hotel_reservation_KNN_with_PCA.ipynb

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        "### This Jupyter Notebook uses a Kellogg's dataset for KNN classification of hotel reservation **non**-cancellations. The analysis is carried out in the following steps:\n",
        "\n",
        "---\n",
        "\n",
        "    1. Creation of a Pandas DataFrame with the data.\n",
        "    2. Displaying a summary of the data.\n",
        "    3. Visualization of the distribution of features.\n",
        "    4. Removing unnecessary features.\n",
        "    5. Conversion of non-ordinal categorical data to binary data.\n",
        "    6. Splitting the data into training, validation, and test sets.\n",
        "    7. Training the model with the training set.\n",
        "    8. Evaluation of the model on the validation set.\n",
        "    9. Simplification of the model by removing certain features (repeating steps 6-8).\n",
        "    10. Identification of the best hyperparameters for the KNN model.\n",
        "    11. Evaluation of the model with the best hyperparameters.\n",
        "    12. Calculation of the model performance metrics. \n",
        "    13. Displaying of the confusion matrix for the test set.\n",
        "    14. Interpretation of results.\n"
      ],
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            "Mounted at /content/gdrive\n"
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            "text/plain": [
              "  Booking_ID  no_of_adults  no_of_children  no_of_weekend_nights  \\\n",
              "0   INN00001             2               0                     1   \n",
              "1   INN00002             2               0                     2   \n",
              "2   INN00003             1               0                     2   \n",
              "3   INN00004             2               0                     0   \n",
              "4   INN00005             2               0                     1   \n",
              "\n",
              "   no_of_week_nights type_of_meal_plan  required_car_parking_space  \\\n",
              "0                  2       Meal Plan 1                           0   \n",
              "1                  3      Not Selected                           0   \n",
              "2                  1       Meal Plan 1                           0   \n",
              "3                  2       Meal Plan 1                           0   \n",
              "4                  1      Not Selected                           0   \n",
              "\n",
              "  room_type_reserved  lead_time  arrival_year  arrival_month  arrival_date  \\\n",
              "0        Room_Type 1        224          2017             10             2   \n",
              "1        Room_Type 1          5          2018             11             6   \n",
              "2        Room_Type 1          1          2018              2            28   \n",
              "3        Room_Type 1        211          2018              5            20   \n",
              "4        Room_Type 1         48          2018              4            11   \n",
              "\n",
              "  market_segment_type  repeated_guest  no_of_previous_cancellations  \\\n",
              "0             Offline               0                             0   \n",
              "1              Online               0                             0   \n",
              "2              Online               0                             0   \n",
              "3              Online               0                             0   \n",
              "4              Online               0                             0   \n",
              "\n",
              "   no_of_previous_bookings_not_canceled  avg_price_per_room  \\\n",
              "0                                     0               65.00   \n",
              "1                                     0              106.68   \n",
              "2                                     0               60.00   \n",
              "3                                     0              100.00   \n",
              "4                                     0               94.50   \n",
              "\n",
              "   no_of_special_requests booking_status  \n",
              "0                       0   Not_Canceled  \n",
              "1                       1   Not_Canceled  \n",
              "2                       0       Canceled  \n",
              "3                       0       Canceled  \n",
              "4                       0       Canceled  "
            ],
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              "      <th>avg_price_per_room</th>\n",
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              "      <th>0</th>\n",
              "      <td>INN00001</td>\n",
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              "      <td>Room_Type 1</td>\n",
              "      <td>5</td>\n",
              "      <td>2018</td>\n",
              "      <td>11</td>\n",
              "      <td>6</td>\n",
              "      <td>Online</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>106.68</td>\n",
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              "      <td>2018</td>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>Online</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>60.00</td>\n",
              "      <td>0</td>\n",
              "      <td>Canceled</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>INN00004</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
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              "      <td>Meal Plan 1</td>\n",
              "      <td>0</td>\n",
              "      <td>Room_Type 1</td>\n",
              "      <td>211</td>\n",
              "      <td>2018</td>\n",
              "      <td>5</td>\n",
              "      <td>20</td>\n",
              "      <td>Online</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100.00</td>\n",
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              "      <td>Not Selected</td>\n",
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              "      <td>Room_Type 1</td>\n",
              "      <td>48</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>Online</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>94.50</td>\n",
              "      <td>0</td>\n",
              "      <td>Canceled</td>\n",
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      ],
      "source": [
        "# We will use KNN model to predict hotel reservation cancellation\n",
        "\n",
        "# Mount Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "\n",
        "\n",
        "# Path to data file\n",
        "filename = \"/content/gdrive/MyDrive/Colab Notebooks/hotel_reservations.csv\" \n",
        "\n",
        "\n",
        "# Import libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
        "\n",
        "\n",
        "\n",
        "# Load dataset\n",
        "df = pd.read_csv(filename)\n",
        "\n",
        "# Check the first 5 rows\n",
        "df.head(5)"
      ]
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          "data": {
            "text/plain": [
              "Index(['Booking_ID', 'no_of_adults', 'no_of_children', 'no_of_weekend_nights',\n",
              "       'no_of_week_nights', 'type_of_meal_plan', 'required_car_parking_space',\n",
              "       'room_type_reserved', 'lead_time', 'arrival_year', 'arrival_month',\n",
              "       'arrival_date', 'market_segment_type', 'repeated_guest',\n",
              "       'no_of_previous_cancellations', 'no_of_previous_bookings_not_canceled',\n",
              "       'avg_price_per_room', 'no_of_special_requests', 'booking_status'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "# Check columns\n",
        "df.columns"
      ]
    },
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            "text/plain": [
              "   no_of_adults  no_of_children  no_of_weekend_nights  no_of_week_nights  \\\n",
              "0             2               0                     1                  2   \n",
              "1             2               0                     2                  3   \n",
              "2             1               0                     2                  1   \n",
              "3             2               0                     0                  2   \n",
              "4             2               0                     1                  1   \n",
              "\n",
              "  type_of_meal_plan  required_car_parking_space room_type_reserved  lead_time  \\\n",
              "0       Meal Plan 1                           0        Room_Type 1        224   \n",
              "1      Not Selected                           0        Room_Type 1          5   \n",
              "2       Meal Plan 1                           0        Room_Type 1          1   \n",
              "3       Meal Plan 1                           0        Room_Type 1        211   \n",
              "4      Not Selected                           0        Room_Type 1         48   \n",
              "\n",
              "   arrival_year  arrival_month  arrival_date market_segment_type  \\\n",
              "0          2017             10             2             Offline   \n",
              "1          2018             11             6              Online   \n",
              "2          2018              2            28              Online   \n",
              "3          2018              5            20              Online   \n",
              "4          2018              4            11              Online   \n",
              "\n",
              "   repeated_guest  no_of_previous_cancellations  \\\n",
              "0               0                             0   \n",
              "1               0                             0   \n",
              "2               0                             0   \n",
              "3               0                             0   \n",
              "4               0                             0   \n",
              "\n",
              "   no_of_previous_bookings_not_canceled  avg_price_per_room  \\\n",
              "0                                     0               65.00   \n",
              "1                                     0              106.68   \n",
              "2                                     0               60.00   \n",
              "3                                     0              100.00   \n",
              "4                                     0               94.50   \n",
              "\n",
              "   no_of_special_requests booking_status  \n",
              "0                       0   Not_Canceled  \n",
              "1                       1   Not_Canceled  \n",
              "2                       0       Canceled  \n",
              "3                       0       Canceled  \n",
              "4                       0       Canceled  "
            ],
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              "      <th>no_of_adults</th>\n",
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              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>Meal Plan 1</td>\n",
              "      <td>0</td>\n",
              "      <td>Room_Type 1</td>\n",
              "      <td>224</td>\n",
              "      <td>2017</td>\n",
              "      <td>10</td>\n",
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              "      <td>Not Selected</td>\n",
              "      <td>0</td>\n",
              "      <td>Room_Type 1</td>\n",
              "      <td>5</td>\n",
              "      <td>2018</td>\n",
              "      <td>11</td>\n",
              "      <td>6</td>\n",
              "      <td>Online</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>106.68</td>\n",
              "      <td>1</td>\n",
              "      <td>Not_Canceled</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Meal Plan 1</td>\n",
              "      <td>0</td>\n",
              "      <td>Room_Type 1</td>\n",
              "      <td>1</td>\n",
              "      <td>2018</td>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>Online</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>60.00</td>\n",
              "      <td>0</td>\n",
              "      <td>Canceled</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Meal Plan 1</td>\n",
              "      <td>0</td>\n",
              "      <td>Room_Type 1</td>\n",
              "      <td>211</td>\n",
              "      <td>2018</td>\n",
              "      <td>5</td>\n",
              "      <td>20</td>\n",
              "      <td>Online</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100.00</td>\n",
              "      <td>0</td>\n",
              "      <td>Canceled</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Not Selected</td>\n",
              "      <td>0</td>\n",
              "      <td>Room_Type 1</td>\n",
              "      <td>48</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>Online</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>94.50</td>\n",
              "      <td>0</td>\n",
              "      <td>Canceled</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1de290c2-2f58-40a7-8214-141fd00c119f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1de290c2-2f58-40a7-8214-141fd00c119f 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-1de290c2-2f58-40a7-8214-141fd00c119f');\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",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# Remove unnessary columns\n",
        "df_processed = df.drop([\"Booking_ID\"], axis=1)\n",
        "df_processed.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HsIRxkN-_zJg",
        "outputId": "5bc35d66-a752-4317-d135-759fc4e2aa42"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "no_of_adults                            0\n",
              "no_of_children                          0\n",
              "no_of_weekend_nights                    0\n",
              "no_of_week_nights                       0\n",
              "type_of_meal_plan                       0\n",
              "required_car_parking_space              0\n",
              "room_type_reserved                      0\n",
              "lead_time                               0\n",
              "arrival_year                            0\n",
              "arrival_month                           0\n",
              "arrival_date                            0\n",
              "market_segment_type                     0\n",
              "repeated_guest                          0\n",
              "no_of_previous_cancellations            0\n",
              "no_of_previous_bookings_not_canceled    0\n",
              "avg_price_per_room                      0\n",
              "no_of_special_requests                  0\n",
              "booking_status                          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# Check for missing data\n",
        "df_processed.isnull().sum(axis=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8vO-oBamAOzq",
        "outputId": "423c4546-4cec-4ae0-df28-4702d84f7347"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 36275 entries, 0 to 36274\n",
            "Data columns (total 18 columns):\n",
            " #   Column                                Non-Null Count  Dtype  \n",
            "---  ------                                --------------  -----  \n",
            " 0   no_of_adults                          36275 non-null  int64  \n",
            " 1   no_of_children                        36275 non-null  int64  \n",
            " 2   no_of_weekend_nights                  36275 non-null  int64  \n",
            " 3   no_of_week_nights                     36275 non-null  int64  \n",
            " 4   type_of_meal_plan                     36275 non-null  object \n",
            " 5   required_car_parking_space            36275 non-null  int64  \n",
            " 6   room_type_reserved                    36275 non-null  object \n",
            " 7   lead_time                             36275 non-null  int64  \n",
            " 8   arrival_year                          36275 non-null  int64  \n",
            " 9   arrival_month                         36275 non-null  int64  \n",
            " 10  arrival_date                          36275 non-null  int64  \n",
            " 11  market_segment_type                   36275 non-null  object \n",
            " 12  repeated_guest                        36275 non-null  int64  \n",
            " 13  no_of_previous_cancellations          36275 non-null  int64  \n",
            " 14  no_of_previous_bookings_not_canceled  36275 non-null  int64  \n",
            " 15  avg_price_per_room                    36275 non-null  float64\n",
            " 16  no_of_special_requests                36275 non-null  int64  \n",
            " 17  booking_status                        36275 non-null  object \n",
            "dtypes: float64(1), int64(13), object(4)\n",
            "memory usage: 5.0+ MB\n"
          ]
        }
      ],
      "source": [
        "# Check for data types of columns (features)\n",
        "df_processed.info()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualise distribution of dataframe data\n",
        "for column in df_processed.columns:\n",
        "    plt.figure()\n",
        "    sns.displot(df_processed, x=column)\n",
        "    plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "pH-hsKVZrXQS",
        "outputId": "a4b7d768-1dfd-4956-e64d-4694ab43fbe1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pfJ88u3mCPzb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d3e71ace-8521-489a-9fca-be3211680d22"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['no_of_adults', 'no_of_children', 'no_of_weekend_nights',\n",
              "       'no_of_week_nights', 'required_car_parking_space', 'lead_time',\n",
              "       'repeated_guest', 'no_of_previous_cancellations',\n",
              "       'no_of_previous_bookings_not_canceled', 'avg_price_per_room',\n",
              "       'no_of_special_requests', 'type_of_meal_plan_Meal Plan 2',\n",
              "       'type_of_meal_plan_Meal Plan 3', 'type_of_meal_plan_Not Selected',\n",
              "       'room_type_reserved_Room_Type 2', 'room_type_reserved_Room_Type 3',\n",
              "       'room_type_reserved_Room_Type 4', 'room_type_reserved_Room_Type 5',\n",
              "       'room_type_reserved_Room_Type 6', 'room_type_reserved_Room_Type 7',\n",
              "       'arrival_year_2018', 'arrival_month_2', 'arrival_month_3',\n",
              "       'arrival_month_4', 'arrival_month_5', 'arrival_month_6',\n",
              "       'arrival_month_7', 'arrival_month_8', 'arrival_month_9',\n",
              "       'arrival_month_10', 'arrival_month_11', 'arrival_month_12',\n",
              "       'arrival_date_2', 'arrival_date_3', 'arrival_date_4', 'arrival_date_5',\n",
              "       'arrival_date_6', 'arrival_date_7', 'arrival_date_8', 'arrival_date_9',\n",
              "       'arrival_date_10', 'arrival_date_11', 'arrival_date_12',\n",
              "       'arrival_date_13', 'arrival_date_14', 'arrival_date_15',\n",
              "       'arrival_date_16', 'arrival_date_17', 'arrival_date_18',\n",
              "       'arrival_date_19', 'arrival_date_20', 'arrival_date_21',\n",
              "       'arrival_date_22', 'arrival_date_23', 'arrival_date_24',\n",
              "       'arrival_date_25', 'arrival_date_26', 'arrival_date_27',\n",
              "       'arrival_date_28', 'arrival_date_29', 'arrival_date_30',\n",
              "       'arrival_date_31', 'market_segment_type_Complementary',\n",
              "       'market_segment_type_Corporate', 'market_segment_type_Offline',\n",
              "       'market_segment_type_Online', 'booking_status_Not_Canceled'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# Convert non-numerical and non ordinal features to binary type\n",
        "data_processed = pd.get_dummies(df_processed, columns=['type_of_meal_plan','room_type_reserved', 'arrival_year', 'arrival_month','arrival_date', 'market_segment_type', 'booking_status' ], drop_first=True)\n",
        "data_processed.columns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check for summary of features\n",
        "data_processed.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "3vqRXhrRWAH7",
        "outputId": "8a3cb9e8-4da0-43c1-baee-e1883cca363f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       no_of_adults  no_of_children  no_of_weekend_nights  no_of_week_nights  \\\n",
              "count  36275.000000    36275.000000          36275.000000       36275.000000   \n",
              "mean       1.844962        0.105279              0.810724           2.204300   \n",
              "std        0.518715        0.402648              0.870644           1.410905   \n",
              "min        0.000000        0.000000              0.000000           0.000000   \n",
              "25%        2.000000        0.000000              0.000000           1.000000   \n",
              "50%        2.000000        0.000000              1.000000           2.000000   \n",
              "75%        2.000000        0.000000              2.000000           3.000000   \n",
              "max        4.000000       10.000000              7.000000          17.000000   \n",
              "\n",
              "       required_car_parking_space     lead_time  repeated_guest  \\\n",
              "count                36275.000000  36275.000000    36275.000000   \n",
              "mean                     0.030986     85.232557        0.025637   \n",
              "std                      0.173281     85.930817        0.158053   \n",
              "min                      0.000000      0.000000        0.000000   \n",
              "25%                      0.000000     17.000000        0.000000   \n",
              "50%                      0.000000     57.000000        0.000000   \n",
              "75%                      0.000000    126.000000        0.000000   \n",
              "max                      1.000000    443.000000        1.000000   \n",
              "\n",
              "       no_of_previous_cancellations  no_of_previous_bookings_not_canceled  \\\n",
              "count                  36275.000000                          36275.000000   \n",
              "mean                       0.023349                              0.153411   \n",
              "std                        0.368331                              1.754171   \n",
              "min                        0.000000                              0.000000   \n",
              "25%                        0.000000                              0.000000   \n",
              "50%                        0.000000                              0.000000   \n",
              "75%                        0.000000                              0.000000   \n",
              "max                       13.000000                             58.000000   \n",
              "\n",
              "       avg_price_per_room  ...  arrival_date_27  arrival_date_28  \\\n",
              "count        36275.000000  ...     36275.000000     36275.000000   \n",
              "mean           103.423539  ...         0.029194         0.031123   \n",
              "std             35.089424  ...         0.168351         0.173653   \n",
              "min              0.000000  ...         0.000000         0.000000   \n",
              "25%             80.300000  ...         0.000000         0.000000   \n",
              "50%             99.450000  ...         0.000000         0.000000   \n",
              "75%            120.000000  ...         0.000000         0.000000   \n",
              "max            540.000000  ...         1.000000         1.000000   \n",
              "\n",
              "       arrival_date_29  arrival_date_30  arrival_date_31  \\\n",
              "count     36275.000000     36275.000000     36275.000000   \n",
              "mean          0.032805         0.033522         0.015934   \n",
              "std           0.178128         0.179997         0.125221   \n",
              "min           0.000000         0.000000         0.000000   \n",
              "25%           0.000000         0.000000         0.000000   \n",
              "50%           0.000000         0.000000         0.000000   \n",
              "75%           0.000000         0.000000         0.000000   \n",
              "max           1.000000         1.000000         1.000000   \n",
              "\n",
              "       market_segment_type_Complementary  market_segment_type_Corporate  \\\n",
              "count                       36275.000000                   36275.000000   \n",
              "mean                            0.010779                       0.055603   \n",
              "std                             0.103261                       0.229157   \n",
              "min                             0.000000                       0.000000   \n",
              "25%                             0.000000                       0.000000   \n",
              "50%                             0.000000                       0.000000   \n",
              "75%                             0.000000                       0.000000   \n",
              "max                             1.000000                       1.000000   \n",
              "\n",
              "       market_segment_type_Offline  market_segment_type_Online  \\\n",
              "count                 36275.000000                36275.000000   \n",
              "mean                      0.290227                    0.639945   \n",
              "std                       0.453873                    0.480023   \n",
              "min                       0.000000                    0.000000   \n",
              "25%                       0.000000                    0.000000   \n",
              "50%                       0.000000                    1.000000   \n",
              "75%                       1.000000                    1.000000   \n",
              "max                       1.000000                    1.000000   \n",
              "\n",
              "       booking_status_Not_Canceled  \n",
              "count                 36275.000000  \n",
              "mean                      0.672364  \n",
              "std                       0.469358  \n",
              "min                       0.000000  \n",
              "25%                       0.000000  \n",
              "50%                       1.000000  \n",
              "75%                       1.000000  \n",
              "max                       1.000000  \n",
              "\n",
              "[8 rows x 67 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>no_of_adults</th>\n",
              "      <th>no_of_children</th>\n",
              "      <th>no_of_weekend_nights</th>\n",
              "      <th>no_of_week_nights</th>\n",
              "      <th>required_car_parking_space</th>\n",
              "      <th>lead_time</th>\n",
              "      <th>repeated_guest</th>\n",
              "      <th>no_of_previous_cancellations</th>\n",
              "      <th>no_of_previous_bookings_not_canceled</th>\n",
              "      <th>avg_price_per_room</th>\n",
              "      <th>...</th>\n",
              "      <th>arrival_date_27</th>\n",
              "      <th>arrival_date_28</th>\n",
              "      <th>arrival_date_29</th>\n",
              "      <th>arrival_date_30</th>\n",
              "      <th>arrival_date_31</th>\n",
              "      <th>market_segment_type_Complementary</th>\n",
              "      <th>market_segment_type_Corporate</th>\n",
              "      <th>market_segment_type_Offline</th>\n",
              "      <th>market_segment_type_Online</th>\n",
              "      <th>booking_status_Not_Canceled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>36275.000000</td>\n",
              "      <td>36275.000000</td>\n",
              "      <td>36275.000000</td>\n",
              "      <td>36275.000000</td>\n",
              "      <td>36275.000000</td>\n",
              "      <td>36275.000000</td>\n",
              "      <td>36275.000000</td>\n",
              "      <td>36275.000000</td>\n",
              "      <td>36275.000000</td>\n",
              "      <td>36275.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>36275.000000</td>\n",
              "      <td>36275.000000</td>\n",
              "      <td>36275.000000</td>\n",
              "      <td>36275.000000</td>\n",
              "      <td>36275.000000</td>\n",
              "      <td>36275.000000</td>\n",
              "      <td>36275.000000</td>\n",
              "      <td>36275.000000</td>\n",
              "      <td>36275.000000</td>\n",
              "      <td>36275.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.844962</td>\n",
              "      <td>0.105279</td>\n",
              "      <td>0.810724</td>\n",
              "      <td>2.204300</td>\n",
              "      <td>0.030986</td>\n",
              "      <td>85.232557</td>\n",
              "      <td>0.025637</td>\n",
              "      <td>0.023349</td>\n",
              "      <td>0.153411</td>\n",
              "      <td>103.423539</td>\n",
              "      <td>...</td>\n",
              "      <td>0.029194</td>\n",
              "      <td>0.031123</td>\n",
              "      <td>0.032805</td>\n",
              "      <td>0.033522</td>\n",
              "      <td>0.015934</td>\n",
              "      <td>0.010779</td>\n",
              "      <td>0.055603</td>\n",
              "      <td>0.290227</td>\n",
              "      <td>0.639945</td>\n",
              "      <td>0.672364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.518715</td>\n",
              "      <td>0.402648</td>\n",
              "      <td>0.870644</td>\n",
              "      <td>1.410905</td>\n",
              "      <td>0.173281</td>\n",
              "      <td>85.930817</td>\n",
              "      <td>0.158053</td>\n",
              "      <td>0.368331</td>\n",
              "      <td>1.754171</td>\n",
              "      <td>35.089424</td>\n",
              "      <td>...</td>\n",
              "      <td>0.168351</td>\n",
              "      <td>0.173653</td>\n",
              "      <td>0.178128</td>\n",
              "      <td>0.179997</td>\n",
              "      <td>0.125221</td>\n",
              "      <td>0.103261</td>\n",
              "      <td>0.229157</td>\n",
              "      <td>0.453873</td>\n",
              "      <td>0.480023</td>\n",
              "      <td>0.469358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>80.300000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99.450000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>443.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>540.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 67 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-616976aa-67bc-450b-aad1-b607154ed588')\"\n",
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              "    .colab-df-convert:hover {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "          document.querySelector('#df-616976aa-67bc-450b-aad1-b607154ed588 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-616976aa-67bc-450b-aad1-b607154ed588');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize correlation between variables after one-hot encoding\n",
        "corr = data_processed.corr()\n",
        "sns.heatmap(corr, annot=True) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "DqxulhCmsnxs",
        "outputId": "a4ff175b-e5b1-4a0c-d618-1c379171ad2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f77b55b7c70>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create X features set and y outcome set\n",
        "X = data_processed.drop([\"booking_status_Not_Canceled\"], axis=1)\n",
        "y = data_processed[\"booking_status_Not_Canceled\"]"
      ],
      "metadata": {
        "id": "8p5YY2KiTKrB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into training and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
        "\n",
        "# Split the training data into training and validation sets\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=0)"
      ],
      "metadata": {
        "id": "xOeIfZXgS5Xj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the model\n",
        "knn = KNeighborsClassifier(n_neighbors=5, weights='uniform', metric='euclidean')\n",
        "\n",
        "# Train the model using the training set\n",
        "knn.fit(X_train, y_train)\n",
        "\n",
        "# Evaluate the model on the validation set\n",
        "val_accuracy = knn.score(X_val, y_val)\n",
        "print(\"Validation Accuracy of Model 1: {:.2f}%\".format(val_accuracy * 100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w4mQeVrVULKB",
        "outputId": "5ae257ef-e903-4f29-daa5-6c770b5b8bf0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation Accuracy of Model 1: 81.08%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict y values for the validation set\n",
        "y_pred = knn.predict(X_val)\n",
        "\n",
        "# Calculate model fit paratmeters for the validation set\n",
        "print(\"Validation Accuracy of Model 1: {:.2f}%\".format(accuracy_score(y_val, y_pred) * 100))\n",
        "print(\"Validation Precision of Model 1: {:.2f}%:\".format(precision_score(y_val, y_pred) * 100))\n",
        "print(\"Validation Recall of Model 1: {:.2f}%\".format(recall_score(y_val, y_pred) * 100))\n",
        "print(\"Validation F1-score of Model 1: {:.2f}%\".format(f1_score(y_val, y_pred) * 100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "43un0peHWXaP",
        "outputId": "c598e7c2-2125-4172-8b31-fd6a4a94ae85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation Accuracy of Model 1: 81.08%\n",
            "Validation Precision of Model 1: 83.22%:\n",
            "Validation Recall of Model 1: 89.91%\n",
            "Validation F1-score of Model 1: 86.44%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Reduce model features to simplify the model (Model 2)\n",
        "# Remove arrival_year arrival_date in addition to Booking_ID\n",
        "df2_processed = df.drop([\"Booking_ID\", \"arrival_year\", \"arrival_date\"], axis=1)\n",
        "# Convert non-numerical and non ordinal features to binary type\n",
        "data2_processed = pd.get_dummies(df_processed, columns=['type_of_meal_plan','room_type_reserved', 'arrival_month', 'market_segment_type', 'booking_status' ], drop_first=True)\n",
        "data2_processed.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5oBxbG98bhvd",
        "outputId": "baadbb80-4218-43ff-e930-6cdae0d6e8cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['no_of_adults', 'no_of_children', 'no_of_weekend_nights',\n",
              "       'no_of_week_nights', 'required_car_parking_space', 'lead_time',\n",
              "       'arrival_year', 'arrival_date', 'repeated_guest',\n",
              "       'no_of_previous_cancellations', 'no_of_previous_bookings_not_canceled',\n",
              "       'avg_price_per_room', 'no_of_special_requests',\n",
              "       'type_of_meal_plan_Meal Plan 2', 'type_of_meal_plan_Meal Plan 3',\n",
              "       'type_of_meal_plan_Not Selected', 'room_type_reserved_Room_Type 2',\n",
              "       'room_type_reserved_Room_Type 3', 'room_type_reserved_Room_Type 4',\n",
              "       'room_type_reserved_Room_Type 5', 'room_type_reserved_Room_Type 6',\n",
              "       'room_type_reserved_Room_Type 7', 'arrival_month_2', 'arrival_month_3',\n",
              "       'arrival_month_4', 'arrival_month_5', 'arrival_month_6',\n",
              "       'arrival_month_7', 'arrival_month_8', 'arrival_month_9',\n",
              "       'arrival_month_10', 'arrival_month_11', 'arrival_month_12',\n",
              "       'market_segment_type_Complementary', 'market_segment_type_Corporate',\n",
              "       'market_segment_type_Offline', 'market_segment_type_Online',\n",
              "       'booking_status_Not_Canceled'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create X features set and y outcome set\n",
        "X2 = data2_processed.drop([\"booking_status_Not_Canceled\"], axis=1)\n",
        "y2 = data2_processed[\"booking_status_Not_Canceled\"]\n",
        "\n",
        "# Split the data into training and test sets\n",
        "X2_train, X2_test, y2_train, y2_test = train_test_split(X2, y2, test_size=0.2, random_state=0)\n",
        "\n",
        "# Split the training data into training and validation sets\n",
        "X2_train, X2_val, y2_train, y2_val = train_test_split(X2_train, y2_train, test_size=0.25, random_state=0)\n",
        "\n",
        "# Initialize the model\n",
        "knn2 = KNeighborsClassifier(n_neighbors=5, weights='uniform', metric='euclidean')\n",
        "\n",
        "# Train the model using the training set\n",
        "knn2.fit(X2_train, y2_train)\n",
        "\n",
        "# Evaluate the model on the validation set\n",
        "val_accuracy = knn2.score(X2_val, y2_val)\n",
        "print(\"Validation Accuracy of Model 2: {:.2f}%\".format(val_accuracy * 100))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y7vy6CHNcDkE",
        "outputId": "270d6c6f-90da-4ab8-f3a9-65944f99d607"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation Accuracy of Model 2: 79.60%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Standardise features to improve on Model 2 (Model 3)\n",
        "# Create a StandardScaler object\n",
        "scaler = StandardScaler()\n",
        "\n",
        "# Fit the StandardScaler to the training data\n",
        "scaler.fit(X2_train)\n",
        "\n",
        "# Transform the training, validation and test data\n",
        "X3_train = scaler.transform(X2_train)\n",
        "X3_val = scaler.transform(X2_val)\n",
        "X3_test = scaler.transform(X2_test)\n",
        "\n",
        "# Use the same y2_train, y2_val and y2_test data\n",
        "y3_train = y2_train\n",
        "y3_val = y2_val\n",
        "y3_test = y2_test"
      ],
      "metadata": {
        "id": "yQBr6KHnm47J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Find the best hyperparameters with GridSearchCV with Model 3 features\n",
        "# Define parameter grid\n",
        "param_grid = {\n",
        "    'n_neighbors': [3, 5, 7],\n",
        "    'weights': ['uniform', 'distance'],\n",
        "    'metric': ['euclidean', 'manhattan']\n",
        "}\n",
        "\n",
        "# Create an instance of the KNeighborsClassifier (KNN)\n",
        "knn3 = KNeighborsClassifier()\n",
        "\n",
        "# Create an instance of GridSearchCV with the KNN model and the parameter grid\n",
        "grid_search = GridSearchCV(knn3, param_grid, cv=5)\n",
        "\n",
        "# Fit the GridSearchCV instance to the training data\n",
        "grid_search.fit(X3_train, y3_train)\n",
        "\n",
        "# Get the best hyperparameters\n",
        "best_params = grid_search.best_params_\n",
        "\n",
        "# Use the best hyperparameters to make predictions on the validation data\n",
        "y3_pred = grid_search.predict(X3_val)\n",
        "\n",
        "# Print the best hyperparameters\n",
        "print(\"Best set of hyperparameters \\n\")\n",
        "print(\"Best Number of Neighbors:\", best_params['n_neighbors'])\n",
        "print(\"Best Weighting Method:\", best_params['weights'])\n",
        "print(\"Best Metric:\", best_params['metric'])\n",
        "\n",
        "# Calculate model fit paratmeters for the validation set\n",
        "print(\"Validation Accuracy with the best hyperparameters: {:.2f}%\".format(accuracy_score(y3_val, y3_pred) * 100))\n",
        "print(\"Validation Precision with the best hyperparameters: {:.2f}%:\".format(precision_score(y3_val, y3_pred) * 100))\n",
        "print(\"Validation Recall with the best hyperparameters: {:.2f}%\".format(recall_score(y3_val, y3_pred) * 100))\n",
        "print(\"Validation F1-score with the best hyperparameters: {:.2f}%\".format(f1_score(y3_val, y3_pred) * 100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mtWVvueXYsm-",
        "outputId": "1cb08c7f-4b75-4f46-b3da-fae783da03ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best set of hyperparameters \n",
            "\n",
            "Best Number of Neighbors: 7\n",
            "Best Weighting Method: distance\n",
            "Best Metric: manhattan\n",
            "Validation Accuracy with the best hyperparameters: 85.76%\n",
            "Validation Precision with the best hyperparameters: 88.28%:\n",
            "Validation Recall with the best hyperparameters: 90.83%\n",
            "Validation F1-score with the best hyperparameters: 89.54%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the final model performance metrics with X3_test and y3_test\n",
        "# Use the best hyperparameters to make predictions on the test data\n",
        "y3_pred = grid_search.predict(X3_test)\n",
        "\n",
        "\n",
        "# Calculate model fit paratmeters for the test set\n",
        "print(\"Hotel Reservation Non-cancellations \\n\")\n",
        "print(\"Test Accuracy with the best hyperparameters: {:.2f}%\".format(accuracy_score(y3_test, y3_pred) * 100))\n",
        "print(\"Test Precision with the best hyperparameters: {:.2f}%:\".format(precision_score(y3_test, y3_pred) * 100))\n",
        "print(\"Test Recall with the best hyperparameters: {:.2f}%\".format(recall_score(y3_test, y3_pred) * 100))\n",
        "print(\"Test F1-score with the best hyperparameters: {:.2f}%\".format(f1_score(y3_test, y3_pred) * 100))\n",
        "\n",
        "# Calculate the confusion matrix\n",
        "cm = confusion_matrix(y3_test, y3_pred)\n",
        "tn, fp, fn, tp = cm.ravel()\n",
        "specificity = tn / (tn + fp)\n",
        "print(\"Test specificity with the best hyperparameters: {:.2f}%\".format(specificity * 100))\n",
        "print(\"Test specificity with the best hyperparameters: {:.2f}%\".format(specificity * 100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V-9Ly-tJfnSD",
        "outputId": "c5c004f9-427f-4887-af27-3075bc4f155b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hotel Reservation Non-cancellations \n",
            "\n",
            "Test Accuracy with the best hyperparameters: 85.76%\n",
            "Test Precision with the best hyperparameters: 87.79%:\n",
            "Test Recall with the best hyperparameters: 91.43%\n",
            "Test F1-score with the best hyperparameters: 89.58%\n",
            "Test specificity with the best hyperparameters: 74.30%\n",
            "Test specificity with the best hyperparameters: 74.30%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "|   | Actual Positive | Actual Negative |\n",
        "|---|---|---|\n",
        "| Predicted Positive | a (True Positive) | c (False Positive) |\n",
        "| Predicted Negative | b (False Negative) | d (True Negative) |\n",
        "\n",
        "#### Metrics:\n",
        "- Accuracy: (a + d) / (a + b + c + d)\n",
        "- Precision: a / (a + c)\n",
        "- Recall (Sensitivity): a / (a + b)\n",
        "- F1 Score: 2 * (Precision * Recall) / (Precision + Recall)\n",
        "- Specificity: d / (c + d)\n"
      ],
      "metadata": {
        "id": "6FDd3ibVch_p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the confusion matrix \n",
        "print(\"Hotel Reservation Non-cancellations \\n\")\n",
        "sns.heatmap(cm, annot=True, annot_kws={\"size\": 16})\n",
        "plt.xlabel(\"Predicted class\")\n",
        "plt.ylabel(\"True class\")\n",
        "plt.title(\"Confusion matrix\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "id": "_WpcJ7vvYRjs",
        "outputId": "ac227dcf-67bf-4f1c-a0b4-2e04e01b113c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hotel Reservation Non-cancellations \n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Interpretation\n",
        "\n",
        "1. The model has a precision of 88% which indicates that out of all the positive predictions made by the model, 88% of them are actual positive cases. \n",
        "2. The recall of 91% shows that the model can successfully recall 92.69% of the positive cases. \n",
        "3. The specificity of 74% shows that model can correctly predict 74% of the actual negative cases. \n",
        "4. Out of all the negative predictions made by the model, 81% [1800/(1800+420)] are actual negative cases, i.e. those who actually cancelled reservations. \n",
        "5. Positive predictions (Non-Cancellations): Generally, the model is good at making accurate positive predictions (high precision) and at recalling a large percentage of the actual positive cases (high recall). \n",
        "6. Negative Predictions (Cancellations): The specificity is 734% and there is a 19% percentage of false negatives out of all negatives predicted. Therefore, the model may not be very good at predicting cancellations."
      ],
      "metadata": {
        "id": "2OsoHrS0e18o"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "VhyJZZv6r2BJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the correlation matrix\n",
        "corr = data2_processed.corr()\n",
        "\n",
        "# Take the absolute value of the correlation matrix\n",
        "corr = abs(corr)\n",
        "\n",
        "# Sort the values in descending order based on the correlation with the target variable\n",
        "corr = corr.sort_values(\"booking_status_Not_Canceled\", ascending=False)\n",
        "\n",
        "# Rank the features by their absolute value of correlation\n",
        "corr[\"booking_status_Not_Canceled\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uFuhqeIBsAID",
        "outputId": "359c83c4-7884-46d3-e0a0-322fcdaf21f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "booking_status_Not_Canceled             1.000000\n",
              "lead_time                               0.438538\n",
              "no_of_special_requests                  0.253070\n",
              "arrival_year                            0.179529\n",
              "avg_price_per_room                      0.142569\n",
              "arrival_month_12                        0.124947\n",
              "market_segment_type_Corporate           0.112993\n",
              "repeated_guest                          0.107287\n",
              "market_segment_type_Online              0.106362\n",
              "no_of_week_nights                       0.092996\n",
              "no_of_adults                            0.086920\n",
              "type_of_meal_plan_Meal Plan 2           0.086370\n",
              "required_car_parking_space              0.086185\n",
              "arrival_month_7                         0.077138\n",
              "market_segment_type_Complementary       0.072867\n",
              "no_of_weekend_nights                    0.061563\n",
              "no_of_previous_bookings_not_canceled    0.060179\n",
              "arrival_month_6                         0.050010\n",
              "arrival_month_8                         0.045717\n",
              "market_segment_type_Offline             0.038351\n",
              "arrival_month_2                         0.035613\n",
              "no_of_previous_cancellations            0.033728\n",
              "no_of_children                          0.033078\n",
              "room_type_reserved_Room_Type 6          0.032652\n",
              "arrival_month_10                        0.022910\n",
              "arrival_month_5                         0.022049\n",
              "arrival_month_4                         0.021928\n",
              "arrival_month_11                        0.021680\n",
              "arrival_month_3                         0.017288\n",
              "room_type_reserved_Room_Type 7          0.014062\n",
              "room_type_reserved_Room_Type 4          0.013309\n",
              "arrival_date                            0.010629\n",
              "room_type_reserved_Room_Type 5          0.010224\n",
              "arrival_month_9                         0.004808\n",
              "type_of_meal_plan_Meal Plan 3           0.003193\n",
              "type_of_meal_plan_Not Selected          0.003072\n",
              "room_type_reserved_Room_Type 3          0.001241\n",
              "room_type_reserved_Room_Type 2          0.000548\n",
              "Name: booking_status_Not_Canceled, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "# Scale the data\n",
        "X4_scaled = (X2 - X2.mean()) / X2.std()\n",
        "\n",
        "# Perform PCA\n",
        "pca = PCA(n_components=10)\n",
        "principalComponents = pca.fit_transform(X4_scaled)\n",
        "\n",
        "# Create the principal components DataFrame\n",
        "X4_principal = pd.DataFrame(data=principalComponents)\n",
        "\n",
        "# Split the data into training and test sets\n",
        "X4_train, X4_test, y4_train, y4_test = train_test_split(X4_principal, y2, test_size=0.2, random_state=0)\n",
        "\n",
        "# Split the training data into training and validation sets\n",
        "X4_train, X4_val, y4_train, y4_val = train_test_split(X4_train, y4_train, test_size=0.25, random_state=0)\n",
        "\n",
        "# Initialize the model\n",
        "knn4 = KNeighborsClassifier(n_neighbors=20, weights='distance', metric='manhattan')\n",
        "\n",
        "# Train the model using the training set\n",
        "knn4.fit(X4_train, y4_train)\n",
        "\n",
        "# Access the column names\n",
        "print(X4_principal.columns)\n",
        "\n",
        "# Evaluate the model on the validation set\n",
        "val_accuracy = knn4.score(X4_val, y4_val)\n",
        "print(\"Validation Accuracy of Model with 10 features selected with PCA: {:.2f}%\".format(val_accuracy * 100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZS5f_7ltnlsb",
        "outputId": "1e2c83c5-efed-423e-ad89-fb9075676c50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RangeIndex(start=0, stop=10, step=1)\n",
            "Validation Accuracy of Model with 10 features selected with PCA: 85.42%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "\n",
        "# Perform LDA\n",
        "lda = LinearDiscriminantAnalysis(n_components=1)\n",
        "X2_train_lda = lda.fit_transform(X2_train, y2_train)\n",
        "X_val_lda = lda.transform(X2_val)\n",
        "\n",
        "# Train a classifier on the transformed data\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn = KNeighborsClassifier(n_neighbors=5)\n",
        "knn.fit(X2_train_lda, y2_train)\n",
        "\n",
        "# Evaluate the model on the validation set\n",
        "val_accuracy = knn2.score(X2_val, y2_val)\n",
        "print(\"Validation Accuracy after LDA with one feature: {:.2f}%\".format(val_accuracy * 100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0nK2hWBvILMJ",
        "outputId": "c5ed61d8-3612-4cce-a604-b8e788ac7e0f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation Accuracy after LDA with one feature: 79.60%\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1VLkrKXJRcYh8dHnvl5tKcPjcvnmiYgor",
      "authorship_tag": "ABX9TyNKy+0HBPUlarErnMDBVjft",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}