hotel_booking_starting_kit.ipynb

{
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  {
   "cell_type": "markdown",
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   },
   "source": [
    "# Hotel booking cancellation prediction\n",
    "Authors:\n",
    "* DAU Hai Dang\n",
    "* LE Ha Quang\n",
    "* TRAN Duc Dung"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "This starting kit requires the following dependencies:\n",
    "\n",
    "* `numpy`\n",
    "* `pandas`\n",
    "* `seaborn`\n",
    "* `scikit-learn`\n",
    "* `matplotlib`\n",
    "* `jupyter`\n",
    "* `lightgbm`\n",
    "\n",
    "You can install all requisite modules with `pip install -r requirements.txt`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition, ramp-workflow is needed. This can be installed from the master branch on GitHub:\n",
    "\n",
    "```bash\n",
    "python -m pip install https://api.github.com/repos/paris-saclay-cds/ramp-workflow/zipball/master\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting the data\n",
    "\n",
    "The public train and test data can be downloaded by running from the root of the starting kit:\n",
    "\n",
    "```bash\n",
    "python download_data.py\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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    "%%javascript\n",
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    "    return false;\n",
    "}"
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  {
   "cell_type": "code",
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   "source": [
    "import numpy as np\n",
    "import problem\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of content\n",
    "1. [Introduction](#ph1)\n",
    "2. [Exploratory Data Analysis](#ph2)\n",
    "3. [Workflow](#ph3)\n",
    "4. [Submission](#ph4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction <a name=\"ph1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](img/illustration_hotel_booking.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the hospitality industry, which includes lodging, event planning, transportation, travelling or cuisine line, booking helps to provide the right product to right customer at the right time. Despite its benefits, booking cancellation option puts the services at risk. In the case of hotels, vacant capacity may happen when a guest cancel a booking or doesn't show up. While cancellations can occur due to reasonable causes like bad weather, working schedule changes, illness, etc, nowadays a great number of them happens because people search for best deals. Although, hotels can use overbooking or cancellation policy, overbooking gives a bad expericence by not allowing a customer to check in, while cancellation policy can reduce the number of bookings and reduce the income.\n",
    "\n",
    "To overcome this problem, we propose using machine learning to predict the probility of cancellation with the reservation's information as input. This prediction helps the management decision, improve the experience of customers and the hotel's revenue. The model can be retrained regularly (as monthly) as the data accumulates, and assuming that the cusomers' behaviors or the hotel's services do not change fast. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Despite important business implications, real-world data about hotel booking cancellation are not abundant. Recently, researchers from Portugal published a set of booking data from two real hotels in the _Data in Bref_ journal. The original article is openly accessible on [ScienceDirect](https://www.sciencedirect.com/science/article/pii/S2352340918315191).\n",
    "\n",
    "The data we use here comes from the \"H1\" dataset described in the article, which concerns a resort hotel in the region of Algarve. Detailed explanations about the meaning of the variables are given in the article. We took this dataset H1, do some slight preprocessing, and splitted it into two files _train.csv_ and _test.csv_. Again, these two files can be downloaded using download_data.py file in the root folder.\n",
    "\n",
    "Each row is a hotel booking reservation and each column is a feature of that reservation (when was the reservation made? How many days the customer wanted to stay? How many adults and how many children? etc...). The target column is the IsCanceled column, indicating whether or not the booking is finally canceled. There are approximately 40000 rows in the original data. The train data contains about 30000 rows and the rest is in the test data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KPI and Metric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the hotel predicts that a customer's reservation is likely to be canceled, the reception has the following solutions:\n",
    "* It can either ask for more guarantee from the concerned customer, for example, by asking for a higher deposit fee.\n",
    "* It can also allow other customers to be on the waitlist for the room/time slot that the likely-to-cancel customer reserved.\n",
    "\n",
    "If a the customer's reservation was predicted to be canceled, but finally it turns out that the customer does not cancel the reservation (false positive), there is a potential cost A for the hotel. If the hotel demanded stricter guarantees from the customer, the customer might feel that the hotel did not trust him, especially if he is a returning customer. If the hotel allowed other customers to be on the waitlist, finally it cannot accomodate their reservations, therefore creates a frustration on these customers.\n",
    "\n",
    "If the prediction was that the customer's reservation is not canceled but in fact it is canceled (false negative), there is a clear cost B for the hotel, because the hotel might fail to take preventive actions and might end up having an empty room.\n",
    "\n",
    "From a business point of view, it is reasonable to assume that the cost B is higher than the cost A (if we look at the analyses above, we see that A is rather a vague psychological cost which is not even sure to be realized).\n",
    "\n",
    "In classification terms, the recall score is more important than the precision score. To reflect this, we use a Modified F1 metric (abbreviated __mf1__ below), where, for simplicity, the precision's weight is 1 and the recall's weight is 3. The exact weight should be determined by a more thorough business analytics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis of our particular hotel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the case of our particular hotel, the exploratory data analysis reveals interesting patterns that can justify the use of machine learning to predict cancellations from a business point of view. Please look at the Exploratory Data Analysis section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory data analysis <a name=\"ph2\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us load the data and have a quick look at its features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IsCanceled</th>\n",
       "      <th>LeadTime</th>\n",
       "      <th>ArrivalDateMonth</th>\n",
       "      <th>ArrivalDateWeekNumber</th>\n",
       "      <th>ArrivalDateDayOfMonth</th>\n",
       "      <th>StaysInWeekendNights</th>\n",
       "      <th>StaysInWeekNights</th>\n",
       "      <th>Adults</th>\n",
       "      <th>Children</th>\n",
       "      <th>Babies</th>\n",
       "      <th>...</th>\n",
       "      <th>AssignedRoomType</th>\n",
       "      <th>BookingChanges</th>\n",
       "      <th>DepositType</th>\n",
       "      <th>Agent</th>\n",
       "      <th>Company</th>\n",
       "      <th>DaysInWaitingList</th>\n",
       "      <th>CustomerType</th>\n",
       "      <th>ADR</th>\n",
       "      <th>RequiredCarParkingSpaces</th>\n",
       "      <th>TotalOfSpecialRequests</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>187</td>\n",
       "      <td>July</td>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>240</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>83.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>September</td>\n",
       "      <td>37</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>146.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>September</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>E</td>\n",
       "      <td>0</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>171</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>98.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>December</td>\n",
       "      <td>51</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient-Party</td>\n",
       "      <td>43.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>October</td>\n",
       "      <td>41</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>250</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>73.67</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   IsCanceled  LeadTime ArrivalDateMonth  ArrivalDateWeekNumber  \\\n",
       "0           0       187             July                     28   \n",
       "1           1         0        September                     37   \n",
       "2           1       156        September                     36   \n",
       "3           0         5         December                     51   \n",
       "4           0         3          October                     41   \n",
       "\n",
       "   ArrivalDateDayOfMonth  StaysInWeekendNights  StaysInWeekNights  Adults  \\\n",
       "0                      9                     2                  5       2   \n",
       "1                      9                     0                  1       1   \n",
       "2                      2                     2                  5       2   \n",
       "3                     19                     0                  1       2   \n",
       "4                      8                     0                  3       2   \n",
       "\n",
       "   Children  Babies           ...            AssignedRoomType BookingChanges  \\\n",
       "0         0       0           ...            A                             0   \n",
       "1         0       0           ...            F                             0   \n",
       "2         0       0           ...            E                             0   \n",
       "3         0       0           ...            D                             0   \n",
       "4         0       0           ...            D                             0   \n",
       "\n",
       "       DepositType        Agent      Company  DaysInWaitingList  \\\n",
       "0  No Deposit               240         NULL                  0   \n",
       "1  No Deposit              NULL         NULL                  0   \n",
       "2  No Deposit               171         NULL                  0   \n",
       "3  No Deposit              NULL         NULL                  0   \n",
       "4  No Deposit               250         NULL                  0   \n",
       "\n",
       "      CustomerType     ADR RequiredCarParkingSpaces  TotalOfSpecialRequests  \n",
       "0        Transient   83.00                        0                       2  \n",
       "1        Transient  146.00                        0                       2  \n",
       "2        Transient   98.00                        0                       0  \n",
       "3  Transient-Party   43.00                        0                       1  \n",
       "4        Transient   73.67                        0                       0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data/train.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32048 entries, 0 to 32047\n",
      "Data columns (total 28 columns):\n",
      "IsCanceled                     32048 non-null int64\n",
      "LeadTime                       32048 non-null int64\n",
      "ArrivalDateMonth               32048 non-null object\n",
      "ArrivalDateWeekNumber          32048 non-null int64\n",
      "ArrivalDateDayOfMonth          32048 non-null int64\n",
      "StaysInWeekendNights           32048 non-null int64\n",
      "StaysInWeekNights              32048 non-null int64\n",
      "Adults                         32048 non-null int64\n",
      "Children                       32048 non-null int64\n",
      "Babies                         32048 non-null int64\n",
      "Meal                           32048 non-null object\n",
      "Country                        31677 non-null object\n",
      "MarketSegment                  32048 non-null object\n",
      "DistributionChannel            32048 non-null object\n",
      "IsRepeatedGuest                32048 non-null int64\n",
      "PreviousCancellations          32048 non-null int64\n",
      "PreviousBookingsNotCanceled    32048 non-null int64\n",
      "ReservedRoomType               32048 non-null object\n",
      "AssignedRoomType               32048 non-null object\n",
      "BookingChanges                 32048 non-null int64\n",
      "DepositType                    32048 non-null object\n",
      "Agent                          32048 non-null object\n",
      "Company                        32048 non-null object\n",
      "DaysInWaitingList              32048 non-null int64\n",
      "CustomerType                   32048 non-null object\n",
      "ADR                            32048 non-null float64\n",
      "RequiredCarParkingSpaces       32048 non-null int64\n",
      "TotalOfSpecialRequests         32048 non-null int64\n",
      "dtypes: float64(1), int64(16), object(11)\n",
      "memory usage: 6.8+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that there is no missing data, except in the Country column. We can therefore fill all missing data with -1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = data.fillna(-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now see how many categories each non-binary categorical variable has."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Column</th>\n",
       "      <th>Number of categories</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ArrivalDateMonth</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Meal</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Country</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MarketSegment</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DistributionChannel</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ReservedRoomType</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AssignedRoomType</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DepositType</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Agent</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Company</td>\n",
       "      <td>226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CustomerType</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Column  Number of categories\n",
       "0      ArrivalDateMonth                    12\n",
       "1                  Meal                     5\n",
       "2               Country                   118\n",
       "3         MarketSegment                     6\n",
       "4   DistributionChannel                     4\n",
       "5      ReservedRoomType                    10\n",
       "6      AssignedRoomType                    11\n",
       "7           DepositType                     3\n",
       "8                 Agent                   178\n",
       "9               Company                   226\n",
       "10         CustomerType                     4"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_cols = [col for col in data.columns if str(data[col].dtype) == 'object']\n",
    "no_cats = [len(set(data[col])) for col in cat_cols]\n",
    "pd.DataFrame({'Column': cat_cols, 'Number of categories': no_cats})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For numerical or binary variables, the **describe** function provides valuable insights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IsCanceled</th>\n",
       "      <th>LeadTime</th>\n",
       "      <th>ArrivalDateWeekNumber</th>\n",
       "      <th>ArrivalDateDayOfMonth</th>\n",
       "      <th>StaysInWeekendNights</th>\n",
       "      <th>StaysInWeekNights</th>\n",
       "      <th>Adults</th>\n",
       "      <th>Children</th>\n",
       "      <th>Babies</th>\n",
       "      <th>IsRepeatedGuest</th>\n",
       "      <th>PreviousCancellations</th>\n",
       "      <th>PreviousBookingsNotCanceled</th>\n",
       "      <th>BookingChanges</th>\n",
       "      <th>DaysInWaitingList</th>\n",
       "      <th>ADR</th>\n",
       "      <th>RequiredCarParkingSpaces</th>\n",
       "      <th>TotalOfSpecialRequests</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>32048.000000</td>\n",
       "      <td>32048.000000</td>\n",
       "      <td>32048.000000</td>\n",
       "      <td>32048.000000</td>\n",
       "      <td>32048.000000</td>\n",
       "      <td>32048.000000</td>\n",
       "      <td>32048.000000</td>\n",
       "      <td>32048.000000</td>\n",
       "      <td>32048.000000</td>\n",
       "      <td>32048.000000</td>\n",
       "      <td>32048.000000</td>\n",
       "      <td>32048.000000</td>\n",
       "      <td>32048.000000</td>\n",
       "      <td>32048.000000</td>\n",
       "      <td>32048.000000</td>\n",
       "      <td>32048.000000</td>\n",
       "      <td>32048.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.276804</td>\n",
       "      <td>92.450012</td>\n",
       "      <td>27.088492</td>\n",
       "      <td>15.802733</td>\n",
       "      <td>1.186252</td>\n",
       "      <td>3.124282</td>\n",
       "      <td>1.866232</td>\n",
       "      <td>0.128838</td>\n",
       "      <td>0.013885</td>\n",
       "      <td>0.044496</td>\n",
       "      <td>0.099663</td>\n",
       "      <td>0.150743</td>\n",
       "      <td>0.289066</td>\n",
       "      <td>0.525119</td>\n",
       "      <td>95.043906</td>\n",
       "      <td>0.138698</td>\n",
       "      <td>0.618198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.447426</td>\n",
       "      <td>97.066314</td>\n",
       "      <td>13.978094</td>\n",
       "      <td>8.892396</td>\n",
       "      <td>1.143512</td>\n",
       "      <td>2.444580</td>\n",
       "      <td>0.683958</td>\n",
       "      <td>0.444471</td>\n",
       "      <td>0.118869</td>\n",
       "      <td>0.206197</td>\n",
       "      <td>1.325955</td>\n",
       "      <td>1.027454</td>\n",
       "      <td>0.727221</td>\n",
       "      <td>7.388141</td>\n",
       "      <td>61.556252</td>\n",
       "      <td>0.352785</td>\n",
       "      <td>0.813272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-6.380000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>737.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>185.000000</td>\n",
       "      <td>450.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         IsCanceled      LeadTime  ArrivalDateWeekNumber  \\\n",
       "count  32048.000000  32048.000000           32048.000000   \n",
       "mean       0.276804     92.450012              27.088492   \n",
       "std        0.447426     97.066314              13.978094   \n",
       "min        0.000000      0.000000               1.000000   \n",
       "25%        0.000000     10.000000              15.000000   \n",
       "50%        0.000000     57.000000              28.000000   \n",
       "75%        1.000000    154.000000              38.000000   \n",
       "max        1.000000    737.000000              53.000000   \n",
       "\n",
       "       ArrivalDateDayOfMonth  StaysInWeekendNights  StaysInWeekNights  \\\n",
       "count           32048.000000          32048.000000       32048.000000   \n",
       "mean               15.802733              1.186252           3.124282   \n",
       "std                 8.892396              1.143512           2.444580   \n",
       "min                 1.000000              0.000000           0.000000   \n",
       "25%                 8.000000              0.000000           1.000000   \n",
       "50%                16.000000              1.000000           3.000000   \n",
       "75%                24.000000              2.000000           5.000000   \n",
       "max                31.000000             19.000000          50.000000   \n",
       "\n",
       "             Adults      Children        Babies  IsRepeatedGuest  \\\n",
       "count  32048.000000  32048.000000  32048.000000     32048.000000   \n",
       "mean       1.866232      0.128838      0.013885         0.044496   \n",
       "std        0.683958      0.444471      0.118869         0.206197   \n",
       "min        0.000000      0.000000      0.000000         0.000000   \n",
       "25%        2.000000      0.000000      0.000000         0.000000   \n",
       "50%        2.000000      0.000000      0.000000         0.000000   \n",
       "75%        2.000000      0.000000      0.000000         0.000000   \n",
       "max       55.000000     10.000000      2.000000         1.000000   \n",
       "\n",
       "       PreviousCancellations  PreviousBookingsNotCanceled  BookingChanges  \\\n",
       "count           32048.000000                 32048.000000    32048.000000   \n",
       "mean                0.099663                     0.150743        0.289066   \n",
       "std                 1.325955                     1.027454        0.727221   \n",
       "min                 0.000000                     0.000000        0.000000   \n",
       "25%                 0.000000                     0.000000        0.000000   \n",
       "50%                 0.000000                     0.000000        0.000000   \n",
       "75%                 0.000000                     0.000000        0.000000   \n",
       "max                26.000000                    28.000000       17.000000   \n",
       "\n",
       "       DaysInWaitingList           ADR  RequiredCarParkingSpaces  \\\n",
       "count       32048.000000  32048.000000              32048.000000   \n",
       "mean            0.525119     95.043906                  0.138698   \n",
       "std             7.388141     61.556252                  0.352785   \n",
       "min             0.000000     -6.380000                  0.000000   \n",
       "25%             0.000000     50.000000                  0.000000   \n",
       "50%             0.000000     75.000000                  0.000000   \n",
       "75%             0.000000    125.000000                  0.000000   \n",
       "max           185.000000    450.000000                  8.000000   \n",
       "\n",
       "       TotalOfSpecialRequests  \n",
       "count            32048.000000  \n",
       "mean                 0.618198  \n",
       "std                  0.813272  \n",
       "min                  0.000000  \n",
       "25%                  0.000000  \n",
       "50%                  0.000000  \n",
       "75%                  1.000000  \n",
       "max                  5.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some first observations:\n",
    "* The problem is imbalanced (because mean of IsCanceled is 0.28). This is confirmed by the plot below\n",
    "* This hotel is very busy. Mean of LeadTime is 92.5, which means that most bookings take place in average 90 days before arrival time. This makes cancellation prediction problem very important from a business point of view.\n",
    "* Mean of IsRepeatedGuest is 0.04, so most guests are in fact new ones. This means that \"manual\" cancellation prediction might be difficult, and machine learning techniques can prove to be useful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a18b1a198>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aunSpioqKlJ2drTFjxqi8vHygdgMA0AcOc519dP/8Id+1OF02r/ClazQVhoutZXnRHgGw\nor+vnQN2ugwAcP0hMgAAa4gMAMAaIgMAsIbIAACsITLAMNPTzX388HnR+r4YsM/JABgYMU6XGsqe\niPYYGGRSC38TleflSAYAYA2RAQBYQ2QAANYQGQCANUQGAGANkQEAWENkAADWEBkAgDVEBgBgDZEB\nAFhDZAAA1hAZAIA1RAYAYA2RAQBYQ2QAANYQGQCANUQGAGANkQEAWENkAADWEBkAgDVEBgBgDZEB\nAFhDZAAA1hAZAIA1RAYAYA2RAQBYQ2QAANYQGQCANUQGAGCNMxpPOn/+fLW3t8vpPPf0q1ev1t//\n/ndt2rRJ3d3deuyxx5SXlydJqqur09q1axUIBDR79mw9/fTTkqQDBw5o+fLl6uzsVFpamlatWhX+\negCAwWHAj2SMMWpqalJNTU34f4mJiaqoqNDWrVtVXV2tbdu26dChQ+rq6lJxcbEqKytVW1urxsZG\n7d69W5K0bNkyrVy5Ujt37pQxRlVVVQO9KwCAKxjwyPztb3+TJD3++OP61re+pS1btqiurk533XWX\nxo4dq1GjRikzM1M+n0/79+9XUlKSJk2aJKfTKa/XK5/Pp5aWFnV1dSklJUWSlJubK5/PN9C7AgC4\nggGPzKlTp5Senq7nnntOL774ov74xz/q6NGjSkhICD/G7XbL7/fr2LFjEa1PSEiQ3+8f0P0AAFzZ\ngF/EuP3223X77beHl+fMmaO1a9eqoKAgvM4YI4fDoZ6eHjkcjojX90VjY+NV7IWUmpp6Vdtj+Gpo\naIjq8/O9iUuJxvfmgEfm/fffVzAYVHp6uqRzgZg4caJaW1vDj2ltbZXb7VZiYmJE69va2uR2u/s0\nR3JysuLi4q5yb4DP40Ueg9XVfG8GAoF+vTkf8NNlp0+fVllZmQKBgDo6OvTqq6/qZz/7mfbs2aP2\n9nadOXNGu3btksfj0fTp03X48GE1NzcrFApp+/bt8ng8mjhxouLi4sJVrqmpkcfjGehdAQBcwYAf\nycycOVP79u3T/fffr56eHs2bN0+pqal6+umnlZ+fr2AwqDlz5ui2226TJK1bt05LlixRIBDQjBkz\nlJWVJUkqLy/XihUr1NHRoWnTpik/P3+gdwUAcAUOY4yJ9hAD6fwh37U4XTav8KVrNBWGi61ledEe\nQZLUUPZEtEfAIJNa+Jur2r6/r5184h8AYA2RAQBYQ2QAANYQGQCANUQGAGANkQEAWENkAADWEBkA\ngDVEBgBgDZEBAFhDZAAA1hAZAIA1RAYAYA2RAQBYQ2QAANYQGQCANUQGAGANkQEAWENkAADWEBkA\ngDVEBgBgDZEBAFhDZAAA1hAZAIA1RAYAYA2RAQBYQ2QAANYQGQCANUQGAGANkQEAWENkAADWEBkA\ngDVEBgBgDZEBAFhDZAAA1hAZAIA1Qzoyr732mr75zW8qIyNDL730UrTHAQBcxBntAfrL7/eroqJC\nr7zyimJjY/XII4/ozjvv1M033xzt0QAA/zFkI1NXV6e77rpLY8eOlSRlZmbK5/Np8eLFl93OGCNJ\nOnv27FXP8D+jXFf9NTC8BAKBaI9wzsgx0Z4Ag8zVfm+ef808/xoaqSEbmWPHjikhISG87Ha7tX//\n/ituFwwGJUkHDx686hm+65181V8Dw0tjY2O0Rzjnfx+N9gQYZK7V92YwGNTIkSMjfvyQjUxPT48c\nDkd42RjTa/lSRo8erSlTpsjlckX0eADAudfYYDCo0aNH92m7IRuZxMREvf/+++Hl1tZWud3uK24X\nExOjMWM4lQAAfdWXI5jzhuxPl919993as2eP2tvbdebMGe3atUsejyfaYwEALjBkj2TGjx+vp59+\nWvn5+QoGg5ozZ45uu+22aI8FALiAw/T1RwUAAIjQkD1dBgAY/IgMAMAaIgMAsIbIAACsITLoN25Q\nisGuo6NDOTk5OnLkSLRHuW4RGfTL+RuUbt26VdXV1dq2bZsOHToU7bGAsH379mnu3LlqamqK9ijX\nNSKDfrnwBqWjRo0K36AUGCyqqqpUUlIS0Z1AYM+Q/TAmoqu/NygFBkppaWm0R4A4kkE/9fcGpQCu\nL0QG/ZKYmKjW1tbwcqQ3KAVwfSEy6BduUAogElyTQb9wg1IAkeAGmQAAazhdBgCwhsgAAKwhMgAA\na4gMAMAaIgMAsIbIAJfx9a9/XR988MFlH/PWW29p/vz5uu+++5Sdna3vf//7+uyzzwZownNeeeUV\nLVq0qE/btLe366tf/aqliYBz+JwMcBVee+01bdq0SZs2bVJSUpKMMXr++eeVn5+vv/zlL4qNjY32\niEBUERkgAhs3btTrr78ul8ulG2+8UWvXrpXb7VZFRYXWrFmjpKQkSZLD4dDChQs1YcIEnT17Vt3d\n3frpT3+q5uZmnThxQqNHj1Z5ebluuukmzZ8/XykpKdq7d68+++wzpaena82aNYqJidFbb72lDRs2\nqKenR6NGjdKqVas0depU7d27V+Xl5Tpz5oxiYmK0ePFizZw5s9esp0+fVmlpqQ4ePKhgMKj09HQV\nFhbK6XRq165dqqio0A033KDk5ORo/FPiemMAXNLMmTNNfX29+drXvmYCgYAxxpjNmzeb119/3bS3\nt5spU6aYf//735fcfseOHWbNmjXh5Z/85Cdm9erVxhhjHn30UfPUU0+ZUChkTp8+be655x6zZ88e\n09raalJTU82HH35ojDFm586d5jvf+Y45ceKEycjIMP/4xz+MMcb885//NB6Px7S0tJg///nPZuHC\nhcYYY4qKiszvfvc7Y4wx3d3d5plnnjHPP/98+Ot+8sknxhhjfvnLX5opU6Zc438xoDeOZIAriI2N\n1dSpU/XAAw/I4/HI4/EoPT1dJ0+elHTujtSXkpWVpUmTJun3v/+9mpub9d577+n2228P//3MmTMV\nExOj+Ph4JSUl6eTJk9q7d69uueUW3XrrrZKkjIwMZWRkaPfu3WptbdWTTz4Z3t7hcOjjjz/u9Zxv\nv/22PvjgA7388suSpK6uLklSQ0ODpkyZoptvvlmS9PDDD2v9+vXX4F8IuDQiA1yBw+HQli1b9MEH\nH2jPnj169tlnde+996qwsFBf/vKXtW/fPt199929tlm6dKkKCgq0d+9eVVVVKS8vT16vV2PHju31\nq4BHjhzZ63mMMXI6nZ/7NQoff/yxQqGQJk+erD/96U/hv/P7/Ro3bpxee+218Lqenh79/Oc/1+TJ\nkyVJp06dksPhUF1dncwFd5FyOvnPH/bx02XAFZw5c0Y5OTmaPHmyFi1apAULFoR/4mzx4sUqLS1V\nc3OzJCkUCqmyslIfffSRbrrpJr377rt64IEH9NBDD+krX/mK3nzzTYVCocs+3/Tp0/Xpp5/qk08+\nkSS98cYbWrZsmVJSUtTc3Kz6+npJ0oEDB5SZmSm/399r+3vuuUcvvviijDE6e/asCgoKtGXLFt1x\nxx06dOiQPvroI0nnfiINsI23MsAV3HDDDZo9e7YefPBBjRo1SiNHjtSKFSskSV6vV8YY/eAHP1B3\nd7cCgYCmTZum3/72t4qNjdXjjz+ulStXhk9dpaSk6ODBg5d9vi9+8YsqLy/Xj370I4VCIcXHx6ui\nokLjxo3Txo0bVVZWpkAgIGOMysrK9KUvfUnvvfdeePvly5ertLRUXq9XwWBQd999t5544gm5XC6V\nl5frmWeekcvl0h133GHvHw34D+7CDACwhtNlAABriAwAwBoiAwCwhsgAAKwhMgAAa4gMAMAaIgMA\nsIbIAACs+T/GEdeOwpNlZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'IsCanceled', data = data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now look at the relation of each variable with the target column. We first consider categorical variables (with a small number of categories, so that they can be visualized) or \"categorical-like\" variables (variables which take less than 12 different values). For each category, we plot the empirical cancellation probability as well as the associated confidence interval. The confidence interval is important because the number of data for each category may be different. While 300 cancellations over 600 bookings and 3 cancellations over 6 bookings give both an empirical cancellation probability of 0.5, in fact in the later example we do not have enough data to conclude."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TJi1evFgWi0XXX3+95syZo2uuuUZxcXGaP3++ateuLUnq2LGjxo8fX+7HBwAAAKoKq9V0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\ngg==\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZQUFBiouLU3x8vHr37u2JXAAAwISoOwAAMCenzYnt27fryJEj2rlzp9LS0lRe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\niWyAy0IC/Rs8AgC8A3UHAAD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AYCiaEx6Sk5OjhIQExcXFaePGjUbH8XmnT5/W8OHDdfTo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\nsuobNA7xrcJ6ZmAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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t9T7vbN85qDd16lQOHz7cpPZ37Nih7Iri5eVF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\nIYQQQgghXEqCE0IIIYQQQgghhHCp/wEy058PcGKuggAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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y8vKIiYlh4MCBzJs373/atmnTpsl+zcz+2I/X\nZDJhbm6OSqW6o5hwPxtNmUymOwZ1k8lEfX29cmxhYQGgXNPY2EivXr1ITU2loaHhjtkWp06dYsuW\nLcTHxwNgbv7Hn6rbsZ0+fZoZM2YQEhLCm2++Sd++fVmyZIly3Z8LPH+9n9ux/PmcyWTC1dWVxMRE\n5VxJSQm2trbk5OTc8/6FEEKI/wqVSnVfOYi1tTWJiYnodDqcnZ0ZNmzYPcfbP+cDJpMJMzMzTCYT\njY2NpKWl0bp1awDKysqwsLC4Zz5wW0NDAw4ODqSnpyuv6fV6rK2t2bFjxx3X/jkGZ2dnjh49ipub\nGwCTJ09m8uTJwK0NzW/nP03lTSaTiQ8//FApSFRVVWEwGABISEjg3LlzHD58mI0bN7J7926lKCOE\n+IN8e4gQT4GLFy+ybt063n33XeW1M2fOoNPpcHBwYOrUqYSEhCi7eavV6jsKBs35/PPPATh9+jRX\nrlyhV69eWFtbc/78eQwGA0ajUVmz2VzfAwYMYOvWrTQ2NlJXV8fOnTvp379/s+/t7OxM586diY2N\nVQbw0tJSoqOjsbe3b7Ztfn4+r776KhMnTqRfv37k5ubS0NBwX/d8N66urhw6dIiffvoJgG+//RZf\nX19qa2sfuk8hhBDi36impqbJHOSvXnjhBaZNm8ayZcuorq6+53ibm5tLZWUlJpOJnTt34ubmhqWl\nJb1792bz5s0A3Lx5k6CgIHJzc5vNB9RqtVJA6N27N5cvXyY/Px+AH3/8EU9PT/R6PQMHDiQzMxOD\nwYDBYFC+sQ1g+vTp7N27l8zMTKXf+vp65RozM7Nm86YBAwawbds26urqMJlMLFy4kJUrV1JWVsbg\nwYNp164dISEhhIeHP1QeJ8R/gcy0EKIFqq2txc/PD7g1GFpYWDBnzhyGDBmifP2no6Mj3t7ejBw5\nkjZt2qDVapUdrN3d3Vm5cuV9zZAoKirC399fWWfZrl075UmFt7c3HTp04I033lCmSfbu3Zu1a9cy\na9asO9ZyLliwgOjoaHx8fDAajQwcOFDZIKs5SUlJrFq1irfffhu1Wo3JZMLf31/ZKLQpOp2O/fv3\n4+3tjclkws3NjYqKCn7//fd7vufddOnShaioKObMmUNjYyPm5uasX7+etm3bPlR/QgghxL9V69at\nm8xB7mbSpElkZmayfv16Pvjgg2bH2/bt2zNlyhTKy8vp27evkkskJCSwdOlSfHx8qKurQ6fT4evr\nS2lpaZP5QJcuXbCwsGDUqFGkp6eTlJREXFwcBoOBxsZG4uLisLe3JzAwkCtXrqDT6WjXrh0vvfSS\nEvuzzz7Ljh07WLNmDSkpKcCt2RK9e/dm586d98ybZsyYwYoVKwgICKChoYEePXoQGRmJpaUl06dP\nJyQkBK1Wi1qtJjo6GoBBgwaxfPlyAKZOnfroP0AhnjKqxvtZUC6EEEIIIYQQj1FycjLl5eUsWrTo\nSYcihGhBZHmIEEIIIYQQQgghWiSZaSGEEEIIIYQQQogWSWZaCCGEEEIIIYQQokWSooUQQgghhBBC\nCCFaJClaCCGEEEIIIYQQokWSooUQQgghhBBCCCFaJClaCCGEEEIIIYQQokX6P1vkTca6R7k+AAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mqXHjxoWOyKhdu7beeecdXXXVVZKk+fPna86cOcrLy9OJ\nEyfUp08f9ejRQwsXLtSqVasUFBSkffv2qVSpUnr11VdVs2ZNpaena9SoUdqzZ4+CgoLUvXt3JSUl\n6dSpUxo7dqx27NihvLw8NW/eXEOGDFFIyMVfov7zn//otddeU25urtLT0xUfH69x48Zp4sSJOnLk\niAYPHqyUlBS9/vrr6tmzp9q0aaOvvvpK7777rux2u0qXLq1hw4apfv36mjRpkg4ePKj09HQdPHhQ\n0dHReu211xQVFaVZs2bps88+U2hoqMLDw5WcnKzrr7/enQ8PAAA8Lj8/X4cOHVK5cuUcyyZPnqyV\nK1fKbreratWqGjVqlKKjo7Vy5UpNnjxZNptNwcHBGjJkiG666aZLvpfHxsbqzjvv1Pbt29WtWzdt\n3LhR77//viRp9+7devjhh7VmzRrt3btXY8eOVUZGhgoKCvTggw86jjR9++23tWzZMpUvX17VqlW7\n5P3JyMhQZmam4/7s3LlTycnJysjIkM1m0yOPPOKocebMmaPp06crKChIlSpV0ksvvaTq1atr6NCh\nKlWqlHbs2KFjx47pjjvuUPny5fXNN98oPT1dY8aMUfPmzS+Zo3fv3mrbtq3uu+8+SdJ7772njIwM\n1a5dW8uXL5fdbtdvv/2m6OhoTZgwQdHR0ZddEwF+xQBwm//7v/8zDRs2LLTsxx9/NDfddJN59913\nzU033WROnTpljDHm+++/Nz169DBZWVnGGGO+/fZb06ZNG2OMMUOGDDEfffSRMcaYXbt2mdtuu80U\nFBSYF154wXz00Ufm+PHjpnnz5uY///mPMcaYHTt2mJtvvtns37/fLFiwwDz++OOO///c+aSkJPP5\n558bY4zZtm2befnll40xxvTt29fMmDHD6f3KzMw09913nzl+/LgxxpiffvrJcT8XLFhgmjRpYg4d\nOmSMMSY5OdkMGTLEGGNM//79zauvvmqMMebkyZOmffv2Zu/evWbo0KHm008/NcYYk5+fbwYPHmym\nTJlijDEmJibGHDt2rFDugQMHmtTUVEeWZs2amZ9//tkYY8ztt99uNm/ebIwxplevXubLL780u3bt\nMvHx8Wb//v3GGGPWrVtnWrRoYU6dOmXeeecdc+eddzp+D3379jVvv/22yc/PN3Xr1jVpaWnGGGMW\nLVpkPvvsM6f7BAAAXxETE2M6dOhgOnToYFq0aGHuuOMOM3r0aHP06FFjzB/vec8++6zJy8szxhjz\n2Wefmccee8wYY8ydd95pfvrpJ2PMH7XKpEmTjDHG5Xv5okWLjDHGnDp1yjRt2tQcOXLEGGNMSkqK\nefPNN01eXp5p166d2bJlizHmjzqhbdu25qeffjKrVq0y7dq1M6dOnTJ5eXnm8ccfN7169TLG/FF3\nNG7c2HTs2NEkJCSYZs2amfvvv9/Mnj3bGGNMXl6eufPOO82KFSuMMcYcPnzY3HrrrebHH38069at\nM3fddZc5duyYY6y2bdsau91uXnjhBXPvvfea3Nxcc+TIERMTE+O4f9OmTTO9e/cutE9TU1NN+/bt\nCy1btWqV6dq1qzHGmIKCAnP77beb3bt3mwULFpiGDRuaPXv2GGOMee2118zTTz/tcj8C/o4WHOBm\nZ86cUadOnSRJBQUFqlChgl577TUdO3ZMtWrVUpkyZSRJa9as0b59+wodfnjy5EllZGTo3nvv1Suv\nvKJHH31UCxYsUNeuXRUU9Of1bDdv3qy//e1vatCggSTphhtuUOPGjfXDDz/IZrM5zda2bVslJydr\n9erVio+P16BBgyRJNptNxhin25UuXVrvv/++1q5dq71792r79u3KyspyrK9bt66qVKkiSapTp45W\nrVol6Y9TLp5//nlJUtmyZfX555877vvPP/+s+fPnO/bZpUyYMEH/+te/9P7772vPnj3Kyckp9P+f\nLzU1VXFxcfrrX/8qSWrevLmuvvpqbdmyRZJ08803O34PderU0YkTJxQcHKw2bdqoe/fuuu2223TL\nLbeoVatWl8wFAICvOHtax9atW/X444+rWbNmqlixoiTpm2++0c8//6yuXbtKkux2u7KzsyVJ7du3\n11NPPfX/27u3kKi6NoDj/3FUTNTeJkQEFXQkiyIk7eApMRhR0Q7ajBaCUlpYQgUaJpiSjkaFZJlo\nRAQmRaiQWWgIXZgVdZGpHSwKD4GpYWWBp5ntdyFt8q3s8+P98C2e39XM3qy199o369nPXgfCw8MJ\nCQkhPT0d+HVfHhgYCICTkxMGg4GGhgZSU1O5ceMGNTU19PT00NfXR25urlpmfHycZ8+e8fr1awwG\ng9pXJyQkUF1dPavuqqoqFEWhoqKCxsZGoqKiAOjp6WFiYoLIyEhgZgRrZGQkra2tjI+PExMTo05v\niY+Px2w28/btWwAiIiKws7PD1dUVR0dHwsLCAPDy8uLjx4+/fMYRERGYzWZevHjB4OAgHh4e+Pj4\n0N7eTkhIiLo+hclkUmPF+cZEQvxJJDkhxP/Z39ec+Kq+vh5HR0f1v6IobNmyRX15VxSFoaEhFi9e\nTGBgIBaLhY6ODhobG6ZJMeUAAAWHSURBVL+bD2q1Wr9LQkxPT2OxWLC3t5+VaJiamlJ/JyUlERER\nQVtbG62trZSXl9PU1IS/vz/t7e0kJyfPqvPq1auMjY0RHR1NYmIiJpOJgIAAoqKiuHPnzqw2f/Vt\nosPW1nbWffb397NkyRIURaGsrAy9Xg/MJGXmSqokJyfj5+dHWFgY0dHRPHnyZM5kiqIoP30+c93v\nqVOnePnyJffu3eP8+fNcv36dsrKyn15HCCGE+N2sXLmSI0eOkJOTw4oVK/Dw8EBRFNLS0ti5cycA\nk5OTfPr0CYBDhw6RkJBAW1sb9fX1XLx4kdra2l/25d/GPCaTiby8PPR6PXq9Hk9PT7q7u3F2dp4V\nM71//x5nZ2dOnDgxq5/XarU/bIuNjQ2ZmZk8fvyYnJwcKisr54yRvk5r/dE5AHt7+1nn5ju1QqvV\nkpiYSG1tLUNDQ7M+QH3bBkVR1P/zjYmE+JPIVqJC/EuEhoZy8+ZNhoaGALhy5QopKSnqeaPRSGFh\nIX5+fri7u88q6+/vz5s3b+jo6ABm5lY+evSIdevWodPpePXqFRMTE0xNTdHc3KyWS0pK4vnz58TH\nx1NYWMjo6CjDw8MkJiby8OFDGhoa1GCgq6uLM2fOsGzZMrq6utDpdOzbt4/Q0FA1MWG1WudsY1BQ\nEHV1dQB8/vyZlJQUenp6CA0N5dKlS0xPTzM5OUlGRgaXL1/+YR2jo6N0dnaSlZVFZGQk7969o6+v\nTw0wtFqtGlR8e927d+/S398PwP379xkYGFBHmvzIyMgI4eHh/PXXX6SmpnLw4EE6OzvnbJ8QQgjx\nO4qNjWX16tWUlJQAMzFJbW0tX758AWbWezh8+DAWi4VNmzYxNjbGjh07yM/Pp7u7m8nJyXn15f7+\n/gCcO3cOo9EIgLe396wPOgMDA8TGxtLV1cXGjRtpampidHQURVF+uehkfn4+bW1ttLS04OPjg62t\nLbdv3wZmFiZvbm4mODiYsLAwbt26xcjICAB1dXX/1ZoW82E0GmlpaeHp06cYDAb1+IMHDxgcHARm\nPv5EREQAzOs5CvGnkZETQvxLhIaGkp6ezq5du9BoNDg5OVFeXq5my7du3UppaSmlpaXfldXpdJSV\nlVFYWMj4+DgajYaSkhK8vb3x9PRk7dq1REdH4+rqyvr16+nu7gYgKyuL4uJiTp8+jUajITMzU92K\nq7q6mpMnT1JVVYWNjQ2LFi3CbDYTEhLC2NgYtbW1REVFodFo1CRIb2/vnG08evQoBQUFxMXFMT09\nzd69e9UtP81mM3FxcUxNTREcHExaWtoP63BxcWHPnj1s27YNR0dH3NzcWLNmDb29vQQFBWEwGMjO\nzqagoEAt4+vrS35+PpmZmVitVhwcHKisrMTZ2fmn96rT6cjIyCA1NRUHBwe0Wi1FRUVztk8IIYT4\nXeXl5bF582ZaW1sxGo0MDg5iMpnQaDS4u7tz/PhxbG1tyc3NJSsrSx0NWVxcjL29/bz6cph5aa+o\nqFC3KLe3t6eiogKz2cyFCxewWCwcOHCAgIAAALq7u0lISMDFxYXly5fz4cOHn9bt5eVFeno6JSUl\nhIWFUVFRQVFREWfPnsVqtbJ//342bNgAQGpqKikpKSiKgk6nU+Oef8rSpUtZtWoVer0eOzs79bib\nmxvZ2dkMDw/j6+vLsWPHAOb9HIX4k2im5xoLLYQQQgghhBDifzIyMsL27dupqalRR77+fSc1IcQM\nmdYhhBBCCCGEEP+wa9euERMTw+7du7+bkiuE+J6MnBBCCCGEEEIIIcSCkpETQgghhBBCCCGEWFCS\nnBBCCCGEEEIIIcSCkuSEEEIIIYQQQgghFpQkJ4QQQgghhBBCCLGgJDkhhBBCCCGEEEKIBSXJCSGE\nEEIIIYQQQiyo/wAaYRK7flKbBgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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83qkjXJRQhIxBrR4rwSOUjqLk7NT0cTcksa8oGR/7x0A3JClcUXJO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\nQgghhBBCCOFX/wJbaHDxLIv1DAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Ws7Ky5Ovra8OKgFtn//79mjBhgl577TX169fP1uU4rG+/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\niFfSfwBsRx1fbdVhVQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sort data by ArrivalDateMonth to make sure that months are ordered correctly on the x-axis.\n",
    "data_sorted = data.copy()\n",
    "months = [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \n",
    "          \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"]\n",
    "data_sorted['ArrivalDateMonth'] = pd.Categorical(data_sorted['ArrivalDateMonth'], categories=months, ordered=True)\n",
    "data_sorted = data_sorted.sort_values(by = 'ArrivalDateMonth')\n",
    "\n",
    "# Categorical-like variables with less than 12 categories\n",
    "plt_cat_cols = [col for col in data.columns if (len(set(data[col])) <= 12) and (col != \"IsCanceled\")]\n",
    "\n",
    "# Plotting probability of cancellation vs categories, with confidence interval.\n",
    "# The iterator makes sure that each row contains two figures, without having to treat odd/even cases manually.\n",
    "iterator_ax = 0\n",
    "for i, col in enumerate(plt_cat_cols):\n",
    "    if i ==0:\n",
    "        fig, current_ax = plt.subplots(figsize = (12,5))\n",
    "    else:\n",
    "        try:\n",
    "            current_ax = next(iterator_ax)\n",
    "        except:\n",
    "            plt.show()\n",
    "            fig, ax = plt.subplots(1, 2, figsize = (18, 5))\n",
    "            iterator_ax = iter(ax)\n",
    "            current_ax = next(iterator_ax)\n",
    "    sns.barplot(x = col, y = 'IsCanceled', data = data_sorted, ax = current_ax)\n",
    "    current_ax.set(ylabel = \"Cancel Proba with Conf. Interval\")\n",
    "try:\n",
    "    while True:\n",
    "        current_ax = next(iterator_ax)\n",
    "        current_ax.set_axis_off()\n",
    "except:\n",
    "    pass\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe some normal patterns here:\n",
    "* In summer months, the probability of cancellation is higher.\n",
    "* Trasient customers are more likely to cancel than group customers or contract customers (variable \"CustomerType\").\n",
    "* If the customer has more special requests, he is less likely to cancel.\n",
    "\n",
    "The interesting thing here is the variable DepositType, where we see that customers who are asked to make a non-refundable deposit are in fact more likely to cancel. This may mean that the hotel does in fact a good job at identifying customers that are likely to cancel, but the deposit amount may not be high enough to prevent the cancellation from happening. In addition, more than 20% of \"no deposit\" customers still cancel their bookings, so machine learning may be useful to help the hotel reduce this number. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us look at non-categorical variables. We plot the probability of cancelation versus values of these variables, using logistic regression. Due to the cost of computation, confidence intervals are not plotted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Y8vl82r17t2644QazZwNQS4eOluj//rVOX+cckCTFu2I0/kf9dOX53RVj5xCOSEEWA4C1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\nTnePTVXXlLp9LQoAAAAAAKga5UQV/H5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d68Pc0AX4K3Tq1IkjR46oy/n5+eo03cbohx9+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\n",
      "text/plain": [
       "<Figure size 1296x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2oTHRrUsM9990Nk/eNYlRg1PUmGhHVItFWs9dcpgj/3yI4iV/w3DX4XPG0HXW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\n",
      "text/plain": [
       "<Figure size 1296x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7GADcgc2VEyaTSXfccYfKysoUHx+v5ORkXX/99Y7I1mk+Xh7q2yNYh3MqlJ5Z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\nQgghhBBCeJQkJ4QQQgghhBBCCOFR/w+Z3ns2yVtxRwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AAAAAAJiKMgIAAAAAAJiKMgIAAAAAAJiKMgIAAAAAAJiKMgIAAAAA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\n",
      "text/plain": [
       "<Figure size 1296x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# non-categorical variables\n",
    "plt_reg_cols = [col for col in data.columns if (len(set(data[col])) >= 12) and (str(data[col].dtype) != \"object\")]\n",
    "\n",
    "# Plot cancellation probability versus values of each variable.\n",
    "# The iterator makes sure that each line contains 3 plots, without having to treat mod = 0, mod = 1 and mod = 2 cases manually\n",
    "iterator_ax = 0\n",
    "for col in plt_reg_cols:\n",
    "    try:\n",
    "        current_ax = next(iterator_ax)\n",
    "    except:\n",
    "        plt.show()\n",
    "        fig, ax = plt.subplots(1, 3, figsize = (18, 5))\n",
    "        iterator_ax = iter(ax)\n",
    "        current_ax = next(iterator_ax)\n",
    "    sns.regplot(x = col, y = 'IsCanceled', data = data, scatter_kws = {'s':0}, ax = current_ax, logistic = True, ci = None)\n",
    "    current_ax.set(ylabel = 'Estimated Cancel Proba')\n",
    "try:\n",
    "    while True:\n",
    "        current_ax = next(iterator_ax)\n",
    "        current_ax.set_axis_off()\n",
    "except:\n",
    "    pass\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, let us summary some important patterns that we discovered:\n",
    "* The longer the customer reserved a hotel before he arrives, the more he is likely to cancel (variable LeadTime)\n",
    "* If a customer had previously made more than 5 bookings without cancellation, then almost surely he won't cancel his future bookings.\n",
    "* The busier the hotel is, the higher the proportion of cancelled bookings will be (variable ADR - Average Daily Rate)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will look at the importance of each feature using the Mutual Information criteria. Look at the help file for more information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import mutual_info_classif\n",
    "#help(mutual_info_classif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function mutual_info_classif can treat categorical variables, but they only take a numpy array. So we need to convert strings to numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "obj_cols = data.select_dtypes(['object']).columns\n",
    "\n",
    "data_num = data.copy()\n",
    "data_num[obj_cols] = data_num[obj_cols].apply(lambda t: t.astype('category'))\n",
    "data_num[obj_cols] = data_num[obj_cols].apply(lambda t: t.cat.codes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The delicate point is that mutual_info_classif distinguishes between discrete and continuous variables. In fact, discrete variables do not have to be categorical. For example, the number of bookings is a discrete variable (because it takes value in $\\mathbb{Z}$ but it is not categorical variable by nature. Again, see the documentation of the function mutual_info_classif. The thing to note here is that in our case, only \"ADR\" is truly a continuous variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_num_except_target = data_num.iloc[:,1:]\n",
    "discrete_index = np.array(data_num_except_target.columns != 'ADR')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we have been waiting long enough... Let us see the importance of each feature using the Mutual information criteria."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/haidang/anaconda/lib/python3.6/site-packages/sklearn/feature_selection/mutual_info_.py:251: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if discrete_features == 'auto':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a18b3c780>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vv/66ZRxfX1/s7e0pX748DRo0IC4ujt27d3P79m3CwsIAyMjI4OTJk1SqVOlp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\nT/d1CtJk4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mi = mutual_info_classif(data_num_except_target.values, data_num['IsCanceled'].values, discrete_features=discrete_index)\n",
    "mi = pd.DataFrame({'Feature': data_num_except_target.columns.values, 'Mutual Information with target': mi}).sort_values(by = 'Mutual Information with target', ascending = False)\n",
    "sns.barplot(x = 'Mutual Information with target', y = 'Feature', data = mi, color = 'b', ax = plt.subplots(figsize = (10, 8))[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Workflow <a name=\"ph3\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The workflow is: first, we use a feature extractor to do preprocessing and feature engineering. Then we pass the processed data to a classifier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./img/FE.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The usual way to treat categorical variables is to use one hot encoding, for example with pandas' get_dummies function. The difficulty here is that there are categorical variables with a high number of categories, and each subset of the train set (that is created for example by the cross validation procedure) may not contain all the categories of a categorical variables. Therefore, naive one hot encoding or dummy encoding may create train and test dataframes of different sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "178\n",
      "139\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('data/train.csv')\n",
    "test = pd.read_csv('data/test.csv')\n",
    "print(len(set(data['Agent']))) # There are 178 different agents on train data...\n",
    "print(len(set(test['Agent']))) # but only 139 different agents on test data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why does this happen? Let us look at the occurence table of the variable \"Agent\" for the train data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Occurences</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Occurences\n",
       "         34           1\n",
       "        114           1\n",
       "        182           1\n",
       "        476           1\n",
       "        167           1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "occurences = pd.DataFrame(Counter(data['Agent']), index=['Occurences']).T\n",
    "occurences.sort_values(by = 'Occurences').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is clear. There are some infrequent categories that occured only once in the train dataset (which contains about 30000 rows). If we split train into two parts for cross-validation, it is sure that these categories only occur in one part. Here we propose one possible solution which consists of grouping all infrequent values together:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def replace_infrequent_values(X, threshold, rvalue):\n",
    "    \"\"\"\n",
    "    In an iterable X, replace values that occur with frequent less than a certain threshold with a user-definied rvalue.\n",
    "    \"\"\"\n",
    "    X = X.copy()\n",
    "    occ = Counter(X)\n",
    "    N = len(X)\n",
    "    for key in occ.keys():\n",
    "        occ[key] /= N\n",
    "    infrequent_values = [key for key in occ.keys() if occ[key] < threshold]\n",
    "    for i, x in enumerate(X):\n",
    "        if x in infrequent_values:\n",
    "            X[i] = rvalue\n",
    "    return X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we remove all categories of the variable \"Agent\" which occur in less than 5% of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "data['Agent'] = replace_infrequent_values(data['Agent'], 0.05, -1)\n",
    "test['Agent'] = replace_infrequent_values(test['Agent'], 0.05, -1)\n",
    "print(len(set(test['Agent'])))\n",
    "print(len(set(data['Agent'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems to be working. It can be proved that with high probability, this procedure leads to a dataframe of consistent size. However, here, to make sure that the starting kit __always__ run, we do not use dummy encoding but rather keep the dataframe as it is (after marking categorical variables as such) and use a classifier that can deal with pandas dataframe directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting submissions/starting_kit/feature_extractor.py\n"
     ]
    }
   ],
   "source": [
    "%%file submissions/starting_kit/feature_extractor.py\n",
    "import pandas as pd\n",
    "class FeatureExtractor:\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    \n",
    "    def fit(self, X_df, y):\n",
    "        pass\n",
    "    \n",
    "    def transform(self, X_df):\n",
    "        # Handle mising data\n",
    "        X_df_2 = X_df.fillna(-1)\n",
    "        \n",
    "        # Transform categorical features into pandas's categorical type\n",
    "        non_numeric_columns = ['ArrivalDateMonth', 'Meal', 'Country', 'MarketSegment', 'DistributionChannel', \n",
    "                               'ReservedRoomType', 'AssignedRoomType', 'DepositType', 'Agent', 'Company', \n",
    "                               'CustomerType']\n",
    "        X_df_2[non_numeric_columns] = X_df_2[non_numeric_columns].astype('category')\n",
    "        \n",
    "        return X_df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting submissions/starting_kit/classifier.py\n"
     ]
    }
   ],
   "source": [
    "%%file submissions/starting_kit/classifier.py\n",
    "from sklearn.base import BaseEstimator\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import make_pipeline\n",
    "import lightgbm as lgb\n",
    "import numpy as np\n",
    "\n",
    "class Classifier(BaseEstimator):\n",
    "    def __init__(self):\n",
    "        self.model = make_pipeline(StandardScaler(), LogisticRegression())\n",
    "    \n",
    "    def fit(self, X, y):\n",
    "        \n",
    "        # Train using traditional Gradient Boosting Decision Tree (of lightgbm package)\n",
    "        # Train data set\n",
    "        train_data = lgb.Dataset(X, label=y) \n",
    "        \n",
    "        # Parameters\n",
    "        param = {'boosting':'gbdt', 'num_leaves':31, 'objective':'binary',\n",
    "            'learning_rate':.05, 'max_bin':255, 'min_data_in_leaf':100,\n",
    "            'bagging_freq':1, 'bagging_fraction':1., 'lambda_l1':1, 'lambda_l2':1,\n",
    "            'verbose':-1}\n",
    "        param['metric'] = 'binary_logloss'\n",
    "        \n",
    "        num_round = 600\n",
    "        \n",
    "        # Train\n",
    "        self.model = lgb.train(param, train_data, num_round)\n",
    "        \n",
    "    def predict_proba(self, X):\n",
    "        pred = self.model.predict(X)\n",
    "        return np.array([1 - pred, pred]).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submission <a name=\"ph4\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is important to test your submission locally before submitting them to [ramp.studio](http://www.ramp.studio). The data set we use at the backend is usually different from what you find in the starting kit, so the score may be different.\n",
    "\n",
    "The usual way to work with RAMP is to explore solutions, add feature transformations, select models, perhaps do some AutoML/hyperopt, etc., locally, and checking them with ramp_test_submission."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "#os.environ[\"KMP_DUPLICATE_LIB_OK\"] = \"TRUE\" # for MacOS compatibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[38;5;178m\u001b[1mTesting Hotel booking cancellation prediction\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mReading train and test files from ./data ...\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mReading cv ...\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mTraining ./submissions/starting_kit ...\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mCV fold 0\u001b[0m\n",
      "/Users/haidang/anaconda/lib/python3.6/site-packages/lightgbm/__init__.py:46: UserWarning: Starting from version 2.2.1, the library file in distribution wheels for macOS is built by the Apple Clang (Xcode_9.4.1) compiler.\n",
      "This means that in case of installing LightGBM from PyPI via the ``pip install lightgbm`` command, you don't need to install the gcc compiler anymore.\n",
      "Instead of that, you need to install the OpenMP library, which is required for running LightGBM on the system with the Apple Clang compiler.\n",
      "You can install the OpenMP library by the following command: ``brew install libomp``.\n",
      "  \"You can install the OpenMP library by the following command: ``brew install libomp``.\", UserWarning)\n",
      "\t\u001b[38;5;178m\u001b[1mscore   mf1  prec   rec\u001b[0m\n",
      "\t\u001b[38;5;10m\u001b[1mtrain\u001b[0m  \u001b[38;5;10m\u001b[1m0.91\u001b[0m  \u001b[38;5;150m0.92\u001b[0m  \u001b[38;5;150m0.90\u001b[0m\n",
      "\t\u001b[38;5;12m\u001b[1mvalid\u001b[0m  \u001b[38;5;12m\u001b[1m\u001b[38;5;105m0.81\u001b[0m\u001b[0m  \u001b[38;5;105m0.82\u001b[0m  \u001b[38;5;12m\u001b[1m\u001b[38;5;105m0.81\u001b[0m\u001b[0m\n",
      "\t\u001b[38;5;1m\u001b[1mtest\u001b[0m   \u001b[38;5;1m\u001b[1m0.82\u001b[0m  \u001b[38;5;218m0.85\u001b[0m  \u001b[38;5;218m0.81\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mCV fold 1\u001b[0m\n",
      "\t\u001b[38;5;178m\u001b[1mscore   mf1  prec   rec\u001b[0m\n",
      "\t\u001b[38;5;10m\u001b[1mtrain\u001b[0m  \u001b[38;5;10m\u001b[1m0.91\u001b[0m  \u001b[38;5;150m0.92\u001b[0m  \u001b[38;5;150m0.90\u001b[0m\n",
      "\t\u001b[38;5;12m\u001b[1mvalid\u001b[0m  \u001b[38;5;12m\u001b[1m\u001b[38;5;105m0.81\u001b[0m\u001b[0m  \u001b[38;5;105m0.83\u001b[0m  \u001b[38;5;12m\u001b[1m\u001b[38;5;105m0.81\u001b[0m\u001b[0m\n",
      "\t\u001b[38;5;1m\u001b[1mtest\u001b[0m   \u001b[38;5;1m\u001b[1m0.82\u001b[0m  \u001b[38;5;218m0.84\u001b[0m  \u001b[38;5;218m0.81\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mCV fold 2\u001b[0m\n",
      "\t\u001b[38;5;178m\u001b[1mscore   mf1  prec   rec\u001b[0m\n",
      "\t\u001b[38;5;10m\u001b[1mtrain\u001b[0m  \u001b[38;5;10m\u001b[1m0.91\u001b[0m  \u001b[38;5;150m0.92\u001b[0m  \u001b[38;5;150m0.90\u001b[0m\n",
      "\t\u001b[38;5;12m\u001b[1mvalid\u001b[0m  \u001b[38;5;12m\u001b[1m0.80\u001b[0m  \u001b[38;5;105m0.83\u001b[0m  \u001b[38;5;105m0.79\u001b[0m\n",
      "\t\u001b[38;5;1m\u001b[1mtest\u001b[0m   \u001b[38;5;1m\u001b[1m0.82\u001b[0m  \u001b[38;5;218m0.84\u001b[0m  \u001b[38;5;218m0.81\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mCV fold 3\u001b[0m\n",
      "\t\u001b[38;5;178m\u001b[1mscore   mf1  prec   rec\u001b[0m\n",
      "\t\u001b[38;5;10m\u001b[1mtrain\u001b[0m  \u001b[38;5;10m\u001b[1m\u001b[38;5;150m0.90\u001b[0m\u001b[0m  \u001b[38;5;150m0.92\u001b[0m  \u001b[38;5;10m\u001b[1m\u001b[38;5;150m0.90\u001b[0m\u001b[0m\n",
      "\t\u001b[38;5;12m\u001b[1mvalid\u001b[0m  \u001b[38;5;12m\u001b[1m0.82\u001b[0m  \u001b[38;5;105m0.85\u001b[0m  \u001b[38;5;105m0.81\u001b[0m\n",
      "\t\u001b[38;5;1m\u001b[1mtest\u001b[0m   \u001b[38;5;1m\u001b[1m0.82\u001b[0m  \u001b[38;5;218m0.84\u001b[0m  \u001b[38;5;218m0.81\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mCV fold 4\u001b[0m\n",
      "\t\u001b[38;5;178m\u001b[1mscore   mf1  prec   rec\u001b[0m\n",
      "\t\u001b[38;5;10m\u001b[1mtrain\u001b[0m  \u001b[38;5;10m\u001b[1m0.90\u001b[0m  \u001b[38;5;150m0.92\u001b[0m  \u001b[38;5;150m0.89\u001b[0m\n",
      "\t\u001b[38;5;12m\u001b[1mvalid\u001b[0m  \u001b[38;5;12m\u001b[1m0.81\u001b[0m  \u001b[38;5;105m0.83\u001b[0m  \u001b[38;5;105m0.80\u001b[0m\n",
      "\t\u001b[38;5;1m\u001b[1mtest\u001b[0m   \u001b[38;5;1m\u001b[1m0.82\u001b[0m  \u001b[38;5;218m0.84\u001b[0m  \u001b[38;5;218m0.81\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mCV fold 5\u001b[0m\n",
      "\t\u001b[38;5;178m\u001b[1mscore   mf1  prec   rec\u001b[0m\n",
      "\t\u001b[38;5;10m\u001b[1mtrain\u001b[0m  \u001b[38;5;10m\u001b[1m\u001b[38;5;150m0.90\u001b[0m\u001b[0m  \u001b[38;5;150m0.92\u001b[0m  \u001b[38;5;10m\u001b[1m\u001b[38;5;150m0.90\u001b[0m\u001b[0m\n",
      "\t\u001b[38;5;12m\u001b[1mvalid\u001b[0m  \u001b[38;5;12m\u001b[1m0.81\u001b[0m  \u001b[38;5;105m0.83\u001b[0m  \u001b[38;5;105m0.80\u001b[0m\n",
      "\t\u001b[38;5;1m\u001b[1mtest\u001b[0m   \u001b[38;5;1m\u001b[1m0.82\u001b[0m  \u001b[38;5;218m0.84\u001b[0m  \u001b[38;5;218m0.81\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mCV fold 6\u001b[0m\n",
      "\t\u001b[38;5;178m\u001b[1mscore   mf1  prec   rec\u001b[0m\n",
      "\t\u001b[38;5;10m\u001b[1mtrain\u001b[0m  \u001b[38;5;10m\u001b[1m\u001b[38;5;150m0.90\u001b[0m\u001b[0m  \u001b[38;5;150m0.92\u001b[0m  \u001b[38;5;10m\u001b[1m\u001b[38;5;150m0.90\u001b[0m\u001b[0m\n",
      "\t\u001b[38;5;12m\u001b[1mvalid\u001b[0m  \u001b[38;5;12m\u001b[1m0.79\u001b[0m  \u001b[38;5;105m0.84\u001b[0m  \u001b[38;5;105m0.78\u001b[0m\n",
      "\t\u001b[38;5;1m\u001b[1mtest\u001b[0m   \u001b[38;5;1m\u001b[1m\u001b[38;5;218m0.81\u001b[0m\u001b[0m  \u001b[38;5;218m0.84\u001b[0m  \u001b[38;5;1m\u001b[1m\u001b[38;5;218m0.81\u001b[0m\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mCV fold 7\u001b[0m\n",
      "\t\u001b[38;5;178m\u001b[1mscore   mf1  prec   rec\u001b[0m\n",
      "\t\u001b[38;5;10m\u001b[1mtrain\u001b[0m  \u001b[38;5;10m\u001b[1m\u001b[38;5;150m0.90\u001b[0m\u001b[0m  \u001b[38;5;150m0.92\u001b[0m  \u001b[38;5;10m\u001b[1m\u001b[38;5;150m0.90\u001b[0m\u001b[0m\n",
      "\t\u001b[38;5;12m\u001b[1mvalid\u001b[0m  \u001b[38;5;12m\u001b[1m0.80\u001b[0m  \u001b[38;5;105m0.83\u001b[0m  \u001b[38;5;105m0.79\u001b[0m\n",
      "\t\u001b[38;5;1m\u001b[1mtest\u001b[0m   \u001b[38;5;1m\u001b[1m\u001b[38;5;218m0.82\u001b[0m\u001b[0m  \u001b[38;5;218m0.84\u001b[0m  \u001b[38;5;1m\u001b[1m\u001b[38;5;218m0.82\u001b[0m\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1m----------------------------\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mMean CV scores\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1m----------------------------\u001b[0m\n",
      "\t\u001b[38;5;178m\u001b[1mscore           mf1          prec           rec\u001b[0m\n",
      "\t\u001b[38;5;10m\u001b[1mtrain\u001b[0m   \u001b[38;5;10m\u001b[1m\u001b[38;5;150m0.9\u001b[0m\u001b[0m \u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m±\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;150m\u001b[38;5;150m0.003\u001b[0m\u001b[0m  \u001b[38;5;10m\u001b[1m\u001b[38;5;150m0.9\u001b[0m\u001b[0m2 \u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m±\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;150m0.002\u001b[0m   \u001b[38;5;10m\u001b[1m\u001b[38;5;150m0.9\u001b[0m\u001b[0m \u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m±\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;150m\u001b[38;5;150m0.003\u001b[0m\u001b[0m\n",
      "\t\u001b[38;5;12m\u001b[1mvalid\u001b[0m  \u001b[38;5;12m\u001b[1m\u001b[38;5;105m0.8\u001b[0m1\u001b[0m \u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m±\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;105m0.007\u001b[0m  \u001b[38;5;105m\u001b[38;5;105m0.8\u001b[0m3\u001b[0m \u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m±\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;105m0.006\u001b[0m    \u001b[38;5;105m0.8\u001b[0m \u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m±\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;105m0.01\u001b[0m\n",
      "\t\u001b[38;5;1m\u001b[1mtest\u001b[0m   \u001b[38;5;1m\u001b[1m0.82\u001b[0m \u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m±\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m0.003\u001b[0m\u001b[0m\u001b[0m  \u001b[38;5;218m0.84\u001b[0m \u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m±\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m0.003\u001b[0m\u001b[0m\u001b[0m  \u001b[38;5;218m0.81\u001b[0m \u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m±\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m0.003\u001b[0m\u001b[0m\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1m----------------------------\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mBagged scores\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1m----------------------------\u001b[0m\n",
      "\t\u001b[38;5;178m\u001b[1mscore   mf1\u001b[0m\n",
      "\t\u001b[38;5;12m\u001b[1mvalid\u001b[0m  \u001b[38;5;12m\u001b[1m0.81\u001b[0m\n",
      "\t\u001b[38;5;1m\u001b[1mtest\u001b[0m   \u001b[38;5;1m\u001b[1m0.82\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!ramp_test_submission --submission starting_kit"
   ]
  }
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