aviakompaniya.ipynb

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          "text": [
            "sample_data  sample_submission.csv  test_dataset.csv\n"
          ]
        }
      ],
      "source": [
        "!ls"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.metrics import jaccard_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import f1_score\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "nrTcHyCrKT1d"
      },
      "execution_count": 101,
      "outputs": []
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      "source": [
        "train_set = pd.read_csv('train_dataset.csv')\n",
        "train_set.head()"
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              "   id  Gender      Customer Type  Age   Type of Travel     Class  \\\n",
              "0   1    Male  disloyal Customer   33  Business travel       Eco   \n",
              "1   2  Female     Loyal Customer   49  Business travel  Business   \n",
              "2   3  Female     Loyal Customer   43  Business travel       Eco   \n",
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              "4   5    Male     Loyal Customer   11  Personal Travel       Eco   \n",
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              "   Cleanliness  Departure Delay in Minutes  Arrival Delay in Minutes  \\\n",
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        "test_set = pd.read_csv('test_dataset.csv')\n",
        "test_set.head()"
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              "   id  Gender   Customer Type  Age   Type of Travel     Class  \\\n",
              "0   1  Female  Loyal Customer   25  Personal Travel       Eco   \n",
              "1   2  Female  Loyal Customer   49  Business travel  Business   \n",
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              "3   4    Male  Loyal Customer   34  Business travel  Eco Plus   \n",
              "4   5    Male  Loyal Customer   34  Personal Travel       Eco   \n",
              "\n",
              "   Flight Distance  Inflight wifi service  Departure/Arrival time convenient  \\\n",
              "0             2704                      2                                  2   \n",
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              "2              338                      4                                  4   \n",
              "3               95                      0                                  0   \n",
              "4              602                      4                                  4   \n",
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              "   Inflight service  Cleanliness  Departure Delay in Minutes  \\\n",
              "0                 1            5                         152   \n",
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              "2                 5            4                           0   \n",
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              "1                      52.0  \n",
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              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Male</td>\n",
              "      <td>Loyal Customer</td>\n",
              "      <td>34</td>\n",
              "      <td>Business travel</td>\n",
              "      <td>Eco Plus</td>\n",
              "      <td>95</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Male</td>\n",
              "      <td>Loyal Customer</td>\n",
              "      <td>34</td>\n",
              "      <td>Personal Travel</td>\n",
              "      <td>Eco</td>\n",
              "      <td>602</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>130</td>\n",
              "      <td>140.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 23 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d7bc749f-18ed-47eb-b379-00cf4fd39741')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\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",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d7bc749f-18ed-47eb-b379-00cf4fd39741 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-d7bc749f-18ed-47eb-b379-00cf4fd39741');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_set.fillna(train_set.mean(), inplace=True)\n",
        "train_set.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w7PbVXYHOVpq",
        "outputId": "9a397603-bc02-421f-c856-fa8542072029"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-110-7c12def24409>:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
            "  train_set.fillna(train_set.mean(), inplace=True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 24)"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_set.fillna(test_set.mean(), inplace=True)\n",
        "test_set.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2kc1LKEvOsN0",
        "outputId": "9561272c-ca8f-4152-ebb9-3c0afd86f4dd"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-112-9a4068a26115>:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
            "  test_set.fillna(test_set.mean(), inplace=True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4000, 23)"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_set.info()"
      ],
      "metadata": {
        "id": "gfz8Bx3bO1mj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f8eb8b6-3590-4976-904d-b10c6af2ad34"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 24 columns):\n",
            " #   Column                             Non-Null Count  Dtype  \n",
            "---  ------                             --------------  -----  \n",
            " 0   id                                 10000 non-null  int64  \n",
            " 1   Gender                             10000 non-null  object \n",
            " 2   Customer Type                      10000 non-null  object \n",
            " 3   Age                                10000 non-null  int64  \n",
            " 4   Type of Travel                     10000 non-null  object \n",
            " 5   Class                              10000 non-null  object \n",
            " 6   Flight Distance                    10000 non-null  int64  \n",
            " 7   Inflight wifi service              10000 non-null  int64  \n",
            " 8   Departure/Arrival time convenient  10000 non-null  int64  \n",
            " 9   Ease of Online booking             10000 non-null  int64  \n",
            " 10  Gate location                      10000 non-null  int64  \n",
            " 11  Food and drink                     10000 non-null  int64  \n",
            " 12  Online boarding                    10000 non-null  int64  \n",
            " 13  Seat comfort                       10000 non-null  int64  \n",
            " 14  Inflight entertainment             10000 non-null  int64  \n",
            " 15  On-board service                   10000 non-null  int64  \n",
            " 16  Leg room service                   10000 non-null  int64  \n",
            " 17  Baggage handling                   10000 non-null  int64  \n",
            " 18  Checkin service                    10000 non-null  int64  \n",
            " 19  Inflight service                   10000 non-null  int64  \n",
            " 20  Cleanliness                        10000 non-null  int64  \n",
            " 21  Departure Delay in Minutes         10000 non-null  int64  \n",
            " 22  Arrival Delay in Minutes           10000 non-null  float64\n",
            " 23  satisfaction                       10000 non-null  int64  \n",
            "dtypes: float64(1), int64(19), object(4)\n",
            "memory usage: 1.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_set.info()"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "IkMT0VSH91dj",
        "outputId": "cfa5237f-5228-4563-ded3-d69a8a0f3a5a"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4000 entries, 0 to 3999\n",
            "Data columns (total 23 columns):\n",
            " #   Column                             Non-Null Count  Dtype  \n",
            "---  ------                             --------------  -----  \n",
            " 0   id                                 4000 non-null   int64  \n",
            " 1   Gender                             4000 non-null   object \n",
            " 2   Customer Type                      4000 non-null   object \n",
            " 3   Age                                4000 non-null   int64  \n",
            " 4   Type of Travel                     4000 non-null   object \n",
            " 5   Class                              4000 non-null   object \n",
            " 6   Flight Distance                    4000 non-null   int64  \n",
            " 7   Inflight wifi service              4000 non-null   int64  \n",
            " 8   Departure/Arrival time convenient  4000 non-null   int64  \n",
            " 9   Ease of Online booking             4000 non-null   int64  \n",
            " 10  Gate location                      4000 non-null   int64  \n",
            " 11  Food and drink                     4000 non-null   int64  \n",
            " 12  Online boarding                    4000 non-null   int64  \n",
            " 13  Seat comfort                       4000 non-null   int64  \n",
            " 14  Inflight entertainment             4000 non-null   int64  \n",
            " 15  On-board service                   4000 non-null   int64  \n",
            " 16  Leg room service                   4000 non-null   int64  \n",
            " 17  Baggage handling                   4000 non-null   int64  \n",
            " 18  Checkin service                    4000 non-null   int64  \n",
            " 19  Inflight service                   4000 non-null   int64  \n",
            " 20  Cleanliness                        4000 non-null   int64  \n",
            " 21  Departure Delay in Minutes         4000 non-null   int64  \n",
            " 22  Arrival Delay in Minutes           4000 non-null   float64\n",
            "dtypes: float64(1), int64(18), object(4)\n",
            "memory usage: 718.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_set['satisfaction'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6oTNBl3a95vk",
        "outputId": "091766cd-3f12-49c2-baf3-43df89efe957"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    5000\n",
              "1    5000\n",
              "Name: satisfaction, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_set.drop(columns=['id'], inplace=True)"
      ],
      "metadata": {
        "id": "g7xCRVzN-EXq"
      },
      "execution_count": 115,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "corr_matrix = train_set.corr().abs()\n",
        "corr_matrix.style.background_gradient(cmap='coolwarm')"
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          "height": 874
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        "outputId": "cd50006b-96e7-40d2-e51b-7378133ef46e"
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      "execution_count": 116,
      "outputs": [
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              "  background-color: #3b4cc0;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row1_col6, #T_145df_row1_col13, #T_145df_row6_col1, #T_145df_row13_col1 {\n",
              "  background-color: #4e68d8;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row1_col8, #T_145df_row8_col1, #T_145df_row11_col2 {\n",
              "  background-color: #7093f3;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row1_col9, #T_145df_row9_col1 {\n",
              "  background-color: #6a8bef;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row1_col10, #T_145df_row2_col8, #T_145df_row8_col2, #T_145df_row14_col15, #T_145df_row15_col14 {\n",
              "  background-color: #5a78e4;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row1_col11, #T_145df_row10_col8, #T_145df_row13_col11, #T_145df_row18_col0 {\n",
              "  background-color: #6485ec;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row1_col12, #T_145df_row3_col12, #T_145df_row8_col14, #T_145df_row12_col1, #T_145df_row12_col3, #T_145df_row14_col8 {\n",
              "  background-color: #506bda;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row1_col14, #T_145df_row3_col10, #T_145df_row14_col1, #T_145df_row17_col14 {\n",
              "  background-color: #4b64d5;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row1_col18 {\n",
              "  background-color: #9dbdff;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row2_col3, #T_145df_row2_col5, #T_145df_row3_col2, #T_145df_row5_col2 {\n",
              "  background-color: #b3cdfb;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row2_col4, #T_145df_row4_col2 {\n",
              "  background-color: #f6a283;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row2_col6, #T_145df_row2_col10, #T_145df_row2_col15, #T_145df_row6_col2, #T_145df_row11_col8, #T_145df_row11_col15, #T_145df_row15_col2 {\n",
              "  background-color: #5f7fe8;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row2_col7, #T_145df_row7_col2, #T_145df_row9_col10 {\n",
              "  background-color: #cbd8ee;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row2_col9, #T_145df_row9_col2 {\n",
              "  background-color: #7a9df8;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row2_col12, #T_145df_row8_col10, #T_145df_row12_col2 {\n",
              "  background-color: #6180e9;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row2_col14, #T_145df_row10_col1, #T_145df_row14_col2, #T_145df_row15_col11 {\n",
              "  background-color: #5d7ce6;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row2_col18 {\n",
              "  background-color: #94b6ff;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row3_col4, #T_145df_row4_col3 {\n",
              "  background-color: #d1dae9;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row3_col5, #T_145df_row5_col3 {\n",
              "  background-color: #d7dce3;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row3_col7, #T_145df_row4_col12, #T_145df_row7_col3, #T_145df_row11_col6 {\n",
              "  background-color: #485fd1;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row3_col9, #T_145df_row4_col15, #T_145df_row5_col8, #T_145df_row9_col3, #T_145df_row15_col4, #T_145df_row17_col0, #T_145df_row17_col3, #T_145df_row18_col5 {\n",
              "  background-color: #3c4ec2;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row3_col13, #T_145df_row7_col14, #T_145df_row13_col3, #T_145df_row14_col7 {\n",
              "  background-color: #516ddb;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row4_col1, #T_145df_row6_col10, #T_145df_row11_col0, #T_145df_row16_col14, #T_145df_row18_col17 {\n",
              "  background-color: #4961d2;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row4_col5, #T_145df_row5_col4 {\n",
              "  background-color: #d6dce4;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row4_col7, #T_145df_row7_col4 {\n",
              "  background-color: #bcd2f7;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row4_col14, #T_145df_row6_col12, #T_145df_row9_col16, #T_145df_row9_col17, #T_145df_row12_col0, #T_145df_row12_col6, #T_145df_row14_col4, #T_145df_row16_col9, #T_145df_row17_col9, #T_145df_row17_col10 {\n",
              "  background-color: #4358cb;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row4_col18, #T_145df_row8_col0 {\n",
              "  background-color: #6c8ff1;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row6_col7, #T_145df_row7_col6, #T_145df_row14_col18, #T_145df_row18_col14 {\n",
              "  background-color: #88abfd;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row6_col8, #T_145df_row8_col6 {\n",
              "  background-color: #edd1c2;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row6_col9 {\n",
              "  background-color: #f2cbb7;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row6_col13, #T_145df_row9_col0 {\n",
              "  background-color: #5572df;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row6_col15, #T_145df_row12_col14, #T_145df_row14_col12, #T_145df_row15_col6 {\n",
              "  background-color: #f6bfa6;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row6_col18 {\n",
              "  background-color: #7b9ff9;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row7_col0, #T_145df_row13_col7, #T_145df_row18_col6 {\n",
              "  background-color: #7da0f9;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row7_col8, #T_145df_row8_col7 {\n",
              "  background-color: #c4d5f3;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row7_col9, #T_145df_row9_col7 {\n",
              "  background-color: #9bbcff;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row7_col10 {\n",
              "  background-color: #6b8df0;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row7_col11 {\n",
              "  background-color: #6282ea;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row7_col12, #T_145df_row8_col12, #T_145df_row12_col7, #T_145df_row12_col8 {\n",
              "  background-color: #536edd;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row7_col13 {\n",
              "  background-color: #7ea1fa;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row7_col15, #T_145df_row15_col7, #T_145df_row18_col8 {\n",
              "  background-color: #adc9fd;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row7_col18 {\n",
              "  background-color: #dddcdc;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row8_col9, #T_145df_row9_col8 {\n",
              "  background-color: #f3c7b1;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row8_col11, #T_145df_row11_col4 {\n",
              "  background-color: #5e7de7;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row8_col13, #T_145df_row13_col15, #T_145df_row15_col13 {\n",
              "  background-color: #799cf8;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row8_col15, #T_145df_row15_col8 {\n",
              "  background-color: #f7b497;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row8_col18, #T_145df_row11_col9 {\n",
              "  background-color: #abc8fd;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row9_col6 {\n",
              "  background-color: #f1ccb8;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row9_col11 {\n",
              "  background-color: #aac7fd;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row9_col12, #T_145df_row12_col9 {\n",
              "  background-color: #bed2f6;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row9_col14 {\n",
              "  background-color: #c5d6f2;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row9_col15, #T_145df_row15_col9 {\n",
              "  background-color: #f7b093;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row9_col18 {\n",
              "  background-color: #bfd3f6;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row10_col2, #T_145df_row11_col7, #T_145df_row15_col10 {\n",
              "  background-color: #6384eb;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row10_col3, #T_145df_row18_col3 {\n",
              "  background-color: #4f69d9;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row10_col9 {\n",
              "  background-color: #cdd9ec;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row10_col11 {\n",
              "  background-color: #b7cff9;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row10_col12 {\n",
              "  background-color: #e7d7ce;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row10_col13, #T_145df_row13_col18, #T_145df_row14_col13 {\n",
              "  background-color: #85a8fc;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row10_col14 {\n",
              "  background-color: #eed0c0;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row10_col15, #T_145df_row11_col13 {\n",
              "  background-color: #6788ee;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row10_col18 {\n",
              "  background-color: #a6c4fe;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row11_col10 {\n",
              "  background-color: #b6cefa;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row11_col12, #T_145df_row14_col11 {\n",
              "  background-color: #bad0f8;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row11_col14 {\n",
              "  background-color: #bbd1f8;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row11_col18, #T_145df_row18_col11 {\n",
              "  background-color: #a5c3fe;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row12_col10 {\n",
              "  background-color: #e5d8d1;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row12_col11 {\n",
              "  background-color: #b9d0f9;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row12_col13, #T_145df_row13_col12, #T_145df_row18_col13 {\n",
              "  background-color: #86a9fc;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row12_col18 {\n",
              "  background-color: #8badfd;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row13_col6 {\n",
              "  background-color: #5470de;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row13_col8 {\n",
              "  background-color: #779af7;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row13_col10 {\n",
              "  background-color: #81a4fb;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row13_col14 {\n",
              "  background-color: #84a7fc;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row14_col9 {\n",
              "  background-color: #c6d6f1;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row14_col10 {\n",
              "  background-color: #edd2c3;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row15_col18 {\n",
              "  background-color: #a1c0ff;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row16_col17, #T_145df_row17_col16 {\n",
              "  background-color: #c12b30;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row18_col1 {\n",
              "  background-color: #9ebeff;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row18_col2 {\n",
              "  background-color: #96b7ff;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row18_col4 {\n",
              "  background-color: #6e90f2;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_145df_row18_col7 {\n",
              "  background-color: #dedcdb;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row18_col9 {\n",
              "  background-color: #c0d4f5;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row18_col10 {\n",
              "  background-color: #a3c2fe;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row18_col12 {\n",
              "  background-color: #8caffe;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_145df_row18_col15 {\n",
              "  background-color: #a2c1ff;\n",
              "  color: #000000;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_145df_\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th class=\"col_heading level0 col0\" >Age</th>\n",
              "      <th class=\"col_heading level0 col1\" >Flight Distance</th>\n",
              "      <th class=\"col_heading level0 col2\" >Inflight wifi service</th>\n",
              "      <th class=\"col_heading level0 col3\" >Departure/Arrival time convenient</th>\n",
              "      <th class=\"col_heading level0 col4\" >Ease of Online booking</th>\n",
              "      <th class=\"col_heading level0 col5\" >Gate location</th>\n",
              "      <th class=\"col_heading level0 col6\" >Food and drink</th>\n",
              "      <th class=\"col_heading level0 col7\" >Online boarding</th>\n",
              "      <th class=\"col_heading level0 col8\" >Seat comfort</th>\n",
              "      <th class=\"col_heading level0 col9\" >Inflight entertainment</th>\n",
              "      <th class=\"col_heading level0 col10\" >On-board service</th>\n",
              "      <th class=\"col_heading level0 col11\" >Leg room service</th>\n",
              "      <th class=\"col_heading level0 col12\" >Baggage handling</th>\n",
              "      <th class=\"col_heading level0 col13\" >Checkin service</th>\n",
              "      <th class=\"col_heading level0 col14\" >Inflight service</th>\n",
              "      <th class=\"col_heading level0 col15\" >Cleanliness</th>\n",
              "      <th class=\"col_heading level0 col16\" >Departure Delay in Minutes</th>\n",
              "      <th class=\"col_heading level0 col17\" >Arrival Delay in Minutes</th>\n",
              "      <th class=\"col_heading level0 col18\" >satisfaction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_145df_level0_row0\" class=\"row_heading level0 row0\" >Age</th>\n",
              "      <td id=\"T_145df_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
              "      <td id=\"T_145df_row0_col1\" class=\"data row0 col1\" >0.110823</td>\n",
              "      <td id=\"T_145df_row0_col2\" class=\"data row0 col2\" >0.020904</td>\n",
              "      <td id=\"T_145df_row0_col3\" class=\"data row0 col3\" >0.036071</td>\n",
              "      <td id=\"T_145df_row0_col4\" class=\"data row0 col4\" >0.026964</td>\n",
              "      <td id=\"T_145df_row0_col5\" class=\"data row0 col5\" >0.012077</td>\n",
              "      <td id=\"T_145df_row0_col6\" class=\"data row0 col6\" >0.021971</td>\n",
              "      <td id=\"T_145df_row0_col7\" class=\"data row0 col7\" >0.215613</td>\n",
              "      <td id=\"T_145df_row0_col8\" class=\"data row0 col8\" >0.169291</td>\n",
              "      <td id=\"T_145df_row0_col9\" class=\"data row0 col9\" >0.097421</td>\n",
              "      <td id=\"T_145df_row0_col10\" class=\"data row0 col10\" >0.073412</td>\n",
              "      <td id=\"T_145df_row0_col11\" class=\"data row0 col11\" >0.060124</td>\n",
              "      <td id=\"T_145df_row0_col12\" class=\"data row0 col12\" >0.040368</td>\n",
              "      <td id=\"T_145df_row0_col13\" class=\"data row0 col13\" >0.038761</td>\n",
              "      <td id=\"T_145df_row0_col14\" class=\"data row0 col14\" >0.036921</td>\n",
              "      <td id=\"T_145df_row0_col15\" class=\"data row0 col15\" >0.062409</td>\n",
              "      <td id=\"T_145df_row0_col16\" class=\"data row0 col16\" >0.015618</td>\n",
              "      <td id=\"T_145df_row0_col17\" class=\"data row0 col17\" >0.019043</td>\n",
              "      <td id=\"T_145df_row0_col18\" class=\"data row0 col18\" >0.144302</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_145df_level0_row1\" class=\"row_heading level0 row1\" >Flight Distance</th>\n",
              "      <td id=\"T_145df_row1_col0\" class=\"data row1 col0\" >0.110823</td>\n",
              "      <td id=\"T_145df_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
              "      <td id=\"T_145df_row1_col2\" class=\"data row1 col2\" >0.002341</td>\n",
              "      <td id=\"T_145df_row1_col3\" class=\"data row1 col3\" >0.021320</td>\n",
              "      <td id=\"T_145df_row1_col4\" class=\"data row1 col4\" >0.052807</td>\n",
              "      <td id=\"T_145df_row1_col5\" class=\"data row1 col5\" >0.017513</td>\n",
              "      <td id=\"T_145df_row1_col6\" class=\"data row1 col6\" >0.065939</td>\n",
              "      <td id=\"T_145df_row1_col7\" class=\"data row1 col7\" >0.213859</td>\n",
              "      <td id=\"T_145df_row1_col8\" class=\"data row1 col8\" >0.170203</td>\n",
              "      <td id=\"T_145df_row1_col9\" class=\"data row1 col9\" >0.150573</td>\n",
              "      <td id=\"T_145df_row1_col10\" class=\"data row1 col10\" >0.115167</td>\n",
              "      <td id=\"T_145df_row1_col11\" class=\"data row1 col11\" >0.140509</td>\n",
              "      <td id=\"T_145df_row1_col12\" class=\"data row1 col12\" >0.073161</td>\n",
              "      <td id=\"T_145df_row1_col13\" class=\"data row1 col13\" >0.066881</td>\n",
              "      <td id=\"T_145df_row1_col14\" class=\"data row1 col14\" >0.058939</td>\n",
              "      <td id=\"T_145df_row1_col15\" class=\"data row1 col15\" >0.100705</td>\n",
              "      <td id=\"T_145df_row1_col16\" class=\"data row1 col16\" >0.011425</td>\n",
              "      <td id=\"T_145df_row1_col17\" class=\"data row1 col17\" >0.002897</td>\n",
              "      <td id=\"T_145df_row1_col18\" class=\"data row1 col18\" >0.298703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_145df_level0_row2\" class=\"row_heading level0 row2\" >Inflight wifi service</th>\n",
              "      <td id=\"T_145df_row2_col0\" class=\"data row2 col0\" >0.020904</td>\n",
              "      <td id=\"T_145df_row2_col1\" class=\"data row2 col1\" >0.002341</td>\n",
              "      <td id=\"T_145df_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
              "      <td id=\"T_145df_row2_col3\" class=\"data row2 col3\" >0.362355</td>\n",
              "      <td id=\"T_145df_row2_col4\" class=\"data row2 col4\" >0.730731</td>\n",
              "      <td id=\"T_145df_row2_col5\" class=\"data row2 col5\" >0.362702</td>\n",
              "      <td id=\"T_145df_row2_col6\" class=\"data row2 col6\" >0.119807</td>\n",
              "      <td id=\"T_145df_row2_col7\" class=\"data row2 col7\" >0.438034</td>\n",
              "      <td id=\"T_145df_row2_col8\" class=\"data row2 col8\" >0.107020</td>\n",
              "      <td id=\"T_145df_row2_col9\" class=\"data row2 col9\" >0.198351</td>\n",
              "      <td id=\"T_145df_row2_col10\" class=\"data row2 col10\" >0.133511</td>\n",
              "      <td id=\"T_145df_row2_col11\" class=\"data row2 col11\" >0.172526</td>\n",
              "      <td id=\"T_145df_row2_col12\" class=\"data row2 col12\" >0.123695</td>\n",
              "      <td id=\"T_145df_row2_col13\" class=\"data row2 col13\" >0.039923</td>\n",
              "      <td id=\"T_145df_row2_col14\" class=\"data row2 col14\" >0.113467</td>\n",
              "      <td id=\"T_145df_row2_col15\" class=\"data row2 col15\" >0.119484</td>\n",
              "      <td id=\"T_145df_row2_col16\" class=\"data row2 col16\" >0.019843</td>\n",
              "      <td id=\"T_145df_row2_col17\" class=\"data row2 col17\" >0.023061</td>\n",
              "      <td id=\"T_145df_row2_col18\" class=\"data row2 col18\" >0.277395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_145df_level0_row3\" class=\"row_heading level0 row3\" >Departure/Arrival time convenient</th>\n",
              "      <td id=\"T_145df_row3_col0\" class=\"data row3 col0\" >0.036071</td>\n",
              "      <td id=\"T_145df_row3_col1\" class=\"data row3 col1\" >0.021320</td>\n",
              "      <td id=\"T_145df_row3_col2\" class=\"data row3 col2\" >0.362355</td>\n",
              "      <td id=\"T_145df_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
              "      <td id=\"T_145df_row3_col4\" class=\"data row3 col4\" >0.456498</td>\n",
              "      <td id=\"T_145df_row3_col5\" class=\"data row3 col5\" >0.479002</td>\n",
              "      <td id=\"T_145df_row3_col6\" class=\"data row3 col6\" >0.001982</td>\n",
              "      <td id=\"T_145df_row3_col7\" class=\"data row3 col7\" >0.046456</td>\n",
              "      <td id=\"T_145df_row3_col8\" class=\"data row3 col8\" >0.002102</td>\n",
              "      <td id=\"T_145df_row3_col9\" class=\"data row3 col9\" >0.007094</td>\n",
              "      <td id=\"T_145df_row3_col10\" class=\"data row3 col10\" >0.071368</td>\n",
              "      <td id=\"T_145df_row3_col11\" class=\"data row3 col11\" >0.027487</td>\n",
              "      <td id=\"T_145df_row3_col12\" class=\"data row3 col12\" >0.072969</td>\n",
              "      <td id=\"T_145df_row3_col13\" class=\"data row3 col13\" >0.078166</td>\n",
              "      <td id=\"T_145df_row3_col14\" class=\"data row3 col14\" >0.062814</td>\n",
              "      <td id=\"T_145df_row3_col15\" class=\"data row3 col15\" >0.001172</td>\n",
              "      <td id=\"T_145df_row3_col16\" class=\"data row3 col16\" >0.010846</td>\n",
              "      <td id=\"T_145df_row3_col17\" class=\"data row3 col17\" >0.008973</td>\n",
              "      <td id=\"T_145df_row3_col18\" class=\"data row3 col18\" >0.067638</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_145df_level0_row4\" class=\"row_heading level0 row4\" >Ease of Online booking</th>\n",
              "      <td id=\"T_145df_row4_col0\" class=\"data row4 col0\" >0.026964</td>\n",
              "      <td id=\"T_145df_row4_col1\" class=\"data row4 col1\" >0.052807</td>\n",
              "      <td id=\"T_145df_row4_col2\" class=\"data row4 col2\" >0.730731</td>\n",
              "      <td id=\"T_145df_row4_col3\" class=\"data row4 col3\" >0.456498</td>\n",
              "      <td id=\"T_145df_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
              "      <td id=\"T_145df_row4_col5\" class=\"data row4 col5\" >0.476405</td>\n",
              "      <td id=\"T_145df_row4_col6\" class=\"data row4 col6\" >0.017193</td>\n",
              "      <td id=\"T_145df_row4_col7\" class=\"data row4 col7\" >0.388220</td>\n",
              "      <td id=\"T_145df_row4_col8\" class=\"data row4 col8\" >0.016831</td>\n",
              "      <td id=\"T_145df_row4_col9\" class=\"data row4 col9\" >0.043574</td>\n",
              "      <td id=\"T_145df_row4_col10\" class=\"data row4 col10\" >0.053613</td>\n",
              "      <td id=\"T_145df_row4_col11\" class=\"data row4 col11\" >0.116333</td>\n",
              "      <td id=\"T_145df_row4_col12\" class=\"data row4 col12\" >0.044259</td>\n",
              "      <td id=\"T_145df_row4_col13\" class=\"data row4 col13\" >0.013875</td>\n",
              "      <td id=\"T_145df_row4_col14\" class=\"data row4 col14\" >0.032168</td>\n",
              "      <td id=\"T_145df_row4_col15\" class=\"data row4 col15\" >0.007301</td>\n",
              "      <td id=\"T_145df_row4_col16\" class=\"data row4 col16\" >0.001474</td>\n",
              "      <td id=\"T_145df_row4_col17\" class=\"data row4 col17\" >0.003711</td>\n",
              "      <td id=\"T_145df_row4_col18\" class=\"data row4 col18\" >0.164662</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_145df_level0_row5\" class=\"row_heading level0 row5\" >Gate location</th>\n",
              "      <td id=\"T_145df_row5_col0\" class=\"data row5 col0\" >0.012077</td>\n",
              "      <td id=\"T_145df_row5_col1\" class=\"data row5 col1\" >0.017513</td>\n",
              "      <td id=\"T_145df_row5_col2\" class=\"data row5 col2\" >0.362702</td>\n",
              "      <td id=\"T_145df_row5_col3\" class=\"data row5 col3\" >0.479002</td>\n",
              "      <td id=\"T_145df_row5_col4\" class=\"data row5 col4\" >0.476405</td>\n",
              "      <td id=\"T_145df_row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
              "      <td id=\"T_145df_row5_col6\" class=\"data row5 col6\" >0.002645</td>\n",
              "      <td id=\"T_145df_row5_col7\" class=\"data row5 col7\" >0.003610</td>\n",
              "      <td id=\"T_145df_row5_col8\" class=\"data row5 col8\" >0.008359</td>\n",
              "      <td id=\"T_145df_row5_col9\" class=\"data row5 col9\" >0.001144</td>\n",
              "      <td id=\"T_145df_row5_col10\" class=\"data row5 col10\" >0.015083</td>\n",
              "      <td id=\"T_145df_row5_col11\" class=\"data row5 col11\" >0.008752</td>\n",
              "      <td id=\"T_145df_row5_col12\" class=\"data row5 col12\" >0.000493</td>\n",
              "      <td id=\"T_145df_row5_col13\" class=\"data row5 col13\" >0.037372</td>\n",
              "      <td id=\"T_145df_row5_col14\" class=\"data row5 col14\" >0.003065</td>\n",
              "      <td id=\"T_145df_row5_col15\" class=\"data row5 col15\" >0.015090</td>\n",
              "      <td id=\"T_145df_row5_col16\" class=\"data row5 col16\" >0.001907</td>\n",
              "      <td id=\"T_145df_row5_col17\" class=\"data row5 col17\" >0.000683</td>\n",
              "      <td id=\"T_145df_row5_col18\" class=\"data row5 col18\" >0.007169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_145df_level0_row6\" class=\"row_heading level0 row6\" >Food and drink</th>\n",
              "      <td id=\"T_145df_row6_col0\" class=\"data row6 col0\" >0.021971</td>\n",
              "      <td id=\"T_145df_row6_col1\" class=\"data row6 col1\" >0.065939</td>\n",
              "      <td id=\"T_145df_row6_col2\" class=\"data row6 col2\" >0.119807</td>\n",
              "      <td id=\"T_145df_row6_col3\" class=\"data row6 col3\" >0.001982</td>\n",
              "      <td id=\"T_145df_row6_col4\" class=\"data row6 col4\" >0.017193</td>\n",
              "      <td id=\"T_145df_row6_col5\" class=\"data row6 col5\" >0.002645</td>\n",
              "      <td id=\"T_145df_row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
              "      <td id=\"T_145df_row6_col7\" class=\"data row6 col7\" >0.239007</td>\n",
              "      <td id=\"T_145df_row6_col8\" class=\"data row6 col8\" >0.573063</td>\n",
              "      <td id=\"T_145df_row6_col9\" class=\"data row6 col9\" >0.598209</td>\n",
              "      <td id=\"T_145df_row6_col10\" class=\"data row6 col10\" >0.062373</td>\n",
              "      <td id=\"T_145df_row6_col11\" class=\"data row6 col11\" >0.045929</td>\n",
              "      <td id=\"T_145df_row6_col12\" class=\"data row6 col12\" >0.030299</td>\n",
              "      <td id=\"T_145df_row6_col13\" class=\"data row6 col13\" >0.087117</td>\n",
              "      <td id=\"T_145df_row6_col14\" class=\"data row6 col14\" >0.035568</td>\n",
              "      <td id=\"T_145df_row6_col15\" class=\"data row6 col15\" >0.641701</td>\n",
              "      <td id=\"T_145df_row6_col16\" class=\"data row6 col16\" >0.011761</td>\n",
              "      <td id=\"T_145df_row6_col17\" class=\"data row6 col17\" >0.011362</td>\n",
              "      <td id=\"T_145df_row6_col18\" class=\"data row6 col18\" >0.207854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_145df_level0_row7\" class=\"row_heading level0 row7\" >Online boarding</th>\n",
              "      <td id=\"T_145df_row7_col0\" class=\"data row7 col0\" >0.215613</td>\n",
              "      <td id=\"T_145df_row7_col1\" class=\"data row7 col1\" >0.213859</td>\n",
              "      <td id=\"T_145df_row7_col2\" class=\"data row7 col2\" >0.438034</td>\n",
              "      <td id=\"T_145df_row7_col3\" class=\"data row7 col3\" >0.046456</td>\n",
              "      <td id=\"T_145df_row7_col4\" class=\"data row7 col4\" >0.388220</td>\n",
              "      <td id=\"T_145df_row7_col5\" class=\"data row7 col5\" >0.003610</td>\n",
              "      <td id=\"T_145df_row7_col6\" class=\"data row7 col6\" >0.239007</td>\n",
              "      <td id=\"T_145df_row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
              "      <td id=\"T_145df_row7_col8\" class=\"data row7 col8\" >0.414889</td>\n",
              "      <td id=\"T_145df_row7_col9\" class=\"data row7 col9\" >0.292397</td>\n",
              "      <td id=\"T_145df_row7_col10\" class=\"data row7 col10\" >0.165847</td>\n",
              "      <td id=\"T_145df_row7_col11\" class=\"data row7 col11\" >0.133977</td>\n",
              "      <td id=\"T_145df_row7_col12\" class=\"data row7 col12\" >0.080804</td>\n",
              "      <td id=\"T_145df_row7_col13\" class=\"data row7 col13\" >0.207733</td>\n",
              "      <td id=\"T_145df_row7_col14\" class=\"data row7 col14\" >0.077808</td>\n",
              "      <td id=\"T_145df_row7_col15\" class=\"data row7 col15\" >0.341899</td>\n",
              "      <td id=\"T_145df_row7_col16\" class=\"data row7 col16\" >0.001727</td>\n",
              "      <td id=\"T_145df_row7_col17\" class=\"data row7 col17\" >0.003680</td>\n",
              "      <td id=\"T_145df_row7_col18\" class=\"data row7 col18\" >0.504986</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_145df_level0_row8\" class=\"row_heading level0 row8\" >Seat comfort</th>\n",
              "      <td id=\"T_145df_row8_col0\" class=\"data row8 col0\" >0.169291</td>\n",
              "      <td id=\"T_145df_row8_col1\" class=\"data row8 col1\" >0.170203</td>\n",
              "      <td id=\"T_145df_row8_col2\" class=\"data row8 col2\" >0.107020</td>\n",
              "      <td id=\"T_145df_row8_col3\" class=\"data row8 col3\" >0.002102</td>\n",
              "      <td id=\"T_145df_row8_col4\" class=\"data row8 col4\" >0.016831</td>\n",
              "      <td id=\"T_145df_row8_col5\" class=\"data row8 col5\" >0.008359</td>\n",
              "      <td id=\"T_145df_row8_col6\" class=\"data row8 col6\" >0.573063</td>\n",
              "      <td id=\"T_145df_row8_col7\" class=\"data row8 col7\" >0.414889</td>\n",
              "      <td id=\"T_145df_row8_col8\" class=\"data row8 col8\" >1.000000</td>\n",
              "      <td id=\"T_145df_row8_col9\" class=\"data row8 col9\" >0.615310</td>\n",
              "      <td id=\"T_145df_row8_col10\" class=\"data row8 col10\" >0.136570</td>\n",
              "      <td id=\"T_145df_row8_col11\" class=\"data row8 col11\" >0.121991</td>\n",
              "      <td id=\"T_145df_row8_col12\" class=\"data row8 col12\" >0.081107</td>\n",
              "      <td id=\"T_145df_row8_col13\" class=\"data row8 col13\" >0.192491</td>\n",
              "      <td id=\"T_145df_row8_col14\" class=\"data row8 col14\" >0.074447</td>\n",
              "      <td id=\"T_145df_row8_col15\" class=\"data row8 col15\" >0.677454</td>\n",
              "      <td id=\"T_145df_row8_col16\" class=\"data row8 col16\" >0.014833</td>\n",
              "      <td id=\"T_145df_row8_col17\" class=\"data row8 col17\" >0.013964</td>\n",
              "      <td id=\"T_145df_row8_col18\" class=\"data row8 col18\" >0.342294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_145df_level0_row9\" class=\"row_heading level0 row9\" >Inflight entertainment</th>\n",
              "      <td id=\"T_145df_row9_col0\" class=\"data row9 col0\" >0.097421</td>\n",
              "      <td id=\"T_145df_row9_col1\" class=\"data row9 col1\" >0.150573</td>\n",
              "      <td id=\"T_145df_row9_col2\" class=\"data row9 col2\" >0.198351</td>\n",
              "      <td id=\"T_145df_row9_col3\" class=\"data row9 col3\" >0.007094</td>\n",
              "      <td id=\"T_145df_row9_col4\" class=\"data row9 col4\" >0.043574</td>\n",
              "      <td id=\"T_145df_row9_col5\" class=\"data row9 col5\" >0.001144</td>\n",
              "      <td id=\"T_145df_row9_col6\" class=\"data row9 col6\" >0.598209</td>\n",
              "      <td id=\"T_145df_row9_col7\" class=\"data row9 col7\" >0.292397</td>\n",
              "      <td id=\"T_145df_row9_col8\" class=\"data row9 col8\" >0.615310</td>\n",
              "      <td id=\"T_145df_row9_col9\" class=\"data row9 col9\" >1.000000</td>\n",
              "      <td id=\"T_145df_row9_col10\" class=\"data row9 col10\" >0.442295</td>\n",
              "      <td id=\"T_145df_row9_col11\" class=\"data row9 col11\" >0.338588</td>\n",
              "      <td id=\"T_145df_row9_col12\" class=\"data row9 col12\" >0.393506</td>\n",
              "      <td id=\"T_145df_row9_col13\" class=\"data row9 col13\" >0.109921</td>\n",
              "      <td id=\"T_145df_row9_col14\" class=\"data row9 col14\" >0.418871</td>\n",
              "      <td id=\"T_145df_row9_col15\" class=\"data row9 col15\" >0.688927</td>\n",
              "      <td id=\"T_145df_row9_col16\" class=\"data row9 col16\" >0.029500</td>\n",
              "      <td id=\"T_145df_row9_col17\" class=\"data row9 col17\" >0.029307</td>\n",
              "      <td id=\"T_145df_row9_col18\" class=\"data row9 col18\" >0.400900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_145df_level0_row10\" class=\"row_heading level0 row10\" >On-board service</th>\n",
              "      <td id=\"T_145df_row10_col0\" class=\"data row10 col0\" >0.073412</td>\n",
              "      <td id=\"T_145df_row10_col1\" class=\"data row10 col1\" >0.115167</td>\n",
              "      <td id=\"T_145df_row10_col2\" class=\"data row10 col2\" >0.133511</td>\n",
              "      <td id=\"T_145df_row10_col3\" class=\"data row10 col3\" >0.071368</td>\n",
              "      <td id=\"T_145df_row10_col4\" class=\"data row10 col4\" >0.053613</td>\n",
              "      <td id=\"T_145df_row10_col5\" class=\"data row10 col5\" >0.015083</td>\n",
              "      <td id=\"T_145df_row10_col6\" class=\"data row10 col6\" >0.062373</td>\n",
              "      <td id=\"T_145df_row10_col7\" class=\"data row10 col7\" >0.165847</td>\n",
              "      <td id=\"T_145df_row10_col8\" class=\"data row10 col8\" >0.136570</td>\n",
              "      <td id=\"T_145df_row10_col9\" class=\"data row10 col9\" >0.442295</td>\n",
              "      <td id=\"T_145df_row10_col10\" class=\"data row10 col10\" >1.000000</td>\n",
              "      <td id=\"T_145df_row10_col11\" class=\"data row10 col11\" >0.379646</td>\n",
              "      <td id=\"T_145df_row10_col12\" class=\"data row10 col12\" >0.542059</td>\n",
              "      <td id=\"T_145df_row10_col13\" class=\"data row10 col13\" >0.227699</td>\n",
              "      <td id=\"T_145df_row10_col14\" class=\"data row10 col14\" >0.575932</td>\n",
              "      <td id=\"T_145df_row10_col15\" class=\"data row10 col15\" >0.142598</td>\n",
              "      <td id=\"T_145df_row10_col16\" class=\"data row10 col16\" >0.039652</td>\n",
              "      <td id=\"T_145df_row10_col17\" class=\"data row10 col17\" >0.043547</td>\n",
              "      <td id=\"T_145df_row10_col18\" class=\"data row10 col18\" >0.326426</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_145df_level0_row11\" class=\"row_heading level0 row11\" >Leg room service</th>\n",
              "      <td id=\"T_145df_row11_col0\" class=\"data row11 col0\" >0.060124</td>\n",
              "      <td id=\"T_145df_row11_col1\" class=\"data row11 col1\" >0.140509</td>\n",
              "      <td id=\"T_145df_row11_col2\" class=\"data row11 col2\" >0.172526</td>\n",
              "      <td id=\"T_145df_row11_col3\" class=\"data row11 col3\" >0.027487</td>\n",
              "      <td id=\"T_145df_row11_col4\" class=\"data row11 col4\" >0.116333</td>\n",
              "      <td id=\"T_145df_row11_col5\" class=\"data row11 col5\" >0.008752</td>\n",
              "      <td id=\"T_145df_row11_col6\" class=\"data row11 col6\" >0.045929</td>\n",
              "      <td id=\"T_145df_row11_col7\" class=\"data row11 col7\" >0.133977</td>\n",
              "      <td id=\"T_145df_row11_col8\" class=\"data row11 col8\" >0.121991</td>\n",
              "      <td id=\"T_145df_row11_col9\" class=\"data row11 col9\" >0.338588</td>\n",
              "      <td id=\"T_145df_row11_col10\" class=\"data row11 col10\" >0.379646</td>\n",
              "      <td id=\"T_145df_row11_col11\" class=\"data row11 col11\" >1.000000</td>\n",
              "      <td id=\"T_145df_row11_col12\" class=\"data row11 col12\" >0.382611</td>\n",
              "      <td id=\"T_145df_row11_col13\" class=\"data row11 col13\" >0.141419</td>\n",
              "      <td id=\"T_145df_row11_col14\" class=\"data row11 col14\" >0.385605</td>\n",
              "      <td id=\"T_145df_row11_col15\" class=\"data row11 col15\" >0.120972</td>\n",
              "      <td id=\"T_145df_row11_col16\" class=\"data row11 col16\" >0.017453</td>\n",
              "      <td id=\"T_145df_row11_col17\" class=\"data row11 col17\" >0.016681</td>\n",
              "      <td id=\"T_145df_row11_col18\" class=\"data row11 col18\" >0.324368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_145df_level0_row12\" class=\"row_heading level0 row12\" >Baggage handling</th>\n",
              "      <td id=\"T_145df_row12_col0\" class=\"data row12 col0\" >0.040368</td>\n",
              "      <td id=\"T_145df_row12_col1\" class=\"data row12 col1\" >0.073161</td>\n",
              "      <td id=\"T_145df_row12_col2\" class=\"data row12 col2\" >0.123695</td>\n",
              "      <td id=\"T_145df_row12_col3\" class=\"data row12 col3\" >0.072969</td>\n",
              "      <td id=\"T_145df_row12_col4\" class=\"data row12 col4\" >0.044259</td>\n",
              "      <td id=\"T_145df_row12_col5\" class=\"data row12 col5\" >0.000493</td>\n",
              "      <td id=\"T_145df_row12_col6\" class=\"data row12 col6\" >0.030299</td>\n",
              "      <td id=\"T_145df_row12_col7\" class=\"data row12 col7\" >0.080804</td>\n",
              "      <td id=\"T_145df_row12_col8\" class=\"data row12 col8\" >0.081107</td>\n",
              "      <td id=\"T_145df_row12_col9\" class=\"data row12 col9\" >0.393506</td>\n",
              "      <td id=\"T_145df_row12_col10\" class=\"data row12 col10\" >0.542059</td>\n",
              "      <td id=\"T_145df_row12_col11\" class=\"data row12 col11\" >0.382611</td>\n",
              "      <td id=\"T_145df_row12_col12\" class=\"data row12 col12\" >1.000000</td>\n",
              "      <td id=\"T_145df_row12_col13\" class=\"data row12 col13\" >0.231689</td>\n",
              "      <td id=\"T_145df_row12_col14\" class=\"data row12 col14\" >0.642380</td>\n",
              "      <td id=\"T_145df_row12_col15\" class=\"data row12 col15\" >0.106570</td>\n",
              "      <td id=\"T_145df_row12_col16\" class=\"data row12 col16\" >0.009337</td>\n",
              "      <td id=\"T_145df_row12_col17\" class=\"data row12 col17\" >0.016227</td>\n",
              "      <td id=\"T_145df_row12_col18\" class=\"data row12 col18\" >0.247965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_145df_level0_row13\" class=\"row_heading level0 row13\" >Checkin service</th>\n",
              "      <td id=\"T_145df_row13_col0\" class=\"data row13 col0\" >0.038761</td>\n",
              "      <td id=\"T_145df_row13_col1\" class=\"data row13 col1\" >0.066881</td>\n",
              "      <td id=\"T_145df_row13_col2\" class=\"data row13 col2\" >0.039923</td>\n",
              "      <td id=\"T_145df_row13_col3\" class=\"data row13 col3\" >0.078166</td>\n",
              "      <td id=\"T_145df_row13_col4\" class=\"data row13 col4\" >0.013875</td>\n",
              "      <td id=\"T_145df_row13_col5\" class=\"data row13 col5\" >0.037372</td>\n",
              "      <td id=\"T_145df_row13_col6\" class=\"data row13 col6\" >0.087117</td>\n",
              "      <td id=\"T_145df_row13_col7\" class=\"data row13 col7\" >0.207733</td>\n",
              "      <td id=\"T_145df_row13_col8\" class=\"data row13 col8\" >0.192491</td>\n",
              "      <td id=\"T_145df_row13_col9\" class=\"data row13 col9\" >0.109921</td>\n",
              "      <td id=\"T_145df_row13_col10\" class=\"data row13 col10\" >0.227699</td>\n",
              "      <td id=\"T_145df_row13_col11\" class=\"data row13 col11\" >0.141419</td>\n",
              "      <td id=\"T_145df_row13_col12\" class=\"data row13 col12\" >0.231689</td>\n",
              "      <td id=\"T_145df_row13_col13\" class=\"data row13 col13\" >1.000000</td>\n",
              "      <td id=\"T_145df_row13_col14\" class=\"data row13 col14\" >0.228145</td>\n",
              "      <td id=\"T_145df_row13_col15\" class=\"data row13 col15\" >0.193895</td>\n",
              "      <td id=\"T_145df_row13_col16\" class=\"data row13 col16\" >0.000731</td>\n",
              "      <td id=\"T_145df_row13_col17\" class=\"data row13 col17\" >0.000633</td>\n",
              "      <td id=\"T_145df_row13_col18\" class=\"data row13 col18\" >0.232359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_145df_level0_row14\" class=\"row_heading level0 row14\" >Inflight service</th>\n",
              "      <td id=\"T_145df_row14_col0\" class=\"data row14 col0\" >0.036921</td>\n",
              "      <td id=\"T_145df_row14_col1\" class=\"data row14 col1\" >0.058939</td>\n",
              "      <td id=\"T_145df_row14_col2\" class=\"data row14 col2\" >0.113467</td>\n",
              "      <td id=\"T_145df_row14_col3\" class=\"data row14 col3\" >0.062814</td>\n",
              "      <td id=\"T_145df_row14_col4\" class=\"data row14 col4\" >0.032168</td>\n",
              "      <td id=\"T_145df_row14_col5\" class=\"data row14 col5\" >0.003065</td>\n",
              "      <td id=\"T_145df_row14_col6\" class=\"data row14 col6\" >0.035568</td>\n",
              "      <td id=\"T_145df_row14_col7\" class=\"data row14 col7\" >0.077808</td>\n",
              "      <td id=\"T_145df_row14_col8\" class=\"data row14 col8\" >0.074447</td>\n",
              "      <td id=\"T_145df_row14_col9\" class=\"data row14 col9\" >0.418871</td>\n",
              "      <td id=\"T_145df_row14_col10\" class=\"data row14 col10\" >0.575932</td>\n",
              "      <td id=\"T_145df_row14_col11\" class=\"data row14 col11\" >0.385605</td>\n",
              "      <td id=\"T_145df_row14_col12\" class=\"data row14 col12\" >0.642380</td>\n",
              "      <td id=\"T_145df_row14_col13\" class=\"data row14 col13\" >0.228145</td>\n",
              "      <td id=\"T_145df_row14_col14\" class=\"data row14 col14\" >1.000000</td>\n",
              "      <td id=\"T_145df_row14_col15\" class=\"data row14 col15\" >0.104448</td>\n",
              "      <td id=\"T_145df_row14_col16\" class=\"data row14 col16\" >0.053246</td>\n",
              "      <td id=\"T_145df_row14_col17\" class=\"data row14 col17\" >0.060696</td>\n",
              "      <td id=\"T_145df_row14_col18\" class=\"data row14 col18\" >0.240584</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_145df_level0_row15\" class=\"row_heading level0 row15\" >Cleanliness</th>\n",
              "      <td id=\"T_145df_row15_col0\" class=\"data row15 col0\" >0.062409</td>\n",
              "      <td id=\"T_145df_row15_col1\" class=\"data row15 col1\" >0.100705</td>\n",
              "      <td id=\"T_145df_row15_col2\" class=\"data row15 col2\" >0.119484</td>\n",
              "      <td id=\"T_145df_row15_col3\" class=\"data row15 col3\" >0.001172</td>\n",
              "      <td id=\"T_145df_row15_col4\" class=\"data row15 col4\" >0.007301</td>\n",
              "      <td id=\"T_145df_row15_col5\" class=\"data row15 col5\" >0.015090</td>\n",
              "      <td id=\"T_145df_row15_col6\" class=\"data row15 col6\" >0.641701</td>\n",
              "      <td id=\"T_145df_row15_col7\" class=\"data row15 col7\" >0.341899</td>\n",
              "      <td id=\"T_145df_row15_col8\" class=\"data row15 col8\" >0.677454</td>\n",
              "      <td id=\"T_145df_row15_col9\" class=\"data row15 col9\" >0.688927</td>\n",
              "      <td id=\"T_145df_row15_col10\" class=\"data row15 col10\" >0.142598</td>\n",
              "      <td id=\"T_145df_row15_col11\" class=\"data row15 col11\" >0.120972</td>\n",
              "      <td id=\"T_145df_row15_col12\" class=\"data row15 col12\" >0.106570</td>\n",
              "      <td id=\"T_145df_row15_col13\" class=\"data row15 col13\" >0.193895</td>\n",
              "      <td id=\"T_145df_row15_col14\" class=\"data row15 col14\" >0.104448</td>\n",
              "      <td id=\"T_145df_row15_col15\" class=\"data row15 col15\" >1.000000</td>\n",
              "      <td id=\"T_145df_row15_col16\" class=\"data row15 col16\" >0.001251</td>\n",
              "      <td id=\"T_145df_row15_col17\" class=\"data row15 col17\" >0.003944</td>\n",
              "      <td id=\"T_145df_row15_col18\" class=\"data row15 col18\" >0.310338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_145df_level0_row16\" class=\"row_heading level0 row16\" >Departure Delay in Minutes</th>\n",
              "      <td id=\"T_145df_row16_col0\" class=\"data row16 col0\" >0.015618</td>\n",
              "      <td id=\"T_145df_row16_col1\" class=\"data row16 col1\" >0.011425</td>\n",
              "      <td id=\"T_145df_row16_col2\" class=\"data row16 col2\" >0.019843</td>\n",
              "      <td id=\"T_145df_row16_col3\" class=\"data row16 col3\" >0.010846</td>\n",
              "      <td id=\"T_145df_row16_col4\" class=\"data row16 col4\" >0.001474</td>\n",
              "      <td id=\"T_145df_row16_col5\" class=\"data row16 col5\" >0.001907</td>\n",
              "      <td id=\"T_145df_row16_col6\" class=\"data row16 col6\" >0.011761</td>\n",
              "      <td id=\"T_145df_row16_col7\" class=\"data row16 col7\" >0.001727</td>\n",
              "      <td id=\"T_145df_row16_col8\" class=\"data row16 col8\" >0.014833</td>\n",
              "      <td id=\"T_145df_row16_col9\" class=\"data row16 col9\" >0.029500</td>\n",
              "      <td id=\"T_145df_row16_col10\" class=\"data row16 col10\" >0.039652</td>\n",
              "      <td id=\"T_145df_row16_col11\" class=\"data row16 col11\" >0.017453</td>\n",
              "      <td id=\"T_145df_row16_col12\" class=\"data row16 col12\" >0.009337</td>\n",
              "      <td id=\"T_145df_row16_col13\" class=\"data row16 col13\" >0.000731</td>\n",
              "      <td id=\"T_145df_row16_col14\" class=\"data row16 col14\" >0.053246</td>\n",
              "      <td id=\"T_145df_row16_col15\" class=\"data row16 col15\" >0.001251</td>\n",
              "      <td id=\"T_145df_row16_col16\" class=\"data row16 col16\" >1.000000</td>\n",
              "      <td id=\"T_145df_row16_col17\" class=\"data row16 col17\" >0.964009</td>\n",
              "      <td id=\"T_145df_row16_col18\" class=\"data row16 col18\" >0.042960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_145df_level0_row17\" class=\"row_heading level0 row17\" >Arrival Delay in Minutes</th>\n",
              "      <td id=\"T_145df_row17_col0\" class=\"data row17 col0\" >0.019043</td>\n",
              "      <td id=\"T_145df_row17_col1\" class=\"data row17 col1\" >0.002897</td>\n",
              "      <td id=\"T_145df_row17_col2\" class=\"data row17 col2\" >0.023061</td>\n",
              "      <td id=\"T_145df_row17_col3\" class=\"data row17 col3\" >0.008973</td>\n",
              "      <td id=\"T_145df_row17_col4\" class=\"data row17 col4\" >0.003711</td>\n",
              "      <td id=\"T_145df_row17_col5\" class=\"data row17 col5\" >0.000683</td>\n",
              "      <td id=\"T_145df_row17_col6\" class=\"data row17 col6\" >0.011362</td>\n",
              "      <td id=\"T_145df_row17_col7\" class=\"data row17 col7\" >0.003680</td>\n",
              "      <td id=\"T_145df_row17_col8\" class=\"data row17 col8\" >0.013964</td>\n",
              "      <td id=\"T_145df_row17_col9\" class=\"data row17 col9\" >0.029307</td>\n",
              "      <td id=\"T_145df_row17_col10\" class=\"data row17 col10\" >0.043547</td>\n",
              "      <td id=\"T_145df_row17_col11\" class=\"data row17 col11\" >0.016681</td>\n",
              "      <td id=\"T_145df_row17_col12\" class=\"data row17 col12\" >0.016227</td>\n",
              "      <td id=\"T_145df_row17_col13\" class=\"data row17 col13\" >0.000633</td>\n",
              "      <td id=\"T_145df_row17_col14\" class=\"data row17 col14\" >0.060696</td>\n",
              "      <td id=\"T_145df_row17_col15\" class=\"data row17 col15\" >0.003944</td>\n",
              "      <td id=\"T_145df_row17_col16\" class=\"data row17 col16\" >0.964009</td>\n",
              "      <td id=\"T_145df_row17_col17\" class=\"data row17 col17\" >1.000000</td>\n",
              "      <td id=\"T_145df_row17_col18\" class=\"data row17 col18\" >0.048582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_145df_level0_row18\" class=\"row_heading level0 row18\" >satisfaction</th>\n",
              "      <td id=\"T_145df_row18_col0\" class=\"data row18 col0\" >0.144302</td>\n",
              "      <td id=\"T_145df_row18_col1\" class=\"data row18 col1\" >0.298703</td>\n",
              "      <td id=\"T_145df_row18_col2\" class=\"data row18 col2\" >0.277395</td>\n",
              "      <td id=\"T_145df_row18_col3\" class=\"data row18 col3\" >0.067638</td>\n",
              "      <td id=\"T_145df_row18_col4\" class=\"data row18 col4\" >0.164662</td>\n",
              "      <td id=\"T_145df_row18_col5\" class=\"data row18 col5\" >0.007169</td>\n",
              "      <td id=\"T_145df_row18_col6\" class=\"data row18 col6\" >0.207854</td>\n",
              "      <td id=\"T_145df_row18_col7\" class=\"data row18 col7\" >0.504986</td>\n",
              "      <td id=\"T_145df_row18_col8\" class=\"data row18 col8\" >0.342294</td>\n",
              "      <td id=\"T_145df_row18_col9\" class=\"data row18 col9\" >0.400900</td>\n",
              "      <td id=\"T_145df_row18_col10\" class=\"data row18 col10\" >0.326426</td>\n",
              "      <td id=\"T_145df_row18_col11\" class=\"data row18 col11\" >0.324368</td>\n",
              "      <td id=\"T_145df_row18_col12\" class=\"data row18 col12\" >0.247965</td>\n",
              "      <td id=\"T_145df_row18_col13\" class=\"data row18 col13\" >0.232359</td>\n",
              "      <td id=\"T_145df_row18_col14\" class=\"data row18 col14\" >0.240584</td>\n",
              "      <td id=\"T_145df_row18_col15\" class=\"data row18 col15\" >0.310338</td>\n",
              "      <td id=\"T_145df_row18_col16\" class=\"data row18 col16\" >0.042960</td>\n",
              "      <td id=\"T_145df_row18_col17\" class=\"data row18 col17\" >0.048582</td>\n",
              "      <td id=\"T_145df_row18_col18\" class=\"data row18 col18\" >1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_set.corrwith(train_set['satisfaction']).abs().sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5a7sypWj-nnl",
        "outputId": "2304f04f-6d21-42fe-db56-dfee879cad8c"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "satisfaction                         1.000000\n",
              "Online boarding                      0.504986\n",
              "Inflight entertainment               0.400900\n",
              "Seat comfort                         0.342294\n",
              "On-board service                     0.326426\n",
              "Leg room service                     0.324368\n",
              "Cleanliness                          0.310338\n",
              "Flight Distance                      0.298703\n",
              "Inflight wifi service                0.277395\n",
              "Baggage handling                     0.247965\n",
              "Inflight service                     0.240584\n",
              "Checkin service                      0.232359\n",
              "Food and drink                       0.207854\n",
              "Ease of Online booking               0.164662\n",
              "Age                                  0.144302\n",
              "Departure/Arrival time convenient    0.067638\n",
              "Arrival Delay in Minutes             0.048582\n",
              "Departure Delay in Minutes           0.042960\n",
              "Gate location                        0.007169\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = train_set.drop('satisfaction', axis=1)\n",
        "y = train_set['satisfaction']"
      ],
      "metadata": {
        "id": "T94q1nMEAwS1"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_column = ['Online boarding', 'Inflight entertainment', 'Seat comfort', 'On-board service', 'Leg room service', 'Cleanliness', \n",
        "              'Flight Distance', 'Inflight wifi service', 'Baggage handling', 'Inflight service', 'Checkin service', 'Food and drink',\n",
        "              'Ease of Online booking', 'Age', 'Departure/Arrival time convenient', 'Arrival Delay in Minutes', 'Departure Delay in Minutes', 'Gate location']\n",
        "obj_column = ['Gender','Customer Type', 'Type of Travel', 'Class']"
      ],
      "metadata": {
        "id": "jlB7zIROBJkQ"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_pipeline = Pipeline([\n",
        "          ('std_scaler', StandardScaler())             \n",
        "])\n",
        "full_pipeline = ColumnTransformer([\n",
        "    ('num', num_pipeline, num_column),\n",
        "    ('cat', OneHotEncoder(), obj_column)\n",
        "])"
      ],
      "metadata": {
        "id": "ZJRWAg1KCnf6"
      },
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_array = full_pipeline.fit_transform(X)"
      ],
      "metadata": {
        "id": "kZrYTtEZDUj7"
      },
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X_array,y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "oi08iip6DfLu"
      },
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=3)\n",
        "\n",
        "knn.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YMeIrEIGESIG",
        "outputId": "1f95b22b-429f-4348-f6b3-8781997d603d"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=3)"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f1 = []\n",
        "for k in range(1,25):\n",
        "    knn = KNeighborsClassifier(n_neighbors=k) \n",
        "    knn.fit(X_train, y_train)\n",
        "    y_predict = knn.predict(X_test)\n",
        "    f1.append(f1_score(y_test, y_predict))\n",
        "\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(range(1,25),f1)\n",
        "plt.xticks(range(1,25))\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "M9hVX1SIEpeM",
        "outputId": "cfa64d38-b96e-4bf1-a76c-f8a83573719a"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "jaccard_score(y_test, y_predict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o0C4keAAEWUU",
        "outputId": "1d321cab-4ad5-4227-918b-f6f0000bf41e"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8086538461538462"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test, y_predict)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mArcXYc6EfaM",
        "outputId": "3e3e5c72-ad30-42bc-a5fa-28e6289a173a"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[960,  42],\n",
              "       [157, 841]])"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "tree_model = DecisionTreeClassifier()\n",
        "tree_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mCSCN-vJFHY5",
        "outputId": "b1894fe4-6cd5-4f03-9c92-0436e6e38234"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predict2 = tree_model.predict(X_test)"
      ],
      "metadata": {
        "id": "_xjrQ3SMGg4Z"
      },
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "jaccard_score(y_test, y_predict2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e58015c4-f828-4d77-9107-c9f397682798",
        "id": "AvO4NSbYGsCv"
      },
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.867231638418079"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test, y_predict2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0fRIh8THGttQ",
        "outputId": "61d1adac-0048-4d07-d192-ba4d095f2464"
      },
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[938,  64],\n",
              "       [ 77, 921]])"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data = full_pipeline.fit_transform(test_set)"
      ],
      "metadata": {
        "id": "lW5C6D6aU21z"
      },
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_predict3 = tree_model.predict(test_data)"
      ],
      "metadata": {
        "id": "EwZ-yP_XVC9x"
      },
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "submission3 = pd.DataFrame(y_predict3)\n",
        "submission3['id'] = test_set['id']\n",
        "submission3.rename({0:'satisfaction'},axis =1,inplace=True)\n",
        "submission3 = submission3[['id','satisfaction']]"
      ],
      "metadata": {
        "id": "H1vmxrWzGxPQ"
      },
      "execution_count": 135,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "submission3.to_csv('submission3.csv',index=False)"
      ],
      "metadata": {
        "id": "neK1AHEhHYSA"
      },
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "subm = pd.read_csv('submission3.csv')\n",
        "subm.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "x7tr4H-HHZEO",
        "outputId": "d9faa640-832d-46bf-c278-e26fabecc8de"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id  satisfaction\n",
              "0   1             0\n",
              "1   2             0\n",
              "2   3             1\n",
              "3   4             1\n",
              "4   5             1"
            ],
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              "      <td>1</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ca34d1a9-160b-4e30-ab98-6bebfc710da8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-ca34d1a9-160b-4e30-ab98-6bebfc710da8 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-ca34d1a9-160b-4e30-ab98-6bebfc710da8');\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",
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          },
          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dFhOua51OX6Y"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}