hotel_reservation_LR.ipynb

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        "<a href=\"https://colab.research.google.com/github/nimnathw/hotel-reservation/blob/main/hotel_reservation_LR.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "### This Jupyter Notebook uses a Kellogg's dataset for logistic regression 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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        "id": "f1GpoSwxeJg2"
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          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ]
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          "data": {
            "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></th>\n",
              "      <th>Booking_ID</th>\n",
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              "      <th>market_segment_type</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>no_of_special_requests</th>\n",
              "      <th>booking_status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>INN00001</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
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              "      <td>0</td>\n",
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              "      <td>224</td>\n",
              "      <td>2017</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <th>1</th>\n",
              "      <td>INN00002</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <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",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>INN00003</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <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>INN00004</td>\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",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>INN00005</td>\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",
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      ],
      "source": [
        "# We will use LR 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.linear_model import LogisticRegression\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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      "outputs": [
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          "output_type": "execute_result",
          "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": 4
        }
      ],
      "source": [
        "# Check columns\n",
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
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        "id": "ASjujJmM-kB1",
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          "output_type": "execute_result",
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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></th>\n",
              "      <th>no_of_adults</th>\n",
              "      <th>no_of_children</th>\n",
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              "      <th>arrival_date</th>\n",
              "      <th>market_segment_type</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>no_of_special_requests</th>\n",
              "      <th>booking_status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <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",
              "      <td>2</td>\n",
              "      <td>Offline</td>\n",
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              "      <td>0</td>\n",
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              "      <td>Not_Canceled</td>\n",
              "    </tr>\n",
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              "      <th>1</th>\n",
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              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <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-06f8e460-9930-41c6-b3da-900fe44c503d')\"\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-06f8e460-9930-41c6-b3da-900fe44c503d 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-06f8e460-9930-41c6-b3da-900fe44c503d');\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": 5
        }
      ],
      "source": [
        "# Remove unnessary columns\n",
        "df_processed = df.drop([\"Booking_ID\"], axis=1)\n",
        "df_processed.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HsIRxkN-_zJg",
        "outputId": "5eec520c-c163-416b-b943-c42e47f94698"
      },
      "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": 6
        }
      ],
      "source": [
        "# Check for missing data\n",
        "df_processed.isnull().sum(axis=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8vO-oBamAOzq",
        "outputId": "a8c35d4a-cb7f-4f55-e859-133cca4c2956"
      },
      "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",
        "df.hist(bins=50, figsize=(20, 15))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 879
        },
        "id": "pH-hsKVZrXQS",
        "outputId": "283a2c2d-f7ae-4914-eea6-4250db7b51b2"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1080 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "pfJ88u3mCPzb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aebcaab7-94fe-4fa8-b711-67af362db13c"
      },
      "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": 9
        }
      ],
      "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": "67f800d7-5763-4932-81df-8911af2c0de3"
      },
      "execution_count": 10,
      "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",
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              "    <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",
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              "    <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",
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              "    <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",
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              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 67 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1828a9e5-4aab-47e4-a032-36232ae41068')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1828a9e5-4aab-47e4-a032-36232ae41068');\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",
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            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "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": "212fa6ad-37a4-48c0-e131-b9748d08f8ce"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fab9aa7dbe0>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "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": 12,
      "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": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pandas.core.reshape.api import lreshape\n",
        "# Initialize the model\n",
        "lr = LogisticRegression()\n",
        "\n",
        "# Train the model using the training set\n",
        "lr.fit(X_train, y_train)\n",
        "\n",
        "# Evaluate the model on the validation set\n",
        "y_pred = lr.predict(X_val)\n",
        "print(\"Accuracy:\", accuracy_score(y_val, y_pred))\n",
        "print(\"Precision:\", precision_score(y_val, y_pred))\n",
        "print(\"Recall:\", recall_score(y_val, y_pred))\n",
        "print(\"F1-score:\", f1_score(y_val, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w4mQeVrVULKB",
        "outputId": "64feaeb2-e16f-4c48-9197-ceb968824f03"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.79710544452102\n",
            "Precision: 0.8191049266641595\n",
            "Recall: 0.8951911220715166\n",
            "F1-score: 0.8554595443833465\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\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": "78710df2-de5f-4c6e-9806-887b1b4d86cb"
      },
      "execution_count": 16,
      "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": 16
        }
      ]
    },
    {
      "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",
        "lr2 = LogisticRegression()\n",
        "\n",
        "# Train the model using the training set\n",
        "lr2.fit(X2_train, y2_train)\n",
        "\n",
        "# Evaluate the model on the validation set\n",
        "y2_pred = lr2.predict(X2_val)\n",
        "print(\"Accuracy:\", accuracy_score(y2_val, y2_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y7vy6CHNcDkE",
        "outputId": "08dc78aa-5518-4e34-9d90-81b9adcecccd"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7925568573397657\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\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": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Find the best hyperparameters with GridSearchCV with Model 3 features\n",
        "# Define parameter grid\n",
        "parameters = {'penalty':['none', 'l1', 'l2'],\n",
        "              'C': [0.01, 0.1, 1, 10, 100],\n",
        "              'solver': ['saga', 'newton-cg']}\n",
        "\n",
        "# Initialize the model\n",
        "lr3 = LogisticRegression()\n",
        "\n",
        "# Create an instance of GridSearchCV with the lr model and the parameter grid\n",
        "grid_search = GridSearchCV(estimator = lr3,  \n",
        "                           param_grid = parameters,\n",
        "                           scoring = 'accuracy',\n",
        "                           cv = 10,\n",
        "                           verbose=1)\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 hyperparameters: \", grid_search.best_params_)\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": "9ecb45e5-4f4e-4766-9e98-f3498d62abe0"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 30 candidates, totalling 300 fits\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: \n",
            "50 fits failed out of a total of 300.\n",
            "The score on these train-test partitions for these parameters will be set to nan.\n",
            "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
            "\n",
            "Below are more details about the failures:\n",
            "--------------------------------------------------------------------------------\n",
            "50 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py\", line 1461, in fit\n",
            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py\", line 447, in _check_solver\n",
            "    raise ValueError(\n",
            "ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.\n",
            "\n",
            "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_search.py:969: UserWarning: One or more of the test scores are non-finite: [0.80941887 0.80914315 0.80886799        nan 0.80850009 0.80854602\n",
            " 0.80941887 0.80914315 0.80918921        nan 0.80960267 0.80974047\n",
            " 0.80941887 0.80914315 0.80923508        nan 0.8094648  0.80914313\n",
            " 0.80941887 0.80914315 0.80941887        nan 0.80941887 0.80914315\n",
            " 0.80941887 0.80914315 0.80941887        nan 0.80941887 0.80914315]\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best hyperparameters:  {'C': 0.1, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
            "Validation Accuracy with the best hyperparameters: 80.18%\n",
            "Validation Precision with the best hyperparameters: 82.47%:\n",
            "Validation Recall with the best hyperparameters: 89.46%\n",
            "Validation F1-score with the best hyperparameters: 85.82%\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",
        "# 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))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V-9Ly-tJfnSD",
        "outputId": "0206c1f8-15a5-460c-a4f8-e8c924b7ebba"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hotel Reservation Non-cancellations \n",
            "\n",
            "Test Accuracy with the best hyperparameters: 80.69%\n",
            "Test Precision with the best hyperparameters: 83.07%:\n",
            "Test Recall with the best hyperparameters: 89.35%\n",
            "Test F1-score with the best hyperparameters: 86.09%\n",
            "Test specificity with the best hyperparameters: 63.18%\n",
            "Test specificity with the best hyperparameters: 63.18%\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",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "id": "_WpcJ7vvYRjs",
        "outputId": "99232b63-ab02-464a-c677-64330f6ff390"
      },
      "execution_count": 30,
      "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 83% which indicates that out of all the positive predictions made by the model, 83% of them are actual positive cases. \n",
        "2. The recall of 89% shows that the model can successfully recall 89% of the positive cases. \n",
        "3. The specificity of 63% shows that model can correctly predict 63% of the actual negtive cases. \n",
        "4. Out of all the negative predictions made by the model, 74% 1500/(1500+520) 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 63% and there is a 26% percentage of false negatives out of all negatives predicted. Therefore, the model may not be very good at predicting cancellations."
      ],
      "metadata": {
        "id": "2OsoHrS0e18o"
      }
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1VLkrKXJRcYh8dHnvl5tKcPjcvnmiYgor",
      "authorship_tag": "ABX9TyM03aabBndtLoR6f4GqCs0f",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}