1_EDA.ipynb

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   "metadata": {},
   "source": [
    "# Table of Contents\n",
    "\n",
    "1. [Importing Libraries](#1.-Importing-Libraries)\n",
    "2. [Importing Data](#2.-Importing-Data)    \n",
    "3. [Initial Exploration](#3.-Initial-Exploration)    \n",
    "4. [Exploratory Data Analysis](#4.-Exploratory-Data-Analysis) <br>\n",
    "    4.1 [Missing Values](#4.1-Missing-Values)<br>\n",
    "    4.2 [Outliers](#4.2-Outliers)<br>\n",
    "    4.3 [Feature Engineering](#4.3-Feature-Engineering)<br>\n",
    "    4.4 [Visualisations](#4.4-Visualisations)<br>\n",
    "5. [Preprocessing fro Sentiment Analysis](#5.-Preprocessing-for-Sentiment-Analysis) <br>   \n",
    "6. [Exporting Preprocessed Data](#6.-Exporting-Preprocessed-data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c73e66cb",
   "metadata": {},
   "source": [
    "In this notebook Exploratory Data Analysis will be performed. This consists of Data Exploration, Missing Values, Outliers, Feature Engineering and VIsualisations. At the end, the data is preprocessed using functions in an external file, *preprocessing*, to make it suitable for Sentiment Analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01ddc5bb",
   "metadata": {},
   "source": [
    "** **"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "081aa230",
   "metadata": {},
   "source": [
    "## 1. Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c67fad32",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-17T16:25:51.433349Z",
     "start_time": "2024-05-17T16:25:47.473880Z"
    }
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     /Users/antoniooliveira/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /Users/antoniooliveira/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     /Users/antoniooliveira/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package omw-1.4 to\n",
      "[nltk_data]     /Users/antoniooliveira/nltk_data...\n",
      "[nltk_data]   Package omw-1.4 is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import emoji\n",
    "\n",
    "# External Functions File\n",
    "import preprocessing\n",
    "\n",
    "# Warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "312a6369",
   "metadata": {},
   "source": [
    "## 2. Importing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ba7ec02f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-17T16:25:51.578829Z",
     "start_time": "2024-05-17T16:25:51.436003Z"
    }
   },
   "outputs": [],
   "source": [
    "#read in dataset in pandas format\n",
    "#dataset = pd.read_csv('airlines_reviews.csv', sep=',')\n",
    "dataset = pd.read_csv('/Users/antoniooliveira/Downloads/airlines_reviews.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0067efad",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-17T16:25:51.602466Z",
     "start_time": "2024-05-17T16:25:51.582992Z"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Name</th>\n",
       "      <th>Review Date</th>\n",
       "      <th>Airline</th>\n",
       "      <th>Verified</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Type of Traveller</th>\n",
       "      <th>Month Flown</th>\n",
       "      <th>Route</th>\n",
       "      <th>Class</th>\n",
       "      <th>Seat Comfort</th>\n",
       "      <th>Staff Service</th>\n",
       "      <th>Food &amp; Beverages</th>\n",
       "      <th>Inflight Entertainment</th>\n",
       "      <th>Value For Money</th>\n",
       "      <th>Overall Rating</th>\n",
       "      <th>Recommended</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Flight was amazing</td>\n",
       "      <td>Alison Soetantyo</td>\n",
       "      <td>2024-03-01</td>\n",
       "      <td>Singapore Airlines</td>\n",
       "      <td>True</td>\n",
       "      <td>Flight was amazing. The crew onboard this fl...</td>\n",
       "      <td>Solo Leisure</td>\n",
       "      <td>December 2023</td>\n",
       "      <td>Jakarta to Singapore</td>\n",
       "      <td>Business Class</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>seats on this aircraft are dreadful</td>\n",
       "      <td>Robert Watson</td>\n",
       "      <td>2024-02-21</td>\n",
       "      <td>Singapore Airlines</td>\n",
       "      <td>True</td>\n",
       "      <td>Booking an emergency exit seat still meant h...</td>\n",
       "      <td>Solo Leisure</td>\n",
       "      <td>February 2024</td>\n",
       "      <td>Phuket to Singapore</td>\n",
       "      <td>Economy Class</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Food was plentiful and tasty</td>\n",
       "      <td>S Han</td>\n",
       "      <td>2024-02-20</td>\n",
       "      <td>Singapore Airlines</td>\n",
       "      <td>True</td>\n",
       "      <td>Excellent performance on all fronts. I would...</td>\n",
       "      <td>Family Leisure</td>\n",
       "      <td>February 2024</td>\n",
       "      <td>Siem Reap to Singapore</td>\n",
       "      <td>Economy Class</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>“how much food was available</td>\n",
       "      <td>D Laynes</td>\n",
       "      <td>2024-02-19</td>\n",
       "      <td>Singapore Airlines</td>\n",
       "      <td>True</td>\n",
       "      <td>Pretty comfortable flight considering I was f...</td>\n",
       "      <td>Solo Leisure</td>\n",
       "      <td>February 2024</td>\n",
       "      <td>Singapore to London Heathrow</td>\n",
       "      <td>Economy Class</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>“service was consistently good”</td>\n",
       "      <td>A Othman</td>\n",
       "      <td>2024-02-19</td>\n",
       "      <td>Singapore Airlines</td>\n",
       "      <td>True</td>\n",
       "      <td>The service was consistently good from start ...</td>\n",
       "      <td>Family Leisure</td>\n",
       "      <td>February 2024</td>\n",
       "      <td>Singapore to Phnom Penh</td>\n",
       "      <td>Economy Class</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                                  Title              Name Review Date  \\\n",
       "0                    Flight was amazing  Alison Soetantyo  2024-03-01   \n",
       "1  seats on this aircraft are dreadful      Robert Watson  2024-02-21   \n",
       "2          Food was plentiful and tasty             S Han  2024-02-20   \n",
       "3          “how much food was available          D Laynes  2024-02-19   \n",
       "4       “service was consistently good”         A Othman   2024-02-19   \n",
       "\n",
       "              Airline Verified  \\\n",
       "0  Singapore Airlines     True   \n",
       "1  Singapore Airlines     True   \n",
       "2  Singapore Airlines     True   \n",
       "3  Singapore Airlines     True   \n",
       "4  Singapore Airlines     True   \n",
       "\n",
       "                                             Reviews Type of Traveller  \\\n",
       "0    Flight was amazing. The crew onboard this fl...      Solo Leisure   \n",
       "1    Booking an emergency exit seat still meant h...      Solo Leisure   \n",
       "2    Excellent performance on all fronts. I would...    Family Leisure   \n",
       "3   Pretty comfortable flight considering I was f...      Solo Leisure   \n",
       "4   The service was consistently good from start ...    Family Leisure   \n",
       "\n",
       "     Month Flown                         Route           Class  Seat Comfort  \\\n",
       "0  December 2023          Jakarta to Singapore  Business Class             4   \n",
       "1  February 2024           Phuket to Singapore   Economy Class             5   \n",
       "2  February 2024        Siem Reap to Singapore   Economy Class             1   \n",
       "3  February 2024  Singapore to London Heathrow   Economy Class             5   \n",
       "4  February 2024       Singapore to Phnom Penh   Economy Class             5   \n",
       "\n",
       "   Staff Service  Food & Beverages  Inflight Entertainment  Value For Money  \\\n",
       "0              4                 4                       4                4   \n",
       "1              3                 4                       4                1   \n",
       "2              5                 2                       1                5   \n",
       "3              5                 5                       5                5   \n",
       "4              5                 5                       5                5   \n",
       "\n",
       "   Overall Rating Recommended  \n",
       "0               9         yes  \n",
       "1               3          no  \n",
       "2              10         yes  \n",
       "3              10         yes  \n",
       "4              10         yes  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c42a5b9",
   "metadata": {},
   "source": [
    "## 3. Initial Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b2c19f1",
   "metadata": {},
   "source": [
    "**Describe & Info**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5e5b508d",
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   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Seat Comfort</th>\n",
       "      <th>Staff Service</th>\n",
       "      <th>Food &amp; Beverages</th>\n",
       "      <th>Inflight Entertainment</th>\n",
       "      <th>Value For Money</th>\n",
       "      <th>Overall Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8100.000000</td>\n",
       "      <td>8100.000000</td>\n",
       "      <td>8100.000000</td>\n",
       "      <td>8100.000000</td>\n",
       "      <td>8100.000000</td>\n",
       "      <td>8100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.414815</td>\n",
       "      <td>3.569877</td>\n",
       "      <td>3.384074</td>\n",
       "      <td>3.636790</td>\n",
       "      <td>3.148642</td>\n",
       "      <td>5.632469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.397183</td>\n",
       "      <td>1.527436</td>\n",
       "      <td>1.455694</td>\n",
       "      <td>1.309564</td>\n",
       "      <td>1.589377</td>\n",
       "      <td>3.523664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       Seat Comfort  Staff Service  Food & Beverages  Inflight Entertainment  \\\n",
       "count   8100.000000    8100.000000       8100.000000             8100.000000   \n",
       "mean       3.414815       3.569877          3.384074                3.636790   \n",
       "std        1.397183       1.527436          1.455694                1.309564   \n",
       "min        1.000000       1.000000          1.000000                0.000000   \n",
       "25%        2.000000       2.000000          2.000000                3.000000   \n",
       "50%        4.000000       4.000000          4.000000                4.000000   \n",
       "75%        5.000000       5.000000          5.000000                5.000000   \n",
       "max        5.000000       5.000000          5.000000                5.000000   \n",
       "\n",
       "       Value For Money  Overall Rating  \n",
       "count      8100.000000     8100.000000  \n",
       "mean          3.148642        5.632469  \n",
       "std           1.589377        3.523664  \n",
       "min           1.000000        1.000000  \n",
       "25%           1.000000        2.000000  \n",
       "50%           3.000000        6.000000  \n",
       "75%           5.000000        9.000000  \n",
       "max           5.000000       10.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8100 entries, 0 to 8099\n",
      "Data columns (total 17 columns):\n",
      " #   Column                  Non-Null Count  Dtype \n",
      "---  ------                  --------------  ----- \n",
      " 0   Title                   8100 non-null   object\n",
      " 1   Name                    8100 non-null   object\n",
      " 2   Review Date             8100 non-null   object\n",
      " 3   Airline                 8100 non-null   object\n",
      " 4   Verified                8100 non-null   object\n",
      " 5   Reviews                 8100 non-null   object\n",
      " 6   Type of Traveller       8100 non-null   object\n",
      " 7   Month Flown             8100 non-null   object\n",
      " 8   Route                   8100 non-null   object\n",
      " 9   Class                   8100 non-null   object\n",
      " 10  Seat Comfort            8100 non-null   int64 \n",
      " 11  Staff Service           8100 non-null   int64 \n",
      " 12  Food & Beverages        8100 non-null   int64 \n",
      " 13  Inflight Entertainment  8100 non-null   int64 \n",
      " 14  Value For Money         8100 non-null   int64 \n",
      " 15  Overall Rating          8100 non-null   int64 \n",
      " 16  Recommended             8100 non-null   object\n",
      "dtypes: int64(6), object(11)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "133f2da4",
   "metadata": {},
   "source": [
    "**Unique Values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "38e58bb0",
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      "text/plain": [
       "Title                     7614\n",
       "Name                      6583\n",
       "Review Date               2549\n",
       "Airline                     10\n",
       "Verified                     5\n",
       "Reviews                   8099\n",
       "Type of Traveller            4\n",
       "Month Flown                107\n",
       "Route                     5566\n",
       "Class                        4\n",
       "Seat Comfort                 5\n",
       "Staff Service                5\n",
       "Food & Beverages             5\n",
       "Inflight Entertainment       6\n",
       "Value For Money              5\n",
       "Overall Rating              10\n",
       "Recommended                  2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
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     "output_type": "execute_result"
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   ],
   "source": [
    "dataset.nunique()"
   ]
  },
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   "execution_count": 7,
   "id": "c9b9af08",
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   "outputs": [
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     "text": [
      "Unique values in column 'Airline': \n",
      " ['Singapore Airlines' 'Qatar Airways' 'All Nippon Airways' 'Emirates'\n",
      " 'Japan Airlines' 'Turkish Airlines' 'Air France' 'Cathay Pacific Airways'\n",
      " 'EVA Air' 'Korean Air'] \n",
      "\n",
      "\n",
      "Unique values in column 'Verified': \n",
      " ['True' 'False' '*Unverified*' 'NotVerified'\n",
      " 'we do appreciate you bringing this matter to our attention. Please accept my apologies for not having met your expectations; I do hope that we can leave you and your family with a more positive impression on your future flights with us'] \n",
      "\n",
      "\n",
      "Unique values in column 'Type of Traveller': \n",
      " ['Solo Leisure' 'Family Leisure' 'Business' 'Couple Leisure'] \n",
      "\n",
      "\n",
      "Unique values in column 'Class': \n",
      " ['Business Class' 'Economy Class' 'Premium Economy' 'First Class'] \n",
      "\n",
      "\n",
      "Unique values in column 'Seat Comfort': \n",
      " [4 5 1 2 3] \n",
      "\n",
      "\n",
      "Unique values in column 'Staff Service': \n",
      " [4 3 5 1 2] \n",
      "\n",
      "\n",
      "Unique values in column 'Food & Beverages': \n",
      " [4 2 5 1 3] \n",
      "\n",
      "\n",
      "Unique values in column 'Inflight Entertainment': \n",
      " [4 1 5 2 3 0] \n",
      "\n",
      "\n",
      "Unique values in column 'Value For Money': \n",
      " [4 1 5 2 3] \n",
      "\n",
      "\n",
      "Unique values in column 'Overall Rating': \n",
      " [ 9  3 10  5  4  2  8  1  7  6] \n",
      "\n",
      "\n",
      "Unique values in column 'Recommended': \n",
      " ['yes' 'no'] \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "list_ = ['Airline', 'Verified', 'Type of Traveller', 'Class', 'Seat Comfort',\n",
    "        'Staff Service', 'Food & Beverages', 'Inflight Entertainment', 'Value For Money',\n",
    "        'Overall Rating', 'Recommended']\n",
    "\n",
    "\n",
    "for value in list_:\n",
    "    unique_values = dataset[value].unique()\n",
    "    print(f\"Unique values in column '{value}': \\n {unique_values} \\n\\n\")\n",
    "\n",
    "    \n",
    "## Verified has some weird values --> Unverified and NotVerified arent the same ?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acd63860",
   "metadata": {},
   "source": [
    "## 4. Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dd901bc",
   "metadata": {},
   "source": [
    "### 4.1 Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "297a6c78",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-17T16:25:51.707446Z",
     "start_time": "2024-05-17T16:25:51.697532Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title                     0\n",
       "Name                      0\n",
       "Review Date               0\n",
       "Airline                   0\n",
       "Verified                  0\n",
       "Reviews                   0\n",
       "Type of Traveller         0\n",
       "Month Flown               0\n",
       "Route                     0\n",
       "Class                     0\n",
       "Seat Comfort              0\n",
       "Staff Service             0\n",
       "Food & Beverages          0\n",
       "Inflight Entertainment    0\n",
       "Value For Money           0\n",
       "Overall Rating            0\n",
       "Recommended               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cc0d37b",
   "metadata": {},
   "source": [
    "### 4.2 Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7eb4d432",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-17T16:25:52.603351Z",
     "start_time": "2024-05-17T16:25:51.714351Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "# Get a list of all numeric column names\n",
    "columns_to_check = dataset.select_dtypes(include=['int', 'float']).columns.to_list()\n",
    "\n",
    "for column in columns_to_check:\n",
    "    plt.figure(figsize=(6, 4))  \n",
    "    sns.boxplot(data=dataset[column], color = 'steelblue')\n",
    "    plt.title(f'Box Plot of {column}')\n",
    "    plt.xticks()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afff4b26",
   "metadata": {},
   "source": [
    "### 4.3 Feature Engineering "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a895781a",
   "metadata": {},
   "source": [
    "**Looking for Emojis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9ff33aa8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-17T16:25:58.188556Z",
     "start_time": "2024-05-17T16:25:52.605811Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is Review: 4708\n",
      "  Valerie, terminal 2A, Paris CDG airport, left our hearts feeling warm with her compassion, patience and understanding whilst checking our bags. She showed us great kindness and deserves recognition for her customer service skills performed. Thank you to Valerie, if you read this, for all the small efforts which made our travels much easier while pregnant. Thank you for going above and beyond ☺️ Turkish airlines hires great assets to their company.\n"
     ]
    }
   ],
   "source": [
    "reviews_column = dataset['Reviews']\n",
    "\n",
    "# Function to check if a string contains emojis\n",
    "def contain_emoji(review):\n",
    "    emoList = emoji.emoji_list(review)\n",
    "    \n",
    "    if emoList:\n",
    "        return True\n",
    "    \n",
    "    return False\n",
    "\n",
    "# Check for emojis in each review\n",
    "emoji_check = [contain_emoji(review) for review in reviews_column]\n",
    "\n",
    "# Print reviews containing emojis\n",
    "for i in range(len(emoji_check)):\n",
    "    if emoji_check[i] == True:\n",
    "        print(\"This is Review: \" + str(i))\n",
    "        print(reviews_column[i])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8be186a7",
   "metadata": {},
   "source": [
    "Since there is only one Review it will be dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8eb16d2b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-17T16:25:58.198550Z",
     "start_time": "2024-05-17T16:25:58.192367Z"
    }
   },
   "outputs": [],
   "source": [
    "dataset.drop(4708, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d8262fe",
   "metadata": {},
   "source": [
    "**Converting Overall Rating to 3 classes**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e603973",
   "metadata": {},
   "source": [
    "Since the column overall rating has 10 classes, and our aim is to predict if the reviews are negative, positive or neutral, we use the following table to better split the existing classes into three groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f9689031",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-17T16:25:58.216743Z",
     "start_time": "2024-05-17T16:25:58.200638Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Overall Rating</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recommended</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>no</th>\n",
       "      <td>1791</td>\n",
       "      <td>669</td>\n",
       "      <td>488</td>\n",
       "      <td>330</td>\n",
       "      <td>305</td>\n",
       "      <td>123</td>\n",
       "      <td>54</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yes</th>\n",
       "      <td>17</td>\n",
       "      <td>23</td>\n",
       "      <td>34</td>\n",
       "      <td>51</td>\n",
       "      <td>131</td>\n",
       "      <td>223</td>\n",
       "      <td>455</td>\n",
       "      <td>728</td>\n",
       "      <td>1035</td>\n",
       "      <td>1589</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Overall Rating    1    2    3    4    5    6    7    8     9     10\n",
       "Recommended                                                        \n",
       "no              1791  669  488  330  305  123   54   14    19    20\n",
       "yes               17   23   34   51  131  223  455  728  1035  1589"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.groupby(['Overall Rating', 'Recommended']).size().unstack(fill_value=0).T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9de4fe94",
   "metadata": {},
   "source": [
    "As one can see, there is a considerable amount of recommended values when the Overall Rating is equal or higher than 8. the opposite happens when looking at Overall Ratings smaller or equal to 3. Consequently, we decided to split Overall Rating as follows:\n",
    "- 1-3 represented by 0, which means a negative sentiment\n",
    "- 4-7 represented by 1, which means a neutral sentiment\n",
    "- 8-10 represented by 2, which means a positive sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "84f77076",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-17T16:25:58.237206Z",
     "start_time": "2024-05-17T16:25:58.219531Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Name</th>\n",
       "      <th>Review Date</th>\n",
       "      <th>Airline</th>\n",
       "      <th>Verified</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Type of Traveller</th>\n",
       "      <th>Month Flown</th>\n",
       "      <th>Route</th>\n",
       "      <th>Class</th>\n",
       "      <th>Seat Comfort</th>\n",
       "      <th>Staff Service</th>\n",
       "      <th>Food &amp; Beverages</th>\n",
       "      <th>Inflight Entertainment</th>\n",
       "      <th>Value For Money</th>\n",
       "      <th>Overall Rating</th>\n",
       "      <th>Recommended</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Flight was amazing</td>\n",
       "      <td>Alison Soetantyo</td>\n",
       "      <td>2024-03-01</td>\n",
       "      <td>Singapore Airlines</td>\n",
       "      <td>True</td>\n",
       "      <td>Flight was amazing. The crew onboard this fl...</td>\n",
       "      <td>Solo Leisure</td>\n",
       "      <td>December 2023</td>\n",
       "      <td>Jakarta to Singapore</td>\n",
       "      <td>Business Class</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>seats on this aircraft are dreadful</td>\n",
       "      <td>Robert Watson</td>\n",
       "      <td>2024-02-21</td>\n",
       "      <td>Singapore Airlines</td>\n",
       "      <td>True</td>\n",
       "      <td>Booking an emergency exit seat still meant h...</td>\n",
       "      <td>Solo Leisure</td>\n",
       "      <td>February 2024</td>\n",
       "      <td>Phuket to Singapore</td>\n",
       "      <td>Economy Class</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Title              Name Review Date  \\\n",
       "0                    Flight was amazing  Alison Soetantyo  2024-03-01   \n",
       "1  seats on this aircraft are dreadful      Robert Watson  2024-02-21   \n",
       "\n",
       "              Airline Verified  \\\n",
       "0  Singapore Airlines     True   \n",
       "1  Singapore Airlines     True   \n",
       "\n",
       "                                             Reviews Type of Traveller  \\\n",
       "0    Flight was amazing. The crew onboard this fl...      Solo Leisure   \n",
       "1    Booking an emergency exit seat still meant h...      Solo Leisure   \n",
       "\n",
       "     Month Flown                 Route           Class  Seat Comfort  \\\n",
       "0  December 2023  Jakarta to Singapore  Business Class             4   \n",
       "1  February 2024   Phuket to Singapore   Economy Class             5   \n",
       "\n",
       "   Staff Service  Food & Beverages  Inflight Entertainment  Value For Money  \\\n",
       "0              4                 4                       4                4   \n",
       "1              3                 4                       4                1   \n",
       "\n",
       "   Overall Rating Recommended  Sentiment  \n",
       "0               9         yes          2  \n",
       "1               3          no          0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create new column, convert scoring into 3 categories\n",
    "dataset[\"Sentiment\"] = dataset[\"Overall Rating\"].apply(preprocessing.score_convert_senti)\n",
    "\n",
    "#dataset has derived sentiment column added\n",
    "dataset.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "756e5eba",
   "metadata": {},
   "source": [
    "**Converting Reviews into a list**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "87f9a762",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-17T16:25:58.243656Z",
     "start_time": "2024-05-17T16:25:58.239812Z"
    }
   },
   "outputs": [],
   "source": [
    "#place reviews column textual data into list\n",
    "reviews = dataset[\"Reviews\"]\n",
    "reviews_list = list(reviews)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d629fdce",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-05T11:30:04.793104Z",
     "start_time": "2024-05-05T11:30:04.789179Z"
    }
   },
   "source": [
    "**Converting Review Date to datetime format and retrieving the year as a new variable**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0f990e02",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-17T16:25:58.256410Z",
     "start_time": "2024-05-17T16:25:58.246055Z"
    }
   },
   "outputs": [],
   "source": [
    "dataset['Review Date'] = pd.to_datetime(dataset['Review Date'])\n",
    "dataset['Year'] = dataset['Review Date'].dt.year"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9b2a526",
   "metadata": {},
   "source": [
    "### 4.4 Visualisations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14b40468",
   "metadata": {},
   "source": [
    "**Frequency Distribution of Overall Ratings**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2abcc6b6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-17T16:25:58.460681Z",
     "start_time": "2024-05-17T16:25:58.259672Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Determine frequency of Overall Ratings Through Plot\n",
    "freq_counts = dataset[\"Overall Rating\"].value_counts()\n",
    "rating_score = freq_counts.index\n",
    "\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.bar(rating_score, freq_counts, color='steelblue')\n",
    "plt.xlabel('Overall Rating Score')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Frequency Distribution of Overall Rating')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92753ed0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-05T11:16:13.227234Z",
     "start_time": "2024-05-05T11:16:13.222740Z"
    }
   },
   "source": [
    "**Average Rating by Airline**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "24bb049b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-17T16:25:58.678888Z",
     "start_time": "2024-05-17T16:25:58.463324Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HIzU1VXkpqWXLlvD29sbOnTvRrl07fP311wgICEDjxo1hamqq0oG1QNu2bWFqaoqlS5fis88+w9y5czF37txC/U/KavDgwfjggw9w4sQJNG7cGFOmTMHEiRPh7u6OBg0aAECRna9L0r59ewQGBqJ9+/aYPn06hgwZAh8fHygUCvzxxx/Ky4avcnR0RNeuXfHw4UMAmnUGLut3X9tU5veVdIjYiYqovAwYMKDIlo6iFLTG7NmzR7ns+fPngpmZmQBAaNy4cYnPT0tLE+bNmyd4eXkJZmZmgrGxsVC7dm2hS5cuwh9//CGkp6crt31TC40gvLx196OPPhJsbGwEU1NTwcvLS1i9enWxf/EKgiDcvHlT6Nmzp2BpaSmYmpoKbdq0Efbu3SssWrRIACDs2rWr0HMePXokTJ06VXB3dxdMTEwEMzMzwc3NTejdu7ewcePGIscbKQpKaKERBEFITEwUTE1NBVNTUyExMVEQBEF48uSJMHbsWKFWrVqCkZGR4OLiIkyfPl3IyMgQatWqVaiFRhBetji1adNG+bnglb/yy6uFRhAE4cWLF8KMGTOE2rVrC4aGhkKtWrWEb775RoiPjxcACD169FDr5/Lq53Xz5k2hW7dugpWVlWBiYiK0a9dOOHjwYInP3717twBAaNWqlVrvV6As331tbKEpUF7fV3o7yAShHKbFJSKtMXDgQGzevBlRUVGoX7++2OVI2pEjR+Dv749p06Zh/vz5Ff5+33//PebMmYM1a9Zg2LBhFf5+RLqEgYZIghQKBR4/flzottpjx46hU6dOaNCgAa5fvy5SddKTkJBQ6LLXkydP4O/vj4sXL+L8+fNFjqFSntLS0uDm5gaFQoH79+/DxMSkQt+PSNfwtm0iCcrJyYGTkxM6duyIBg0aQF9fHzdu3MCRI0dgZGSElStXil2ipHz55Ze4cuUK2rVrBxsbG8THx+PAgQNISUnBuHHjKjTM/Pvvv4iIiMDevXuRlJSEJUuWMMwQlQIDDZEEGRgYYNy4cQgJCcH58+eRnp4Oa2trfPzxx/jmm2/QrFkzsUuUlN69eyM5ORn79+9HSkoKjIyM0LhxY+XtzhXpn3/+wfr162Fvb4/vvvtO4/GNiOgl0S85RUZGYs+ePYiNjcXTp08xZcoUlV76giDg77//xrFjx5Ceno66deti+PDhyuGyiYiIiES/bTs7Oxu1a9cutgPc7t27sW/fPgwbNgzz58+HlZUV5s6dW+RAXERERPR2Ej3QeHp6on///kVO3CcIAvbv34+ePXuidevWcHZ2xvjx45GdnV3qETyJiIhI94geaEry+PFjpKamqvQHMDAwQKNGjXDr1q1in5ebm4vMzEyVf7m5uZVRMhEREYlAqzsFp6amAig8kZulpSWSk5OLfV5QUBB27NihfOzt7Y2JEydWSI1EREQkPq0ONAVen/H1Tf2Ye/bsia5duxZ6/tOnT5GXl1f+BRIREVG509fXR9WqVdXbtoJrKRMrKysAL1tqXt2htLS0Qq02rzIwMCg0oy8A5OXl8dITERGRDtLqPjS2trawsrLC1atXlcvy8vIQGRnJId2JiIhISfQWmqysLCQmJiofP378GHFxcTA3N4e1tTW6dOmCoKAg1KhRA/b29ggKCoKRkRHeeecdEasmIiIibSL6wHo3btzA7NmzCy3v0KEDxo8frxxY7+jRo8jIyICbmxuGDx8OZ2dnjd8rKSmJl5yIiIgkwsDAADY2NmptK3qgqUwMNERERNKhSaDR6j40REREROpgoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiydMXu4A3yc/Px99//40TJ04gNTUVVatWha+vL3r16gW5nHmMiIiIJBBodu/ejSNHjmD8+PFwdHRETEwMVq5cCVNTU3Tp0kXs8oiIiEgLaH2g+e+//9CiRQt4eXkBAGxtbXHy5EncuXNH5MqIiIhIW2h9oGnQoAGOHDmChIQEODg4IC4uDrdu3UJAQECxz8nNzUVubq7ysUwmg4mJifL/REREpFtkgiAIYhdREkEQsGXLFuzevRtyuRwKhQL9+/dHz549i33O9u3bsWPHDuXjOnXqYMGCBZVRLhEREYlA6wPNqVOnsGnTJgwaNAhOTk6Ii4vDunXrMHjwYPj6+hb5nOJaaJKSkpCXl1dJlRMREVFZ6Ovrw8bGRr1tK7iWMtu0aRO6d+8Ob29vAICzszOSkpKwa9euYgONgYEBDAwMilz3an6Tm1Yr93rLkyIzRewSiIiIJEHr73vOzs4udHu2XC6HljcsERERUSXS+haa5s2bY+fOnbC2toajoyPi4uKwd+9edOzYUezSiIiISEtofR+aFy9eYNu2bTh37hyePXuGatWqwdvbG3369IG+vmZ5LCkpSaVvDS85ERERaS8DAwO1+9BofaApTww0RERE0qFJoNH6PjREREREb8JAQ0RERJLHQENERESSx0BDREREksdAQ0RERJLHQENERESSx0BDREREksdAQ0RERJLHQENERESSx0BDREREksdAQ0RERJLHQENERESSx0BDREREksdAQ0RERJKnL3YBVHZpgqnYJRTLQpYpdglERPQWYAsNERERSR4DDREREUkeAw0RERFJHgMNERERSR4DDREREUkeAw0RERFJHgMNERERSR4DDREREUkeB9YjrRD3XLuzde0qCrFLICKiEmj3bxEiIiIiNTDQEBERkeTxkhNROToZnyF2CcV6x9FM7BKIiCoMW2iIiIhI8hhoiIiISPIYaIiIiEjyGGiIiIhI8hhoiIiISPIYaIiIiEjyGGiIiIhI8hhoiIiISPIYaIiIiEjyGGiIiIhI8hhoiIiISPIYaIiIiEjyGGiIiIhI8hhoiIiISPIYaIiIiEjyGGiIiIhI8vQ1fUK/fv2KXSeTyWBmZgYXFxd0794djRs3LlNxREREROrQuIWmd+/esLGxgbm5OTp06IDu3bujffv2MDc3h7W1NXx8fJCSkoK5c+fi6tWrFVEzERERkQqNW2jMzc1hZWWFxYsXw9jYWLk8KysLc+bMQbVq1bBw4ULMnTsXO3fuRNOmTcu1YCIiIqLXadxCc+DAAXz00UcqYQYAjI2N8dFHH+HQoUPQ09PD+++/j9jY2HIrlIiIiKg4GrfQpKSkQE9Pr8h1enp6SE1NBQBYWVkhLy+vTMURUeXbdPmR2CWUaJCHndglEJEW0riFxsHBAQcOHEB+fr7K8vz8fOzfvx8ODg4AgNTUVFhYWJRPlUREREQl0LiFpm/fvvj555/x+eefo2XLlrC0tMSzZ89w/vx5pKSkYPLkyQCAq1evol69euVeMBGROn48ckfsEkr0zfuuYpdApFM0DjQtW7bEtGnTsG3bNhw4cEC53NXVFSNHjoSHhwcAYMyYMeVWJBEREVFJNA40AODh4QEPDw9kZ2cjIyMDZmZmMDIyKu/aiIiIiNRSqkBTwMjIiEGGiIiIRFeqQPP48WOcPn0aSUlJyMnJUVknk8kwduzYcimOiIiISB0aB5qLFy9i8eLFUCgUsLS0hL6+6kvIZLJyK46IiIhIHRoHmi1btqB+/fr44osvYGlpWRE1EREREWlE43FoEhMT0b17d4YZIiIi0hoaBxpra2tkZWVVRC1EREREpaJxoOnZsyf+/fdfZGdnV0Q9RERERBrTuA9NdHQ00tLS8Pnnn8Pd3R3m5uYq62UyGYYOHVpuBRIRERG9icaB5tChQ8r/nzp1qshtGGiIiMrH+C2XxC6hWL9+4il2CURKGgeabdu2VUQdRERERKWmcR8aIiIiIm1TpqkPKktKSgo2bdqEy5cvIycnBzVq1MDYsWPh4uIidmlERESkBdQKNBMmTMCUKVNQu3ZtjB8/vsTRgGUyGX755ZdyKzA9PR0zZsyAu7s7vvnmG1hYWODRo0cwNTUtt/cgIiIiaVMr0DRq1EgZIBo1alSp0xvs3r0b1atXx7hx45TLbG1tK+39iYiISPupFWheDRPjx4+vsGKKEhERgWbNmuF///sfIiMjUa1aNfj7++O9994r9jm5ubnIzc1VPpbJZDAxMVH+XyrUrlWo2DrKQko/75Lown7owj4A3A9togv7QLpD4z40YWFh8PLyQpUqVQqtS09Px4ULF9ChQ4dyKQ54ObP3kSNH8OGHH6Jnz56Ijo7G2rVrYWBgUOz7BAUFYceOHcrHderUwYIFC2BjY6OyXWKqdo94XKNGDbW2e/YgtWILKQN19yE2LbGCKykbdfcD929XbCFloPY+XNKRzwLRFVpHWam/H9pLF/aBdIfGgWblypWYN29ekYHm8ePHWLlyZbkGGoVCAVdXVwwYMADAy3By//59HD58uNj36dmzJ7p27ap8XPBXRFJSEvLy8v5vuUnVcquzIjx8+FDNLU0qtI6yUH8ftPsvPfX3Q3vpwj4A3A9togv7QNpNX1+/UGNEsduW5xvn5ORALi/fO8GrVq0KR0dHlWWOjo44e/Zssc8xMDCAgYFBkesE4f+uz2j3r1DVWqVK/X3Q7k/j7fostBv3Q3vowj6Q7lAr0CQnJ+Px48fKx7GxscjJyVHZJicnB0ePHoW1tXW5Fli/fn0kJCSoLEtISFA7sREREZHuUyvQhISEqPRJCQwMLHbbIUOGlLmoV3344YeYMWMGdu7ciXbt2iE6OhrHjh3DqFGjyvV9iIiISLrUCjRt27aFk5MTAGDJkiX45JNPYG9vr7KNgYEBnJycyv2Wajc3N0yZMgWbN2/GP//8A1tbWwQEBMDHx6dc34eIiIikS61A4+joqOzHMnbsWDRv3rzITsEVpXnz5mjevHmlvR8RERFJi8adgn19fSugDCIiIqLSK9VdTunp6Th58iTi4+MLdQ6WyWQYO3ZsuRRHREREpA6NA01ycjKmT5+O7OxsZGdnw8LCAunp6VAoFDAzM+McS0RERFTpNB405q+//oKjoyNWr14NAJg+fTo2btyIoUOHwsDAANOmTSv3IomIiIhKonGg+e+//+Dv768ycJ2+vj4++OAD+Pn5YdOmTeVaIBEREdGbaBxonj17hqpVq0Iul0MulyMzM1O5rlGjRoiKiirXAomIiIjeRONAY2lpifT0dACAjY0NYmJilOuSkpKgp6dXftURERERqUHjTsF169ZFbGwsWrRogVatWmHHjh3Izc2Fvr4+9uzZA3d394qok4iIiKhYGgeabt26Ked16tOnDx48eIDt27cDABo2bFjuUx8QERERvYnGgcbFxQUuLi4AAGNjY0ydOhWZmZmQyWQwMTEp9wKJiIiI3kTjPjRFMTU1hYmJCbKysvDPP/+Ux0sSERERqU2jFpq8vDxkZGTAwsICMplMuTw7OxsHDhzAv//+i/T0dPTu3bvcCyUiIiIqjlqBJi8vD3/++SfCwsKQl5cHMzMzDBw4EO+++y7Cw8Oxfv16pKamwtnZGRMmTKjomomIiIhUqBVo9uzZg2PHjsHe3h61a9fG48ePsWrVKiQlJSEoKAiWlpYYO3YsOnTooNJyQ0RERFQZ1Ao0J0+eRIsWLTB58mTI5S+73Wzfvh3//PMPateujRkzZsDc3LxCCyUiIiIqjlqdgh89eoR3331XGWYAwN/fHwDQq1cvhhkiIiISlVqBJi8vDxYWFirLCh7b2NiUf1VEREREGijzbdvsM0NERERiU/u27eXLl8PQ0LDQ8qVLl6rMvC2TybBo0aLyqY6IiIhIDWoFmoYNGxbZEtOoUaNyL4iIiIhIU2oFmlmzZlVwGURERESlVy5THxARERGJiYGGiIiIJI+BhoiIiCSPgYaIiIgkj4GGiIiIJI+BhoiIiCRP7YH1XvfgwQNERkbi+fPn8PPzg5WVFVJSUmBubl7kAHxEREREFUXjQKNQKPDHH38gNDRUuczDwwNWVlZYtWoV6tSpg379+pVnjUREREQl0viS086dO3Hy5El8+umn+Pnnn1XWeXp64vLly+VVGxEREZFaNG6hCQ0NRe/evdG1a1coFAqVdba2tnj8+HG5FUdERESkDo1baFJSUlCvXr0i1xkYGCArK6vMRRERERFpQuNAY2lpWWwrTEJCAqpVq1bmooiIiIg0oXGg8fT0xM6dO5GSkqJcJpPJkJmZiQMHDqB58+blWiARERHRm2jch6Zv3764dOkSJk2aBHd3dwDAli1bcP/+fejp6aFPnz7lXiQRERFRSTRuobGyssL8+fPh7e2N2NhYyOVy3L17Fx4eHpg7dy7Mzc0rok4iIiKiYpVqYD0rKyuMGjWqvGshIiIiKhVOfUBERESSp3ELzcqVK4tdJ5fLYWpqCjc3N7Rq1Qr6+qWeWYGIiIhIbRonjhs3biAzMxOZmZmQy+WoUqUKnj9/DoVCAVNTUwDAvn374ODggJkzZ8LKyqq8ayYiIiJSoXGgmTx5MhYvXoyRI0eiTZs2kMvlUCgUOH36NP766y98+eWXyM/Px+LFi7FlyxaMHTu2IuomIiIiUtK4D82GDRvw0UcfoV27dpDLXz5dLpfD29sbXbt2xfr161G/fn10796d8zoRERFRpdC4hebOnTvo3bt3keucnJywZcsWAEDt2rXx/PnzslVHRESS12dpmNgllGjHFx3ELoHKgcaBxsTEBDdu3ECTJk0Krbt+/TpMTEwAADk5Ocr/ExERSV3Hb3aJXUKxQn7sIXYJotM40LzzzjvYvXs3BEFA27ZtYWlpiWfPniE8PBz//vsvunTpAgCIiYlBzZo1y71gIiIiotdpHGgGDBiAp0+fYteuXdi1a5fKOm9vb3zyyScAgHr16sHDw6M8aiQiIiIqkcaBRl9fHxMnTkTv3r0RGRmJ9PR0mJubo1GjRnB0dFRu17Rp03ItlIiIiKg4pR75ztHRUSXAEBEREYmlTEP5pqWlIScnp9Bya2vrsrwsERERkUZKFWj++ecfHDhwoNjbsrdt21amooiIiIg0ofHAesHBwdi1axc6d+4MAOjZsyd69uyJ6tWro0aNGhgzZky5F0lERERUEo0DzaFDh5QhBgBatWqF/v37Y+nSpTAxMeFgekRERFTpNA40iYmJqFevHmQyGQAgLy8PAGBoaIiuXbvi6NGj5VshERER0RtoHGj09PQAADKZDCYmJkhJSVGuq1KlispjIiIiosqgcaCpUaMGkpOTAQCurq44duwY8vLyoFAocPToUdjY2JR7kUREREQl0TjQeHh44ObNmwBedgi+fv06hg4diqFDh+Ls2bPo3r17uRdJREREVBKNb9v++OOPlf9v3Lgx5syZg/DwcACAl5cXGjduXH7VEREREalBo0CTk5OD48ePo0GDBspRgt3c3ODm5lYhxRERERGpQ6NLToaGhli7di3S0tIqqh4iIiIijWnch8bW1hapqakVUAoRERFR6WgcaLp06YJdu3YhMzOzIuohIiIi0pjGnYLv37+P58+fY/z48WjcuDGqVq2qsl4mk2Ho0KHlViARERHRm2gcaA4dOqT8/7lz54rchoGGiIiIKpPGgUbsmbSDgoKwZcsWdOnSBUOGDBG1FiIiItIOGvehEVN0dDSOHj2KWrVqiV0KERERaZFSB5rLly9j8+bN+P3335VTIURHR1fYLd1ZWVn45ZdfMHr0aJiZmVXIexAREZE0aXzJKTs7GwsXLsT169eVy/z9/WFtbY1///0X1atXx+DBg8u1SAAIDAyEp6cnmjZtip07d5a4bW5uLnJzc5WPCybSLPi/VKhdq1CxdZSFlH7eJdGF/dCFfQC4H9pEF/YB0I390IV9KCuNA82WLVsQExODyZMno2nTpggICFCua9asGQ4cOFCuBQLAqVOnEBsbi/nz56u1fVBQEHbs2KF8XKdOHSxYsKDQxJmJqVnlWmd5q1GjhlrbPXuQWrGFlIG6+xCblljBlZSNuvuB+7crtpAyUHsfLunIZ4HoCq2jrNTfD+2lC/sA6MZ+6MI+lJXGgebMmTPo168fWrVqBYVCobLO2tpaefmpvCQnJ2PdunX49ttvYWhoqNZzevbsia5duyofFyTXpKQk5OXl/d9yk6qFnqtNHj58qOaWJhVaR1movw/a/deF+vuhvXRhHwDuhzbRhX0AdGM/dGEfiqKvr1+oMaLYbTV98bS0NOU8Tq+TyWTIycnR9CVLFBMTg2fPnmHatGnKZQqFAjdv3sTBgwexefNmyOWqXYEMDAxgYGBQ5OsJwv9dn9HuX6GqtUqV+vug3Z/G2/VZaDfuh/bQhX0AdGM/dGEfykrjQFOtWjXcu3evyFm17969C1tb23IprECTJk2wePFilWW//fYbHBwc0L1790JhhoiIiN4+GgeaVq1aISgoCA0bNlTePi2TyZCUlIR9+/ahY8eO5VqgiYkJnJ2dVZYZGRmhSpUqhZYTERHR20njQPPxxx/j+vXr+Oabb+Dk5AQAWLlyJR49egQHBwf06NGjvGskIiIiKpHGgcbExARz587F/v37cfHiRdjb28PIyAg9evTAhx9+qHbH3bKYNWtWhb8HERERSYfGgQYADA0N0aNHD7bGEBERkVbQuEfthg0bEB8fXxG1EBEREZVKqWbb3rdvH1xcXODn5wdvb2+YmppWRG1EREREatG4hWb16tUYPnw45HI5AgMDMWrUKCxfvhzXrl2riPqIiIiI3kjjFhpTU1P4+/vD398f8fHxCA0NxYkTJ3Dq1ClUr14dvr6+6Nu3b0XUSkRERFSkUnUKLuDo6IhBgwZhwIABuHjxItasWYN//vmHgYaIiEgLtRjxh9gllCgicHSpn1umQAMACQkJCA0NxfHjx/H06VNUr169rC9JREREpJFSBZqsrCyEh4cjJCQE//33H/T19dGyZUv4+fmhSZMm5V0jERERUYk0DjQrVqzA2bNnkZOTAxcXFwwfPhze3t4wMzOriPqIiIiI3kjjQHPlyhW8//778PX1LXIupbS0NFhYWJRLcURERETq0DjQ/P7779DT01NZJggCLl26hODgYFy8eBGbN28utwKJiIiI3kTjQPNqmElMTERISAjCwsLw9OlT6Ovro3Xr1uVaIBEREdGbaBxocnJycObMGQQHB+PmzZvK5V27dkWPHj1QpUqVci2QiIiI6E3UDjTR0dEIDg5GeHg4Xrx4AWNjY/j6+qJ169ZYsGABmjdvzjBDREREolAr0EyZMgX3798HANSrVw8dO3ZEu3btYGxsjMzMzAotkIiIiOhN1Ao0BWHGy8sLAwcOhKOjY4UWRURERKQJtQJNQEAAQkNDcfHiRVy8eBFubm7w8/NDu3btKro+IiIiojdSK9B06dIFXbp0wZ07d5T9aFatWoV169bBy8sLACCTySq0UCIiIqLiaHSXk6urK1xdXREQEKC80+nMmTMAXo5P895778HX15edg4mIiKhSlWouJ0NDQ7Rv3x7t27dHYmIigoODcfz4cWzatAnbtm3Dpk2byrtOIiIiomKVebZte3t7DBgwAP3798elS5cQEhJSHnURERERqa3MgaaAXC5H8+bN0bx58/J6SSIiIiK1yMUugIiIiKisGGiIiIhI8hhoiIiISPIYaIiIiEjyGGiIiIhI8hhoiIiISPIYaIiIiEjyGGiIiIhI8hhoiIiISPIYaIiIiEjyGGiIiIhI8hhoiIiISPIYaIiIiEjyGGiIiIhI8hhoiIiISPIYaIiIiEjyGGiIiIhI8hhoiIiISPIYaIiIiEjyGGiIiIhI8hhoiIiISPIYaIiIiEjyGGiIiIhI8hhoiIiISPIYaIiIiEjyGGiIiIhI8hhoiIiISPIYaIiIiEjyGGiIiIhI8hhoiIiISPIYaIiIiEjyGGiIiIhI8hhoiIiISPIYaIiIiEjyGGiIiIhI8hhoiIiISPIYaIiIiEjyGGiIiIhI8hhoiIiISPIYaIiIiEjyGGiIiIhI8vTFLuBNgoKCcO7cOTx48ACGhoaoV68eBg0aBAcHB7FLIyIiIi2h9YEmMjISnTp1gqurK/Lz87F161bMnTsX//vf/2BsbCx2eURERKQFtD7QfPvttyqPx40bhxEjRiAmJgaNGjUSqSoiIiLSJlofaF6XmZkJADA3Ny92m9zcXOTm5iofy2QymJiYKP8vFWrXKlRsHWUhpZ93SXRhP3RhHwDuhzbRhX0AdGM/dGEfgLLth6QCjSAIWL9+PRo0aABnZ+ditwsKCsKOHTuUj+vUqYMFCxbAxsZGZbvE1KwKq7U81KhRQ63tnj1IrdhCykDdfYhNS6zgSspG3f3A/dsVW0gZqL0Pl3Tks0B0hdZRVurvh/bShX0AdGM/dGEfgLLth6QCzZo1a3Dv3j388MMPJW7Xs2dPdO3aVfm4IPElJSUhLy/v/5abVK2YQsvJw4cP1dzSpELrKAv190G7/7pQfz+0ly7sA8D90Ca6sA+AbuyHLuwDUHg/9PX1CzVGFEcygebPP//EhQsXMHv2bFSvXr3EbQ0MDGBgYFDkOkH4v+sz2v0rVLVWqVJ/H7T703i7Pgvtxv3QHrqwD4Bu7Icu7ANQtv3Q+nFoBEHAmjVrcPbsWXz//fewtbUVuyQiIiLSMlofaNasWYMTJ05g4sSJMDExQWpqKlJTU5GTkyN2aURERKQltP6S0+HDhwEAs2bNUlk+btw4+Pr6Vn5BREREpHW0PtBs375d7BKIiIhIy2n9JSciIiKiN2GgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsljoCEiIiLJY6AhIiIiyWOgISIiIsnTF7sAdR06dAh79uxBamoqHB0dMWTIEDRs2FDssoiIiEgLSKKFJjw8HOvWrUOvXr2wYMECNGzYED/++COSk5PFLo2IiIi0gCQCzd69e+Hn54d3331X2TpjbW2Nw4cPi10aERERaQGtv+SUl5eHmJgY9OjRQ2V506ZNcevWrSKfk5ubi9zcXOVjmUwGExMT6Our7q5MT1bu9ZYnwcBAre2MBL0KrqT0DGTq7YOZkXZ/FgYGglrbWZkaVXAlpWeg5vfJzsKkgispG3X3w7GaWQVXUjbq7oeLrUUFV1J66u5DvZpVK7iSslF3PxrVsq7gSkpP3X1o4lqjgispm9f34/Xf2yWRCYKg3plaJCkpKRgzZgzmzJmD+vXrK5fv3LkTYWFhWLZsWaHnbN++HTt27FA+9vb2xsSJEyulXiIiIqp8krjkBLxsZVFnGQD07NkT69atU/4bOXKkSotNRXjx4gWmTp2KFy9eVOj7VCRd2AeA+6FNdGEfAN3YD13YB4D7oU20bR+0/pKThYUF5HI5UlNTVZY/e/YMlpaWRT7HwMBA7ea38iIIAmJjY6HlDV4l0oV9ALgf2kQX9gHQjf3QhX0AuB/aRNv2QetbaPT19eHi4oKrV6+qLL969arKJSgiIiJ6e2l9Cw0AdO3aFb/88gtcXFxQr149HD16FMnJyXj//ffFLo2IiIi0gCQCTbt27fD8+XP8888/ePr0KZycnDB9+nTY2NiIXZqSgYEB+vTpU+mXusqTLuwDwP3QJrqwD4Bu7Icu7APA/dAm2rYPWn+XExEREdGbaH0fGiIiIqI3YaAhIiIiyWOgISIiIsljoCEiIiLJY6ApRxkZGWKXoLb8/HyEhoYWGrCQ6G2Xl5eH2bNnIyEhQexSSEfl5OSIXYJOYqAppV27diE8PFz5+H//+x+GDRuG0aNHIy4uTrzC1KSnp4fAwMAKnxKiMiQnJ+PJkyfKx9HR0Vi3bh2OHj0qYlWauXz5MqKiopSPDx48iK+++grLli1Denq6iJWVjUKhQFxcnKT2QV9fH/fu3St2ahWp0IXjQpcoFArs2LEDo0ePxuDBg/Ho0SMAwNatWxEcHCxyderR9vMUA00pHT16FNWrVwfwctTia9eu4ZtvvoGnpyc2bdokcnXqqVu3riTC15ssX74cN27cAACkpqZizpw5iI6OxpYtW1QmKdVmGzduRGZmJgDg3r172LhxIzw9PfH48WNs2LBB5OrUt27dOuXJWaFQYObMmZg6dSrGjh2r/IykoEOHDpL5JVMcXTgudEnBhMqDBg1SmUHa2dkZx44dE7Ey9Wn7eUoSA+tpo6dPnyoDzYULF9C2bVs0a9YMNjY2+Pbbb0WuTj3+/v7YsGEDnjx5AhcXFxgZGamsr1WrlkiVaeb+/ftwc3MDAISHh8PZ2Rlz5szBlStXsHr1avTp00fkCt/s8ePHcHR0BACcOXMGXl5eGDBgAGJiYjB//nyRq1PfmTNn4OPjAwCIiIjA48ePsWTJEhw/fhxbt27FnDlzRK5QPXl5eTh27BiuXr0KV1fXQsdGQECASJWpTxeOiwKhoaGwsLCAl5cXAGDTpk04evQoHB0dMXHiRK0aZLU4YWFhGDVqFJo0aYLVq1crl9eqVUsylze1/TzFQFNK5ubmePLkCaytrXH58mX0799fuU6hUIhYmfqWLl0KAFi7dm2R67dt21aJ1ZReXl6e8i+ea9euoXnz5gCAmjVr4unTp2KWpjZ9fX3ldfVr166hQ4cOAF5+z7RlJlt1PH/+HFZWVgCAS5cuoW3btnBwcICfnx8OHDggbnEauH//PlxcXAAADx8+FLma0tGF46JAUFAQRo4cCQD477//cPDgQQwZMgQXLlzA+vXrMWXKFJErfLOUlBTY29sXWi4IAvLy8kSoSHPafp5ioCmlVq1aYfny5bC3t0d6ejo8PT0BAHFxcUV+abXRihUrxC6hXDg5OeHIkSPw8vLC1atX0a9fPwAvTyBVqlQRuTr1NGjQAOvXr0f9+vURHR2NSZMmAXj5y7SgJVAKLC0tER8fj6pVq+Ly5csYMWIEACA7OxtyuXSucM+cOVPsEspMF46LAk+ePFGeV8+dO4c2bdrgvffeQ/369TFr1ixxi1OTk5MTbt68Wag16fTp06hTp45IVWlG289TDDSlFBAQABsbGzx58gSDBg2CsbExgJeXovz9/UWuTj1SaKZVx8CBA7F48WLs2bMHHTp0QO3atQG8vORR0OSu7YYPH47AwECcPXsWI0eORLVq1QC8bOVo1qyZyNWpz9fXF0uWLEHVqlUhk8nQtGlTAMDt27fh4OAgcnVvF104LgoYGxvj+fPnsLa2xtWrV/Hhhx8CeDmXkFTuGOrTpw9WrFiBlJQUCIKAs2fPIiEhAcePH8e0adPELk8t2n6e4lxOpZSVlaUMMVISEREBDw8P6OvrIyIiosRtW7RoUUlVlZ1CoUBmZibMzc2Vyx4/fgwjIyNYWlqKWNnb58yZM0hOTkbbtm2Vf7WFhobCzMwMLVu2FLm64i1evBjjxo2DqakpFi9eXOK2UrjEAejOcbF8+XI8ePAAderUwalTp7By5UpUqVIFERER2LJlC37++WexS1TL5cuXERQUhJiYGAiCgDp16qBPnz5aEQZ0AVtoSmnkyJFo27Yt/Pz80KBBA7HLUduiRYuwatUqWFpaYtGiRSVuK5U+NNu3b0fHjh0LtTjZ2tqKVFHpJCYmIjQ0FImJiRg6dCgsLS1x+fJlVK9eHU5OTmKXp7Y2bdoAUB1rw9fXV6Rq1Gdqaqq8VdvU1FTkaspOV44L4GXLwNatW/HkyRNMnjxZecksJiYG3t7eIlenPg8PD3h4eIhdRplo83mKLTSlFBERgdDQUFy8eBE2Njbo2LEj2rdvr2yCo8ozdepU3L17F40aNYKfnx9atWoFQ0NDscvSSGRkJH788UfUr18fN2/exJIlS2BnZ4fdu3cjOjoakydPFrtEtSgUCuzcuRNHjhzBs2fPsGzZMtjZ2WHr1q2wtbWFn5+f2CW+NXThuNAl0dHREAQBdevWVVl++/ZtyOVyuLq6ilSZ+rT9PCWdXnpapkWLFpgyZQr++OMPvP/++zh16hTGjx+Pn376CWfPnkV+fr7YJZba8+fPsW/fPrHLUNuCBQuwYMECODs7Y/369Rg9ejRWr16N6OhosUtT219//YX+/ftjxowZKmNUuLu747///hOxMs3owlgbxUlPT8eBAwfw1VdfiV2KWnThuHjVzZs3sXz5cnz33XdISUkBABw/flxloDdttmbNGpWBDgukpKRgzZo1IlSkOW0/T/GSUxlVqVIFXbt2RdeuXXHgwAFs2rQJly5dQpUqVfD++++jR48ehcaw0EaCIODKlSsIDg5GREQETExMlB3vpKBWrVoYMmQIPv30U1y4cAEhISGYMWMGHBwc8O6778LX11erLyPcu3cPEydOLLTcwsJCK0bgVJcujLXxuqtXryI4OBjnz5+HhYUFWrVqJXZJapP6cVHgzJkzWLFiBd555x3ExsYqRzh/8eIFgoKCMH36dJErfLP4+Pgi72aqU6cO4uPjRahIc9p+nmKgKaPU1FSEhYUhNDQUycnJaN26Nfz8/PD06VPs2rULt2/fxnfffSd2mcV6/PgxQkJCEBoaipSUFPj4+GDatGlo3Lix2KWVSsGYDgXjOpibm+Pw4cPYtm0bRo8ejXbt2olcYdHMzMzw9OnTQv0b4uLiJHUZUxfG2gBeThtQcFxkZWUhPT0dkyZNUvYPkhqpHhcFdu7ciZEjR6JDhw4qU87Ur19fMqMeGxgY4NmzZ7Czs1NZ/vTpU+jp6YlUlWa0/TzFQFNKZ8+eRUhICK5cuQJHR0d06tQJPj4+MDMzU25Tu3ZtfP311yJWWbTc3FycPXsWwcHBuHXrFjw9PREQEIBly5ahR48eypEgpSQmJgYhISE4deoUDAwM0L59ewwfPlz5y/Xff//F2rVrtfbE/c477+Cvv/7Cl19+CZlMBkEQEBUVhY0bN6J9+/Zil6c2qY+1ER4ernJcDBkyBJ6envj00095XIgoISEBDRs2LLTcxMREORS/tmvatCk2b96Mr7/+WtkqlpGRgS1btqBJkyYiV6cebT9PMdCU0sqVK+Ht7Y05c+YUO6aDnZ0devXqVcmVvdmYMWPg6OgIHx8ffPnll8pbOpctWyZyZaUzZcoUPHjwAE2bNsWYMWPQokWLQoO4dejQQavn2Orfvz9WrlyJ0aNHAwAmTZoEhUKBd955B7179xa5OvVJfayN5cuXo1u3bpg8eTJMTEzELqdMdOG4KFC1alUkJiYWahmIioqSzF1bgwcPxsyZMzFu3DhluI+Li4OlpSUmTJggcnXq0fbzFO9yKqXs7GxJ9I0pytChQ+Hs7AwfHx+0a9dO+dfCJ598gkWLFknuL9EdO3bAz89PK5o8yyoxMRFxcXFQKBSoU6cOatSoIXZJGpPyWBt//PEHTp8+DScnJ+XxYW5uLsljQ5eOi927dyMsLAxjx47F3LlzMX36dCQlJWH9+vXo06cPPvjgA7FLVEtWVhZOnjyJuLg4GBoaolatWvD29lbpYCsF2nqeYqApBzk5OYX6B2hzR7ucnBzlJafbt2/Dw8MD7du3x5IlSyR30iYqbzk5OQgPD0dISAiio6PRrFkzXLx4EQsXLoSzs7PY5b21tmzZgn379ik7BOvr6+Ojjz5SmUeP3m4MNKWUlZWFv/76C6dPn8bz588LrZfKoHQFgySFhYUhJSUF3t7e8PX1RePGjSU1986TJ08QERGB5OTkQuFSCjMjKxQKhIaG4tq1a0hLSys0wanU5hbKy8vDs2fP8PrpxdraWqSKSufhw4cICQlBWFgYsrKy4OXlhTZt2qB169Zil6YWqR8Xr8vOzkZ8fDwEQYCjo6PkRmtPSEhAZGRkkceGFGY/1/bzFANNKQUGBuLGjRvo168fVqxYgeHDhyMlJQVHjx7FgAED4OPjI3aJGlEoFMrbti9cuAATExPJjI1w7do1LFy4ELa2tkhISICTkxOSkpKUlzvEPsjUsWbNGoSGhsLLywtVq1YttH7IkCGVX1QpPHz4EL/99htu3bpV5HqpBP3XKRQKXLx4EcHBwbh8+TI2b94sdklvpAvHRYGVK1di6NChhfo1ZWVl4c8//8S4ceNEqkx9R48eRWBgIKpUqQIrKyvlqNQAIJPJsGDBAhGrU4+2n6ekdeFOi1y4cAETJkyAu7s7fvvtNzRs2BD29vawsbHByZMnJRdo5HI5PD094enpibS0NBw/flzsktS2efNmdO3aFf369cPgwYMxefJkWFpaYvny5ZIZZjw8PByTJk2Cl5eX2KWUycqVKyGXyzFt2rQiT3hSJZfL0aJFC7Ro0QLPnj0Tuxy16MJxUSAsLAwDBw4sFGhycnJw/PhxSQSanTt3on///ujRo4fYpZSatp+npHNNQcukp6cre9ebmJgoBxVq0KABIiMjxSytzCwsLNC1a1exy1DbgwcP0KFDBwCAnp4ecnJyYGxsjL59+2L37t0iV6cefX39IsdvkZq4uDiMGjUKnp6eqF27dqF/ukAqkzrqwnGRmZmpvC37xYsXyseZmZlIT0/HpUuXJPN5ZGRkoG3btmKXUSbafp5iC00p2dnZISkpCTY2NnB0dER4eDjc3NwQERGhMhYNVTwjIyNl/4CqVavi0aNHyknS0tLSxCxNbV27dsX+/fsxfPhwlaZoqXF0dCyyTxlVPl04LoYOHar8f1Ej1MpkMvTt27cySyq1Nm3a4MqVK/D39xe7lFLT9vMUA00p+fr6Ii4uDo0aNUKPHj3w008/4eDBg8jPz5dkZzspq1u3LqKiouDo6AgvLy9s2LAB9+7dw9mzZ1GvXj2xy1NLVFQUbty4gcuXL8PR0bHQbZxTpkwRqTLNDBw4EJs2bcInn3wCZ2fnQiOgavPdf7pGF46LmTNnQhAE/PDDD5g8ebJyzCzgZWuBtbW1ZG5Lt7e3x7Zt23D79u0ij40uXbqIVJn6tP08xU7B5SQ5ORl37tyBnZ2dzjStS8WjR4+QlZWFWrVqITs7Gxs3bkRUVBTs7e0REBBQaNRabbRy5coS10uhjwAA9OvXr8T1Uu0ULEW6cFwUSEpKQvXq1SV15+Xrxo8fX+w6mUyGFStWVGI1paPt5ykGmlKS8sB6b3Lv3j0EBweL3mOdpOdN/ccaNWpUSZWUD125/VxXZGdnF3kLeq1atUSqiLQJLzmV0pAhQ+Dq6opGjRqhUaNGaNCggeTGRHhVZmYmTp06heDgYMTExEhqALEtW7bA3d0d9evX19mQKRVSCyzF0YXbz3XpuEhLS8PKlStx6dKlItdL4fOgisdAU0qzZ89GZGQkbty4gUOHDiEnJwcuLi7KgOPp6Sl2iWqJjIxEcHAwzp49i5ycHHTr1g0TJ07U6p7sr4uJicHBgweRm5uLOnXqwN3dXRIhc+rUqZgxYwbMzc3x9ddfl9jJTpvHqLh79y6cnJwgl8tx9+7dEreVyl/SunD7uVSPi6KsW7cOGRkZmDdvHmbPno2vvvoKqamp2LlzJwYPHix2eWqT4kCHUjpPMdCUUr169VCvXj306NEDCoUC0dHROHLkCPbu3Ys9e/Zo9V8MT58+RUhICEJCQpCdnQ1vb2/MmjUL3333HTp06CCpMAMA3377rfIzKCpkzps3T+wSi9SiRQsYGBgAAFq2bClyNaX39ddfY9WqVbC0tHzj7PLafFy8Ki4uDj/99BNq1qwpdimlJtXjoijXr1/H119/DTc3N8jlclhbW6Np06YwNTVFUFCQ1o6L8qo3DXSoraR0nmKgKYMHDx7gxo0biIyMRGRkJPLz89G8eXOtb3afMGEC2rRpg+HDh6Np06aS7mhXQC6Xo169ejA3N4eZmRlMTExw/vx5JCYmil1asT7++OMi/y81K1asgIWFhfL/ukBXbj+X4nFRlOzsbOV3zNzcHGlpaXBwcICzszNiY2NFrk49Uh3oUErnKQaaUho5ciTy8/PRuHFjNGrUCL169ZJMvxNra2tERUXB2toaNjY2kv4rFAAOHz6sDJUKhQINGjRAo0aN0Lt3b8lc4pCyV++WkdKdMyXRhdvPdem4cHBwQEJCAmxtbVGrVi0cPXoUtra2OHz4sGQuCT548EA5ls7rAx0uWrRI0uPTaAsGmlKysrLCgwcPkJycjOTkZDx58gS2traSuDa9bNkyREVFITg4GNOnT0eNGjUkN1XDq9asWaMc3fj999+XxC8bQHXQsDdZu3ZtBVZSNhEREWpv26JFiwqspPzMmTMHAPDDDz8UuV4Kl86kelwUpUuXLkhNTQXwspVg3rx5OHHiBPT19Uu8HVqbSHWgQymdp3jbdhlkZGTg5s2byr+C7t+/j1q1asHd3R0DBw4Uuzy1ZGVl4eTJkwgJCUF0dDQaNWoEb29vtGrVStnEq+3OnTun/Bzi4+NRq1YtNGrUCO7u7mjYsKHWhszQ0FC1t/X19a2wOsrqTWPPvEoKQQDQjdvPpXpcqCM7OxsPHjyAtbW1ZM5TCxcuhJeXF9577z1s2rQJ586dg6+vL86ePQtzc3PMmDFD7BKLJKXzFANNOUhPT8eNGzdw/vx5nDp1CgqFQqtP3Pn5+YWa0AEgPj4ewcHBOHHiBNLT07FlyxYRqiubzMxM3Lx5E2fOnMHJkychk8m0fmbk/Px8nDhxAh4eHrCyshK7HNJBUjwuCuTl5eGLL77AtGnT4OjoKHY5pSb1gQ6lcJ7iJadSOnfunLJD8P3791GlShU0aNAAAQEBcHd3F7u8Eo0aNQodOnSAn5+fygnC0dERgwcPxsCBAzW6jKAN0tPTlXdyREZG4t69e6hSpYok/pLW09NDYGAglixZInYpZZKXl4d58+Zh5MiRcHBwELucciH1gdykfFwU0NfXR25urthllIlCocCTJ0+U/SyNjIwwYsQIkavSjBTOUww0pbRq1So0bNgQ7777Lho1aiSZDsHAywnGQkNDsW/fPri5ueHdd99Fu3btlE3Qenp6aN26tchVqm/KlCmIj4+Hubm5ZD+TunXrIjY2Vuv/SiuJvr4+7t27p5WT1mlKFwZy04XjokDnzp2xe/dujBkzpsjWZW0nl8sxb948LFmyRGU+KqnR9vMULzmVQkHTW7NmzSTTw74oN2/eREhICM6cOQPg5Wywfn5+aNCggciVaebgwYOSPVEXOH36NDZv3owPP/wQLi4uhUZ2lUqLwIYNG6CnpyeZPmTFWb58OZKSkhAQEFDkQG5SGPdEF46LAosWLcL169dhbGwMZ2fnQseH2JMiqmP69OkYMGAAmjRpInYppabt5ym20JSCFJre1NGwYUM0bNgQw4YNQ3h4OEJDQzFz5kzY29vDz88P3bt3F7vEN8rLy8PevXvRuHFjsUspk6VLlwIo/i4BKbQIAC8/j2PHjuHq1atwdXUtdMLT1tFQXyf1gdx05bgoYGZmJqlW46L0798fGzduRL9+/YoMA1K4C03bz1MMNKWk7U1vmjA2Noafnx/8/Pxw8eJFrFixAps3b5ZEoNGF6+uA7gxId//+fbi4uAB4OR+SVEl9IDddOS4KiD2Lc3n48ccfAby826koYocBdWj7eYqBppT8/f2xceNGpKSkaGXTmyays7OVLTQFve67desmdllqk/r1dUB3BqSbOXOm2CWUC10YyE0XjgtdogvHhrafp9iHppTeNPaGFNJ2QR+as2fPQqFQoHXr1vDz85PUHRCAdK+vR0REwMPDA/r6+m+8q0wqA9LpihMnTiA/Px++vr6IjY3FvHnz8Pz5c+VAbu3atRO7xDeS6nFRQEqTIpZkxYoVGD58OExMTAC8nCfM0dER+vrSaE+Q0nlKGj9RLaTtTW8l2blzJ8LCwpCYmAhXV1cMGjQI3t7ekriGWxSpXl9ftGiRclLHRYsWlbitNgfkxYsXY9y4cTA1NcXixYtL3Fbbf4kWeHXk7Dp16uDXX3+V3EBuUj0uCkhpUsSSnDhxAp9++qky0MycORMLFy6EnZ2dyJWpR0rnKQaaUtL2preS7Nu3Dz4+PvDz89OJOyCken391YNf7BNBWZiamir/epZqKC5OXl4eHj9+DDs7O2XfIKmQ6nFRQEqTImpCahdFpHSeYqDRgJSa3kryxx9/SKa58233/PlzHD9+HB9++KHYpRTr1V+cUv8lWiA7Oxt//vknwsLCALyc/8zOzg5//vknqlWrhh49eohb4FssKysLCoVCZZmuBWmp0ZbzFH+raUBKTW8lWbRoESZOnKg8CezcuROdOnWCmZkZgJdfzu+//16rb0vXlevrRREEAVeuXEFwcDAiIiJgYmIi+omiLNLT03HixAkEBwe/8bjRFps3b8bdu3cxa9YszJs3T7m8adOm2L59u9YGGl09Lh4/fow1a9YgMjISOTk5hdZr8/kWeDmtTMHkmoIg4MGDB8jKylLZRko3kgDaeZ5ioNGAlJreSnL58mWVWzp3794Nb29vZaDJz89HQkKCWOWpRVeur7/q8ePHCAkJQWhoKFJSUuDj44Np06ZJdiyRq1evIjg4GOfPn4eFhQVatWoldklqO3/+PL744gvUq1dPJRQ4Ojri0aNHIlZWMl08LoCXAx0CwNixY2FpaSm50ahfn7W9qDApld8p2nyeYqApZ9rS9KYJqV3TBdS/vp6fn18Z5ZRabm4uzp49i+DgYNy6dQuenp4ICAjAsmXL0KNHD8lNxpecnKw82WVlZSE9PR2TJk1CmzZtxC5NI2lpabC0tCy0/PW/qrWNrhwXr7t79y4WLFggyTnCpHwDSQGpnKcYaMqBNja9ve1enTl89erVYpdTrDFjxsDR0RE+Pj748ssvlfO8LFu2TOTKNBMeHq5yshsyZAg8PT3x6aefas3JThOurq64ePEiOnfuDADKFoFjx46hXr16YpZWJlI5Ll7n5uaG5ORkSQYaKd9AUkAq5ykGmjLQ5qa3N3m9yVZqTbhFycrKwqlTpxAcHIw7d+6gbt26Wj/a8audG+VyuYiVlM3y5cvRrVs3TJ48WXl7qpQNGDAAP/74I+Lj45Gfn4/9+/fj/v37+O+//zB79myxy9OIFI+L140ePRqrV69GSkoKnJ2dCw0UKLX+J1IjlfMUA42GpNL09ia//vqr8lp7bm4uVq9erRx4S2pDpkdFReHYsWM4e/YsbG1tER8fj1mzZkliks0//vhD+X1at24dPDw80L59e7HL0ljHjh1x+PBh3Lx5Ez4+PmjXrp2kZxWuX78+5syZgz179sDe3h5XrlxBnTp1MG/ePMkMdSDl4+J1aWlpePToEX777bci10ul/4lUSeU8xZGCNTR8+HBl01ubNm2UJ+1PPvkEixYtkkSgWblypVrbafstuLt370ZISAiysrLg7e0NHx8f1K5dW1KfxasSExMRGhqKsLAwpKSkwNvbG76+vmjcuLFW/1VUICcnB+Hh4QgJCUF0dDSaNWuGixcvYuHChZIJAcDLsWdWrVqF3r17S2bws1fp2nEBAJMmTULNmjXRvXv3IjsF68JlHanQ5vMUW2g0JJWmt5Joe1BR15YtW9C9e3f069dPsp/Fq+zt7dG/f3/07dtX2Sfrp59+gomJCdasWSN2eW9kaGgIX19f+Pr64uHDhwgJCcGdO3cwY8YMeHl5oU2bNpIYuVZfXx/nzp1D7969xS6lVHTtuABedjafOnUq7O3txS6lVARBQHJyMiwtLWFoaCh2OWWizecpBhoNSaXp7W3Qt29fhIWF4fjx4/D29kb79u0l1RJQHLlcDk9PT3h6eiItLQ3Hjx8XuySN1ahRAwMGDED//v1x8eJFBAcHY9myZdi8ebPYpamlVatWOH/+PLp27Sp2KRrTxePC3d0dcXFxkg40n3/+Of73v/+hRo0aYpdTLrTxPMVLTmWgzU1vb5PIyEgEBwfj7NmzsLe3x/379yXbV0CXPXv2rMhbobXRzp078e+//6Jx48ZwcXEpNLFjly5dRKpMfbp0XBw9ehT//PMPOnbsCGdn50IjnWvzyOwFvvzyS4wZM0bSd8lpOwaacqBQKJRNbxcuXNCKpre30YsXL3DixAmEhoYiJiYGbm5uaNOmjST/yiZxjR8/vth1MplMUmOL6MJx0a9fvxLXS6FT8MWLF7Fr1y6MGDFC8i1m2oqBppwVNL1J6WShi+7du4fg4GCcPHkSgYGBYpdDpBV4XIhn6NChyM7ORn5+PvT19Qv1pVm7dq1IlekOBhrSaXl5eZyIk8qk4BSpC2M1FZDKcTF//nzJzztXIDQ0tMT1vr6+lVKHLtP+bzRRGUjhpE3aKSwsDHv27EFiYiKAlx2du3XrphM3AUjluNCFeecKMLBUPGl8q4l0nEKhQGhoKK5du4a0tDSV4QEAYObMmSJVprmMjAycOXMGjx49Qrdu3WBubo6YmBhYWVmhWrVqYpenlr1792Lbtm3o1KkTGjRoAEEQcOvWLaxevRppaWm8pCwSqV1QyMzMVLYuZWZmlrhtwXbaTNvPUww0RFpg7dq1CA0NhZeXF5ycnMQup9Tu3r2LOXPmwNTUFElJSXj33Xdhbm6Oc+fOITk5GRMmTBC7RLUcOHAAI0aMQIcOHZTLWrZsCUdHR/z9998MNKSWoUOHYtWqVbC0tMTQoUNL3FYKHZu1/TzFQEOSlp+fjxMnTsDDwwNWVlZil1Nq4eHhmDRpEry8vMQupUw2bNgAX19fDBo0CIMHD1Yu9/T0xPLly0WsTDOpqamoX79+oeX169dHampq5Rf0FpPyvHMzZ85UjiYvdutFedD28xQDTSlpe9Pb20JPTw+BgYGS6BRYEn19fckOGvaq6OhojBw5stDyatWqSSoI2NvbIzw8HL169VJZHh4eLpnP6eLFi5DL5fDw8FBZfvnyZQiCAE9PT3EK05CU551r1KhRkf9/XVxcXCVUU3bafp5ioCklbW96e5vUrVsXcXFxkp7PpWvXrti/fz+GDx8uqb9AX2doaIgXL14UWp6QkAALCwsRKiqdjz/+GEuXLsXNmzdRv359yGQyREVF4fr165g0aZLY5all8+bNGDBgQLHrpBBoXr3kBwA+Pj5v3EYqMjMzceLECQQHByMuLk4Sl5y0/TzFQFNK2t709jbx9/fHhg0b8OTJkyJHda1Vq5ZIlakvKioKN27cwOXLl+Ho6FjoLpQpU6aIVJlmWrRogR07dih/6ctkMiQnJ2Pz5s2SmMepQJs2bfDjjz9i7969OH/+PACgZs2a+PHHHyUzYeXDhw+LnIjSwcFBeeeWttOVeededf36dQQHB+PcuXOwsbFB69atMWbMGLHLKtbixYtVHl+/fl1rz1MMNKWk7U1vb5OlS5cCKH5gKin85WNmZoZWrVqJXUaZffrpp5g/fz5GjhyJnJwczJw5E6mpqahXrx769+8vdnlvtGfPHnTr1g0A4OLigs8//1xlfWZmJubNm4d58+aJUZ5GTE1N8fjxY9ja2qosT0xMLBT6qWI9efIEoaGhCAkJQXZ2Ntq2bYv8/HxMnjxZ62c/f/3uK20+T3FgvVL6999/8ejRI61tenubJCUllbheypeipOr69euIiYmBIAioU6cOmjZtKnZJahk4cCBGjBiBjh07FlqXlZWFOXPmIDMzUxJ9tv744w/cvn0bU6ZMUf7xlZiYiJ9//hmurq5a3SqgS+bPn4+oqCh4eXnBx8cHHh4ekMvl+OSTT7Bo0SKtDzRSwhaaUtKVSwS6gIFFO+Tn52PgwIFYuHAhGjdujMaNG4tdksY+++wz/PLLL4VazLKysjB37lykp6dj9uzZIlaovk8//RTz5s3DpEmTlOP/pKSkoEGDBvj0009Fru7tceXKFXTu3Bn+/v6Sn2k7JycHgiAoW/iSkpJw7tw5ODo6olmzZiJXx0BTarpyiUCXxMfHIzk5GXl5eSrLpTATLwCcOXMG4eHhePLkSaF9WLBggUhVqU9PTw82NjaF7viTkjZt2iAjIwPLly/HtGnT0LhxY2RlZWHevHl4/vw5Zs2aJZnhAUxNTTF37lxcvXoVd+/ehaGhIZydnUu824bK3w8//IDg4GBMmzYNNWvWRPv27dGuXTuxyyqVhQsXolWrVvD390dGRga++eYb6OvrIy0tDQEBAfD39xe1Pl5yIsl79OgRFi9ejHv37hW5Xgp9aPbv34+tW7eiQ4cOOHbsGHx9ffHo0SPcuXMHnTp1wieffCJ2iWoJCQnBmTNn8NlnnynH35Ci3bt3Y+fOnfj666+xbds2PH36FLNnz5bMSMekfbKzs3Hq1CmEhIQgOjoaCoUCAQEB6NixI0xMTMQuTy3Dhw/HrFmz4OTkhGPHjuHgwYNYsGABzp49i+3bt4t+KZYtNGWUlpamnEvEwcFBUrem6oq1a9fCxsYGM2bMwIQJE/Djjz8iPT0dGzZskEzT+uHDhzFq1Ci88847CAsLQ/fu3WFnZ4dt27YhPT1d7PLUduDAASQmJmL06NGwtraGsbGxynoptDQBQPfu3ZGRkYEffvgBtra2mDVrliTCzP79+/Hee+/B0NAQ+/fvL3HbLl26VFJVBABGRkbw8/ODn58fEhISEBwcjF27duGvv/5C06ZNMXXqVLFLfKPs7Gxl+Lpy5QpatWoFuVyOunXrvrEvY2VgoCmlrKws/Pnnnzh+/LhyfhG5XI727dtj2LBhvIugEt2+fRvff/89LCwsIJPJIJfL0aBBAwwYMABr167FwoULxS7xjZKTk5Uj0746lkv79u3x7bffYvjw4WKWp7aWLVuKXUKZvH6Lqr6+PqpUqVLoDjpt7SO3b98++Pj4wNDQEPv27St2O5lMxkAjIgcHBwwaNAgDBgxAREQEQkJCxC5JLfb29jh37hxatWqFK1euKKcASUtL04pWJgaaUtqwYQNu3ryJqVOnKn8RRUVFYe3atdiwYUORo6VSxVAoFMqWAAsLC6SkpMDBwQHW1taSmYnXysoKz58/h42NDWxsbHD79m3Url0bjx8/ltSEfB9//LHYJZTJ67eoent7i1RJ6SxatEi5D7/++qvI1dCbyOVytGrVSjL9Mfv06YNly5Zh/fr1aNKkCerVqwfgZWtNnTp1RK6OgabUzp49iy+//BLu7u7KZV5eXjA0NMSSJUsYaCqRk5MT7t69Czs7O7i5uWHPnj3Q19fH0aNHJTMIWuPGjXHhwgW4uLigY8eOWL9+Pc6cOYOYmBjJnOxeFRMTg/j4eMhkMjg6OmrFyU4dUh/I7dXJEGfPno0pU6bAzMxM7LJIR7Rp0wYNGjTA06dPVQYsbdKkiVacpxhoSik7OxuWlpaFlltaWiInJ0eEit5evXr1QnZ2NgCgf//++Omnn5STwn3xxRfiFqemUaNGKVti/P39YW5ujqioKDRv3lz0Owc08ezZMyxduhSRkZHKloLMzEy4u7vjiy++YB+zCmZsbIznz5/D0tISkZGRyM/PF7sk0jFWVlaF7vRzc3MTp5jX8C6nUvrhhx9gbm6OCRMmwNDQEMDLe/RXrFiBjIwMzJgxQ+QK327p6ekwMzPjoIeVbMmSJXj06BEmTJigHDAsPj4ev/76K+zs7CQTMKVq8eLFuHXrFhwdHREZGYl69eoVGiOrACfQJXUsXrwY48aNg6mpaaE+Zq8Tu28ZW2hKaciQIfjxxx8xduxY1KpVCzKZDHFxcTAwMMC3334rdnlvlZUrV2Lo0KEqndLMzc2VHbelchkhPT0dwcHBePDgAWQyGWrWrImOHTtK6vbny5cvY8aMGSqjnzo6OmL48OGYO3euiJW9HT7//HOEhobi0aNHiIyMhJOTk/IPLqLSMDU1Vf5h+HofM23DQFNKzs7OWL58OY4fP46EhAQIgoB27dop7zCgyhMWFoaBAwcW6mWfk5OD48ePSyLQREZGYuHChTAxMYGrqyuAl7dA79ixA1OnTpXMYGiCIBTZIqCnpyepzs1SZWhoqLxEGRMTg4EDB7IPDZXJq+dPbT+XMtCUgaGhId577z2xy3hrZWZmKv//4sULGBgYKB8rFApcunSpyH5O2mjNmjVo27YtRo4cCblcDuDlPgQGBmLNmjX4+eefRa5QPY0bN8batWsxceJEleH2169fL8mpEKSMl5SovB0/fhzt27cvct3GjRtFH/eLgaYMEhIScODAAeUlAgcHB3zwwQeoWbOm2KW9FYYOHar8/8SJEwutl8lk6Nu3b2WWVGqJiYmYPHmyMswAL2/p7Nq1K8LCwkSsTDPDhg3DokWLMH78eFhbWwN4OcaOs7MzPvvsM5Gr033r169Hv379YGxsjPXr15e4bUBAQCVVRbrizz//hJmZGZo3b66yfN26dQgPD2egkaozZ85g2bJlcHFxUd6LXzCz7eeff462bduKXKHumzlzJgRBwA8//IDJkyer9DXR19eHtbW1JEZ3BQAXFxfEx8fDwcFBZXl8fDxq164tTlGlYG1tjQULFuDq1at48OABBEGAo6OjZGbblrq4uDjlnU1xcXHiFkM654svvsDSpUvx9ddfKy+D//nnnzh79qxWtAjyLqdSmjBhAnx8fNCvXz+V5du3b8fx48exYsUKkSp7+yQlJaF69eoqrRtSEx4ejk2bNuGDDz5QBuT//vsPhw4dwsCBA1Va/V4d/4GIqDKdOnUKgYGB+O677xASEoLz589j5syZhf4YEwMDTSkNGjQIixcvhr29vcryhw8f4quvvsKmTZtEquztlZ2dXeRs21IIAK8H45Jo+2Sb165dw759+/DgwQMAQM2aNdGlSxe20lSyzMxMKBSKQnfJpaenQy6Xa/0dK6S9Dh8+jPXr18PCwgIzZ84s9HtQLLzkVEru7u64efNmoQ8yKioKDRs2FKmqt1NaWhpWrlyJS5cuFble2wMAAJ1p0Tt48CDWr1+P1q1bo3PnzgBeXoqdP38+AgIC8MEHH4hc4dtj6dKlaN68OTp16qSyPDw8HBcuXMD06dNFqoykpLi+WBYWFqhTpw4OHTqkXCZ2vywGmlJq3rw5/vrrL8TExKBu3boAXp64z5w5g48//hgRERHKbVu0aCFWmW+FdevWISMjA/PmzcPs2bPx1VdfITU1FTt37sTgwYPFLk8tNjY2YpdQLoKCgooMLvXr10dQUBADTSW6fft2kd9/d3d3bN26VYSKSIqK64tlb2+PFy9eaFVfLQaaUlqzZg2Al01vhw8fLnJdASm0EEjZ9evX8fXXX8PNzQ1yuRzW1tZo2rQpTE1NERQUBC8vL7FLVFt8fHyRl82kEopfvHgBDw+PQsubNWuGv/76q/ILeovl5eVBoVAUWp6fn8/pWUht2tDZV10MNKXEkKI9srOzlXMEmZubIy0tDQ4ODnB2dkZsbKzI1ann0aNHWLx4Me7du1fkeql831q0aIFz586hW7duKsvPnz9f6FZPqliurq44evQohg0bprL88OHDcHFxEakqkqr8/HwMHDgQCxcuhLOzs9jlFImBhiTPwcEBCQkJsLW1Ra1atXD06FHY2tri8OHDqFq1qtjlqWXt2rWwsbHBjBkzMGHCBPz4449IT0/Hhg0bRB/bQRM1a9bEzp07cePGDZXhDG7duoWuXbti//79ym27dOkiVplvhf79+2POnDm4e/euclDD69evIzo6Gt99953I1ZHU6OnpwcbGpshWP23Bu5zKgHdzaIcTJ04gPz8fvr6+iI2Nxbx58/D8+XPo6+tj/PjxaNeundglvtHw4cPx/fffo1atWggICMD8+fPh4OCA69evY8OGDVi4cKHYJapl/Pjxam0nk8l0piO0NouLi8OePXsQFxcHQ0NDODs7o2fPnqhRo4bYpZEEhYSE4MyZM/jss8+0co45ttCUEu/m0B4+Pj7K/9epUwe//vorHjx4AGtra+WlKG2nUChgbGwM4OXdAykpKXBwcIC1tTUSEhJErk59v/76q9gl0Ctq166Nzz//XOwySEccOHAAiYmJGD16NKytrZXnrAILFiwQqbKXGGhKiXdzaJ+0tDTo6+vD1NRUcn0EnJyccPfuXdjZ2cHNzQ179uyBvr4+jh49Cjs7O7HLI4nLyckp1NGc49CQplq2bCl2CSVioCkl3s2hHTIyMrBlyxacPn0a6enpAF62cHTs2BG9e/eGkZGRyBWqp1evXsjOzgbwcpC9BQsWYObMmTA3N8cXX3whbnEaevLkCSIiIoq8W0vscSreJtnZ2di0aRNOnz6N58+fF1ovlY7mpD0+/vhjsUsoEQNNKfFuDvGlp6fj22+/RUpKCnx8fFCzZk0IgoAHDx7gwIEDuHr1Kn744QfcvXsXt2/f1upOqK+GY3t7eyxZsgTp6ekwMzODTCYTrzANXbt2DQsXLoStrS0SEhLg5OSEpKQkCIKAOnXqiF3eW2Xjxo24ceMGRowYgRUrVmD48OFISUnB0aNHMWDAALHLIyp3DDSlxLs5xLdjxw7o6+vjl19+gZWVlcq6fv36Ye7cuVixYgWuXLmiMjO3Nlm8ePEbt5HL5bCyskLTpk21fjyazZs3o2vXrujXrx8GDx6MyZMnw9LSEsuXLy+yRZMqzoULFzBhwgS4u7vjt99+Q8OGDWFvbw8bGxucPHlSpe8ZkTreNEWL2K1+DDSlFBwcDDMzM8THxyM+Pl653NTUFMHBwcrHMpmMgaaCnD9/HiNHjiwUZgDAysoKgwYNwvz589GnTx/4+vpWen3qUKcfg0KhQGJiIoKDg/HRRx9pNO9TZXvw4AEmTpwI4OVtnjk5OTA2Nkbfvn2xaNEi+Pv7i1zh2yM9PR22trYAABMTE+Ul2QYNGmD16tVilkYSNWXKFJXH+fn5iI2NRVhYGPr27StSVf+HgaaUeDeH+J4+fQonJ6di1zs5OUEmk2n1dd9x48apve3FixexevVqrQ40RkZGyn4zVatWxaNHj5SfUVpampilvXXs7OyQlJQEGxsbODo6Ijw8HG5uboiIiICZmZnY5ZEEFdUpuE2bNnByckJ4eDj8/PxEqOr/yEV9d6IyqFKlCpKSkopd//jxY1haWlZiRRWrfv36cHV1FbuMEtWtWxdRUVEAAC8vL2zYsAE7d+7Eb7/9prw0S5XD19dXOc9Ojx49cPjwYQwYMADr168v1PePqCzc3Nxw7do1scvgwHqaWL9+Pfr16wdjY+NiZyAtwLs5Kt5vv/2GxMREzJgxA/r6qo2Nubm5mDdvHmxtbTVqBaGyefToEbKyslCrVi1kZ2dj48aNiIqKgr29PQICAnRmEk4pSk5Oxp07d2BnZ4fatWuLXQ7piJycHGzevBmXLl3CsmXLRK2Fl5w0EBcXh/z8fOX/SVx9+/bFtGnT8Pnnn6NTp06oWbMmgJcTPB46dAh5eXmYMGGCyFW+XV4dM8fIyAgjRowQsZq3k0KhwN69e3H+/Hnk5eWhSZMm6NOnD6ytrWFtbS12eSRBK1euxJAhQ4ocCfzFixcwMjLCZ599JkJlqthCQ5L2+PFjBAYG4sqVKyrLmzZtiuHDh8Pe3l6kyojEERQUhK1bt6JJkyYwNDTElStX0L59e4wePVrs0kii+vXrh1WrVuHSpUsqy+VyOSwsLODm5qYVUyGwhYYkzdbWFt988w3S09ORmJgI4OU4LtpwcL2Nirs9XiaTwcDAAPb29vD19UXHjh0rubK3R2hoKIYNG4ZOnToBAC5fvoxFixZh1KhRkhrTiLSPtt4tWoCBRkMrV6584zYymQxjx46thGqogLm5Odzc3MQu463Xu3dvBAUFwcPDA25ubhAEAXfu3MHly5fRqVMnZYtafn4+3nvvPbHL1UnJyckqg3s2a9YMgiDg6dOnqFatmoiVkZRJIQwz0GgoIyOj2HUKhQLXrl1Dbm4uAw29laKiotCvX79C480cOXIEV65cwZQpU1CrVi0cOHCAgaaC5OXlwdDQUPlYJpNBX18fubm5IlZFUlcwvlRJ1q5dWwmVFI+BRkNfffVVkcvPnz+PLVu2wMDAAH369Knkqoi0w5UrVzBo0KBCy5s0aYINGzYAADw9PbF58+bKLu2tsnXrVpV5zPLy8rBz506VgRx5JyZp4uOPP9b6CU0ZaMooKioKf/31F+Li4tCpUyf06NGD/TforWVubo6IiAh07dpVZXlERITyuMjOzoaxsbEY5b0VGjZsiIcPH6osq1+/Ph4/fixSRaQLvL29tX5cLwaaUrp//z7++usv5R0EX3zxBapXry52WUSi6t27NwIDA3Hjxg24ublBJpMhOjoaly5dwsiRIwEAV69eRaNGjUSuVHfNmjVL7BKIRMHbtjWUnJyM7du348SJE/Dy8sInn3wCR0dHscsi0hpRUVE4ePAgHj58CEEQ4ODggM6dO6N+/fpil0ZEpVBw27a2t9Aw0Gho0KBBkMlk+OCDD0o8QWv7rMhERES6hJecNFRwp8CePXtK3E7sadSJxFIwO3haWhoUCoXKOl5qIqKKwhYaIio3//33H5YvX17spKEM+kRUURhoiKjcfPXVV6hRowb69u2LqlWrFhqMS9tv+yQi6ZKLXQAR6Y7ExEQMGDAAjo6OMDMzg6mpqco/qjzjx4/Hjh07kJycLHYpRJWCfWiIqNy4ubkhMTGRk4Jqga5duyI0NBQ7duyAu7s7/Pz80KpVKxgYGIhdGlGF4CUnIio3586dw9atW9GtWzc4OztDT09PZX2tWrVEquztFRcXh5CQEJw6dQr5+fl455130LFjR7i4uIhdGlG5YqAhonLTr1+/EtezU7B48vLycPjwYfz111/Iy8uDs7MzOnfujI4dO0pi4kGiN2GgIaJyU9zdTQVsbGwqqRIqkJeXh3PnziE0NBRXr15F3bp14efnh6dPn+LgwYNwd3dXa+JBIm3HPjQaGDp0qNrbij3rKJEYGFi0R0xMDEJDQ3Hq1CnI5XL4+PggICAANWvWVG7TtGlTzJw5U8QqicoPA40GODstUWERERHw8PCAvr4+IiIiStyWI2hXnunTp6Np06YYMWIEWrZsCX39wqd7R0dHeHt7i1AdUfnjJSciKpNX53lhHxrtkZSUxBYzeqsw0GggMzNT7W055gYREVHlYaDRwJv++nwV/xIlIjEpFArs3bsXp0+fRnJyMvLy8lTWs58f6Rr2odEAO88RFe327dtIT0+Hp6encllYWBi2b9+O7OxstGzZEsOGDeOgbpXo77//RnBwMD788ENs27YNvXr1QlJSEs6fP4/evXuLXR5RuWOg0YC6MwXHxcVVbCFEWubvv/9Go0aNlIHm3r17+P3339GhQwc4Ojpiz549qFq1Kvr27StypW+PkydPYvTo0fDy8sKOHTvg7e0Ne3t7ODs74/bt22KXR1TuGGjKSWZmJk6cOIHg4GDExcXxkhO9VeLi4lQuyZ46dQpubm4YM2YMAKB69erYvn07A00lSk1NhbOzMwDA2NhY2QewefPmPD+RTmKgKaPr168jODgY586dg42NDVq3bq08iRO9LTIyMmBpaal8HBkZCQ8PD+VjV1dXPHnyRITK3l7VqlXD06dPYW1tDXt7e1y9ehUuLi64c+cOL/2RTmKgKYUnT54gNDQUISEhyM7ORtu2bZGfn4/JkyfD0dFR7PKIKp2lpSUeP34Ma2tr5OXlITY2VqU1Jisrq9C8TlSxWrVqhWvXrqFu3bro0qULli1bhuDgYCQnJ+PDDz8UuzyicsdAo6H58+cjKioKXl5eGDZsGDw8PCCXy3HkyBGxSyMSjYeHBzZv3oyBAwfi/PnzMDIyQsOGDZXr7969yxm4K9nAgQOV/2/Tpg2qVauG//77D/b29hzgkHQSA42Grly5gs6dO8Pf3x81atQQuxwirdC/f38sXrwYs2bNgrGxMcaPH68yMm1ISAiaNm0qYoVUr1491KtXT+wyiCoMx6HR0H///Yfg4GCcPn0aNWvWRPv27dGuXTuMHj0aixYt4iUneqtlZmbC2NgYcrlcZXl6ejqMjY2LHH6fKk5CQgIiIyPx7NkzvH6q79Onj0hVEVUMBppSys7OxqlTpxASEoLo6GgoFAoEBASgY8eOMDExEbs8InrLHT16FIGBgahSpQqsrKwgk8mU62QyGRYsWCBidUTlj4GmHCQkJCA4OBjHjx9HRkYGmjZtiqlTp4pdFhG9xcaNGwd/f3/06NFD7FKIKgXbf8uBg4MDBg0ahAEDBiAiIgIhISFil0REb7mMjAy0bdtW7DKIKg1baIiIdNBvv/0GV1dX+Pv7i10KUaVgoCEi0hH79+9X/j87Oxt79+6Fl5cXnJ2dC40D1KVLl8ouj6hCMdAQEemI8ePHq7WdTCbDihUrKrgaosrFQENERESSJ3/zJkREJDU7duxAdnZ2oeU5OTnYsWOHCBURVSwGGiIiHfT3338jKyur0PLs7Gz8/fffIlREVLEYaIiIdNSrg+kVuHv3LszNzUWohqhicRwaIiIdMnToUOX/J06cqLJOoVAgKysL77//fmWXRVTh2CmYiEiHhIaGAng5Dk1AQABMTU2V6/T19WFra8tJKkknMdAQEemgyMhI1KtXjxOC0luDgYaISMfl5OQgLy9PZdmrLTdEuoDRnYhIB2VnZ2PTpk04ffo0nj9/Xmj9tm3bRKiKqOLwLiciIh20ceNGXL9+HSNGjICBgQHGjBmDvn37olq1apgwYYLY5RGVOwYaIiIddOHCBYwYMQJt2rSBnp4eGjZsiN69e+OTTz7ByZMnxS6PqNwx0BAR6aD09HTY2toCAExMTJCeng4AaNCgASIjI8UsjahCMNAQEekgOzs7JCUlAQAcHR0RHh4OAIiIiICZmZmYpRFVCN7lRESkg/bu3Qu5XI4uXbrg+vXr+Omnn6BQKJCfn4+AgAB06dJF7BKJyhUDDRHRWyA5ORl37tyBnZ0dateuLXY5ROWOgYaIiIgkj31oiIh0yPXr1zFp0iRkZmYWWpeZmYkvv/wSN2/eFKEyoorFQENEpEP27duHd999t8iRgE1NTfHee+9h7969IlRGVLEYaIiIdMjdu3fh4eFR7PpmzZohJiam8goiqiQMNEREOuTZs2clTkipp6eHtLS0SqyIqHIw0BAR6ZBq1arh3r17xa6/e/cuqlatWokVEVUOBhoiIh3i6emJbdu2IScnp9C6nJwcbN++HV5eXiJURlSxeNs2EZEOSU1NxdSpUyGXy/HBBx/AwcEBMpkM8fHxOHToEBQKBRYsWAArKyuxSyUqVww0REQ6JikpCYGBgbh8+bLKcg8PDwwfPlw5xxORLmGgISLSUenp6UhMTAQA2Nvbw9zcXOSKiCoOAw0RERFJHjsFExERkeQx0BAREZHkMdAQERGR5DHQEFGl2b9/P/r27YvJkycXub5v377Yvn27Wq8VGhqKvn374vHjx8plv/76K8aPH18utRKRtBQ/PjYRUTkLCQkBANy/fx+3b99G3bp1VdbPnTsX1atXL/Xr9+7dG126dClTjUQkTWyhIaJKcefOHdy9e1c5Sm1wcHChberVq/fGQJOTk4Pibs60t7dHnTp1yl4sEUkOW2iIqFIUBJgBAwYgIyMD4eHhGDJkCIyMjJTb9O3bF3369EHfvn0BvLystHLlSnz77bc4deoULly4gOfPn2PTpk1Fvsevv/6KyMhI/Prrryqv2alTJ9StWxdBQUFISkpCjRo10L9/fzRv3lzl+Q8fPsT27dtx7do1ZGZmws7ODp06dcIHH3xQ3j8OIipnbKEhogqXk5ODU6dOwdXVFc7OzujYsSNevHiB06dPq/X83377Dfr6+vjss8/w5ZdfljibdFEuXryIgwcPKvvvmJubY/HixXj06JFym/j4eEyfPh3379/H4MGDMW3aNHh6emLt2rX4+++/NXo/Iqp8bKEhogp35swZZGZmws/PDwDQrl07rFu3DiEhIfD19X3j8xs3boxRo0aV+v1zcnIwY8YMmJiYAADq1KmD0aNH4/Tp0+jRowcAYP369TAxMcEPP/wAU1NTAEDTpk2Rl5eHXbt2oXPnzhxpl0iLsYWGiCpccHAwDA0N4e3tDQAwNjZGmzZtcPPmTTx8+PCNz2/Tpk2Z3t/d3V0ZZgDAysoKlpaWSEpKAvAy8Fy/fh0tW7aEkZER8vPzlf88PT2Rm5uL27dvl6kGIqpYbKEhogqVmJiImzdvonXr1hAEARkZGQBehpTQ0FCEhIRgwIABJb5GWWeGrlKlSqFlBgYGyMnJAfByzqP8/HwcPHgQBw8eLPI1nj9/XqYaiKhiMdAQUYUKDg6GIAg4c+YMzpw5U2h9WFgY+vfvD7m8+AZjmUxWkSXCzMwMcrkc7du3R6dOnYrchjNUE2k3BhoiqjAKhQJhYWGws7PDmDFjCq2/cOEC9u7di0uXLhW646gyGRkZwd3dHbGxsahVq5bGnY6JSHw8aomowly6dAlPnz7FwIED4e7uXmi9k5MTDh06hODgYFEDDQAMHToUM2bMwPfffw9/f3/Y2NjgxYsXSExMxIULFzBz5kxR6yOikjHQEFGFCQ4Ohr6+Pjp27FjkegsLC7Rs2RJnz55Fampq5Rb3GkdHRyxYsAD//PMPtm7dimfPnsHMzAw1atSAp6enqLUR0ZvJhOKG3CQiIiKSCN62TURERJLHQENERESSx0BDREREksdAQ0RERJLHQENERESSx0BDREREksdAQ0RERJLHQENERESSx0BDREREksdAQ0RERJLHQENERESSx0BDREREkvf/AHn8HcC8cO3nAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_temp = dataset[['Overall Rating','Airline']].groupby(['Airline']).mean().sort_values(by = 'Overall Rating', ascending= False).reset_index()\n",
    "sns.barplot(data= dataset_temp, x='Airline', y='Overall Rating', palette = 'Blues')\n",
    "\n",
    "plt.ylim(0, 10) \n",
    "plt.xticks(rotation=90) \n",
    "plt.xlabel('Airline')\n",
    "plt.ylabel('Average Rating')\n",
    "plt.title('Average Rating by Airline')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3e81373",
   "metadata": {},
   "source": [
    "**Variation of Overall Rating, Seat Comfort,  Staff Service, Food & Beverages, Inflight Entertainment and Value For Money across the years**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e76a9093",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-17T16:25:59.023153Z",
     "start_time": "2024-05-17T16:25:58.681257Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg_overall_rating_per_year = dataset.groupby(['Year'])['Overall Rating'].mean()/2\n",
    "avg_seat_comfort_rating_per_year = dataset.groupby(['Year'])['Seat Comfort'].mean()\n",
    "avg_staff_service_rating_per_year = dataset.groupby(['Year'])['Staff Service'].mean()\n",
    "avg_Food_n_Beverages_ratings_per_year =dataset.groupby(['Year'])['Food & Beverages'].mean()\n",
    "avg_Inflight_Entertainment_ratings_per_year = dataset.groupby(['Year'])['Inflight Entertainment'].mean()\n",
    "avg_Value_For_Money_ratings_per_year = dataset.groupby(['Year'])['Value For Money'].mean()\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15,8))\n",
    "avg_overall_rating_per_year.plot(kind = 'line',ax = ax, color = 'lightblue', linewidth = 5.3)\n",
    "avg_seat_comfort_rating_per_year.plot(kind = 'line',ax = ax, color = 'darkblue')\n",
    "avg_staff_service_rating_per_year.plot(kind = 'line',ax = ax, color = 'aqua')\n",
    "avg_Food_n_Beverages_ratings_per_year.plot(kind = 'line',ax = ax, color = 'deepskyblue')\n",
    "avg_Inflight_Entertainment_ratings_per_year.plot(kind = 'line',ax = ax)\n",
    "avg_Value_For_Money_ratings_per_year.plot(kind = 'line',ax = ax, color = 'midnightblue')\n",
    "\n",
    "ax.set_title('Avg rating per year')\n",
    "ax.set_xlabel('Year')\n",
    "ax.set_ylabel('Rating')\n",
    "ax.set_ylim(0,5)\n",
    "ax.grid(True, which = 'both')\n",
    "ax.grid(color='lightgrey', linestyle='--', linewidth=0.5)\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "579f83b7",
   "metadata": {},
   "source": [
    "**Length vs Sentiment**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7b5fd8c",
   "metadata": {},
   "source": [
    "Here we wanted to explore if there was any relation between the length of the reviews and the Overall Sentiment associated with it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "875a5cd7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-17T16:25:59.202284Z",
     "start_time": "2024-05-17T16:25:59.025459Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Relationship Between Review Length and Rating\n",
    "dataset['review_length'] = dataset['Reviews'].str.len()\n",
    "\n",
    "avg_review_length = dataset.groupby('Overall Rating')['review_length'].mean()\n",
    "ratings = dataset['Overall Rating'].unique()\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (6,4))\n",
    "ax.bar(ratings, avg_review_length, color='steelblue')\n",
    "ax.set_xlabel('Overall Rating')\n",
    "ax.set_ylabel('Average Review Length')\n",
    "ax.set_title('Relationship Between Review Length and Overall Rating')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4acb1d4",
   "metadata": {},
   "source": [
    "As one can see, no relation was found"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b5f7052",
   "metadata": {},
   "source": [
    "**Average Sentiment per Class**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "378e3c75",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-17T16:25:59.394532Z",
     "start_time": "2024-05-17T16:25:59.204806Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "average_sentiment = dataset.groupby('Class')['Sentiment'].mean().reset_index()\n",
    "\n",
    "# Plot sentiment per class \n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x='Class', y='Sentiment', data=average_sentiment, ci=None, palette = 'Blues')\n",
    "plt.title('Sentiment per Class Traveled')\n",
    "plt.xlabel(' ')\n",
    "plt.ylabel('Average Sentiment Score')\n",
    "plt.xticks(rotation=45)  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9360da37",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-05T11:46:29.697952Z",
     "start_time": "2024-05-05T11:46:29.693350Z"
    }
   },
   "source": [
    "**Average Sentiment per Type of Traveler**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "839f4af0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-17T16:25:59.573079Z",
     "start_time": "2024-05-17T16:25:59.397101Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "average_sentiment = dataset.groupby('Type of Traveller')['Sentiment'].mean().reset_index()\n",
    "\n",
    "# Plot sentiment per Type of Traveller\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x='Type of Traveller', y='Sentiment', data=average_sentiment, ci=None, palette = 'Blues')\n",
    "plt.title('Sentiment per Type of Traveller')\n",
    "plt.xlabel('Type of Traveller')\n",
    "plt.ylabel('Average Sentiment Score')\n",
    "plt.xticks(rotation=45)  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ce91a11",
   "metadata": {},
   "source": [
    "**Dropping the Column Overall Rating, since it was replaced by Sentiment**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b7077ac0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-17T16:25:59.580519Z",
     "start_time": "2024-05-17T16:25:59.575506Z"
    }
   },
   "outputs": [],
   "source": [
    "dataset = dataset.drop('Overall Rating', axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2be41081",
   "metadata": {},
   "source": [
    "## 5. Preprocessing for Sentiment Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7831c07a",
   "metadata": {},
   "source": [
    "**Preprocessing with the function for Lexicon-based**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d86badd4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-17T16:26:11.553406Z",
     "start_time": "2024-05-17T16:25:59.582890Z"
    }
   },
   "outputs": [],
   "source": [
    "dataset['Reviews_1'] = dataset['Reviews'].apply(\n",
    "    lambda x: preprocessing.reviews_preprocessor(x,\n",
    "                                                     remove_punctuation=False,\n",
    "                                                     lowercase=False,\n",
    "                                                     remove_stopwords=True,\n",
    "                                                     lemmatization=False,\n",
    "                                                     stemming=False,\n",
    "                                                     tokenized_output=False,\n",
    "                                                     sentence_output=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a315155f",
   "metadata": {},
   "source": [
    "**Preprocessing for LR and RF**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9cf9f4d8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-17T16:26:35.259534Z",
     "start_time": "2024-05-17T16:26:11.555872Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "dataset['Reviews_4'] = preprocessing.preprocess_reviews(dataset['Reviews'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9dd9515",
   "metadata": {},
   "source": [
    "**Partial Preprocessing with the function for BERT usage**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f003ff30",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-17T16:26:41.670104Z",
     "start_time": "2024-05-17T16:26:35.262107Z"
    }
   },
   "outputs": [],
   "source": [
    "dataset['Reviews_2'] = dataset['Reviews'].apply(\n",
    "    lambda x: preprocessing.reviews_preprocessor(x,\n",
    "                                                     remove_punctuation=True,\n",
    "                                                     lowercase=True,\n",
    "                                                     remove_stopwords=True,\n",
    "                                                     lemmatization=False,\n",
    "                                                     stemming=False,\n",
    "                                                     tokenized_output=False,\n",
    "                                                     sentence_output=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41fb894f",
   "metadata": {},
   "source": [
    "**Preprocessing with the function for LSTM usage**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4c1deca0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-17T16:26:41.675038Z",
     "start_time": "2024-05-17T16:26:41.672432Z"
    }
   },
   "outputs": [],
   "source": [
    "# dataset['Reviews_3'] = dataset['Reviews'].apply(\n",
    "#      lambda x: preprocessing.lstm_preprocessing(dataset=dataset))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "004ad9fa",
   "metadata": {},
   "source": [
    "## 6. Exporting Preprocessed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "00590089",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-17T16:26:41.702177Z",
     "start_time": "2024-05-17T16:26:41.683239Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Name</th>\n",
       "      <th>Review Date</th>\n",
       "      <th>Airline</th>\n",
       "      <th>Verified</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Type of Traveller</th>\n",
       "      <th>Month Flown</th>\n",
       "      <th>Route</th>\n",
       "      <th>Class</th>\n",
       "      <th>...</th>\n",
       "      <th>Food &amp; Beverages</th>\n",
       "      <th>Inflight Entertainment</th>\n",
       "      <th>Value For Money</th>\n",
       "      <th>Recommended</th>\n",
       "      <th>Sentiment</th>\n",
       "      <th>Year</th>\n",
       "      <th>review_length</th>\n",
       "      <th>Reviews_1</th>\n",
       "      <th>Reviews_4</th>\n",
       "      <th>Reviews_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Flight was amazing</td>\n",
       "      <td>Alison Soetantyo</td>\n",
       "      <td>2024-03-01</td>\n",
       "      <td>Singapore Airlines</td>\n",
       "      <td>True</td>\n",
       "      <td>Flight was amazing. The crew onboard this fl...</td>\n",
       "      <td>Solo Leisure</td>\n",
       "      <td>December 2023</td>\n",
       "      <td>Jakarta to Singapore</td>\n",
       "      <td>Business Class</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>2024</td>\n",
       "      <td>467</td>\n",
       "      <td>Flight amazing. The crew onboard flight welcom...</td>\n",
       "      <td>flight amaz crew onboard flight welcom gave go...</td>\n",
       "      <td>flight amazing crew onboard flight welcoming g...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>seats on this aircraft are dreadful</td>\n",
       "      <td>Robert Watson</td>\n",
       "      <td>2024-02-21</td>\n",
       "      <td>Singapore Airlines</td>\n",
       "      <td>True</td>\n",
       "      <td>Booking an emergency exit seat still meant h...</td>\n",
       "      <td>Solo Leisure</td>\n",
       "      <td>February 2024</td>\n",
       "      <td>Phuket to Singapore</td>\n",
       "      <td>Economy Class</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>2024</td>\n",
       "      <td>249</td>\n",
       "      <td>Booking emergency exit seat still meant huge d...</td>\n",
       "      <td>book emerg exit seat still meant huge discomfo...</td>\n",
       "      <td>booking emergency exit seat still meant huge d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Food was plentiful and tasty</td>\n",
       "      <td>S Han</td>\n",
       "      <td>2024-02-20</td>\n",
       "      <td>Singapore Airlines</td>\n",
       "      <td>True</td>\n",
       "      <td>Excellent performance on all fronts. I would...</td>\n",
       "      <td>Family Leisure</td>\n",
       "      <td>February 2024</td>\n",
       "      <td>Siem Reap to Singapore</td>\n",
       "      <td>Economy Class</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>2024</td>\n",
       "      <td>196</td>\n",
       "      <td>Excellent performance fronts. I would definite...</td>\n",
       "      <td>excel perform front would definit choos use ai...</td>\n",
       "      <td>excellent performance fronts would definitely ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>“how much food was available</td>\n",
       "      <td>D Laynes</td>\n",
       "      <td>2024-02-19</td>\n",
       "      <td>Singapore Airlines</td>\n",
       "      <td>True</td>\n",
       "      <td>Pretty comfortable flight considering I was f...</td>\n",
       "      <td>Solo Leisure</td>\n",
       "      <td>February 2024</td>\n",
       "      <td>Singapore to London Heathrow</td>\n",
       "      <td>Economy Class</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>2024</td>\n",
       "      <td>991</td>\n",
       "      <td>Pretty comfortable flight considering I flying...</td>\n",
       "      <td>pretti comfort flight consid fli economi class...</td>\n",
       "      <td>pretty comfortable flight considering flying e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>“service was consistently good”</td>\n",
       "      <td>A Othman</td>\n",
       "      <td>2024-02-19</td>\n",
       "      <td>Singapore Airlines</td>\n",
       "      <td>True</td>\n",
       "      <td>The service was consistently good from start ...</td>\n",
       "      <td>Family Leisure</td>\n",
       "      <td>February 2024</td>\n",
       "      <td>Singapore to Phnom Penh</td>\n",
       "      <td>Economy Class</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>2024</td>\n",
       "      <td>310</td>\n",
       "      <td>The service consistently good start finish. Th...</td>\n",
       "      <td>servic consist good start finish cabin crew sh...</td>\n",
       "      <td>service consistently good start finish cabin c...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Title              Name Review Date  \\\n",
       "0                    Flight was amazing  Alison Soetantyo  2024-03-01   \n",
       "1  seats on this aircraft are dreadful      Robert Watson  2024-02-21   \n",
       "2          Food was plentiful and tasty             S Han  2024-02-20   \n",
       "3          “how much food was available          D Laynes  2024-02-19   \n",
       "4       “service was consistently good”         A Othman   2024-02-19   \n",
       "\n",
       "              Airline Verified  \\\n",
       "0  Singapore Airlines     True   \n",
       "1  Singapore Airlines     True   \n",
       "2  Singapore Airlines     True   \n",
       "3  Singapore Airlines     True   \n",
       "4  Singapore Airlines     True   \n",
       "\n",
       "                                             Reviews Type of Traveller  \\\n",
       "0    Flight was amazing. The crew onboard this fl...      Solo Leisure   \n",
       "1    Booking an emergency exit seat still meant h...      Solo Leisure   \n",
       "2    Excellent performance on all fronts. I would...    Family Leisure   \n",
       "3   Pretty comfortable flight considering I was f...      Solo Leisure   \n",
       "4   The service was consistently good from start ...    Family Leisure   \n",
       "\n",
       "     Month Flown                         Route           Class  ...  \\\n",
       "0  December 2023          Jakarta to Singapore  Business Class  ...   \n",
       "1  February 2024           Phuket to Singapore   Economy Class  ...   \n",
       "2  February 2024        Siem Reap to Singapore   Economy Class  ...   \n",
       "3  February 2024  Singapore to London Heathrow   Economy Class  ...   \n",
       "4  February 2024       Singapore to Phnom Penh   Economy Class  ...   \n",
       "\n",
       "   Food & Beverages  Inflight Entertainment  Value For Money  Recommended  \\\n",
       "0                 4                       4                4          yes   \n",
       "1                 4                       4                1           no   \n",
       "2                 2                       1                5          yes   \n",
       "3                 5                       5                5          yes   \n",
       "4                 5                       5                5          yes   \n",
       "\n",
       "   Sentiment  Year  review_length  \\\n",
       "0          2  2024            467   \n",
       "1          0  2024            249   \n",
       "2          2  2024            196   \n",
       "3          2  2024            991   \n",
       "4          2  2024            310   \n",
       "\n",
       "                                           Reviews_1  \\\n",
       "0  Flight amazing. The crew onboard flight welcom...   \n",
       "1  Booking emergency exit seat still meant huge d...   \n",
       "2  Excellent performance fronts. I would definite...   \n",
       "3  Pretty comfortable flight considering I flying...   \n",
       "4  The service consistently good start finish. Th...   \n",
       "\n",
       "                                           Reviews_4  \\\n",
       "0  flight amaz crew onboard flight welcom gave go...   \n",
       "1  book emerg exit seat still meant huge discomfo...   \n",
       "2  excel perform front would definit choos use ai...   \n",
       "3  pretti comfort flight consid fli economi class...   \n",
       "4  servic consist good start finish cabin crew sh...   \n",
       "\n",
       "                                           Reviews_2  \n",
       "0  flight amazing crew onboard flight welcoming g...  \n",
       "1  booking emergency exit seat still meant huge d...  \n",
       "2  excellent performance fronts would definitely ...  \n",
       "3  pretty comfortable flight considering flying e...  \n",
       "4  service consistently good start finish cabin c...  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
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     "end_time": "2024-05-17T16:26:42.379457Z",
     "start_time": "2024-05-17T16:26:41.704664Z"
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   },
   "outputs": [],
   "source": [
    "dataset.to_csv('processed_data.csv', index=False)"
   ]
  }
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