Data_Analysis_of_British_Airways.ipynb

{
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      "cell_type": "markdown",
      "metadata": {
        "id": "SbloYhcCTmBA"
      },
      "source": [
        "The competition in the airline industry is at an all-time high due to the introduction of new airlines and expansion of existing services. Thus, to stay relevant in the industry it is crucial to understand the drivers of customer satisfaction for service improvements and effective marketing strategies. This analysis aims to analyze customer satisfaction within the airline industry, focusing on British Airways. By identifying factors influencing customer ratings and understanding seasonal booking trends, the analysis provides actionable insights that could enhance service delivery and customer experience."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The project follows a traditional data analysis methodology starting with data preprocessing, exploratory data analysis (EDA), and hypothesis testing to determine factors affecting customer satisfaction. The analysis was performed using Python. The data preprocessing of this dataset is challenging. So lets start the analysis\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "wo3v_1zbTYdI"
      },
      "outputs": [],
      "source": [
        "# This project is solution of the final Assessment for the subject Big Data Analytics (LD7186) which is taught under the course of MSC Big Data and Data Science Technology Northumbria University London Campus\n",
        "# Developed by Muhammad Abrar\n",
        "\n",
        "#Important Libraries\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import scipy as sp\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "custom_params = {\"axes.spines.right\": False, \"axes.spines.top\": False}\n",
        "sns.set_theme(style=\"ticks\", rc=custom_params, palette = 'Set2')          "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "xLa5v7U6UQEJ"
      },
      "outputs": [],
      "source": [
        "#Loading Dataset\n",
        "data_raw= pd.read_csv(r'Airline_Review.csv') # check pd.read_csv parameters to directly import the data in a right way"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HYWEyKVtVefn",
        "outputId": "e29c9975-7c31-432a-a08b-aa947b898c64"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3580 entries, 0 to 3579\n",
            "Data columns (total 9 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   Passanger_Name  3580 non-null   object \n",
            " 1   Flying_month    2815 non-null   object \n",
            " 2   Route           2816 non-null   object \n",
            " 3   Rating          3575 non-null   float64\n",
            " 4   Verified        1270 non-null   object \n",
            " 5   Review_title    3580 non-null   object \n",
            " 6   Review_content  3580 non-null   object \n",
            " 7   Traveller_type  3580 non-null   object \n",
            " 8   Class           3579 non-null   object \n",
            "dtypes: float64(1), object(8)\n",
            "memory usage: 251.8+ KB\n"
          ]
        }
      ],
      "source": [
        "data_raw.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "kD-hSvwJViA_",
        "outputId": "788f29fa-d445-488c-debb-ac9503cfbb3a"
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      "outputs": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Passanger_Name</th>\n",
              "      <th>Flying_month</th>\n",
              "      <th>Route</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Verified</th>\n",
              "      <th>Review_title</th>\n",
              "      <th>Review_content</th>\n",
              "      <th>Traveller_type</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1937</th>\n",
              "      <td>R Sanyal</td>\n",
              "      <td>16-Jul</td>\n",
              "      <td>LHR to ZRH</td>\n",
              "      <td>7.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>a matter-of-fact efficient trip</td>\n",
              "      <td>London Heathrow to Zurich - after reading all ...</td>\n",
              "      <td>Business</td>\n",
              "      <td>Business Class</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>985</th>\n",
              "      <td>David Ellis</td>\n",
              "      <td>Cape Town to London</td>\n",
              "      <td>First Class</td>\n",
              "      <td>9.0</td>\n",
              "      <td>Trip Verified</td>\n",
              "      <td>An excellent flight</td>\n",
              "      <td>Cape Town to London. I can report a positive e...</td>\n",
              "      <td>Boeing 747-400</td>\n",
              "      <td>Couple Leisure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3000</th>\n",
              "      <td>R Worton</td>\n",
              "      <td>23-Mar</td>\n",
              "      <td>London Heathrow to Munich</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Trip Verified</td>\n",
              "      <td>a horror show of incompetence</td>\n",
              "      <td>The usual shambolic unfolding that BA has now ...</td>\n",
              "      <td>Family Leisure</td>\n",
              "      <td>Economy Class</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2853</th>\n",
              "      <td>Mark Freyton</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>British Airways customer review</td>\n",
              "      <td>Got a wonderful cheap fare from London City to...</td>\n",
              "      <td>Economy Class</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1752</th>\n",
              "      <td>C Stratton</td>\n",
              "      <td>Seoul Incheon to London Heathrow</td>\n",
              "      <td>Business Class</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>BA is way behind other carriers</td>\n",
              "      <td>Verified Review   Seoul Incheon to London Heat...</td>\n",
              "      <td>Boeing 787</td>\n",
              "      <td>Solo Leisure</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Passanger_Name                      Flying_month  \\\n",
              "1937       R Sanyal                            16-Jul   \n",
              "985     David Ellis               Cape Town to London   \n",
              "3000       R Worton                            23-Mar   \n",
              "2853   Mark Freyton                               NaN   \n",
              "1752     C Stratton  Seoul Incheon to London Heathrow   \n",
              "\n",
              "                          Route  Rating       Verified  \\\n",
              "1937                 LHR to ZRH     7.0            NaN   \n",
              "985                 First Class     9.0  Trip Verified   \n",
              "3000  London Heathrow to Munich     1.0  Trip Verified   \n",
              "2853                        NaN     9.0            NaN   \n",
              "1752             Business Class     6.0            NaN   \n",
              "\n",
              "                         Review_title  \\\n",
              "1937  a matter-of-fact efficient trip   \n",
              "985               An excellent flight   \n",
              "3000    a horror show of incompetence   \n",
              "2853  British Airways customer review   \n",
              "1752  BA is way behind other carriers   \n",
              "\n",
              "                                         Review_content  Traveller_type  \\\n",
              "1937  London Heathrow to Zurich - after reading all ...        Business   \n",
              "985   Cape Town to London. I can report a positive e...  Boeing 747-400   \n",
              "3000  The usual shambolic unfolding that BA has now ...  Family Leisure   \n",
              "2853  Got a wonderful cheap fare from London City to...   Economy Class   \n",
              "1752  Verified Review   Seoul Incheon to London Heat...      Boeing 787   \n",
              "\n",
              "               Class  \n",
              "1937  Business Class  \n",
              "985   Couple Leisure  \n",
              "3000   Economy Class  \n",
              "2853             yes  \n",
              "1752    Solo Leisure  "
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data_raw.sample(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yhflRZAsW1gy"
      },
      "source": [
        "\n",
        "# Data Preproccessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fWkl6vMlXAGY",
        "outputId": "250323e8-bf09-4aad-dc68-db887b3fbd25"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Passanger_Name       0\n",
              "Flying_month       765\n",
              "Route              764\n",
              "Rating               5\n",
              "Verified          2310\n",
              "Review_title         0\n",
              "Review_content       0\n",
              "Traveller_type       0\n",
              "Class                1\n",
              "dtype: int64"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Checking Missing Values\n",
        "data_raw.isnull().sum()\n",
        "\n",
        "# Verified has 2310 missing values, this shows that majority of the records are null and dropping the column would be a great option for us."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "gCt2N5j1XYx7"
      },
      "outputs": [],
      "source": [
        "data = data_raw.drop('Verified', axis='columns')\n",
        "\n",
        "# verified dropped"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The data has severe inconcistency. The details can be found in the data preprocessing section of the project description. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7G0BW-Ws11d2",
        "outputId": "483cec01-ae13-47eb-d78c-cf5eafe8997e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Correct Flying month:\n",
            "1834    16-Jan\n",
            "1835    17-Jan\n",
            "1836    17-Jan\n",
            "1837    17-Jan\n",
            "1838    17-Jan\n",
            "         ...  \n",
            "3573    22-Dec\n",
            "3574    22-Dec\n",
            "3575    22-Dec\n",
            "3576    22-Dec\n",
            "3577    22-Dec\n",
            "Name: Flying_month, Length: 947, dtype: object\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# Identify correct and incorrect records\n",
        "correct_mask = data['Flying_month'].str.match(r'^\\d{2}-[a-zA-Z]+$')\n",
        "\n",
        "# Separate into different series\n",
        "correct_flying_month = data.loc[correct_mask & ~data['Flying_month'].isna(), 'Flying_month']\n",
        "\n",
        "# Display the results\n",
        "print(\"Correct Flying month:\")\n",
        "print(correct_flying_month)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZB3kXtRiStBs"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lvHoSnZRYE3T"
      },
      "source": [
        "# Data Correction\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "A Systemetic approach has been followed. The correct values of each column is identified using regex and boolean array and the identified values are then moved to the correct column."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "_vcDRJ1CYUgj"
      },
      "outputs": [],
      "source": [
        "# Creating copy of origional data\n",
        "data_new = data.copy()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "dB6OR2o8W1NS"
      },
      "outputs": [],
      "source": [
        "#Regular Expressions for all columns\n",
        "\n",
        "# For Correct Data of Flying Month '^\\d{2}-[a-zA-Z]+\n",
        "# For Correct Data of Aircraft Type '[A-Z]*\\d'\n",
        "# For Correct Data of Route         'to+'\n",
        "# For Correct Data of Class         'Class' | 'Premium Economy'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MR4BMbV2zRka"
      },
      "source": [
        "Populating **Flying Month** with valid values from every other column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Y0yvhVQcZ7G8"
      },
      "outputs": [],
      "source": [
        "#Looking for all valid data of the form (22-DEC) in other columns and transfering it to flying_month.\n",
        "\n",
        "# 1. Boolean Array for correct values in the Flying Month\n",
        "mask_fl = data.Flying_month.str.contains(pat='^\\d{2}-[a-zA-Z]+$') #data.Flying_month.str.match(r'^\\d{2}-[a-zA-Z]+$')  using regular expression\n",
        "\n",
        "\n",
        "#print(data[mask_fl & ~data.Flying_month.isna()])\n",
        "\n",
        "#2. Boolean Array for Route column that has flying month values\n",
        "mask_rt_fl = data.Route.str.contains(pat='^\\d{2}-[a-zA-Z]+$')\n",
        "\n",
        "#3. Boolean Array for Route column that has flying month values\n",
        "mask_cl_fl = data.Class.str.contains(pat='^\\d{2}-[a-zA-Z]+$')\n",
        "\n",
        "#print(data.loc[mask_cl_fl & ~data.Route.isna()]) # This returns empty dataset which means that it contains no value that is related to flying month.\n",
        "\n",
        "#Copying all values into the new column for flying month [Valid Value (flying month and Route) --> New Flying Month]\n",
        "data_new.loc[mask_fl & ~data.Flying_month.isna(), 'NewFlying_month'] = data_new.Flying_month  # Correct values from flying Month\n",
        "\n",
        "\n",
        "data_new.loc[mask_rt_fl & ~data.Route.isna(), 'NewFlying_month'] = data_new.Route # Correct Values from Route Column into New Flying Month\n",
        "\n",
        "data_new.loc[mask_rt_fl & ~data.Route.isna(), 'Route'] = np.nan   # Replace values of route that are related to flying month with nan values.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xTUm1ItHPDNx"
      },
      "source": [
        "Traveller Type Fixing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ngW99-N51EZ1"
      },
      "outputs": [],
      "source": [
        "mask_tr = data_new.Traveller_type.str.contains(pat='[A-Z]*\\d')  #Get all charachters in range alphabets plus any numbers (Aircraft Carrier)\n",
        "\n",
        "\n",
        "\n",
        "data_new.loc[mask_tr & ~data_new.Traveller_type.isna(), 'Aircraft_type'] = data_new.Traveller_type # copy into new column Aircraft_type\n",
        "data_new.loc[mask_tr & ~data_new.Traveller_type.isna(), 'Traveller_type'] = np.nan"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_i9EagNezoUO"
      },
      "source": [
        "Populating **Route** with valid values from every other column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s5Y1HzLmOv5F"
      },
      "outputs": [],
      "source": [
        "mask_fl_rt = data.Flying_month.str.contains(pat=\"to+\") #get all route values from flying_month to ROute\n",
        "#print(mask_fl_rt.sum())\n",
        "\n",
        "#boolean array for Traveller_type to Route\n",
        "mask_Tr_rt = data_new.Traveller_type.str.contains(pat=\"to+\")\n",
        "print(mask_Tr_rt.sum())\n",
        "\n",
        "#boolean array for Class to Route\n",
        "mask_cl_rt = data_new.Class.str.contains(pat=\"to+\")\n",
        "print(mask_cl_rt.sum())\n",
        "\n",
        "#Copying all relevante to Route\n",
        "data_new.loc[mask_fl_rt & ~data_new.Flying_month.isna(), 'Route'] = data_new.Flying_month\n",
        "data_new.loc[mask_Tr_rt & ~data_new.Traveller_type.isna(), 'Route'] = data_new.Traveller_type\n",
        "data_new.loc[mask_cl_rt & ~data_new.Class.isna(), 'Route'] = data_new.Class\n",
        "\n",
        "#Filling Class with nan for route values\n",
        "data_new.loc[mask_cl_rt & ~data_new.Class.isna(), 'Class'] =np.nan\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RsfFluVYmR2f"
      },
      "source": [
        "Populating Class column from all other columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WLEMIsdq3JcA"
      },
      "outputs": [],
      "source": [
        "\n",
        "mask_tr_cl = data.Traveller_type.str.contains('Class') | data.Traveller_type.str.contains(pat='Premium Economy', case=False )\n",
        "#765 cases found when checked in excel, this is exactly right. Traveller to Class\n",
        "\n",
        "\n",
        "#mask_tr_cl.sum()\n",
        "\n",
        "data_new.loc[mask_tr_cl & ~data_new.Class.isna(), 'Class'] = data_new.Traveller_type\n",
        "\n",
        "# Class to Traveller\n",
        "\n",
        "mask_cl_tr = data_new.Class.str.contains('Leisure') | data.Class.str.contains(pat='Business$', regex=True)\n",
        "\n",
        "\n",
        "\n",
        "data_new.loc[mask_cl_tr & ~data_new.Class.isna(), 'Traveller_type'] = data_new.Class\n",
        "\n",
        "data_new.loc[mask_cl_tr & ~data_new.Class.isna(), 'Class'] =np.nan\n",
        "\n",
        "\n",
        "\n",
        "data_new.loc[mask_tr_cl & ~data_new.Traveller_type.isna(), 'Traveller_type'] = np.nan\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "print((mask_tr_cl & ~data_new.Class.isna()).sum())\n",
        "\n",
        "print(mask_tr_cl.sum())\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "VSaNwkHH9eSF"
      },
      "outputs": [],
      "source": [
        "data_new.loc[~data_new.Traveller_type.isna() & data_new.Traveller_type.str.contains(pat='no'),'Traveller_type'] = np.nan"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "uhVv0pkz6rPq"
      },
      "outputs": [
        {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Passanger_Name</th>\n",
              "      <th>Flying_month</th>\n",
              "      <th>Route</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Review_title</th>\n",
              "      <th>Review_content</th>\n",
              "      <th>Traveller_type</th>\n",
              "      <th>Class</th>\n",
              "      <th>Aircraft_type</th>\n",
              "      <th>Verified</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>D Brown</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Buenos Aires to Heathrow</td>\n",
              "      <td>9.0</td>\n",
              "      <td>British Airways customer review</td>\n",
              "      <td>Very good flight in Club World from start to f...</td>\n",
              "      <td>Business</td>\n",
              "      <td>NaN</td>\n",
              "      <td>777</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>S Mayir</td>\n",
              "      <td>NaN</td>\n",
              "      <td>San Francisco to London</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Cramped uncomfortable seats</td>\n",
              "      <td>San Francisco to London. Terrible service and ...</td>\n",
              "      <td>Couple Leisure</td>\n",
              "      <td>NaN</td>\n",
              "      <td>777</td>\n",
              "      <td>Trip Verified</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>H Warner</td>\n",
              "      <td>NaN</td>\n",
              "      <td>St Lucia to Gatwick</td>\n",
              "      <td>1.0</td>\n",
              "      <td>both involuntarily downgraded</td>\n",
              "      <td>St Lucia to Gatwick on which my wife and I wer...</td>\n",
              "      <td>Couple Leisure</td>\n",
              "      <td>NaN</td>\n",
              "      <td>777</td>\n",
              "      <td>Trip Verified</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>G Mantimo</td>\n",
              "      <td>NaN</td>\n",
              "      <td>LHR to YYC</td>\n",
              "      <td>3.0</td>\n",
              "      <td>experience has really declined</td>\n",
              "      <td>Verified Review   The British Airways experien...</td>\n",
              "      <td>Family Leisure</td>\n",
              "      <td>NaN</td>\n",
              "      <td>787</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Scott Harris</td>\n",
              "      <td>NaN</td>\n",
              "      <td>LHR to ARN</td>\n",
              "      <td>6.0</td>\n",
              "      <td>behind their competitors</td>\n",
              "      <td>London to Stockholm flight was delayed a littl...</td>\n",
              "      <td>Family Leisure</td>\n",
              "      <td>NaN</td>\n",
              "      <td>767-300</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3575</th>\n",
              "      <td>Martin McCormack</td>\n",
              "      <td>22-Dec</td>\n",
              "      <td>Dublin to Orlando via Heathrow</td>\n",
              "      <td>1.0</td>\n",
              "      <td>one of the worst experiences</td>\n",
              "      <td>This was literally one of the worst experience...</td>\n",
              "      <td>Solo Leisure</td>\n",
              "      <td>Economy Class</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Not Verified</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3576</th>\n",
              "      <td>R Marton</td>\n",
              "      <td>22-Dec</td>\n",
              "      <td>London to Phoenix</td>\n",
              "      <td>5.0</td>\n",
              "      <td>luggage was finally delivered on Thursday</td>\n",
              "      <td>Missing baggage customer service was the worst...</td>\n",
              "      <td>Solo Leisure</td>\n",
              "      <td>Premium Economy</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Trip Verified</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3577</th>\n",
              "      <td>S Marton</td>\n",
              "      <td>22-Dec</td>\n",
              "      <td>London Heathrow to Frankfurt</td>\n",
              "      <td>1.0</td>\n",
              "      <td>minimal communication from their team</td>\n",
              "      <td>Absolutely terrible experience with British Ai...</td>\n",
              "      <td>Solo Leisure</td>\n",
              "      <td>Economy Class</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Trip Verified</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3578</th>\n",
              "      <td>Tom Moore</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Los Angeles to London to Paris to Rome</td>\n",
              "      <td>1.0</td>\n",
              "      <td>British Airways customer review</td>\n",
              "      <td>Overall good experience. BA owns Vueling/Iberi...</td>\n",
              "      <td>Solo Leisure</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3579</th>\n",
              "      <td>B Pearson</td>\n",
              "      <td>NaN</td>\n",
              "      <td>BKK to LHR via LAD &amp; ABV</td>\n",
              "      <td>1.0</td>\n",
              "      <td>a worn out and low cost discount carrier</td>\n",
              "      <td>Verified Review   I booked and bought an onlin...</td>\n",
              "      <td>Business</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3580 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Passanger_Name Flying_month                                    Route  \\\n",
              "0              D Brown          NaN                 Buenos Aires to Heathrow   \n",
              "1              S Mayir          NaN                  San Francisco to London   \n",
              "2             H Warner          NaN                      St Lucia to Gatwick   \n",
              "3            G Mantimo          NaN                               LHR to YYC   \n",
              "4         Scott Harris          NaN                               LHR to ARN   \n",
              "...                ...          ...                                      ...   \n",
              "3575  Martin McCormack       22-Dec           Dublin to Orlando via Heathrow   \n",
              "3576          R Marton       22-Dec                        London to Phoenix   \n",
              "3577          S Marton       22-Dec             London Heathrow to Frankfurt   \n",
              "3578         Tom Moore          NaN  Los Angeles to London to Paris to Rome    \n",
              "3579         B Pearson          NaN                 BKK to LHR via LAD & ABV   \n",
              "\n",
              "      Rating                               Review_title  \\\n",
              "0        9.0            British Airways customer review   \n",
              "1        1.0                Cramped uncomfortable seats   \n",
              "2        1.0              both involuntarily downgraded   \n",
              "3        3.0             experience has really declined   \n",
              "4        6.0                   behind their competitors   \n",
              "...      ...                                        ...   \n",
              "3575     1.0               one of the worst experiences   \n",
              "3576     5.0  luggage was finally delivered on Thursday   \n",
              "3577     1.0      minimal communication from their team   \n",
              "3578     1.0            British Airways customer review   \n",
              "3579     1.0   a worn out and low cost discount carrier   \n",
              "\n",
              "                                         Review_content  Traveller_type  \\\n",
              "0     Very good flight in Club World from start to f...        Business   \n",
              "1     San Francisco to London. Terrible service and ...  Couple Leisure   \n",
              "2     St Lucia to Gatwick on which my wife and I wer...  Couple Leisure   \n",
              "3     Verified Review   The British Airways experien...  Family Leisure   \n",
              "4     London to Stockholm flight was delayed a littl...  Family Leisure   \n",
              "...                                                 ...             ...   \n",
              "3575  This was literally one of the worst experience...    Solo Leisure   \n",
              "3576  Missing baggage customer service was the worst...    Solo Leisure   \n",
              "3577  Absolutely terrible experience with British Ai...    Solo Leisure   \n",
              "3578  Overall good experience. BA owns Vueling/Iberi...    Solo Leisure   \n",
              "3579  Verified Review   I booked and bought an onlin...        Business   \n",
              "\n",
              "                Class Aircraft_type       Verified  \n",
              "0                 NaN           777            NaN  \n",
              "1                 NaN           777  Trip Verified  \n",
              "2                 NaN           777  Trip Verified  \n",
              "3                 NaN           787            NaN  \n",
              "4                 NaN       767-300            NaN  \n",
              "...               ...           ...            ...  \n",
              "3575    Economy Class           NaN   Not Verified  \n",
              "3576  Premium Economy           NaN  Trip Verified  \n",
              "3577    Economy Class           NaN  Trip Verified  \n",
              "3578              NaN           NaN            NaN  \n",
              "3579              NaN           NaN            NaN  \n",
              "\n",
              "[3580 rows x 10 columns]"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Final Changes NewFlyingmonth to Flying Month , Adding Verified again to the final dataset.\n",
        "\n",
        "data_new['Flying_month'] = data_new['NewFlying_month']\n",
        "data_new.drop(['NewFlying_month'], axis=1, inplace=True )\n",
        "data_new['Verified'] = data_raw.Verified\n",
        "data_new"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B6BDqtdLL1tV"
      },
      "source": [
        "All the columns have valid data now Now moving towards Step 2 which is Data Cleaning and missing data\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kxxHuSEV6qar"
      },
      "source": [
        "# Checking Missing Values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2aWYahOm9u4v"
      },
      "source": [
        "**Handling Missing Values**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IfNOiFyKWIf8",
        "outputId": "944b28a0-4c21-4c02-8952-51d97dc37803"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Passanger_Name       0\n",
              "Flying_month      2633\n",
              "Route              769\n",
              "Rating               5\n",
              "Review_title         0\n",
              "Review_content       0\n",
              "Traveller_type     766\n",
              "Class             1862\n",
              "Aircraft_type     1728\n",
              "Verified          2310\n",
              "dtype: int64"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data_new.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "uIF-_dS_9hnC"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\mabra\\AppData\\Local\\Temp\\ipykernel_10784\\3911070049.py:11: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  barplot = sns.barplot(x=percent.values, y=percent.index, palette='Set2')\n"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Calculate the percentage of missing values for each column\n",
        "missing_values = data_new.isnull().mean()\n",
        "percent = missing_values * 100\n",
        "percent.sort_values(inplace=True, ascending=False)\n",
        "\n",
        "# Create bar plot\n",
        "plt.figure(figsize=(12, 8))\n",
        "barplot = sns.barplot(x=percent.values, y=percent.index, palette='Set2')\n",
        "\n",
        "# Annotate each bar with the percentage of missing values\n",
        "for bar in barplot.patches:\n",
        "    barplot.annotate(format(bar.get_width(), '.2f') + '%',\n",
        "                     (bar.get_width(), bar.get_y() + bar.get_height() / 2),\n",
        "                     ha='left', va='center',\n",
        "                     size=10, xytext=(3, 0),\n",
        "                     textcoords='offset points')\n",
        "\n",
        "# Set the title and show the plot\n",
        "plt.title('Percentage of Missing Values in Each Column')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rhxE6DER-tUZ"
      },
      "source": [
        "**Extracting Dates from Reviews and filling Flying month column in order to fill As much missing values as possible**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rmkU8hLFTIOF"
      },
      "source": [
        "**Convert the values extracted from reviews to standard format of flying month**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6O8JYvVfRAL4",
        "outputId": "530deac2-e4d8-405a-e853-c3bd3417a321"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "51      18-Dec\n",
            "182     22-Apr\n",
            "256     16-May\n",
            "334     17-Apr\n",
            "338     22-Oct\n",
            "         ...  \n",
            "3573    22-Dec\n",
            "3574    22-Dec\n",
            "3575    22-Dec\n",
            "3576    22-Dec\n",
            "3577    22-Dec\n",
            "Name: Flying_month, Length: 1030, dtype: object\n"
          ]
        }
      ],
      "source": [
        "import re\n",
        "from dateutil.parser import parse\n",
        "\n",
        "def convert_to_standard_format(date_str):\n",
        "    if pd.notna(date_str):\n",
        "        try:\n",
        "            # Convert to datetime and then format as \"YY-Mon\" (e.g., \"19-Apr\")\n",
        "            return parse(date_str).strftime(\"%y-%b\")\n",
        "        except ValueError:\n",
        "            # In case of parsing error, return None\n",
        "            return None\n",
        "    else:\n",
        "        return None\n",
        "\n",
        "# Apply the conversion to the 'New_Flying_month' column\n",
        "data_new['New_Flying_month'] = data_new['New_Flying_month'].apply(convert_to_standard_format)\n",
        "\n",
        "# **Now Merging Flying Month and New Flying Month to Fill the NAN Values with New Flying Month Values that are extracted dates from reviews**\n",
        "\n",
        "# Replace NaN or empty values in 'Flying_month' with values from 'New_Flying_month'\n",
        "data_new['Flying_month'] = data_new['Flying_month'].combine_first(data_new['New_Flying_month'])\n",
        "\n",
        "# Optionally, if you want to remove the 'New_Flying_month' column afterwards\n",
        "data_new = data_new.drop(columns=['New_Flying_month'])\n",
        "\n",
        "# Show the updated dataframe\n",
        "#data_new.head()\n",
        "\n",
        "mask = ~data_new.Flying_month.isna()\n",
        "print(data_new.Flying_month.loc[mask])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 790
        },
        "id": "okRew3BtVWoH",
        "outputId": "2066d6a6-0453-4ea1-ce0d-a7b2783b7c13"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\mabra\\AppData\\Local\\Temp\\ipykernel_10784\\3911070049.py:11: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  barplot = sns.barplot(x=percent.values, y=percent.index, palette='Set2')\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Calculate the percentage of missing values for each column\n",
        "missing_values = data_new.isnull().mean()\n",
        "percent = missing_values * 100\n",
        "percent.sort_values(inplace=True, ascending=False)\n",
        "\n",
        "# Create bar plot\n",
        "plt.figure(figsize=(12, 8))\n",
        "barplot = sns.barplot(x=percent.values, y=percent.index, palette='Set2')\n",
        "\n",
        "# Annotate each bar with the percentage of missing values\n",
        "for bar in barplot.patches:\n",
        "    barplot.annotate(format(bar.get_width(), '.2f') + '%',\n",
        "                     (bar.get_width(), bar.get_y() + bar.get_height() / 2),\n",
        "                     ha='left', va='center',\n",
        "                     size=10, xytext=(3, 0),\n",
        "                     textcoords='offset points')\n",
        "\n",
        "# Set the title and show the plot\n",
        "plt.title('Percentage of Missing Values in Each Column')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BHLtDbAvjcOd"
      },
      "source": [
        "# Handling Duplicate Values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GV-bPCwObwDb",
        "outputId": "70e4f80b-a76c-4a85-a1f1-c784fb7bcf35"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "74"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "duplicate_rows = data_new.duplicated().sum()\n",
        "\n",
        "duplicate_rows"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nZwMoYFEcEYA",
        "outputId": "ecc4412f-4766-4d2d-ae9e-13ea34561858"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Shape Before : (3580, 10)\n",
            "Shape After : (3506, 10)\n"
          ]
        }
      ],
      "source": [
        "# Removing duplicates\n",
        "df_no_duplicates = data_new.drop_duplicates()\n",
        "\n",
        "# Shape of the dataset before and after removing duplicates\n",
        "shape_before = data_new.shape\n",
        "shape_after = df_no_duplicates.shape\n",
        "\n",
        "print('Shape Before :',shape_before)\n",
        "print ('Shape After :',shape_after)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z3MM_kup10ZT"
      },
      "source": [
        "# Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192
        },
        "id": "kVf6MEfKtDwV",
        "outputId": "daf5b590-b4a9-4757-edb3-80b1914a4fc3"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"data_new\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Passanger_Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2764,\n          \"44\",\n          \"3580\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Flying_month\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          112,\n          \"25\",\n          \"1038\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Route\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1534,\n          \"16\",\n          \"2811\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Review_title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2570,\n          \"952\",\n          \"3580\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Review_content\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3506,\n          \"2\",\n          \"3580\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Traveller_type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          \"949\",\n          \"2814\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          \"1003\",\n          \"1718\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Aircraft_type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          191,\n          \"342\",\n          \"1852\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Verified\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"1067\",\n          \"1270\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-d64c53de-df6f-44b7-b2db-823a3982a7b4\" class=\"colab-df-container\">\n",
              "    <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>Passanger_Name</th>\n",
              "      <th>Flying_month</th>\n",
              "      <th>Route</th>\n",
              "      <th>Review_title</th>\n",
              "      <th>Review_content</th>\n",
              "      <th>Traveller_type</th>\n",
              "      <th>Class</th>\n",
              "      <th>Aircraft_type</th>\n",
              "      <th>Verified</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3580</td>\n",
              "      <td>1038</td>\n",
              "      <td>2811</td>\n",
              "      <td>3580</td>\n",
              "      <td>3580</td>\n",
              "      <td>2814</td>\n",
              "      <td>1718</td>\n",
              "      <td>1852</td>\n",
              "      <td>1270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>2764</td>\n",
              "      <td>112</td>\n",
              "      <td>1534</td>\n",
              "      <td>2570</td>\n",
              "      <td>3506</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>191</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>David Ellis</td>\n",
              "      <td>15-Sep</td>\n",
              "      <td>London to Johannesburg</td>\n",
              "      <td>British Airways customer review</td>\n",
              "      <td>I have used British Airways over a number of y...</td>\n",
              "      <td>Couple Leisure</td>\n",
              "      <td>Economy Class</td>\n",
              "      <td>A320</td>\n",
              "      <td>Trip Verified</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>44</td>\n",
              "      <td>25</td>\n",
              "      <td>16</td>\n",
              "      <td>952</td>\n",
              "      <td>2</td>\n",
              "      <td>949</td>\n",
              "      <td>1003</td>\n",
              "      <td>342</td>\n",
              "      <td>1067</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d64c53de-df6f-44b7-b2db-823a3982a7b4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-d64c53de-df6f-44b7-b2db-823a3982a7b4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d64c53de-df6f-44b7-b2db-823a3982a7b4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f3b8f6a0-4851-4187-ba27-202d6a8e1c4b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f3b8f6a0-4851-4187-ba27-202d6a8e1c4b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f3b8f6a0-4851-4187-ba27-202d6a8e1c4b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       Passanger_Name Flying_month                   Route  \\\n",
              "count            3580         1038                    2811   \n",
              "unique           2764          112                    1534   \n",
              "top       David Ellis       15-Sep  London to Johannesburg   \n",
              "freq               44           25                      16   \n",
              "\n",
              "                           Review_title  \\\n",
              "count                              3580   \n",
              "unique                             2570   \n",
              "top     British Airways customer review   \n",
              "freq                                952   \n",
              "\n",
              "                                           Review_content  Traveller_type  \\\n",
              "count                                                3580            2814   \n",
              "unique                                               3506               4   \n",
              "top     I have used British Airways over a number of y...  Couple Leisure   \n",
              "freq                                                    2             949   \n",
              "\n",
              "                Class Aircraft_type       Verified  \n",
              "count            1718          1852           1270  \n",
              "unique              4           191              2  \n",
              "top     Economy Class          A320  Trip Verified  \n",
              "freq             1003           342           1067  "
            ]
          },
          "execution_count": 122,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data_new.describe(include=object)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5CokiNBqtDGf"
      },
      "source": [
        "**Distribution of Ratings:**\n",
        "\n",
        "This histogram shows how the ratings are distributed. The use of a Kernel Density Estimate (KDE) provides a smooth estimate of the distribution. From the graph, you can observe the central tendency and dispersion of ratings.\n",
        "Count of Reviews per Class:\n",
        "\n",
        "This bar chart displays the number of reviews for each class. It helps in understanding which classes are most reviewed, indicating their popularity or the volume of passengers.\n",
        "These visualizations are effective in conveying the general distributions and characteristics of the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "id": "HMFg8tVbPerH",
        "outputId": "b0e298ad-f738-4689-c250-0e0bb224160f"
      },
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "<Figure size 1500x700 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Setting up the matplotlib figure\n",
        "plt.figure(figsize=(15, 7))\n",
        "\n",
        "# Distribution of Ratings\n",
        "plt.subplot(1, 2, 1)\n",
        "sns.histplot(df_no_duplicates['Rating'], bins=10, kde=True)\n",
        "plt.title('Distribution of Ratings')\n",
        "plt.xlabel('Rating')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "# Count of Reviews per Class\n",
        "plt.subplot(1, 2, 2)\n",
        "sns.countplot(y='Class', data=df_no_duplicates, order = df_no_duplicates['Class'].value_counts().index)\n",
        "plt.title('Count of Reviews per Class')\n",
        "plt.xlabel('Count')\n",
        "plt.ylabel('Class')\n",
        "\n",
        "# Adjust layout\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Note: Additional visualizations can be created for other variables as needed.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "opg66EP5gkz0"
      },
      "source": [
        "# 1. Identify the factors affecting customer choices."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iZ96rGUDhQN0"
      },
      "outputs": [],
      "source": [
        "# Analyzing correlation of 'Rating' with 'Traveller_type', 'Class', 'Aircraft_type', and 'Verified'\n",
        "# Since these are categorical variables, we will use groupby to understand their impact on average ratings\n",
        "avg_rating_by_traveller_type = data_new.groupby('Traveller_type')['Rating'].mean()\n",
        "avg_rating_by_class = data_new.groupby('Class')['Rating'].mean()\n",
        "avg_rating_by_aircraft = data_new.groupby('Aircraft_type')['Rating'].mean()\n",
        "avg_rating_by_verified = data_new.groupby('Verified')['Rating'].mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        },
        "id": "9OQ7TT7MqsKV",
        "outputId": "aa6c8596-a0d4-41ae-a87d-454f9b7ae301"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-128-4a7ada1c1155>:7: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=avg_rating_by_traveller_type.index, y=avg_rating_by_traveller_type.values , palette='GnBu')\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1600x1000 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Visualization setup\n",
        "plt.figure(figsize=(16, 10))\n",
        "sns.set_theme(style=\"ticks\", palette = 'Set2')\n",
        "\n",
        "# Average Ratings by Traveller Type\n",
        "plt.subplot(2, 2, 1)\n",
        "sns.barplot(x=avg_rating_by_traveller_type.index, y=avg_rating_by_traveller_type.values , palette='GnBu')\n",
        "plt.title('Average Ratings by Traveller Type')\n",
        "plt.xlabel('Traveller Type')\n",
        "plt.ylabel('Average Rating')\n",
        "plt.xticks(rotation=0)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        },
        "id": "k-Y6bziLhROy",
        "outputId": "ce8f20bd-5197-405c-9ad4-c868595d3513"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-129-81f8c39e564c>:7: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=avg_rating_by_class.index, y=avg_rating_by_class.values  , palette='GnBu')\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1600x1000 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Visualization setup\n",
        "plt.figure(figsize=(16, 10))\n",
        "\n",
        "\n",
        "# Average Ratings by Traveller Class\n",
        "plt.subplot(2, 2, 1)\n",
        "sns.barplot(x=avg_rating_by_class.index, y=avg_rating_by_class.values  , palette='GnBu')\n",
        "plt.title('Average Ratings by Class')\n",
        "plt.xlabel('Class')\n",
        "plt.ylabel('Average Rating')\n",
        "plt.xticks(rotation=0)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r2zGHLCOlblo"
      },
      "source": [
        "# 2.\tEvaluate the influence of holiday booking time on customer behaviour."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZgO4bm5IsMtm",
        "outputId": "85db9aa4-b56c-435e-b356-ce607cd85189"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['18-Dec', '22-Apr', '16-May', '17-Apr', '22-Oct', '23-Feb',\n",
              "       '16-Aug', '17-Jun', '18-Oct', '15-Sep', '16-Mar', '15-May',\n",
              "       '18-May', '19-Sep', '18-Jun', '15-Dec', '99-Mar', '18-Aug',\n",
              "       '22-Nov', '17-Aug', '17-Mar', '19-May', '18-Sep', '17-Jan',\n",
              "       '19-Feb', '20-Jan', '20-May', '16-Sep', '02-Oct', '16-Jan',\n",
              "       '18-Jan', '19-Jan', '22-Jan', '23-Jan', '16-Feb', '17-Feb',\n",
              "       '18-Feb', '20-Feb', '18-Mar', '20-Mar', '22-Mar', '23-Mar',\n",
              "       '16-Apr', '18-Apr', '19-Apr', '20-Apr', '21-Apr', '23-Apr',\n",
              "       '17-May', '22-May', '23-May', '15-Jun', '19-Jun', '20-Jun',\n",
              "       '21-Jun', '22-Jun', '23-Jun', '15-Jul', '16-Jul', '17-Jul',\n",
              "       '18-Jul', '19-Jul', '15-Aug', '19-Aug', '21-Aug', '14-Sep',\n",
              "       '17-Sep', '21-Sep', '22-Sep', '15-Oct', '16-Oct', '17-Oct',\n",
              "       '19-Oct', '20-Oct', '15-Nov', '16-Nov', '17-Nov', '18-Nov',\n",
              "       '19-Nov', '21-Nov', '16-Dec', '22-Dec', '22-Jul', '15-Apr',\n",
              "       '12-Aug', '15-Jan', '15-Mar', '14-Aug', '14-Jul', '14-Oct',\n",
              "       '21-Feb', '22-Feb', '19-Mar', '16-Jun', '21-Jul', '22-Aug',\n",
              "       '21-Oct', '14-Nov', '17-Dec', '19-Dec', '14-Jun', '14-May',\n",
              "       '15-Feb', '14-Dec', '21-Jan', '20-Jul', '20-Nov', '20-Dec',\n",
              "       '21-Dec', '13-Sep', '12-Jul', '20-Sep'], dtype=object)"
            ]
          },
          "execution_count": 130,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# filtering the data to correctly include rows where 'Flying_month' is not missing\n",
        "correct_filtered_data = data_new[data_new['Flying_month'].notna()]\n",
        "\n",
        "\n",
        "# Now, let's check the unique values in the 'Flying_month' column again\n",
        "correct_unique_flying_months = correct_filtered_data['Flying_month'].unique()\n",
        "correct_unique_flying_months\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OZFrE3gwsHh-",
        "outputId": "ed52a837-8b7c-42f5-e5b5-d1f2490a09fa"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(Month_Num\n",
              " 1      64\n",
              " 2      58\n",
              " 3     100\n",
              " 4      83\n",
              " 5     105\n",
              " 6      98\n",
              " 7      79\n",
              " 8      96\n",
              " 9      97\n",
              " 10     74\n",
              " 11     89\n",
              " 12     95\n",
              " Name: count, dtype: int64,\n",
              " Month_Num\n",
              " 1     3.093750\n",
              " 2     3.120690\n",
              " 3     3.090000\n",
              " 4     2.819277\n",
              " 5     3.314286\n",
              " 6     2.612245\n",
              " 7     2.810127\n",
              " 8     3.312500\n",
              " 9     3.381443\n",
              " 10    3.594595\n",
              " 11    3.258427\n",
              " 12    3.294737\n",
              " Name: Rating, dtype: float64)"
            ]
          },
          "execution_count": 131,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import calendar\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# Extracting month information from the 'Flying_month' values\n",
        "\n",
        "# The month is the last three characters of the string\n",
        "correct_filtered_data['Month'] = correct_filtered_data['Flying_month'].str[-3:]\n",
        "\n",
        "# Map the abbreviated month names to their numerical representation\n",
        "month_to_num_corrected = {month: index for index, month in enumerate(calendar.month_abbr) if month}\n",
        "correct_filtered_data['Month_Num'] = correct_filtered_data['Month'].map(month_to_num_corrected)\n",
        "\n",
        "# Check the distribution of flights across different months and calculate average rating per month\n",
        "month_distribution_corrected = correct_filtered_data['Month_Num'].value_counts().sort_index()\n",
        "average_monthly_rating_corrected = correct_filtered_data.groupby('Month_Num')['Rating'].mean()\n",
        "\n",
        "month_distribution_corrected, average_monthly_rating_corrected\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "GcItdCaMqznZ",
        "outputId": "4e240f5a-13cc-4761-e760-6ddbd956623f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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kpo9rpwY1y9/wmI7NqqlT82rKNkvvLvlNicnpDqoWQEEQQAEAAAAAHO677Sf00tyflJyaoXo1yumdsW11V4VSNh07sm8jValQUhcS0jRj6U6ZzWaDqwVQUARQAAAAAACHMZvN+mLdfr3/xQ5lZpnVuvFdmjqylXxLedp8Di9PNz03uLncXF207c8zWr3lqIEVAygMBFAAAAAAAIfIyMzWB0t36vO1f0mS7u9YW88Pbi4Pd9d8n6tW1TJ6pFd9SdKn0X/q6OmEQq0VQOEigAIAAAAAGO5yaoZenrdVG389IRcXk0Y/0FjDejWQi4vJ7nOGta2pe+pXUkZmtt5auF1pVzILsWIAhYkACgAAAABgqHMXU/T8zB+169AFeXm6avLwYIWG1CjweU0mk54aEKRyPiV06vxlzV2xu+DFAjAEARQAAAAAwDAHT1zShBmbdeJsksr5lNC0J9uqeb1KhXZ+31KemvBQM5lM0vpf/tamHScL7dwACg8BFAAAAADAEL/8eUaRH27RpaQrqlHZR+8+1U41q/gW+vME1r5D/bv4S5Jmff2H/rmQXOjPAaBgCKAAAAAAAIUu5n9H9Ppn23QlPUtNAyrqrTFtdEcZL8Oe78GuAarvV06pVzL1zuLtysjMNuy5AOQfARQAAAAAoNBkZZs1b+VuffTf3co2S/cG363JjwbLu4S7oc/r6uqiCQ81Vykvdx08Ea/F3+4z9PkA5A8BFAAAAACgUKSlZ2ragl+0avMRSdLQHvU0pl9jubk65p+eFcp6adyAIEnS8h8O6be/zjrkeQHcHAEUAAAAAKDALiWl6cXZW/TznjNyc3XR84Obq19nf5lMJofWERJYWT1b+0mS/vPFTl1KTHPo8wPIGwEUAAAAAKBATpxN0oQZP+rA3/Eq7e2uqSNbqW1QFafVMzysgWpU9lH85St67/Mdys42O60WAFcRQAEAAAAA7Lb70AU9N/NHnbuYosrlS+qdce3UoGZ5p9bk4e6q54c0l6eHq34/eF7Lfzjk1HoAEEABAAAAAOz0/W8n9NLcn5ScmqG6d5fVO+PaqkqFUs4uS5JUrVJpPXFfoCRp0bf79Nfxi06uCLi9EUABAAAAAPLFbDbry/X79d7nO5SZZVbrxndp6qjW8i3l6ezSrHRtUV3tmlRRdrZZ7yz+TZdTM5xdEnDbIoACAAAAANgsIzNbHyzdqSVr/pIk3d+xtp4f3Fye7q5Oriw3k8mk0Q80VqVy3jp3MUVRy36X2cx6UIAzEEABAAAAAGxyOTVDr3y8VRt/PSEXkzT6/kYa1quBXFwc+013+VHSy13PD2kuVxeTtvxxWuu2HXd2ScBtiQAKAAAAAHBT5y6m6IWoH/XHwQsq4eGqyY+2VGgrP2eXZRP/6mU1JLSeJGnuij36+0yikysCbj8EUAD+H3v3HR5VmbYB/J6amfTegSSEBAiEJPQSQrNRBBHEhmVVbKiwNiy46qeCu2JDV8WCawdFEVBEpIQOAgmEFlIIaaT3ZPqc748UiQQIyWTOzOT+XddeJHNmzrnzbpzMPPO870tEREREdEkZeZV44t0dyC2qhbe7CksfHoMh/QLEjnVFbhgXifgoP+gNJvz7y4PQGUxiRyLqVliAIiIiIiIioos6cLwIz/x3NyprdQgLcseyx8aid6in2LGumFQqwcJbE+Dp5oSzRbX4dN0xsSMRdSssQBEREREREVGbNuzKxqsr90OnNyE+yg+vzx8DX0+12LE6zMtNhYW3JAAANu7Jwe6jhSInIuo+WIAiIiIiIiKiVkxmAZ/8fAwf/ZQGswBcPbwXXrh3BJxVCrGjdVpCtD9uHB8JAFi+OhUlFQ0iJyLqHliAIiIiIiIiohZavRGvf/Enft6RBQC4Y3I/zJ89CHKZ47x9vP26foju6YV6jQFvfH0IJpNZ7EhEDs9xnkGIiIiIiIioUyprtXjug93Ym3YOcpkUT94+GLMnRkEikYgdzaLkMimeuH0wnFVynMypwLe/p4sdicjhsQBFREREREREyCuuxZPv7sTp3Cq4OSvwygOjMDY+VOxYXSbQxwXzZ8UBAFZvOY0jGaXiBiJycCxAERERERERdXNpWWV4cvlOFFc0IMjHBf95dCxiInzEjtXlEuNDcNWwnhAE4M1vDqG6Tid2JCKHxQIUERERERFRN7btUB5e+GgP6jUG9O3lhf88mogQP1exY1nNvBkD0SPAFRU1Orz9XQoEQRA7EpFDYgGKiIiIiIioGxIEAd9tTseb3xyG0SRgdGwwXnlwNDxcncSOZlUqJzmemjsUCrkUB08WY93ObLEjETkkFqCIiIiIiIi6GaPJjHdXpeLr304BAGaOi8RTc4fASSETOZk4woLccc/1AwAAn284jsy8KnEDETkgFqCIiIiIiIi6kTqNAS9+vBd//JkLqQR48MZY3D0tBlKpY+10d6UmjwrDyIFBMJoE/Purg2jQGsSORORQWIAiIiIiIiLqJkoqGvD0eztxJKMMKqUMi+8ZgcmjwsWOZRMkEgkeuSkOvp5qnCurx4c/HhU7EpFDYQGKiIiIiIioG8jMq8IT7+5AblEtvN1VWPrwGAzpFyB2LJvi5qzEE7cNhlQCbDuUj60Hc8WOROQwWIAiIiIiIiJycAeOF2HRf3ehslaHsCB3vPHoWPQO9RQ7lk2KifDBLdf0BQB8sOYoCkrrRE5E5BhYgCIiIiIiInJgv+zKxqsr90OnNyE+yg+vzx8DPy+12LFs2uyJURjY2xdavQn//vIgDEaT2JGI7B4LUERERERERA7IbBbw6bpj+PCnNJgF4OrhvfDCvSPgrFKIHc3myaQSPH5bAtyclcguqMbnv5wQOxKR3WMBioiIiIiIyMFo9UYs/eJPrE3OAgDcMbkf5s8eBLmMbwHby8dDjQW3xAMA1u3IxoETRSInIrJvfPYhIiIiIiJyIFW1Ojz3wW7sTTsHuUyKJ24bjNkToyCRSMSOZneG9Q/E9YkRAIC3v01BebVG5ERE9osFKCIiIiIiIgeRV1yLJ97dgdO5VXBzVuCVB0YhKSFU7Fh27a6p/RER4oHaBj2WfX0YJrMgdiQiu8QCFBERERERkQNIyyrDU8t3oriiAYE+zvj3I4mIifARO5bdU8hleGruEKiUMqRlleGHLafFjkRkl1iAIiIiIiIisnPbD+XhhY/2oE5jQHQvL7zx6FiE+ruJHcthhPi54sEbYwEA3/yejuPZ5SInIrI/LEARERERERHZKUEQsGpzOpZ9cxhGk4BRsUF49cHR8HB1Ejuaw5kwpCfGDQ6F2Szgja8PobZBL3YkIrvCAhQREREREZEdMprMeHdVKr767RQAYOa4SDw9dyicFDKRkzmuB2fGIsjXBWVVGixfnQpB4HpQRO3FAhQREREREZGdqdcY8NLH+/DHn7mQSoAHZsbi7mkxkEq5011XclYp8NTtQyCXSbA37Rw27s0ROxKR3WABioiIiIiIyI6UVDbgqfd2IjWjFCqlDM//YzimjA4XO1a3EdnDE3dOiQEAfPLzMZwprBY5EZF9YAGKiIiIiIjITmTmVeGJd3Ygt6gW3u5OWPrwGAztHyh2rG5n+tgIDOkXAIPRjP98dRBanVHsSEQ2jwUoIiIiIiIiO3DgRBEW/XcXKmt16BXohjceTULvUE+xY3VLEokEC26Oh7e7E/KK6/Dxz8fEjkRk81iAIiIiIiIisnG/7D6DVz/bD53ehLgoP7w+PxF+XmqxY3VrHq5O+OetgyGRAL/vP4udKQViRyKyaSxAERERERER2SizWcCn647hwx+PwiwAVw3riX/dOwIuaoXY0QjAoD5+mD0xCgDw3g+pKCqvFzkRke1iAYqIiIiIiMgGafVGLP3iT6xNzgIAzL2uHx65KQ5yGd/G2ZJbr45GvzBvNGiNeOOrQzCazGJHIrJJfOYiIiIiIiKyMVW1Ojz/wR7sTTsHuUyKJ24bjJsmRUEikYgdjf5G1vT/j4tagfTcSny18aTYkYhsEgtQRERERERENiSvuBZPvLsD6bmVcFUr8MoDo5CUECp2LLoEf29nPHJTHABgzbZMHE4vETcQkQ1iAYqIiIiIiMhGHMsqw1PLd6K4ogGBPs74z6OJiInwETsWtcPo2GBcNzIMAPDWt4dRWasVNxCRjWEBioiIiIiIyAZsP5yPxR/tRZ3GgOheXnjj0bEI9XcTOxZdgXumD0CvQDdU1erw1jeHYTYLYkcishksQBEREREREYlIEASs2pyOZV83LmA9KjYIrz44Gh6uTmJHoyvkpJDhyblDoFTIkHK6FGuTM8WORGQzWIAiIiIiIiISidFkxvLVqfjqt1MAgBvGReLpuUPhpJCJnIw6qlegO+bNGAAA+OLXkzidWylyIiLbwAIUERERERGRCOo1Brz08T5sPpALqQR4YGYs/jEtBlIpd7qzd1cP74XRg4JhMgv495cHUa8xiB2JSHQsQBEREREREVlZSWUDnn5vJ1IzSqFSyvD8P4ZjyuhwsWORhUgkEsyfHQd/LzWKKxrw3x+OQBC4HhR1byxAERERERERWVFmfhWeeGcHzhbVwtvdCUseHoOh/QPFjkUW5qpW4Mnbh0AqlWBHagH+OJArdiQiUbEARUREREREZCUHThThmfd3obJWh16BbvjPo2MRGeopdizqIn3DvHH7tX0BAB+tTUNeca3IiYjEY9MFqOTkZNx+++0YMWIEBgwYgIkTJ2LJkiWorW3/f7R//PEHoqOjMXXq1C5MSkREREREdGm/7D6DVz/bD63ehLgoP7w+PxH+Xs5ix6IuduP4Pojr4wed3oR/f3kQeoNJ7EhEopCLHeBSqqqqEBsbi7lz58LT0xMZGRlYvnw5MjIy8Nlnn1328VqtFq+99hp8fX2tkJaIiIiIiOhCZrOAlRuOY21yFgDgqmE98dCsQZDLbLofgCxEKpVg4a0JeHTZNuScq8Fn64/jgZmxYscisjqbLkBNnz691ffDhw+HUqnE4sWLUVxcjICAgEs+/qOPPkJwcDBCQ0Nx7NixroxKRERERER0AZ3BhDe/OYQ9R88BAG6/ri9umhgFiYQ73XUn3u4qLLwlAS9+vA+/7D6DQX38MHJgkNixiKzK7krunp6eAACD4dLbWObm5mLlypV4/vnnrZCKiIiIiIiotapaHZ77YDf2HD0HuUyKx28bjDmToll86qYG9w3ADeMiAQDvrkpBaaVG5ERE1mXTHVDNTCYTjEYjMjMz8f7772PChAkIDQ295GNeffVVTJ8+HX379rVoFkEQ0NDQYNFzWptGo2n1L3UNjrP1cKyth2NtPRxr6+A4Ww/H2no41tZzqbEuLK3Hki9TUFKpgYtajidujUP/MC+7fy8hFkf5vZ6V1AtHM0qQVVCDf395AC/cPRgyG5qK6SjjbA8cZawFQWh3UV0iCILQxXk6bezYsSguLgYAJCYm4t1334Wz88UX69u6dSsWLVqE3377Dd7e3li0aBGOHTuGDRs2dCpHWloa9Hp9p85BRERERESOLadEh+92lEGrF+DlKsNt43zh664QOxbZiIpaIz7cWAy9UUDSADeMj/UQOxJRpyiVSgwcOPCy97OLDqgVK1ZAo9EgMzMTH3zwAR544AGsXLkSMpnsgvvqdDq89tpreOSRR+Dt7W3xLAqFApGRkRY/rzVpNBrk5OQgLCwMarVa7DgOi+NsPRxr6+FYWw/H2jo4ztbDsbYejrX1tDXWu46cw1fbjsNoEhAZ6oGnbouDh6tS5KT2z9F+rwUnXyz/4Rh2HK/FuGFR6B9u+feuHeFo42zLHGWsMzMz231fuyhANU+ji4+Px8CBAzF9+nRs3rwZ11577QX3/d///gepVIopU6agpqYGQON6UWazGTU1NVCpVFAqO/4HQCKRXLL7yp6o1WqH+VlsGcfZejjW1sOxth6OtXVwnK2HY209HGvrUKlUUKvVUKvVWL3lNL7aeAoAMHJgEB6/bTCcFBd+aE4d5yi/11eP7I2TZ2vwx5+5eG/Ncbzzz3HwcHUSO1YLRxlne2DvY30la9rZzmTTdoqOjoZCoUBubm6bx7Ozs3H27FmMHDkSQ4cOxdChQ7FhwwZkZWVh6NChWLNmjZUTExERERGRo9HqjVAoVQgKjYBC6YSMvCpsP5QPAJiR1BuL7hjK4hNd0v03DESInyvKq7V4d1Uq7GB1HKJOsYsOqPMdOXIEBoPhoouQ33fffbjhhhta3bZixQqcOXMGS5YsQVhYmBVSEhERERGRo9IbTFizLRPrd2ajXmOAi1qBqaPDsfThMUg9XYqkhEtvmEQEAConOZ6aOwSPv7MDB04UYf2ubFyf2FvsWERdxqYLUPPnz8eAAQMQHR0NlUqFU6dO4dNPP0V0dDQmTZoEAHj22Wexdu1anDhxAgDQu3dv9O7d+j/an376CcXFxRg+fLjVfwYiIiIiInIcWr0Ra7Zl4rvf01tuq9cYsOqP05BIJLhxgn2vF0vWFRHigX9Mi8GKtWlYuf4EYsJ90DvUU+xYRF3CpqfgxcbG4rfffsPjjz+Ohx56CGvWrMHs2bPxzTfftKzjZDabYTKZRE5KRERERETdgUwqxfqd2W0eW78rGzKpTb/FIhs0dUw4hscEwmgy4z9fHYRGZxQ7ElGXsOkOqHnz5mHevHmXvM/SpUuxdOnSy96HiIiIiIjoSgmCgLNFtTh8qhj5JXWYc1UU6jWGNu9brzGgQWuwqcWkyfZJJBI8Oicejy3bhoLSenz441EsvCVB7FhEFmfTBSgiIiIiIiJrq23QI/V0KQ6fKsHh9BJU1GgBAO4uSsybMRAuakWbRSgXtQLOKoW145IDcHdR4vHbBuO5D3Zj68E8xEf5YdzgHmLHIrIoFqCIiIiIiKhbM5kFZOZV4vCpEhxKL0FGbiXM521IplTIEBvpi4Rof+gMJlyfGIFvz1sDqtn1iREwmc1Q2PZKJ2SjBvT2xZyrovHt7+n475ojiOrlhWBfV7FjEVkMC1BERERERNTtVNRoWzqcUk+XoLahdUdTz0A3JET7Y3Bff/QP94FSIWs5NmtCHwDAuvN2wbs+MQKzJvRpdT+iKzVnUhSOZpbheHY5/vPlQfz7kbFQyFnQJMfAAhQRERERETk8g9GMkznlLUWnM4U1rY67qOSIi/JHQl9/JET7w9dTfdFzKRUyzBwfidkTo1DXoIOrsxNMZjOLT9RpMpkUT9w2GI8u24bM/Gp88esJ3HP9ALFjEVkEC1BEREREROSQisrrcTi9BIdPleBoZik0ur92z5ZIgMhQTyT09cfg6ABE9fSETNb+ThOVUo6GhgYU5p1BeHg4nJ2du+JHoG7I11ONR+fE49WVB7A2OQuD+vhhSL8AsWMRdRoLUERERERE5BC0OiOOZZfj0KliHD5VgsKy+lbHPV2dEB/th4S+AYiP8rPIbnVarbbT5yD6uxEDgjB1dDg27D6Dt749jHcfHwcfj4t35RHZAxagiIiIiIjILgmCgNzi2sZpdadKcCy7HEaTueW4TCpB3zBvDG6aVhce7AGpVCJiYqL2u3taDI6fKceZwhq8+c1hvHz/KMj4+0t2jAUoIiIiIiKyG3UaA46cLsWhU8VISS9BWXXrDiR/LzUS+gYgIdofg/r4wlmlECkpUecoFTI8NXcIFryVjKOZZVizNQM3TYoSOxZRh7EARURERERENstsFpCZX9WyllN6biXMZqHluFIuxYBIXwyO9kd8tD9C/V0hkbBLhBxDqL8bHrhhIN5ZlYqvN53CwN6+6BfuLXYsog5hAYqIiIiIiGxKZa0WKemlOHyqBCmnS1BTr291vEeAK+KjGxcPj+ntAyfuPkcObOLQnkg5XYodKQV44+uDeOef4+DqrBQ7FtEVYwGKiIiIiIhEZTSZcSqnAofTS3DoVAmyC6pbHVc7yREX5YeE6Ma1nPy9ueMcdR8SiQQPzxqE07mVKCpvwPLvU7HojqHs9CO7wwIUERERERFZXXFFQ9O0umIcySiDRmdsdbx3qAcSov0xuG8Aont5QS6TipSUSHzOKgWevH0Inlq+E3uOnsNv+87iupFhYsciuiIsQBERERERUZfTGUw4llXWuGNdegnyS+paHXd3UTZ2OPX1R1yUH7zcVCIlJbJNUT29cMfk/li54Tg+WZuG/mHe6BXkLnYsonZjAYqIiIiIiCxOEATkl9Th0KkSpKSX4FhWGfRGc8txqVSCvr28WopOvUM8IeUW80SXNCOpN45klOJwegn+/dVBLHtsLFRKvq0n+8DfVCIiIiIisoh6jaHlzfHh9BKUVmpaHff1UCGhbwAS+vpjUB8/uKoVIiUlsk9SqQQLb0nAo8u2IbeoFp/8fAzzZ8eJHYuoXViAIiIiIiKiDjGbBWQXVLcUnE7mVMBsFlqOK+RSxET4YHDfxsXDewS4ceFkok7ydHPCP29NwAsr9mLTvrOIi/LDmEEhYsciuiwWoIiIiIiIqN2q63RISS/BofTGqXXVdfpWx0P8XBq7nKL9MaC3D6cHEXWBuCh/3Di+D37YmoH3VqeiTw8vBHB3SLJx/GtAREREREQXZTKZcepsZcuOdVkF1RD+anKC2kmG2Eg/DO7rj/hofwT6uIgXlqgbue3avkjLKkP62Uq88dVBLHl4DHeLJJvGAhQREREREbVSUtmAlKZpdUdOl6Jea2x1PCLYA/HRfhjcNwB9w7yhkPNNL5G1yWVSPHHbYCx4cztOna3EN5tO4Y7J/cWORXRRLEARERHZKJWKW5ATkXXoDSYcyy5vnFp3qgR5xbWtjrs5KxAf1bhbXXy0P7zd+fxEZAsCfVww/6Y4vP7FQfywNQODIv0wKMpP7FhEbWIBioiIyMZo9UYolCoEhUZAoXSCVm/kGipEdEUuV8AWBAGFZfU4dKoYh0+VIC2rHHqDqeW4VAJE9fRCQt8ADO7rj96hnpBJuXg4kS0aMygEqSNKsWnfWSz75hDefXw8PN2cxI5FdAG+miUiIrIheoMJa7ZlYv3ObNRrDHBRK3B9YgRmTegDpUImdjwisnGXKmA3aA04mlmGw6caFxAvqWho9Vhvd1XjbnV9/TGojx/cnJVi/AhE1AH3Th+AE2cqkFdci7e/O4wX7hkBKYvGZGNYgCIiIrIRWr0Ra7Zl4rvf01tuq9cY8G3T9zPHR7ITioguqq0C9rTECNyQ1Bsf/nQUOw4XwGT+a/VwuUyC/uE+TUWnAPQKdINEwjesRPZIpZTjqblD8PjbyTh0qgQ/78jCDeMixY5F1ApfxRIREdkImVSK9Tuz2zy2bmc2Zk+MsnIiIrIXFytgf/d7OgSzgFEDg7HtYD6CfFyQ0NTlNLC3L9ROfDtA5CjCgtxx7/QB+O+ao/ji1xMY0NsHfXp4iR2LqAX/4hAREdmIeq0B9RpD28c0BjRoDfBw5ZoORHShSxWwN+w+gy/+dQ0+fnYiAn1crZyMiKzp2pFhSM0oxZ6j5/CfLw/h7X8mwVmlEDsWEQCA+6USERHZCBeVAi7qtl8kuqgVfAFJRBd1uQK2Rmdk8YmoG5BIJHhkdhz8vNQ4V16P//5wFIIgXP6BRFbAAhQREZGNMJnMmDo6vM1j1ydGwGQ2WzkREdkLFrCJqJmrsxJP3DYYUqkEySn52PJnntiR6CIut2Opo2EBioiIyEZkFVRhWmIE5kyKankj6aJW4JarozFrQh8uQE5EF2UymzFtDAvYRNSof7gPbr0mGgDw4U9HkV9SK3IiOl/rHUtV0OqNYkeyCr6SJSIisgGCIGDlhhOo1xjwxG2DMeeqKFTW6ODuokRlrRZKhUzsiERkw+o1BkwdEwFBaFzzqXkXvOsTIzBrQh8+hxB1Q7MmROFoRhmOZpbhP18ewhuPJUIh53OB2NrasbS7PFezAEVERGQDUk6XIv1sJZRyKbzdVTDodfglOQ1/pFQgupcXXrhnhNgRiciGrf7jNI5mluHhWYMw56po1DXo4OrsBJPZ7PBvaIiobTKpBP+8NQGPLtuO7MJqrNxwAvNmDBQ7Vrd2sR1Lv236fub4SIfueOcUPCIiIpEJgtDyQuTaUWHwcm9cDyDIQ0BNvR6HT5Wgpl4vZkQismHl1Rr8vj8X+SV1EATAoNeiMC8bBr3Wod/IENHl+XioseDmeADA+p3Z2H/snMiJuqfaBj0y8iohlUguumPpup3ZkEkdu0TDv0hEREQiO5JRipM5FVDKpbhxfJ+W2/08FOgV6IqzRXXYc7QQ144MEy8kEdmsH7dlwmgyo3+4Nwb09oFGo4FWqxU7FhHZiKH9AzF9bG/8vCML76xKwbuhnvD1VIsdy+FodUacK69HQWkdCkrrUFhaj8LSOhSU1qO2QY9egW5Y/I/hl9yxtEFrgIerk5WTWw8LUERERCISBKGl7fqakWHwdm+9G8ro2CCcLcpAcko+C1BEdIHKGi1+25sDAJhzVTQkEom4gYjIJt05pR+OZZchK78ab3x9CK8+OBoyKZ8vrpTRZEZxRUNTgamxuFTY9HVZ9aUL/xIJ4Ommgota0WYRqjvsWMoCFBERkYiOZpThxJkKKORS3Dg+8oLjowcG4JvfM3A8uxxlVRp+YklErfyUnAW90Yzonl6Ij/ITOw4R2SiFXIanbh+CBW9tx/HscqzenI5brukrdiybZDYLKKvW4FxpPQrK/upmKiitQ3FFA8xm4aKPdXNWINjPFSF+rgj2c0Gwb+PXQb4uUDvJodUbcX1iRMuHj+dr3rFU4cArJbEARUREJBJBEPDt5qbupxG94ONxYXHJ11ON/uHeOHGmAjtTC3DDuAuLVETUPVXX6bBxzxkAwJyrotj9RESXFOznigdvHIQ3vzmM7zanY2CkLwb09hU7ligEoXGdzeZOpsKy+lbT5vRG80Uf66SUIdjXpaXQFOLX+HWwryvcXZSXvK5KKcesCY3LLazjLnhERERkLWlZZTieXQ65TNryYqQtSQmhOHGmAskp+SxAEVGLn3dkQas3oXeoB4b0CxA7DhHZgfGDeyD1dCm2HszDsq8P4Z3Hx1+2aGLPGrQGFJbVt54uV9b49cXWYgIadxAM9HH5q5OpqdAU4ucKb3dVpwr+SoUMM8dHYvbEqG63YykLUERERCJpWfvpIt1PzUbHBmPFT2nIyq9GfkktQv3drBWRiGxUbYMeG3Y1dT9NYvcTEbXfAzNjcSqnAoVl9Xh3VQqeu3uYXT+HGIwmnCurb1Voau5sqqzVXfKxfl5qhPg2Fpkai02NXwd4OUMm67qpcCqlHA0NDSjMO4Pw8HA4Ozt32bVsCQtQREREIkjLKsOxrMt3PwGAh6sT4qL8cOhUCXakFOBWrtlA1O2t35kNjc6IsCB3DI8JEjsOEdkRtZMcT84dgiff3YH9x4vwy+4zmDomQuxYl2QyCyitbGhZi6mw7K91mUorG3CJZZng6eqEIN+/uplCmqbOBfq6wEnkrqPutmMpC1BEREQi+K6p++mq4T3btbB4UkIoDp0qQfLhfNxyNXe6IurOGrQGrNuZDQC4aVIUpNzJioiuUGSoJ+6eGoOPfz6Gz9YfR0yED8KDPUTNJAgCqmp1KDhvulxB0/pM58rqYTRdfF0mtZO8ZS2mED/XljWagv1c4ap27J3l7AkLUERERFZ2LKsMRzPLIJdJLtv91Gx4TCCUChkKy+qRlV+NyB6eXRuSiGzWhl1nUK8xINTfFaNig8WOQ0R2alpiBFIzSvHniWK8/sVBvL0wySrXrdMYGtdiOr/QVNb4vUZnuujj5DJpUyfTX9PlmotNnm5O/HDODrAARUREZGXNaz9dNawX/L3aN+ffWaXAsP4B2HWkEMkp+SxAEXVTGp0Ra5OzADSu/SRj9xMRdZBEIsFjc+Lx6LLtKCitw4q1abh3WjRUKlWnz60zNK7L1LLL3HlT56rr9Bd9nFQC+Hs7N+0q17rQ5Oup5nOenbNIAeq999675HGJRAInJycEBgZi6NChCAjgLh1ERNQ9Hc8u/6v7aWL7up+aJSWEYteRQuxIKcBdU2P4IoyoG9q45wxqG/QI8nVBYlyI2HGIyM55uDrh8dsS8OGPRzEsJhByhROCQiOgUDpBqzdCpbx4ycBkMqP4vHWZWopNZfUordRc8rre7k7ndTD9tTZToI8zFHLH3w2uu7JYAaq53U0QWq/+9ffbZTIZZs+ejRdeeAFSadetKk9ERGSLmtd+mji0Z7u7n5oN7usPF7UCFTVanMgux8BI366ISEQ2Sqs34qftjd1PN03s06U7NBFR9xEb6Yf/PDIWP23PxNvfpaBeY4CLWoHrEyMwa0If1GsMyCupbb0uU2kdisobYLrE6t8uakWrdZmad5sL8nWBs4rrMnVHFilAJScn4/7770e/fv0wd+5c9OzZEwBw9uxZfPXVV0hPT8dbb72FhoYG/O9//8OqVavg7++Phx56yBKXJyIisgsnz1QgNaMUMqkEsydGXfHjFXIZRg0MwuYDuUhOyWcBiqib2bTvLKrqdPD3dsa4wT3EjkNEDkKrN2Ltjiys+uN0y231GgO+/T0dZrOAyB6eeHXlgTYfq1TIzpsq54JgX9eWr91dlFyXiVqxSAHqpZdeQkREBJYsWdLq9piYGCxZsgQLFy7EsmXL8O6772Lp0qUoLy/Hzz//zAIUERF1K9/+fgpAY/dTgPeVdT81S0oIxeYDudh9pBD33xALhZwdEETdgd5gwo/bMgAAsyf0gZzdT0RkITKpFOubdtb8uw27z+DzxVcjqocn3F2dGjuZmgpNwX6u8PFQcSdOajeLFKD27duHJ5988qLHhw4dimXLlrV8n5SUhNdff90SlyYiIrILp3IqkHK6ufvpytZ+Ot+A3r7wdndCRY0OKeklGBYTaMGURGSrNh/IRUWNDr4eKkwcyu4nIrKceq0B9RpD28c0BugMJixbYJ0d8sixWeSjE6VSiaNHj170+JEjR6BQ/DXH02g0wtm5Y5/8EhER2aPmne8mDOmBQB+XDp9HJpVgTNPCw8mH8y2SjYhsm8Foxg9bG7ufbpzQhwv0EpFFuagUcFG3vSaTi1rB9ZrIYixSgJo6dSrWrl2L119/Hbm5uTCbzTCbzcjNzcXSpUuxbt06TJ06teX++/fvR2RkpCUuTUREZPPSz1bgcHoJpFIJbpp05Ws//V1SfCgAYN/xImh0xk6fj4hs29aDuSir0sDb3QlXD+8ldhwicjAmsxnXJ0a0eez6xAiYzGYrJyJHZZEpeE8++STKysqwcuVKfP755y2725nNZgiCgKuvvrplip5Op0NMTAwSEhIscWkiIiKb19L9NLhz3U/N+vTwRJCvC86V1WP/8SKMSwjt9DmJyDYZTWZ8v6Wx++mGcX2gVLD7iYgsS6WUY9aExuUB1u3MvmAXPD7vkKVYpADl5OSEt99+GydOnMDOnTtRUFAAAAgJCcGYMWMQExPT6r7z58+3xGWJiIhs3uncShw6ZbnuJwCQSCQYGx+CVZtPI/lwPgtQRA4s+XA+iisa4OGqxLUj2f1ERF1DqZBh5vhIzJ4YhboGHVydnWAym1l8IouySAGqWf/+/dG/f39LnpKIiMiuNXc/jUsIRZBv57ufmiXFh2LV5tNISS9BTb0e7i5Ki52biGyDySxgddO26DckRUKltOhLdyKiVlRKORoaGlCYdwbh4eFct5kszuJ/xerr61FTUwNBEC44FhwcbOnLERER2azTuZU4eLIYUgkwx0LdT816BLghIsQD2QXV2H20ENeNDLPo+YlIfDtTC1BYVg83ZwWuGxUmdhwi6ia0Wq3YEchBWaQApdPp8N577+GHH35AVVXVRe938uRJS1yOiIjILny3uan7aXAPBPu5Wvz8SfEhyC6oRvLhfBagiByM2Sxg9R+NzyHTx/bmLlRERGT3LFKAevHFF7F27VpMmjQJgwcPhoeHhyVOS0REZLcy86rw54mu6X5qlhgXipUbTuB4djlKKzXw81J3yXWIyPr2pBUir7gOLio5po5pe3cqIiIie2KRAtTmzZsxe/ZsvPzyy5Y4HRERkd1r7n4amxDaJd1PAODnpUZMhA+OZ5djZ2oBZo6P7JLrEJF1mc0CVm1uXPtpWmJvuKjZ/URERPZPaomTSCQSLj5ORETUJCu/CvuPF3Vp91OzpPgQAEBySn6XXoeIrOfAiSLknKuB2kmG68ey+4mIiByDRQpQEydOxJ49eyxxKiIiIrvX3P2UGBeKUH+3Lr3WqNhgyKQSZBdUI6+4tkuvRURdTxCElueQqWMi4ObMHS6JiMgxWKQA9dBDDyE/Px+LFy/GsWPHUFFRgaqqqgv+R0RE5OiyC6qx71gRJBJgzlVd2/0EAB6uToiP9gcA7Egp6PLrEVHXOnSqBFn51XBSyjB9bG+x4xAREVmMRdaAuvrqqwEAJ06cwA8//HDR+3EXPCIicnR/dT+FoEdA13Y/NUuKD8HBk8VITsnHrddEQyKRWOW6RGRZ53c/XTcyDB6uTiInIiIishyLFKAefvhhvtglIqJu70xhNfamnYNEAtx8VbTVrjt8QBCUChnOldUjI68KUT29rHZtIrKcIxmlSD9bCaVcipnjuKkAERE5FosUoB555BFLnIaIiMiuNXcujBlkve4nAFA7yTEiJhA7UguwI6WABSgiO/Vd085314wMg5e7SuQ0RERElmWRNaC6SnJyMm6//XaMGDECAwYMwMSJE7FkyRLU1l58kdW6ujosX74cs2bNwpAhQzBq1Cg88MADSE9Pt2JyIiLqbnLO1WDP0XNWW/vp78Y27Ya3MzUfJrNg9esTUeekZZXheHY55DIpbhzP7iciInI8HeqAeu+99yCRSPDggw9CKpXivffeu+xjJBIJHn744Su6TlVVFWJjYzF37lx4enoiIyMDy5cvR0ZGBj777LM2H1NYWIhVq1bhxhtvxIIFC6DT6fDZZ59hzpw5WLNmDXr35mKORERkec3dT6Nig9Er0N3q10/oGwBXtQIVNTocyyrDoD5+Vs9ARB23quk55KphPeHjoRY5DRERkeV1qgB13333QalUdlkBavr06a2+Hz58OJRKJRYvXozi4mIEBARc8JjQ0FBs3rwZavVff7hHjBiBCRMm4JtvvsHixYuvKAMREdHlnD1Xg91HCgFYd+2n8ynkUoweFIxN+85iR0oBC1BEduTkmQocySiDTCrBrAl9xI5DRETUJTpUgDp16tQlv+9Knp6eAACDwdDmcWdn5wtuc3FxQc+ePVFSUtKV0YiIqJtq7n4aHRuMsCDrdz81Gxsfgk37zmL30UI8MHMgFHKZaFmIqP2++6PxOWTCkB7w977wtSwREZEjsMgi5F3NZDLBaDQiMzMT77//PiZMmIDQ0NB2P76mpgYZGRkYNWpUp7MIgoCGhoZOn0dMGo2m1b/UNTjO1sOxth6O9YXyS+qw+2hj99OMxJ4W+xvRkbGOCHSGl5sTKmt12HskD0P6+VskiyPj77T1cKzblplfjcOnSiCVSjB1dA+LPIdwrK2HY209HGvr4Dhbj6OMtSAIkEgk7bqvRBCETq9U2q9fP/z73//GtGnT2jz+66+/4vHHH8fJkyc7dP6xY8eiuLgYAJCYmIh33323zU6ni1m8eDE2bNiAjRs3IjAwsEMZACAtLQ16vb7DjyciIsfzw+5yHDurQb8easxJ9BE7DjYdrsLeU3WI6anG7DHi5yGiS/smuQynC7QYFO6MG0Z6ix2HiIjoiimVSgwcOPCy97NIB9Tlalgmk6ndFbG2rFixAhqNBpmZmfjggw/wwAMPYOXKlZDJLj+1YM2aNVi9ejWWLl3aqeJTM4VCgchI+96ZRKPRICcnB2FhYa3WyiLL4jhbD8faejjWreWX1OF4bj4A4K5pgxAW5Gaxc3d0rJXu1dh76gAyzukQHtEHKie7aHYWDX+nrYdjfaGcc7U4XZAPiQS4a1ocgv1cLHJejrX1cKyth2NtHRxn63GUsc7MzGz3fS32qvRiBaa6ujrs2rULXl5eHT533759AQDx8fEYOHAgpk+fjs2bN+Paa6+95OOSk5Pxwgsv4KGHHsINN9zQ4eufTyKRXFH3lS1Tq9UO87PYMo6z9XCsrYdj3WjdrpMQBGDEgED0733hxhiWcKVjPSBSjWBfFxSW1eNodjXGDe7RJbkcDX+nrYdj/Zefdx0DACQOCkFkL8tvHMCxth6OtfVwrK2D42w99j7WV9Js1OEC1HvvvYf333+/5YJPPvkknnzyyTbvKwgC5s6d29FLtRIdHQ2FQoHc3NxL3i81NRWPPfYYZsyYgccee8wi1yYiImqWV1yLHamN3U9i7XzXFolEgqSEUHz7ezqSUwpYgCKyUWfP1WDP0XMAgJsmRYmchoiIqOt1uAA1cOBA3HrrrRAEAd988w1Gjx6NsLCwVveRSCRQq9WIiYnB1Vdf3dmsAIAjR47AYDBcchHyzMxM3H///RgxYgReeukli1yXiIjofKv/OA1BAIbHBKJ3qKfYcVoZGx+Cb39PR0p6CarrdPBwdRI7EhH9zeo/TgMARsUGoZeIu2cSERFZS4cLUElJSUhKSgLQOHfx5ptvxqBBgywWDADmz5+PAQMGIDo6GiqVCqdOncKnn36K6OhoTJo0CQDw7LPPYu3atThx4gQAoLy8HPfccw+cnJxw55134tixYy3nc3V1tfv1m4iISHwFpXXYkdLU/XS17XQ/NQv1d0PvUA9k5Vdjz9FCXDcqXOxIRHSe/JJa7DxSAACYM8n2nkOIiIi6gkXWgFqyZIklTnOB2NhY/Prrr1ixYgUEQUBISAhmz56Ne+65B0qlEgBgNpthMplaHpOZmYmioiIAwF133dXqfMOGDcOXX37ZJVmJiKj7WLU5HWYBGNY/EJE21v3ULCk+FFn51UhOKWABisjGfL8lA0LTc0hEiIfYcYiIiKzColvjFBUV4cSJE6itrW1zZ7wZM2Zc0fnmzZuHefPmXfI+S5cuxdKlS1u+Hz58ONLT06/oOkRERO1VWFqH5MON3U+32GD3U7PEuBCs3HAcx7PLUVLZAH8v+13cksiRnCurx/am55A5V3HtJyIi6j4sUoDS6XR4+umn8fvvv8NsNkMikbQUoM5fEf1KC1BERES2ZtUfp2EWgCH9AhDZw1PsOBfl66lGTIQPjmWVY1dqAWaO7yN2JCIC8P2W0zCbBST09UdUz47vEk1ERGRvpJY4yZtvvonNmzdjwYIF+PLLLyEIApYuXYrPPvsMY8eORd++ffHzzz9b4lJERESiKSyra+lcsOXup2ZJ8Y0bdiQfLhA5CREBQElFA7YezAMA3My1n4iIqJuxSAFq06ZNmDlzJubNm9eyyHdAQABGjRqFjz76CG5ubvj6668tcSkiIiLRrP6jsXNhSL8Au+hcGBUbDJlUguzCauQV14odh6jb+2FrBkxmAYP6+KJfuLfYcYiIiKzKIgWo8vJyxMbGAgBUKhWAxp3xml1zzTXYvHmzJS5FREQkinNl9dh2yH66nwDA3UWJhL7+AIDkpl37iEgc5dUabD6QCwCYc5V9PIcQERFZkkUKUL6+vqisrAQAqNVqeHh44MyZMy3H6+rqoNPpLHEpIiIiUdjrui3N0/B2HC5oc4MQIrKONdsyYTSZERPhg4G9fcWOQ0REZHUWWYQ8NjYWhw8fbvl+/Pjx+PTTT+Hn5wez2YzPP/8ccXFxlrgUERGR1RWV17es22Iv3U/NhscEwkkpw7nyemTkVdlV8YzIUVTWaLFpbw4AYM4k7nxHRETdk0U6oObOnYvQ0FDo9XoAwGOPPQY3Nzc89dRTWLRoEdzc3PDcc89Z4lJERERW9/2WxnVb4qP80LeXfa3bonKSY3hMIABOwyMSy4/bM6E3mhHdywtxUX5ixyEiIhKFRTqghgwZgiFDhrR8HxQUhI0bN+L06dOQSqWIiIiAXG6RSxEREVlVcUUDtvzZuG7LLVf3FTlNxyQlhGJHSgF2phTgH9MGQCaViB2JqNuortNhY1P3081XRUMi4X9/RETUPVmkA6rNE0ul6Nu3L6KioiCRSLB27dquuhQREVGX+X7LaZjMAuKi/Ox216r4KH+4qhWorNXhWGaZ2HGIupWfd2RBpzchMtQDg5s2BSAiIuqOuqwABQBarRZffPEFrrrqKjzzzDNdeSkiIiKLK6lowB8Hmruf7Gvtp/Mp5FKMHhQMgNPwiKyptkGPDbuyATTufMfuJyIi6s46VYD6/vvvMXXqVMTGxmLMmDF45ZVXoNfrIQgCPv/8c4wfPx6vvfYaXFxcsGTJEktlJiIisorvtzau/TSojy/6h/uIHadTkhIad8Pbc7QQBqNJ5DRE3cO6HdnQ6EwIC3JvWYuNiIiou+rwwkxr167F4sWL4ezsjKioKBQVFeHrr7+GRqNBTU0NNm/ejKFDh+K+++7D2LFjLZmZiIioy5VUNuCPA2cB2O/aT+eLCfeBj4cK5dVaHDxZgpEDg8SOROTQ6jUGrN+ZBQCYc1UUu5+IiKjb63AB6uuvv0Z4eDi+/vpreHt7w2Qy4ZlnnsGaNWvg4eGBjz76CElJSZbMSkREZDU/bM2A0SQgNtIXMRH23f0EAFKpBIlxIVibnIUdKfksQBF1sQ27slGvNaJHgBtGDQwWOw4REZHoOjwFLzMzE7Nnz4a3d+OCrDKZDPfddx8A4MEHH2TxiYiI7FZppQab9zd2P91sx2s//V3zNLwDx4vQoDWInIbIcTVoDfh5R2P3002ToiDlzpNEREQdL0BpNBr4+fm1us3X1xcA0KdPn86lIiIiEtEPW0/DaBIwoLcPBvb2FTuOxfQO8UCInwv0RjP2HSsSOw6Rw9q4Jwe1DQYE+7ogMS5E7DhEREQ2oVOLkF9sLrtMJuvMaYmIiERTVqXB7/sbd7671QHWfjqfRCJBUnxjF9QO7oZH1CW0eiN+Ss4E0Nj9JGP3ExEREYBOrAEFAJ999hk2bNjQ8r3RaAQAvP322/D09Gx1X4lEgg8++KAzlyOyOyqVSuwIRHSF1mzNgNFkRkyEDwZGOk73U7OxCaH45vd0pJwuRXWdDh6uTmJHInIov+09i+o6PQK8nVumvRIREVEnClDBwcGoqqpCVVXVBbeXlJSgpKSk1e3c+YO6E63eCIVShaDQCCiUTtDqjVApO1XvJSIrKK/WYNP+5p3vHGftp/OF+LkiMtQDmfnV2HWkEFNGh4sdichh6A0m/LQ9AwAwe2IfyGWdmmxARETkUDr8jnjr1q2WzEHkMPQGE9Zsy8T6ndmo1xjgolbg+sQIzJrQB0oFp6cS2bI12zJhMJrRP9wbsQ7Y/dQsKSEUmfnV2JGSzwIUkQVt3n8WFTU6+HqqMWFIT7HjEBER2RR+LENkQVq9Ed9vzcB3v6ejXtO4w1S9xoBvf0/HD1szoNUbRU5IRBdTXq3Bb3tzADR2Pzly525iXAgkEuDEmQqUVDSIHYfIIRiMJvywtbH7adaEPlDI+TKbiIjofPzLSGQBJrOAihoNZFIJ1u/MbvM+63ZmQyaVwmQWrJyOiNrjx6bup35h3hjUx+/yD7BjPh5qDIho7PDakVogchoix7DlzzyUVWvh7e6Eq4ax+4mIiOjvuCgN0WUYTWZUVGtRVq1BeVXjv+d/XV6lQUWtDj38XbH4H8NbOp/+rl5jQEWNFq+t3I8GnRH+Xs4I8HaGv7cz/L3U8Pdq/NrHQ80dc4isrKJG2226n5olJYQgLasMO1LyMWtCH7HjENk1o8mM75u6n2aO55R7IiKitrAARd2a3mBCefVfhaSyam3Tv399XVWng9COpqXqej083Jzgola0WYRyUSvg4apEWbUWNfV6FJW3Pe1FJpXA11PdWJxqKkoFeLNARdSVftyWCb3RjL69vBAX5djdT81GxQbjwx+P4kxhDc4W1aBXoLvYkYjs1vZDeSipaICnqxOuGdFL7DhEREQ2iQUoclhanRHlNVqUVWlQXq1BWVXrLqbyag2q6/TtOpdcJoGPhxq+nmr4eKjg66GGj2fjv823ebqpYDCacH1iBL79Pf2Cc1yfGAFBAN755zgUVzSgpLIBJRUN532tQWlVA4wmAcVNt7flsgUqdxVk3HWHqN0qa7XY2NL91LdbdD8BgJuzEgnRAThwogg7Ugow9zoWoIg6wmQyY/WWxu6nG8b15q63REREF8G/kGSXGrQGlLXqWNI2FZk0jR1NVRrUXWQq3N8p5VL4ep5XXPJUNxabPFTw8VTD10MNdxclpO3oOpIp5S1TWdZdZBc8tZMcvp5qxMDngsebzAIqa7QXFKhKKzUormxAaeXlC1TS5gKVlzP8vZv/bSpUeTnDx4MFKqLz/bgtE3qDCdE9vRAf3T26n5olJYQ0FaDycfu13af4RmRJO1MLcK6sHm7OSlw3irtKEhERXYxFC1B6vR7Hjx9HeXk5EhIS4O3tbcnTUzcgCALqNQaUVbfuXCqv1qD0vO81uvbtJqdSyhqLS+d1LDUWlf4qNLk5Kyz6pkupkGHm+EjMnhiFugYdXJ2dYDKb27UeRHN306UKVFW1TQWqigYUN3VONX/dXKAqaTqOrAuvwQIV0V8qa7X4dU8OAODmbrL20/mG9Q+ESilDUXkDTudWIroX/24TXQmTWcDqLacBADOSekPtxM92iYiILsZifyW/+OILvPfee6itrQUAfPbZZxg5ciQqKipw3XXX4cknn8SsWbMsdTnqJJVKZfVrCoKAmnr9X11Kf+tYKm9ad0mnN7XrfC5qRasupfO/bi42OavkoryhVCnlaGhoQGHeGYSHh8PZ2dki55VJG6cC+nio0T/8wgKV2Syg0oIFKj+vv6b6NS+Y7ssCFTmQn7ZnQW8woU8PTwzu6y92HKtTOckxPCYIySn5SE4pYAGK6ArtOVqIvOI6uKgVmDqG3U9ERESXYpEC1Jo1a/Daa69hypQpGD16NJ599tmWY97e3hgxYgR+/fVXFqBsgFZvhEKpQlBoBBRKJ2j1RousVWA2C6iu0zUVldpYd6m6sdBkMJrbdT43ZyV8PVUt6y75ejR//ddt9vApo1arter1pO0sUJVUaJqKU41T/ZoLViWVGhhN5r8KVBe5hq+Hqmn3PmcWqMhuVdXq8OueMwCAW6/pvtPPkhJCkJySj52pBbjn+gHc5IConcxmAav/aOx+uj4xAs4qhciJiIiIbJtF3sGvXLkSEydOxLJly1BZWXnB8ZiYGHz55ZeWuBR1gt5gwpptmVh/kbWJLsZkMqOyVtdqAe+/dy6VV2thMrdjqzgAnm5O5xWU/lp3qblzycdDDSduX9wlzi9Q9Qu/sNOh3QWqSg1KKjUAytu8xoUFKnXL976easi7oEAlRlcf2be1yZnQ6U2I7KbdT83io/3h5qxEVa0OaZmliIvqvmNBdCX2Hy9CzrkaqJ3kuD4xQuw4RERENs8iBaizZ89i7ty5Fz3u6emJqqoqS1yKOkirN2LNtkx8d97ubPUaQ8tubVeP6IUT2eUt6y2dX2yqrNGiPbUliQTwclNdpHOpsdDk46GCQs7ikq260gJVaaviVAOKK9pRoJIAPp7qv3VPdbxA1VVdfeTYqut0+GV3Y/fTLd1w7afzyWVSjB4UjN/25iD5cAELUETtIAgCvtvc+Bpq6phwuDorRU5ERERk+yzyLs3d3b3NzqdmmZmZ8PPrXjsL2RqZVIr1O7PbPLZuZzZmjovERz+loaZe3+Z9GgsTTYt4n79TXPPC3h5qeLk7dUlnC9mO9hSoqup0Lbv3lfytQFVSqYHBaEZppQallRocz+5cgaqjXX1Ea5OzoNWbEBnqgaH9AsSOI7qk+BD8tjcHe9MK8eCNsfzvh+gyDp4sRnZBNVRKGaaP7S12HCIiIrtgkQLU2LFjsXr1atx6660XHMvIyMD333+PG2+80RKXog6q1xpQrzG0fUxjQE2DHkP6BcBoMrdaxLt5RzYPVyeuC0KXJZVK4O2ugre7Cn3DrrRApUFJZUO7ClTeHmo8fmsCjpwuxXdN628Arbv6Zo6PZCcUtam6TocNuxoL8jdf1b27n5r1D/eBr4cKZdVaHDpVjJEDg8WORGSzBEHAqs2Nf3uuGxUOD1cnkRMRERHZB4u8O1uwYAFuuukmTJ06FePHj4dEIsHatWuxZs0a/P777/Dz88NDDz1kiUtRB7moFHBRK9osQrmoFfByU2HhLQkiJKPupD0Fquo6Xcv6U8XNhanzClYGoxl6gwmRoZ54ZeWBNq+zbmc2Zk+M6uofh+zUzzsau58iQjwwLCZQ7Dg2QSqVIDE+FD9tz0Ty4QIWoIguIeV0KdJzK6GUS3HDOHY/ERERtZdFClABAQH48ccf8eabb2Ljxo0QBAE///wzXFxcMGXKFDzxxBPw9ubWzmIymc24PjGipTvkfNcnRsBkNkMBTp8jcUmlEni5q+DlrkLfNraDby5QVdXp0KA1XrKrr0Fr4KfSdIGaen1L91N3X/vp75LiQ/DT9kz8eaIIDVoDd/QiakNj91Pja6lrR4bBy40bYBAREbWXxean+Pj44NVXX8Wrr76KiooKmM1meHt7QyplUcMWqJRyzJrQB0BjdwjXyyF7dH6BymA0X7Krj2+eqS0/78iCRmdCRLAHhrP7qZWIEA+E+rsiv6QO+46dw4QhPcWORGRzjmWV48SZCshlUswcHyl2HCIiIrvSJQuksNvJNikVMswcH4nZE6NQ16CDq7MTTGYzi09kly7V1Td1dDg0OiMUcu5KRH+pbdC3bMZw89VR7H76G4lEgrHxofhm0ykkHy5gAYqoDc073109vCd8PNQipyEiIrIvFilAvffee5c8LpFI4OTkhMDAQAwdOhQBAdxxSCwqpRwNDQ0ozDuD8PBwODs7ix2JqEMu1tU3dXQ4piVG4M1vDmHhLQmchkctGrufjAgLcsfwmCCx49ikpPgQfLPpFFIzSlFVq4OnG//7IWp24kw5jmaWQS6T4Mamvz9ERETUfhYrQDV/kiwIQqtjf79dJpNh9uzZeOGFFzg9T0RarVbsCESd1lZXn1ZnxJIv/kRaZhn+79P9eOWBUVA5cTe87q6uVfdTNKTc1bNNwX6u6NPDExl5Vdh9pABTxkSIHYnIZjTvfDdhSE/4e/EDPCIioitlkQpQcnIyoqOjMWPGDPz44484ePAgDh48iDVr1mDGjBno168fNm3ahJ9++gnTpk3DqlWr8OGHH1ri0kTUzamUchj0WhTmZcOg18LNRYkHZ8bCzVmB9NxKLPniTxhNZrFjksh+3pGNBq0RvQLdMHIAu58uZWx8KAAgOaVA5CREtuN0biUOp5dAKpVg9kR2PxEREXWERQpQL730EiIiIrBkyRL0798frq6ucHV1RUxMDJYsWYJevXph2bJl6NevH5YuXYoxY8bg559/tsSliYgAtO7q6xHghn/dOwJOShkOnyrBO6tSYDYLl3g0ObI6jQHrd2YBAG65ui+7ny4jMS4YEglwMqcCxRUNYschsgnNaz+NSwhFoI+LyGmIiIjsk0UKUPv27cPQoUMvenzo0KHYvXt3y/dJSUkoLCy0xKWJiNoU3csbi+4YCplUgu2H8rFyw3GxI5FI1u/IQn1z99NAdj9djo+HGgN7+wIAdqTki5yGSHxZ+VX480QxpBLgpklRYschIiKyWxYpQCmVShw9evSix48cOQKF4q8t0Y1GIxe/JqIuN6RfAB6dEw8AWJuchR+3ZYiciKytXmPAz01rP825ims/tVfzNLwdnIZHhFV/NK79NCYuBCF+riKnISIisl8WKUBNnToVa9euxeuvv47c3FyYzWaYzWbk5uZi6dKlWLduHaZOndpy//379yMyMtISlyYiuqQJQ3rgH9NiAAArN5zAlj9zRU5E1rR+V+MOiT0C3DA6NljsOHZjdGwQ5DIJcs7V4Oy5GrHjEIkm51wN9qadg4TdT0RERJ1mka2hnnzySZSVlWHlypX4/PPPW3a3M5vNEAQBV199NZ588kkAgE6nQ0xMDBISEixxaSKiy7phXCSqanX4cXsm3l2dCncXJYb2DxQ7FnWxeo0Ba5Mb1366+aoodj9dAVdnJQb3DcD+40VITsnHHUH9xY5EJIrVTd1PowYGo1egu8hpiIiI7JtFClBOTk54++23ceLECezcuRMFBY0t+yEhIRgzZgxiYmJa3Xf+/PmWuCwRUbvdOaU/qup02HowD0u/OIhXHxiFvmHeYseiLrShqfsp1N8VoweFiB3H7iTFh2L/8SLsSCnA3Ov6QSJhAY+6l7ziWuw60viads5V7H4iIiLqLIsUoJr1798f/fvzU1Iisj1SqQSP3BSHmno9Dp4sxkuf7MPr88egJz/RdkgN2vO7n6IhY/fTFRsaEwCVUobiigak51aiby8WbKl7+X7LaQgCMDwmEOHBHmLHISIisnsWWQOKiMgeyGVSPD13CKJ7eaFOY8C/VuxFaaVG7FjUBTbsOoO6pu6nMXHsfuoIlVKOEU27BiYf5m541L0UltW1/N7ffFW0yGmIiIgcg8UKUMnJybj77rsxfPhw9O/fH/369bvgf0REYlM5yfHCPSPQI8AVZdVa/OvjPaip14sdiyyosfspEwAwZ1IUu586IalpN7xdqYUwmcwipyGynh+2ZMAsAIP7+iOyh6fYcYiIiByCRQpQmzZtwgMPPICysjJMnjwZZrMZU6ZMweTJk6FSqRAdHY2HH37YEpciIuo0dxclXrpvFHw9VMgrrsPLn+6DVmcUOxZZyC+7z6C2wYAQPxckNhVQqGPiovzg5qxEVZ0ORzPLxI5DZBXFFQ3YejAPALufiIiILMkiBaiPPvoIsbGxWLt2LR555BEAwI033ohly5Zh/fr1KC0tRWgo3wQQke3w81LjpXkj4apWIP1sJV7/8iCM7PCwexqdET9tb1z76aZJXPups+QyKcbEBQMAklM4DY+6hx+2ZsBkFhDXx4+bVRAREVmQRQpQWVlZmDx5MmQyGeTyxnXNjcbGboLQ0FDccsst+Pjjjy1xKSIii+kZ6I5/3TsCSoUMB08WY/nqVJjNgtixqBMau5/0CPJ1QVI8136yhOZpeHvTzkFvMImchqhrlVVp8MeBXADc+Y6IiMjSLFKAUqlUUCgUAAB3d3colUqUlpa2HPf19UV+Pj85JSLb0zfMG4vuGAKpVIKtB/Pwv19OiB2JOqix+6lx7aebr4qCTMZ9NiyhX5g3fD3VaNAacfBksdhxiLrUmm0ZMJrMiInwwYDevmLHISIicigWeXUeHh6OrKyslu/79euHn3/+GUajETqdDhs2bEBQUJAlLkVEZHFD+wfisTlxAIAft2e2FDHIvmzccwY19c3dT5z2bSlSqaSlm4zT8MiRVdRosWnfWQDALVz7iYiIyOIsUoC66qqrsGXLFuj1jTtJPfDAAzhw4ACGDh2KESNG4ODBg5g3b54lLkVE1CUmDOmJu6f2BwB8tv54ywK0ZB+0OiN+bCoc3jSR3U+WNrapoPfniWLUawwipyHqGj9tz4TBaEbfXl6I7cPuJyIiIkuTW+Ik99xzD+65556W78ePH48vv/wSv//+O2QyGZKSkjBixAhLXIqIqMvMHN8HlbU6rE3OwrurUuDuosSQfgFix6J22Lg3B9V1egT6OGP8YHY/WVp4sDt6BLgir7gO+46dw8ShPcWORGRR1XU6bNybAwCYc1U0JBJuYEBERGRpnS5A6fV67Ny5EyEhIejbt2/L7UOGDMGQIUM6e3oiIqu6e2oMqup02H4oH0u/+BOvPDAKfXtxFyRbptUb8eM2dj91JYlEgqT4UHz12ykkH85nAYocztrkLOj0JkT28MTgvv5ixyEiInJInX6VrlAo8NhjjyElJcUSeYiIRCWVSvDYnHgM7usPnd6Elz/Zh7ziWrFj0SX8tjcHVXU6BHg7Y/yQHmLHcViJTetAHckoRWWtVuQ0RJZTU6/HL7uzAQA3T4pi9xMREVEX6XQBSiKRICwsDJWVlZbIQ0QkOrlMikV3DEV0Ty/UNhjwwoq9KKvSiB2L2qDVG7GmuftpUhTk7H7qMsG+rojq6QmzAOw+Uih2HCKLWbczCxqdCeHB7hgWEyh2HCIiIodlkVfq999/P77++mtkZ2db4nRERKJTOcnxwr0jEOrvirIqDV5YsRe1DXqxY9HfbNp3FlW1Ovh7O2MCu5+6XPPugsmHuRseOYY6jQEbdja+fuXaT0RERF3LIouQHzlyBJ6enpg2bRqGDRuGkJAQqFSqC+73/PPPW+JyRERW4e6ixEvzRuKp5TuRV1yL//t0P16+fyRUSos8dVIn6QwmrNmaAQC4aWIfdj9ZwZi4EHy67hhOna1EUXk9An1cxI5E1CkbdmWjXmtEz0A3jBwQJHYcIiIih2aRd1FfffVVy9d79+5t8z4SiYQFKCKyO/5eznhp3kg8/d4unMypwL+/PIhn7xrGYocN2LQvB5W1Ovh5qTFhCBfFtgZvdxUGRvriSEYZdqYWYPbEKLEjEXVYg9aAdTuyADRuYCCVsvuJiIioK1mkAHXq1ClLnOYCycnJ+Pjjj5GZmYm6ujoEBARg0qRJmD9/Ptzc3C752O+//x6ffPIJCgsLER4ejoULF2L8+PFdkpOIHFuvQHe8cM9wLP5wD/48UYz3vk/FY3PiOVVDRPrzup9mT4yCQs6CoLUkxYfiSEYZkg/nswBFdu3XPTmobTAgxM8FY+JCxI5DRETk8Gz6FXtVVRViY2Px0ksv4dNPP8Xdd9+NtWvX4rHHHrvk43755RcsXrwY1113HT7++GPExcVh/vz5SE1NtU5wInI4/cN98PQdQyGVSrDlzzz875cTYkfq1jbtO4uKGh18PdWYNJTdT9Y0MjYYcpkUZ4tqkXOuRuw4RB2i1Rnx0/a/NjCQsfuJiIioy1l0IZPU1FTs378f5eXluPXWWxEWFgaNRoPs7GyEhYXBxeXK1oqYPn16q++HDx8OpVKJxYsXo7i4GAEBAW0+7t1338WUKVOwYMECAMCIESNw+vRpvP/++/j444879LMREQ2LCcQjswfhnVWpWLMtE55uKsxI6i12rG5HbzDhh/PWfmL3k3W5qhUY0s8f+44VYUdKPsKC+osdieiK/bYvBzX1egT6OLcsrk9ERERdyyKv2vV6PebPn49bbrkFb731Fr788kucO3eu8QJSKf7xj3/giy++sMSl4OnpCQAwGAxtHs/Ly0NOTg6uu+66VrdPnjwZe/fuhV7PXayIqOMmDeuFO6c0vuH+dN0xbD+UJ3Ki7mfz/rOoqNHC10OFScPY/SSGpIS/dsMTBEHkNERXRmcw4cdtjd1PsyZEQcY1/YiIiKzCIh1Q77zzDrZv344XX3wRw4cPx7XXXttyzMnJCddeey22bNmCBx98sEPnN5lMMBqNyMzMxPvvv48JEyYgNLTtT6uysxu30g0PD291e+/evWEwGJCXl4fevTvesSAIAhoaGjr8eFug0Wha/Utdg+NsPdYe6+uGB6O0og6/7s3F29+lQCkXENfH1yrXFpvYv9cGoxnfbzkNALg+MQwGvQ4GB/1cQeyxvpQBYe5QKWUoqdQgNf0cont6ih2pw2x5nB2NrYz1xn25qKzVwddDhZH9fez+dV1bbGWsuwOOtfVwrK2D42w9jjLWgiC0e21cixSgfvnlF9x8882YM2cOKisrLzjeu3dv/Pbbbx0+//jx41FcXAwASExMxLJlyy563+rqagCAu7t7q9ubv28+3lEGgwEnT57s1DlsRU5OjtgRugWOs/VYc6yHhAnIK1Qj7awGb3yTijsn+CHUV2m164tNrN/rPzPqUF6jg5tahmCXWod5Pr4UW30OiQp2wtGcBmxIPgHzEC+x43SarY6zIxJzrI0mAWu2FgEAhkepkJGRLloWa+DvtfVwrK2HY20dHGfrcYSxVirb9z7IIgWo8vJyREdHX/S4TCaDVqvt8PlXrFgBjUaDzMxMfPDBB3jggQewcuVKyGSyDp+zoxQKBSIjI61+XUvSaDTIyclBWFgY1Gq12HEcFsfZesQa6+goM/79dSqOZJZj1a5KvHzvUAT7Xdlad/ZGzN9rg9GM5Rt2AQBmTYjEwAGOPf3O1p9DpsjLcDQnBacK9Fhwa7TdTmOy9XF2JLYw1pv/zEetpgBebk64+boEKBXWfy1pDbYw1t0Fx9p6ONbWwXG2HkcZ68zMzHbf1yIFqKCgoJapb205fPgwevbs+BuFvn37AgDi4+MxcOBATJ8+HZs3b2411a+Zh4cHAKC2thZ+fn4tt9fU1LQ63lESiQTOzs6dOoetUKvVDvOz2DKOs/WIMdbP/WMEnvtgNzLyqvDalyn4zyOJ8PGw3z8g7SXGWG/ccwblNTp4u6swNbGPw75x/DtbfQ4ZNiAU7i7HUVOvR0ZBAxL6+osdqVNsdZwdkVhjbTSZsW5nDgBg1sQ+8PRws3oGa+PvtfVwrK2HY20dHGfrsfexbu/0O8BCi5BPnToV3333HVJSUi4IsXr1amzcuBEzZsywxKUQHR0NhUKB3NzcNo9HREQAwAUFsezsbCgUCvTo0cMiOYiIAEDtJMe/7h2BED8XlFZq8K8Ve1HX4KCLEonIYDTj+6ad72ZN6D7FJ1sml0kxZlAwACA5JV/kNESXt+1gHkoqNfB0c8I1I8LEjkNERNTtWKQA9cADDyAhIQG333477rjjDkgkEixZsgTjxo3DCy+8gMTERNx1112WuBSOHDkCg8Fw0UXIe/TogbCwsAvWnPr1118xcuTIds9NJCJqLw9XJ7w8bxS83VU4W1SL//tsP3QGk9ixHMqWP3NRWqmBt7sTrhnRS+w41KR5N7y9aef4O082zWQy4/stjUXsG5Ii4cQiNhERkdVZZAqeUqnEJ598gnXr1mHTpk0wm83Q6/WIjo7GggULMH369Ctqy2o2f/58DBgwANHR0VCpVDh16hQ+/fRTREdHY9KkSQCAZ599FmvXrsWJEydaHvfII4/giSeeQM+ePTF8+HD8+uuvOHr0KL766itL/LhERBfw93bGS/NGYtH7u3DiTAX+8+VBPHPnULtdF8eWnL/z3Y3j2f1kS/r28oaflxqllRocPFGM0U0dUUS2ZkdqAc6V18PdRYnrRoWJHYeIiKhbskgBCmiccjd9+nRMnz7dUqdEbGwsfv31V6xYsQKCICAkJASzZ8/GPffc09LJZDabYTK1/tR16tSp0Gg0+Pjjj7FixQqEh4fjvffeQ3x8vMWyERH9XViQOxb/Yzhe+GgP9h8vwvs/HMEjN8V1qABPf9l6MBcllRp4uTnhmpFhYseh80ilEoyNC8GabZlITslnAYpsksksYNXmxiL2jKTeUDtZ7OUvERERXQGL/AX+97//jalTp6J///6WOF2LefPmYd68eZe8z9KlS7F06dILbp89ezZmz55t0TxERJcTE+GDp+YOwWufH8DmA7nwdHPCHZMt+9zYnRhNZqxumjYzc3wfTpuxQUkJoVizLRMHTxajTmOAq1ohdiSiVvYcKURBaR1c1QpMGR0udhwiIqJuyyJzQ7766ivceOONuPrqq/H2228jPT3dEqclIrJLwwcE4eHZcQCA77dkYN3OLHED2bGtB/NQUtEATzcnXDuSaz/ZorAgd/QIcIPBaMa+tEKx4xC1YjYLWPVH4+vS6xMj4KxigZSIiEgsFilA7dmzB0uWLEFYWBg++eQTzJgxA1OmTMH7779/wW50RETdwdXDe2Hudf0AAB+vPYbkw9wl7EoZTWas/qN57adIqJScNmOLJBIJkhJCAADJKQUipyFqbd+xczhbVAtnlRzTEiPEjkNERNStWaQA5erqihkzZmDFihXYs2cPXn75ZQQGBuKDDz7AlClTMH36dKxYscISlyIishuzJ/ZpecPz9neHcTi9RORE9mX7oTwUVzTA09UJ13LtJ5s2Nq5xN7yjGaWorNGKnIaokSAIWNVUxJ46JgKuztwJmYiISEwW357J3d0ds2fPxqeffoqdO3fi6aefRn5+Pt566y1LX4qIyKZJJBLce/0AjI0PgdEkYMnnB3A6t1LsWHbBZDJj9R9NW6aPY/eTrQvydUF0Ty+YBWDnEXZBkW3482QxsguqoVLKcD27n4iIiETXJfuDGwwGbNmyBa+88greffdd1NfXIzAwsCsuRURk06RSCRbcnIC4KD9o9Sa89Mk+FJTWiR3L5m07lI9z5fXwcFViMrdMtwtjm6bh7eA0PLIBgiBg1ebGtZ+mjA6Hh6uTyImIiIjIYgUoo9GI5ORkPP300xg5ciQefvhhHDhwADNnzsQ333yDbdu2WepSRER2RSGX4pk7hyKyhydq6vV44aM9KK/WiB3LZpnOW/vphqRIqLhlul1IHBQCqQRIP1uJovJ6seNQN5eSXorTuVVQKmSYkRQpdhwiIiICYJFX9c8++yy2bNmC6upqeHl5YcqUKZgyZQqGDh0KiURiiUsQEdk1Z5UCL947Ak8t34nCsnq8+PE+LHl4DLesb0NySmP3k7uLEpO5Zbrd8HJXITbSD6kZpUhOycecSdFiR6JuShAEfNfU/XTtyF7wdGP3ExERkS2wSAfUli1bMGnSJHz66afYtWsXXnrpJQwbNqxV8am6utoSlyIislserk54+f5R8HZ3Qs65Grzy2X7oDCaxY9kUk8mMVZubup/GRULN7ie70rIb3uECCIIgchrqrtKyynAypwIKuRQzx7H7iYiIyFZYpAC1e/duvPrqqxg9ejRkMlnL7Xq9Hhs3bsRDDz2EMWPGWOJSRER2LcDbGS/eNxIuKjmOZ5fjja8OwmQyix3LZuxILUBhWT3cnJWYwu4nuzNiYDDkMinyimuRc65G7DjUTX33e2MR++rhveDjoRY5DRERETWzSAFKLv/rE2pBELBnzx4888wzGDVqFBYuXIjU1FRMnTrVEpciIrJ74cEeeP4fw6GQS7HvWBH+u+You0UAmMx/LRp8w7je7H6yQ65qBYb2DwAAJB/OFzkNdUfHs8uRllUGuUyCG8f3ETsOERERncdir+6PHTuG9evX45dffkFZWRkkEgkmT56M22+/HXFxcVwLiojoPAN6++LJ24dg6f8O4Pf9Z+Hl5oTbr+sndixR7UzJR0FpPdycFex+smNJ8aHYm3YOO1MLcMfk/pBK+fefrKe5iD1xaE/4ebH7iYiIyJZ0qgMqLy8P77//Pq699lrMnj0bmzZtwrRp0/DWW29BEARcc801iI+PZ/GJiKgNIwcG4aFZgwAAq/44jfU7s0VOJB6TWcB3TWs/TU/qDWcVF2e3V0P6B0DtJEdJpQanzlaIHYe6kfSzFUg5XQqpVIJZE9j9REREZGs63AE1Z84cHD16FF5eXrjmmmvwyiuvYMiQIQCA3NxciwUkInJk14wIQ1WtDl/9dgof/5wGT1cnJMaHiB3L6nalFqCgtA6uagWmjYkQOw51gpNChpEDg7D1YB6SD+ejf7iP2JGom2guYo8fHIpAHxeR0xAREdHfdbgD6siRIwgJCcHLL7+M5557rqX4REREV+amSVGYOjocggC8+e0hpJ4uETuSVZnMAlb90ThtZga7nxxCUnwoAGD30UIYucg+WUFmfhUOniyGVALcNDFK7DhERETUhg4XoBYvXgw/Pz/Mnz8fo0ePxgsvvIB9+/ZxIV0ioiskkUhw74yBGDMoGEaTgNc+P4DMvCqxY1nNniOFyCuug4tagansfnIIg/r4wsNVieo6PY5klIodh7qB1X80dj+NjQ9FsJ+ryGmIiIioLR0uQN1222349ttvsXnzZtx55504ePAg7rrrLiQmJuKdd96BRCLh2k9ERO0kk0rwz1sTMKiPLzQ6E178ZC8KS+vEjtXlzGYB3zYtGjx9bG+4qNn95AhkMinGDGqcSsrd8KirnSmsxt60c5BIGjtKiYiIyDZ1ahFyAOjRowceeugh/Prrr/jhhx8wZcoUHDhwAIIg4KWXXsLixYuxbds26HQ6S+QlInJYCrkMz941DL1DPVBdp8cLK/aiokYrdqwutftoIfKKa+GikmNaIrufHEnzNLx9x85BZzCJnIYcWXP306jYYPQIcBM5DREREV1MpwtQ5xswYACeeeYZJCcn47PPPsOYMWPw66+/4sEHH8SIESMseSkiIofkrFLgxXtHIsjXBcUVDXjx472o1xjEjtUlzGYB3zV1P10/tjdc2f3kUPqGecHfSw2NzoQ/TxSJHYccVF5xLXYfLQQAzGH3ExERkU2zaAGq5aRSKUaNGoWlS5diz549ePPNN1mAIiJqJ083J7w8byS83JxwprAGr6zcD70DdpDsTTuH3KJaOKvkuJ7dTw5HIpFgbFMXFKfhUVdZ/cdpCAIwYkAgwoM9xI5DREREl9AlBajzOTk5YfLkyfjggw+6+lJERA4j0McFL943Es4qOY5lleONrw/BZHacTR5adT8l9oars1LkRNQVkhIaC1AHT5agzkE7+Ug8haV12JHSWNycMyla5DRERER0OV1egCIioo6JCPHA83cPh0Iuxd60c/hgzRGH2Wl037FzyDlXA2eVHNPHsvvJUYUFuaNnoBuMJjP2Nk2TIrKU77dkwCwAQ/oFILKHp9hxiIiI6DJYgCIismEDI33xxG2DIZEAm/adxTeb0sWO1Gnndz9NGxPB7icH17wYeXIKp+GR5RSV12ProTwAwJyruPYTERGRPWABiojIxo2KDcaDNw4CAHy3OR2/7D4jcqLO2X/8HM4U1kDtJMf0pN5ix6EuNjY+BACQllnm8Ls6kvX8sDUDZrOAuCg/9O3lLXYcIiIiagcWoIiI7MB1I8Nw6zV9AQAf/XQUu44UiJyoYwRBwHe/N26ZPnVMONzY/eTwAn1cEN3LC2YB2JVqn7+3ZFtKKzXY8mcuAODmq7j2ExERkb1gAYqIyE7cfFUUJo8KgyAAy74+jCMZpWJHumL7jxchu7AaaicZZiRFih2HrITT8MiSftyWAaNJwMDevoiJ8BE7DhEREbUTC1BERHZCIpFg3g2xGB0bDKPJjFdXHkBmfpXYsdpNEAR8+3vj2k9Tx0TA3YXdT93FmLhgSCXA6dwqnCurFzsO2bGKGi027T8LgGs/ERER2RsWoIiI7IhMKsHjtyUgNtIXGp0RL328D4VldWLHapc/TxQju6AaKqUM08dy7afuxMtNhdg+fgCAHeyCok74cVsmDEYz+oV5IzbSV+w4REREdAVYgCIisjMKuQzP3T0MEcEeqKrT4V8r9qLSxhd3bux+OgUAmDI6HB6uTiInIms7fxqeIAgipyF7VFWrw8a9OQAa136SSCTiBiIiIqIrwgIUEZEdclYp8OJ9IxDo44yi8ga8+PE+1GsMYse6qD9PFiMzvxpOShluGMe1n7qjkQODoJBLkVdch5xzNWLHITu0NjkTeoMJfXp4Ij7aT+w4REREdIVYgCIislNe7iq8PG8UPN2ckF1YjVdXHoDeYBI71gXOX/tpyih2P3VXLmoFhvQLAAAkH+Y0PLoyNfV6/LL7DAB2PxEREdkrFqCIiOxYkK8LXrx3BNROcqRllWHZN4dgMtvW9KZDp0qQmVfF7idCUkLzNLwCmG3s95Rs27odWdDqTYgI9sDQ/gFixyEiIqIOYAGKiMjO9Q71xPP/GAa5TIo9R8/hox+P2swaO+ev/TR5VDg83dj91J0N6RcAZ5UcZVUanMypEDsO2Yk6jQHrd2UDaNz5jt1PRERE9okFKCIiBxAb6YcnbhsMiQTYuDcH3zVNeRPb4fQSnM6tglIhww3juPNdd+ekkGHEgCAAjYuRE7XH+p3ZaNAa0SvQreX3h4iIiOwPC1BERA5i9KBgPDAzFgDwze/p2LjnjKh5zl/7afKoMHi5qUTNQ7aheRrertRCGE1mkdOQrWvQGrBuRxYA4KZJUZBK2f1ERERkr1iAIiJyIJNHheOWq6MBAB/8eBS7jxaKliUlvRTpZyuhlEsxk2s/UZNBkb7wdHVCbYMeqadLxY5DNu6X3WdQpzEgxM8VoweFiB2HiIiIOoEFKCIiB3PL1dG4dmQYBAF446tDSMsss3qG89d+unZUGLzc2f1EjWQyKcYMCgbAaXh0aVqdEWuT/+p+krH7iYiIyK6xAEVE5GAkEgkemBmLkQODYDSZ8crK/cguqLZqhtTTpTjV1P104/g+Vr022b7maXj70s5BqzeKnIZs1ca9Oaip1yPIxwVJ8ex+IiIisncsQBEROSCZVIInbhuMgb190aA14l8f70VReb1Vrn3+2k/XjgyDN7uf6G+ie3nB39sZWr0Jf54oFjsO2SCdwYQft2cCAGZP7AOZjC9ZiYiI7B3/mhMROSilQobn7h6G8GB3VNXq8MJHe1FZq+3y6x7NKMPJnAoo5FLMHM+1n+hCEomkpaMl+TCn4dGFNu3LQVWtDv5eaowf0kPsOERERGQBLEARETkwF7UCL943EgHezjhXXo8XP96HBq2hy64nCAK+3dzY/XTNiF7w8VB32bXIviXFN07DO3SqGHUNepHTkC0xGE34cVtj99OsCX0gZ/cTERGRQ+BfdCIiB+ftrsLL94+Eh6sS2QXVeHXlARiMpi651tHMMhzPLodcJsWsCVz7iS6uV5A7woLcYTQJ2JN2Tuw4ZEP+OJCL8motfDxUmDSsp9hxiIiIyEJYgCIi6gaCfV3x4r0joXaS4WhmGZZ9cxgms2Dx6zSv/cTuJ2qPsZyGR39jMJrx/dYMAMCN4/tAIZeJnIiIiIgshQUoIqJuIrKHJ567azjkMgl2HynEx2vTIAiWK0KlsfuJrlBiXGMBKi2rDOXVGpHTkC3YdigPpZUaeLo54eoRvcSOQ0RERBbEAhQRUTcyKMoP/7x1MCQS4JfdZ7D6j9MWO3dz99PVw3vC15PdT3R5gT4u6BfmDUEAdh0pFDsOicxkMuP7LY3PSTPHRcJJwe4nIiIiR8ICFBFRN5MYF4J5MwYCAL767RR+25vT6XMeyypDWlYZ5DIJZk2I6vT5qPvgNDxqlpxSgKLyBri7KHHdyDCx4xAREZGFsQBFRNQNTR0TgTmTGgtFH6w5gr1pnes+ae5+umpYL/h5sfuJ2m/0oGBIpRJk5FWhsLRO7DgkEpNZwOo/Gp9HZiT1hspJLnIiIiIisjQWoIiIuqnbru2La0b0glkA/vPVIaRllXXoPMezy3E0s6n7aSLXfqIr4+WmQlwfPwDAjtQCkdOQWHYfKUBBaT1c1QpMGR0udhwiIiLqAixAERF1UxKJBA/OjMWIAYEwGM145bP9OFNYfcXn+fb3UwCAiUN7wt/L2dIxqRtonoa3/VC+RRfGJ/tgNgtY1bQe3fSk3nBWKURORERERF2BBSgiom5MJpPiyduHICbCBw1aI/61Yi+Kyuvb/fgTZ8pxJKMMMqkEsydy7SfqmJEDg6CQS1FQWofsgisvgpJ923vsHHKLauGskmPqmAix4xAREVEXYQGKiKibUypkeP4fwxEW5I7KWh1eWLEXVbW6dj22ee2nScN6IsCb3U/UMc4qBYb1DwQA7EjhNLzuRBAErN7c2P00bUwEXNXsfiIiInJULEARERFc1Qq8eN8I+Hs741xZPV76ZC8atIZLPuZUTgVST5ey+4ksonka3o6UfJjNnIbXXfx5ohjZhdVQO8lw/djeYschIiKiLsQCFBERAQB8PNR4ed5IuLsokZlfjSWf/wmD0XTR+zd3P00Y0oPdT9RpQ/oFwFklR1m1FifOlIsdh6xAEAR8u7nxeWTyqHC4uyhFTkRERERdiQUoIiJqEeLnihfvGwGVUobUjFK89W1Km90op/OqcDi9BFKpBDdNYvcTdZ5SIcOogcEAOA2vuzicXoLMvCooFTLMSIoUOw4RERF1MRagiIiolT49vPDsXcMgl0mwM7UAH/+cdsHOZGu2ZQMAJgzugUAfFzFikgNqnoa360gBDEazyGmoKwmCgFVNaz9dNzIMnm5OIiciIiKirsYCFBERXSA+2h8Lb0mARAJs2HUG32/JaDlWrZUju7CW3U9kcbGRvvB0c0JtgwGpp0vEjkNd6GhmGU7mVEAhl2LmeHY/ERERdQcsQBERUZvGxofivukDAQDbDuWhoLQOCqUKA/r3wafPXYX/PJKIIF92P5HlyGRSJMY1L0bOaXiO7LumtZ+uGd4L3u4qkdMQERGRNcjFDkBERLZrWmIEzGYzxg3ugfU7s7Fh9xnUawxwUSswLTECYUHuUCpkYsckBzI2PgTrd2Zj37Fz0OqMUDnxpYqjOZlTiWNZ5ZDLJJg5vo/YcYiIiMhK2AFFRESXdM2IMGzYmY1Vf5xGvcYAAKjXGPDd7+n4YWsGtHqjyAnJkUT39EKAtzO0ehMOnCgSOw5ZmEqlwm/78gAAk4b1gp+XWuREREREZC0sQBER0SXJZFKs332mzWPrdmZDJuWfErIciUSCpIRQAJyG50i0eiMUShUCQ8Lx+G1D8NzdwzCHa8gRERF1K+xrJyKiS6rXGlo6ny44pjGgQWuAhyt3sCLLGRsfgtV/nMahU8WobdDDzVkpdiTqBL3BhDXbMrF+Z/ZfU3jHhCMh2l/saERERGRF/NiaiIguyUWlgIta0fYxtQLOqraPEXVUr0B3hAW5w2gSsOdoodhxqBO0eiO+35qB735Pbz2Fd/NpTuElIiLqZmy6ALVx40Y8+OCDGDt2LOLi4jB9+nT88MMPEAThko+rrKzECy+8gHHjxiEuLg5Tp07Ft99+a6XURESOxWQ24/rEiDaPXZ8YAZPZbOVE1B1wGp5jkEmlWL8zu81jnMJLRETUvdj0FLzPP/8cISEhWLRoEby8vLBnzx4sXrwYRUVFmD9//kUf99hjjyE7Oxv//Oc/ERQUhB07duDFF1+ETCbDTTfdZMWfgIjI/qmUcsya0LhT1brzptBcnxiBWRP6cBc86hJj40Lwv19OIC2rDOXVGvh4cLFqe8QpvERERNTMpgtQH3zwAby9vVu+HzlyJKqqqrBy5Uo89NBDkLbxqVlpaSn279+PJUuWYObMmS2PS0tLwy+//MICFBFRBygVMswcH4nZE6NQ16CDq7MTTGYzi0/UZfy9ndEvzBsncyqwM7UAM5IixY5EHeDsJIeLWtFmEYpTeImIiLoXm+57Pr/41Kxfv36oq6tDQ0NDm48xGhvXEnBzc2t1u6ur62Wn7hER0cWplHIY9FoU5mXDoNdCpbTpzzDIATRPw0vmNDy7YzYL+Pb3dBxOL8HU0eFt3odTeImIiLoXu3v3cOjQIQQEBMDV1bXN40FBQRgzZgw+/PBDhIeHIzAwEDt27MDu3bvxxhtvdPr6giBctPhlLzQaTat/qWtwnK2HY209Go0GWq2WY20F/L0GBkd5QSqVIDOvCpm5pQj2dbH4NTjOlqfRGfH+mmP482QpQv1d8Z9Hx0IqlbQ5hddk1KOhQS92ZIfD32vr4VhbD8faOjjO1uMoYy0IAiQSSbvuKxHsqC3o4MGDmDt3Lp5++mncddddF71fQ0MDFi5ciO3btwMAZDIZnn/+edx6662dun5aWhr0er5IIiIispavtpUi85wO4wa6Y9xAd7Hj0GWU1xjw3c5ylFYbIZMCU4Z6YVSMD/wCguDp4YF6jR4uaiWqqqtRWnwOWq1W7MhERETUSUqlEgMHDrzs/eymA6qoqAgLFy7E8OHDcccdd1z0foIg4JlnnkFOTg6WLVsGPz8/7NmzB6+99ho8PDwwZcqUTuVQKBSIjLTvdSg0Gg1ycnIQFhYGtZqLunYVjrP1cKyth2NtPRzrRtfoPJC55jhOnzPigdl92/0JW3txnC0n5XQZPv0jDQ1aI7zcnPD4LYPQp4dHy/G62mqcO3cOQUFBcHVWwTW87al51Hn8vbYejrX1cKytg+NsPY4y1pmZme2+r10UoGpqanDffffB09MTy5cvb3Px8Wbbt2/Hb7/9hnXr1iE6OhoAMHz4cJSXl2Pp0qWdLkBJJBI4Ozt36hy2Qq1WO8zPYss4ztbDsbYejrX1dPexHpvQCx//fBKFZQ04V2lAZKhnl1ynu49zZwiCgB+2ZuDLjSchCEC/MG88c+dQeLmrLrivVquFSqXiWFsJf6+th2NtPRxr6+A4W4+9j/WVfDho04uQA40vVO6//37U1tbik08+uWBx8b/LzMyETCZDVFRUq9v79euHkpISu59fSURE1J04qxQYGhMIAEg+nC9yGvo7rc6I1788iC9+bSw+XTOiF159cHSbxSciIiLq3my6AGU0GrFgwQJkZ2fjk08+QUBAwGUfExISApPJhPT09Fa3Hz9+HD4+Pnbd2kZERNQdJcU37oa3M7UAZrPdLF3p8IrK6/Hk8p3YfaQQcpkED80ahPmz46CQ2/TLSyIiIhKJTU/Be+mll7Bt2zYsWrQIdXV1SE1NbTnWv39/KJVK3HnnnSgsLMTmzZsBAGPHjkVwcDAeffRRPPzww/D398euXbvw008/4ZFHHhHpJyEiIqKOGtLPHy4qOcqrtTh+phwDe/uKHanbSz1dgn9/eRC1DQZ4ujlh0R1DERPhI3YsIiIismE2XYDavXs3AGDp0qUXHNuyZQtCQ0NhNpthMplabnd1dcXnn3+Ot956C2+88QZqa2sRGhqKRYsW4fbbb7dadiIiIrIMhVyGUbHB2HwgF8mH81mAEpEgCPh5RxZWrj8OswD06eGJZ+8aBl9PdpgTERHRpdl0AWrr1q2Xvc+XX355wW29evXC22+/3QWJiIiISAxJ8aHYfCAXe44W4v4bYjnNSwQ6gwnvrU7F9qa1uCYM6YGHZw2CUiETORkRERHZA5suQBEREREBwIBIX3i5OaGyVoeU0yUY1j9Q7EjdSkllA177/ACy8qshlUpw7/UDMHVM+BXtfENERETdGz8+JCIiIpsnk0qQGBcCgLvhWVtaVhn++XYysvKr4e6ixCv3j8K0xAgWn4iIiOiKsABFREREdiEpoXE3vP3Hi6DVGUVO4/gEQcCGXdlY/OEeVNfpERHigbcWJGFgJNfgIiIioivHAhQRERHZhT49PBHk4wKd3oT9x4vEjuPQ9AYTlq9OxUc/pcFkFpAUH4rX54+Bv7ez2NGIiIjITrEARURERHZBIpFgbHzTNLwUTsPrKuXVGjzz313YfCAXUgnwj2kxePy2BKiUXDqUiIiIOo4FKCIiIrIbzdPwDp8qQU29XuQ0jufEmXIseCsZp3Or4KpW4MX7RuKGcZFc74mIiIg6jQUoIiIishs9AtwQHuwOk1nAnqOFYsdxKL/tzcFzH+xGVa0OYUHueGthEuKj/cWORURERA6CBSgiIiKyK0nxjV1QnIZnGQajGe//cATv/3AERpOA0bHB+PcjiQj0cRE7GhERETkQFqCIiIjIriQ2rQN1PLscZVUakdPYt8oaLZ77YDd+25sDiQS4Y3I/PH3HEKiduN4TERERWRYLUERERGRX/L2c0T/cG4IA7EwtEDuO3TqdW4mFbyfjZE4FXFRyvHDPCMyeGMX1noiIiKhLsABFREREdqd5MXJOw+uYPw7kYtH7u1BerUWPAFcsW5CEIf0CxI5FREREDowFKCIiIrI7o2ODIZNKkJVfjfySWrHj2A2jyYwVa9PwzqoUGIxmDI8JxBuPjkWIn6vY0YiIiMjBsQBFREREdsfD1QlxUX4AgB0pnIbXHtV1Orzw0V6s35kNALj16mg8e9cwOKsUIicjIiKi7oAFKCIiIrJLLdPwDudDEASR09i2rPwqLHw7GWlZZVA7yfDc3cNwyzV9IZVyvSciIiKyDm5xQkRERHZpeEwglAoZCsvqkZVfjcgenmJHsknbD+dj+epU6A0mBPu64Lm7h6FnoLvYsYiIiKibYQcUERER2SVnlQLD+jcunM3FyC9kMpnx6bpjWPb1IegNJgzu649lC5JYfCIiIiJRsABFREREdqt5Gt6OlAKYzJyG16ymXo8XP96HtclZAIDZE/tg8T0j4Krmek9EREQkDk7BIyIiIrs1uK8/XNQKVNRocSK7HAMjfcWOJLozhdV4deUBFFc0wEkpw4Kb4zFmUIjYsYiIiKibYwcUERER2S2FXIZRA4MAcBoeAOw+Uognl+9EcUUDAryd8Z9HEll8IiIiIpvAAhQRERHZteZpeLuPFMJgNIucRhwms4Avfj2BpV/8CZ3ehLg+fnhzQRLCgz3EjkZEREQEgFPwiIiIyM4N6O0Lb3cnVNTokJJegmExgWJHsqo6jQHLvj6EgyeLAQAzknrjrin9IZPxc0YiIiKyHXxlQkRERHZNJpVgTFzjNLPkw91rGl5ecS0efzsZB08WQymX4vHbBuOe6wew+EREREQ2h69OiIiIyO4lxTdOw9t3vAganVHkNNax79g5PP7ODhSW1cPPS43XH0nEuKbpiERERES2hgUoIiIisnt9engiyNcFeoMJ+48XiR2nS5nNAr7ddAqvrjwAjc6Igb198daCJESGeoodjYiIiOiiWIAiIiIiuyeRSDA23vGn4TVoDXjt8wP45vd0AMC0xAi8fP9IeLg6iZyMiIiI6NJYgCIiIiKH0DwNLyW9BDX1epHTWF5BaR2eeHcH9h8vgkIuxWNz4jFvxkDIud4TERER2QG+YiEiIiKH0CPADREhHjCZBew+Wih2HIs6eLIYj7+djLziOvh4qLD04TGYNKyn2LGIiIiI2o0FKCIiInIYSQ42DU8QBHy/5TRe/nQf6rVG9AvzxlsLkhDV00vsaERERERXhAUoIiIichiJcY3T8I5nl6O0UiNyms7R6Ix4/YuD+OLXkxAE4NqRYXj1wdHwcleJHY2IiIjoirEARURERA7Dz0uNmAgfAMDO1AKR03RcUXk9nlq+E7uPFkIuk+ChWYPw8KxBUMj50o2IiIjsE1/FEBERkUNpmYaXYp/T8FLSS7DwrWTknKuBl5sTXn1wNK4bGSZ2LCIiIqJOYQGKiIiIHMqo2GDIpBJkF1Qjr7hW7DjtJggCftqeiRc/3os6jQFRPT3x1sIk9A/3ETsaERERUaexAEVEREQOxcPVCfHR/gCAHSn2MQ1Pqzdi2deH8dn64zALwKShPbHkoTHw8VCLHY2IiIjIIliAIiIiIodz/jQ8QRBETnNpJRUNePq9XUhOyYdMKsEDNwzEo3PioFTIxI5GREREZDFysQMQERERWdrwAUFQKmQ4V1aPjLwqRPX0EjtSm9Iyy7D0iz9RU6+Hu4sSi+4cioG9fcWORURERGRx7IAiIiIih6N2kmNETCAA25yGJwgC1u/MxvMf7UFNvR4RIR54a2ESi09ERETksFiAIiIiIoc0tmka3s7UfJjMtjMNT28w4Z1VKVixNg1ms4BxCaF4ff4Y+Hs5ix2NiIiIqMtwCh4RERE5pIS+AXBVK1BRo8OxrDIM6uMndiSUVWnw2ucHkJFXBakEuHtaDKaP7Q2JRCJ2NCIiIqIuxQ4oIiIickgKuRSjBwUDsI1peCfOlGPh28nIyKuCm7MCL80biRlJkSw+ERERUbfAAhQRERE5rOZpeLuPFsJgNImWY+PeHDz3wW5U1eoQFuSONxckIS7KX7Q8RERERNbGKXhERETksGIifOHtrkJFjRaHTpVgxIAgq17fYDTjo5+OYtO+swCAMYOC8diceKic+BKMiIiIuhd2QBEREZHDkkklLV1Q1p6GV1GjxXMf7MamfWchkQB3TumPp+YOYfGJiIiIuiUWoIiIiMihNReg9h8vQoPWYJVrns6txMK3knEypwIuKjleuGcEZk3ow/WeiIiIqNtiAYqIiIgcWmSoJ4J9XaA3mLD/eFGXX++PA2fx9Hu7UFGjRY8AV7y5IAlD+gV0+XWJiIiIbBkLUEREROTQJBIJkhJCAXTtNDyjqXG9p3dWpcJoMmPEgEC88ehYBPu5dtk1iYiIiOwFC1BERETk8Jqn4R1OL0F1nc7i56+u02HxR3uwYdcZAMCt1/TFM3cOg7NKYfFrEREREdkjFqCIiIjI4YX6u6F3qAfMZgG7jxZa9NyZ+VVY+HYyjmWVQ+0kx/N3D8MtV0dDKuV6T0RERETNWIAiIiKibiEp3vLT8LYfysPTy3eitFKDYF8XLHtsLIYPCLLY+YmIiIgcBQtQRERE1C0kxoVAIgGOZ5ejpLKhU+cymcz4dN0xLPvmMPRGM4b0C8CyBUnoEeBmobREREREjoUFKCIiIuoWfD3ViInwAQDs7EQXVE29Hi9+vA9rk7MAADdNisLz/xgOVzXXeyIiIiK6GBagiIiIqNvo7DS8M4XV+OfbyUjNKIVKKcOiO4Zi7nX9ION6T0RERESXxAIUERERdRujYoMhk0qQXViN3KKaK3rsriMFeHL5ThRXNCDQxxn/eXQsRg8K7qKkRERERI6FBSgiIiLqNtxdlEjo6w+g/V1QJrOAL349gde/OAid3oS4KD+8uSAJYUHuXRmViIiIyKGwAEVERETdyvnT8ARBuOR96zQG/N+n+/D9lgwAwMxxkXjx3hFwc1Z2eU4iIiIiRyIXOwARERGRNQ2PCYSTUoZz5fXIKrj4NLzcohq8uvIACsvqoVTI8OhNcUhKCLViUiIiIiLHwQ4oIiIi6lZUTnIMjwkEAOw6WtTmffamncMT7+5AYVk9/LzU+Pf8MSw+EREREXUCC1BERETU7TQXk45lVcDJSdVyu9ks4JtNp/Da5weg0ZkwsLcv3lqQhN6hniIlJSIiInIMnIJHRERE3U58lD9euGc4Bvb2Rb1GD4VShQaNAf/79QR+3ZMDALg+MQJ3T4uBXMbP64iIiIg6iwUoIiIi6nYEQcDps5VY9s1h1GsMcFErMHV0OG69pi9O5FRgxtjemDi0p9gxiYiIiBwGC1BERETUrWj1RqzZlonv/jjdclu9xoBVTd+/dO8IeHuoxYpHRERE5JDYU05ERETdikwqxfqd2W0e27D7DNxcnKyciIiIiMjx2XQBauPGjXjwwQcxduxYxMXFYfr06fjhhx8gCMJlH1tcXIynn34aI0aMQGxsLK677jqsW7fOCqmJiIjIltVrDajXGNo+pjGgQdv2MSIiIiLqOJuegvf5558jJCQEixYtgpeXF/bs2YPFixejqKgI8+fPv+jjSkpKMGfOHISHh+P//u//4OrqioyMDOj1eiumJyIiIlvkolLARa1oswjlolbAWaUQIRURERGRY7PpAtQHH3wAb2/vlu9HjhyJqqoqrFy5Eg899BCk0rYbuP7zn/8gMDAQn3zyCWQyWctjiYiIiExmM65PjMC3v6dfcOz6xAiYzGYobLtJnIiIiMju2PSrq/OLT8369euHuro6NDQ0tPmYuro6bNy4EbfeemtL8YmIiIiomUopx6wJfXDL1dFwUTd2O7moFbjl6mjMmtAHKqVNfz5HREREZJfs7hXWoUOHEBAQAFdX1zaPHz9+HAaDAXK5HLfffjtSUlLg6emJGTNmYMGCBVAoOtdWLwjCRYtf9kKj0bT6l7oGx9l6ONbWw7G2Ho5115JIJJgxNgKzJ/ZBXYMers5K6A0mmIx6NBguv9YkXTn+TlsPx9p6ONbWw7G2Do6z9TjKWAuCAIlE0q77SoT2rOhtIw4ePIi5c+fi6aefxl133dXmfX755Rf885//hIuLC2666SaMGzcOR48exbvvvou7774bjz/+eIevn5aWxnWkiIiIHIhcLodcLofRaITRaBQ7DhEREZHdUSqVGDhw4GXvZzcdUEVFRVi4cCGGDx+OO+6446L3M5vNAIBRo0Zh0aJFAIARI0agvr4en332GR5++GGoVKoO51AoFIiMjOzw422BRqNBTk4OwsLCoFarxY7jsDjO1sOxth6OtfVwrK2D42w9HGvr4VhbD8faejjW1sFxth5HGevMzMx239cuClA1NTW477774OnpieXLl1908XEAcHd3B9BYdDrfyJEj8eGHH+Ls2bOIjo7ucBaJRAJnZ+cOP96WqNVqh/lZbBnH2Xo41tbDsbYejrV1cJyth2NtPRxr6+FYWw/H2jo4ztZj72Pd3ul3gB0UoLRaLe6//37U1tZi1apVcHNzu+T9L9edpNPpLBmPiIiIiIiIiIguw6Z3wTMajViwYAGys7PxySefICAg4LKPCQkJQVRUFPbs2dPq9j179kClUtn99DkiIiIiIiIiIntj0x1QL730ErZt24ZFixahrq4OqampLcf69+8PpVKJO++8E4WFhdi8eXPLsYULF+Khhx7Cq6++inHjxiEtLQ2fffYZ7rnnHrtubSMiIiIiIiIiskc2XYDavXs3AGDp0qUXHNuyZQtCQ0NhNpthMplaHZswYQLefPNN/Pe//8W3334Lf39/PPLII5g3b55VchMRERERERER0V9sugC1devWy97nyy+/bPP2yZMnY/LkyZaOREREREREREREV8im14AiIiIiIiIiIiL7xwIUERERERERERF1KRagiIiIiIiIiIioS7EARUREREREREREXUoiCIIgdgh7cfjwYQiCAKVSKXaUThEEAQaDAQqFAhKJROw4DovjbD0ca+vhWFsPx9o6OM7Ww7G2Ho619XCsrYdjbR0cZ+txlLHW6/WQSCRISEi47H1tehc8W2PPvxTnk0gkdl9EswccZ+vhWFsPx9p6ONbWwXG2Ho619XCsrYdjbT0ca+vgOFuPo4y1RCJpd62EHVBERERERERERNSluAYUERERERERERF1KRagiIiIiIiIiIioS7EARUREREREREREXYoFKCIiIiIiIiIi6lIsQBERERERERERUZdiAYqIiIiIiIiIiLoUC1BERERERERERNSlWIAiIiIiIiIiIqIuxQIUERERERERERF1KRagiIiIiIiIiIioS7EARUREREREREREXYoFKCIiIiIiIiIi6lIsQDmQ5cuXIz4+XuwYDm358uWIjo6+4H9Tp05t1+P379+P6OhopKWldXFS+9Y8zomJiTCbzRccv/nmmxEdHY1FixaJkM6xXX/99YiOjsbBgwfFjuIw+PssHv5dtL6OjDn/f2o/Pkd3vXXr1mHWrFkYPHgwEhIScN111+G5555DeXm52NEc2rp163DzzTcjPj4e8fHxmDNnDtauXXtF56ipqcHy5cuRmZnZNSHtVPPrkNtuu+2CY6+++iomTJggQirHdP57xb59+2Lw4MGYNm0aXn75ZWRlZYkdzybIxQ5AZG9UKhX+97//XXAbWZZCoUBlZSX+/PNPDB8+vOX2goICpKamwtnZWcR0jikjIwPp6ekAgPXr12PIkCEiJ3Ic/H0mos7ic3TX+/jjj7Fs2TLcddddePTRRyEIAjIyMrB+/XqUlJTAx8dH7IgO6f/+7//w9ddf48Ybb8RDDz0EiUSCTZs2YdGiRUhLS8PixYvbdZ6amhq899576NOnDyIjI7s4tf05ePAg9u/f3+p1CFne+e8V6+vrcfr0aaxatQqrV6/Gq6++iunTp4ucUFwsQBFdIalUiri4OLFjODyFQoGRI0fil19+afWH8pdffkGfPn0glXa+gVOr1bJ4eJ7169dDKpVi6NCh+O233/D8889DoVB0+rwcZ+v8PhORY+uq52j6y5dffokbbrihVUdqUlIS7r333jY7WKnztmzZgq+++grz58/HI4880nJ7YmIi/P398f7772P06NHs0ukkZ2dnREZG4r///S8LUF3s7+8VR48ejVtvvRXz5s3Dc889h4SEBPTo0UO8gCLjK14H9sYbb2DatGmIj49HYmIi/vnPf6KkpKTVfebOnYv7778fv/32G6655hrEx8fjjjvuQG5urkip7dv27dsxe/ZsxMbGYsSIEfjXv/6FhoaGC+5XUVGB+fPnIy4uDmPGjMGHH34oQlrbN3XqVGzatAkGg6Hltg0bNlww5TErKwsLFy5EUlISBg0ahMmTJ+P/27v3sKqqvA/gX+4iCBxUFCGLSg+CiojJVREJuYc6IY4BIk4SpIhKjSiko01iqKQcIm00b3kZjCfywHg7o+LDpEyK4ww65WA+cREShIS4CfL+wbBftwfrOHJA8Pt5Hp7Hs/Y6m7U2y7X3/u211t61a5foYrG0tBRSqRRZWVlITEyEo6MjgoODe6wuT7v29nbI5XI4OTlhwYIFqK2txblz54TtndNHz549+4ttt3MqzZUrVxASEoJx48bh888/7+nqPJW6sz3Pnj0bK1asUPodKSkpcHNzQ1tbm/oq0kc9agp0TEwMwsLChM+dbfjbb7/Fb3/7W9jZ2SEgIED0/4FUo+oxp1/3a310VlYWpFIp7ty5I/peUFCQ0vTeQ4cOwcPDA3Z2dliwYAGuXr0qnB+fdXfv3oWZmVmX2x5+UJCVlYXAwECMGzcOU6ZMQWpqqqjv7fybXL58GeHh4bCzs8P06dNx5MgRtdahr9mzZw+MjY0RGRmptG3hwoUwNjYWzTwoLCxEZGQkJk6cCHt7ewQHByM/Px+lpaXw9PQEACxdulSYBlVaWtpjdXnaxcTE4Pz587h06dIj85SVlSE2NhYODg6YMGECFi5cKIy8BICVK1d2ufTI6dOnIZVKcePGDbWUva/T09NDUlIS7t27h8zMTCH91/oRAKisrMS7774LFxcXjB8/Hj4+PkqzcfoSBqD6serqakRFRWH79u1YvXo1ysrKEBYWhtbWVlG+a9euYefOnYiPj8eGDRvwww8/4J133umlUvcNra2top/29nYcO3YM0dHRGD16NGQyGd555x2cPHkSq1evVvp+UlISnnvuOaSlpSEwMBCpqak4ePBgL9Tk6ebh4YGWlhbk5+cDAP7zn//g22+/hZ+fnyjfjz/+CCsrK6xZswY7duzAnDlzkJ6ejo8//lhpn1u2bEF7ezs2b97Mdv6AS5cuoaysDAEBAXBzc4OJiQnkcrlSPlXa7r1797BixQq89tpr+PTTT+Hq6tpT1XiqdWd7Dg4OxqlTp1BXVyektbW1ITs7G7NmzYKWllbPVKqfunfvHuLj4zF79mzIZDKYmpoiNjYWNTU1vV00ekap2kf/GoVCgTVr1sDV1RUymQzOzs6Ii4vr/gL3Uba2tjh06BAyMzNx+/btR+b77LPPkJiYKDyIefPNN7F3716kpqYq5V2+fLlwvB0dHbF69Wrk5eWpsxp9RmtrKwoLC+Ho6AgDAwOl7QYGBnB0dERhYSFaW1tx8eJFhIWFoaWlBe+//z7S0tLg6emJ8vJymJmZQSaTAeg45ocPH8bhw4cfGVB8Fnl4eMDGxgbp6eldbq+vr0dYWBiuXr2KP/zhD0hJSUFNTQ1CQ0Nx69YtAIC/vz+uX7+O7777TvRduVwOW1tbvPjii2qvR1/18ssvY9iwYSgsLASgWj9SU1ODkJAQFBQUYNmyZdi+fTsiIiJQWVnZW9V4YpyC149t2LBB+HdbWxvs7e0xdepUnD9/Hm5ubsK2uro6fPnllzA1NQUANDQ0ICEhARUVFRg+fHiPl/tp19DQAFtbW1Haxo0bsW3bNvj5+eGPf/yjkD506FAsWrQIMTExGDVqlJDu5OSE3//+9wA6hhhXV1cjIyMDISEhnIrzAH19fUyfPh05OTmYNm0a5HI57O3tlYatOjs7w9nZGUDHU2IHBwc0NTUJQ7ofZG1tLfobUQe5XA49PT3MmDEDOjo68Pb2xldffYWff/5ZdFGoStu9d+8eli1bphRYedZ1Z3sODAzExo0bcfToUcybNw8AcPbsWdy+fRu/+c1verZi/VBnAMrd3R0AYGVlBU9PT+TlzF5fdwAAERtJREFU5T3zazdQ71C1j/41GRkZcHJywvvvvw+gox9vbW3F1q1b1VX0PmXNmjVYvHgxEhMTAQCWlpbw8PBAREQELC0tAXTcpG/btg2/+93vsHz5cgAdU2x0dHSQnJyMhQsXQiKRCPsMCgpCVFQUgI7jXVJSgvT0dEydOrWHa/f0qampQUtLC8zNzR+Zx9zcHM3NzaitrUVKSgqef/557NmzR3jQ8uA9zZgxYwAAzz//PJfLeITo6GgsWbIEV65cwfjx40XbsrKyUF5ejpycHLz00ksAgFdeeQUeHh7Ys2cPVq5cCWdnZ5iamiInJwejR48GADQ2NuKvf/2r0jU3KTM3N0dVVZXK/cju3btRXV2Nv/zlL0If1HmN2FfxTrcfO3v2LObOnQsHBwfY2NgIJ7qbN2+K8llbWwvBJwDCon0VFRU9Vta+ZMCAAThy5Ijox8rKCmVlZfD19RWNjJo8eTI0NTXxr3/9S7QPLy8v0Wdvb29UVlbymHchICAACoUCTU1NyM3Nhb+/v1Ke5uZmbNu2DV5eXhg3bhxsbW2RmpqK27dv4+effxblnTZtWg+VvO9obW3FsWPH4O7ujkGDBgHoCHA0Njbi5MmToryqtt3OG3cS6672bGhoCF9fX3zxxRfC97KysjBp0iS88MILPVWdfktTU1N0gWdpaYkBAwb06SeO1Hc9Th/9S9ra2nDt2jWltXQ6py0RMHr0aMjlcuzYsQPh4eEYNGgQ9u3bh9deew3Xrl0D0DEFrKGhAT4+PqJrPhcXFzQ1NeH69euifT583pwxYwaKioo4VfoxNTc34x//+AdmzpzJUb5PwMvLC6NHj+5yFNQ333yDUaNGCcEnADAxMYGLiwsuXrwIANDW1oaPjw9yc3OFPKdPn0ZjY2OX1zQk1t7eDg0NDZX7ka+//hpOTk5C8Kk/4AiofurKlSuIiYmBp6cn3nzzTQwePBgaGhqYM2cOmpubRXmNjIxEnzsXtHw4H3XQ1NTEuHHjRGmdnfLbb7/d5Xc6h612ejDgBwBDhgwBANy+fRsjRozorqL2C25ubtDR0cHWrVtRWloKX19fpTwpKSnIzMzE22+/jbFjx2LQoEFQKBTIyMhAc3Oz6Okw32CjLD8/H3fu3IGHhwfu3r0LoOMifOjQoZDL5Zg5c6aQV5W2q6+v/1hP5J8l3dme58yZg7lz5+Lf//43zMzMcObMGaxbt66nq9QvDRgwALq6uqI0HR0dnhepVzxOH/1L7ty5g9bWVqV+nOdFMV1dXbi7uwsPUs6dO4eoqCikp6dDJpMJU3FnzZrV5fcfvuZ7+PgOGTIE9+7dQ01NjXAOfVZJJBLo6uoqHbMH3bp1C3p6egCA+/fvc0rdE9LQ0MBbb72F5cuXo6ioSLTt7t27XbbJwYMHiwKr/v7+OHDggDCKKicnB5MmTeLMGRVUVFTghRdeULkfqa2tFc2i6Q8YgOqnTp06BUNDQ3z00UfCtJiysrJeLlX/ZWJiAgB47733lIazAlA6WT68SGhVVRWAjil7JKajo4MZM2Zg9+7dcHZ27vLEeOzYMYSEhGDRokVC2tmzZ7vcn4aGhtrK2lcdPXoUAJCQkICEhATRtpqaGlRXVwufVWm7PMaP1p3t2d7eHqNGjcIXX3yBESNGQFdXFz4+Pmotf1/WeQPz4CLwQMcFN9usevCYdw9V+uhfOtadTE1Noa2trdSPP9jHk7IpU6bA2toaxcXFAABjY2MAgEwm6/KG++GRCtXV1Rg2bJjwuaqqCjo6OqJpes8qbW1t2Nvbo6CgAA0NDRg4cKBoe0NDAwoKCmBvbw+JRAJNTU2lFyrR4/P19UVaWho+/vhj0YNvY2NjfP/990r5q6urhXYPAA4ODjA3N0dOTg6srKyQl5eHVatW9UjZ+7Lr16+jsrISs2bNUrkfMTEx6XdtngGofqqpqQk6OjqiC7zOCxjqfi+++CKGDx+OkpISvPHGG7+a/+TJk6Ih2cePH4eZmRmfHDxCcHAwqqurMWfOnC63Nzc3i15F3dbWhpycnJ4qXp/W2NgIhUKBV199FeHh4aJtVVVVWL58OXJzc4V5/my7T64723NwcDAyMjIwePBg+Pn5KV280//rbKPFxcWYOHEigI6AalFREcaOHdubReu3eMyfnKp9dOfaNzdu3BCCHcXFxaKRJVpaWhgzZgwUCgXmz58vpJ86daoHatI3VFVVKT0YaGpqwq1bt4QlKuzt7aGvr4+Kigql6XVdOXnyJGxsbITPJ06cgK2tLaeR/df8+fMRExODXbt2Ka0htGvXLtTW1mL+/PkYOHAgJkyYgOzsbERGRnZ5/DiLQzWampp46623sHLlSkyePFlId3BwwPHjx3Hjxg1hMfGffvoJf/vb3xASEiLk09DQgJ+fH+RyOUaNGoX79+/D29u7x+vRlzQ3N2P9+vXQ1dVFcHAwjIyMVOpHnJ2dsWvXLpSXl/ebWTIMQPVTrq6u2LNnD9avXw8vLy8UFhYiOzu7t4vVb2loaGDlypWIj49HQ0MDpk2bBn19fZSXl+Ps2bNYtmwZrKyshPznz5/Hxo0b4erqivz8fGRnZ+O9997jAuSPMH78+C7faNfJxcUFmZmZePnllyGRSHDgwAG0tLT0YAn7LoVCgYaGBoSFhcHR0VFp+5/+9CfI5XJhgUS23SfXne05KCgImzZtQk1NDRfX/xXDhw+HnZ0d0tPTMWjQIGhra+PTTz8V1tSh7sdj/uRU7aPnzp0Lc3NzfPDBB1ixYgXq6+uxY8cOYYR2p+joaMTExCAxMRE+Pj64evUqvvzySwBgP46OtbU8PDzg5uYGMzMzVFZWYv/+/aipqRGCdkZGRoiNjUVKSgoqKiowefJkaGlpoaSkBAqFAmlpadDX1xf2mZ2djQEDBsDGxga5ubn4+9//jh07dvRWFZ86np6eCA0NhUwmQ0VFhTCS98SJE/jzn/+M0NBQYd2yFStWICIiAhEREZg3bx6MjY1RVFQEiUSC119/HUOHDoWRkRFycnJgaWkJXV1dSKVSpSnV1NHW09PTceHCBVhYWAAAZs+ejd27dyMqKgpxcXHQ09NDRkYGtLW1RUFroGNNy507d2Lr1q1wdXVVmtr7LLt//z4uX74MoGMU33fffYfDhw+jpKQEycnJwugmVfqRiIgIZGdnIzQ0FNHR0XjuuedQUlKCmzdv9tm3eTMA1Y80NTUJHay7uzvi4+Oxf/9+ZGVlYeLEidi+fTuj02rk6+sLIyMjfPLJJ8JoMwsLC0yZMkXpadq6detw+PBhHDx4EAYGBli6dKlKI6eoa0lJSVizZg3Wr18PfX19zJo1C15eXsJbbOjR5HI5RowY0eWNDQDMnDkTH3zwAX744QcAbLs94XHas4mJCSZPnoyKigq+8acLD54XAWDTpk1ITExEQkIChgwZgri4OOTk5KCurq4XS9m/8Jh3L1X76Fu3bkEmk2Ht2rVYunQpRo4ciVWrViE5OVmU39PTE2vXrsX27dvx1Vdfwc7ODmvXrkVkZCQMDQ17okpPtcWLF+P06dNITk7GnTt3IJFIIJVKsXv3bjg5OQn5IiMjMWzYMHz22WfYv38/tLW1MXLkSEybNk00ghUANm/ejC1btiA9PR2DBw/G+vXr+aKOhyQlJcHOzg4HDhzAkiVLAHSsc5acnCxa42zSpEnYu3cvPvroIyQkJEBTUxOjRo1CXFwcgI4g6oYNG7BlyxZERESgpaUFCoWiXy3g3F20tLSwaNEi0bWFoaEh9u3bh+TkZCQlJeH+/fuYOHEi9u/fr/SmQhsbG1hZWeH7779HfHx8Txf/qdbU1CSMGBs4cCAsLS3h7OwMmUwmWuBdlX5EIpHg4MGD2Lx5MzZt2oTGxkZYWFgIb0DuizTa29vbe7sQ1D0WL16M8vJyZGVl9XZRiKifuXDhAsLDw3HkyBGlRfip99TX12PKlClYsmQJIiMje7s4Tx2eF3sej3nfk5mZicTERN6od7OsrCwkJCTg66+/5ugQIqL/4giofuDatWsoKCjAmTNnhKcGRETUf9XX16O4uBgHDhyAhoYGZs+e3dtFeqrwvNjzeMz7htraWshkMjg5OcHAwAD//Oc/8cknn8DT05PBJyIiUjsGoPqBVatW4aeffsKCBQuwcOHC3i4OERGpWVFREcLDw2Fubo6NGzcqrfPyrON5sefxmPcN2traKCkpgVwuR11dHSQSCYKCgjiFhoiIegSn4BERERERERERkVrxdRdERERERERERKRWDEAREREREREREZFaMQBFRERERERERERqxQAUERERERERERGpFQNQRERERERERESkVgxAERERET2DpFIp1q1b19vFICIiomcEA1BERERE3SwrKwtSqRRSqRTffPON0vb29na4u7tDKpUiKipKbeW4dOkS0tLScPfuXbX9DiIiIiJVMABFREREpCZ6enqQy+VK6QUFBaioqICurq5af39hYSFkMhkDUERERNTrGIAiIiIiUhN3d3ccO3YMra2tonS5XA5bW1sMHTq0l0pGRERE1LMYgCIiIiJSE39/f9TW1iI/P19Ia2lpwfHjxxEYGKiUv6GhAcnJyXB3d8fYsWPh7e2NnTt3or29XZSvc/2mU6dOISAgAGPHjoW/vz/y8vKEPGlpafjwww8BAJ6ensKUwNLSUtG+fmkfRERERN1Fu7cLQERERNRfWVhYYMKECcjJyYG7uzsAIC8vD3V1dfDz88O+ffuEvO3t7YiOjsaFCxfw+uuvY8yYMTh37hw+/PBDVFZWYtWqVaJ9X7x4ESdOnMC8efNgYGCAffv2ITY2FqdPn4ZEIoGXlxdu3rwJuVyOhIQESCQSAICpqanK+yAiIiLqLgxAEREREalRYGAgNm/ejKamJgwYMABHjx7FK6+8gmHDhonyKRQKnD9/HnFxcYiOjgYAvPHGG4iNjcXevXsRGhqKkSNHCvmLi4uRm5srpDk6OiIoKAg5OTkIDQ2FtbU1bGxsIJfL8eqrr8LS0lKpbL+2DyIiIqLuwil4RERERGrk6+uL5uZmnD59GvX19Thz5kyX0+/y8vKgpaWFsLAwUXpkZCTa29uVpsa5uLiIAlLW1tYwNDRESUmJymXrjn0QERERqYIjoIiIiIjUyNTUFM7OzpDL5WhqakJbWxu8vb2V8pWVlcHMzAyGhoai9JdeeknY/iBzc3OlfRgbGz/WG++6Yx9EREREqmAAioiIiEjNAgICkJSUhKqqKkydOhVGRkZPvE8tLa0u0x9esFzd+yAiIiJSBafgEREREamZl5cXNDU1cfnyZQQEBHSZx8LCAj/++CPq6+tF6Tdu3BC2Py4NDY3HLywRERGRGjAARURERKRmBgYGWLt2LZYsWYLp06d3mWfq1Kloa2vD559/LkrfvXs3NDQ0MHXq1Mf+vfr6+gCAurq6xy80ERERUTfiFDwiIiKiHjBr1qxf3D59+nQ4OjoiNTUVZWVlkEqlyM/Ph0KhwPz580WLhavK1tYWAJCamgo/Pz/o6OjAw8MDAwcO/J/qQERERPS/YgCKiIiI6CmgqamJjIwMbNu2Dbm5ucjKyoKFhQXeffddREZG/k/7HD9+PJYuXYpDhw7h3LlzuH//PhQKBQNQRERE1OM02rnKJBERERERERERqRHXgCIiIiIiIiIiIrViAIqIiIiIiIiIiNSKASgiIiIiIiIiIlIrBqCIiIiIiIiIiEitGIAiIiIiIiIiIiK1YgCKiIiIiIiIiIjUigEoIiIiIiIiIiJSKwagiIiIiIiIiIhIrRiAIiIiIiIiIiIitWIAioiIiIiIiIiI1IoBKCIiIiIiIiIiUisGoIiIiIiIiIiISK3+D66f2SSg/d3rAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 1200x1000 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Setting the style for the plots\n",
        "sns.set(style=\"whitegrid\")\n",
        "\n",
        "# Creating a figure with two subplots\n",
        "fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(12, 10))\n",
        "\n",
        "# Bar chart for flight distribution by month\n",
        "sns.barplot(x=month_distribution_corrected.index, y=month_distribution_corrected.values, ax=axes[0], palette='GnBu')\n",
        "axes[0].set_title('Flight Distribution by Month')\n",
        "axes[0].set_xlabel('Month')\n",
        "axes[0].set_ylabel('Number of Flights')\n",
        "axes[0].set_xticklabels([calendar.month_abbr[i] for i in month_distribution_corrected.index])\n",
        "\n",
        "# Line chart for average customer rating by month\n",
        "sns.lineplot(x=average_monthly_rating_corrected.index, y=average_monthly_rating_corrected.values, ax=axes[1], marker='o',palette='GnBu')\n",
        "axes[1].set_title('Average Customer Rating by Month')\n",
        "axes[1].set_xlabel('Month')\n",
        "axes[1].set_ylabel('Average Rating')\n",
        "axes[1].set_xticks(range(1, 13))\n",
        "axes[1].set_xticklabels([calendar.month_abbr[i] for i in range(1, 13)])\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "JEtajyVDQtiy",
        "outputId": "2ea8b934-e8a6-4152-e796-3ac29a3afb93"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.barplot(x=month_distribution_corrected.index, y=month_distribution_corrected.values ,palette='GnBu')\n",
        "plt.title('Flight Distribution by Month')\n",
        "plt.xlabel('Month')\n",
        "plt.ylabel('Number of Flights')\n",
        "plt.xticks(ticks=range(12), labels=[calendar.month_abbr[i] for i in range(1, 13)])\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "wfp_OYS6QHtc",
        "outputId": "a93d7403-efac-4570-855a-97273da6eafe"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Second Plot - Average Customer Rating by Month\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.lineplot(x=average_monthly_rating_corrected.index, y=average_monthly_rating_corrected.values, marker='o')\n",
        "plt.title('Average Customer Rating by Month')\n",
        "plt.xlabel('Month')\n",
        "plt.ylabel('Average Rating')\n",
        "plt.xticks(ticks=range(12), labels=[calendar.month_abbr[i] for i in range(1, 13)])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S2W8bg_syFyr"
      },
      "source": [
        "#3.Find the likelihood of a successful holiday booking based on customer characteristics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jwqx-yZ5yKfq",
        "outputId": "06feeaab-4d57-4098-cfaa-21e63222f9ce"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "count    1038.000000\n",
              "mean        3.143545\n",
              "std         2.826656\n",
              "min         1.000000\n",
              "25%         1.000000\n",
              "50%         2.000000\n",
              "75%         4.000000\n",
              "max        10.000000\n",
              "Name: Rating, dtype: float64"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Checking the distribution of ratings in the dataset\n",
        "rating_distribution = correct_filtered_data['Rating'].describe()\n",
        "rating_distribution\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 799
        },
        "id": "bAE1MjLwyTgp",
        "outputId": "4c6c520e-19ca-48fb-8c2e-34fc8cb29a57"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "1     28.125000\n",
              "2     31.034483\n",
              "3     28.000000\n",
              "4     24.096386\n",
              "5     31.428571\n",
              "6     20.408163\n",
              "7     25.316456\n",
              "8     31.250000\n",
              "9     36.082474\n",
              "10    35.135135\n",
              "11    28.089888\n",
              "12    31.578947\n",
              "Name: Month_Num, dtype: float64"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Segmenting the data based on the high rating threshold (4 or above)\n",
        "high_ratings_data = correct_filtered_data[correct_filtered_data['Rating'] >= 4]\n",
        "\n",
        "# Analyzing the distribution of high ratings across different months\n",
        "high_ratings_monthly_distribution = high_ratings_data['Month_Num'].value_counts().sort_index()\n",
        "\n",
        "# Normalizing the high ratings distribution by the total number of flights per month to get the likelihood\n",
        "total_flights_per_month = correct_filtered_data['Month_Num'].value_counts().sort_index()\n",
        "likelihood_high_rating = (high_ratings_monthly_distribution / total_flights_per_month) * 100\n",
        "\n",
        "# Plotting the results\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.barplot(x=likelihood_high_rating.index, y=likelihood_high_rating.values, palette=\"mako\")\n",
        "plt.title('Likelihood of High Rating by Flying Month')\n",
        "plt.xlabel('Month')\n",
        "plt.ylabel('Likelihood of High Rating (%)')\n",
        "plt.xticks(ticks=range(12), labels=[calendar.month_abbr[i] for i in range(1, 13)])\n",
        "plt.show()\n",
        "\n",
        "likelihood_high_rating\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9wtus8Ev-qTJ"
      },
      "source": [
        "# 4. Investigate the popularity of various routes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 764
        },
        "id": "iJ9cYI9A-pZ5",
        "outputId": "3cc88413-9a51-4de8-efd2-12e71404a9bc"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "LHR to MIA    13\n",
              "BKK to LHR    13\n",
              "JFK to LHR    10\n",
              "LHR to BKK    10\n",
              "HKG to LHR     9\n",
              "LHR to SFO     9\n",
              "LHR to CPT     9\n",
              "LHR to HKG     9\n",
              "LHR to JFK     8\n",
              "LHR to SIN     8\n",
              "Name: Route, dtype: int64"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Excluding rows with missing 'Route' values in the original dataset\n",
        "original_data_no_nan = data_new[data_new['Route'].notna()]\n",
        "\n",
        "# Filtering for routes represented by airport codes in the original dataset\n",
        "original_airport_code_routes_no_nan = original_data_no_nan[original_data_no_nan['Route'].str.contains(r'^[A-Z]{3} to [A-Z]{3}$', regex=True)]\n",
        "\n",
        "# Analyzing the popularity of airport code routes in the original dataset without NaN values\n",
        "original_airport_code_route_popularity_no_nan = original_airport_code_routes_no_nan['Route'].value_counts()\n",
        "\n",
        "# Top 10 airport code-based routes in the original dataset without NaN values\n",
        "top_10_original_airport_code_routes_no_nan = original_airport_code_route_popularity_no_nan.head(10)\n",
        "\n",
        "# Plotting the top 10 airport code-based routes from the original dataset without NaN values\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.barplot(x=top_10_original_airport_code_routes_no_nan.values, y=top_10_original_airport_code_routes_no_nan.index, palette=\"summer\")\n",
        "plt.title('Top 10 Airport Code-Based Routes (Original Dataset, No NaN)')\n",
        "plt.xlabel('Number of Flights')\n",
        "plt.ylabel('Route (Airport Codes)')\n",
        "plt.show()\n",
        "\n",
        "top_10_original_airport_code_routes_no_nan\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9tHRNBu4kUjv"
      },
      "source": [
        "This one should be used in assessment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 868
        },
        "id": "Te3NmTzA7VXQ",
        "outputId": "d5a8dad5-e8a1-4e33-c972-d86680841f4a"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-41-bafdcafd9bc5>:20: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  original_data_no_nan['Route_City_Name'] = original_data_no_nan['Route'].apply(convert_airport_codes_to_city)\n"
          ]
        },
        {
          "data": {
            "image/png": 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XX5k8PT1N06dPt+jf09PTNGnSJKtrGDhwoEVsN/P222+bPD09TQcPHixU/YiICFPDhg1NR48eNZedPn3a5O/vb3rxxRfNZePHjzd5enqa9u7day5LTU01BQQEWIy1O/W52LBhQ9OYMWMKVRdAycFSOAAA7OTrr79WqVKlrB6v3rdvX5lMJqulT/7+/uZ/EZak6tWrq3379vr222+Vm5tbLDFdvny5wH1J8ssKs7zo0KFDatGihVq0aKGOHTtq3rx5Cg4OVmxsrLnOli1bJEkvvfSSxWvzN1HOP18cvvvuO6WlpSk0NFTnzp0z/zg4OKhRo0bm5XJlypSRo6OjduzYoYsXLxapjwsXLsjNza3YYg4KClLNmjXNx/Xr11e5cuV07NixYml/3bp1CggIUPny5S3uSVBQkHJzc81LLL/++muVLl1aL7zwgvm1pUqVspgldDN//fXXTWdt3I6wsDDt3btXR48eNZetXbtW1apVU7NmzQrdTtOmTRUYGKi5c+fq8uXLN6x37WymixcvKj09XQEBATp48KBV3RYtWljM5MifTdKhQweVK1fOXJ4/+yj/fU1KStKRI0f01FNP6fz58+b3JSMjQy1atNDOnTvNS5rKly+vvXv3Wi3VupVrryMnJ0fnz59XzZo1Vb58+QKvpXv37hbLaJs0aaLc3FylpKRIuvr3lZOTo549e1rU6927d6Fj+vnnn/XLL7+oc+fO5rLQ0FCdP39e3377baHa6NChg9zd3Qs816VLF4v7/uSTT8rDw8P8OfPTTz8pNTVVL7zwgsXMnscee0x16tTR5s2brdq89m/CFn/99Zekm89qypebm6utW7fq8ccf18MPP2wur1y5sjp37qzdu3eb29uyZYv8/PzMY0u6upz5qaeesmjzTn0uurm56fz587e+AQBKFJbCAQBgJykpKapcubLFFx7p/5Zv5X9xy/fII49YtVGrVi1lZmbq3Llz8vDwuO2YypQpU+A+SvllhVleVKNGDY0bN055eXk6evSo3nvvPZ0/f97itSkpKXJwcLBInkiSh4eHypcvb3Xtt+PIkSOSbvxFN//+Ozk5KSoqSu+8845atmypRo0a6bHHHlOXLl0KdW9NxfikqmrVqlmVXbu053b98ccfSk5OVosWLQo8n7+HTkpKijw8PKy+/NauXbtQ/ZQrV06XLl26vWBvoFOnTnr77be1Zs0aRUZGKj09XV999ZX69OlT5I2Dhw4dqp49e2rZsmU3fKLXV199pXfffVdJSUkWfyMF9XX9+5c/xq5/uperq6skmd/X/LF6s6WG6enpcnNzU1RUlKKjo/XYY4+pYcOGatu2rbp06WKReCjI5cuXNXv2bK1cuVKnT5+2GLcF7Z1VvXp1i+P8JWH5MZ84cUKSrJ4Y6e7uXuhk65o1a+Ti4qKHH35Yf/zxh6SrnzU1atTQ2rVrrZYaFuRmS1Wv/+w0GAx65JFHzJ8z+ddQ0LiuU6eO1f5WpUuXvu0nteWPicL8fZw7d06ZmZkFxle3bl3l5eXp5MmTevTRR3XixAlzIvNa17/2Tn0umkwmNu4G7kMklgAAgJmHh4fOnDljVf7nn39Kuvov5Lfi4uJisYl148aN1a1bN02dOlWvv/66Rd3b+QJS2Fla+V+c4+LiCvwidO2T1fr06aPg4GBt3LhR3377raZPn645c+Zo4cKFN90zpEKFCgUmffKTaTeaCZOZmVlgsu5GT84rruRVXl6eWrZsqf79+xd4/vokga3q1KljTsQU9xO63Nzc1K5dO61du1aRkZFat26dsrOzC71nzbWaNm2qZs2aae7cuQU+2W/Xrl16+eWX1bRpU40ZM0YeHh5ydHTUxx9/rE8//dSq/o3ev1u9r/n/+69//UteXl4F1s1/+linTp3UpEkTffHFF9q6davmzZunxMRExcfHq23btje81rfeesu8Z5Ofn59cXV1lMBj0yiuvFDi+rt9v7fqYb5fJZNJnn32mjIwMderUyer8uXPndOnSpVvO7Pk7n+rn5OR0w/tSWPn7wCUnJ9/wvb6T7tTnYlpa2g33gQNQcpFYAgDATmrUqKHvv/9ef/31l8WspUOHDpnPXyv/X/KvdeTIETk7O99wCUhR1a9fX7t371ZeXp7FF6d9+/bJ2dm50DNVrm/z6aef1rJly9S3b19Vr15dNWrUUF5env744w+Lp4WdPXtWaWlpFtde0Eyd7OxsqwTYjZJU+TM4KlasWKinttWsWVN9+/ZV3759deTIEXXp0kXz58/XpEmTbviaOnXqaO3atUpPTzfPQpH+b7bH4cOHC3zd4cOHrWaE/B1q1qypjIyMW96PGjVqaNu2bVZf7G90Pddr166d9uzZow0bNlgscyqsWyUew8LCFBERoX379mnt2rVq0KCBHn300SL3I12dtRQeHq5ly5ZZnVu/fr0eeOABzZs3zyJB9vHHH9vU143kj9Vy5coVaqxWrlxZL774ol588UWlpqaqa9eueu+9926aWFq/fr26dOli8ZTArKysQj/p73r54/fIkSMWs6XOnTtXqKVTO3bs0KlTpzRs2DCrJwempaXpP//5jzZu3KiwsDCb4pOsPztNJpP++OMP84MIrv07vX4WX1H+RouSKG/Tpo1KlSqltWvXqkuXLjet6+7uLmdn5wL/7g4dOiQHBwfzLLnq1asX+P8V17/2Tnwunj59Wjk5OQU+ARJAycYeSwAA2EmbNm2Um5urJUuWWJQvWLBABoNBbdq0sSjfs2ePxaPET548qU2bNqlly5Y3nAlRVE8++aTOnj2rDRs2mMvOnTundevWqV27djbPOunfv7+uXLmi999/X5LMX3wXLlxoUe/689LVL0C7du2yqLdixQqrGUv5T7O6/gty69atVa5cOc2ePbvAp1TlL/vKzMy02kOqZs2aKlu2bIHLA6/l5+cnk8mkn376yaK8cuXK8vLy0tq1a62SYz/99JP27t1r9T7/HTp27Kg9e/bom2++sTqXlpamK1euSLo6Rq9cuaKlS5eaz+fm5mrx4sWF6uf555+Xh4eHJkyYUOCX4tTUVM2aNeuGr89/T2+0BLBNmzZ68MEHNXfuXO3cudOm2Ur5mjVrZp61dP04KFWqlAwGg8WYO378uDZt2mRzfwXx9vZWzZo1NX/+/AKXSOWP1dzcXKtxXrFiRVWuXPmWY7Wgz4pFixbZvE9bUFCQHB0dtXjxYotZTNf/bd9I/jK4/v3768knn7T46d69u2rVqqW1a9faFFu+1atXm/cgkq7uMXbmzBnz3563t7cqVqyoZcuWWdy/LVu26Pfffy/UUjzp6ngtbIKuWrVqevbZZ/Xtt99q0aJFVufz8vI0f/58nTp1SqVKlVLLli21adMmHT9+3Fzn7Nmz+vTTTxUQEGD+x4m2bdvqxx9/1L59+8z1zp07Z3UP78TnYv7nn7+/f6HuAYCSgxlLAADYSXBwsAIDAzV16lSlpKTIaDRq69at2rRpk3r37m21/5Cnp6f69eun8PBwOTk5mb/sDx069JZ9ffnll/r5558lXd2wNzk52fyFPjg4WPXr15ckhYSEyM/PTzExMfrtt9/04IMPaunSpcrNzS1UPzdSr149tW3bVh999JEiIiJUv359de3aVcuXL1daWpqaNm2q/fv3a9WqVXr88cfVvHlz82ufffZZjRkzRkOHDlVQUJB+/vlnffvtt1bLLby8vFSqVCklJiYqPT1dTk5Oat68uSpWrKg33nhD//rXv9StWzd16tRJ7u7uOnHihLZs2aLGjRtr9OjROnLkiPr06aMnn3xS9erVU6lSpbRx40adPXtWoaGhN72+gIAAVahQQd9//73VjIfo6Gj1799fXbp0UdeuXVW5cmX9/vvvWrFihTw8PDRo0CCb76ut+vXrpy+//FKDBw9W165d1bBhQ2VmZuqXX37R+vXrtWnTJrm7uys4OFiNGzfW5MmTlZKSonr16mnDhg2F/vLs5uamhIQEDRw4UF26dNHTTz+thg0bSpIOHjyoTz/99KZfQvM3lV62bJnKli0rFxcX+fr6mmdbODo6KjQ0VIsXL1apUqVu+T7dSmRkpHr16mVV3rZtW73//vvq37+/OnfurNTUVH344YeqWbOmkpOTb6vPazk4OGjcuHEaMGCAOnfurG7duqlKlSo6ffq0tm/frnLlyum9997TpUuX1LZtW4WEhKh+/fpycXHRd999p/3791vMRCrIY489pk8++UTlypVTvXr19OOPP+q7775ThQoVbIrZ3d1dffv21ezZszVo0CC1bdtWBw8e1Ndff33LJVHZ2dnasGGDgoKCbrh/W3BwsD744AOlpqaqYsWKNsXo5uamHj16qFu3bkpNTdXChQv1yCOPqHv37pKujqOoqCjFxMSoZ8+eCg0NVWpqqj744APVqFHjhvtuXa9hw4b63//+p9jYWPn4+MjFxUXBwcE3rB8dHa1jx45p3Lhx2rBhg9q1a6fy5cvr5MmTWrdunQ4dOmQe0//85z/13XffqUePHurRo4dKlSql5cuXKzs7WyNHjjS32b9/f33yySfq37+/evXqJWdnZ61YsULVq1e3GKvlypUr9s/F7777TtWrV7/psmEAJROJJQAA7MTBwUHvvvuuZsyYof/9739auXKlatSooX/961/mp6Ndq2nTpvLz81NCQoJOnDihevXqKTY21pwUupkNGzZo1apV5uODBw+anwBVtWpVcxulSpXSnDlzFBcXp0WLFikrK0s+Pj6KjY017wliq379+mnz5s1avHixhg4dqnHjxumhhx7SqlWrtHHjRlWqVEmDBg1SZGSkxeu6d++u48eP66OPPtI333yjgIAAvf/++1Zf9jw8PDR27FjNnj1br732mnJzc/XBBx+oYsWKeuqpp1S5cmXNmTNH8+bNU3Z2tqpUqaImTZqoW7du5vsQGhqq77//XmvWrFGpUqVUp04dTZs2TSEhITe9NicnJz311FNat26dXn31VYtzzZs315IlS/Tuu+9q0aJFunTpkipWrKjOnTtr6NChNn9Zvh3Ozs5atGiRZs+erXXr1mn16tUqV66catWqpaFDh5qX8+WP0fxNsg0Gg4KDgxUdHX3L5Tv5GjVqpLVr12revHnavHmzPvnkEzk4OKhOnToaOHDgTZ8w5+joqAkTJmjKlCl64403dOXKFcXGxlosuQoLC9PixYvVokWLQu0BdjOBgYFq1qyZduzYYVHeokULjR8/XomJiXr77bf10EMPKSoqSikpKcWaWMqPYfny5Zo1a5YWL16sjIwMeXh4yNfXV88995ykq/sJvfDCC9q6das2bNggk8mkmjVrasyYMerRo8dN23/ttdfk4OCgtWvXKisrS40bNzYnzWz1z3/+U05OTlq2bJm2b98uX19fzZ8//5ZJ082bNystLU3t2rW7YZ127dpp/vz5+uyzzwpM+hXG4MGDlZycrDlz5ujSpUtq0aKFxowZY54RJ0ndunVTmTJllJiYqEmTJsnFxUWPP/64Ro4cad6w/FZ69OihpKQkrVy5UgsWLFCNGjVumlhydnZWYmKiVq5cqdWrV2vWrFm6fPmyKleurMDAQE2aNElVqlSRJD366KNasmSJJk+erNmzZ8tkMsnX11cTJ0602Ky7cuXK+uCDDzRu3DjNmTNHFSpU0PPPP6/KlSvrtddes+i/OD8X8/LytH79ev3jH/9g827gPmQwFecjTAAAwB1hNBr14osvavTo0fYOBTdw7NgxdezYUYmJiTd82hqK388//6ywsDC98847hU524f6wfft29erVS9OnT9eTTz5p73BKtI0bN2rEiBH64osvbjvBC+Dewx5LAAAAxeDhhx/WM888ozlz5tg7lPvKihUr5OLiog4dOtg7FOC+lZiYqBdffJGkEnCfYikcAABAMRk7dqy9Q7hvfPnll/rtt9+0YsUKvfjii3JxcbF3SMB9a/ny5fYOAYAdkVgCAADAPWfcuHE6e/as2rRpc1sbywMAgNvDHksAAAAAAACwCXssAQAAAAAAwCYklgAAAAAAAGAT9lgC7mJ79uyRyWSSo6OjvUMBAAAAANwHcnJyZDAY5O/vX6j6zFgC7mImk8n8A5Q0JpNJ2dnZjG+UWIxxlGSMb5R0jHGUZLca30X9DsqMJeAu5ujoqOzsbNWrV4/HKKPEycjIUFJSEuMbJRZjHCUZ4xslHWMcJdmtxvf+/fuL1B4zlgAAAAAAAGATEksAAAAAAACwCYkl4B5gMBjsHQJQ7AwGg5ydnRnfKLEY4yjJGN8o6RjjQOGxxxJwl3NycpKzs7O9wwCKnbOzsxo0aGDvMIA7hjGOkozxjZKOMY7ilJebJ4dSJXdeD4kl4B4QFz1fxw6dsncYAAAAAIAieLhOVf1rQl97h3FHkVgC7gHHDp3S70nH7B0GAAAAAAAWSu5cLAAAAAAAANxRJJYAAAAAAABgExJLAAAAAAAAsAmJJQAAAAAAANiExBIAAAAAAABsQmIJAAAAAAAANiGxBAAAAAAAAJuQWAIAAAAAAIBNSCwBAAAAAADAJqXtHcDfIT4+XvPnz9eePXvsFkNYWJi8vLw0YcKEv6W/lStXytHRUU899VSxtrtr1y7NmzdPP/74o9LT0+Xu7q5mzZqpd+/e8vHxKda+bBUeHq4dO3bctE7Xrl3/tvcCAAAAAICS6r5ILN2PVq1aJRcXl2JNLC1ZskRvvfWWmjdvrtdee01VqlTR6dOntXbtWvXt21c7d+4str5ux5gxY/TXX3+Zj8eOHasyZcpo1KhR5jJ3d3d7hAYAAAAAQIlCYgmF8vPPP+vtt99WWFiYJkyYIIPBYD7XuXNnffXVV3aMzlK9evUsjsuVKycXFxf5+fnZJyAAAAAAAEoo9lj6/5KTk9WvXz/5+fkpICBAw4YN04kTJyzqGI1GJSYmKj4+XkFBQQoMDFRMTIwyMjIs6v3www/q1q2bfHx81LlzZ23ZsqXAPjds2KCwsDD5+PioVatWio2NVVZWlvn89u3bZTQatXXrVo0YMUL+/v5q166dEhMTb3ot+UvBNm/eLKPRKKPRqPj4ePP5ZcuWKSQkRN7e3goODtasWbOUl5d30zY/+OADGQwGjRo1yiKplK9du3bm31evXq0XXnhBzZo1U9OmTRUeHq59+/ZZ1I+Pj5e/v7/27dunf/zjH/Lx8VHHjh0LTFBt3rxZzz77rHx9fdW8eXONGTPG6p4X1a3u/eOPP65p06aZj9evXy+j0ah33nnHXPbNN9/IaDTq3Llzkq7e90GDBmndunUKCQmRv7+/evXqpaNHj95WrAAAAAAA3K1ILEk6efKkevbsqfPnz2vixIkaO3asDhw4oJ49e1osqZKuLgc7cuSIJkyYoCFDhmjt2rWaNWuW+fyZM2fUr18/OTk5adq0aerXr5/Gjh2r06dPW7SzadMmDRs2TPXq1VNCQoL69++vZcuWaeTIkVbxjRkzRrVq1VJCQoLatWunSZMm6euvv77h9YwZM0YNGjRQ48aNtXz5ci1fvlzPPvusJGnRokUaM2aMWrdurffee09du3bVzJkzNXHixJveo507d8rb27tQS8iOHz+uLl26aPr06Zo0aZKqVaumF198UYcPH7aol5OTo1deecUcwyOPPKLIyEglJyeb66xbt04vv/yyPD09NXPmTI0cOVJffPGFXnvttVvGcSOFufdNmzbVrl27LK7/gQcesCqrU6eOxT1JSkrSvHnzFBUVpdjYWB09erTA9xQAAAAAgJKApXCSFixYoCtXrmj+/PmqUKGCJMnLy0uhoaFatWqVwsPDzXU9PDw0efJkSVKbNm108OBBrV+/XlFRUZKkhQsXymAwKDExUa6urpKkqlWrqk+fPhZ9zpw5U35+fhZtOTs7a/To0UpOTpbRaDTX7dChg4YOHSpJatGihTZv3qz169erTZs2BV5PvXr1Clz+lZubq4SEBIWGhur111+XJLVq1Uo5OTmaP3++Bg4cqAcffLDANk+fPl3ozbkjIyPNv+fl5ally5bat2+fVq1apVdffdV8LicnRy+//LL+8Y9/mGPp0KGDZs+erSlTpshkMikuLk6dOnXS+PHjza/z8PDQwIEDFRERoUcffbRQMV2rMPe+SZMm+uyzz5SdnS0nJyft3LlTzz77rJYtW6ZLly6pbNmy2rlzp5o2bWrRdnp6ulavXm1ONmVkZCgmJkanTp1S1apVixwrAAAAAAB3M2Ys6eqTzgIDA81JJUmqW7eu6tevr927d1vUDQoKsjiuW7euTp06ZT7eu3evAgMDzUkl6Woy6Nq2L126pKSkJIWEhFi01alTJ0my6rNVq1bm3w0Gg1WfhXXo0CGdP39eTz75pFW/OTk5VsvVrlfQEriC/P777xoyZIiCgoLk5eWlhg0b6vDhwzpy5IhV3SeeeML8e6lSpfT4449r7969kqTDhw8rJSVFHTt21JUrV8w/zZo1k4ODg3766adCxXOtwt77pk2bKisrS/v27VNaWpp++eUX9ejRQ+XKldMPP/ygrKws7d+/X02aNLFop379+hYzmPL3e7Ll/QIAAAAA4G7HjCVJaWlp8vLysiqvWLGiLl68aFFWvnx5i2NHR0dlZ2ebj8+cOaNHHnnEqq1rkw3p6ekymUyqWLGiRR1XV1c5OTlZ9Xltkiq/z/T09FtclbX8dq/vN//4+n6vVaVKFas9pwry119/qW/fvnJ3d1d0dLSqV6+uBx54QK+//rrFHkbS1etwc3OziuXMmTOSpPPnz0uShgwZUmBfJ0+evGU81yvsva9Zs6aqVKminTt3Kj09XRUrVlTdunXVuHFj7dq1S05OTsrJybGasVTQ+JBkde0AAAAAAJQEJJYkubm5KTU11ao8NTVVtWrVKlJbHh4eBbaVv8GzdDWJYTAYLMqkq0mP7Oxsq2RLccmfNXV9v/nx3qzfZs2aac2aNbpw4YLF7Kvr/fjjjzp16pRmz56t+vXrm8vT09OtloLl5OTo4sWLFv2mpqbKw8PDIt7Ro0fL19fXqq/KlSvfMI4bKcq9z99nKT09XQEBAeayL774Qo6OjqpRo4aqVatW5BgAAAAAACgpWAonKSAgQNu2bbOYsXPo0CElJyebEwqF5evrq+3bt1vMKPr+++914cIF83HZsmXl5eWldevWWbz2888/N8dzuxwdHa1mydSuXVvu7u4F9uvo6Fhg8iZfeHi48vLyLJ6Kdq3NmzdLki5fvmzuP98PP/yglJSUAl/3xRdfmH/Pzc3Vxo0b1ahRI0lSnTp1VLVqVR07dkw+Pj5WP1WqVLlhvDdSlHvfpEkT/fDDD9q2bZuaNWsm6Wpiaf/+/fr222+tlsEBAAAAAHC/uW9mLOXm5lolE6SriaA+ffpo5cqV6tu3r15++WVlZWVp2rRpqlatmrp27Vqkfnr37q0PP/xQAwYM0IABA5SWlqb4+HirWT6RkZEaMmSIoqKi9PTTT+vw4cOaOnWqQkJCLDbutlWdOnW0evVqffnll/Lw8FDlypVVpUoVRUREaNy4cXJ3d1fbtm31448/KjExUb17977hxt3S1b2D/v3vf+utt97S6dOn9cwzz6hKlSo6ffq0PvvsM+3atUs7duyQn5+fXFxcNHbsWA0cOFCnT59WfHx8gUkgR0dHvfvuu8rKytJDDz2kpUuX6tSpU0pISJB0dU+n6OhoRUVFKSMjQ4899picnZ114sQJbdmyRa+88opq165d5HtT2HvftGlTZWRk6MCBA4qNjZUkNWjQQE5OTtqzZ4+eeeaZIvcNAAAAAEBJct8klrKysjR8+HCr8ri4OIWFhWnRokWKi4tTVFSUHBwc1LJlS0VHR6tcuXJF6qdy5cpKTEzUuHHjNHz4cNWsWVOjR4/W1KlTLeq1b99e06dPV0JCgiIiIlShQgV1795dI0aMuK3rzDdgwAAdPXpUo0aNUlpamiIjIzV06FCFh4erdOnSWrBggZYuXSoPDw9FRkZq8ODBt2zzxRdflNFo1Lx58/Tmm2/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            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "London to Cape Town       24\n",
              "Hong Kong to London       20\n",
              "Bangkok to London         20\n",
              "London to Hong Kong       20\n",
              "New York to London        19\n",
              "London to Dubai           18\n",
              "London to New York        17\n",
              "London to Johannesburg    16\n",
              "London to Singapore       16\n",
              "London to Miami           16\n",
              "Name: Route_City_Name, dtype: int64"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Excluding rows with missing 'Route' values in the original dataset\n",
        "original_data_no_nan = data_new[data_new['Route'].notna()]\n",
        "\n",
        "# Manually mapping some well-known airport codes to city names\n",
        "airport_code_to_city = {\n",
        "    \"LHR\": \"London\", \"BKK\": \"Bangkok\", \"MIA\": \"Miami\",\n",
        "    \"JFK\": \"New York\", \"HKG\": \"Hong Kong\", \"SFO\": \"San Francisco\",\n",
        "    \"CPT\": \"Cape Town\", \"SIN\": \"Singapore\", \"LGW\": \"London Gatwick\",\n",
        "    \"DXB\": \"Dubai\", \"CDG\": \"Paris\", \"NAP\": \"Naples\", \"PRG\": \"Prague\",\n",
        "    \"BUD\": \"Budapest\", \"LAS\": \"Las Vegas\"\n",
        "}\n",
        "\n",
        "# Function to convert airport codes to city names in the route\n",
        "def convert_airport_codes_to_city(route):\n",
        "    for code, city in airport_code_to_city.items():\n",
        "        route = route.replace(code, city)\n",
        "    return route\n",
        "\n",
        "# Applying the conversion to the 'Route' column in the original data\n",
        "original_data_no_nan['Route_City_Name'] = original_data_no_nan['Route'].apply(convert_airport_codes_to_city)\n",
        "\n",
        "# Analyzing the popularity of routes in the unified format\n",
        "unified_route_popularity = original_data_no_nan['Route_City_Name'].value_counts()\n",
        "\n",
        "# Top 10 routes in the unified format\n",
        "top_10_unified_routes = unified_route_popularity.head(10)\n",
        "\n",
        "# Plotting the top 10 routes in the unified format\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.barplot(x=top_10_unified_routes.values, y=top_10_unified_routes.index, palette=\"viridis\")\n",
        "plt.title('Top 10 Routes (Unified City Names and Airport Codes)')\n",
        "plt.xlabel('Number of Flights')\n",
        "plt.ylabel('Route')\n",
        "plt.show()\n",
        "\n",
        "top_10_unified_routes\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k5riwWhMiklD"
      },
      "source": [
        "# STASTICAL Tests"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "This section presents the code for hypothesis testing based on research questions in the context of the study and then based on statistical tests we present the final recommendations. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "56jzPsWUin40"
      },
      "outputs": [],
      "source": [
        "# Excluding rows with missing 'Traveller_type' values\n",
        "new_airline_data_no_nan = data_new.dropna(subset=['Traveller_type'])\n",
        "\n",
        "# Re-filtering data for business and leisure travelers\n",
        "business_travelers_no_nan = new_airline_data_no_nan[new_airline_data_no_nan['Traveller_type'] == 'Business']['Rating']\n",
        "leisure_travelers_no_nan = new_airline_data_no_nan[new_airline_data_no_nan['Traveller_type'].str.contains('Leisure')]['Rating']\n",
        "\n",
        "# Ensuring that the 'Rating' column contains only numeric values and dropping NaNs\n",
        "business_ratings_no_nan = pd.to_numeric(business_travelers_no_nan, errors='coerce').dropna()\n",
        "leisure_ratings_no_nan = pd.to_numeric(leisure_travelers_no_nan, errors='coerce').dropna()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5yCftfxYnzMK"
      },
      "source": [
        "**Shapiro Wilk Test for normality**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lg54-CO8iqdm",
        "outputId": "f0ab1666-b7d4-41be-bf22-7fb5b4afb2a4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(ShapiroResult(statistic=0.8452683091163635, pvalue=3.534128409826342e-24),\n",
              " ShapiroResult(statistic=0.8816855549812317, pvalue=5.59996925610488e-38))"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from scipy.stats import shapiro\n",
        "\n",
        "# Testing for normality using the Shapiro-Wilk test\n",
        "# Business travelers' ratings\n",
        "shapiro_test_business = shapiro(business_ratings_no_nan)\n",
        "\n",
        "# Leisure travelers' ratings\n",
        "shapiro_test_leisure = shapiro(leisure_ratings_no_nan)\n",
        "\n",
        "shapiro_test_business, shapiro_test_leisure"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7eQ-5Y1Vn67Q"
      },
      "source": [
        "**Mann Whitney U test a non parametric test **"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QMDoV1M1n5ri",
        "outputId": "721ccfa8-307c-42a1-9c49-2247f91626d2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(542946.0, 3.0507929986040537e-15)"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from scipy.stats import mannwhitneyu\n",
        "\n",
        "# Conducting the Mann-Whitney U test\n",
        "u_statistic, u_p_value = mannwhitneyu(business_ratings_no_nan, leisure_ratings_no_nan, alternative='two-sided')\n",
        "\n",
        "u_statistic, u_p_value\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "S2W8bg_syFyr",
        "9wtus8Ev-qTJ",
        "k5riwWhMiklD"
      ],
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}