Club.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"data/train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv(\"data/test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "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>reservation_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>checkin_date</th>\n",
       "      <th>checkout_date</th>\n",
       "      <th>channel_code</th>\n",
       "      <th>main_product_code</th>\n",
       "      <th>numberofadults</th>\n",
       "      <th>numberofchildren</th>\n",
       "      <th>persontravellingid</th>\n",
       "      <th>resort_region_code</th>\n",
       "      <th>resort_type_code</th>\n",
       "      <th>room_type_booked_code</th>\n",
       "      <th>roomnights</th>\n",
       "      <th>season_holidayed_code</th>\n",
       "      <th>state_code_residence</th>\n",
       "      <th>state_code_resort</th>\n",
       "      <th>total_pax</th>\n",
       "      <th>member_age_buckets</th>\n",
       "      <th>booking_type_code</th>\n",
       "      <th>memberid</th>\n",
       "      <th>cluster_code</th>\n",
       "      <th>reservationstatusid_code</th>\n",
       "      <th>resort_id</th>\n",
       "      <th>amount_spent_per_room_night_scaled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>07659f3758d8aee27f5a7e2887adeacb67021cb95ada1b...</td>\n",
       "      <td>05/04/2018</td>\n",
       "      <td>05/04/2018</td>\n",
       "      <td>06/04/2018</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>3d1539e56495b6991f0a3ef5a61ca3d03ce4fff7380e9a...</td>\n",
       "      <td>F</td>\n",
       "      <td>C</td>\n",
       "      <td>4e07408562bedb8b60ce05c1decfe3ad16b72230967de0...</td>\n",
       "      <td>7.706428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03930f033646d073462b35d411616323597715ac4fc398...</td>\n",
       "      <td>23/01/2015</td>\n",
       "      <td>11/04/2015</td>\n",
       "      <td>16/04/2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>3d1539e56495b6991f0a3ef5a61ca3d03ce4fff7380e9a...</td>\n",
       "      <td>F</td>\n",
       "      <td>A</td>\n",
       "      <td>39fa9ec190eee7b6f4dff1100d6343e10918d044c75eac...</td>\n",
       "      <td>6.662563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>d145a32920e6587ad95bfe299d80c0affa268220535aaf...</td>\n",
       "      <td>28/01/2015</td>\n",
       "      <td>01/02/2015</td>\n",
       "      <td>05/02/2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>3d1539e56495b6991f0a3ef5a61ca3d03ce4fff7380e9a...</td>\n",
       "      <td>E</td>\n",
       "      <td>A</td>\n",
       "      <td>535fa30d7e25dd8a49f1536779734ec8286108d115da50...</td>\n",
       "      <td>7.871602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cfd77f44811ed62f25a220b53324cdbafc662a4c9e5f04...</td>\n",
       "      <td>02/05/2015</td>\n",
       "      <td>11/06/2015</td>\n",
       "      <td>16/06/2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>3d1539e56495b6991f0a3ef5a61ca3d03ce4fff7380e9a...</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>d4735e3a265e16eee03f59718b9b5d03019c07d8b6c51f...</td>\n",
       "      <td>5.344943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>937cff9e4dcfc2459620153dfc8b9962ac22bea67dfb29...</td>\n",
       "      <td>02/09/2015</td>\n",
       "      <td>14/12/2015</td>\n",
       "      <td>19/12/2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>3d1539e56495b6991f0a3ef5a61ca3d03ce4fff7380e9a...</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>d4735e3a265e16eee03f59718b9b5d03019c07d8b6c51f...</td>\n",
       "      <td>7.059346</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                      reservation_id booking_date  \\\n",
       "0  07659f3758d8aee27f5a7e2887adeacb67021cb95ada1b...   05/04/2018   \n",
       "1  03930f033646d073462b35d411616323597715ac4fc398...   23/01/2015   \n",
       "2  d145a32920e6587ad95bfe299d80c0affa268220535aaf...   28/01/2015   \n",
       "3  cfd77f44811ed62f25a220b53324cdbafc662a4c9e5f04...   02/05/2015   \n",
       "4  937cff9e4dcfc2459620153dfc8b9962ac22bea67dfb29...   02/09/2015   \n",
       "\n",
       "  checkin_date checkout_date  channel_code  main_product_code  numberofadults  \\\n",
       "0   05/04/2018    06/04/2018             3                  1               2   \n",
       "1   11/04/2015    16/04/2015             1                  1               2   \n",
       "2   01/02/2015    05/02/2015             1                  1               2   \n",
       "3   11/06/2015    16/06/2015             1                  1               2   \n",
       "4   14/12/2015    19/12/2015             1                  1               2   \n",
       "\n",
       "   numberofchildren  persontravellingid  resort_region_code  resort_type_code  \\\n",
       "0                 0                  46                   3                 3   \n",
       "1                 0                  46                   3                 3   \n",
       "2                 0                  47                   1                 5   \n",
       "3                 2                  46                   2                 2   \n",
       "4                 0                  46                   2                 2   \n",
       "\n",
       "   room_type_booked_code  roomnights  season_holidayed_code  \\\n",
       "0                      3           1                    2.0   \n",
       "1                      4           5                    2.0   \n",
       "2                      4           4                    2.0   \n",
       "3                      3           5                    2.0   \n",
       "4                      4           5                    2.0   \n",
       "\n",
       "   state_code_residence  state_code_resort  total_pax member_age_buckets  \\\n",
       "0                   7.0                  3          3                  F   \n",
       "1                   7.0                  5          2                  F   \n",
       "2                   7.0                  1          2                  F   \n",
       "3                   7.0                  2          2                  F   \n",
       "4                   7.0                  2          2                  F   \n",
       "\n",
       "   booking_type_code                                           memberid  \\\n",
       "0                  1  3d1539e56495b6991f0a3ef5a61ca3d03ce4fff7380e9a...   \n",
       "1                  1  3d1539e56495b6991f0a3ef5a61ca3d03ce4fff7380e9a...   \n",
       "2                  1  3d1539e56495b6991f0a3ef5a61ca3d03ce4fff7380e9a...   \n",
       "3                  1  3d1539e56495b6991f0a3ef5a61ca3d03ce4fff7380e9a...   \n",
       "4                  1  3d1539e56495b6991f0a3ef5a61ca3d03ce4fff7380e9a...   \n",
       "\n",
       "  cluster_code reservationstatusid_code  \\\n",
       "0            F                        C   \n",
       "1            F                        A   \n",
       "2            E                        A   \n",
       "3            D                        A   \n",
       "4            D                        A   \n",
       "\n",
       "                                           resort_id  \\\n",
       "0  4e07408562bedb8b60ce05c1decfe3ad16b72230967de0...   \n",
       "1  39fa9ec190eee7b6f4dff1100d6343e10918d044c75eac...   \n",
       "2  535fa30d7e25dd8a49f1536779734ec8286108d115da50...   \n",
       "3  d4735e3a265e16eee03f59718b9b5d03019c07d8b6c51f...   \n",
       "4  d4735e3a265e16eee03f59718b9b5d03019c07d8b6c51f...   \n",
       "\n",
       "   amount_spent_per_room_night_scaled  \n",
       "0                            7.706428  \n",
       "1                            6.662563  \n",
       "2                            7.871602  \n",
       "3                            5.344943  \n",
       "4                            7.059346  "
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reservation_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>checkin_date</th>\n",
       "      <th>checkout_date</th>\n",
       "      <th>channel_code</th>\n",
       "      <th>main_product_code</th>\n",
       "      <th>numberofadults</th>\n",
       "      <th>numberofchildren</th>\n",
       "      <th>persontravellingid</th>\n",
       "      <th>resort_region_code</th>\n",
       "      <th>resort_type_code</th>\n",
       "      <th>room_type_booked_code</th>\n",
       "      <th>roomnights</th>\n",
       "      <th>season_holidayed_code</th>\n",
       "      <th>state_code_residence</th>\n",
       "      <th>state_code_resort</th>\n",
       "      <th>total_pax</th>\n",
       "      <th>member_age_buckets</th>\n",
       "      <th>booking_type_code</th>\n",
       "      <th>memberid</th>\n",
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       "      <th>0</th>\n",
       "      <td>7dae1ce6bc8f69481328f2be5c4943077dad5598b5f66d...</td>\n",
       "      <td>05/04/2018</td>\n",
       "      <td>05/04/2018</td>\n",
       "      <td>06/04/2018</td>\n",
       "      <td>3</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>H</td>\n",
       "      <td>1</td>\n",
       "      <td>2114944930dcc42ce5b9b50ae965cf8a9c04e46be63d84...</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>9f14025af0065b30e47e23ebb3b491d39ae8ed17d33739...</td>\n",
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       "      <th>1</th>\n",
       "      <td>fe0d4e444e1818436c88f72f1cf800536c2f785e59baeb...</td>\n",
       "      <td>24/05/2015</td>\n",
       "      <td>23/06/2015</td>\n",
       "      <td>27/06/2015</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>2114944930dcc42ce5b9b50ae965cf8a9c04e46be63d84...</td>\n",
       "      <td>F</td>\n",
       "      <td>A</td>\n",
       "      <td>e7f6c011776e8db7cd330b54174fd76f7d0216b612387a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>540bd4285ad8168e8388f84ee74a82cd4f97dc0a404d3e...</td>\n",
       "      <td>21/07/2015</td>\n",
       "      <td>07/08/2015</td>\n",
       "      <td>10/08/2015</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>H</td>\n",
       "      <td>1</td>\n",
       "      <td>2114944930dcc42ce5b9b50ae965cf8a9c04e46be63d84...</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>d4735e3a265e16eee03f59718b9b5d03019c07d8b6c51f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>09593c907ae262e46f655b4db9e14f54a19eadcfdd2679...</td>\n",
       "      <td>31/07/2015</td>\n",
       "      <td>22/10/2015</td>\n",
       "      <td>25/10/2015</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>H</td>\n",
       "      <td>1</td>\n",
       "      <td>2114944930dcc42ce5b9b50ae965cf8a9c04e46be63d84...</td>\n",
       "      <td>F</td>\n",
       "      <td>A</td>\n",
       "      <td>49d180ecf56132819571bf39d9b7b342522a2ac6d23c14...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>f4c50caac68051faf37551d70bb17eebef2a20e2244cb1...</td>\n",
       "      <td>01/07/2016</td>\n",
       "      <td>01/10/2016</td>\n",
       "      <td>05/10/2016</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>H</td>\n",
       "      <td>1</td>\n",
       "      <td>2114944930dcc42ce5b9b50ae965cf8a9c04e46be63d84...</td>\n",
       "      <td>F</td>\n",
       "      <td>A</td>\n",
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      ],
      "text/plain": [
       "                                      reservation_id booking_date  \\\n",
       "0  7dae1ce6bc8f69481328f2be5c4943077dad5598b5f66d...   05/04/2018   \n",
       "1  fe0d4e444e1818436c88f72f1cf800536c2f785e59baeb...   24/05/2015   \n",
       "2  540bd4285ad8168e8388f84ee74a82cd4f97dc0a404d3e...   21/07/2015   \n",
       "3  09593c907ae262e46f655b4db9e14f54a19eadcfdd2679...   31/07/2015   \n",
       "4  f4c50caac68051faf37551d70bb17eebef2a20e2244cb1...   01/07/2016   \n",
       "\n",
       "  checkin_date checkout_date  channel_code  main_product_code  numberofadults  \\\n",
       "0   05/04/2018    06/04/2018             3                  2               2   \n",
       "1   23/06/2015    27/06/2015             1                  2               4   \n",
       "2   07/08/2015    10/08/2015             3                  2               3   \n",
       "3   22/10/2015    25/10/2015             1                  2               3   \n",
       "4   01/10/2016    05/10/2016             1                  2               4   \n",
       "\n",
       "   numberofchildren  persontravellingid  resort_region_code  resort_type_code  \\\n",
       "0                 0                  45                   3                 3   \n",
       "1                 0                  45                   1                 1   \n",
       "2                 0                  45                   2                 2   \n",
       "3                 0                  45                   1                 1   \n",
       "4                 0                  45                   1                 2   \n",
       "\n",
       "   room_type_booked_code  roomnights  season_holidayed_code  \\\n",
       "0                      4           1                    2.0   \n",
       "1                      4           8                    2.0   \n",
       "2                      2           3                    4.0   \n",
       "3                      2           3                    2.0   \n",
       "4                      3           8                    2.0   \n",
       "\n",
       "   state_code_residence  state_code_resort  total_pax member_age_buckets  \\\n",
       "0                   2.0                  3          3                  H   \n",
       "1                   2.0                  4          2                  H   \n",
       "2                   2.0                  2          3                  H   \n",
       "3                   2.0                  6          3                  H   \n",
       "4                   2.0                 11          2                  H   \n",
       "\n",
       "   booking_type_code                                           memberid  \\\n",
       "0                  1  2114944930dcc42ce5b9b50ae965cf8a9c04e46be63d84...   \n",
       "1                  1  2114944930dcc42ce5b9b50ae965cf8a9c04e46be63d84...   \n",
       "2                  1  2114944930dcc42ce5b9b50ae965cf8a9c04e46be63d84...   \n",
       "3                  1  2114944930dcc42ce5b9b50ae965cf8a9c04e46be63d84...   \n",
       "4                  1  2114944930dcc42ce5b9b50ae965cf8a9c04e46be63d84...   \n",
       "\n",
       "  cluster_code reservationstatusid_code  \\\n",
       "0            A                        C   \n",
       "1            F                        A   \n",
       "2            D                        A   \n",
       "3            F                        A   \n",
       "4            F                        A   \n",
       "\n",
       "                                           resort_id  \n",
       "0  9f14025af0065b30e47e23ebb3b491d39ae8ed17d33739...  \n",
       "1  e7f6c011776e8db7cd330b54174fd76f7d0216b612387a...  \n",
       "2  d4735e3a265e16eee03f59718b9b5d03019c07d8b6c51f...  \n",
       "3  49d180ecf56132819571bf39d9b7b342522a2ac6d23c14...  \n",
       "4  e29c9c180c6279b0b02abd6a1801c7c04082cf486ec027...  "
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>channel_code</th>\n",
       "      <th>main_product_code</th>\n",
       "      <th>numberofadults</th>\n",
       "      <th>numberofchildren</th>\n",
       "      <th>persontravellingid</th>\n",
       "      <th>resort_region_code</th>\n",
       "      <th>resort_type_code</th>\n",
       "      <th>room_type_booked_code</th>\n",
       "      <th>roomnights</th>\n",
       "      <th>season_holidayed_code</th>\n",
       "      <th>state_code_residence</th>\n",
       "      <th>state_code_resort</th>\n",
       "      <th>total_pax</th>\n",
       "      <th>booking_type_code</th>\n",
       "      <th>amount_spent_per_room_night_scaled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>341424.000000</td>\n",
       "      <td>341424.000000</td>\n",
       "      <td>341424.000000</td>\n",
       "      <td>341424.000000</td>\n",
       "      <td>341424.000000</td>\n",
       "      <td>341424.000000</td>\n",
       "      <td>341424.000000</td>\n",
       "      <td>341424.000000</td>\n",
       "      <td>341424.000000</td>\n",
       "      <td>341310.000000</td>\n",
       "      <td>336660.000000</td>\n",
       "      <td>341424.000000</td>\n",
       "      <td>341424.000000</td>\n",
       "      <td>341424.000000</td>\n",
       "      <td>341424.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.902810</td>\n",
       "      <td>2.208738</td>\n",
       "      <td>3.275361</td>\n",
       "      <td>0.362696</td>\n",
       "      <td>210.288905</td>\n",
       "      <td>1.772960</td>\n",
       "      <td>2.193832</td>\n",
       "      <td>2.890321</td>\n",
       "      <td>3.738346</td>\n",
       "      <td>2.673862</td>\n",
       "      <td>7.260708</td>\n",
       "      <td>4.763303</td>\n",
       "      <td>3.193390</td>\n",
       "      <td>1.124209</td>\n",
       "      <td>7.720801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.915311</td>\n",
       "      <td>0.823779</td>\n",
       "      <td>1.756062</td>\n",
       "      <td>0.758019</td>\n",
       "      <td>865.735577</td>\n",
       "      <td>0.750806</td>\n",
       "      <td>1.551451</td>\n",
       "      <td>0.804260</td>\n",
       "      <td>2.479054</td>\n",
       "      <td>0.885650</td>\n",
       "      <td>4.701283</td>\n",
       "      <td>3.103909</td>\n",
       "      <td>1.167824</td>\n",
       "      <td>0.329821</td>\n",
       "      <td>1.090109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-45.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.187260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.860814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.419144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>4995.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>10.816652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        channel_code  main_product_code  numberofadults  numberofchildren  \\\n",
       "count  341424.000000      341424.000000   341424.000000     341424.000000   \n",
       "mean        1.902810           2.208738        3.275361          0.362696   \n",
       "std         0.915311           0.823779        1.756062          0.758019   \n",
       "min         1.000000           1.000000        0.000000          0.000000   \n",
       "25%         1.000000           2.000000        2.000000          0.000000   \n",
       "50%         2.000000           2.000000        3.000000          0.000000   \n",
       "75%         3.000000           3.000000        4.000000          0.000000   \n",
       "max         3.000000           7.000000       32.000000         13.000000   \n",
       "\n",
       "       persontravellingid  resort_region_code  resort_type_code  \\\n",
       "count       341424.000000       341424.000000     341424.000000   \n",
       "mean           210.288905            1.772960          2.193832   \n",
       "std            865.735577            0.750806          1.551451   \n",
       "min             45.000000            1.000000          0.000000   \n",
       "25%             45.000000            1.000000          1.000000   \n",
       "50%             45.000000            2.000000          2.000000   \n",
       "75%             45.000000            2.000000          3.000000   \n",
       "max           4995.000000            3.000000          7.000000   \n",
       "\n",
       "       room_type_booked_code     roomnights  season_holidayed_code  \\\n",
       "count          341424.000000  341424.000000          341310.000000   \n",
       "mean                2.890321       3.738346               2.673862   \n",
       "std                 0.804260       2.479054               0.885650   \n",
       "min                 1.000000     -45.000000               1.000000   \n",
       "25%                 2.000000       2.000000               2.000000   \n",
       "50%                 3.000000       3.000000               3.000000   \n",
       "75%                 3.000000       4.000000               3.000000   \n",
       "max                 6.000000      60.000000               4.000000   \n",
       "\n",
       "       state_code_residence  state_code_resort      total_pax  \\\n",
       "count         336660.000000      341424.000000  341424.000000   \n",
       "mean               7.260708           4.763303       3.193390   \n",
       "std                4.701283           3.103909       1.167824   \n",
       "min                1.000000           1.000000       0.000000   \n",
       "25%                4.000000           2.000000       2.000000   \n",
       "50%                8.000000           5.000000       3.000000   \n",
       "75%                8.000000           7.000000       4.000000   \n",
       "max               38.000000          13.000000      24.000000   \n",
       "\n",
       "       booking_type_code  amount_spent_per_room_night_scaled  \n",
       "count      341424.000000                       341424.000000  \n",
       "mean            1.124209                            7.720801  \n",
       "std             0.329821                            1.090109  \n",
       "min             1.000000                            1.600397  \n",
       "25%             1.000000                            7.187260  \n",
       "50%             1.000000                            7.860814  \n",
       "75%             1.000000                            8.419144  \n",
       "max             2.000000                           10.816652  "
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>channel_code</th>\n",
       "      <th>main_product_code</th>\n",
       "      <th>numberofadults</th>\n",
       "      <th>numberofchildren</th>\n",
       "      <th>persontravellingid</th>\n",
       "      <th>resort_region_code</th>\n",
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       "      <th>roomnights</th>\n",
       "      <th>season_holidayed_code</th>\n",
       "      <th>state_code_residence</th>\n",
       "      <th>state_code_resort</th>\n",
       "      <th>total_pax</th>\n",
       "      <th>booking_type_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>146765.000000</td>\n",
       "      <td>146765.000000</td>\n",
       "      <td>146765.000000</td>\n",
       "      <td>146765.000000</td>\n",
       "      <td>146765.000000</td>\n",
       "      <td>146765.000000</td>\n",
       "      <td>146765.000000</td>\n",
       "      <td>146765.000000</td>\n",
       "      <td>146765.000000</td>\n",
       "      <td>146730.000000</td>\n",
       "      <td>144505.000000</td>\n",
       "      <td>146765.000000</td>\n",
       "      <td>146765.000000</td>\n",
       "      <td>146765.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.902068</td>\n",
       "      <td>2.204483</td>\n",
       "      <td>3.275897</td>\n",
       "      <td>0.362287</td>\n",
       "      <td>209.179852</td>\n",
       "      <td>1.774101</td>\n",
       "      <td>2.196675</td>\n",
       "      <td>2.888754</td>\n",
       "      <td>3.730372</td>\n",
       "      <td>2.681088</td>\n",
       "      <td>7.254206</td>\n",
       "      <td>4.750560</td>\n",
       "      <td>3.188410</td>\n",
       "      <td>1.123354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.914653</td>\n",
       "      <td>0.817804</td>\n",
       "      <td>1.783842</td>\n",
       "      <td>0.758218</td>\n",
       "      <td>862.926844</td>\n",
       "      <td>0.753281</td>\n",
       "      <td>1.544679</td>\n",
       "      <td>0.804043</td>\n",
       "      <td>2.482388</td>\n",
       "      <td>0.882366</td>\n",
       "      <td>4.755982</td>\n",
       "      <td>3.101036</td>\n",
       "      <td>1.163872</td>\n",
       "      <td>0.328844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4995.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        channel_code  main_product_code  numberofadults  numberofchildren  \\\n",
       "count  146765.000000      146765.000000   146765.000000     146765.000000   \n",
       "mean        1.902068           2.204483        3.275897          0.362287   \n",
       "std         0.914653           0.817804        1.783842          0.758218   \n",
       "min         1.000000           1.000000        0.000000          0.000000   \n",
       "25%         1.000000           2.000000        2.000000          0.000000   \n",
       "50%         2.000000           2.000000        3.000000          0.000000   \n",
       "75%         3.000000           3.000000        4.000000          0.000000   \n",
       "max         3.000000           7.000000       32.000000         10.000000   \n",
       "\n",
       "       persontravellingid  resort_region_code  resort_type_code  \\\n",
       "count       146765.000000       146765.000000     146765.000000   \n",
       "mean           209.179852            1.774101          2.196675   \n",
       "std            862.926844            0.753281          1.544679   \n",
       "min             45.000000            1.000000          0.000000   \n",
       "25%             45.000000            1.000000          1.000000   \n",
       "50%             45.000000            2.000000          2.000000   \n",
       "75%             45.000000            2.000000          3.000000   \n",
       "max           4995.000000            3.000000          7.000000   \n",
       "\n",
       "       room_type_booked_code     roomnights  season_holidayed_code  \\\n",
       "count          146765.000000  146765.000000          146730.000000   \n",
       "mean                2.888754       3.730372               2.681088   \n",
       "std                 0.804043       2.482388               0.882366   \n",
       "min                 1.000000       0.000000               1.000000   \n",
       "25%                 2.000000       2.000000               2.000000   \n",
       "50%                 3.000000       3.000000               3.000000   \n",
       "75%                 3.000000       4.000000               3.000000   \n",
       "max                 6.000000      80.000000               4.000000   \n",
       "\n",
       "       state_code_residence  state_code_resort      total_pax  \\\n",
       "count         144505.000000      146765.000000  146765.000000   \n",
       "mean               7.254206           4.750560       3.188410   \n",
       "std                4.755982           3.101036       1.163872   \n",
       "min                1.000000           1.000000       0.000000   \n",
       "25%                4.000000           2.000000       2.000000   \n",
       "50%                8.000000           5.000000       3.000000   \n",
       "75%                8.000000           7.000000       4.000000   \n",
       "max               38.000000          13.000000      21.000000   \n",
       "\n",
       "       booking_type_code  \n",
       "count      146765.000000  \n",
       "mean            1.123354  \n",
       "std             0.328844  \n",
       "min             1.000000  \n",
       "25%             1.000000  \n",
       "50%             1.000000  \n",
       "75%             1.000000  \n",
       "max             2.000000  "
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "341424"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.reservation_id.unique().size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reservation_id: 341424, booking_date: 1621, checkin_date: 1532, checkout_date: 1531, memberid: 101327, resort_id: 32\n"
     ]
    }
   ],
   "source": [
    "print(\"reservation_id: {}, booking_date: {}, checkin_date: {}, checkout_date: {}, memberid: {}, resort_id: {}\".format(train.reservation_id.unique().size,\n",
    "                                                                                                     train.booking_date.unique().size,\n",
    "                                                                                                     train.checkin_date.unique().size,\n",
    "                                                                                                     train.checkout_date.unique().size,train.memberid.unique().size,train.resort_id.unique().size))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['reservation_id', 'booking_date', 'checkin_date', 'checkout_date',\n",
       "       'channel_code', 'main_product_code', 'numberofadults',\n",
       "       'numberofchildren', 'persontravellingid', 'resort_region_code',\n",
       "       'resort_type_code', 'room_type_booked_code', 'roomnights',\n",
       "       'season_holidayed_code', 'state_code_residence', 'state_code_resort',\n",
       "       'total_pax', 'member_age_buckets', 'booking_type_code', 'memberid',\n",
       "       'cluster_code', 'reservationstatusid_code', 'resort_id',\n",
       "       'amount_spent_per_room_night_scaled'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\5029874\\AppData\\Local\\Continuum\\anaconda3\\envs\\trainingenv\\lib\\site-packages\\sklearn\\utils\\deprecation.py:58: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    " from sklearn.preprocessing import Imputer\n",
    " imputer = Imputer(missing_values='NaN',strategy='most_frequent',axis=0)  \n",
    " imputer = imputer.fit(train[['season_holidayed_code']])\n",
    " train['season_holidayed_code']= imputer.transform(train[['season_holidayed_code']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    " test['season_holidayed_code']= imputer.transform(test[['season_holidayed_code']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train[train.roomnights != -45]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[['state_code_residence']] = train[['state_code_residence']].fillna(value=17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "test[['state_code_residence']] = test[['state_code_residence']].fillna(value=17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "reservation_id                        0\n",
       "booking_date                          0\n",
       "checkin_date                          0\n",
       "checkout_date                         0\n",
       "channel_code                          0\n",
       "main_product_code                     0\n",
       "numberofadults                        0\n",
       "numberofchildren                      0\n",
       "persontravellingid                    0\n",
       "resort_region_code                    0\n",
       "resort_type_code                      0\n",
       "room_type_booked_code                 0\n",
       "roomnights                            0\n",
       "season_holidayed_code                 0\n",
       "state_code_residence                  0\n",
       "state_code_resort                     0\n",
       "total_pax                             0\n",
       "member_age_buckets                    0\n",
       "booking_type_code                     0\n",
       "memberid                              0\n",
       "cluster_code                          0\n",
       "reservationstatusid_code              0\n",
       "resort_id                             0\n",
       "amount_spent_per_room_night_scaled    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "reservation_id              0\n",
       "booking_date                0\n",
       "checkin_date                0\n",
       "checkout_date               0\n",
       "channel_code                0\n",
       "main_product_code           0\n",
       "numberofadults              0\n",
       "numberofchildren            0\n",
       "persontravellingid          0\n",
       "resort_region_code          0\n",
       "resort_type_code            0\n",
       "room_type_booked_code       0\n",
       "roomnights                  0\n",
       "season_holidayed_code       0\n",
       "state_code_residence        0\n",
       "state_code_resort           0\n",
       "total_pax                   0\n",
       "member_age_buckets          0\n",
       "booking_type_code           0\n",
       "memberid                    0\n",
       "cluster_code                0\n",
       "reservationstatusid_code    0\n",
       "resort_id                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 341423 entries, 0 to 341423\n",
      "Data columns (total 24 columns):\n",
      "reservation_id                        341423 non-null object\n",
      "booking_date                          341423 non-null object\n",
      "checkin_date                          341423 non-null object\n",
      "checkout_date                         341423 non-null object\n",
      "channel_code                          341423 non-null int64\n",
      "main_product_code                     341423 non-null int64\n",
      "numberofadults                        341423 non-null int64\n",
      "numberofchildren                      341423 non-null int64\n",
      "persontravellingid                    341423 non-null int64\n",
      "resort_region_code                    341423 non-null int64\n",
      "resort_type_code                      341423 non-null int64\n",
      "room_type_booked_code                 341423 non-null int64\n",
      "roomnights                            341423 non-null int64\n",
      "season_holidayed_code                 341423 non-null float64\n",
      "state_code_residence                  341423 non-null float64\n",
      "state_code_resort                     341423 non-null int64\n",
      "total_pax                             341423 non-null int64\n",
      "member_age_buckets                    341423 non-null object\n",
      "booking_type_code                     341423 non-null int64\n",
      "memberid                              341423 non-null object\n",
      "cluster_code                          341423 non-null object\n",
      "reservationstatusid_code              341423 non-null object\n",
      "resort_id                             341423 non-null object\n",
      "amount_spent_per_room_night_scaled    341423 non-null float64\n",
      "dtypes: float64(3), int64(12), object(9)\n",
      "memory usage: 65.1+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove D it has only one row\n",
    "train = train[train.reservationstatusid_code != 'D']"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "train = train[~train['total_pax'].isin([14,20,24])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_booking_date(row):\n",
    "    return datetime.strptime(row['booking_date'], '%d/%m/%Y')\n",
    "\n",
    "def convert_checkin_date(row):\n",
    "    return datetime.strptime(row['checkin_date'], '%d/%m/%Y') \n",
    "\n",
    "def convert_checkout_date(row):\n",
    "    return datetime.strptime(row['checkout_date'], '%d/%m/%Y') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['booking_date'] = train.apply(convert_booking_date, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['checkin_date'] = train.apply(convert_checkin_date, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['checkout_date'] = train.apply(convert_checkout_date, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['actual_days'] = (train['checkout_date'] - train['checkin_date']).dt.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_booking_date_t(row):\n",
    "    return datetime.strptime(row['booking_date'], '%d/%m/%Y')\n",
    "\n",
    "def convert_checkin_date_t(row):\n",
    "    return datetime.strptime(row['checkin_date'], '%d/%m/%Y') \n",
    "\n",
    "def convert_checkout_date_t(row):\n",
    "    return datetime.strptime(row['checkout_date'], '%d/%m/%Y') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['booking_date'] = test.apply(convert_booking_date_t, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['checkin_date'] = test.apply(convert_checkin_date_t, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['checkout_date'] = test.apply(convert_checkout_date_t, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['actual_days'] = (test['checkout_date'] - test['checkin_date']).dt.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import display\n",
    "pd.options.display.max_columns = None"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "train[train['roomnights'].isin([3,29,31,34,37,41,42,47,60])]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "train.to_csv(\"data/train_v2.csv\")"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "train.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b984c76358>"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.heatmap(train.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b987d712b0>"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.plot(kind='scatter', x='main_product_code', y='season_holidayed_code')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b984f5ab70>"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAELCAYAAAA7h+qnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAG+pJREFUeJzt3XuUXWWZ5/HvL6EkQAiEpEgiBBNNGkWIAUtMwES8YZCrIoOsCZfpoelx7F7a3Y5EZSk6MKCuBnVsu8Vwx2ba4RYgSoJcDBhAipgbBEyGgiSSG5FLSkkIqWf+OLvwVFGn6pzK2WdX7f37rHVW1XnqnL2fJ5fz1H7f/e6tiMDMzIptSNYJmJlZ9twMzMzMzcDMzNwMzMwMNwMzM8PNwMzMcDMwMzPcDMzMDDcDMzMD9sg6gWqNHj06JkyYkHUaZmaDyhNPPPFiRDT39bpB0wwmTJhAa2tr1mmYmQ0qkp6v5nUeJjIzMzcDMzNzMzAzM9wMzMwMNwMzM8PNwHLipsVtnPFvi7lpcVvWqZgNSoPm1FKzSt538T28sn0XAI8/9xLfW/gMyy6elXFWZoOLjwxsULtpcdubjaDTK9t3+QjBrEZuBjaozVu+oaa4mfXMzcAGtVOnjKspbmY9czOwQW32MRPZb9jQLrH9hg1l9jETM8rIbHDyBLINessunsVNi9uYt3wDp04Z50Zg1g9uBpYLs4+Z6CZgths8TGRmZuk2A0njJT0gaZWkJyV9MYkfIOleSauTryPTzMPMzHqX9pHBG8A/RcR7gGnAFyQdBswB7ouIycB9yXMzM8tIqs0gIjZExJLk+23AKuAg4FTg+uRl1wOnpZmHmZn1rmFzBpImAEcCjwFjImIDlBoGcGCj8jAzs7dqSDOQNBy4FfhSRLxaw/sukNQqqXXLli3pJWhmVnCpNwNJTZQawc8i4rYkvEnSuOTn44DNPb03Iq6KiJaIaGlu7vN+zmZm1k9pn00k4GpgVURcUfajO4Fzk+/PBealmYeZmfUu7UVnxwJnAyskLU1iXwMuB34u6b8Ca4EzUs7DzMx6kWoziIiHAVX48cfS3LeZmVXPK5DNzMzNwMzM3AzMzAw3AzMzw83AzMxwMzAzM9wMzMwMNwMzM8PNwMzMcDMwMzPcDMzMDDcDMzPDzcDMzHAzMDMz3AzMzAw3AzMzw83AzMxwMzAzM9wMzMwMNwMzM8PNwMzMcDMwMzPcDMzMDDcDMzPDzcDMzHAzMDMz3AzMzAw3AzMzw83AzMxwMzAzM9wMzMwMNwMzM8PNwMzMcDMwMzPcDMzMDDcDMzPDzcDMzHAzMDMzUm4Gkq6RtFnSyrLYxZL+IGlp8vhUmjmYmVnf0j4yuA6Y1UP8yoiYmjx+kXIOZmbWh1SbQUQsAv6Y5j7MzGz3ZTVn8HeSlifDSCMzysHMzBJZNIN/Bd4FTAU2AP9c6YWSLpDUKql1y5YtjcrPzKxwGt4MImJTROyKiA7gp8DRvbz2qohoiYiW5ubmxiVpZlYwDW8GksaVPf00sLLSa83MrDH2SHPjkm4GjgNGS1oPfBM4TtJUIIDngL9NMwczM+tbqs0gIs7qIXx1mvs0M7PaeQWymZm5GZiZmZuBmZnhZmBmZrgZmJkZbgZmZoabgZmZ4WZgZma4GZiZGW4GZmaGm4GZmVGAZrBm0zZuaV3Hmk3bsk7FzGzASvVCdVn7xh0ruOHRtW8+P2f6IXz71CMyzMjMbGDK7ZHBmk3bujQCgBseWesjBDOzHuS2GSxd93JNcTOzIsttM5g6fv+a4mZmRVZVM5D08R5i59Y/nfqZNGZfzpl+SJfYOdMPYdKYfTPKyMxs4FJE9P0iaRHwJPBlYDgwF9gREZ9NN72/aGlpidbW1prft2bTNpaue5mp4/d3IzCzwpH0RES09PW6as8m+jDwT8DS5Pk3IuLm/ibXSJPG7OsmYGbWh2rnDEYCHwT+H7ADeIckpZaVmZk1VLXN4FHglxExC/gA8HbgN6llVUcX3baM9//PhVx027KsU2mY1ratXLHwGVrbtmadipkNEtXOGRwSEWu7xWZGxKLUMuumP3MGE+bMf0vsuctPrFdKA9LsuY/y8Jq/NIEZk0Zx4/nTMszIzLJU7ZxBVUcGEbFW0khJR0uaKWnm7qeYrkpHAnk+Qmht29qlEQA8tGarjxDMrE/Vnlp6PrAIWAB8K/l6cXpp7b5fPrmppngeLFr9Yk1xM7NO1c4ZfJHSXMHzEfER4EhgS2pZ1cEJ7x1TUzwPZk4eXVPczKxTtc1ge0RsB5C0Z0Q8DRyaXlq775LPvK+meB60TBzFjEmjusRmTBpFy8RRFd5hZlZS7TqD9ZL2B+4A7pX0EvBCemnVx3OXn8hFty3jl09u4oT3jsl1I+h04/nTaG3byqLVLzJz8mg3AjOrSlVnE3V5g/RhYD/gnoh4PZWsetDfFchmZkVW7xXISDoK+BAQwG8a2QjMzCxd1Z5N9A3gemAUMBq4VtJFaSZWL+fNfYS/+vp8zpv7SNapNMzW9h0sW/cyW9t3ZJ1Kw3ihndnuqXbR2SrgyLJJ5L2AJRHxnpTze5MXnVVn3tI/cOGty2kaMoSdHR189/QpnDL1oKzTSpUX2plVVtdFZ8BzwLCy53tSuk7RgFXpSCDPRwhb23dw4a3L2b6zg2073mD7zg6+cuvyXB8heKGdWX1U2wx2AE9Kuk7StcBKoF3SDyX9ML30+m9x2x9riufB+pdeo2lI17/SpiFDWP/SaxlllD4vtDOrj2onkG9PHp0erH8q9XXMxAN4cM1bP/iPmXhABtk0xsEj92JnR0eX2M6ODg4euVdGGaVv5uTR/PD+NT3Gzax6VTWDiLi+t59LujUiTq9PSvVx3fnTe5wzuO786Rlk0xijhu/Jd0+fwle6zRmMGr5n1qmlpnOh3UPd5gy8vsKsNjWvM+hxI9LvIuLIOuRTUX/XGZw39xEWt/2RYyYekOtGUG5r+w7Wv/QaB4/cK9eNoJwX2pn1rNoJ5Ho1gyURcdRub6gXXnRmZla7ep9NZGZmOVavZtDjLTAlXSNps6SVZbEDJN0raXXydWSdcjAzs36qVzO4sEL8OmBWt9gc4L6ImAzclzxPzYQ58998FMWsKx5gwpz5zLrigaxTaZgirkC+csEqjvveA1y5YFXWqVgO9DpnIGkFpWsRveVHQETElD53IE0A7o6Iw5PnzwDHRcQGSeOAByOiz8thewVydYpYcxFXIE/+6nx2lv3PbBKsvizff8/WP/WaMzgJOLmHR2e8P8ZExAaA5OuB/dxOryodCeT5CKHSkUCejxCKuAL5ygWrujQCgJ2BjxBst/TaDCLi+d4eaScn6QJJrZJat2wZ0DdWGxCe3vznmuJ5UMQVyPOWb6wpblaNaq9aOk3S45LaJb0uaZekV/u5z03J8BDJ182VXhgRV0VES0S0NDc393N3xfHuA/euKZ4HRbzV56lTxtYUN6tGtRPIPwLOAlYDewHnA/+7n/u8Ezg3+f5cYF4/t9OrSuPkeR4/v+cfP1JTPA+KeKvPf/jke2jqdv5ek0pxs/6q9hLWrRHRIml556SxpMURcUwf77sZOI7SPRA2Ad+kdOvMnwOHAGuBMyKiz6vH9XfRWfkcQZ4bQblZVzzA05v/zLsP3DvXjaBcEVcgX7lgFfOWb+TUKWPdCKyiuq5AlrQI+DgwF9gIbADOi4iG3VTYK5DNzGpX7xXIZyev/TvgT8B44DP9T8/MzAaSai9hfVpE/ADYDnwLQNIXgR+klVi9FHGYqIg1r9m0jaXrXmbq+P2ZNGbfrNNpCA8TWT1VO0z0lgvRNeJKpeW86Kw6Raz5G3es4IZH1775/Jzph/DtU4/IMKP0edGZVasuw0SSzpJ0FzBR0p1ljweBAb2qp4iLzopY85pN27o0AoAbHlnLmk3bMsoofV50Zmnoa5hoMaXJ4tHAP5fFtwHL00rKrFpL171cMZ7X4aLeFp15uMj6q5oVyA9GxHTgaWDf5LE+It5oRIJmvZk6fv+a4nngRWeWhmpXIJ8B/BY4A/hPwGOSPptmYruriIvOiljzpDH7cs70Q7rEzpl+SG6PCsCLziwd1U4gLwM+ERGbk+fNwK8GwzqDIp5ZU8SafTaRG4H1rN6LzlZExBFlz4cAy8pjafOiMzOz2lXbDKpdZ/BLSQuAm5PnZwK/6G9yZmY2sFS7AjmAnwBTgPcBV6WWkZmZNVy1RwafiIgLgds6A5K+ReXbXQ4YRRw/d83FqHnaJQvZ2L6TscObePSi47NOxwa5vhadfT659eWhkpaXPdoYBOsMui+2yvPiq06uuTg1b2zfCcDG9p2FqNnS1dcw0b9Tur3lnXS97eX7I2J2yrntliKuxnXNfcfzYNolC2uKm1Wj12GiiHgFeIXSjW3MbADoPCKoNm5WjWonkM1sgBg7vKmmuFk1ctsMirga1zX3Hc+DSpPFnkS23ZHbZgBv/UDI8wdEJ9dcnJo7jwTGDm8qRM2WrqpWIA8EXoFsZla7et/20szMcqzaRWeDVhEXI7lm12xWq1wfGRR1MVJvz/PINRejZktXbptBERcjuea+43lQxJotfbltBmZmVj03AzMzy28zKOJiJNfcdzwPilizpS+3zQCKuxipt+d55JqLUbOly4vOzMxyzIvOzMysal50lkOu2TXn0R1L1nH3io2cdMRYTjtqfNbp5E6uh4l6Ou867/9pXHOJa86Xaf/rXja++vqbz8eNeBuPfO0TGWY0eBR+mKiIC3Ncc9/xPChazXcsWdelEQBsePV17liyLqOM8im3zcDM8uHuFRtrilv/uBmY2YB20hFja4pb/+S2GRRxYY5r7jueB0Wr+bSjxjNuxNu6xMaNeJsnkess1xPIULwzLsA1u+Z88tlE/VPtBHLum4GZWZEV/mwiMzOrXmaLziQ9B2wDdgFvVNO5zMwsHVmvQP5IRLyY5g6KNq4Krtk159Old63k7pUbOenwsXz95MOzTid3cj1MVMRbA7pm15xH75wzn5/+5nk2vLKDn/7med6Z83qzkGUzCGChpCckXVDvjRdtlSa45mrieVC0mi+9ayUd3WIdSdzqJ8tmcGxEHAWcAHxB0szuL5B0gaRWSa1btmxpfIZmlrm7V1ZYgVwhbv2TWTOIiBeSr5uB24Gje3jNVRHREhEtzc3NjU7RzAaAkw6vsAK5Qtz6J5NmIGkfSft2fg8cD9T1mK9oqzTBNVcTz4Oi1fz1kw9/ywfVkCRu9ZPVkcEY4GFJy4DfAvMj4p5676SItwZ0za45j569/ET+5th3MG6/PfmbY9/BszmvNwtegWxmlmNegWxmZlXLetFZ6oq2MAdcs2vOp6LVC3DT4jbmLd/AqVPGMfuYianuK9fDREW7NSC45k6uOV+KVi/A+y6+h1e273rz+X7DhrLs4lk1b6fww0RFW5gDrrmaeB4Ureai1QulI4LyRgDwyvZd3LS4LbV95rYZmJkNVvOWb6gpXg9uBmZmA8ypU8bVFK+H3DaDoi3MAddcTTwPilZz0eoFmH3MRPYbNrRLbL9hQ1OdRM5tM4DiLcwB19zT8zwqWs1Fqxdg2cWzuOSUw/jAhJFccsph/Zo8rkWuzyYyMyu6wp9NZGZm1XMzMDMzr0DOI9fsmvOoaPUCtLZtZdHqF5k5eTQtE0eluq9czxkUcdWiay5xzflStHoBZs99lIfXbH3z+YxJo7jx/Gk1b6fwcwZFXLXomvuO50HRai5avVA6IihvBAAPrdlKa9vWCu/YfbltBmZmg9Wi1S/WFK8HNwMzswFm5uTRNcXrIbfNoIirFl1z3/E8KFrNRasXoGXiKGZM6jphPGPSqFQnkXM9gQzFPAPBNbvmPCpavVCfs4mqnUDOfTMwMyuywp9NZGZm1fOisxxyza45j4pWL8DW9h2sf+k1Dh65F6OG75nqvnJ9ZND9POQ8n5fcyTW75jwqWr0A85b+gWO/cz+z5z7Gsd+5nzuX/iHV/eW2GRRxoYpr7jueB0WruWj1QumI4MJbl7N9ZwfbdrzB9p0dfOXW5Wxt35HaPnPbDMzMBqv1L71G05CuH89NQ4aw/qXXUtunm4GZ2QBz8Mi92NnR0SW2s6ODg0fuldo+c9sMirhQxTX3Hc+DotVctHoBRg3fk++ePoVhTUPYd889GNY0hO+ePiXVSeTcrzMo4hkIrtk151HR6oX6nE3kRWdmZuZFZ2ZmVj03AzMzy/8K5GmXLGRj+07GDm/i0YuOzzqdhiji2Kprzn/NZ/z4IZase5Wjxo/g//73GVmnkzu5PjKYMGc+G9t3ArCxfWeuF6l0KuJKTdec/5onzJnP42tfZVfA42tfzX29WchtM5h2ycKa4nlQxJWarrnv+GB3xo8fqilu/ZPbZtB5RFBt3MwGpiXrXq0pbv2T22YwdnhTTXEzG5iOGj+iprj1T26bQaXJ4jxPIhdxpaZr7js+2FWaLPYkcn3lthlA6T9H55HA2OFNuf3PUq57ja45n4pW83OXn8gHDhnBUMEHDhmR+3qz4BXIZmY5NuBXIEuaJekZSWskzckqDzMzy6gZSBoK/AtwAnAYcJakw7LIJY/ue2ojF96yjPue2ph1Kg1z6V0rmX7Zr7j0rpVZp9IwrW1buWLhM7S2bc06FcuBTIaJJE0HLo6ITybPvwoQEZdVeo+Hiapz/JUP8vtNf3rz+aFj9mHBPxyXXUIN8M458ym/8vsQ4NmcjynPnvsoD6/5SxOYMWkUN54/LcOMbKAa6MNEBwHryp6vT2K2G+57amOXRgDwzKY/5foI4dK7VtLRLdaRxPOqtW1rl0YA8NCarT5CsN2SVTNQD7G3HKJIukBSq6TWLVu2NCCtwW3hU5tqiufB3St7bnSV4nmwaPWLNcXNqpFVM1gPjC97fjDwQvcXRcRVEdESES3Nzc0NS26wOv6wMTXF8+Ckw8fWFM+DmZNH1xQ3q0ZWzeBxYLKkiZLeBnwOuDOjXHLjY4eN5dAx+3SJHTpmHz52WH4/GL9+8uFv+Uc8JInnVcvEUcyYNKpLbMakUbRMHFXhHWZ9y2ydgaRPAd8HhgLXRMSlvb3eE8jVu++pjSx8ahPHHzYm142g3KV3reTulRs56fCxuW4E5VrbtrJo9YvMnDzajcAq8m0vzcxswJ9NZGZmA4ibgZmZuRmYmZmbgZmZ4WZgZmYMorOJJG0Bnu/n20cDRVue6ZqLoWg1F61e2P2a3xERfa7aHTTNYHdIaq3m1Ko8cc3FULSai1YvNK5mDxOZmZmbgZmZFacZXJV1AhlwzcVQtJqLVi80qOZCzBmYmVnvinJkYGZmvch1M5B0jaTNkvJ726tuJI2X9ICkVZKelPTFrHNKm6Rhkn4raVlS87eyzqkRJA2V9DtJd2edSyNIek7SCklLJRXiqpWS9pd0i6Snk//T01PbV56HiSTNBNqBGyKiENc1ljQOGBcRSyTtCzwBnBYRT2WcWmokCdgnItolNQEPA1+MiEczTi1Vkv4RaAFGRMRJWeeTNknPAS0RUZh1BpKuBx6KiLnJvV/2joiX09hXro8MImIR8Mes82ikiNgQEUuS77cBq8j5/aWjpD152pQ88vtbDiDpYOBEYG7WuVg6JI0AZgJXA0TE62k1Ash5Myg6SROAI4HHss0kfcmQyVJgM3BvROS95u8DXwE6sk6kgQJYKOkJSRdknUwDvBPYAlybDAfOlbRPX2/qLzeDnJI0HLgV+FJEvJp1PmmLiF0RMZXS/bSPlpTbYUFJJwGbI+KJrHNpsGMj4ijgBOALyTBwnu0BHAX8a0QcCfwJmJPWztwMcigZN78V+FlE3JZ1Po2UHEY/CMzKOJU0HQuckoyh/x/go5Juyjal9EXEC8nXzcDtwNHZZpS69cD6sqPcWyg1h1S4GeRMMpl6NbAqIq7IOp9GkNQsaf/k+72AjwNPZ5tVeiLiqxFxcERMAD4H3B8RszNOK1WS9klOiCAZKjkeyPVZghGxEVgn6dAk9DEgtRNB9khrwwOBpJuB44DRktYD34yIq7PNKnXHAmcDK5IxdICvRcQvMswpbeOA6yUNpfQLzs8johCnWxbIGOD20u867AH8e0Tck21KDfH3wM+SM4meBf5LWjvK9amlZmZWHQ8TmZmZm4GZmbkZmJkZbgZmZoabgZmZ4WZgZma4GVhOSJoq6VMN2tdx9bpstKT2vl9V8b0XS/pyPfLoYdvXSfpsGtu2gcnNwFKjkkb9G5sKNKQZmOWRm4HVlaQJyU04fgwsAc5ObkiyUtJ3yl53VoV4u6TvJFem/JWkoyU9KOlZSadU2OfbgG8DZyY3PjlT0mpJzcnPh0haI2l08hvvv0l6SNLvk4u+dV719HuSHpe0XNLf9lHqCEm3S3oq2d6QPurqMV7289GSHpF0YvL8f5Tl8q2y131d0jOSfgUc2n073bY5KfkzXCZpiaR3JQ36e0keKySdmbxWkn6U1DMfOLBsO++X9Ovk72SBSvfMsLyJCD/8qNsDmEDpssrTgLcDa4FmSpcQuB84rVI8eX8AJyTf3w4spHR/gvcBS3vZ73nAj8qef5PSFVuhdB2bW5PvrwPuofSL0GRKFwMbBlwAXJS8Zk+gFZhYYV/HAdspXWJ4KHAv8Nl+1ttO6VILjwGfKMv3KkBJnndTuq79+4EVwN7ACGAN8OVe/kweAz6dfD8sed/pSb5Dk/2upXQ5j8+Uxd8OvJzU1AQsBpqT7ZwJXJP1vzM/6v/I9bWJLDPPR8Sjkk4FHoyILQCSfkbpQy0qxO8AXqf0YQ2lD74dEbFT0gpKjaZa1wDzKF33/6+Ba8t+9vOI6ABWS3oWeDelD+ApZePk+1FqFm0Vtv/biHg2yf9m4EPAzn7U2wTcB3whIn6dbPv45PG75PnwJJd9gdsj4s/Jdu6sVHxyUbeDIuJ2gIjYnsQ/BNwcEbuATZJ+DXwgyacz/oKk+5NNHQocDtybXBdoKLCh0n5t8HIzsDT8KfmqCj+vFAfYGRGdF8zqAHYARESHpKr/vUbEOkmbJH0U+CDwn8t/3P3lSU5/HxELqt1FhW30pLd636B0a9JPAp3NQMBlEfGTLhuRvtTDfivpTy49bVvAkxGR2r13bWDwnIGl6THgw8l4+FDgLEofeJXiu2Mbpd+cy80FbqJ0JLCrLH5GMo/wLkpDPc8AC4DPq3QvCCT9lXq/q9TRkiYmcwVnUrrvcn/qDUpHLu+W1HnjkgXAX6t0gyIkHSTpQGAR8GlJeyW/+Z9cKbko3dBovaTTkm3sKWnvZBtnJnMkzZSOCH6bxD+XxMcBH0k29QzQrORG7JKaJL23lz8XG6R8ZGCpiYgNkr4KPEDpN8xfRMQ8gErx3fAAMEely3ZfFhH/AdxJaXjo2m6vfYbSh/EY4L9FxHZJcykNQy1RaTxkC6Xx/koeAS4HjqD0QXp7cvRSc70RsUvS54C7JL0aET+W9B7gkWRoph2YHRFLJP0HsBR4Hniojz+Ts4GfSPo2pSGsMyjNw0wHllFqRF+JiI2Sbgc+Smlo7vfJnw8R8XoydPZDSftR+sz4PvBkH/u2QcaXsLbcktQCXBkRM8pi1wF3R8QtmSVmNgD5yMByKRly+Txd5wrMrAIfGdigIumTQPfz9Nsi4tMp7OsI4MZu4R0R8cF672t3SPoXSne4K/eDiOg+PGZWkZuBmZn5bCIzM3MzMDMz3AzMzAw3AzMzw83AzMyA/w9eG9jX5PkYbAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.plot(kind='scatter', x='room_type_booked_code', y='total_pax')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b9889cc5c0>"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test.plot(kind='scatter', x='room_type_booked_code', y='total_pax')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b98bf77438>"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.plot(kind='scatter', x='numberofadults', y='roomnights') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b991ce3c88>"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test.plot(kind='scatter', x='numberofadults', y='roomnights') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b991d149b0>"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.plot(kind='scatter', x='roomnights', y='actual_days')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b9e8aa7828>"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test.plot(kind='scatter', x='roomnights', y='actual_days')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TRAIN - remove total_pax 24 and cap above 13 to 21 \n",
    "train = train[train.total_pax != 24]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.loc[(train.total_pax > 13), \"total_pax\"] = 21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TRAIN - actual days above 15 removed\n",
    "train = train[train.actual_days < 15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_roomnight(row):\n",
    "    if row['roomnights'] == 80:\n",
    "        return 3\n",
    "    else:\n",
    "        return row['roomnights']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TEST - change roomnights 80 to 3\n",
    "test['roomnights'] = test.apply(replace_roomnight, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TRAIN - numberofchildren remove 13 and cap 12 to 10\n",
    "train = train[train.numberofchildren != 15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_child(row):\n",
    "    if row['numberofchildren'] == 12:\n",
    "        return 10\n",
    "    else:\n",
    "        return row['numberofchildren']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['numberofchildren'] = train.apply(replace_child, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TEST - numberofchildren cap 9 to 10\n",
    "def replace_child_t(row):\n",
    "    if row['numberofchildren'] == 9:\n",
    "        return 10\n",
    "    else:\n",
    "        return row['numberofchildren']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['numberofchildren'] = test.apply(replace_child_t, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TRAIN - remove numberofadults 23 and 25\n",
    "train = train[~train['numberofadults'].isin([23,25])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TEST - numberofadults cap 29 to 30 and change children to 0\n",
    "def replace_adult_t(row):\n",
    "    if row['numberofadults'] == 29:\n",
    "        return 30\n",
    "    else:\n",
    "        return row['numberofadults']\n",
    "    \n",
    "def replace_child_adult(row):\n",
    "    if row['numberofadults'] == 30:\n",
    "        return 0\n",
    "    else:\n",
    "        return row['numberofchildren']\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['numberofadults'] = test.apply(replace_adult_t, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['numberofchildren'] = test.apply(replace_child_adult, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TRAIN - roomnights cap 29, 31, 34, to 30 and 37,41,42,47,60 to 40\n",
    "def replace_roomnights(row):\n",
    "    if row['roomnights'] in [29,31,34,37]:\n",
    "        return 30\n",
    "    elif row['roomnights'] in [41,42,47,60]:\n",
    "        return 40\n",
    "    else:\n",
    "        return row['roomnights']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['roomnights'] = train.apply(replace_roomnights, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TEST - roomnights cap 40,44,48 to 40\n",
    "def replace_roomnights_t(row):\n",
    "    if row['roomnights'] in [40,44,48]:\n",
    "        return 40\n",
    "    else:\n",
    "        return row['roomnights']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['roomnights'] = test.apply(replace_roomnights_t, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['booking_checkin_days'] = (train['checkin_date'] - train['booking_date']).dt.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['booking_checkout_days'] = (train['checkout_date'] - train['booking_date']).dt.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['booking_checkin_days'] = (test['checkin_date'] - test['booking_date']).dt.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['booking_checkout_days'] = (test['checkout_date'] - test['booking_date']).dt.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['reservation_id', 'booking_date', 'checkin_date', 'checkout_date',\n",
       "       'channel_code', 'main_product_code', 'numberofadults',\n",
       "       'numberofchildren', 'persontravellingid', 'resort_region_code',\n",
       "       'resort_type_code', 'room_type_booked_code', 'roomnights',\n",
       "       'season_holidayed_code', 'state_code_residence', 'state_code_resort',\n",
       "       'total_pax', 'member_age_buckets', 'booking_type_code', 'memberid',\n",
       "       'cluster_code', 'reservationstatusid_code', 'resort_id', 'actual_days',\n",
       "       'booking_checkin_days', 'booking_checkout_days',\n",
       "       'amount_spent_per_room_night_scaled'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['reservation_id', 'booking_date', 'checkin_date', 'checkout_date',\n",
       "       'channel_code', 'main_product_code', 'numberofadults',\n",
       "       'numberofchildren', 'persontravellingid', 'resort_region_code',\n",
       "       'resort_type_code', 'room_type_booked_code', 'roomnights',\n",
       "       'season_holidayed_code', 'state_code_residence', 'state_code_resort',\n",
       "       'total_pax', 'member_age_buckets', 'booking_type_code', 'memberid',\n",
       "       'cluster_code', 'reservationstatusid_code', 'resort_id', 'actual_days',\n",
       "       'booking_checkin_days', 'booking_checkout_days'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train[['reservation_id', 'booking_date', 'checkin_date', 'checkout_date',\n",
    "       'channel_code', 'main_product_code', 'numberofadults',\n",
    "       'numberofchildren', 'persontravellingid', 'resort_region_code',\n",
    "       'resort_type_code', 'room_type_booked_code', 'roomnights',\n",
    "       'season_holidayed_code', 'state_code_residence', 'state_code_resort',\n",
    "       'total_pax', 'member_age_buckets', 'booking_type_code', 'memberid',\n",
    "       'cluster_code', 'reservationstatusid_code', 'resort_id', 'actual_days',\n",
    "       'booking_checkin_days', 'booking_checkout_days', 'amount_spent_per_room_night_scaled']]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "train.to_csv(\"data/train_v2.csv\")"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "test.to_csv(\"data/test_v2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_v2 = pd.read_csv(\"data/train_v2.csv\").iloc[:, 1:]\n",
    "test_v2 = pd.read_csv(\"data/test_v2.csv\").iloc[:, 1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\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>reservation_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>checkin_date</th>\n",
       "      <th>checkout_date</th>\n",
       "      <th>channel_code</th>\n",
       "      <th>main_product_code</th>\n",
       "      <th>numberofadults</th>\n",
       "      <th>numberofchildren</th>\n",
       "      <th>persontravellingid</th>\n",
       "      <th>resort_region_code</th>\n",
       "      <th>...</th>\n",
       "      <th>member_age_buckets</th>\n",
       "      <th>booking_type_code</th>\n",
       "      <th>memberid</th>\n",
       "      <th>cluster_code</th>\n",
       "      <th>reservationstatusid_code</th>\n",
       "      <th>resort_id</th>\n",
       "      <th>actual_days</th>\n",
       "      <th>booking_checkin_days</th>\n",
       "      <th>booking_checkout_days</th>\n",
       "      <th>amount_spent_per_room_night_scaled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>03930f033646d073462b35d411616323597715ac4fc398...</td>\n",
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       "      <td>39fa9ec190eee7b6f4dff1100d6343e10918d044c75eac...</td>\n",
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       "      <td>83</td>\n",
       "      <td>6.662563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>d145a32920e6587ad95bfe299d80c0affa268220535aaf...</td>\n",
       "      <td>28/01/2015</td>\n",
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       "      <td>3d1539e56495b6991f0a3ef5a61ca3d03ce4fff7380e9a...</td>\n",
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       "      <td>535fa30d7e25dd8a49f1536779734ec8286108d115da50...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>7.871602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cfd77f44811ed62f25a220b53324cdbafc662a4c9e5f04...</td>\n",
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       "      <td>5.344943</td>\n",
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       "    <tr>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      reservation_id booking_date  \\\n",
       "0  07659f3758d8aee27f5a7e2887adeacb67021cb95ada1b...   05/04/2018   \n",
       "1  03930f033646d073462b35d411616323597715ac4fc398...   23/01/2015   \n",
       "2  d145a32920e6587ad95bfe299d80c0affa268220535aaf...   28/01/2015   \n",
       "3  cfd77f44811ed62f25a220b53324cdbafc662a4c9e5f04...   02/05/2015   \n",
       "4  937cff9e4dcfc2459620153dfc8b9962ac22bea67dfb29...   02/09/2015   \n",
       "\n",
       "  checkin_date checkout_date  channel_code  main_product_code  numberofadults  \\\n",
       "0   05/04/2018    06/04/2018             3                  1               2   \n",
       "1   11/04/2015    16/04/2015             1                  1               2   \n",
       "2   01/02/2015    05/02/2015             1                  1               2   \n",
       "3   11/06/2015    16/06/2015             1                  1               2   \n",
       "4   14/12/2015    19/12/2015             1                  1               2   \n",
       "\n",
       "   numberofchildren  persontravellingid  resort_region_code  \\\n",
       "0                 0                  46                   3   \n",
       "1                 0                  46                   3   \n",
       "2                 0                  47                   1   \n",
       "3                 2                  46                   2   \n",
       "4                 0                  46                   2   \n",
       "\n",
       "                 ...                  member_age_buckets  booking_type_code  \\\n",
       "0                ...                                   F                  1   \n",
       "1                ...                                   F                  1   \n",
       "2                ...                                   F                  1   \n",
       "3                ...                                   F                  1   \n",
       "4                ...                                   F                  1   \n",
       "\n",
       "                                            memberid  cluster_code  \\\n",
       "0  3d1539e56495b6991f0a3ef5a61ca3d03ce4fff7380e9a...             F   \n",
       "1  3d1539e56495b6991f0a3ef5a61ca3d03ce4fff7380e9a...             F   \n",
       "2  3d1539e56495b6991f0a3ef5a61ca3d03ce4fff7380e9a...             E   \n",
       "3  3d1539e56495b6991f0a3ef5a61ca3d03ce4fff7380e9a...             D   \n",
       "4  3d1539e56495b6991f0a3ef5a61ca3d03ce4fff7380e9a...             D   \n",
       "\n",
       "   reservationstatusid_code  \\\n",
       "0                         C   \n",
       "1                         A   \n",
       "2                         A   \n",
       "3                         A   \n",
       "4                         A   \n",
       "\n",
       "                                           resort_id  actual_days  \\\n",
       "0  4e07408562bedb8b60ce05c1decfe3ad16b72230967de0...            1   \n",
       "1  39fa9ec190eee7b6f4dff1100d6343e10918d044c75eac...            5   \n",
       "2  535fa30d7e25dd8a49f1536779734ec8286108d115da50...            4   \n",
       "3  d4735e3a265e16eee03f59718b9b5d03019c07d8b6c51f...            5   \n",
       "4  d4735e3a265e16eee03f59718b9b5d03019c07d8b6c51f...            5   \n",
       "\n",
       "  booking_checkin_days  booking_checkout_days  \\\n",
       "0                    0                      1   \n",
       "1                   78                     83   \n",
       "2                    4                      8   \n",
       "3                   40                     45   \n",
       "4                  103                    108   \n",
       "\n",
       "  amount_spent_per_room_night_scaled  \n",
       "0                           7.706428  \n",
       "1                           6.662563  \n",
       "2                           7.871602  \n",
       "3                           5.344943  \n",
       "4                           7.059346  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_v2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_v2 = train_v2[train_v2.booking_checkin_days >= 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = train_v2\n",
    "test_df = test_v2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>540bd4285ad8168e8388f84ee74a82cd4f97dc0a404d3e...</td>\n",
       "      <td>21/07/2015</td>\n",
       "      <td>07/08/2015</td>\n",
       "      <td>10/08/2015</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>H</td>\n",
       "      <td>1</td>\n",
       "      <td>2114944930dcc42ce5b9b50ae965cf8a9c04e46be63d84...</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>d4735e3a265e16eee03f59718b9b5d03019c07d8b6c51f...</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>09593c907ae262e46f655b4db9e14f54a19eadcfdd2679...</td>\n",
       "      <td>31/07/2015</td>\n",
       "      <td>22/10/2015</td>\n",
       "      <td>25/10/2015</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>H</td>\n",
       "      <td>1</td>\n",
       "      <td>2114944930dcc42ce5b9b50ae965cf8a9c04e46be63d84...</td>\n",
       "      <td>F</td>\n",
       "      <td>A</td>\n",
       "      <td>49d180ecf56132819571bf39d9b7b342522a2ac6d23c14...</td>\n",
       "      <td>3</td>\n",
       "      <td>83</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>f4c50caac68051faf37551d70bb17eebef2a20e2244cb1...</td>\n",
       "      <td>01/07/2016</td>\n",
       "      <td>01/10/2016</td>\n",
       "      <td>05/10/2016</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>H</td>\n",
       "      <td>1</td>\n",
       "      <td>2114944930dcc42ce5b9b50ae965cf8a9c04e46be63d84...</td>\n",
       "      <td>F</td>\n",
       "      <td>A</td>\n",
       "      <td>e29c9c180c6279b0b02abd6a1801c7c04082cf486ec027...</td>\n",
       "      <td>4</td>\n",
       "      <td>92</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      reservation_id booking_date  \\\n",
       "0  7dae1ce6bc8f69481328f2be5c4943077dad5598b5f66d...   05/04/2018   \n",
       "1  fe0d4e444e1818436c88f72f1cf800536c2f785e59baeb...   24/05/2015   \n",
       "2  540bd4285ad8168e8388f84ee74a82cd4f97dc0a404d3e...   21/07/2015   \n",
       "3  09593c907ae262e46f655b4db9e14f54a19eadcfdd2679...   31/07/2015   \n",
       "4  f4c50caac68051faf37551d70bb17eebef2a20e2244cb1...   01/07/2016   \n",
       "\n",
       "  checkin_date checkout_date  channel_code  main_product_code  numberofadults  \\\n",
       "0   05/04/2018    06/04/2018             3                  2               2   \n",
       "1   23/06/2015    27/06/2015             1                  2               4   \n",
       "2   07/08/2015    10/08/2015             3                  2               3   \n",
       "3   22/10/2015    25/10/2015             1                  2               3   \n",
       "4   01/10/2016    05/10/2016             1                  2               4   \n",
       "\n",
       "   numberofchildren  persontravellingid  resort_region_code  \\\n",
       "0                 0                  45                   3   \n",
       "1                 0                  45                   1   \n",
       "2                 0                  45                   2   \n",
       "3                 0                  45                   1   \n",
       "4                 0                  45                   1   \n",
       "\n",
       "           ...           total_pax  member_age_buckets  booking_type_code  \\\n",
       "0          ...                   3                   H                  1   \n",
       "1          ...                   2                   H                  1   \n",
       "2          ...                   3                   H                  1   \n",
       "3          ...                   3                   H                  1   \n",
       "4          ...                   2                   H                  1   \n",
       "\n",
       "                                            memberid  cluster_code  \\\n",
       "0  2114944930dcc42ce5b9b50ae965cf8a9c04e46be63d84...             A   \n",
       "1  2114944930dcc42ce5b9b50ae965cf8a9c04e46be63d84...             F   \n",
       "2  2114944930dcc42ce5b9b50ae965cf8a9c04e46be63d84...             D   \n",
       "3  2114944930dcc42ce5b9b50ae965cf8a9c04e46be63d84...             F   \n",
       "4  2114944930dcc42ce5b9b50ae965cf8a9c04e46be63d84...             F   \n",
       "\n",
       "   reservationstatusid_code  \\\n",
       "0                         C   \n",
       "1                         A   \n",
       "2                         A   \n",
       "3                         A   \n",
       "4                         A   \n",
       "\n",
       "                                           resort_id actual_days  \\\n",
       "0  9f14025af0065b30e47e23ebb3b491d39ae8ed17d33739...           1   \n",
       "1  e7f6c011776e8db7cd330b54174fd76f7d0216b612387a...           4   \n",
       "2  d4735e3a265e16eee03f59718b9b5d03019c07d8b6c51f...           3   \n",
       "3  49d180ecf56132819571bf39d9b7b342522a2ac6d23c14...           3   \n",
       "4  e29c9c180c6279b0b02abd6a1801c7c04082cf486ec027...           4   \n",
       "\n",
       "   booking_checkin_days booking_checkout_days  \n",
       "0                     0                     1  \n",
       "1                    30                    34  \n",
       "2                    17                    20  \n",
       "3                    83                    86  \n",
       "4                    92                    96  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 341392 entries, 0 to 341396\n",
      "Data columns (total 27 columns):\n",
      "reservation_id                        341392 non-null object\n",
      "booking_date                          341392 non-null object\n",
      "checkin_date                          341392 non-null object\n",
      "checkout_date                         341392 non-null object\n",
      "channel_code                          341392 non-null int64\n",
      "main_product_code                     341392 non-null int64\n",
      "numberofadults                        341392 non-null int64\n",
      "numberofchildren                      341392 non-null int64\n",
      "persontravellingid                    341392 non-null int64\n",
      "resort_region_code                    341392 non-null int64\n",
      "resort_type_code                      341392 non-null int64\n",
      "room_type_booked_code                 341392 non-null int64\n",
      "roomnights                            341392 non-null int64\n",
      "season_holidayed_code                 341392 non-null int64\n",
      "state_code_residence                  341392 non-null int64\n",
      "state_code_resort                     341392 non-null int64\n",
      "total_pax                             341392 non-null int64\n",
      "member_age_buckets                    341392 non-null object\n",
      "booking_type_code                     341392 non-null int64\n",
      "memberid                              341392 non-null object\n",
      "cluster_code                          341392 non-null object\n",
      "reservationstatusid_code              341392 non-null object\n",
      "resort_id                             341392 non-null object\n",
      "actual_days                           341392 non-null int64\n",
      "booking_checkin_days                  341392 non-null int64\n",
      "booking_checkout_days                 341392 non-null int64\n",
      "amount_spent_per_room_night_scaled    341392 non-null float64\n",
      "dtypes: float64(1), int64(17), object(9)\n",
      "memory usage: 72.9+ MB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmpvar = {}\n",
    "col_index = {}\n",
    "\n",
    "for i in train_df.columns:\n",
    "    tmpvar[i] = train_df[i].unique().size\n",
    "    col_index[i] = train_df.columns.get_loc(i)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'reservation_id': 341392,\n",
       " 'booking_date': 1621,\n",
       " 'checkin_date': 1527,\n",
       " 'checkout_date': 1527,\n",
       " 'channel_code': 3,\n",
       " 'main_product_code': 5,\n",
       " 'numberofadults': 26,\n",
       " 'numberofchildren': 11,\n",
       " 'persontravellingid': 6,\n",
       " 'resort_region_code': 3,\n",
       " 'resort_type_code': 7,\n",
       " 'room_type_booked_code': 6,\n",
       " 'roomnights': 36,\n",
       " 'season_holidayed_code': 4,\n",
       " 'state_code_residence': 38,\n",
       " 'state_code_resort': 11,\n",
       " 'total_pax': 15,\n",
       " 'member_age_buckets': 10,\n",
       " 'booking_type_code': 2,\n",
       " 'memberid': 101327,\n",
       " 'cluster_code': 6,\n",
       " 'reservationstatusid_code': 3,\n",
       " 'resort_id': 32,\n",
       " 'actual_days': 14,\n",
       " 'booking_checkin_days': 127,\n",
       " 'booking_checkout_days': 129,\n",
       " 'amount_spent_per_room_night_scaled': 278059}"
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmpvar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmpvar_test = {}\n",
    "col_index_test = {}\n",
    "\n",
    "for i in test_df.columns:\n",
    "    tmpvar_test[i] = test_df[i].unique().size\n",
    "    col_index_test[i] = test_df.columns.get_loc(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'reservation_id': 146765,\n",
       " 'booking_date': 1620,\n",
       " 'checkin_date': 1523,\n",
       " 'checkout_date': 1524,\n",
       " 'channel_code': 3,\n",
       " 'main_product_code': 5,\n",
       " 'numberofadults': 26,\n",
       " 'numberofchildren': 10,\n",
       " 'persontravellingid': 6,\n",
       " 'resort_region_code': 3,\n",
       " 'resort_type_code': 7,\n",
       " 'room_type_booked_code': 6,\n",
       " 'roomnights': 36,\n",
       " 'season_holidayed_code': 4,\n",
       " 'state_code_residence': 38,\n",
       " 'state_code_resort': 11,\n",
       " 'total_pax': 15,\n",
       " 'member_age_buckets': 10,\n",
       " 'booking_type_code': 2,\n",
       " 'memberid': 43496,\n",
       " 'cluster_code': 6,\n",
       " 'reservationstatusid_code': 3,\n",
       " 'resort_id': 32,\n",
       " 'actual_days': 15,\n",
       " 'booking_checkin_days': 126,\n",
       " 'booking_checkout_days': 131}"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmpvar_test"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "train_df.apply(LabelEncoder().fit_transform)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "#Check MemberID in test and train\n",
    "train_members = train_df.memberid.unique()\n",
    "test_members = test_df.memberid.unique()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "members = []\n",
    "for i in test_members:\n",
    "    if i not in train_members:\n",
    "        members.append(i)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "len(members)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping MEMBER ID coz it will create issue in the model.\n",
    "train_df = train_df.drop(['memberid'], axis=1)\n",
    "test_df = test_df.drop(['memberid'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in ['member_age_buckets','cluster_code','reservationstatusid_code','resort_id']:\n",
    "    label_encoder = LabelEncoder()\n",
    "    fitted = label_encoder.fit(train_df[i]) \n",
    "    train_df[i] = fitted.transform(train_df[i])\n",
    "    test_df[i] = fitted.transform(test_df[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['checkin_date'] = train_df.apply(convert_checkin_date, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df['checkin_date'] = test_df.apply(convert_checkin_date_t, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting month may help in identifying vacation months.\n",
    "train_df['booking_month'] = train_df['checkin_date'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df['booking_month'] = test_df['checkin_date'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mark_vacation(row):\n",
    "    if row['booking_month'] in [12,1,4,5]:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['is_vacation'] = train_df.apply(mark_vacation, axis=1)\n",
    "test_df['is_vacation'] = test_df.apply(mark_vacation, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = train_df[['reservation_id', 'booking_date', 'checkin_date', 'checkout_date',\n",
    "       'channel_code', 'main_product_code', 'numberofadults',\n",
    "       'numberofchildren', 'persontravellingid', 'resort_region_code',\n",
    "       'resort_type_code', 'room_type_booked_code', 'roomnights',\n",
    "       'season_holidayed_code', 'state_code_residence', 'state_code_resort',\n",
    "       'total_pax', 'member_age_buckets', 'booking_type_code', 'cluster_code',\n",
    "       'reservationstatusid_code', 'resort_id', 'actual_days',\n",
    "       'booking_checkin_days', 'booking_checkout_days',\n",
    "       'booking_month', 'is_vacation', 'amount_spent_per_room_night_scaled']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop Date columns\n",
    "train_df = train_df.drop(['booking_date'], axis=1)\n",
    "test_df = test_df.drop(['booking_date'], axis=1)\n",
    "\n",
    "train_df = train_df.drop(['checkin_date'], axis=1)\n",
    "test_df = test_df.drop(['checkin_date'], axis=1)\n",
    "\n",
    "train_df = train_df.drop(['checkout_date'], axis=1)\n",
    "test_df = test_df.drop(['checkout_date'], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>reservation_id</th>\n",
       "      <th>channel_code</th>\n",
       "      <th>main_product_code</th>\n",
       "      <th>numberofadults</th>\n",
       "      <th>numberofchildren</th>\n",
       "      <th>persontravellingid</th>\n",
       "      <th>resort_region_code</th>\n",
       "      <th>resort_type_code</th>\n",
       "      <th>room_type_booked_code</th>\n",
       "      <th>roomnights</th>\n",
       "      <th>...</th>\n",
       "      <th>booking_type_code</th>\n",
       "      <th>cluster_code</th>\n",
       "      <th>reservationstatusid_code</th>\n",
       "      <th>resort_id</th>\n",
       "      <th>actual_days</th>\n",
       "      <th>booking_checkin_days</th>\n",
       "      <th>booking_checkout_days</th>\n",
       "      <th>booking_month</th>\n",
       "      <th>is_vacation</th>\n",
       "      <th>amount_spent_per_room_night_scaled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>07659f3758d8aee27f5a7e2887adeacb67021cb95ada1b...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7.706428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03930f033646d073462b35d411616323597715ac4fc398...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>78</td>\n",
       "      <td>83</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6.662563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>d145a32920e6587ad95bfe299d80c0affa268220535aaf...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7.871602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cfd77f44811ed62f25a220b53324cdbafc662a4c9e5f04...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>40</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5.344943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>937cff9e4dcfc2459620153dfc8b9962ac22bea67dfb29...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>103</td>\n",
       "      <td>108</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>7.059346</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      reservation_id  channel_code  \\\n",
       "0  07659f3758d8aee27f5a7e2887adeacb67021cb95ada1b...             3   \n",
       "1  03930f033646d073462b35d411616323597715ac4fc398...             1   \n",
       "2  d145a32920e6587ad95bfe299d80c0affa268220535aaf...             1   \n",
       "3  cfd77f44811ed62f25a220b53324cdbafc662a4c9e5f04...             1   \n",
       "4  937cff9e4dcfc2459620153dfc8b9962ac22bea67dfb29...             1   \n",
       "\n",
       "   main_product_code  numberofadults  numberofchildren  persontravellingid  \\\n",
       "0                  1               2                 0                  46   \n",
       "1                  1               2                 0                  46   \n",
       "2                  1               2                 0                  47   \n",
       "3                  1               2                 2                  46   \n",
       "4                  1               2                 0                  46   \n",
       "\n",
       "   resort_region_code  resort_type_code  room_type_booked_code  roomnights  \\\n",
       "0                   3                 3                      3           1   \n",
       "1                   3                 3                      4           5   \n",
       "2                   1                 5                      4           4   \n",
       "3                   2                 2                      3           5   \n",
       "4                   2                 2                      4           5   \n",
       "\n",
       "                  ...                  booking_type_code  cluster_code  \\\n",
       "0                 ...                                  1             5   \n",
       "1                 ...                                  1             5   \n",
       "2                 ...                                  1             4   \n",
       "3                 ...                                  1             3   \n",
       "4                 ...                                  1             3   \n",
       "\n",
       "   reservationstatusid_code  resort_id  actual_days  booking_checkin_days  \\\n",
       "0                         2          7            1                     0   \n",
       "1                         0          1            5                    78   \n",
       "2                         0          9            4                     4   \n",
       "3                         0         25            5                    40   \n",
       "4                         0         25            5                   103   \n",
       "\n",
       "   booking_checkout_days  booking_month  is_vacation  \\\n",
       "0                      1              4            1   \n",
       "1                     83              4            1   \n",
       "2                      8              2            0   \n",
       "3                     45              6            0   \n",
       "4                    108             12            1   \n",
       "\n",
       "   amount_spent_per_room_night_scaled  \n",
       "0                            7.706428  \n",
       "1                            6.662563  \n",
       "2                            7.871602  \n",
       "3                            5.344943  \n",
       "4                            7.059346  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_child_n(row):\n",
    "    if row['numberofchildren'] > 10:\n",
    "        return 10\n",
    "    else:\n",
    "        return row['numberofchildren']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['numberofchildren'] = train_df.apply(replace_child_n, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = train_df.drop(['booking_checkin_days'], axis=1)\n",
    "test_df = test_df.drop(['booking_checkin_days'], axis=1)\n",
    "\n",
    "train_df = train_df.drop(['booking_checkout_days'], axis=1)\n",
    "test_df = test_df.drop(['booking_checkout_days'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 579,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = train_df.drop(['booking_month'], axis=1)\n",
    "test_df = test_df.drop(['booking_month'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 619,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = train_df.drop(['actual_days'], axis=1)\n",
    "test_df = test_df.drop(['actual_days'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_lbd = train_df\n",
    "test_df_lbd = test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_lbd.columns.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>reservation_id</th>\n",
       "      <th>channel_code</th>\n",
       "      <th>main_product_code</th>\n",
       "      <th>numberofadults</th>\n",
       "      <th>numberofchildren</th>\n",
       "      <th>persontravellingid</th>\n",
       "      <th>resort_region_code</th>\n",
       "      <th>resort_type_code</th>\n",
       "      <th>room_type_booked_code</th>\n",
       "      <th>roomnights</th>\n",
       "      <th>...</th>\n",
       "      <th>booking_type_code</th>\n",
       "      <th>cluster_code</th>\n",
       "      <th>reservationstatusid_code</th>\n",
       "      <th>resort_id</th>\n",
       "      <th>actual_days</th>\n",
       "      <th>booking_checkin_days</th>\n",
       "      <th>booking_checkout_days</th>\n",
       "      <th>booking_month</th>\n",
       "      <th>is_vacation</th>\n",
       "      <th>amount_spent_per_room_night_scaled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>07659f3758d8aee27f5a7e2887adeacb67021cb95ada1b...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7.706428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03930f033646d073462b35d411616323597715ac4fc398...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>78</td>\n",
       "      <td>83</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6.662563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>d145a32920e6587ad95bfe299d80c0affa268220535aaf...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7.871602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cfd77f44811ed62f25a220b53324cdbafc662a4c9e5f04...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>40</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5.344943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>937cff9e4dcfc2459620153dfc8b9962ac22bea67dfb29...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>103</td>\n",
       "      <td>108</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>7.059346</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      reservation_id  channel_code  \\\n",
       "0  07659f3758d8aee27f5a7e2887adeacb67021cb95ada1b...             3   \n",
       "1  03930f033646d073462b35d411616323597715ac4fc398...             1   \n",
       "2  d145a32920e6587ad95bfe299d80c0affa268220535aaf...             1   \n",
       "3  cfd77f44811ed62f25a220b53324cdbafc662a4c9e5f04...             1   \n",
       "4  937cff9e4dcfc2459620153dfc8b9962ac22bea67dfb29...             1   \n",
       "\n",
       "   main_product_code  numberofadults  numberofchildren  persontravellingid  \\\n",
       "0                  1               2                 0                  46   \n",
       "1                  1               2                 0                  46   \n",
       "2                  1               2                 0                  47   \n",
       "3                  1               2                 2                  46   \n",
       "4                  1               2                 0                  46   \n",
       "\n",
       "   resort_region_code  resort_type_code  room_type_booked_code  roomnights  \\\n",
       "0                   3                 3                      3           1   \n",
       "1                   3                 3                      4           5   \n",
       "2                   1                 5                      4           4   \n",
       "3                   2                 2                      3           5   \n",
       "4                   2                 2                      4           5   \n",
       "\n",
       "                  ...                  booking_type_code  cluster_code  \\\n",
       "0                 ...                                  1             5   \n",
       "1                 ...                                  1             5   \n",
       "2                 ...                                  1             4   \n",
       "3                 ...                                  1             3   \n",
       "4                 ...                                  1             3   \n",
       "\n",
       "   reservationstatusid_code  resort_id  actual_days  booking_checkin_days  \\\n",
       "0                         2          7            1                     0   \n",
       "1                         0          1            5                    78   \n",
       "2                         0          9            4                     4   \n",
       "3                         0         25            5                    40   \n",
       "4                         0         25            5                   103   \n",
       "\n",
       "   booking_checkout_days  booking_month  is_vacation  \\\n",
       "0                      1              4            1   \n",
       "1                     83              4            1   \n",
       "2                      8              2            0   \n",
       "3                     45              6            0   \n",
       "4                    108             12            1   \n",
       "\n",
       "   amount_spent_per_room_night_scaled  \n",
       "0                            7.706428  \n",
       "1                            6.662563  \n",
       "2                            7.871602  \n",
       "3                            5.344943  \n",
       "4                            7.059346  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_lbd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_reservation_id = train_df_lbd.iloc[:, 0].values\n",
    "train_df_lbd = train_df_lbd.drop(['reservation_id'], axis=1)\n",
    "\n",
    "X2_reservation_id = test_df_lbd.iloc[:, 0].values\n",
    "test_df_lbd = test_df_lbd.drop(['reservation_id'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = train_df_lbd.iloc[:, 23].values\n",
    "train_df_lbd = train_df_lbd.drop(['amount_spent_per_room_night_scaled'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.70642846, 6.66256293, 7.87160208, ..., 8.38193917, 8.85603547,\n",
       "       7.86462734])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['channel_code', 'main_product_code', 'numberofadults',\n",
       "       'numberofchildren', 'persontravellingid', 'resort_region_code',\n",
       "       'resort_type_code', 'room_type_booked_code', 'roomnights',\n",
       "       'season_holidayed_code', 'state_code_residence', 'state_code_resort',\n",
       "       'total_pax', 'member_age_buckets', 'booking_type_code', 'cluster_code',\n",
       "       'reservationstatusid_code', 'resort_id', 'actual_days',\n",
       "       'booking_checkin_days', 'booking_checkout_days', 'booking_month',\n",
       "       'is_vacation'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_lbd.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['channel_code', 'main_product_code', 'numberofadults',\n",
       "       'numberofchildren', 'persontravellingid', 'resort_region_code',\n",
       "       'resort_type_code', 'room_type_booked_code', 'roomnights',\n",
       "       'season_holidayed_code', 'state_code_residence', 'state_code_resort',\n",
       "       'total_pax', 'member_age_buckets', 'booking_type_code', 'cluster_code',\n",
       "       'reservationstatusid_code', 'resort_id', 'actual_days',\n",
       "       'booking_checkin_days', 'booking_checkout_days', 'booking_month',\n",
       "       'is_vacation'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df_lbd.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_lbd_one = pd.get_dummies(train_df_lbd, columns= ['channel_code', 'main_product_code', 'numberofadults',\n",
    "       'numberofchildren', 'persontravellingid', 'resort_region_code',\n",
    "       'resort_type_code', 'room_type_booked_code', 'roomnights',\n",
    "       'season_holidayed_code', 'state_code_residence', 'state_code_resort',\n",
    "       'total_pax', 'member_age_buckets', 'booking_type_code', 'cluster_code',\n",
    "       'reservationstatusid_code', 'resort_id',\n",
    "       'is_vacation'], drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df_lbd_one = pd.get_dummies(test_df_lbd, columns=['channel_code', 'main_product_code', 'numberofadults',\n",
    "       'numberofchildren', 'persontravellingid', 'resort_region_code',\n",
    "       'resort_type_code', 'room_type_booked_code', 'roomnights',\n",
    "       'season_holidayed_code', 'state_code_residence', 'state_code_resort',\n",
    "       'total_pax', 'member_age_buckets', 'booking_type_code', 'cluster_code',\n",
    "       'reservationstatusid_code', 'resort_id',\n",
    "       'is_vacation'], drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .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>actual_days</th>\n",
       "      <th>booking_checkin_days</th>\n",
       "      <th>booking_checkout_days</th>\n",
       "      <th>booking_month</th>\n",
       "      <th>channel_code_2</th>\n",
       "      <th>channel_code_3</th>\n",
       "      <th>main_product_code_2</th>\n",
       "      <th>main_product_code_3</th>\n",
       "      <th>main_product_code_4</th>\n",
       "      <th>main_product_code_7</th>\n",
       "      <th>...</th>\n",
       "      <th>resort_id_23</th>\n",
       "      <th>resort_id_24</th>\n",
       "      <th>resort_id_25</th>\n",
       "      <th>resort_id_26</th>\n",
       "      <th>resort_id_27</th>\n",
       "      <th>resort_id_28</th>\n",
       "      <th>resort_id_29</th>\n",
       "      <th>resort_id_30</th>\n",
       "      <th>resort_id_31</th>\n",
       "      <th>is_vacation_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>78</td>\n",
       "      <td>83</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>40</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>103</td>\n",
       "      <td>108</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 210 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   actual_days  booking_checkin_days  booking_checkout_days  booking_month  \\\n",
       "0            1                     0                      1              4   \n",
       "1            5                    78                     83              4   \n",
       "2            4                     4                      8              2   \n",
       "3            5                    40                     45              6   \n",
       "4            5                   103                    108             12   \n",
       "\n",
       "   channel_code_2  channel_code_3  main_product_code_2  main_product_code_3  \\\n",
       "0               0               1                    0                    0   \n",
       "1               0               0                    0                    0   \n",
       "2               0               0                    0                    0   \n",
       "3               0               0                    0                    0   \n",
       "4               0               0                    0                    0   \n",
       "\n",
       "   main_product_code_4  main_product_code_7      ...        resort_id_23  \\\n",
       "0                    0                    0      ...                   0   \n",
       "1                    0                    0      ...                   0   \n",
       "2                    0                    0      ...                   0   \n",
       "3                    0                    0      ...                   0   \n",
       "4                    0                    0      ...                   0   \n",
       "\n",
       "   resort_id_24  resort_id_25  resort_id_26  resort_id_27  resort_id_28  \\\n",
       "0             0             0             0             0             0   \n",
       "1             0             0             0             0             0   \n",
       "2             0             0             0             0             0   \n",
       "3             0             1             0             0             0   \n",
       "4             0             1             0             0             0   \n",
       "\n",
       "   resort_id_29  resort_id_30  resort_id_31  is_vacation_1  \n",
       "0             0             0             0              1  \n",
       "1             0             0             0              1  \n",
       "2             0             0             0              0  \n",
       "3             0             0             0              0  \n",
       "4             0             0             0              1  \n",
       "\n",
       "[5 rows x 210 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_lbd_one.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['actual_days', 'booking_checkin_days', 'booking_checkout_days',\n",
       "       'booking_month', 'channel_code_2', 'channel_code_3',\n",
       "       'main_product_code_2', 'main_product_code_3', 'main_product_code_4',\n",
       "       'main_product_code_7',\n",
       "       ...\n",
       "       'resort_id_23', 'resort_id_24', 'resort_id_25', 'resort_id_26',\n",
       "       'resort_id_27', 'resort_id_28', 'resort_id_29', 'resort_id_30',\n",
       "       'resort_id_31', 'is_vacation_1'],\n",
       "      dtype='object', length=210)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_lbd_one.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['actual_days', 'booking_checkin_days', 'booking_checkout_days',\n",
       "       'booking_month', 'channel_code_2', 'channel_code_3',\n",
       "       'main_product_code_2', 'main_product_code_3', 'main_product_code_4',\n",
       "       'main_product_code_7',\n",
       "       ...\n",
       "       'resort_id_23', 'resort_id_24', 'resort_id_25', 'resort_id_26',\n",
       "       'resort_id_27', 'resort_id_28', 'resort_id_29', 'resort_id_30',\n",
       "       'resort_id_31', 'is_vacation_1'],\n",
       "      dtype='object', length=210)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df_lbd_one.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in test_df_lbd_one.columns:\n",
    "    if i not in train_df_lbd_one.columns:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "actual_days_16\n",
    "booking_checkin_days_177\n",
    "booking_checkout_days_130\n",
    "booking_checkout_days_180"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "numberofchildren_13\n",
    "booking_checkin_days_125\n",
    "booking_checkin_days_126"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_lbd_one = train_df_lbd_one.drop(['numberofchildren_13'], axis=1)\n",
    "train_df_lbd_one = train_df_lbd_one.drop(['booking_checkin_days_125'], axis=1)\n",
    "train_df_lbd_one = train_df_lbd_one.drop(['booking_checkin_days_126'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 593,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df_lbd_one = test_df_lbd_one.drop(['actual_days_16'], axis=1)\n",
    "#test_df_lbd_one = test_df_lbd_one.drop(['booking_checkin_days_177'], axis=1)\n",
    "#test_df_lbd_one = test_df_lbd_one.drop(['booking_checkout_days_130'], axis=1)\n",
    "#test_df_lbd_one = test_df_lbd_one.drop(['booking_checkout_days_180'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train_df_lbd_one.iloc[:, 0:].values\n",
    "X2 = test_df_lbd_one.iloc[:, 0:].values # Test SET"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "146765 rows × 486"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n# Feature Scaling\\nfrom sklearn.preprocessing import StandardScaler\\n\\nsc = StandardScaler()\\nX_train = sc.fit_transform(X_train)\\nX_test = sc.transform(X_test)\\nX2_Scaled = sc.transform(X2)\\n'"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Splitting the dataset into the Training set and Test set\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "\"\"\"\n",
    "# Feature Scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)\n",
    "X2_Scaled = sc.transform(X2)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(X_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras import backend\n",
    "from tensorflow.keras.wrappers.scikit_learn import KerasRegressor\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import backend\n",
    " \n",
    "def rmse(y_true, y_pred):\n",
    "    return backend.sqrt(backend.mean(backend.square(y_pred - y_true), axis=-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 245801 samples, validate on 27312 samples\n",
      "Epoch 1/50\n",
      "245801/245801 [==============================] - 1s 5us/sample - loss: 13.9537 - rmse: 2.8785 - val_loss: 1.9262 - val_rmse: 1.0868\n",
      "Epoch 2/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 1.4471 - rmse: 0.9113 - val_loss: 1.2722 - val_rmse: 0.8319\n",
      "Epoch 3/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 1.1528 - rmse: 0.8008 - val_loss: 1.1236 - val_rmse: 0.7774\n",
      "Epoch 4/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 1.0610 - rmse: 0.7629 - val_loss: 1.0740 - val_rmse: 0.7814\n",
      "Epoch 5/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 1.0274 - rmse: 0.7488 - val_loss: 1.0648 - val_rmse: 0.7869\n",
      "Epoch 6/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 1.0107 - rmse: 0.7416 - val_loss: 1.0305 - val_rmse: 0.7509\n",
      "Epoch 7/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9993 - rmse: 0.7363 - val_loss: 1.0320 - val_rmse: 0.7308\n",
      "Epoch 8/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9905 - rmse: 0.7319 - val_loss: 1.0408 - val_rmse: 0.7735\n",
      "Epoch 9/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9992 - rmse: 0.7368 - val_loss: 1.0146 - val_rmse: 0.7310\n",
      "Epoch 10/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9852 - rmse: 0.7300 - val_loss: 1.0100 - val_rmse: 0.7432\n",
      "Epoch 11/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9820 - rmse: 0.7288 - val_loss: 1.0088 - val_rmse: 0.7306\n",
      "Epoch 12/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9809 - rmse: 0.7285 - val_loss: 1.0412 - val_rmse: 0.7859\n",
      "Epoch 13/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9781 - rmse: 0.7281 - val_loss: 1.0116 - val_rmse: 0.7534\n",
      "Epoch 14/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9871 - rmse: 0.7316 - val_loss: 1.0045 - val_rmse: 0.7395\n",
      "Epoch 15/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9813 - rmse: 0.7288 - val_loss: 1.0313 - val_rmse: 0.7776\n",
      "Epoch 16/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9840 - rmse: 0.7302 - val_loss: 1.0309 - val_rmse: 0.7788\n",
      "Epoch 17/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9745 - rmse: 0.7260 - val_loss: 1.0405 - val_rmse: 0.7906\n",
      "Epoch 18/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9790 - rmse: 0.7284 - val_loss: 1.0090 - val_rmse: 0.7570\n",
      "Epoch 19/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9714 - rmse: 0.7242 - val_loss: 1.0048 - val_rmse: 0.7497\n",
      "Epoch 20/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9724 - rmse: 0.7254 - val_loss: 1.0125 - val_rmse: 0.7195\n",
      "Epoch 21/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9653 - rmse: 0.7214 - val_loss: 1.0015 - val_rmse: 0.7467\n",
      "Epoch 22/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9739 - rmse: 0.7259 - val_loss: 1.0144 - val_rmse: 0.7168\n",
      "Epoch 23/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9660 - rmse: 0.7220 - val_loss: 0.9994 - val_rmse: 0.7427\n",
      "Epoch 24/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9793 - rmse: 0.7284 - val_loss: 1.0102 - val_rmse: 0.7185\n",
      "Epoch 25/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9646 - rmse: 0.7216 - val_loss: 1.0000 - val_rmse: 0.7206\n",
      "Epoch 26/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9721 - rmse: 0.7257 - val_loss: 0.9963 - val_rmse: 0.7333\n",
      "Epoch 27/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9629 - rmse: 0.7212 - val_loss: 1.0072 - val_rmse: 0.7581\n",
      "Epoch 28/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9680 - rmse: 0.7235 - val_loss: 0.9947 - val_rmse: 0.7336\n",
      "Epoch 29/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9592 - rmse: 0.7192 - val_loss: 0.9989 - val_rmse: 0.7464\n",
      "Epoch 30/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9887 - rmse: 0.7341 - val_loss: 1.0995 - val_rmse: 0.7275\n",
      "Epoch 31/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9659 - rmse: 0.7220 - val_loss: 0.9978 - val_rmse: 0.7438\n",
      "Epoch 32/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9765 - rmse: 0.7286 - val_loss: 1.0180 - val_rmse: 0.7152\n",
      "Epoch 33/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9644 - rmse: 0.7221 - val_loss: 1.0243 - val_rmse: 0.7147\n",
      "Epoch 34/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9606 - rmse: 0.7203 - val_loss: 1.0285 - val_rmse: 0.7779\n",
      "Epoch 35/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9607 - rmse: 0.7208 - val_loss: 1.0020 - val_rmse: 0.7454\n",
      "Epoch 36/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9565 - rmse: 0.7186 - val_loss: 1.0057 - val_rmse: 0.7175\n",
      "Epoch 37/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9575 - rmse: 0.7190 - val_loss: 0.9965 - val_rmse: 0.7420\n",
      "Epoch 38/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9575 - rmse: 0.7194 - val_loss: 0.9972 - val_rmse: 0.7284\n",
      "Epoch 39/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9582 - rmse: 0.7195 - val_loss: 0.9950 - val_rmse: 0.7277\n",
      "Epoch 40/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9712 - rmse: 0.7258 - val_loss: 0.9962 - val_rmse: 0.7390\n",
      "Epoch 41/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9588 - rmse: 0.7202 - val_loss: 0.9992 - val_rmse: 0.7479\n",
      "Epoch 42/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9568 - rmse: 0.7192 - val_loss: 0.9967 - val_rmse: 0.7296\n",
      "Epoch 43/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9676 - rmse: 0.7244 - val_loss: 1.0382 - val_rmse: 0.7178\n",
      "Epoch 44/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9622 - rmse: 0.7215 - val_loss: 1.0259 - val_rmse: 0.7168\n",
      "Epoch 45/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9738 - rmse: 0.7268 - val_loss: 1.0001 - val_rmse: 0.7468\n",
      "Epoch 46/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9527 - rmse: 0.7174 - val_loss: 1.0678 - val_rmse: 0.7202\n",
      "Epoch 47/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9630 - rmse: 0.7224 - val_loss: 1.0050 - val_rmse: 0.7196\n",
      "Epoch 48/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9627 - rmse: 0.7221 - val_loss: 1.0667 - val_rmse: 0.8122\n",
      "Epoch 49/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9639 - rmse: 0.7236 - val_loss: 0.9989 - val_rmse: 0.7315\n",
      "Epoch 50/50\n",
      "245801/245801 [==============================] - 1s 4us/sample - loss: 0.9497 - rmse: 0.7161 - val_loss: 0.9990 - val_rmse: 0.7279\n"
     ]
    }
   ],
   "source": [
    "with tf.device('/device:GPU:0'):\n",
    "    # Initialising the ANN\n",
    "    Regressior = Sequential()\n",
    "    \n",
    "    # Adding the input layer and the first hidden layer\n",
    "    Regressior.add(Dense(units=210, kernel_initializer='uniform', activation='relu', input_dim=210))\n",
    "    \n",
    "    # Adding the second hidden layer\n",
    "    Regressior.add(Dense(units=210, kernel_initializer='uniform', activation='relu'))\n",
    "    \n",
    "    # Adding the third hidden layer\n",
    "    Regressior.add(Dense(units=210, kernel_initializer='uniform', activation='relu'))\n",
    "    \n",
    "    # Adding the output layer\n",
    "    Regressior.add(Dense(units=1, kernel_initializer='uniform', activation='linear'))\n",
    "    \n",
    "    # Compiling the ANN\n",
    "    Regressior.compile(loss='mse', optimizer='adam', metrics=[rmse])\n",
    "    \n",
    "    # Fitting the ANN to the Training set\n",
    "    Regressior.fit(X_train, y_train, batch_size=5000, epochs=50, validation_split=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred= Regressior.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(y_test, y_pred)\n",
    "ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=4)\n",
    "ax.set_xlabel('Measured')\n",
    "ax.set_ylabel('Predicted')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Submit = Regressior.predict(X2_Scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 613,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.7572117, 7.5140996, 7.268451 , ..., 8.027311 , 7.137011 ,\n",
       "       7.750776 ], dtype=float32)"
      ]
     },
     "execution_count": 613,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Submit.reshape(146765,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 614,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(146765,)"
      ]
     },
     "execution_count": 614,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2_reservation_id.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 615,
   "metadata": {},
   "outputs": [],
   "source": [
    "Submission = pd.DataFrame({'reservation_id':X2_reservation_id,'amount_spent_per_room_night_scaled':np.around(Submit.reshape(146765,))})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 616,
   "metadata": {},
   "outputs": [],
   "source": [
    "Submission['amount_spent_per_room_night_scaled'] = Submission.amount_spent_per_room_night_scaled.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 617,
   "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>reservation_id</th>\n",
       "      <th>amount_spent_per_room_night_scaled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7dae1ce6bc8f69481328f2be5c4943077dad5598b5f66d...</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fe0d4e444e1818436c88f72f1cf800536c2f785e59baeb...</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>540bd4285ad8168e8388f84ee74a82cd4f97dc0a404d3e...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>09593c907ae262e46f655b4db9e14f54a19eadcfdd2679...</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>f4c50caac68051faf37551d70bb17eebef2a20e2244cb1...</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      reservation_id  \\\n",
       "0  7dae1ce6bc8f69481328f2be5c4943077dad5598b5f66d...   \n",
       "1  fe0d4e444e1818436c88f72f1cf800536c2f785e59baeb...   \n",
       "2  540bd4285ad8168e8388f84ee74a82cd4f97dc0a404d3e...   \n",
       "3  09593c907ae262e46f655b4db9e14f54a19eadcfdd2679...   \n",
       "4  f4c50caac68051faf37551d70bb17eebef2a20e2244cb1...   \n",
       "\n",
       "   amount_spent_per_room_night_scaled  \n",
       "0                                   8  \n",
       "1                                   8  \n",
       "2                                   7  \n",
       "3                                   8  \n",
       "4                                   8  "
      ]
     },
     "execution_count": 617,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 618,
   "metadata": {},
   "outputs": [],
   "source": [
    "Submission.to_csv(\"submission_four.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  8,  3,  4,  2,  5,  6,  7, 12, 20, 10, 15,  9, 16, 25, 18, 14,\n",
       "       24, 17, 11, 32, 21, 13, 28, 36, 30, 39, 19, 40, 22, 23, 33, 27, 26,\n",
       "        0, 35], dtype=int64)"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.roomnights.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_room = [ 1,  5,  4,  2,  6,  3,  8, 12, 16,  9,  7, 10, 14, 15, 11, 22, 18,\n",
    "       25, 20, 13, 21, 26, 30, 17, 24, 27, 39, 28, 19, 23, 36, 40, 33, 35,\n",
    "       32,  0]\n",
    "test_room = [ 1,  8,  3,  4,  2,  5,  6,  7, 12, 20, 10, 15,  9, 16, 25, 18, 14,\n",
    "       24, 17, 11, 32, 21, 13, 28, 36, 30, 39, 19, 40, 22, 23, 33, 27, 26,\n",
    "        0, 35]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_room.sort()\n",
    "test_room.sort()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "test_room"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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