \n",
       "    Checkin.service + Inflight.service + Cleanliness + Departure.Delay.in.Minutes + \n",
       "    Arrival.Delay.in.Minutes, family = \"binomial\", data = ds)\n",
       "\n",
       "Deviance Residuals: \n",
       "    Min       1Q   Median       3Q      Max  \n",
       "-3.3397  -0.3204  -0.0753   0.2283   4.4552  \n",
       "\n",
       "Coefficients: (3 not defined because of singularities)\n",
       "                                     Estimate Std. Error z value Pr(>|z|)    \n",
       "(Intercept)                        -2.648e+01  3.657e+02  -0.072   0.9423    \n",
       "Customer.TypeLoyal Customer         2.610e+00  3.753e-02  69.546  < 2e-16 ***\n",
       "Age                                -6.129e-03  8.406e-04  -7.291 3.09e-13 ***\n",
       "Type.of.TravelPersonal Travel      -3.615e+00  4.080e-02 -88.604  < 2e-16 ***\n",
       "ClassEco                           -5.276e-01  2.829e-02 -18.649  < 2e-16 ***\n",
       "ClassEco Plus                      -7.102e-01  4.660e-02 -15.241  < 2e-16 ***\n",
       "Departure.Arrival.time.convenient1  5.155e-01  6.972e-02   7.394 1.43e-13 ***\n",
       "Departure.Arrival.time.convenient2  7.332e-01  6.736e-02  10.886  < 2e-16 ***\n",
       "Departure.Arrival.time.convenient3  4.850e-01  6.513e-02   7.447 9.56e-14 ***\n",
       "Departure.Arrival.time.convenient4 -4.014e-01  5.840e-02  -6.872 6.32e-12 ***\n",
       "Departure.Arrival.time.convenient5 -6.631e-01  6.115e-02 -10.844  < 2e-16 ***\n",
       "Ease.of.Online.booking1            -3.564e+00  1.245e-01 -28.632  < 2e-16 ***\n",
       "Ease.of.Online.booking2            -3.905e+00  1.232e-01 -31.695  < 2e-16 ***\n",
       "Ease.of.Online.booking3            -3.559e+00  1.216e-01 -29.256  < 2e-16 ***\n",
       "Ease.of.Online.booking4            -2.271e+00  1.189e-01 -19.101  < 2e-16 ***\n",
       "Ease.of.Online.booking5            -1.719e+00  1.192e-01 -14.418  < 2e-16 ***\n",
       "Online.boarding1                   -7.797e-01  1.283e-01  -6.077 1.22e-09 ***\n",
       "Online.boarding2                   -8.683e-01  1.280e-01  -6.782 1.19e-11 ***\n",
       "Online.boarding3                   -9.024e-01  1.267e-01  -7.122 1.06e-12 ***\n",
       "Online.boarding4                    1.089e+00  1.260e-01   8.647  < 2e-16 ***\n",
       "Online.boarding5                    2.903e+00  1.284e-01  22.618  < 2e-16 ***\n",
       "Seat.comfort1                       1.520e+01  3.247e+02   0.047   0.9627    \n",
       "Seat.comfort2                       1.478e+01  3.247e+02   0.046   0.9637    \n",
       "Seat.comfort3                       1.372e+01  3.247e+02   0.042   0.9663    \n",
       "Seat.comfort4                       1.433e+01  3.247e+02   0.044   0.9648    \n",
       "Seat.comfort5                       1.496e+01  3.247e+02   0.046   0.9633    \n",
       "Inflight.entertainment1             1.111e+01  9.239e+01   0.120   0.9043    \n",
       "Inflight.entertainment2             1.174e+01  9.239e+01   0.127   0.8989    \n",
       "Inflight.entertainment3             1.254e+01  9.239e+01   0.136   0.8920    \n",
       "Inflight.entertainment4             1.263e+01  9.239e+01   0.137   0.8912    \n",
       "Inflight.entertainment5             1.193e+01  9.239e+01   0.129   0.8972    \n",
       "On.board.service1                   8.535e-01  1.919e+02   0.004   0.9965    \n",
       "On.board.service2                   9.471e-01  1.919e+02   0.005   0.9961    \n",
       "On.board.service3                   1.400e+00  1.919e+02   0.007   0.9942    \n",
       "On.board.service4                   1.518e+00  1.919e+02   0.008   0.9937    \n",
       "On.board.service5                   1.948e+00  1.919e+02   0.010   0.9919    \n",
       "Leg.room.service1                   8.371e-01  1.751e-01   4.781 1.74e-06 ***\n",
       "Leg.room.service2                   1.105e+00  1.737e-01   6.364 1.97e-10 ***\n",
       "Leg.room.service3                   9.754e-01  1.736e-01   5.618 1.94e-08 ***\n",
       "Leg.room.service4                   1.792e+00  1.738e-01  10.310  < 2e-16 ***\n",
       "Leg.room.service5                   1.993e+00  1.743e-01  11.430  < 2e-16 ***\n",
       "Baggage.handling2                  -2.589e-01  6.231e-02  -4.154 3.26e-05 ***\n",
       "Baggage.handling3                  -7.519e-01  5.799e-02 -12.966  < 2e-16 ***\n",
       "Baggage.handling4                  -1.384e-01  5.657e-02  -2.446   0.0144 *  \n",
       "Baggage.handling5                   5.189e-01  6.010e-02   8.634  < 2e-16 ***\n",
       "Checkin.service1                   -1.145e+00  4.515e-02 -25.361  < 2e-16 ***\n",
       "Checkin.service2                   -9.847e-01  4.488e-02 -21.941  < 2e-16 ***\n",
       "Checkin.service3                   -5.575e-01  3.628e-02 -15.366  < 2e-16 ***\n",
       "Checkin.service4                   -5.807e-01  3.608e-02 -16.096  < 2e-16 ***\n",
       "Checkin.service5                           NA         NA      NA       NA    \n",
       "Inflight.service1                  -3.149e-01  6.270e-02  -5.023 5.08e-07 ***\n",
       "Inflight.service2                  -5.852e-01  5.703e-02 -10.261  < 2e-16 ***\n",
       "Inflight.service3                  -1.234e+00  4.672e-02 -26.420  < 2e-16 ***\n",
       "Inflight.service4                  -5.964e-01  3.675e-02 -16.227  < 2e-16 ***\n",
       "Inflight.service5                          NA         NA      NA       NA    \n",
       "Cleanliness1                       -6.790e-01  6.217e-02 -10.921  < 2e-16 ***\n",
       "Cleanliness2                       -5.830e-01  6.089e-02  -9.574  < 2e-16 ***\n",
       "Cleanliness3                       -3.249e-01  5.123e-02  -6.342 2.27e-10 ***\n",
       "Cleanliness4                       -4.193e-01  4.962e-02  -8.450  < 2e-16 ***\n",
       "Cleanliness5                               NA         NA      NA       NA    \n",
       "Departure.Delay.in.Minutes          4.772e-03  1.083e-03   4.406 1.05e-05 ***\n",
       "Arrival.Delay.in.Minutes           -9.210e-03  1.072e-03  -8.594  < 2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "(Dispersion parameter for binomial family taken to be 1)\n",
       "\n",
       "    Null deviance: 142189  on 103903  degrees of freedom\n",
       "Residual deviance:  50288  on 103845  degrees of freedom\n",
       "AIC: 50406\n",
       "\n",
       "Number of Fisher Scoring iterations: 11\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "set.seed(123)\n",
    "model2 <- glm(satisfaction ~ Customer.Type + Age + \n",
    "                 \n",
    "                 Type.of.Travel + Class +\n",
    "                 Departure.Arrival.time.convenient + Ease.of.Online.booking + \n",
    "                 Online.boarding + Seat.comfort +\n",
    "                 Inflight.entertainment + On.board.service + Leg.room.service +\n",
    "                 Baggage.handling + Checkin.service + Inflight.service +Cleanliness +\n",
    "                 Departure.Delay.in.Minutes + Arrival.Delay.in.Minutes , \n",
    "               data = ds, family = \"binomial\")\n",
    "summary(model2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49eac7cd",
   "metadata": {
    "papermill": {
     "duration": 0.034732,
     "end_time": "2022-04-29T06:22:16.053957",
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     "start_time": "2022-04-29T06:22:16.019225",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Observations**\n",
    "\n",
    "- The AIC of Model 1 is 50406 which is higher than Model 1. Therefore,we'll keep using Model 1 for prediction. \n",
    "\n",
    "### ANOVA test\n",
    "\n",
    "To be more sure, I'd like to compare the two models using ANOVA test. My NULL hypothesis (H0) is that Model 1 is better than Model 2. Therefore, if p < 0.05, the NULL hypothesis will be rejected , suggesting Model 1 is better. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8a7e4856",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T06:22:16.126469Z",
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     "exception": false,
     "start_time": "2022-04-29T06:22:16.088360",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A anova: 2 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Resid. Df</th><th scope=col>Resid. Dev</th><th scope=col>Df</th><th scope=col>Deviance</th><th scope=col>Pr(&gt;Chi)</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>103828</td><td>37004.13</td><td> NA</td><td>       NA</td><td>NA</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>103845</td><td>50288.10</td><td>-17</td><td>-13283.97</td><td> 0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A anova: 2 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & Resid. Df & Resid. Dev & Df & Deviance & Pr(>Chi)\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 103828 & 37004.13 &  NA &        NA & NA\\\\\n",
       "\t2 & 103845 & 50288.10 & -17 & -13283.97 &  0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A anova: 2 × 5\n",
       "\n",
       "| <!--/--> | Resid. Df &lt;dbl&gt; | Resid. Dev &lt;dbl&gt; | Df &lt;dbl&gt; | Deviance &lt;dbl&gt; | Pr(&gt;Chi) &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| 1 | 103828 | 37004.13 |  NA |        NA | NA |\n",
       "| 2 | 103845 | 50288.10 | -17 | -13283.97 |  0 |\n",
       "\n"
      ],
      "text/plain": [
       "  Resid. Df Resid. Dev Df  Deviance  Pr(>Chi)\n",
       "1 103828    37004.13    NA        NA NA      \n",
       "2 103845    50288.10   -17 -13283.97  0      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "anova(model1, model2, test=\"Chisq\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77a9438a",
   "metadata": {
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     "start_time": "2022-04-29T06:22:16.258463",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Observations**\n",
    "\n",
    "- The p-value is <0.05 which is significant. Thus, H0 is thus rejected, and Model 1 is confirmed to be better than Model 2. \n",
    "\n",
    "<a id=\"8\"></a> <br>\n",
    "### Prediction on Test Data \n",
    "\n",
    "Okay, now that the trained model is ready, its time to test Model 1 on the test dataset. \n",
    "\n",
    "**Step 1**\n",
    "\n",
    "- Read and process test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ac33a198",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t25976 obs. of  23 variables:\n",
      " $ Gender                           : Factor w/ 2 levels \"Female\",\"Male\": 1 1 2 2 1 2 1 1 2 1 ...\n",
      " $ Customer.Type                    : Factor w/ 2 levels \"disloyal Customer\",..: 2 2 1 2 2 2 2 2 2 2 ...\n",
      " $ Age                              : int  52 36 20 44 49 16 77 43 47 46 ...\n",
      " $ Type.of.Travel                   : Factor w/ 2 levels \"Business travel\",..: 1 1 1 1 1 1 1 1 1 1 ...\n",
      " $ Class                            : Factor w/ 3 levels \"Business\",\"Eco\",..: 2 1 2 1 2 2 1 1 2 1 ...\n",
      " $ Flight.Distance                  : int  160 2863 192 3377 1182 311 3987 2556 556 1744 ...\n",
      " $ Inflight.wifi.service            : Factor w/ 6 levels \"0\",\"1\",\"2\",\"3\",..: 6 2 3 1 3 4 6 3 6 3 ...\n",
      " $ Departure.Arrival.time.convenient: Factor w/ 6 levels \"0\",\"1\",\"2\",\"3\",..: 5 2 1 1 4 4 6 3 3 3 ...\n",
      " $ Ease.of.Online.booking           : Factor w/ 6 levels \"0\",\"1\",\"2\",\"3\",..: 4 4 3 1 5 4 6 3 3 3 ...\n",
      " $ Gate.location                    : Factor w/ 5 levels \"1\",\"2\",\"3\",\"4\",..: 4 1 4 2 3 3 5 2 2 2 ...\n",
      " $ Food.and.drink                   : Factor w/ 6 levels \"0\",\"1\",\"2\",\"3\",..: 4 6 3 4 5 6 4 5 6 4 ...\n",
      " $ Online.boarding                  : Factor w/ 6 levels \"0\",\"1\",\"2\",\"3\",..: 5 5 3 5 2 6 6 5 6 5 ...\n",
      " $ Seat.comfort                     : Factor w/ 5 levels \"1\",\"2\",\"3\",\"4\",..: 3 5 2 4 2 3 5 5 5 4 ...\n",
      " $ Inflight.entertainment           : Factor w/ 6 levels \"0\",\"1\",\"2\",\"3\",..: 6 5 3 2 3 6 6 5 6 5 ...\n",
      " $ On.board.service                 : Factor w/ 6 levels \"0\",\"1\",\"2\",\"3\",..: 6 5 5 2 3 5 6 5 3 5 ...\n",
      " $ Leg.room.service                 : Factor w/ 6 levels \"0\",\"1\",\"2\",\"3\",..: 6 5 2 2 3 4 6 5 3 5 ...\n",
      " $ Baggage.handling                 : Factor w/ 5 levels \"1\",\"2\",\"3\",\"4\",..: 5 4 3 1 2 1 5 4 5 4 ...\n",
      " $ Checkin.service                  : Factor w/ 5 levels \"1\",\"2\",\"3\",\"4\",..: 2 3 2 3 4 1 4 5 3 5 ...\n",
      " $ Inflight.service                 : Factor w/ 6 levels \"0\",\"1\",\"2\",\"3\",..: 6 5 3 2 3 3 6 5 4 5 ...\n",
      " $ Cleanliness                      : Factor w/ 6 levels \"0\",\"1\",\"2\",\"3\",..: 6 6 3 5 5 6 4 4 6 5 ...\n",
      " $ Departure.Delay.in.Minutes       : num  50 0 0 0 0 0 0 77 1 28 ...\n",
      " $ Arrival.Delay.in.Minutes         : num  44 0 0 6 20 0 0 65 0 14 ...\n",
      " $ satisfaction                     : Factor w/ 2 levels \"0\",\"1\": 2 2 1 2 2 2 2 2 2 2 ...\n"
     ]
    }
   ],
   "source": [
    "ds_test <- read.csv(\"/kaggle/input/airline-passenger-satisfaction/test.csv\")\n",
    "\n",
    "ds_test <- subset(ds_test, select=-c(id,X))\n",
    "ds_test$Departure.Delay.in.Minutes = as.numeric(ds_test$Departure.Delay.in.Minutes)\n",
    "ds_test$Arrival.Delay.in.Minutes[is.na(ds_test$Arrival.Delay.in.Minutes)] <-0\n",
    "\n",
    "ds_test$satisfaction <- ifelse(ds_test$satisfaction==\"satisfied\",1,0)\n",
    "ds_test$satisfaction <- factor(ds_test$satisfaction, levels = c(0, 1))\n",
    "\n",
    "ds_test$Gender <- as.factor(ds_test$Gender)\n",
    "ds_test$Customer.Type <- as.factor(ds_test$Customer.Type)\n",
    "ds_test$Type.of.Travel <- as.factor(ds_test$Type.of.Travel)\n",
    "ds_test$Class <- as.factor(ds_test$Class)\n",
    "ds_test$Inflight.wifi.service <- as.factor(ds_test$Inflight.wifi.service)\n",
    "ds_test$Departure.Arrival.time.convenient <- as.factor(ds_test$Departure.Arrival.time.convenient)\n",
    "ds_test$Ease.of.Online.booking <- as.factor(ds_test$Ease.of.Online.booking)\n",
    "ds_test$Gate.location <- as.factor(ds_test$Gate.location)\n",
    "ds_test$Food.and.drink <- as.factor(ds_test$Food.and.drink)\n",
    "ds_test$Online.boarding <- as.factor(ds_test$Online.boarding)\n",
    "ds_test$Seat.comfort <- as.factor(ds_test$Seat.comfort)\n",
    "ds_test$Leg.room.service <- as.factor(ds_test$Leg.room.service)\n",
    "ds_test$Inflight.entertainment <- as.factor(ds_test$Inflight.entertainment)\n",
    "ds_test$On.board.service <- as.factor(ds_test$On.board.service)\n",
    "ds_test$Checkin.service <- as.factor(ds_test$Checkin.service)\n",
    "ds_test$Inflight.service <- as.factor(ds_test$Inflight.service)\n",
    "ds_test$Cleanliness <- as.factor(ds_test$Cleanliness)\n",
    "ds_test$Baggage.handling <- as.factor(ds_test$Baggage.handling)\n",
    "  \n",
    "str(ds_test)"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "    Gender                Customer.Type        Age       \n",
       " Female:13172   disloyal Customer: 4799   Min.   : 7.00  \n",
       " Male  :12804   Loyal Customer   :21177   1st Qu.:27.00  \n",
       "                                          Median :40.00  \n",
       "                                          Mean   :39.62  \n",
       "                                          3rd Qu.:51.00  \n",
       "                                          Max.   :85.00  \n",
       "         Type.of.Travel       Class       Flight.Distance Inflight.wifi.service\n",
       " Business travel:18038   Business:12495   Min.   :  31    0: 813               \n",
       " Personal Travel: 7938   Eco     :11564   1st Qu.: 414    1:4488               \n",
       "                         Eco Plus: 1917   Median : 849    2:6490               \n",
       "                                          Mean   :1194    3:6317               \n",
       "                                          3rd Qu.:1744    4:4981               \n",
       "                                          Max.   :4983    5:2887               \n",
       " Departure.Arrival.time.convenient Ease.of.Online.booking Gate.location\n",
       " 0:1381                            0:1195                 1:4429       \n",
       " 1:3911                            1:4361                 2:4837       \n",
       " 2:4343                            2:6030                 3:7140       \n",
       " 3:4412                            3:5944                 4:6040       \n",
       " 4:6334                            4:4873                 5:3530       \n",
       " 5:5595                            5:3573                              \n",
       " Food.and.drink Online.boarding Seat.comfort Inflight.entertainment\n",
       " 0:  25         0: 652          1:3033       0:   4                \n",
       " 1:3214         1:2569          2:3632       1:3197                \n",
       " 2:5395         2:4429          3:4632       2:4331                \n",
       " 3:5494         3:5313          4:7991       3:4745                \n",
       " 4:6204         4:7706          5:6688       4:7368                \n",
       " 5:5644         5:5307                       5:6331                \n",
       " On.board.service Leg.room.service Baggage.handling Checkin.service\n",
       " 0:   2           0: 126           1:1791           1:3218         \n",
       " 1:2915           1:2542           2:2841           2:3209         \n",
       " 2:3670           2:5015           3:5219           3:7007         \n",
       " 3:5709           3:4958           4:9378           4:7278         \n",
       " 4:7836           4:7097           5:6747           5:5264         \n",
       " 5:5844           5:6238                                           \n",
       " Inflight.service Cleanliness Departure.Delay.in.Minutes\n",
       " 0:   2           0:   2      Min.   :   0.00           \n",
       " 1:1778           1:3411      1st Qu.:   0.00           \n",
       " 2:2851           2:3981      Median :   0.00           \n",
       " 3:5017           3:6065      Mean   :  14.31           \n",
       " 4:9378           4:6790      3rd Qu.:  12.00           \n",
       " 5:6950           5:5727      Max.   :1128.00           \n",
       " Arrival.Delay.in.Minutes satisfaction\n",
       " Min.   :   0.00          0:14573     \n",
       " 1st Qu.:   0.00          1:11403     \n",
       " Median :   0.00                      \n",
       " Mean   :  14.69                      \n",
       " 3rd Qu.:  13.00                      \n",
       " Max.   :1115.00                      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(ds_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d627433",
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    "tags": []
   },
   "source": [
    "**Step 2**\n",
    "\n",
    "- Run prediction on it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7ee9614f",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :\n",
      "“prediction from a rank-deficient fit may be misleading”\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "0.00000 0.01021 0.19479 0.43881 0.97520 1.00000 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred <- predict(model1, type = \"response\", newdata = ds_test)\n",
    "summary(pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b48d9e56",
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     "duration": 0.049746,
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Step 3**\n",
    "\n",
    "- Find out the accuracy, sensitivity, AUC ROC, etc. of the prediction. We will find out how reliable the logistic regression model is, and how accurately it makes predictions. \n",
    "\n",
    "- Unlike linear regression, R-squared value is not present in logistic regression that can explain the proportion of variance in the DV caused by the IVs \n",
    "\n",
    "**AUC-ROC**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0162892a",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] The AUC ROC value is : "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "0.979155385323938"
      ],
      "text/latex": [
       "0.979155385323938"
      ],
      "text/markdown": [
       "0.979155385323938"
      ],
      "text/plain": [
       "[1] 0.9791554"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "[1] The threshold value for cutoff from the ROC curve appears to be 0.7 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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efOjQoe3atSus13ZxOVL/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\naeuIGT0md56zefZoTfmylMsP/rpNU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     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 840
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC plot (sensitivity versus specificity)\n",
    "ROC_pred <- prediction(pred, ds_test$satisfaction)\n",
    "ROC_perf<- performance(ROC_pred, measure = \"tpr\", x.measure = \"fpr\")\n",
    "options(repr.plot.width = 14, repr.plot.height = 8)\n",
    "plot(ROC_perf, colorize=TRUE, print.cutoffs.at=seq(0,1,by =0.1), text.adj=c(-0.3,1.6), text.cex = 1.2 , main=\"ROC Curve\", xlab=\"1-Specificity\", ylab=\"Sensitivity\")\n",
    "\n",
    "# AUC of ROC plot\n",
    "auc <- as.numeric(performance(ROC_pred, measure = \"auc\")@y.values)\n",
    "noquote(\"The AUC ROC value is : \")\n",
    "auc\n",
    "noquote(\"The threshold value for cutoff from the ROC curve appears to be 0.7 \")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ef7b7f8",
   "metadata": {
    "papermill": {
     "duration": 0.03694,
     "end_time": "2022-04-29T06:22:25.922925",
     "exception": false,
     "start_time": "2022-04-29T06:22:25.885985",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Confusion Matrix**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6278ca1e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T06:22:26.001046Z",
     "iopub.status.busy": "2022-04-29T06:22:25.999396Z",
     "iopub.status.idle": "2022-04-29T06:22:26.037208Z",
     "shell.execute_reply": "2022-04-29T06:22:26.035449Z"
    },
    "papermill": {
     "duration": 0.078458,
     "end_time": "2022-04-29T06:22:26.039225",
     "exception": false,
     "start_time": "2022-04-29T06:22:25.960767",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] Confusion Matrix"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "                         \n",
       "                          FALSE  TRUE\n",
       "  neutral or dissatisfied 14162   411\n",
       "  satisfied                1540  9863"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion matrix\n",
    "confusion_matrix <- table(ds_test$satisfaction, pred>0.7)\n",
    "row.names(confusion_matrix)<- c(\"neutral or dissatisfied\", \"satisfied\")\n",
    "noquote(\"Confusion Matrix\")\n",
    "confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ab36b4b",
   "metadata": {
    "papermill": {
     "duration": 0.037213,
     "end_time": "2022-04-29T06:22:26.113771",
     "exception": false,
     "start_time": "2022-04-29T06:22:26.076558",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Observations**\n",
    "\n",
    "The columns are predictions and the rows are actual values. The values are :\n",
    "\n",
    "- True Negative (TN) = 14162\n",
    "\n",
    "- False Negative (FN) = 411\n",
    "\n",
    "- False Positive (FP) = 1540\n",
    "\n",
    "- True Positive (TP) = 9863\n",
    "\n",
    "Using the values of the confusion matrix, lets calculate other metrics. \n",
    "\n",
    " - Accuracy (all correct / all) = TP + TN / total\n",
    " \n",
    " - Misclassification (all incorrect / all) = FP + FN / total\n",
    " \n",
    " - Precision (true positives / predicted positives) = TP / TP + FP\n",
    " \n",
    " - Sensitivity (true positives / all actual positives) = TP / TP + FN\n",
    " \n",
    " - Specificity (true negatives / all actual negatives) =TN / TN + FP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a6f78bdd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T06:22:26.193989Z",
     "iopub.status.busy": "2022-04-29T06:22:26.192238Z",
     "iopub.status.idle": "2022-04-29T06:22:26.258947Z",
     "shell.execute_reply": "2022-04-29T06:22:26.257222Z"
    },
    "papermill": {
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     "exception": false,
     "start_time": "2022-04-29T06:22:26.151230",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] Accuracy"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "0.924892208192177"
      ],
      "text/latex": [
       "0.924892208192177"
      ],
      "text/markdown": [
       "0.924892208192177"
      ],
      "text/plain": [
       "[1] 0.9248922"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[1] Misclassification"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "0.0751077918078226"
      ],
      "text/latex": [
       "0.0751077918078226"
      ],
      "text/markdown": [
       "0.0751077918078226"
      ],
      "text/plain": [
       "[1] 0.07510779"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[1] Precision"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "0.864947820748926"
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      "text/latex": [
       "0.864947820748926"
      ],
      "text/markdown": [
       "0.864947820748926"
      ],
      "text/plain": [
       "[1] 0.8649478"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[1] Sensitivity"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "0.959996106677049"
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      "text/latex": [
       "0.959996106677049"
      ],
      "text/markdown": [
       "0.959996106677049"
      ],
      "text/plain": [
       "[1] 0.9599961"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[1] Specificity"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "0.901923321869825"
      ],
      "text/latex": [
       "0.901923321869825"
      ],
      "text/markdown": [
       "0.901923321869825"
      ],
      "text/plain": [
       "[1] 0.9019233"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tp <- 9863\n",
    "fp <- 1540\n",
    "fn <- 411\n",
    "tn <- 14162\n",
    "noquote(\"Accuracy\")\n",
    "(tp+tn)/(tp+tn+fp+fn)\n",
    "noquote(\"Misclassification\")\n",
    "(fp+fn)/(tp+tn+fp+fn)\n",
    "noquote(\"Precision\")\n",
    "tp/(tp+fp)\n",
    "noquote(\"Sensitivity\")\n",
    "tp/(tp+fn)\n",
    "noquote(\"Specificity\")\n",
    "tn/(tn+fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5ff9b53",
   "metadata": {
    "papermill": {
     "duration": 0.038147,
     "end_time": "2022-04-29T06:22:26.337527",
     "exception": false,
     "start_time": "2022-04-29T06:22:26.299380",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id=\"9\"></a> <br>\n",
    "### Results of Logistic Regression\n",
    "\n",
    "- The model's prediction accuracy is 92% and misclassification is 7%. Both measures indicate reliability of the model.\n",
    "\n",
    "- Precision is 86% , perhaps this needs to be improved.  \n",
    "\n",
    " - Logistic regression showed that for customer satisfaction, the following factors are significantly positively significant. This means when these variables increase, the probability of satisfaction increases. \n",
    " \n",
    "    1. Loyal customers\n",
    " \n",
    "    2. Convenient departure and arrival time\n",
    " \n",
    "    3. Ease of booking\n",
    "\n",
    "- Increase in delay of flight arrival decreases the probability of satisfaction\n",
    "\n",
    "Now that we know the high contribution variables from logistic regression and the latent variables from factor analysis, lets visualize them .\n",
    "\n",
    "<a id=\"10\"></a> <br>\n",
    "### Visualizations\n",
    "\n",
    "**Customer Ratings Distribution**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "665dcfa6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T06:22:26.417237Z",
     "iopub.status.busy": "2022-04-29T06:22:26.415625Z",
     "iopub.status.idle": "2022-04-29T06:22:30.359340Z",
     "shell.execute_reply": "2022-04-29T06:22:30.356602Z"
    },
    "papermill": {
     "duration": 3.987783,
     "end_time": "2022-04-29T06:22:30.363373",
     "exception": false,
     "start_time": "2022-04-29T06:22:26.375590",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“attributes are not identical across measure variables;\n",
      "they will be dropped”\n",
      "Warning message:\n",
      "“Ignoring unknown parameters: binwidth, bins, pad”\n"
     ]
    },
    {
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hFwBAlYX0oYHzuEDqKCBkYMCuAizLSjtNr9s8/X0AACAASURBVJPzBkNy4hyIUalU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\nyyO561POC7Z5zz+z6Wxxs3u6t09QZuzYvOjNcVdeWTS0lcFvB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w29SaVNmTNrptVdap/OIwbWJKLAlmPbrhm8Z86YOXG9wny5s/vWH00zREwYr340Gs4B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\nxLDqwT8cHFHVoyRRWEpRBU0cJBDvoAZ53L17N3dCxNTUVGe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     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 840
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_rating <- subset(ds, select=c(\"Inflight.wifi.service\", \"Departure.Arrival.time.convenient\",\"Ease.of.Online.booking\", \"Gate.location\", \"Food.and.drink\", \"Online.boarding\", \"Seat.comfort\", \"Inflight.entertainment\", \"On.board.service\", \"Leg.room.service\", \"Baggage.handling\", \"Checkin.service\", \"Inflight.service\",\"Cleanliness\"))\n",
    "options(repr.plot.width = 14, repr.plot.height = 8)\n",
    "ds_rating %>%\n",
    "  keep(is.factor) %>%\n",
    "  gather()%>%\n",
    "  ggplot(aes(value, fill = value)) +\n",
    "    facet_wrap(~ key, scales = \"free\", ncol=3) +\n",
    "    geom_histogram(stat=\"count\", col=\"cadetblue\")+\n",
    "  scale_fill_brewer(palette = \"Blues\")+\n",
    "  labs(title=\"Rating of Airline Services\")+\n",
    "theme(text= element_text(size=14))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcd6fbca",
   "metadata": {
    "papermill": {
     "duration": 0.041222,
     "end_time": "2022-04-29T06:22:30.448221",
     "exception": false,
     "start_time": "2022-04-29T06:22:30.406999",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Categorical Variables**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d87dfb96",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T06:22:30.545088Z",
     "iopub.status.busy": "2022-04-29T06:22:30.543387Z",
     "iopub.status.idle": "2022-04-29T06:22:32.410891Z",
     "shell.execute_reply": "2022-04-29T06:22:32.409048Z"
    },
    "papermill": {
     "duration": 1.924414,
     "end_time": "2022-04-29T06:22:32.413730",
     "exception": false,
     "start_time": "2022-04-29T06:22:30.489316",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“attributes are not identical across measure variables;\n",
      "they will be dropped”\n",
      "Warning message:\n",
      "“Ignoring unknown parameters: binwidth, bins, pad”\n"
     ]
    },
    {
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     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 840
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_cat <- subset(ds, select= c(\"Gender\", \"Customer.Type\",\"Type.of.Travel\",\"Class\",\"satisfaction\"))\n",
    "ds_cat$satisfaction <- ifelse(ds_cat$satisfaction ==1,\"Satisfied\",\"Dissatisfied/Neutral\")\n",
    "ds_cat$satisfaction <- as.factor(ds_cat$satisfaction)\n",
    "options(repr.plot.width = 14, repr.plot.height = 8)\n",
    "ds_cat %>%\n",
    "  keep(is.factor)%>%\n",
    "  gather()%>%\n",
    "  ggplot(aes(value))+\n",
    "  geom_histogram(stat=\"count\", fill=\"cadetblue4\", alpha=0.6)+\n",
    "  facet_wrap(~key, ncol=2,scales=\"free\")+\n",
    "  labs(title=\"Categorical Variables\")+\n",
    "  stat_count(aes(y=..count..,label=..count..), geom=\"text\", vjust=1.3,size=4.2,color=\"white\")+\n",
    "theme(text= element_text(size=16))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ed53e9e",
   "metadata": {
    "papermill": {
     "duration": 0.042887,
     "end_time": "2022-04-29T06:22:32.501491",
     "exception": false,
     "start_time": "2022-04-29T06:22:32.458604",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Observations**\n",
    "\n",
    "- The gender ratio is quite balanced with almost equal numbers of male and female passengers\n",
    "\n",
    "- Loyal customers are much higher in number than disloyal customers\n",
    "\n",
    "- While the difference is not too high, more passengers are dissatisfied or neutral than satisfied\n",
    "\n",
    "- Passengers who travel for business are more than the double of personal travel passengers. This explains why maximum passengers go for business class. \n",
    "\n",
    "**Customer Loyalty and Satisfaction**\n",
    "\n",
    "Do loyal customers report satisfaction more than disloyal customers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b988b447",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T06:22:32.590224Z",
     "iopub.status.busy": "2022-04-29T06:22:32.588630Z",
     "iopub.status.idle": "2022-04-29T06:22:33.160234Z",
     "shell.execute_reply": "2022-04-29T06:22:33.157305Z"
    },
    "papermill": {
     "duration": 0.619347,
     "end_time": "2022-04-29T06:22:33.163338",
     "exception": false,
     "start_time": "2022-04-29T06:22:32.543991",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gAAAACQIAI7AAAAAEgQgR0AAAAAJIjADgAAAAASRGAHAAAAAAmSnekC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\nUwPGL/woPbvg22+/zPosacH4oLC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     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 840
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 14, repr.plot.height = 8)\n",
    "ggplot(ds, aes(x=`Customer.Type`, fill = `satisfaction`))+\n",
    "  geom_bar(alpha=0.7)+\n",
    "  geom_text(aes(label= scales::percent((..count..)/sum(..count..))), stat=\"count\",\n",
    "        vjust = 1.5, size=5)+\n",
    "  scale_fill_discrete(name = \"Satisfaction Status\", labels=c(\"Neutral or Dissatisfied\", \"Satisfied\"))+\n",
    "  labs(x=\"Customer Type\", y=\"Count\", title=\"Customer Satisfaction and Loyalty \")+\n",
    "  theme(legend.position=\"right\",text= element_text(size=16) )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0db09f6a",
   "metadata": {
    "papermill": {
     "duration": 0.044069,
     "end_time": "2022-04-29T06:22:33.253967",
     "exception": false,
     "start_time": "2022-04-29T06:22:33.209898",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Observation**\n",
    "\n",
    "- 39 % loyal customers are satisfied and 42.7 % loyal customers are dissatisfied / neutral. \n",
    "\n",
    "- A greater % of disloyal customers (13.9 %) are dissatisfied / neutral compared to disloyal and satisfied customers (4.3 %)\n",
    "\n",
    "**Convenience of Arrival/Departure and Satisfaction**\n",
    "With increasing conveneince of arrival/departure time, the number of satisfied customers should gradually increase with the rating. The opposite trend should be observed for dissatisfied customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ae7a3675",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T06:22:33.344207Z",
     "iopub.status.busy": "2022-04-29T06:22:33.342426Z",
     "iopub.status.idle": "2022-04-29T06:22:33.831184Z",
     "shell.execute_reply": "2022-04-29T06:22:33.829291Z"
    },
    "papermill": {
     "duration": 0.536262,
     "end_time": "2022-04-29T06:22:33.833389",
     "exception": false,
     "start_time": "2022-04-29T06:22:33.297127",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 840
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 14, repr.plot.height = 8)\n",
    "ggplot(ds, aes(x=`Departure.Arrival.time.convenient`, fill =`satisfaction`))+\n",
    "  geom_bar(position=\"dodge\", alpha=0.5)+\n",
    "  labs(x=\"Rating\", y=\"Count\", title=\"Convenience of Departure/Arrival Time\")+\n",
    "  scale_fill_discrete(name = \"Satisfaction Status\", labels=c(\"Neutral or Dissatisfied\", \"Satisfied\"))+\n",
    "  theme(legend.position=\"right\", text= element_text( size = 16))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "237bdce0",
   "metadata": {
    "papermill": {
     "duration": 0.045898,
     "end_time": "2022-04-29T06:22:33.926280",
     "exception": false,
     "start_time": "2022-04-29T06:22:33.880382",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Observation**\n",
    "\n",
    "- Contrary to my assumption, the number of dissatisfied customers increases with the increase of rating. \n",
    "\n",
    "**Customer Satisfaction and Ease of Online Booking**\n",
    "\n",
    "Since increase in the ease of online booking tends to increase satisfaction, as indicated by logistic regression analysis, the number of satisfied customers should should be higher than dissatisfied/neutral customers for the highest rating (=5) in ease of online booking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b903d223",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-29T06:22:34.019169Z",
     "iopub.status.busy": "2022-04-29T06:22:34.017539Z",
     "iopub.status.idle": "2022-04-29T06:22:34.487012Z",
     "shell.execute_reply": "2022-04-29T06:22:34.485020Z"
    },
    "papermill": {
     "duration": 0.518443,
     "end_time": "2022-04-29T06:22:34.489700",
     "exception": false,
     "start_time": "2022-04-29T06:22:33.971257",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ELQ9++NKje63VuPD7taePVVKydNmQCy8PIXQ47dJBbfNWLcxr1m7A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\nVZpMZjfY9dlXrqrOggEAAABSpv4OL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     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 840
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 14, repr.plot.height = 8)\n",
    "ggplot(ds, aes(x=`Ease.of.Online.booking`, fill =`satisfaction`))+\n",
    "  geom_bar(position=\"dodge\", alpha=0.5)+\n",
    "  labs(x=\"Rating\", y=\"Count\", title=\"Ease of online booking rating\")+\n",
    "  scale_fill_discrete(name = \"Satisfaction Status\", labels=c(\"Neutral or Dissatisfied\", \"Satisfied\"))+\n",
    "  theme(legend.position=\"right\", text= element_text( size = 16))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d24f510",
   "metadata": {
    "papermill": {
     "duration": 0.045174,
     "end_time": "2022-04-29T06:22:34.583845",
     "exception": false,
     "start_time": "2022-04-29T06:22:34.538671",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Observation**\n",
    "\n",
    "- As predicted, the highest rating of 5 has been given by a higher number of satisfied customers to ease of online booking. \n",
    "\n",
    "- As the rating decreases, the number of dissatisfied customers increases. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
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   "file_extension": ".r",
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   "pygments_lexer": "r",
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  "papermill": {
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   "end_time": "2022-04-29T06:22:34.748545",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2022-04-29T06:16:23.894397",
   "version": "2.3.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}