Classification_using_Frequent_Patterns.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Môi trường lập trình"
      ],
      "metadata": {
        "id": "BSDlk6bSQi00"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {
        "id": "u52pE_B0DdQt"
      },
      "outputs": [],
      "source": [
        "# Thư viện\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import math\n",
        "import warnings\n",
        "import joblib            as jlb\n",
        "from sklearn.decomposition   import PCA\n",
        "from sklearn.preprocessing   import LabelEncoder, OneHotEncoder\n",
        "from sklearn.preprocessing   import Binarizer, PolynomialFeatures, MinMaxScaler, StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors       import KNeighborsClassifier\n",
        "from sklearn                 import metrics\n",
        "from sklearn.metrics         import accuracy_score, confusion_matrix\n",
        "from sklearn.metrics         import auc,roc_curve\n",
        "from sklearn.metrics         import precision_score, recall_score, f1_score\n",
        "from sklearn.metrics         import roc_auc_score\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn                 import svm\n",
        "from mlxtend.preprocessing import TransactionEncoder\n",
        "from mlxtend.frequent_patterns import fpgrowth\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.naive_bayes     import GaussianNB, BernoulliNB, MultinomialNB\n",
        "from sklearn.tree            import DecisionTreeClassifier\n",
        "from sklearn.neighbors       import KNeighborsClassifier"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Bỏ qua các cảnh báo\n",
        "np.seterr(divide = 'ignore')\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "ntPMtLqLRAML"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tiền xử lý dữ liệu"
      ],
      "metadata": {
        "id": "pQZ8hH4kRLkc"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "fPk7d3utXUJY",
        "outputId": "8e4eba3c-de5d-4672-f8dd-8afbba565ba2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "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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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Booking_ID</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>type_of_meal_plan</th>\n",
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              "      <th>lead_time</th>\n",
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              "      <th>arrival_month</th>\n",
              "      <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>INN00001</td>\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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>65.00</td>\n",
              "      <td>0</td>\n",
              "      <td>Not_Canceled</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>INN00003</td>\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>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",
              "    </tr>\n",
              "    <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",
              "    </tr>\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ],
      "source": [
        "df = pd.read_csv('Hotel Reservations.csv')\n",
        "\n",
        "df.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Thăm dò dữ liệu"
      ],
      "metadata": {
        "id": "mEXcUD-tSkmg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hiển thị thông tin bộ dữ liệu."
      ],
      "metadata": {
        "id": "sI8-1ppZS7Ce"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Kích thước của bộ dữ liệu gồm {df.shape[0]} dòng và {df.shape[1]} cột.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9UV3p33KS4Wq",
        "outputId": "3ff1eadf-2d01-4c81-92f8-dbffb38f298d"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Kích thước của bộ dữ liệu gồm 36275 dòng và 19 cột.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9CdcKRNRS_Ez",
        "outputId": "0bb0aaf4-f798-4306-f89f-f1ef52c32c1b"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 36275 entries, 0 to 36274\n",
            "Data columns (total 19 columns):\n",
            " #   Column                                Non-Null Count  Dtype  \n",
            "---  ------                                --------------  -----  \n",
            " 0   Booking_ID                            36275 non-null  object \n",
            " 1   no_of_adults                          36275 non-null  int64  \n",
            " 2   no_of_children                        36275 non-null  int64  \n",
            " 3   no_of_weekend_nights                  36275 non-null  int64  \n",
            " 4   no_of_week_nights                     36275 non-null  int64  \n",
            " 5   type_of_meal_plan                     36275 non-null  object \n",
            " 6   required_car_parking_space            36275 non-null  int64  \n",
            " 7   room_type_reserved                    36275 non-null  object \n",
            " 8   lead_time                             36275 non-null  int64  \n",
            " 9   arrival_year                          36275 non-null  int64  \n",
            " 10  arrival_month                         36275 non-null  int64  \n",
            " 11  arrival_date                          36275 non-null  int64  \n",
            " 12  market_segment_type                   36275 non-null  object \n",
            " 13  repeated_guest                        36275 non-null  int64  \n",
            " 14  no_of_previous_cancellations          36275 non-null  int64  \n",
            " 15  no_of_previous_bookings_not_canceled  36275 non-null  int64  \n",
            " 16  avg_price_per_room                    36275 non-null  float64\n",
            " 17  no_of_special_requests                36275 non-null  int64  \n",
            " 18  booking_status                        36275 non-null  object \n",
            "dtypes: float64(1), int64(13), object(5)\n",
            "memory usage: 5.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# In số giá trị duy nhất của mỗi cột\n",
        "df.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IwFevR47zRla",
        "outputId": "b0dede63-c47e-455f-c079-4deb4629f2b7"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Booking_ID                              36275\n",
              "no_of_adults                                5\n",
              "no_of_children                              6\n",
              "no_of_weekend_nights                        8\n",
              "no_of_week_nights                          18\n",
              "type_of_meal_plan                           4\n",
              "required_car_parking_space                  2\n",
              "room_type_reserved                          7\n",
              "lead_time                                 352\n",
              "arrival_year                                2\n",
              "arrival_month                              12\n",
              "arrival_date                               31\n",
              "market_segment_type                         5\n",
              "repeated_guest                              2\n",
              "no_of_previous_cancellations                9\n",
              "no_of_previous_bookings_not_canceled       59\n",
              "avg_price_per_room                       3930\n",
              "no_of_special_requests                      6\n",
              "booking_status                              2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# In các giá trị duy nhất của các cột kiểu object\n",
        "for column in df.select_dtypes(include=['object']).columns:\n",
        "    unique_values = df[column].unique()\n",
        "    print(f'Cột {column}:', unique_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GceDnYlt0hUY",
        "outputId": "3e577218-8e66-4fee-90a1-0633476bab73"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cột Booking_ID: ['INN00001' 'INN00002' 'INN00003' ... 'INN36273' 'INN36274' 'INN36275']\n",
            "Cột type_of_meal_plan: ['Meal Plan 1' 'Not Selected' 'Meal Plan 2' 'Meal Plan 3']\n",
            "Cột room_type_reserved: ['Room_Type 1' 'Room_Type 4' 'Room_Type 2' 'Room_Type 6' 'Room_Type 5'\n",
            " 'Room_Type 7' 'Room_Type 3']\n",
            "Cột market_segment_type: ['Offline' 'Online' 'Corporate' 'Aviation' 'Complementary']\n",
            "Cột booking_status: ['Not_Canceled' 'Canceled']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# In các giá trị duy nhất của các cột kiểu number\n",
        "for column in df.select_dtypes(include=['number']).columns:\n",
        "    unique_values = df[column].unique()\n",
        "    print(f'Cột {column}:', unique_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sBEkZ1VLajWp",
        "outputId": "b2a8de47-e527-449d-bb65-ff6054f5fd9c"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cột no_of_adults: [2 1 3 0 4]\n",
            "Cột no_of_children: [ 0  2  1  3 10  9]\n",
            "Cột no_of_weekend_nights: [1 2 0 4 3 6 5 7]\n",
            "Cột no_of_week_nights: [ 2  3  1  4  5  0 10  6 11  7 15  9 13  8 14 12 17 16]\n",
            "Cột required_car_parking_space: [0 1]\n",
            "Cột lead_time: [224   5   1 211  48 346  34  83 121  44   0  35  30  95  47 256  99  12\n",
            " 122   2  37 130  60  56   3 107  72  23 289 247 186  64  96  41  55 146\n",
            "  32  57   7 124 169   6  51  13 100 139 117  39  86  19 192 179  26  74\n",
            " 143 177  18 267 155  46 128  20  40 196 188  17 110  68  73  92 171 134\n",
            " 320 118 189  16  24   8  10 182 116 123 105 443 317 286 148  14  85  25\n",
            "  28  80  11 162  82  27 245 266 112  88  69 273   4  97  31  62 197 280\n",
            " 185 160 104  22 292 109 126 303  81  54  15 161 147  87 127 418 156  58\n",
            " 433 111 195 119  59  78 335 103  70  76 144  49  77  36  79  21  33 164\n",
            " 152  43 102  71 209  93  53 302 239  45 167 113  84   9 166 174  61 151\n",
            "  52  67 282  38 175  89 133  65  66  50 159 386 115 237 125  91  29 221\n",
            " 213 198  75 180 236 120 230  63 136 309 157 268 217  94 305  98  42 154\n",
            " 330 137 184 232 304 114 257 265 191 101 259 149 170 271 207 108 210 222\n",
            " 296 194 145 153 275 158 301 349 200 315 181 263 176 141 270 150 359 244\n",
            " 219 142 138 276 178 163 377 290 216 226 258 254 193 131 208 215 190 381\n",
            " 231 248 106 308 140 173 168 172  90 249 205 129 212 135 220 277 253 132\n",
            " 183 255 223 336 288 229 319 199 203 228 246 235 294 281 202 361 287 291\n",
            " 313 206 269 279 261 214 274 250 187 240 241 323 322 227 225 233 338 283\n",
            " 327 204 352 165 251 299 314 285 238 328 278 332 243 201 307 272 252 242\n",
            " 284 297 324 260 262 326 295 218 234 353 300 355 306 298 331 341 318 333\n",
            " 372 311 310 345 264 325 293 348 350 351]\n",
            "Cột arrival_year: [2017 2018]\n",
            "Cột arrival_month: [10 11  2  5  4  9 12  7  6  8  3  1]\n",
            "Cột arrival_date: [ 2  6 28 20 11 13 15 26 18 30  5 10  4 25 22 21 19 17  7  9 27  1 29 16\n",
            "  3 24 14 31 23  8 12]\n",
            "Cột repeated_guest: [0 1]\n",
            "Cột no_of_previous_cancellations: [ 0  3  1  2 11  4  5 13  6]\n",
            "Cột no_of_previous_bookings_not_canceled: [ 0  5  1  3  4 12 19  2 15 17  7 20 16 50 13  6 14 34 18  8 10 23 11 49\n",
            " 47 53  9 33 22 24 52 21 48 28 39 25 31 38 26 51 42 37 35 56 44 27 32 55\n",
            " 45 30 57 46 54 43 58 41 29 40 36]\n",
            "Cột avg_price_per_room: [ 65.   106.68  60.   ... 118.43 137.25 167.8 ]\n",
            "Cột no_of_special_requests: [0 1 3 2 4 5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Loại bỏ cột Booking_ID\n",
        "df = df.drop(columns = ['Booking_ID'])"
      ],
      "metadata": {
        "id": "pgofuU7AcXNQ"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Chuyển kiểu cột dữ liệu\n",
        "df['repeated_guest'] = df['repeated_guest'].astype(object)\n",
        "df['required_car_parking_space'] = df['required_car_parking_space'].astype(object)"
      ],
      "metadata": {
        "id": "1ZtJcfigaM5C"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Đặt cột class là target\n",
        "target = df['booking_status']\n",
        "df = df.drop(columns = 'booking_status')"
      ],
      "metadata": {
        "id": "YMFZHgn6zA50"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# In số giá trị duy nhất của cột target\n",
        "tar_unique_values = target.unique()\n",
        "tar_unique_values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gkCTNNNzZrs5",
        "outputId": "23310de7-b26e-4336-d99f-83ed6ea0580f"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Not_Canceled', 'Canceled'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Làm sạch dữ liệu"
      ],
      "metadata": {
        "id": "zJdnObNiU_ay"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Kiểm tra giá trị thiếu."
      ],
      "metadata": {
        "id": "vZpkuGFFVFr8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tEDU-kcZVIvs",
        "outputId": "01fb591d-dac8-4d09-d89b-feb8108ce99b"
      },
      "execution_count": 115,
      "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",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Kiểm tra giá trị nhiễu."
      ],
      "metadata": {
        "id": "xBuUQoNNVMzy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Sử dụng hàm để kiểm tra outlier cho tất cả các cột trong DataFrame\n",
        "def check_outliers_using_3_sigma(df):\n",
        "    has_outliers = True\n",
        "    while has_outliers:\n",
        "        has_outliers = False\n",
        "        for col in df.columns:\n",
        "            if df[col].dtype in ['int64', 'float64']:\n",
        "                lech3sigma_tren = round(df[col].mean() + 3 * df[col].std(ddof=1))\n",
        "                lech3sigma_duoi = round(df[col].mean() - 3 * df[col].std(ddof=1))\n",
        "                df_filter = df[(df[col] > lech3sigma_tren) | (df[col] < lech3sigma_duoi)]\n",
        "\n",
        "                if df_filter[col].count() > 0:\n",
        "                    print(f'Cột {col} có {df_filter[col].count()} giá trị nhiễu là các giá trị ngoài khoảng ({lech3sigma_duoi}, {lech3sigma_tren})')\n",
        "                    # Thay thế nhiễu bằng trung vị\n",
        "                    trungvi = df[col].median()\n",
        "                    df.loc[(df[col] > lech3sigma_tren) | (df[col] < lech3sigma_duoi), col] = trungvi\n",
        "                    has_outliers = True\n",
        "                else:\n",
        "                    print(f'Cột {col}: Không có outlier.')\n",
        "\n",
        "# Gọi hàm trên DataFrame df\n",
        "check_outliers_using_3_sigma(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6m7ZrxtTbF5A",
        "outputId": "411ef024-4a9f-4150-bc1a-167256693782"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cột no_of_adults có 16 giá trị nhiễu là các giá trị ngoài khoảng (0, 3)\n",
            "Cột no_of_children có 1080 giá trị nhiễu là các giá trị ngoài khoảng (-1, 1)\n",
            "Cột no_of_weekend_nights có 184 giá trị nhiễu là các giá trị ngoài khoảng (-2, 3)\n",
            "Cột no_of_week_nights có 324 giá trị nhiễu là các giá trị ngoài khoảng (-2, 6)\n",
            "Cột lead_time có 376 giá trị nhiễu là các giá trị ngoài khoảng (-173, 343)\n",
            "Cột arrival_year: Không có outlier.\n",
            "Cột arrival_month: Không có outlier.\n",
            "Cột arrival_date: Không có outlier.\n",
            "Cột no_of_previous_cancellations có 140 giá trị nhiễu là các giá trị ngoài khoảng (-1, 1)\n",
            "Cột no_of_previous_bookings_not_canceled có 267 giá trị nhiễu là các giá trị ngoài khoảng (-5, 5)\n",
            "Cột avg_price_per_room có 353 giá trị nhiễu là các giá trị ngoài khoảng (-2, 209)\n",
            "Cột no_of_special_requests có 86 giá trị nhiễu là các giá trị ngoài khoảng (-2, 3)\n",
            "Cột no_of_adults: Không có outlier.\n",
            "Cột no_of_children: Không có outlier.\n",
            "Cột no_of_weekend_nights: Không có outlier.\n",
            "Cột no_of_week_nights: Không có outlier.\n",
            "Cột lead_time có 100 giá trị nhiễu là các giá trị ngoài khoảng (-159, 323)\n",
            "Cột arrival_year: Không có outlier.\n",
            "Cột arrival_month: Không có outlier.\n",
            "Cột arrival_date: Không có outlier.\n",
            "Cột no_of_previous_cancellations có 198 giá trị nhiễu là các giá trị ngoài khoảng (0, 0)\n",
            "Cột no_of_previous_bookings_not_canceled có 317 giá trị nhiễu là các giá trị ngoài khoảng (-1, 1)\n",
            "Cột avg_price_per_room có 737 giá trị nhiễu là các giá trị ngoài khoảng (5, 199)\n",
            "Cột no_of_special_requests: Không có outlier.\n",
            "Cột no_of_adults: Không có outlier.\n",
            "Cột no_of_children: Không có outlier.\n",
            "Cột no_of_weekend_nights: Không có outlier.\n",
            "Cột no_of_week_nights: Không có outlier.\n",
            "Cột lead_time có 141 giá trị nhiễu là các giá trị ngoài khoảng (-157, 319)\n",
            "Cột arrival_year: Không có outlier.\n",
            "Cột arrival_month: Không có outlier.\n",
            "Cột arrival_date: Không có outlier.\n",
            "Cột no_of_previous_cancellations: Không có outlier.\n",
            "Cột no_of_previous_bookings_not_canceled có 228 giá trị nhiễu là các giá trị ngoài khoảng (0, 0)\n",
            "Cột avg_price_per_room có 257 giá trị nhiễu là các giá trị ngoài khoảng (16, 190)\n",
            "Cột no_of_special_requests: Không có outlier.\n",
            "Cột no_of_adults: Không có outlier.\n",
            "Cột no_of_children: Không có outlier.\n",
            "Cột no_of_weekend_nights: Không có outlier.\n",
            "Cột no_of_week_nights: Không có outlier.\n",
            "Cột lead_time có 102 giá trị nhiễu là các giá trị ngoài khoảng (-153, 314)\n",
            "Cột arrival_year: Không có outlier.\n",
            "Cột arrival_month: Không có outlier.\n",
            "Cột arrival_date: Không có outlier.\n",
            "Cột no_of_previous_cancellations: Không có outlier.\n",
            "Cột no_of_previous_bookings_not_canceled: Không có outlier.\n",
            "Cột avg_price_per_room có 76 giá trị nhiễu là các giá trị ngoài khoảng (19, 187)\n",
            "Cột no_of_special_requests: Không có outlier.\n",
            "Cột no_of_adults: Không có outlier.\n",
            "Cột no_of_children: Không có outlier.\n",
            "Cột no_of_weekend_nights: Không có outlier.\n",
            "Cột no_of_week_nights: Không có outlier.\n",
            "Cột lead_time có 29 giá trị nhiễu là các giá trị ngoài khoảng (-151, 310)\n",
            "Cột arrival_year: Không có outlier.\n",
            "Cột arrival_month: Không có outlier.\n",
            "Cột arrival_date: Không có outlier.\n",
            "Cột no_of_previous_cancellations: Không có outlier.\n",
            "Cột no_of_previous_bookings_not_canceled: Không có outlier.\n",
            "Cột avg_price_per_room có 94 giá trị nhiễu là các giá trị ngoài khoảng (20, 185)\n",
            "Cột no_of_special_requests: Không có outlier.\n",
            "Cột no_of_adults: Không có outlier.\n",
            "Cột no_of_children: Không có outlier.\n",
            "Cột no_of_weekend_nights: Không có outlier.\n",
            "Cột no_of_week_nights: Không có outlier.\n",
            "Cột lead_time có 3 giá trị nhiễu là các giá trị ngoài khoảng (-151, 309)\n",
            "Cột arrival_year: Không có outlier.\n",
            "Cột arrival_month: Không có outlier.\n",
            "Cột arrival_date: Không có outlier.\n",
            "Cột no_of_previous_cancellations: Không có outlier.\n",
            "Cột no_of_previous_bookings_not_canceled: Không có outlier.\n",
            "Cột avg_price_per_room có 46 giá trị nhiễu là các giá trị ngoài khoảng (20, 184)\n",
            "Cột no_of_special_requests: Không có outlier.\n",
            "Cột no_of_adults: Không có outlier.\n",
            "Cột no_of_children: Không có outlier.\n",
            "Cột no_of_weekend_nights: Không có outlier.\n",
            "Cột no_of_week_nights: Không có outlier.\n",
            "Cột lead_time: Không có outlier.\n",
            "Cột arrival_year: Không có outlier.\n",
            "Cột arrival_month: Không có outlier.\n",
            "Cột arrival_date: Không có outlier.\n",
            "Cột no_of_previous_cancellations: Không có outlier.\n",
            "Cột no_of_previous_bookings_not_canceled: Không có outlier.\n",
            "Cột avg_price_per_room có 2 giá trị nhiễu là các giá trị ngoài khoảng (21, 184)\n",
            "Cột no_of_special_requests: Không có outlier.\n",
            "Cột no_of_adults: Không có outlier.\n",
            "Cột no_of_children: Không có outlier.\n",
            "Cột no_of_weekend_nights: Không có outlier.\n",
            "Cột no_of_week_nights: Không có outlier.\n",
            "Cột lead_time: Không có outlier.\n",
            "Cột arrival_year: Không có outlier.\n",
            "Cột arrival_month: Không có outlier.\n",
            "Cột arrival_date: Không có outlier.\n",
            "Cột no_of_previous_cancellations: Không có outlier.\n",
            "Cột no_of_previous_bookings_not_canceled: Không có outlier.\n",
            "Cột avg_price_per_room: Không có outlier.\n",
            "Cột no_of_special_requests: Không có outlier.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Rời rạc hóa dữ liệu là các biến liên tục."
      ],
      "metadata": {
        "id": "ynUk8CEqbzaP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Rời rạc các biến lead_time và avg_price_per_room\n",
        "df['lead_time_group'] = pd.qcut(df['lead_time'], 4)\n",
        "df['avg_price_per_room_group'] = pd.qcut(df['avg_price_per_room'], 4)\n",
        "df = df.drop(columns = ['lead_time', 'avg_price_per_room'])"
      ],
      "metadata": {
        "id": "wOjtMfHLb8P9"
      },
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.astype(object)"
      ],
      "metadata": {
        "id": "KVxyi03jcPSD"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "columns_to_exclude = ['type_of_meal_plan', 'room_type_reserved', 'market_segment_type']\n",
        "\n",
        "# Chuyển giá trị ở các ô thành chuỗi có dạng \"tên cột (giá trị ở ô tương ứng)\" cho các cột trừ các cột có giá trị số rời rạc và nhị phân\n",
        "df = df.apply(lambda x: x.astype(str).map(lambda y: f'{x.name} ({y})') if x.name not in columns_to_exclude else x)"
      ],
      "metadata": {
        "id": "8-13lfhrcqVk"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xbKDGizmz5O8",
        "outputId": "ea2671ad-70a2-4b92-a274-47cd81949914"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(36275, 17)"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "gbHfeOcez3Vq",
        "outputId": "6b42d6e3-8a38-4149-f315-b1a20e60d4ab"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       no_of_adults      no_of_children      no_of_weekend_nights  \\\n",
              "0  no_of_adults (2)  no_of_children (0)  no_of_weekend_nights (1)   \n",
              "1  no_of_adults (2)  no_of_children (0)  no_of_weekend_nights (2)   \n",
              "2  no_of_adults (1)  no_of_children (0)  no_of_weekend_nights (2)   \n",
              "3  no_of_adults (2)  no_of_children (0)  no_of_weekend_nights (0)   \n",
              "4  no_of_adults (2)  no_of_children (0)  no_of_weekend_nights (1)   \n",
              "\n",
              "       no_of_week_nights type_of_meal_plan      required_car_parking_space  \\\n",
              "0  no_of_week_nights (2)       Meal Plan 1  required_car_parking_space (0)   \n",
              "1  no_of_week_nights (3)      Not Selected  required_car_parking_space (0)   \n",
              "2  no_of_week_nights (1)       Meal Plan 1  required_car_parking_space (0)   \n",
              "3  no_of_week_nights (2)       Meal Plan 1  required_car_parking_space (0)   \n",
              "4  no_of_week_nights (1)      Not Selected  required_car_parking_space (0)   \n",
              "\n",
              "  room_type_reserved         arrival_year       arrival_month  \\\n",
              "0        Room_Type 1  arrival_year (2017)  arrival_month (10)   \n",
              "1        Room_Type 1  arrival_year (2018)  arrival_month (11)   \n",
              "2        Room_Type 1  arrival_year (2018)   arrival_month (2)   \n",
              "3        Room_Type 1  arrival_year (2018)   arrival_month (5)   \n",
              "4        Room_Type 1  arrival_year (2018)   arrival_month (4)   \n",
              "\n",
              "        arrival_date market_segment_type      repeated_guest  \\\n",
              "0   arrival_date (2)             Offline  repeated_guest (0)   \n",
              "1   arrival_date (6)              Online  repeated_guest (0)   \n",
              "2  arrival_date (28)              Online  repeated_guest (0)   \n",
              "3  arrival_date (20)              Online  repeated_guest (0)   \n",
              "4  arrival_date (11)              Online  repeated_guest (0)   \n",
              "\n",
              "       no_of_previous_cancellations      no_of_previous_bookings_not_canceled  \\\n",
              "0  no_of_previous_cancellations (0)  no_of_previous_bookings_not_canceled (0)   \n",
              "1  no_of_previous_cancellations (0)  no_of_previous_bookings_not_canceled (0)   \n",
              "2  no_of_previous_cancellations (0)  no_of_previous_bookings_not_canceled (0)   \n",
              "3  no_of_previous_cancellations (0)  no_of_previous_bookings_not_canceled (0)   \n",
              "4  no_of_previous_cancellations (0)  no_of_previous_bookings_not_canceled (0)   \n",
              "\n",
              "       no_of_special_requests                   lead_time_group  \\\n",
              "0  no_of_special_requests (0)  lead_time_group ((118.0, 309.0])   \n",
              "1  no_of_special_requests (1)  lead_time_group ((-0.001, 17.0])   \n",
              "2  no_of_special_requests (0)  lead_time_group ((-0.001, 17.0])   \n",
              "3  no_of_special_requests (0)  lead_time_group ((118.0, 309.0])   \n",
              "4  no_of_special_requests (0)    lead_time_group ((17.0, 57.0])   \n",
              "\n",
              "                     avg_price_per_room_group  \n",
              "0  avg_price_per_room_group ((22.999, 81.08])  \n",
              "1  avg_price_per_room_group ((99.45, 118.96])  \n",
              "2  avg_price_per_room_group ((22.999, 81.08])  \n",
              "3  avg_price_per_room_group ((99.45, 118.96])  \n",
              "4   avg_price_per_room_group ((81.08, 99.45])  "
            ],
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              "      <th></th>\n",
              "      <th>no_of_adults</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>no_of_adults (2)</td>\n",
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              "      <td>arrival_year (2018)</td>\n",
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              "      <td>arrival_date (28)</td>\n",
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              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-628d90c1-4be4-40f6-a82f-979707dd7b1e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZcnNYw900jCJ",
        "outputId": "9ebe1407-3e75-4c17-8a97-513516e86661"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "no_of_adults                            object\n",
              "no_of_children                          object\n",
              "no_of_weekend_nights                    object\n",
              "no_of_week_nights                       object\n",
              "type_of_meal_plan                       object\n",
              "required_car_parking_space              object\n",
              "room_type_reserved                      object\n",
              "arrival_year                            object\n",
              "arrival_month                           object\n",
              "arrival_date                            object\n",
              "market_segment_type                     object\n",
              "repeated_guest                          object\n",
              "no_of_previous_cancellations            object\n",
              "no_of_previous_bookings_not_canceled    object\n",
              "no_of_special_requests                  object\n",
              "lead_time_group                         object\n",
              "avg_price_per_room_group                object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Xây dựng bài toán"
      ],
      "metadata": {
        "id": "4UtMLFBImUQX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zoHs0deUm2Zp",
        "outputId": "ea737940-7c7d-4747-9c20-f9fd31505cce"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(36275, 17)"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "da2NWqKSnTZK",
        "outputId": "5adafb2b-ed0f-4f73-c75b-8590f8eb691a"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(36275,)"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 125,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "IcS36cbO90y0",
        "outputId": "6619fa08-31a5-43ce-a093-382339962803"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           no_of_adults      no_of_children      no_of_weekend_nights  \\\n",
              "0      no_of_adults (2)  no_of_children (0)  no_of_weekend_nights (1)   \n",
              "1      no_of_adults (2)  no_of_children (0)  no_of_weekend_nights (2)   \n",
              "2      no_of_adults (1)  no_of_children (0)  no_of_weekend_nights (2)   \n",
              "3      no_of_adults (2)  no_of_children (0)  no_of_weekend_nights (0)   \n",
              "4      no_of_adults (2)  no_of_children (0)  no_of_weekend_nights (1)   \n",
              "...                 ...                 ...                       ...   \n",
              "36270  no_of_adults (3)  no_of_children (0)  no_of_weekend_nights (2)   \n",
              "36271  no_of_adults (2)  no_of_children (0)  no_of_weekend_nights (1)   \n",
              "36272  no_of_adults (2)  no_of_children (0)  no_of_weekend_nights (2)   \n",
              "36273  no_of_adults (2)  no_of_children (0)  no_of_weekend_nights (0)   \n",
              "36274  no_of_adults (2)  no_of_children (0)  no_of_weekend_nights (1)   \n",
              "\n",
              "           no_of_week_nights type_of_meal_plan  \\\n",
              "0      no_of_week_nights (2)       Meal Plan 1   \n",
              "1      no_of_week_nights (3)      Not Selected   \n",
              "2      no_of_week_nights (1)       Meal Plan 1   \n",
              "3      no_of_week_nights (2)       Meal Plan 1   \n",
              "4      no_of_week_nights (1)      Not Selected   \n",
              "...                      ...               ...   \n",
              "36270  no_of_week_nights (6)       Meal Plan 1   \n",
              "36271  no_of_week_nights (3)       Meal Plan 1   \n",
              "36272  no_of_week_nights (6)       Meal Plan 1   \n",
              "36273  no_of_week_nights (3)      Not Selected   \n",
              "36274  no_of_week_nights (2)       Meal Plan 1   \n",
              "\n",
              "           required_car_parking_space room_type_reserved         arrival_year  \\\n",
              "0      required_car_parking_space (0)        Room_Type 1  arrival_year (2017)   \n",
              "1      required_car_parking_space (0)        Room_Type 1  arrival_year (2018)   \n",
              "2      required_car_parking_space (0)        Room_Type 1  arrival_year (2018)   \n",
              "3      required_car_parking_space (0)        Room_Type 1  arrival_year (2018)   \n",
              "4      required_car_parking_space (0)        Room_Type 1  arrival_year (2018)   \n",
              "...                               ...                ...                  ...   \n",
              "36270  required_car_parking_space (0)        Room_Type 4  arrival_year (2018)   \n",
              "36271  required_car_parking_space (0)        Room_Type 1  arrival_year (2018)   \n",
              "36272  required_car_parking_space (0)        Room_Type 1  arrival_year (2018)   \n",
              "36273  required_car_parking_space (0)        Room_Type 1  arrival_year (2018)   \n",
              "36274  required_car_parking_space (0)        Room_Type 1  arrival_year (2018)   \n",
              "\n",
              "            arrival_month       arrival_date market_segment_type  \\\n",
              "0      arrival_month (10)   arrival_date (2)             Offline   \n",
              "1      arrival_month (11)   arrival_date (6)              Online   \n",
              "2       arrival_month (2)  arrival_date (28)              Online   \n",
              "3       arrival_month (5)  arrival_date (20)              Online   \n",
              "4       arrival_month (4)  arrival_date (11)              Online   \n",
              "...                   ...                ...                 ...   \n",
              "36270   arrival_month (8)   arrival_date (3)              Online   \n",
              "36271  arrival_month (10)  arrival_date (17)              Online   \n",
              "36272   arrival_month (7)   arrival_date (1)              Online   \n",
              "36273   arrival_month (4)  arrival_date (21)              Online   \n",
              "36274  arrival_month (12)  arrival_date (30)             Offline   \n",
              "\n",
              "           repeated_guest      no_of_previous_cancellations  \\\n",
              "0      repeated_guest (0)  no_of_previous_cancellations (0)   \n",
              "1      repeated_guest (0)  no_of_previous_cancellations (0)   \n",
              "2      repeated_guest (0)  no_of_previous_cancellations (0)   \n",
              "3      repeated_guest (0)  no_of_previous_cancellations (0)   \n",
              "4      repeated_guest (0)  no_of_previous_cancellations (0)   \n",
              "...                   ...                               ...   \n",
              "36270  repeated_guest (0)  no_of_previous_cancellations (0)   \n",
              "36271  repeated_guest (0)  no_of_previous_cancellations (0)   \n",
              "36272  repeated_guest (0)  no_of_previous_cancellations (0)   \n",
              "36273  repeated_guest (0)  no_of_previous_cancellations (0)   \n",
              "36274  repeated_guest (0)  no_of_previous_cancellations (0)   \n",
              "\n",
              "           no_of_previous_bookings_not_canceled      no_of_special_requests  \\\n",
              "0      no_of_previous_bookings_not_canceled (0)  no_of_special_requests (0)   \n",
              "1      no_of_previous_bookings_not_canceled (0)  no_of_special_requests (1)   \n",
              "2      no_of_previous_bookings_not_canceled (0)  no_of_special_requests (0)   \n",
              "3      no_of_previous_bookings_not_canceled (0)  no_of_special_requests (0)   \n",
              "4      no_of_previous_bookings_not_canceled (0)  no_of_special_requests (0)   \n",
              "...                                         ...                         ...   \n",
              "36270  no_of_previous_bookings_not_canceled (0)  no_of_special_requests (1)   \n",
              "36271  no_of_previous_bookings_not_canceled (0)  no_of_special_requests (2)   \n",
              "36272  no_of_previous_bookings_not_canceled (0)  no_of_special_requests (2)   \n",
              "36273  no_of_previous_bookings_not_canceled (0)  no_of_special_requests (0)   \n",
              "36274  no_of_previous_bookings_not_canceled (0)  no_of_special_requests (0)   \n",
              "\n",
              "                        lead_time_group  \\\n",
              "0      lead_time_group ((118.0, 309.0])   \n",
              "1      lead_time_group ((-0.001, 17.0])   \n",
              "2      lead_time_group ((-0.001, 17.0])   \n",
              "3      lead_time_group ((118.0, 309.0])   \n",
              "4        lead_time_group ((17.0, 57.0])   \n",
              "...                                 ...   \n",
              "36270   lead_time_group ((57.0, 118.0])   \n",
              "36271  lead_time_group ((118.0, 309.0])   \n",
              "36272  lead_time_group ((118.0, 309.0])   \n",
              "36273   lead_time_group ((57.0, 118.0])   \n",
              "36274  lead_time_group ((118.0, 309.0])   \n",
              "\n",
              "                         avg_price_per_room_group  \n",
              "0      avg_price_per_room_group ((22.999, 81.08])  \n",
              "1      avg_price_per_room_group ((99.45, 118.96])  \n",
              "2      avg_price_per_room_group ((22.999, 81.08])  \n",
              "3      avg_price_per_room_group ((99.45, 118.96])  \n",
              "4       avg_price_per_room_group ((81.08, 99.45])  \n",
              "...                                           ...  \n",
              "36270  avg_price_per_room_group ((118.96, 184.0])  \n",
              "36271   avg_price_per_room_group ((81.08, 99.45])  \n",
              "36272   avg_price_per_room_group ((81.08, 99.45])  \n",
              "36273   avg_price_per_room_group ((81.08, 99.45])  \n",
              "36274  avg_price_per_room_group ((118.96, 184.0])  \n",
              "\n",
              "[36275 rows x 17 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d1d3ca99-e462-47dd-a617-26490a148339\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>type_of_meal_plan</th>\n",
              "      <th>required_car_parking_space</th>\n",
              "      <th>room_type_reserved</th>\n",
              "      <th>arrival_year</th>\n",
              "      <th>arrival_month</th>\n",
              "      <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>no_of_special_requests</th>\n",
              "      <th>lead_time_group</th>\n",
              "      <th>avg_price_per_room_group</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>no_of_adults (2)</td>\n",
              "      <td>no_of_children (0)</td>\n",
              "      <td>no_of_weekend_nights (1)</td>\n",
              "      <td>no_of_week_nights (2)</td>\n",
              "      <td>Meal Plan 1</td>\n",
              "      <td>required_car_parking_space (0)</td>\n",
              "      <td>Room_Type 1</td>\n",
              "      <td>arrival_year (2017)</td>\n",
              "      <td>arrival_month (10)</td>\n",
              "      <td>arrival_date (2)</td>\n",
              "      <td>Offline</td>\n",
              "      <td>repeated_guest (0)</td>\n",
              "      <td>no_of_previous_cancellations (0)</td>\n",
              "      <td>no_of_previous_bookings_not_canceled (0)</td>\n",
              "      <td>no_of_special_requests (0)</td>\n",
              "      <td>lead_time_group ((118.0, 309.0])</td>\n",
              "      <td>avg_price_per_room_group ((22.999, 81.08])</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>no_of_adults (2)</td>\n",
              "      <td>no_of_children (0)</td>\n",
              "      <td>no_of_weekend_nights (2)</td>\n",
              "      <td>no_of_week_nights (3)</td>\n",
              "      <td>Not Selected</td>\n",
              "      <td>required_car_parking_space (0)</td>\n",
              "      <td>Room_Type 1</td>\n",
              "      <td>arrival_year (2018)</td>\n",
              "      <td>arrival_month (11)</td>\n",
              "      <td>arrival_date (6)</td>\n",
              "      <td>Online</td>\n",
              "      <td>repeated_guest (0)</td>\n",
              "      <td>no_of_previous_cancellations (0)</td>\n",
              "      <td>no_of_previous_bookings_not_canceled (0)</td>\n",
              "      <td>no_of_special_requests (1)</td>\n",
              "      <td>lead_time_group ((-0.001, 17.0])</td>\n",
              "      <td>avg_price_per_room_group ((99.45, 118.96])</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>no_of_adults (1)</td>\n",
              "      <td>no_of_children (0)</td>\n",
              "      <td>no_of_weekend_nights (2)</td>\n",
              "      <td>no_of_week_nights (1)</td>\n",
              "      <td>Meal Plan 1</td>\n",
              "      <td>required_car_parking_space (0)</td>\n",
              "      <td>Room_Type 1</td>\n",
              "      <td>arrival_year (2018)</td>\n",
              "      <td>arrival_month (2)</td>\n",
              "      <td>arrival_date (28)</td>\n",
              "      <td>Online</td>\n",
              "      <td>repeated_guest (0)</td>\n",
              "      <td>no_of_previous_cancellations (0)</td>\n",
              "      <td>no_of_previous_bookings_not_canceled (0)</td>\n",
              "      <td>no_of_special_requests (0)</td>\n",
              "      <td>lead_time_group ((-0.001, 17.0])</td>\n",
              "      <td>avg_price_per_room_group ((22.999, 81.08])</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>no_of_adults (2)</td>\n",
              "      <td>no_of_children (0)</td>\n",
              "      <td>no_of_weekend_nights (0)</td>\n",
              "      <td>no_of_week_nights (2)</td>\n",
              "      <td>Meal Plan 1</td>\n",
              "      <td>required_car_parking_space (0)</td>\n",
              "      <td>Room_Type 1</td>\n",
              "      <td>arrival_year (2018)</td>\n",
              "      <td>arrival_month (5)</td>\n",
              "      <td>arrival_date (20)</td>\n",
              "      <td>Online</td>\n",
              "      <td>repeated_guest (0)</td>\n",
              "      <td>no_of_previous_cancellations (0)</td>\n",
              "      <td>no_of_previous_bookings_not_canceled (0)</td>\n",
              "      <td>no_of_special_requests (0)</td>\n",
              "      <td>lead_time_group ((118.0, 309.0])</td>\n",
              "      <td>avg_price_per_room_group ((99.45, 118.96])</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>no_of_adults (2)</td>\n",
              "      <td>no_of_children (0)</td>\n",
              "      <td>no_of_weekend_nights (1)</td>\n",
              "      <td>no_of_week_nights (1)</td>\n",
              "      <td>Not Selected</td>\n",
              "      <td>required_car_parking_space (0)</td>\n",
              "      <td>Room_Type 1</td>\n",
              "      <td>arrival_year (2018)</td>\n",
              "      <td>arrival_month (4)</td>\n",
              "      <td>arrival_date (11)</td>\n",
              "      <td>Online</td>\n",
              "      <td>repeated_guest (0)</td>\n",
              "      <td>no_of_previous_cancellations (0)</td>\n",
              "      <td>no_of_previous_bookings_not_canceled (0)</td>\n",
              "      <td>no_of_special_requests (0)</td>\n",
              "      <td>lead_time_group ((17.0, 57.0])</td>\n",
              "      <td>avg_price_per_room_group ((81.08, 99.45])</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36270</th>\n",
              "      <td>no_of_adults (3)</td>\n",
              "      <td>no_of_children (0)</td>\n",
              "      <td>no_of_weekend_nights (2)</td>\n",
              "      <td>no_of_week_nights (6)</td>\n",
              "      <td>Meal Plan 1</td>\n",
              "      <td>required_car_parking_space (0)</td>\n",
              "      <td>Room_Type 4</td>\n",
              "      <td>arrival_year (2018)</td>\n",
              "      <td>arrival_month (8)</td>\n",
              "      <td>arrival_date (3)</td>\n",
              "      <td>Online</td>\n",
              "      <td>repeated_guest (0)</td>\n",
              "      <td>no_of_previous_cancellations (0)</td>\n",
              "      <td>no_of_previous_bookings_not_canceled (0)</td>\n",
              "      <td>no_of_special_requests (1)</td>\n",
              "      <td>lead_time_group ((57.0, 118.0])</td>\n",
              "      <td>avg_price_per_room_group ((118.96, 184.0])</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36271</th>\n",
              "      <td>no_of_adults (2)</td>\n",
              "      <td>no_of_children (0)</td>\n",
              "      <td>no_of_weekend_nights (1)</td>\n",
              "      <td>no_of_week_nights (3)</td>\n",
              "      <td>Meal Plan 1</td>\n",
              "      <td>required_car_parking_space (0)</td>\n",
              "      <td>Room_Type 1</td>\n",
              "      <td>arrival_year (2018)</td>\n",
              "      <td>arrival_month (10)</td>\n",
              "      <td>arrival_date (17)</td>\n",
              "      <td>Online</td>\n",
              "      <td>repeated_guest (0)</td>\n",
              "      <td>no_of_previous_cancellations (0)</td>\n",
              "      <td>no_of_previous_bookings_not_canceled (0)</td>\n",
              "      <td>no_of_special_requests (2)</td>\n",
              "      <td>lead_time_group ((118.0, 309.0])</td>\n",
              "      <td>avg_price_per_room_group ((81.08, 99.45])</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36272</th>\n",
              "      <td>no_of_adults (2)</td>\n",
              "      <td>no_of_children (0)</td>\n",
              "      <td>no_of_weekend_nights (2)</td>\n",
              "      <td>no_of_week_nights (6)</td>\n",
              "      <td>Meal Plan 1</td>\n",
              "      <td>required_car_parking_space (0)</td>\n",
              "      <td>Room_Type 1</td>\n",
              "      <td>arrival_year (2018)</td>\n",
              "      <td>arrival_month (7)</td>\n",
              "      <td>arrival_date (1)</td>\n",
              "      <td>Online</td>\n",
              "      <td>repeated_guest (0)</td>\n",
              "      <td>no_of_previous_cancellations (0)</td>\n",
              "      <td>no_of_previous_bookings_not_canceled (0)</td>\n",
              "      <td>no_of_special_requests (2)</td>\n",
              "      <td>lead_time_group ((118.0, 309.0])</td>\n",
              "      <td>avg_price_per_room_group ((81.08, 99.45])</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36273</th>\n",
              "      <td>no_of_adults (2)</td>\n",
              "      <td>no_of_children (0)</td>\n",
              "      <td>no_of_weekend_nights (0)</td>\n",
              "      <td>no_of_week_nights (3)</td>\n",
              "      <td>Not Selected</td>\n",
              "      <td>required_car_parking_space (0)</td>\n",
              "      <td>Room_Type 1</td>\n",
              "      <td>arrival_year (2018)</td>\n",
              "      <td>arrival_month (4)</td>\n",
              "      <td>arrival_date (21)</td>\n",
              "      <td>Online</td>\n",
              "      <td>repeated_guest (0)</td>\n",
              "      <td>no_of_previous_cancellations (0)</td>\n",
              "      <td>no_of_previous_bookings_not_canceled (0)</td>\n",
              "      <td>no_of_special_requests (0)</td>\n",
              "      <td>lead_time_group ((57.0, 118.0])</td>\n",
              "      <td>avg_price_per_room_group ((81.08, 99.45])</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36274</th>\n",
              "      <td>no_of_adults (2)</td>\n",
              "      <td>no_of_children (0)</td>\n",
              "      <td>no_of_weekend_nights (1)</td>\n",
              "      <td>no_of_week_nights (2)</td>\n",
              "      <td>Meal Plan 1</td>\n",
              "      <td>required_car_parking_space (0)</td>\n",
              "      <td>Room_Type 1</td>\n",
              "      <td>arrival_year (2018)</td>\n",
              "      <td>arrival_month (12)</td>\n",
              "      <td>arrival_date (30)</td>\n",
              "      <td>Offline</td>\n",
              "      <td>repeated_guest (0)</td>\n",
              "      <td>no_of_previous_cancellations (0)</td>\n",
              "      <td>no_of_previous_bookings_not_canceled (0)</td>\n",
              "      <td>no_of_special_requests (0)</td>\n",
              "      <td>lead_time_group ((118.0, 309.0])</td>\n",
              "      <td>avg_price_per_room_group ((118.96, 184.0])</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>36275 rows × 17 columns</p>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Chuẩn bị dữ liệu"
      ],
      "metadata": {
        "id": "J2sRrorpnk5w"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 126,
      "metadata": {
        "id": "_UCVQiGvpjUT"
      },
      "outputs": [],
      "source": [
        "def onehot(col, df):\n",
        "  enc = OneHotEncoder(sparse = False) # dạng\n",
        "  df_enc = pd.DataFrame(enc.fit_transform(df[[col]]), columns = df[col].unique())\n",
        "  df.drop(columns = col, inplace = True)\n",
        "  df_combined = pd.concat([df, df_enc], axis=1)\n",
        "  return df_combined"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 127,
      "metadata": {
        "id": "hbvcepKaqYAC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "62fa059d-9778-47e3-aa79-f16d7596407a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       no_of_adults (2)  no_of_adults (1)  no_of_adults (3)  no_of_adults (0)  \\\n",
              "0                 False             False              True             False   \n",
              "1                 False             False              True             False   \n",
              "2                 False              True             False             False   \n",
              "3                 False             False              True             False   \n",
              "4                 False             False              True             False   \n",
              "...                 ...               ...               ...               ...   \n",
              "36270             False             False             False              True   \n",
              "36271             False             False              True             False   \n",
              "36272             False             False              True             False   \n",
              "36273             False             False              True             False   \n",
              "36274             False             False              True             False   \n",
              "\n",
              "       no_of_children (0)  no_of_children (1)  no_of_weekend_nights (1)  \\\n",
              "0                    True               False                     False   \n",
              "1                    True               False                     False   \n",
              "2                    True               False                     False   \n",
              "3                    True               False                      True   \n",
              "4                    True               False                     False   \n",
              "...                   ...                 ...                       ...   \n",
              "36270                True               False                     False   \n",
              "36271                True               False                     False   \n",
              "36272                True               False                     False   \n",
              "36273                True               False                      True   \n",
              "36274                True               False                     False   \n",
              "\n",
              "       no_of_weekend_nights (2)  no_of_weekend_nights (0)  \\\n",
              "0                          True                     False   \n",
              "1                         False                      True   \n",
              "2                         False                      True   \n",
              "3                         False                     False   \n",
              "4                          True                     False   \n",
              "...                         ...                       ...   \n",
              "36270                     False                      True   \n",
              "36271                      True                     False   \n",
              "36272                     False                      True   \n",
              "36273                     False                     False   \n",
              "36274                      True                     False   \n",
              "\n",
              "       no_of_weekend_nights (3)  ...  no_of_special_requests (3)  \\\n",
              "0                         False  ...                       False   \n",
              "1                         False  ...                       False   \n",
              "2                         False  ...                       False   \n",
              "3                         False  ...                       False   \n",
              "4                         False  ...                       False   \n",
              "...                         ...  ...                         ...   \n",
              "36270                     False  ...                       False   \n",
              "36271                     False  ...                        True   \n",
              "36272                     False  ...                        True   \n",
              "36273                     False  ...                       False   \n",
              "36274                     False  ...                       False   \n",
              "\n",
              "       no_of_special_requests (2)  lead_time_group ((118.0, 309.0])  \\\n",
              "0                           False                             False   \n",
              "1                           False                              True   \n",
              "2                           False                              True   \n",
              "3                           False                             False   \n",
              "4                           False                             False   \n",
              "...                           ...                               ...   \n",
              "36270                       False                             False   \n",
              "36271                       False                             False   \n",
              "36272                       False                             False   \n",
              "36273                       False                             False   \n",
              "36274                       False                             False   \n",
              "\n",
              "       lead_time_group ((-0.001, 17.0])  lead_time_group ((17.0, 57.0])  \\\n",
              "0                                  True                           False   \n",
              "1                                 False                           False   \n",
              "2                                 False                           False   \n",
              "3                                  True                           False   \n",
              "4                                 False                            True   \n",
              "...                                 ...                             ...   \n",
              "36270                             False                           False   \n",
              "36271                              True                           False   \n",
              "36272                              True                           False   \n",
              "36273                             False                           False   \n",
              "36274                              True                           False   \n",
              "\n",
              "       lead_time_group ((57.0, 118.0])  \\\n",
              "0                                False   \n",
              "1                                False   \n",
              "2                                False   \n",
              "3                                False   \n",
              "4                                False   \n",
              "...                                ...   \n",
              "36270                             True   \n",
              "36271                            False   \n",
              "36272                            False   \n",
              "36273                             True   \n",
              "36274                            False   \n",
              "\n",
              "       avg_price_per_room_group ((22.999, 81.08])  \\\n",
              "0                                           False   \n",
              "1                                           False   \n",
              "2                                           False   \n",
              "3                                           False   \n",
              "4                                           False   \n",
              "...                                           ...   \n",
              "36270                                        True   \n",
              "36271                                       False   \n",
              "36272                                       False   \n",
              "36273                                       False   \n",
              "36274                                        True   \n",
              "\n",
              "       avg_price_per_room_group ((99.45, 118.96])  \\\n",
              "0                                            True   \n",
              "1                                           False   \n",
              "2                                            True   \n",
              "3                                           False   \n",
              "4                                           False   \n",
              "...                                           ...   \n",
              "36270                                       False   \n",
              "36271                                       False   \n",
              "36272                                       False   \n",
              "36273                                       False   \n",
              "36274                                       False   \n",
              "\n",
              "       avg_price_per_room_group ((81.08, 99.45])  \\\n",
              "0                                          False   \n",
              "1                                          False   \n",
              "2                                          False   \n",
              "3                                          False   \n",
              "4                                           True   \n",
              "...                                          ...   \n",
              "36270                                      False   \n",
              "36271                                       True   \n",
              "36272                                       True   \n",
              "36273                                       True   \n",
              "36274                                      False   \n",
              "\n",
              "       avg_price_per_room_group ((118.96, 184.0])  \n",
              "0                                           False  \n",
              "1                                            True  \n",
              "2                                           False  \n",
              "3                                            True  \n",
              "4                                           False  \n",
              "...                                           ...  \n",
              "36270                                       False  \n",
              "36271                                       False  \n",
              "36272                                       False  \n",
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              "      <td>False</td>\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ],
      "source": [
        "for i in df.select_dtypes(include = ['object']).columns:\n",
        "  df = onehot(i, df)\n",
        "\n",
        "df = df.replace({1: True, 0: False})\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Khai thác mẫu phổ biến"
      ],
      "metadata": {
        "id": "GbViG8rsoSfD"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 128,
      "metadata": {
        "id": "qvHA0D4u5Skv"
      },
      "outputs": [],
      "source": [
        "frequent_patterns = fpgrowth(df, min_support = 0.5, use_colnames=True)\n",
        "frequent_patterns['itemsets'] = frequent_patterns['itemsets'].apply(lambda x: ', '.join(x))\n",
        "\n",
        "# Sắp xếp chỉ số dựa trên số lượng từ\n",
        "word_counts = frequent_patterns['itemsets'].str.split(\",\").apply(len)\n",
        "sorted_indices = np.argsort(word_counts)\n",
        "\n",
        "# Sắp xếp lại cột \"Itemsets\" dựa trên chỉ số đã sắp xếp\n",
        "frequent_patterns['itemsets'] = frequent_patterns['itemsets'].iloc[sorted_indices].values\n",
        "\n",
        "# Lưu các tập phổ biến lớn nhất\n",
        "frequent_patterns_filter = frequent_patterns.copy()\n",
        "frequent_patterns_reverse = frequent_patterns.iloc[::-1]\n",
        "for valuehigh in frequent_patterns_reverse['itemsets']:\n",
        "  for valuelow in frequent_patterns['itemsets']:\n",
        "    dem = 0\n",
        "    if len(valuelow.split(', ')) == len(valuehigh.split(', ')):\n",
        "      break\n",
        "    for l in valuelow.split(', '):\n",
        "      if l in valuehigh.split(', '):\n",
        "        dem = dem + 1\n",
        "    if dem == len(valuelow.split(', ')):\n",
        "      frequent_patterns_filter = frequent_patterns_filter[frequent_patterns['itemsets'] != valuelow]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "frequent_patterns"
      ],
      "metadata": {
        "id": "TnpsDtph_7vT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "d0a4b8a5-9038-4995-e795-58f8ed4357a6"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      support                                           itemsets\n",
              "0    1.000000           no_of_previous_bookings_not_canceled (0)\n",
              "1    1.000000                                      Complementary\n",
              "2    0.974363                                arrival_year (2018)\n",
              "3    0.969014                                   no_of_adults (3)\n",
              "4    0.955396                                        Meal Plan 1\n",
              "..        ...                                                ...\n",
              "422  0.502329  no_of_previous_bookings_not_canceled (0), requ...\n",
              "423  0.504121  no_of_previous_bookings_not_canceled (0), requ...\n",
              "424  0.502329  no_of_previous_bookings_not_canceled (0), requ...\n",
              "425  0.502329  no_of_previous_bookings_not_canceled (0), requ...\n",
              "426  0.502329  no_of_previous_bookings_not_canceled (0), Comp...\n",
              "\n",
              "[427 rows x 2 columns]"
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              "      <th></th>\n",
              "      <th>support</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.000000</td>\n",
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              "      <td>0.974363</td>\n",
              "      <td>arrival_year (2018)</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.969014</td>\n",
              "      <td>no_of_adults (3)</td>\n",
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              "      <td>0.955396</td>\n",
              "      <td>Meal Plan 1</td>\n",
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              "      <td>0.502329</td>\n",
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              "    <tr>\n",
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              "      <td>0.502329</td>\n",
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              "    <tr>\n",
              "      <th>425</th>\n",
              "      <td>0.502329</td>\n",
              "      <td>no_of_previous_bookings_not_canceled (0), requ...</td>\n",
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              "      <td>0.502329</td>\n",
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              "\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "</div>\n",
              "\n",
              "  <div id=\"id_509622e5-65be-4c98-a5a8-c2d031994d13\">\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('frequent_patterns')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
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              "    </button>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('frequent_patterns');\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Tìm mẫu phổ biến lớn nhất"
      ],
      "metadata": {
        "id": "T44uIM9rol_G"
      }
    },
    {
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      "execution_count": 130,
      "outputs": [
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            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Chọn lọc lại bộ dữ liệu từ mẫu phổ biến tìm được"
      ],
      "metadata": {
        "id": "FJGTEpDuOv8r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Lấy các cột là mẫu phổ biến\n",
        "df_filtered = df[df.columns[df.columns.isin(frequent_patterns.itemsets)]]\n",
        "\n",
        "## XOR của các tập phổ biến\n",
        "for item in frequent_patterns_filter['itemsets']:\n",
        "  row = []\n",
        "  value = []\n",
        "  for index in item.split(', '):\n",
        "    row.append(index)\n",
        "  name = ' x '.join(row)\n",
        "\n",
        "  for i in row:\n",
        "    value.append(df_filtered[i])\n",
        "\n",
        "  result = value[0]\n",
        "  for i in range(1, len(value)):\n",
        "    result = [x ^ y for x, y in zip(result, value[i])]\n",
        "\n",
        "  df_filtered[name] = result\n",
        "\n",
        "# Xây dựng đặc trưng\n",
        "features = df.astype(int).values"
      ],
      "metadata": {
        "id": "DTis-LuJRonr"
      },
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train = pd.concat([df_filtered, target], axis=1)\n",
        "train"
      ],
      "metadata": {
        "id": "b4enkvSpVExK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "f0ae42a8-a906-42f0-8c94-f68ec448c8ea"
      },
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       no_of_adults (3)  no_of_children (0)  Meal Plan 1  \\\n",
              "0                  True                True         True   \n",
              "1                  True                True        False   \n",
              "2                 False                True         True   \n",
              "3                  True                True         True   \n",
              "4                  True                True        False   \n",
              "...                 ...                 ...          ...   \n",
              "36270             False                True         True   \n",
              "36271              True                True         True   \n",
              "36272              True                True         True   \n",
              "36273              True                True        False   \n",
              "36274              True                True         True   \n",
              "\n",
              "       required_car_parking_space (0)  Room_Type 1  arrival_year (2018)  \\\n",
              "0                                True         True                False   \n",
              "1                                True         True                 True   \n",
              "2                                True         True                 True   \n",
              "3                                True         True                 True   \n",
              "4                                True         True                 True   \n",
              "...                               ...          ...                  ...   \n",
              "36270                            True        False                 True   \n",
              "36271                            True         True                 True   \n",
              "36272                            True         True                 True   \n",
              "36273                            True         True                 True   \n",
              "36274                            True         True                 True   \n",
              "\n",
              "       Complementary  repeated_guest (0)  no_of_previous_cancellations (0)  \\\n",
              "0              False                True                              True   \n",
              "1               True                True                              True   \n",
              "2               True                True                              True   \n",
              "3               True                True                              True   \n",
              "4               True                True                              True   \n",
              "...              ...                 ...                               ...   \n",
              "36270           True                True                              True   \n",
              "36271           True                True                              True   \n",
              "36272           True                True                              True   \n",
              "36273           True                True                              True   \n",
              "36274          False                True                              True   \n",
              "\n",
              "       no_of_previous_bookings_not_canceled (0)  ...  \\\n",
              "0                                          True  ...   \n",
              "1                                          True  ...   \n",
              "2                                          True  ...   \n",
              "3                                          True  ...   \n",
              "4                                          True  ...   \n",
              "...                                         ...  ...   \n",
              "36270                                      True  ...   \n",
              "36271                                      True  ...   \n",
              "36272                                      True  ...   \n",
              "36273                                      True  ...   \n",
              "36274                                      True  ...   \n",
              "\n",
              "       no_of_previous_bookings_not_canceled (0) x required_car_parking_space (0) x no_of_previous_cancellations (0) x Meal Plan 1 x repeated_guest (0) x no_of_adults (3)  \\\n",
              "0                                                  False                                                                                                                    \n",
              "1                                                   True                                                                                                                    \n",
              "2                                                   True                                                                                                                    \n",
              "3                                                  False                                                                                                                    \n",
              "4                                                   True                                                                                                                    \n",
              "...                                                  ...                                                                                                                    \n",
              "36270                                               True                                                                                                                    \n",
              "36271                                              False                                                                                                                    \n",
              "36272                                              False                                                                                                                    \n",
              "36273                                               True                                                                                                                    \n",
              "36274                                              False                                                                                                                    \n",
              "\n",
              "       no_of_previous_bookings_not_canceled (0) x no_of_previous_cancellations (0) x Meal Plan 1 x repeated_guest (0) x Room_Type 1 x no_of_children (0)  \\\n",
              "0                                                  False                                                                                                   \n",
              "1                                                   True                                                                                                   \n",
              "2                                                  False                                                                                                   \n",
              "3                                                  False                                                                                                   \n",
              "4                                                   True                                                                                                   \n",
              "...                                                  ...                                                                                                   \n",
              "36270                                               True                                                                                                   \n",
              "36271                                              False                                                                                                   \n",
              "36272                                              False                                                                                                   \n",
              "36273                                               True                                                                                                   \n",
              "36274                                              False                                                                                                   \n",
              "\n",
              "       no_of_previous_bookings_not_canceled (0) x required_car_parking_space (0) x no_of_previous_cancellations (0) x Meal Plan 1 x Room_Type 1 x no_of_children (0)  \\\n",
              "0                                                  False                                                                                                               \n",
              "1                                                   True                                                                                                               \n",
              "2                                                  False                                                                                                               \n",
              "3                                                  False                                                                                                               \n",
              "4                                                   True                                                                                                               \n",
              "...                                                  ...                                                                                                               \n",
              "36270                                               True                                                                                                               \n",
              "36271                                              False                                                                                                               \n",
              "36272                                              False                                                                                                               \n",
              "36273                                               True                                                                                                               \n",
              "36274                                              False                                                                                                               \n",
              "\n",
              "       no_of_previous_bookings_not_canceled (0) x required_car_parking_space (0) x no_of_previous_cancellations (0) x Meal Plan 1 x repeated_guest (0) x Room_Type 1  \\\n",
              "0                                                  False                                                                                                               \n",
              "1                                                   True                                                                                                               \n",
              "2                                                  False                                                                                                               \n",
              "3                                                  False                                                                                                               \n",
              "4                                                   True                                                                                                               \n",
              "...                                                  ...                                                                                                               \n",
              "36270                                               True                                                                                                               \n",
              "36271                                              False                                                                                                               \n",
              "36272                                              False                                                                                                               \n",
              "36273                                               True                                                                                                               \n",
              "36274                                              False                                                                                                               \n",
              "\n",
              "       no_of_previous_bookings_not_canceled (0) x required_car_parking_space (0) x no_of_previous_cancellations (0) x Meal Plan 1 x repeated_guest (0) x arrival_year (2018) x no_of_children (0)  \\\n",
              "0                                                  False                                                                                                                                            \n",
              "1                                                  False                                                                                                                                            \n",
              "2                                                   True                                                                                                                                            \n",
              "3                                                   True                                                                                                                                            \n",
              "4                                                  False                                                                                                                                            \n",
              "...                                                  ...                                                                                                                                            \n",
              "36270                                               True                                                                                                                                            \n",
              "36271                                               True                                                                                                                                            \n",
              "36272                                               True                                                                                                                                            \n",
              "36273                                              False                                                                                                                                            \n",
              "36274                                               True                                                                                                                                            \n",
              "\n",
              "       no_of_previous_bookings_not_canceled (0) x required_car_parking_space (0) x no_of_previous_cancellations (0) x repeated_guest (0) x no_of_adults (3) x arrival_year (2018) x no_of_children (0)  \\\n",
              "0                                                  False                                                                                                                                                 \n",
              "1                                                   True                                                                                                                                                 \n",
              "2                                                  False                                                                                                                                                 \n",
              "3                                                   True                                                                                                                                                 \n",
              "4                                                   True                                                                                                                                                 \n",
              "...                                                  ...                                                                                                                                                 \n",
              "36270                                              False                                                                                                                                                 \n",
              "36271                                               True                                                                                                                                                 \n",
              "36272                                               True                                                                                                                                                 \n",
              "36273                                               True                                                                                                                                                 \n",
              "36274                                               True                                                                                                                                                 \n",
              "\n",
              "       no_of_previous_bookings_not_canceled (0) x required_car_parking_space (0) x no_of_previous_cancellations (0) x repeated_guest (0) x no_of_adults (3) x Room_Type 1 x no_of_children (0)  \\\n",
              "0                                                   True                                                                                                                                         \n",
              "1                                                   True                                                                                                                                         \n",
              "2                                                  False                                                                                                                                         \n",
              "3                                                   True                                                                                                                                         \n",
              "4                                                   True                                                                                                                                         \n",
              "...                                                  ...                                                                                                                                         \n",
              "36270                                               True                                                                                                                                         \n",
              "36271                                               True                                                                                                                                         \n",
              "36272                                               True                                                                                                                                         \n",
              "36273                                               True                                                                                                                                         \n",
              "36274                                               True                                                                                                                                         \n",
              "\n",
              "       no_of_previous_bookings_not_canceled (0) x required_car_parking_space (0) x no_of_previous_cancellations (0) x repeated_guest (0) x Room_Type 1 x arrival_year (2018) x no_of_children (0)  \\\n",
              "0                                                  False                                                                                                                                            \n",
              "1                                                   True                                                                                                                                            \n",
              "2                                                   True                                                                                                                                            \n",
              "3                                                   True                                                                                                                                            \n",
              "4                                                   True                                                                                                                                            \n",
              "...                                                  ...                                                                                                                                            \n",
              "36270                                              False                                                                                                                                            \n",
              "36271                                               True                                                                                                                                            \n",
              "36272                                               True                                                                                                                                            \n",
              "36273                                               True                                                                                                                                            \n",
              "36274                                               True                                                                                                                                            \n",
              "\n",
              "       no_of_previous_bookings_not_canceled (0) x Complementary x required_car_parking_space (0) x no_of_previous_cancellations (0) x repeated_guest (0) x arrival_year (2018) x no_of_children (0)  \\\n",
              "0                                                   True                                                                                                                                              \n",
              "1                                                   True                                                                                                                                              \n",
              "2                                                   True                                                                                                                                              \n",
              "3                                                   True                                                                                                                                              \n",
              "4                                                   True                                                                                                                                              \n",
              "...                                                  ...                                                                                                                                              \n",
              "36270                                               True                                                                                                                                              \n",
              "36271                                               True                                                                                                                                              \n",
              "36272                                               True                                                                                                                                              \n",
              "36273                                               True                                                                                                                                              \n",
              "36274                                              False                                                                                                                                              \n",
              "\n",
              "       booking_status  \n",
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              "3            Canceled  \n",
              "4            Canceled  \n",
              "...               ...  \n",
              "36270    Not_Canceled  \n",
              "36271        Canceled  \n",
              "36272    Not_Canceled  \n",
              "36273        Canceled  \n",
              "36274    Not_Canceled  \n",
              "\n",
              "[36275 rows x 22 columns]"
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              "    [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-29e887f2-e560-4bf9-b4e3-f7c227f9e6eb 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-29e887f2-e560-4bf9-b4e3-f7c227f9e6eb');\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",
              "\n",
              "\n",
              "<div id=\"df-6c417e65-8d74-425a-ab42-7cd4426b80ee\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6c417e65-8d74-425a-ab42-7cd4426b80ee')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6c417e65-8d74-425a-ab42-7cd4426b80ee button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_78182945-0ce5-431e-b007-e5adef22e2d9\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('train')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_78182945-0ce5-431e-b007-e5adef22e2d9 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('train');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Xây dựng mô hình phân lớp"
      ],
      "metadata": {
        "id": "eLw_CRvjph2K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y = train['booking_status']\n",
        "X = train.drop(columns = 'booking_status')"
      ],
      "metadata": {
        "id": "HNH7ZDtf10Gq"
      },
      "execution_count": 157,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2, random_state = 1)"
      ],
      "metadata": {
        "id": "FFEKcz2c3Bb5"
      },
      "execution_count": 158,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Naive Bayes"
      ],
      "metadata": {
        "id": "SwyQ-D36qHXU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "##------------------------------------------------------------------------------\n",
        "## Huấn luyện mô hình Gaussian NB\n",
        "##------------------------------------------------------------------------------\n",
        "NBmodel = GaussianNB()\n",
        "NBmodel.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "taiJy1tQ2nK4",
        "outputId": "56aaebfa-11df-4dd4-ce40-48287d41a929"
      },
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-12 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-12 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = NBmodel.predict(X_test)\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f'Accuracy của mô hình Naive Bayes: {accuracy:6f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G7JRBycy3NGV",
        "outputId": "12eafa8d-0234-43dc-b07a-251685645054"
      },
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy của mô hình Naive Bayes: 0.491661\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Cây quyết định"
      ],
      "metadata": {
        "id": "STOlCM-DA9kg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf   = DecisionTreeClassifier()\n",
        "DCTmodel = clf.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "gEdpnZFd4h1m"
      },
      "execution_count": 161,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = DCTmodel.predict(X_test)\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f'Accuracy của mô hình cây quyết định: {accuracy:6f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q6gkTtjI4m7b",
        "outputId": "6197fa7f-b77e-4c03-8703-da3ccb23d47d"
      },
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy của mô hình cây quyết định: 0.749552\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### K-nn"
      ],
      "metadata": {
        "id": "4tAb-ZM8BAPl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Phân tích giá trị của k theo chỉ số accuracy\n",
        "list_k   = []\n",
        "list_acc = []\n",
        "\n",
        "for k in range(2, 50):\n",
        "    list_k.append(k)\n",
        "\n",
        "    neigh = KNeighborsClassifier(n_neighbors = k)\n",
        "    neigh.fit(X_train, y_train)\n",
        "\n",
        "    y_pred   = neigh.predict(X_test)\n",
        "    acc      = accuracy_score(y_test, y_pred)\n",
        "    list_acc.append(acc)\n",
        "    print(f'k = {k:d} --> accuracy ~ {acc * 100:.1f}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K_ZSXGDn5ADJ",
        "outputId": "f65fccb7-5a11-479b-c27f-6ed6d2859b58"
      },
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "k = 2 --> accuracy ~ 56.1%\n",
            "k = 3 --> accuracy ~ 60.9%\n",
            "k = 4 --> accuracy ~ 72.0%\n",
            "k = 5 --> accuracy ~ 72.7%\n",
            "k = 6 --> accuracy ~ 71.8%\n",
            "k = 7 --> accuracy ~ 73.8%\n",
            "k = 8 --> accuracy ~ 73.8%\n",
            "k = 9 --> accuracy ~ 72.0%\n",
            "k = 10 --> accuracy ~ 73.8%\n",
            "k = 11 --> accuracy ~ 73.8%\n",
            "k = 12 --> accuracy ~ 73.8%\n",
            "k = 13 --> accuracy ~ 72.1%\n",
            "k = 14 --> accuracy ~ 73.9%\n",
            "k = 15 --> accuracy ~ 73.9%\n",
            "k = 16 --> accuracy ~ 73.9%\n",
            "k = 17 --> accuracy ~ 73.9%\n",
            "k = 18 --> accuracy ~ 73.8%\n",
            "k = 19 --> accuracy ~ 72.1%\n",
            "k = 20 --> accuracy ~ 73.8%\n",
            "k = 21 --> accuracy ~ 73.0%\n",
            "k = 22 --> accuracy ~ 73.8%\n",
            "k = 23 --> accuracy ~ 73.0%\n",
            "k = 24 --> accuracy ~ 73.0%\n",
            "k = 25 --> accuracy ~ 73.0%\n",
            "k = 26 --> accuracy ~ 73.0%\n",
            "k = 27 --> accuracy ~ 73.0%\n",
            "k = 28 --> accuracy ~ 73.0%\n",
            "k = 29 --> accuracy ~ 73.0%\n",
            "k = 30 --> accuracy ~ 73.0%\n",
            "k = 31 --> accuracy ~ 73.1%\n",
            "k = 32 --> accuracy ~ 73.0%\n",
            "k = 33 --> accuracy ~ 73.1%\n",
            "k = 34 --> accuracy ~ 73.1%\n",
            "k = 35 --> accuracy ~ 73.1%\n",
            "k = 36 --> accuracy ~ 73.0%\n",
            "k = 37 --> accuracy ~ 73.0%\n",
            "k = 38 --> accuracy ~ 73.0%\n",
            "k = 39 --> accuracy ~ 73.0%\n",
            "k = 40 --> accuracy ~ 73.0%\n",
            "k = 41 --> accuracy ~ 73.0%\n",
            "k = 42 --> accuracy ~ 73.0%\n",
            "k = 43 --> accuracy ~ 73.0%\n",
            "k = 44 --> accuracy ~ 73.0%\n",
            "k = 45 --> accuracy ~ 73.0%\n",
            "k = 46 --> accuracy ~ 73.0%\n",
            "k = 47 --> accuracy ~ 73.0%\n",
            "k = 48 --> accuracy ~ 73.0%\n",
            "k = 49 --> accuracy ~ 73.1%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**k = 7**"
      ],
      "metadata": {
        "id": "YLSgldxI94v4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors = 7)\n",
        "knn.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "7Q0nEAni91h3",
        "outputId": "0082d07c-ac76-433e-8ec8-4dcb21e52125"
      },
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=7)"
            ],
            "text/html": [
              "<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-13 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-13 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=7)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=7)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = knn.predict(X_test)\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f'Accuracy của mô hình knn: {accuracy:6f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qWdICms198Jz",
        "outputId": "b0e64169-3903-4a57-fd89-49f6f8c9951c"
      },
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy của mô hình knn: 0.738387\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### SVM"
      ],
      "metadata": {
        "id": "YMi6c2SnBEHd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "svmmodel = svm.SVC(kernel = 'linear')\n",
        "svmmodel.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "W39fgbMo-hfK",
        "outputId": "0a003e9a-2f88-4899-b708-0ace18764d9a"
      },
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(kernel='linear')"
            ],
            "text/html": [
              "<style>#sk-container-id-14 {color: black;background-color: white;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-14 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-14 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = svmmodel.predict(X_test)\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f'Accuracy của mô hình SVM: {accuracy:6f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gz26aIENFf6t",
        "outputId": "7733ea6c-7458-4356-86e3-3a0a16eb4a2b"
      },
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy của mô hình SVM: 0.738939\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Đánh giá với mô hình SVM"
      ],
      "metadata": {
        "id": "WcTbgEY6BIP7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Số fold\n",
        "k = 5\n",
        "\n",
        "# Tạo đối tượng KFold\n",
        "kf = KFold(n_splits=k, shuffle=True, random_state=42)\n",
        "\n",
        "# Xây dựng mô hình\n",
        "model = svm.SVC(kernel = 'linear')\n",
        "\n",
        "# Biến để lưu trữ các kết quả đánh giá\n",
        "accuracy_scores = []\n",
        "\n",
        "# Chia dữ liệu và thực hiện cross-validation\n",
        "for train_index, test_index in kf.split(X):\n",
        "    X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
        "    y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
        "\n",
        "    # Huấn luyện mô hình\n",
        "    model.fit(X_train, y_train)\n",
        "\n",
        "    # Dự đoán nhãn lớp cho tập kiểm tra\n",
        "    y_pred = model.predict(X_test)\n",
        "\n",
        "    # Đánh giá mô hình bằng độ chính xác\n",
        "    accuracy = accuracy_score(y_test, y_pred)\n",
        "    accuracy_scores.append(accuracy)"
      ],
      "metadata": {
        "id": "-WRz-9GB7k-w"
      },
      "execution_count": 168,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# In kết quả đánh giá từ mỗi fold\n",
        "for i, accuracy in enumerate(accuracy_scores):\n",
        "    print(f\"Fold {i+1}: Accuracy = {accuracy}\")\n",
        "\n",
        "# In trung bình độ chính xác qua tất cả các fold\n",
        "print(f\"\\nAverage Accuracy across all folds: {np.mean(accuracy_scores)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xdgri6X28xHw",
        "outputId": "1ea73153-0aa4-4953-e031-6dd537e4deba"
      },
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fold 1: Accuracy = 0.729841488628532\n",
            "Fold 2: Accuracy = 0.7422467263955892\n",
            "Fold 3: Accuracy = 0.7399035148173674\n",
            "Fold 4: Accuracy = 0.7346657477601654\n",
            "Fold 5: Accuracy = 0.7436250861474845\n",
            "\n",
            "Average Accuracy across all folds: 0.7380565127498278\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Tập dữ liệu không sử dụng mẫu thường xuyên dùng SVM\n",
        "\n"
      ],
      "metadata": {
        "id": "OghdDn2bqtQU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('Hotel Reservations.csv')\n",
        "data = data.drop(columns = ['booking_status', 'Booking_ID'] )\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "TqUkvWR_X7T0",
        "outputId": "58eb2fa1-4568-4e7d-f2f3-6d97cc71f227"
      },
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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",
              "36270             3               0                     2                  6   \n",
              "36271             2               0                     1                  3   \n",
              "36272             2               0                     2                  6   \n",
              "36273             2               0                     0                  3   \n",
              "36274             2               0                     1                  2   \n",
              "\n",
              "      type_of_meal_plan  required_car_parking_space room_type_reserved  \\\n",
              "0           Meal Plan 1                           0        Room_Type 1   \n",
              "1          Not Selected                           0        Room_Type 1   \n",
              "2           Meal Plan 1                           0        Room_Type 1   \n",
              "3           Meal Plan 1                           0        Room_Type 1   \n",
              "4          Not Selected                           0        Room_Type 1   \n",
              "...                 ...                         ...                ...   \n",
              "36270       Meal Plan 1                           0        Room_Type 4   \n",
              "36271       Meal Plan 1                           0        Room_Type 1   \n",
              "36272       Meal Plan 1                           0        Room_Type 1   \n",
              "36273      Not Selected                           0        Room_Type 1   \n",
              "36274       Meal Plan 1                           0        Room_Type 1   \n",
              "\n",
              "       lead_time  arrival_year  arrival_month  arrival_date  \\\n",
              "0            224          2017             10             2   \n",
              "1              5          2018             11             6   \n",
              "2              1          2018              2            28   \n",
              "3            211          2018              5            20   \n",
              "4             48          2018              4            11   \n",
              "...          ...           ...            ...           ...   \n",
              "36270         85          2018              8             3   \n",
              "36271        228          2018             10            17   \n",
              "36272        148          2018              7             1   \n",
              "36273         63          2018              4            21   \n",
              "36274        207          2018             12            30   \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",
              "36270              Online               0                             0   \n",
              "36271              Online               0                             0   \n",
              "36272              Online               0                             0   \n",
              "36273              Online               0                             0   \n",
              "36274             Offline               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",
              "36270                                     0              167.80   \n",
              "36271                                     0               90.95   \n",
              "36272                                     0               98.39   \n",
              "36273                                     0               94.50   \n",
              "36274                                     0              161.67   \n",
              "\n",
              "       no_of_special_requests  \n",
              "0                           0  \n",
              "1                           1  \n",
              "2                           0  \n",
              "3                           0  \n",
              "4                           0  \n",
              "...                       ...  \n",
              "36270                       1  \n",
              "36271                       2  \n",
              "36272                       2  \n",
              "36273                       0  \n",
              "36274                       0  \n",
              "\n",
              "[36275 rows x 17 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6e2da21a-c57b-4687-84b3-118e48dfeff7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <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>type_of_meal_plan</th>\n",
              "      <th>required_car_parking_space</th>\n",
              "      <th>room_type_reserved</th>\n",
              "      <th>lead_time</th>\n",
              "      <th>arrival_year</th>\n",
              "      <th>arrival_month</th>\n",
              "      <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",
              "    </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",
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              "      <td>0</td>\n",
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              "      <td>2</td>\n",
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              "      <td>20</td>\n",
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              "      <td>1</td>\n",
              "      <td>Not Selected</td>\n",
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              "      <td>2018</td>\n",
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              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>0</td>\n",
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              "      <th>36271</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
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              "      <th>36272</th>\n",
              "      <td>2</td>\n",
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              "      <th>36273</th>\n",
              "      <td>2</td>\n",
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              "      <th>36274</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
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              "      <td>2</td>\n",
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              "      <td>0</td>\n",
              "      <td>Room_Type 1</td>\n",
              "      <td>207</td>\n",
              "      <td>2018</td>\n",
              "      <td>12</td>\n",
              "      <td>30</td>\n",
              "      <td>Offline</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>161.67</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
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          },
          "metadata": {},
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "check_outliers_using_3_sigma(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mrk3F7qg851J",
        "outputId": "afe74c9c-5cc8-4daf-9044-24213e50354d"
      },
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cột no_of_adults có 16 giá trị nhiễu là các giá trị ngoài khoảng (0, 3)\n",
            "Cột no_of_children có 1080 giá trị nhiễu là các giá trị ngoài khoảng (-1, 1)\n",
            "Cột no_of_weekend_nights có 184 giá trị nhiễu là các giá trị ngoài khoảng (-2, 3)\n",
            "Cột no_of_week_nights có 324 giá trị nhiễu là các giá trị ngoài khoảng (-2, 6)\n",
            "Cột required_car_parking_space: Không có outlier.\n",
            "Cột lead_time có 376 giá trị nhiễu là các giá trị ngoài khoảng (-173, 343)\n",
            "Cột arrival_year: Không có outlier.\n",
            "Cột arrival_month: Không có outlier.\n",
            "Cột arrival_date: Không có outlier.\n",
            "Cột repeated_guest có 930 giá trị nhiễu là các giá trị ngoài khoảng (0, 0)\n",
            "Cột no_of_previous_cancellations có 140 giá trị nhiễu là các giá trị ngoài khoảng (-1, 1)\n",
            "Cột no_of_previous_bookings_not_canceled có 267 giá trị nhiễu là các giá trị ngoài khoảng (-5, 5)\n",
            "Cột avg_price_per_room có 353 giá trị nhiễu là các giá trị ngoài khoảng (-2, 209)\n",
            "Cột no_of_special_requests có 86 giá trị nhiễu là các giá trị ngoài khoảng (-2, 3)\n",
            "Cột no_of_adults: Không có outlier.\n",
            "Cột no_of_children: Không có outlier.\n",
            "Cột no_of_weekend_nights: Không có outlier.\n",
            "Cột no_of_week_nights: Không có outlier.\n",
            "Cột required_car_parking_space: Không có outlier.\n",
            "Cột lead_time có 100 giá trị nhiễu là các giá trị ngoài khoảng (-159, 323)\n",
            "Cột arrival_year: Không có outlier.\n",
            "Cột arrival_month: Không có outlier.\n",
            "Cột arrival_date: Không có outlier.\n",
            "Cột repeated_guest: Không có outlier.\n",
            "Cột no_of_previous_cancellations có 198 giá trị nhiễu là các giá trị ngoài khoảng (0, 0)\n",
            "Cột no_of_previous_bookings_not_canceled có 317 giá trị nhiễu là các giá trị ngoài khoảng (-1, 1)\n",
            "Cột avg_price_per_room có 737 giá trị nhiễu là các giá trị ngoài khoảng (5, 199)\n",
            "Cột no_of_special_requests: Không có outlier.\n",
            "Cột no_of_adults: Không có outlier.\n",
            "Cột no_of_children: Không có outlier.\n",
            "Cột no_of_weekend_nights: Không có outlier.\n",
            "Cột no_of_week_nights: Không có outlier.\n",
            "Cột required_car_parking_space: Không có outlier.\n",
            "Cột lead_time có 141 giá trị nhiễu là các giá trị ngoài khoảng (-157, 319)\n",
            "Cột arrival_year: Không có outlier.\n",
            "Cột arrival_month: Không có outlier.\n",
            "Cột arrival_date: Không có outlier.\n",
            "Cột repeated_guest: Không có outlier.\n",
            "Cột no_of_previous_cancellations: Không có outlier.\n",
            "Cột no_of_previous_bookings_not_canceled có 228 giá trị nhiễu là các giá trị ngoài khoảng (0, 0)\n",
            "Cột avg_price_per_room có 257 giá trị nhiễu là các giá trị ngoài khoảng (16, 190)\n",
            "Cột no_of_special_requests: Không có outlier.\n",
            "Cột no_of_adults: Không có outlier.\n",
            "Cột no_of_children: Không có outlier.\n",
            "Cột no_of_weekend_nights: Không có outlier.\n",
            "Cột no_of_week_nights: Không có outlier.\n",
            "Cột required_car_parking_space: Không có outlier.\n",
            "Cột lead_time có 102 giá trị nhiễu là các giá trị ngoài khoảng (-153, 314)\n",
            "Cột arrival_year: Không có outlier.\n",
            "Cột arrival_month: Không có outlier.\n",
            "Cột arrival_date: Không có outlier.\n",
            "Cột repeated_guest: Không có outlier.\n",
            "Cột no_of_previous_cancellations: Không có outlier.\n",
            "Cột no_of_previous_bookings_not_canceled: Không có outlier.\n",
            "Cột avg_price_per_room có 76 giá trị nhiễu là các giá trị ngoài khoảng (19, 187)\n",
            "Cột no_of_special_requests: Không có outlier.\n",
            "Cột no_of_adults: Không có outlier.\n",
            "Cột no_of_children: Không có outlier.\n",
            "Cột no_of_weekend_nights: Không có outlier.\n",
            "Cột no_of_week_nights: Không có outlier.\n",
            "Cột required_car_parking_space: Không có outlier.\n",
            "Cột lead_time có 29 giá trị nhiễu là các giá trị ngoài khoảng (-151, 310)\n",
            "Cột arrival_year: Không có outlier.\n",
            "Cột arrival_month: Không có outlier.\n",
            "Cột arrival_date: Không có outlier.\n",
            "Cột repeated_guest: Không có outlier.\n",
            "Cột no_of_previous_cancellations: Không có outlier.\n",
            "Cột no_of_previous_bookings_not_canceled: Không có outlier.\n",
            "Cột avg_price_per_room có 94 giá trị nhiễu là các giá trị ngoài khoảng (20, 185)\n",
            "Cột no_of_special_requests: Không có outlier.\n",
            "Cột no_of_adults: Không có outlier.\n",
            "Cột no_of_children: Không có outlier.\n",
            "Cột no_of_weekend_nights: Không có outlier.\n",
            "Cột no_of_week_nights: Không có outlier.\n",
            "Cột required_car_parking_space: Không có outlier.\n",
            "Cột lead_time có 3 giá trị nhiễu là các giá trị ngoài khoảng (-151, 309)\n",
            "Cột arrival_year: Không có outlier.\n",
            "Cột arrival_month: Không có outlier.\n",
            "Cột arrival_date: Không có outlier.\n",
            "Cột repeated_guest: Không có outlier.\n",
            "Cột no_of_previous_cancellations: Không có outlier.\n",
            "Cột no_of_previous_bookings_not_canceled: Không có outlier.\n",
            "Cột avg_price_per_room có 46 giá trị nhiễu là các giá trị ngoài khoảng (20, 184)\n",
            "Cột no_of_special_requests: Không có outlier.\n",
            "Cột no_of_adults: Không có outlier.\n",
            "Cột no_of_children: Không có outlier.\n",
            "Cột no_of_weekend_nights: Không có outlier.\n",
            "Cột no_of_week_nights: Không có outlier.\n",
            "Cột required_car_parking_space: Không có outlier.\n",
            "Cột lead_time: Không có outlier.\n",
            "Cột arrival_year: Không có outlier.\n",
            "Cột arrival_month: Không có outlier.\n",
            "Cột arrival_date: Không có outlier.\n",
            "Cột repeated_guest: Không có outlier.\n",
            "Cột no_of_previous_cancellations: Không có outlier.\n",
            "Cột no_of_previous_bookings_not_canceled: Không có outlier.\n",
            "Cột avg_price_per_room có 2 giá trị nhiễu là các giá trị ngoài khoảng (21, 184)\n",
            "Cột no_of_special_requests: Không có outlier.\n",
            "Cột no_of_adults: Không có outlier.\n",
            "Cột no_of_children: Không có outlier.\n",
            "Cột no_of_weekend_nights: Không có outlier.\n",
            "Cột no_of_week_nights: Không có outlier.\n",
            "Cột required_car_parking_space: Không có outlier.\n",
            "Cột lead_time: Không có outlier.\n",
            "Cột arrival_year: Không có outlier.\n",
            "Cột arrival_month: Không có outlier.\n",
            "Cột arrival_date: Không có outlier.\n",
            "Cột repeated_guest: Không có outlier.\n",
            "Cột no_of_previous_cancellations: Không có outlier.\n",
            "Cột no_of_previous_bookings_not_canceled: Không có outlier.\n",
            "Cột avg_price_per_room: Không có outlier.\n",
            "Cột no_of_special_requests: Không có outlier.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Chuyển dạng\n",
        "for i in data.select_dtypes(include = ['object']).columns:\n",
        "  data = onehot(i, data)\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "GQRkH4i05ygV",
        "outputId": "c1e3c893-fd89-4444-f131-500854872e88"
      },
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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",
              "36270             3               0                     2                  6   \n",
              "36271             2               0                     1                  3   \n",
              "36272             2               0                     2                  6   \n",
              "36273             2               0                     0                  3   \n",
              "36274             2               0                     1                  2   \n",
              "\n",
              "       required_car_parking_space  lead_time  arrival_year  arrival_month  \\\n",
              "0                               0        224          2017             10   \n",
              "1                               0          5          2018             11   \n",
              "2                               0          1          2018              2   \n",
              "3                               0        211          2018              5   \n",
              "4                               0         48          2018              4   \n",
              "...                           ...        ...           ...            ...   \n",
              "36270                           0         85          2018              8   \n",
              "36271                           0        228          2018             10   \n",
              "36272                           0        148          2018              7   \n",
              "36273                           0         63          2018              4   \n",
              "36274                           0        207          2018             12   \n",
              "\n",
              "       arrival_date  repeated_guest  ...  Room_Type 2  Room_Type 6  \\\n",
              "0                 2               0  ...          0.0          0.0   \n",
              "1                 6               0  ...          0.0          0.0   \n",
              "2                28               0  ...          0.0          0.0   \n",
              "3                20               0  ...          0.0          0.0   \n",
              "4                11               0  ...          0.0          0.0   \n",
              "...             ...             ...  ...          ...          ...   \n",
              "36270             3               0  ...          0.0          1.0   \n",
              "36271            17               0  ...          0.0          0.0   \n",
              "36272             1               0  ...          0.0          0.0   \n",
              "36273            21               0  ...          0.0          0.0   \n",
              "36274            30               0  ...          0.0          0.0   \n",
              "\n",
              "       Room_Type 5  Room_Type 7  Room_Type 3  Offline  Online  Corporate  \\\n",
              "0              0.0          0.0          0.0      0.0     0.0        0.0   \n",
              "1              0.0          0.0          0.0      0.0     0.0        0.0   \n",
              "2              0.0          0.0          0.0      0.0     0.0        0.0   \n",
              "3              0.0          0.0          0.0      0.0     0.0        0.0   \n",
              "4              0.0          0.0          0.0      0.0     0.0        0.0   \n",
              "...            ...          ...          ...      ...     ...        ...   \n",
              "36270          0.0          0.0          0.0      0.0     0.0        0.0   \n",
              "36271          0.0          0.0          0.0      0.0     0.0        0.0   \n",
              "36272          0.0          0.0          0.0      0.0     0.0        0.0   \n",
              "36273          0.0          0.0          0.0      0.0     0.0        0.0   \n",
              "36274          0.0          0.0          0.0      0.0     0.0        0.0   \n",
              "\n",
              "       Aviation  Complementary  \n",
              "0           1.0            0.0  \n",
              "1           0.0            1.0  \n",
              "2           0.0            1.0  \n",
              "3           0.0            1.0  \n",
              "4           0.0            1.0  \n",
              "...         ...            ...  \n",
              "36270       0.0            1.0  \n",
              "36271       0.0            1.0  \n",
              "36272       0.0            1.0  \n",
              "36273       0.0            1.0  \n",
              "36274       1.0            0.0  \n",
              "\n",
              "[36275 rows x 30 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4d100d4e-3b4e-4733-a6ba-6d61cd1724f5\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>arrival_year</th>\n",
              "      <th>arrival_month</th>\n",
              "      <th>arrival_date</th>\n",
              "      <th>repeated_guest</th>\n",
              "      <th>...</th>\n",
              "      <th>Room_Type 2</th>\n",
              "      <th>Room_Type 6</th>\n",
              "      <th>Room_Type 5</th>\n",
              "      <th>Room_Type 7</th>\n",
              "      <th>Room_Type 3</th>\n",
              "      <th>Offline</th>\n",
              "      <th>Online</th>\n",
              "      <th>Corporate</th>\n",
              "      <th>Aviation</th>\n",
              "      <th>Complementary</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>0</td>\n",
              "      <td>224</td>\n",
              "      <td>2017</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2018</td>\n",
              "      <td>11</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</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>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2018</td>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</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>0</td>\n",
              "      <td>211</td>\n",
              "      <td>2018</td>\n",
              "      <td>5</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</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>0</td>\n",
              "      <td>48</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>36270</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>85</td>\n",
              "      <td>2018</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36271</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>228</td>\n",
              "      <td>2018</td>\n",
              "      <td>10</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>36272</th>\n",
              "      <td>2</td>\n",
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              "      <td>2</td>\n",
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              "      <td>0</td>\n",
              "      <td>148</td>\n",
              "      <td>2018</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36273</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>63</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36274</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>207</td>\n",
              "      <td>2018</td>\n",
              "      <td>12</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>36275 rows × 30 columns</p>\n",
              "</div>\n",
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              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "  <div id=\"id_0264271f-b803-4dfd-ae0e-712e0300b6bb\">\n",
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              "        border: none;\n",
              "        border-radius: 50%;\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        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",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Giảm chiều dữ liệu."
      ],
      "metadata": {
        "id": "THhVSjlZX10x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pca = PCA().fit(data)\n",
        "nb_features = data.shape[1] - 1"
      ],
      "metadata": {
        "id": "fk-9FO3BX1Zd"
      },
      "execution_count": 173,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "var = 0.0\n",
        "for k in range(1, nb_features + 1):\n",
        "    pca = PCA(k)\n",
        "    pca.fit(data)\n",
        "\n",
        "    newVar = pca.explained_variance_ratio_.sum() * 100\n",
        "    print('   * k = %2d' %k, ': phương sai tích lũy ~ %.2f%%' %newVar,\n",
        "          '--> tăng ~ %.2f%%' %(newVar - var))\n",
        "    var = newVar"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u8BQjr3CYDSn",
        "outputId": "d225bce2-901a-45b7-d72d-1d72389d1a57"
      },
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   * k =  1 : phương sai tích lũy ~ 87.69% --> tăng ~ 87.69%\n",
            "   * k =  2 : phương sai tích lũy ~ 98.65% --> tăng ~ 10.96%\n",
            "   * k =  3 : phương sai tích lũy ~ 99.80% --> tăng ~ 1.14%\n",
            "   * k =  4 : phương sai tích lũy ~ 99.94% --> tăng ~ 0.14%\n",
            "   * k =  5 : phương sai tích lũy ~ 99.96% --> tăng ~ 0.02%\n",
            "   * k =  6 : phương sai tích lũy ~ 99.97% --> tăng ~ 0.01%\n",
            "   * k =  7 : phương sai tích lũy ~ 99.98% --> tăng ~ 0.01%\n",
            "   * k =  8 : phương sai tích lũy ~ 99.98% --> tăng ~ 0.00%\n",
            "   * k =  9 : phương sai tích lũy ~ 99.99% --> tăng ~ 0.00%\n",
            "   * k = 10 : phương sai tích lũy ~ 99.99% --> tăng ~ 0.00%\n",
            "   * k = 11 : phương sai tích lũy ~ 99.99% --> tăng ~ 0.00%\n",
            "   * k = 12 : phương sai tích lũy ~ 100.00% --> tăng ~ 0.00%\n",
            "   * k = 13 : phương sai tích lũy ~ 100.00% --> tăng ~ 0.00%\n",
            "   * k = 14 : phương sai tích lũy ~ 100.00% --> tăng ~ 0.00%\n",
            "   * k = 15 : phương sai tích lũy ~ 100.00% --> tăng ~ 0.00%\n",
            "   * k = 16 : phương sai tích lũy ~ 100.00% --> tăng ~ 0.00%\n",
            "   * k = 17 : phương sai tích lũy ~ 100.00% --> tăng ~ 0.00%\n",
            "   * k = 18 : phương sai tích lũy ~ 100.00% --> tăng ~ 0.00%\n",
            "   * k = 19 : phương sai tích lũy ~ 100.00% --> tăng ~ 0.00%\n",
            "   * k = 20 : phương sai tích lũy ~ 100.00% --> tăng ~ 0.00%\n",
            "   * k = 21 : phương sai tích lũy ~ 100.00% --> tăng ~ 0.00%\n",
            "   * k = 22 : phương sai tích lũy ~ 100.00% --> tăng ~ 0.00%\n",
            "   * k = 23 : phương sai tích lũy ~ 100.00% --> tăng ~ 0.00%\n",
            "   * k = 24 : phương sai tích lũy ~ 100.00% --> tăng ~ 0.00%\n",
            "   * k = 25 : phương sai tích lũy ~ 100.00% --> tăng ~ 0.00%\n",
            "   * k = 26 : phương sai tích lũy ~ 100.00% --> tăng ~ 0.00%\n",
            "   * k = 27 : phương sai tích lũy ~ 100.00% --> tăng ~ 0.00%\n",
            "   * k = 28 : phương sai tích lũy ~ 100.00% --> tăng ~ 0.00%\n",
            "   * k = 29 : phương sai tích lũy ~ 100.00% --> tăng ~ 0.00%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Chọn k = 5."
      ],
      "metadata": {
        "id": "UDdAOMkuYHDS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_norm = pd.DataFrame(StandardScaler().fit_transform(df)) # tự động loại cột Class\n",
        "\n",
        "## Áp dụng PCA\n",
        "pca = PCA(n_components = 5)\n",
        "pca.fit(data_norm)"
      ],
      "metadata": {
        "id": "kqer_p-DYF2d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "a91d5eeb-c885-445f-90d9-cf69347fbd71"
      },
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PCA(n_components=5)"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "B = pca.transform(data_norm)\n",
        "df_pca = pd.DataFrame(B, columns = ['PC1', 'PC2', 'PC3', 'PC4', 'PC5'])\n",
        "df_combined = pd.concat([df_pca, target], axis=1)\n",
        "df_combined.head()"
      ],
      "metadata": {
        "id": "bKBxKOy0YLPO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "74ce7dd3-4603-4707-fb67-3a548441b05e"
      },
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        PC1       PC2       PC3       PC4       PC5 booking_status\n",
              "0  2.814652 -2.358024 -1.250612  1.430136  0.740220   Not_Canceled\n",
              "1 -1.052639 -0.639909  3.080365 -0.949159 -1.309646   Not_Canceled\n",
              "2  1.368878  2.151670  0.286700 -2.032257  0.258532       Canceled\n",
              "3 -0.060909 -1.011661 -0.522157 -1.085182  0.308512       Canceled\n",
              "4 -0.567491 -0.806760  2.696639 -1.892263 -1.397678       Canceled"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PC1</th>\n",
              "      <th>PC2</th>\n",
              "      <th>PC3</th>\n",
              "      <th>PC4</th>\n",
              "      <th>PC5</th>\n",
              "      <th>booking_status</th>\n",
              "    </tr>\n",
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              "      <th>0</th>\n",
              "      <td>2.814652</td>\n",
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              "      <td>-1.250612</td>\n",
              "      <td>1.430136</td>\n",
              "      <td>0.740220</td>\n",
              "      <td>Not_Canceled</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-1.052639</td>\n",
              "      <td>-0.639909</td>\n",
              "      <td>3.080365</td>\n",
              "      <td>-0.949159</td>\n",
              "      <td>-1.309646</td>\n",
              "      <td>Not_Canceled</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.368878</td>\n",
              "      <td>2.151670</td>\n",
              "      <td>0.286700</td>\n",
              "      <td>-2.032257</td>\n",
              "      <td>0.258532</td>\n",
              "      <td>Canceled</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.060909</td>\n",
              "      <td>-1.011661</td>\n",
              "      <td>-0.522157</td>\n",
              "      <td>-1.085182</td>\n",
              "      <td>0.308512</td>\n",
              "      <td>Canceled</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.567491</td>\n",
              "      <td>-0.806760</td>\n",
              "      <td>2.696639</td>\n",
              "      <td>-1.892263</td>\n",
              "      <td>-1.397678</td>\n",
              "      <td>Canceled</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "        document.querySelector('#df-f32eb1fd-1f7c-45ea-a5cb-47fb5a1e3531 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-f32eb1fd-1f7c-45ea-a5cb-47fb5a1e3531');\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",
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              "\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    height: 32px;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a81d1ad9-487b-44f9-a7ba-eb34e55d5b0f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = target\n",
        "X = df_pca"
      ],
      "metadata": {
        "id": "xNDzPS4bHHLr"
      },
      "execution_count": 177,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Số fold\n",
        "k = 5\n",
        "\n",
        "# Tạo đối tượng KFold\n",
        "kf = KFold(n_splits=k, shuffle=True, random_state=42)\n",
        "\n",
        "# Xây dựng mô hình\n",
        "model = svm.SVC(kernel = 'linear')\n",
        "\n",
        "# Biến để lưu trữ các kết quả đánh giá\n",
        "accuracy_scores = []\n",
        "\n",
        "# Chia dữ liệu và thực hiện cross-validation\n",
        "for train_index, test_index in kf.split(X):\n",
        "    X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
        "    y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
        "\n",
        "    # Huấn luyện mô hình\n",
        "    model.fit(X_train, y_train)\n",
        "\n",
        "    # Dự đoán nhãn lớp cho tập kiểm tra\n",
        "    y_pred = model.predict(X_test)\n",
        "\n",
        "    # Đánh giá mô hình bằng độ chính xác\n",
        "    accuracy = accuracy_score(y_test, y_pred)\n",
        "    accuracy_scores.append(accuracy)"
      ],
      "metadata": {
        "id": "nQGqCpaZYNvJ"
      },
      "execution_count": 178,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# In kết quả đánh giá từ mỗi fold\n",
        "for i, accuracy in enumerate(accuracy_scores):\n",
        "    print(f\"Fold {i+1}: Accuracy = {accuracy}\")\n",
        "\n",
        "# In trung bình độ chính xác qua tất cả các fold\n",
        "print(f\"\\nAverage Accuracy across all folds: {np.mean(accuracy_scores)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tp-qGzWHHQfo",
        "outputId": "0c8c6488-cbb5-404d-bddf-613c89f26141"
      },
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fold 1: Accuracy = 0.6669882839421089\n",
            "Fold 2: Accuracy = 0.6758097863542385\n",
            "Fold 3: Accuracy = 0.6665747760165404\n",
            "Fold 4: Accuracy = 0.6719503790489317\n",
            "Fold 5: Accuracy = 0.6804962095106822\n",
            "\n",
            "Average Accuracy across all folds: 0.6723638869745003\n"
          ]
        }
      ]
    }
  ],
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