customer-segment-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9352f71f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd # for data manupulation\n",
    "import numpy as np # to work with array\n",
    "import matplotlib.pyplot as plt # for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cad0157b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('travel_tide.csv') # load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8713b969",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>session_id</th>\n",
       "      <th>trip_id</th>\n",
       "      <th>session_start</th>\n",
       "      <th>session_end</th>\n",
       "      <th>flight_discount</th>\n",
       "      <th>hotel_discount</th>\n",
       "      <th>flight_discount_amount</th>\n",
       "      <th>hotel_discount_amount</th>\n",
       "      <th>flight_booked</th>\n",
       "      <th>...</th>\n",
       "      <th>destination_airport_lat</th>\n",
       "      <th>destination_airport_lon</th>\n",
       "      <th>base_fare_usd</th>\n",
       "      <th>trip_id.2</th>\n",
       "      <th>hotel_name</th>\n",
       "      <th>nights</th>\n",
       "      <th>rooms</th>\n",
       "      <th>check_in_time</th>\n",
       "      <th>check_out_time</th>\n",
       "      <th>hotel_per_room_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>447951</td>\n",
       "      <td>447951-dc80e75ea3234c0398a9911953386d90</td>\n",
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       "      <td>2023-01-04 20:35:00</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>449980</td>\n",
       "      <td>449980-e740c9ba5e3945db803565a5239b6d2e</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-01-04 22:48:00</td>\n",
       "      <td>2023-01-04 22:50:10</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>485785</td>\n",
       "      <td>485785-b77e273846f34973993dae383568a47c</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-01-05 18:24:00</td>\n",
       "      <td>2023-01-05 18:24:47</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>487352</td>\n",
       "      <td>487352-58491d5ab9d84a34ae58e6ccb3ade0c3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-01-05 09:01:00</td>\n",
       "      <td>2023-01-05 09:03:29</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>489949</td>\n",
       "      <td>489949-a3eec794414a4e13a5929e73c7157f1b</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-01-05 20:58:00</td>\n",
       "      <td>2023-01-05 20:59:23</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49206</th>\n",
       "      <td>622343</td>\n",
       "      <td>622343-8de1e79234d94afca297fa7dee9af5ce</td>\n",
       "      <td>622343-2061dc37f60d410f9af97a51a7c8b676</td>\n",
       "      <td>2023-07-20 18:42:37</td>\n",
       "      <td>2023-07-20 19:51:01.770539</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>40.640</td>\n",
       "      <td>-73.779</td>\n",
       "      <td>214.03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49207</th>\n",
       "      <td>480313</td>\n",
       "      <td>480313-f46ac4d965a2410e84ddf400ede116ad</td>\n",
       "      <td>480313-129a85cc3c5244b5962afe10c7932cda</td>\n",
       "      <td>2023-02-09 22:01:16</td>\n",
       "      <td>2023-02-10 00:01:16</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>37.362</td>\n",
       "      <td>-121.929</td>\n",
       "      <td>83.44</td>\n",
       "      <td>480313-129a85cc3c5244b5962afe10c7932cda</td>\n",
       "      <td>Shangri-La - san jose</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-02-13 09:37:10.74</td>\n",
       "      <td>2023-02-15 11:00:00</td>\n",
       "      <td>174.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49208</th>\n",
       "      <td>534295</td>\n",
       "      <td>534295-0a137c45cac34b1e834f23ecab4233fb</td>\n",
       "      <td>534295-3e20b84b40c946b1a8124395231d4412</td>\n",
       "      <td>2023-07-10 18:55:00</td>\n",
       "      <td>2023-07-10 18:57:18</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>32.699</td>\n",
       "      <td>-117.215</td>\n",
       "      <td>3205.63</td>\n",
       "      <td>534295-3e20b84b40c946b1a8124395231d4412</td>\n",
       "      <td>Aman Resorts - san diego</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2023-07-20 20:54:53.1</td>\n",
       "      <td>2023-07-29 11:00:00</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49209</th>\n",
       "      <td>518416</td>\n",
       "      <td>518416-fd45c188d94a4d30a7e4468056701f1e</td>\n",
       "      <td>518416-cca5cea3fac04cdf8d069e13891f016a</td>\n",
       "      <td>2023-07-15 07:38:00</td>\n",
       "      <td>2023-07-15 07:42:04</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>29.529</td>\n",
       "      <td>-98.279</td>\n",
       "      <td>103.01</td>\n",
       "      <td>518416-cca5cea3fac04cdf8d069e13891f016a</td>\n",
       "      <td>InterContinental  - san antonio</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2023-07-22 15:22:38.37</td>\n",
       "      <td>2023-07-24 11:00:00</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49210</th>\n",
       "      <td>510093</td>\n",
       "      <td>510093-2d3e14a9e10b4a0a942a4312b914e557</td>\n",
       "      <td>510093-e71475580a7c4948ba8f13114a60fa30</td>\n",
       "      <td>2023-07-18 17:06:00</td>\n",
       "      <td>2023-07-18 17:08:42</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>50.056</td>\n",
       "      <td>-97.032</td>\n",
       "      <td>381.22</td>\n",
       "      <td>510093-e71475580a7c4948ba8f13114a60fa30</td>\n",
       "      <td>Marriott  - winnipeg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-07-30 11:45:27.81</td>\n",
       "      <td>2023-07-30 11:00:00</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49211 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id                               session_id  \\\n",
       "0       447951  447951-dc80e75ea3234c0398a9911953386d90   \n",
       "1       449980  449980-e740c9ba5e3945db803565a5239b6d2e   \n",
       "2       485785  485785-b77e273846f34973993dae383568a47c   \n",
       "3       487352  487352-58491d5ab9d84a34ae58e6ccb3ade0c3   \n",
       "4       489949  489949-a3eec794414a4e13a5929e73c7157f1b   \n",
       "...        ...                                      ...   \n",
       "49206   622343  622343-8de1e79234d94afca297fa7dee9af5ce   \n",
       "49207   480313  480313-f46ac4d965a2410e84ddf400ede116ad   \n",
       "49208   534295  534295-0a137c45cac34b1e834f23ecab4233fb   \n",
       "49209   518416  518416-fd45c188d94a4d30a7e4468056701f1e   \n",
       "49210   510093  510093-2d3e14a9e10b4a0a942a4312b914e557   \n",
       "\n",
       "                                       trip_id        session_start  \\\n",
       "0                                          NaN  2023-01-04 20:35:00   \n",
       "1                                          NaN  2023-01-04 22:48:00   \n",
       "2                                          NaN  2023-01-05 18:24:00   \n",
       "3                                          NaN  2023-01-05 09:01:00   \n",
       "4                                          NaN  2023-01-05 20:58:00   \n",
       "...                                        ...                  ...   \n",
       "49206  622343-2061dc37f60d410f9af97a51a7c8b676  2023-07-20 18:42:37   \n",
       "49207  480313-129a85cc3c5244b5962afe10c7932cda  2023-02-09 22:01:16   \n",
       "49208  534295-3e20b84b40c946b1a8124395231d4412  2023-07-10 18:55:00   \n",
       "49209  518416-cca5cea3fac04cdf8d069e13891f016a  2023-07-15 07:38:00   \n",
       "49210  510093-e71475580a7c4948ba8f13114a60fa30  2023-07-18 17:06:00   \n",
       "\n",
       "                      session_end  flight_discount  hotel_discount  \\\n",
       "0             2023-01-04 20:35:52            False           False   \n",
       "1             2023-01-04 22:50:10            False           False   \n",
       "2             2023-01-05 18:24:47             True           False   \n",
       "3             2023-01-05 09:03:29            False           False   \n",
       "4             2023-01-05 20:59:23            False           False   \n",
       "...                           ...              ...             ...   \n",
       "49206  2023-07-20 19:51:01.770539             True            True   \n",
       "49207         2023-02-10 00:01:16             True            True   \n",
       "49208         2023-07-10 18:57:18            False           False   \n",
       "49209         2023-07-15 07:42:04            False           False   \n",
       "49210         2023-07-18 17:08:42             True           False   \n",
       "\n",
       "       flight_discount_amount  hotel_discount_amount  flight_booked  ...  \\\n",
       "0                         NaN                    NaN          False  ...   \n",
       "1                         NaN                    NaN          False  ...   \n",
       "2                        0.15                    NaN          False  ...   \n",
       "3                         NaN                    NaN          False  ...   \n",
       "4                         NaN                    NaN          False  ...   \n",
       "...                       ...                    ...            ...  ...   \n",
       "49206                     NaN                    NaN           True  ...   \n",
       "49207                     NaN                    NaN           True  ...   \n",
       "49208                     NaN                    NaN           True  ...   \n",
       "49209                     NaN                    NaN           True  ...   \n",
       "49210                    0.20                    NaN           True  ...   \n",
       "\n",
       "       destination_airport_lat  destination_airport_lon  base_fare_usd  \\\n",
       "0                          NaN                      NaN            NaN   \n",
       "1                          NaN                      NaN            NaN   \n",
       "2                          NaN                      NaN            NaN   \n",
       "3                          NaN                      NaN            NaN   \n",
       "4                          NaN                      NaN            NaN   \n",
       "...                        ...                      ...            ...   \n",
       "49206                   40.640                  -73.779         214.03   \n",
       "49207                   37.362                 -121.929          83.44   \n",
       "49208                   32.699                 -117.215        3205.63   \n",
       "49209                   29.529                  -98.279         103.01   \n",
       "49210                   50.056                  -97.032         381.22   \n",
       "\n",
       "                                     trip_id.2  \\\n",
       "0                                          NaN   \n",
       "1                                          NaN   \n",
       "2                                          NaN   \n",
       "3                                          NaN   \n",
       "4                                          NaN   \n",
       "...                                        ...   \n",
       "49206                                      NaN   \n",
       "49207  480313-129a85cc3c5244b5962afe10c7932cda   \n",
       "49208  534295-3e20b84b40c946b1a8124395231d4412   \n",
       "49209  518416-cca5cea3fac04cdf8d069e13891f016a   \n",
       "49210  510093-e71475580a7c4948ba8f13114a60fa30   \n",
       "\n",
       "                            hotel_name  nights  rooms           check_in_time  \\\n",
       "0                                  NaN     NaN    NaN                     NaN   \n",
       "1                                  NaN     NaN    NaN                     NaN   \n",
       "2                                  NaN     NaN    NaN                     NaN   \n",
       "3                                  NaN     NaN    NaN                     NaN   \n",
       "4                                  NaN     NaN    NaN                     NaN   \n",
       "...                                ...     ...    ...                     ...   \n",
       "49206                              NaN     NaN    NaN                     NaN   \n",
       "49207            Shangri-La - san jose     2.0    1.0  2023-02-13 09:37:10.74   \n",
       "49208         Aman Resorts - san diego     8.0    4.0   2023-07-20 20:54:53.1   \n",
       "49209  InterContinental  - san antonio     1.0    2.0  2023-07-22 15:22:38.37   \n",
       "49210             Marriott  - winnipeg     0.0    1.0  2023-07-30 11:45:27.81   \n",
       "\n",
       "            check_out_time hotel_per_room_usd  \n",
       "0                      NaN                NaN  \n",
       "1                      NaN                NaN  \n",
       "2                      NaN                NaN  \n",
       "3                      NaN                NaN  \n",
       "4                      NaN                NaN  \n",
       "...                    ...                ...  \n",
       "49206                  NaN                NaN  \n",
       "49207  2023-02-15 11:00:00              174.0  \n",
       "49208  2023-07-29 11:00:00               85.0  \n",
       "49209  2023-07-24 11:00:00               80.0  \n",
       "49210  2023-07-30 11:00:00              109.0  \n",
       "\n",
       "[49211 rows x 43 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "25de0ee2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['user_id', 'session_id', 'trip_id', 'session_start', 'session_end',\n",
       "       'flight_discount', 'hotel_discount', 'flight_discount_amount',\n",
       "       'hotel_discount_amount', 'flight_booked', 'hotel_booked', 'page_clicks',\n",
       "       'cancellation', 'birthdate', 'gender', 'married', 'has_children',\n",
       "       'home_country', 'home_city', 'home_airport', 'home_airport_lat',\n",
       "       'home_airport_lon', 'sign_up_date', 'trip_id.1', 'origin_airport',\n",
       "       'destination', 'destination_airport', 'seats', 'return_flight_booked',\n",
       "       'departure_time', 'return_time', 'checked_bags', 'trip_airline',\n",
       "       'destination_airport_lat', 'destination_airport_lon', 'base_fare_usd',\n",
       "       'trip_id.2', 'hotel_name', 'nights', 'rooms', 'check_in_time',\n",
       "       'check_out_time', 'hotel_per_room_usd'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b924309",
   "metadata": {},
   "source": [
    "# Free Hotel Meal Perk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1751dc46",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['total_hotel_spent'] = df['rooms'] * df['hotel_per_room_usd'] * df['nights'] # total spend on hotel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "30bdce2a",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>session_id</th>\n",
       "      <th>trip_id</th>\n",
       "      <th>session_start</th>\n",
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       "      <th>flight_discount</th>\n",
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       "      <th>check_out_time</th>\n",
       "      <th>hotel_per_room_usd</th>\n",
       "      <th>total_hotel_spent</th>\n",
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       "      <th>49207</th>\n",
       "      <td>480313</td>\n",
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       "      <td>480313-129a85cc3c5244b5962afe10c7932cda</td>\n",
       "      <td>2023-02-09 22:01:16</td>\n",
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       "      <td>83.44</td>\n",
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       "      <td>85.0</td>\n",
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       "      <td>2023-07-22 15:22:38.37</td>\n",
       "      <td>2023-07-24 11:00:00</td>\n",
       "      <td>80.0</td>\n",
       "      <td>160.0</td>\n",
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       "    <tr>\n",
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       "      <td>510093</td>\n",
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       "      <td>381.22</td>\n",
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       "      <td>Marriott  - winnipeg</td>\n",
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       "      <td>2023-07-30 11:45:27.81</td>\n",
       "      <td>2023-07-30 11:00:00</td>\n",
       "      <td>109.0</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "       user_id                               session_id  \\\n",
       "0       447951  447951-dc80e75ea3234c0398a9911953386d90   \n",
       "1       449980  449980-e740c9ba5e3945db803565a5239b6d2e   \n",
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       "4       489949  489949-a3eec794414a4e13a5929e73c7157f1b   \n",
       "...        ...                                      ...   \n",
       "49206   622343  622343-8de1e79234d94afca297fa7dee9af5ce   \n",
       "49207   480313  480313-f46ac4d965a2410e84ddf400ede116ad   \n",
       "49208   534295  534295-0a137c45cac34b1e834f23ecab4233fb   \n",
       "49209   518416  518416-fd45c188d94a4d30a7e4468056701f1e   \n",
       "49210   510093  510093-2d3e14a9e10b4a0a942a4312b914e557   \n",
       "\n",
       "                                       trip_id        session_start  \\\n",
       "0                                          NaN  2023-01-04 20:35:00   \n",
       "1                                          NaN  2023-01-04 22:48:00   \n",
       "2                                          NaN  2023-01-05 18:24:00   \n",
       "3                                          NaN  2023-01-05 09:01:00   \n",
       "4                                          NaN  2023-01-05 20:58:00   \n",
       "...                                        ...                  ...   \n",
       "49206  622343-2061dc37f60d410f9af97a51a7c8b676  2023-07-20 18:42:37   \n",
       "49207  480313-129a85cc3c5244b5962afe10c7932cda  2023-02-09 22:01:16   \n",
       "49208  534295-3e20b84b40c946b1a8124395231d4412  2023-07-10 18:55:00   \n",
       "49209  518416-cca5cea3fac04cdf8d069e13891f016a  2023-07-15 07:38:00   \n",
       "49210  510093-e71475580a7c4948ba8f13114a60fa30  2023-07-18 17:06:00   \n",
       "\n",
       "                      session_end  flight_discount  hotel_discount  \\\n",
       "0             2023-01-04 20:35:52            False           False   \n",
       "1             2023-01-04 22:50:10            False           False   \n",
       "2             2023-01-05 18:24:47             True           False   \n",
       "3             2023-01-05 09:03:29            False           False   \n",
       "4             2023-01-05 20:59:23            False           False   \n",
       "...                           ...              ...             ...   \n",
       "49206  2023-07-20 19:51:01.770539             True            True   \n",
       "49207         2023-02-10 00:01:16             True            True   \n",
       "49208         2023-07-10 18:57:18            False           False   \n",
       "49209         2023-07-15 07:42:04            False           False   \n",
       "49210         2023-07-18 17:08:42             True           False   \n",
       "\n",
       "       flight_discount_amount  hotel_discount_amount  flight_booked  ...  \\\n",
       "0                         NaN                    NaN          False  ...   \n",
       "1                         NaN                    NaN          False  ...   \n",
       "2                        0.15                    NaN          False  ...   \n",
       "3                         NaN                    NaN          False  ...   \n",
       "4                         NaN                    NaN          False  ...   \n",
       "...                       ...                    ...            ...  ...   \n",
       "49206                     NaN                    NaN           True  ...   \n",
       "49207                     NaN                    NaN           True  ...   \n",
       "49208                     NaN                    NaN           True  ...   \n",
       "49209                     NaN                    NaN           True  ...   \n",
       "49210                    0.20                    NaN           True  ...   \n",
       "\n",
       "       destination_airport_lon  base_fare_usd  \\\n",
       "0                          NaN            NaN   \n",
       "1                          NaN            NaN   \n",
       "2                          NaN            NaN   \n",
       "3                          NaN            NaN   \n",
       "4                          NaN            NaN   \n",
       "...                        ...            ...   \n",
       "49206                  -73.779         214.03   \n",
       "49207                 -121.929          83.44   \n",
       "49208                 -117.215        3205.63   \n",
       "49209                  -98.279         103.01   \n",
       "49210                  -97.032         381.22   \n",
       "\n",
       "                                     trip_id.2  \\\n",
       "0                                          NaN   \n",
       "1                                          NaN   \n",
       "2                                          NaN   \n",
       "3                                          NaN   \n",
       "4                                          NaN   \n",
       "...                                        ...   \n",
       "49206                                      NaN   \n",
       "49207  480313-129a85cc3c5244b5962afe10c7932cda   \n",
       "49208  534295-3e20b84b40c946b1a8124395231d4412   \n",
       "49209  518416-cca5cea3fac04cdf8d069e13891f016a   \n",
       "49210  510093-e71475580a7c4948ba8f13114a60fa30   \n",
       "\n",
       "                            hotel_name nights  rooms           check_in_time  \\\n",
       "0                                  NaN    NaN    NaN                     NaN   \n",
       "1                                  NaN    NaN    NaN                     NaN   \n",
       "2                                  NaN    NaN    NaN                     NaN   \n",
       "3                                  NaN    NaN    NaN                     NaN   \n",
       "4                                  NaN    NaN    NaN                     NaN   \n",
       "...                                ...    ...    ...                     ...   \n",
       "49206                              NaN    NaN    NaN                     NaN   \n",
       "49207            Shangri-La - san jose    2.0    1.0  2023-02-13 09:37:10.74   \n",
       "49208         Aman Resorts - san diego    8.0    4.0   2023-07-20 20:54:53.1   \n",
       "49209  InterContinental  - san antonio    1.0    2.0  2023-07-22 15:22:38.37   \n",
       "49210             Marriott  - winnipeg    0.0    1.0  2023-07-30 11:45:27.81   \n",
       "\n",
       "            check_out_time hotel_per_room_usd total_hotel_spent  \n",
       "0                      NaN                NaN               NaN  \n",
       "1                      NaN                NaN               NaN  \n",
       "2                      NaN                NaN               NaN  \n",
       "3                      NaN                NaN               NaN  \n",
       "4                      NaN                NaN               NaN  \n",
       "...                    ...                ...               ...  \n",
       "49206                  NaN                NaN               NaN  \n",
       "49207  2023-02-15 11:00:00              174.0             348.0  \n",
       "49208  2023-07-29 11:00:00               85.0            2720.0  \n",
       "49209  2023-07-24 11:00:00               80.0             160.0  \n",
       "49210  2023-07-30 11:00:00              109.0               0.0  \n",
       "\n",
       "[49211 rows x 44 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "464f44d0",
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>total_hotel_spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>447951</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>449980</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>485785</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>487352</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>489949</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49206</th>\n",
       "      <td>622343</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49207</th>\n",
       "      <td>480313</td>\n",
       "      <td>348.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49208</th>\n",
       "      <td>534295</td>\n",
       "      <td>2720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49209</th>\n",
       "      <td>518416</td>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49210</th>\n",
       "      <td>510093</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49211 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id  total_hotel_spent\n",
       "0       447951                NaN\n",
       "1       449980                NaN\n",
       "2       485785                NaN\n",
       "3       487352                NaN\n",
       "4       489949                NaN\n",
       "...        ...                ...\n",
       "49206   622343                NaN\n",
       "49207   480313              348.0\n",
       "49208   534295             2720.0\n",
       "49209   518416              160.0\n",
       "49210   510093                0.0\n",
       "\n",
       "[49211 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df[['user_id', 'total_hotel_spent']] # get the related column\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "032ff6ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get total spending on hotel by each user, sort it descending order on total spent\n",
    "new_df = new_df.groupby('user_id', as_index=False).sum('total_hotel_spent').sort_values('total_hotel_spent', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "1967f7c3",
   "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>user_id</th>\n",
       "      <th>total_hotel_spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2675</th>\n",
       "      <td>534594</td>\n",
       "      <td>28114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5077</th>\n",
       "      <td>596863</td>\n",
       "      <td>27489.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780</th>\n",
       "      <td>507805</td>\n",
       "      <td>27241.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2954</th>\n",
       "      <td>539710</td>\n",
       "      <td>22619.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5818</th>\n",
       "      <td>669329</td>\n",
       "      <td>22356.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3244</th>\n",
       "      <td>544926</td>\n",
       "      <td>-349.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4595</th>\n",
       "      <td>577534</td>\n",
       "      <td>-398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5064</th>\n",
       "      <td>596320</td>\n",
       "      <td>-477.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2961</th>\n",
       "      <td>539868</td>\n",
       "      <td>-562.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1393</th>\n",
       "      <td>515835</td>\n",
       "      <td>-575.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5998 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  total_hotel_spent\n",
       "2675   534594            28114.0\n",
       "5077   596863            27489.0\n",
       "780    507805            27241.0\n",
       "2954   539710            22619.0\n",
       "5818   669329            22356.0\n",
       "...       ...                ...\n",
       "3244   544926             -349.0\n",
       "4595   577534             -398.0\n",
       "5064   596320             -477.0\n",
       "2961   539868             -562.0\n",
       "1393   515835             -575.0\n",
       "\n",
       "[5998 rows x 2 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "728343fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     5998.000000\n",
       "mean      1949.746582\n",
       "std       2335.797809\n",
       "min       -575.000000\n",
       "25%        464.000000\n",
       "50%       1256.000000\n",
       "75%       2588.750000\n",
       "max      28114.000000\n",
       "Name: total_hotel_spent, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['total_hotel_spent'].describe() # summery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "818c15f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_df['total_hotel_spent'].hist() # plot histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2aec32d8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x22c737b1250>,\n",
       "  <matplotlib.lines.Line2D at 0x22c737c1250>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x22c737c1ed0>,\n",
       "  <matplotlib.lines.Line2D at 0x22c737c2990>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x22c73552b10>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x22c737c3410>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x22c737c3e10>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(new_df['total_hotel_spent']) # plot boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2aabda67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original DataFrame:\n",
      "      user_id  total_hotel_spent\n",
      "2675   534594            28114.0\n",
      "5077   596863            27489.0\n",
      "780    507805            27241.0\n",
      "2954   539710            22619.0\n",
      "5818   669329            22356.0\n",
      "...       ...                ...\n",
      "3244   544926             -349.0\n",
      "4595   577534             -398.0\n",
      "5064   596320             -477.0\n",
      "2961   539868             -562.0\n",
      "1393   515835             -575.0\n",
      "\n",
      "[5998 rows x 2 columns]\n",
      "\n",
      "DataFrame after removing outliers:\n",
      "      user_id  total_hotel_spent\n",
      "3576   551359             5770.0\n",
      "5779   662596             5760.0\n",
      "1778   520898             5760.0\n",
      "5319   611839             5758.0\n",
      "2750   535905             5755.0\n",
      "...       ...                ...\n",
      "3244   544926             -349.0\n",
      "4595   577534             -398.0\n",
      "5064   596320             -477.0\n",
      "2961   539868             -562.0\n",
      "1393   515835             -575.0\n",
      "\n",
      "[5644 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "# Calculate Q1, Q3, and IQR\n",
    "Q1 = new_df['total_hotel_spent'].quantile(0.25)\n",
    "Q3 = new_df['total_hotel_spent'].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# Define bounds for outliers\n",
    "lower_bound = Q1 - 1.5 * IQR\n",
    "upper_bound = Q3 + 1.5 * IQR\n",
    "\n",
    "# Remove outliers\n",
    "df_filtered = new_df[(new_df['total_hotel_spent'] >= lower_bound) & (new_df['total_hotel_spent'] <= upper_bound)]\n",
    "\n",
    "print(\"Original DataFrame:\")\n",
    "print(new_df)\n",
    "print(\"\\nDataFrame after removing outliers:\")\n",
    "print(df_filtered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1a49d6d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Hjvp4O9vjBqeQTevIJziyCS5YNuHKKuSFZ8aMGWpoaNCAAQPUpUsXNTY26v7771deXp4kyev1SpKSk5MD7pecnOys83q96tWrV+BEo6KUlJTkjDlTcXGx5s6d22J5WVmZ4uPjv/ZxdUQej8f5uWR0G06kHZqf0RS2bW/cuDFs2z4fTn/cIBDZtI58giOb4M7M5tixY2HZT8gLz5/+9CetWbNGa9eu1eDBg1VdXa3p06crNTVV+fn5od6dY+bMmSoqKnJuNzQ0KC0tTTk5OXK73WHbb3vk9/vl8Xg0btw4RUdHS5KGzNnUxrNqH1yRRvMzmjRrR6R8TRFh2ceuOblh2W64ne1xg1PIpnXkExzZBBcsm+ZXaEIt5IXnzjvv1IwZMzRp0iRJ0tChQ/Xhhx+quLhY+fn5SklJkSTV1NSod+/ezv1qamo0YsQISVJKSopqa2sDtnvy5EnV1dU59z+Ty+WSy+VqsTw6OrrTPshOP3ZfY3h+uXdUvqaIsGXS0R9vnfnfzBchm9aRT3BkE9yZ2YQrp5AXnmPHjikyMvDiry5duqip6dRLCOnp6UpJSdHmzZudgtPQ0KDKykpNmzZNkpSVlaX6+npVVVVp1KhRkqQtW7aoqalJmZmZoZ4yEFL9ZrzY1lM4Zx8smNDWUwCAsAp54bn22mt1//33q0+fPho8eLDefvttLVq0SL/4xS8kSREREZo+fbruu+8+XXzxxUpPT9esWbOUmpqq66+/XpI0cOBAXX311ZoyZYpWrFghv9+vwsJCTZo0iSu0AADAOQt54Vm6dKlmzZqlX/7yl6qtrVVqaqr+8z//U7Nnz3bG3HXXXTp69KimTp2q+vp6XX755SotLVVsbKwzZs2aNSosLNTYsWMVGRmpiRMnasmSJaGeLgAA6ARCXni6deumxYsXa/HixUHHREREaN68eZo3b17QMUlJSVq7dm2opwcAADohvksLAABYj8IDAACsR+EBAADWo/AAAADrUXgAAID1KDwAAMB6FB4AAGA9Cg8AALAehQcAAFiPwgMAAKxH4QEAANaj8AAAAOtReAAAgPUoPAAAwHoUHgAAYD0KDwAAsB6FBwAAWC+qrSfQEfWb8WJbT6FVri5GJaOlIXM2ydcY0dbTAQCgzXGGBwAAWI/CAwAArEfhAQAA1qPwAAAA61F4AACA9Sg8AADAehQeAABgPQoPAACwHoUHAABYj8IDAACsR+EBAADWo/AAAADrUXgAAID1KDwAAMB6FB4AAGA9Cg8AALAehQcAAFiPwgMAAKxH4QEAANaj8AAAAOtReAAAgPUoPAAAwHoUHgAAYD0KDwAAsB6FBwAAWI/CAwAArEfhAQAA1gtL4fn444/105/+VD169FBcXJyGDh2qHTt2OOuNMZo9e7Z69+6tuLg4ZWdna//+/QHbqKurU15entxutxITEzV58mQdOXIkHNMFAACWC3nh+eyzz3TZZZcpOjpaL730kvbs2aPf/va36t69uzOmpKRES5Ys0YoVK1RZWamuXbsqNzdXx48fd8bk5eVp9+7d8ng82rBhg8rLyzV16tRQTxcAAHQCUaHe4MKFC5WWlqZVq1Y5y9LT052fjTFavHix7rnnHl133XWSpCeffFLJyclav369Jk2apL1796q0tFTbt29XRkaGJGnp0qW65ppr9NBDDyk1NTXU0wYAABYL+Rme559/XhkZGfrRj36kXr16aeTIkXrsscec9QcOHJDX61V2drazLCEhQZmZmaqoqJAkVVRUKDEx0Sk7kpSdna3IyEhVVlaGesoAAMByIT/D8/7772v58uUqKirSb37zG23fvl2//vWvFRMTo/z8fHm9XklScnJywP2Sk5OddV6vV7169QqcaFSUkpKSnDFn8vl88vl8zu2GhgZJkt/vl9/vD9nxSZKriwnp9kLNFWkC/sa/kM3Z9f+vDXJFGs3PkEbNK5WvKaKtp/SFds3JPW/7an4OCfVziS3IJziyCS5YNuHKKuSFp6mpSRkZGXrggQckSSNHjtSuXbu0YsUK5efnh3p3juLiYs2dO7fF8rKyMsXHx4d0XyWjQ7q5sJmf0dTWU2i3yCa4jpLNxo0bz/s+PR7Ped9nR0I+wZFNcGdmc+zYsbDsJ+SFp3fv3ho0aFDAsoEDB+p///d/JUkpKSmSpJqaGvXu3dsZU1NToxEjRjhjamtrA7Zx8uRJ1dXVOfc/08yZM1VUVOTcbmhoUFpamnJycuR2u7/2cZ1uyJxNId1eqJ36P/UmzdoR2SH+T/18IpvgOlo25/sMj8fj0bhx4xQdHX3e9ttRkE9wZBNcsGyaX6EJtZAXnssuu0z79u0LWPbee++pb9++kk69gTklJUWbN292Ck5DQ4MqKys1bdo0SVJWVpbq6+tVVVWlUaNGSZK2bNmipqYmZWZmnnW/LpdLLperxfLo6OiQP8h8je3/l4Ek+ZoiOsxczzeyCa6jZNMWvzzC8XxiE/IJjmyCOzObcOUU8sJz++2369JLL9UDDzygH//4x9q2bZtWrlyplStXSpIiIiI0ffp03Xfffbr44ouVnp6uWbNmKTU1Vddff72kU2eErr76ak2ZMkUrVqyQ3+9XYWGhJk2axBVaAADgnIW88HznO9/Rc889p5kzZ2revHlKT0/X4sWLlZeX54y56667dPToUU2dOlX19fW6/PLLVVpaqtjYWGfMmjVrVFhYqLFjxyoyMlITJ07UkiVLQj1dAADQCYS88EjS97//fX3/+98Puj4iIkLz5s3TvHnzgo5JSkrS2rVrwzE9AADQyfBdWgAAwHoUHgAAYD0KDwAAsB6FBwAAWI/CAwAArEfhAQAA1qPwAAAA61F4AACA9Sg8AADAehQeAABgPQoPAACwHoUHAABYj8IDAACsR+EBAADWo/AAAADrUXgAAID1KDwAAMB6FB4AAGA9Cg8AALAehQcAAFiPwgMAAKxH4QEAANaj8AAAAOtReAAAgPUoPAAAwHoUHgAAYD0KDwAAsB6FBwAAWI/CAwAArEfhAQAA1qPwAAAA61F4AACA9Sg8AADAehQeAABgPQoPAACwHoUHAABYj8IDAACsR+EBAADWo/AAAADrUXgAAID1KDwAAMB6FB4AAGA9Cg8AALAehQcAAFiPwgMAAKxH4QEAANYLe+FZsGCBIiIiNH36dGfZ8ePHVVBQoB49euiCCy7QxIkTVVNTE3C/gwcPasKECYqPj1evXr1055136uTJk+GeLgAAsFBYC8/27dv1+9//XsOGDQtYfvvtt+uFF17QM888o61bt+qTTz7RD3/4Q2d9Y2OjJkyYoBMnTuiNN97QE088odWrV2v27NnhnC4AALBU2ArPkSNHlJeXp8cee0zdu3d3lh86dEh/+MMftGjRIn3ve9/TqFGjtGrVKr3xxht68803JUllZWXas2eP/vjHP2rEiBEaP3685s+fr2XLlunEiRPhmjIAALBUVLg2XFBQoAkTJig7O1v33Xefs7yqqkp+v1/Z2dnOsgEDBqhPnz6qqKjQmDFjVFFRoaFDhyo5OdkZk5ubq2nTpmn37t0aOXJki/35fD75fD7ndkNDgyTJ7/fL7/eH9NhcXUxItxdqrkgT8Df+hWyC62jZhPrf9ZfZ1/ncZ0dCPsGRTXDBsglXVmEpPOvWrdNbb72l7du3t1jn9XoVExOjxMTEgOXJycnyer3OmNPLTvP65nVnU1xcrLlz57ZYXlZWpvj4+K9yGEGVjA7p5sJmfkZTW0+h3SKb4DpKNhs3bjzv+/R4POd9nx0J+QRHNsGdmc2xY8fCsp+QF56PPvpIt912mzwej2JjY0O9+aBmzpypoqIi53ZDQ4PS0tKUk5Mjt9sd0n0NmbMppNsLNVek0fyMJs3aESlfU0RbT6ddIZvgOlo2u+bknrd9+f1+eTwejRs3TtHR0edtvx0F+QRHNsEFy6b5FZpQC3nhqaqqUm1trS655BJnWWNjo8rLy/XII49o06ZNOnHihOrr6wPO8tTU1CglJUWSlJKSom3btgVst/kqruYxZ3K5XHK5XC2WR0dHh/xB5mts/78MJMnXFNFh5nq+kU1wHSWbtvjlEY7nE5uQT3BkE9yZ2YQrp5C/aXns2LHauXOnqqurnT8ZGRnKy8tzfo6OjtbmzZud++zbt08HDx5UVlaWJCkrK0s7d+5UbW2tM8bj8cjtdmvQoEGhnjIAALBcyM/wdOvWTUOGDAlY1rVrV/Xo0cNZPnnyZBUVFSkpKUlut1u/+tWvlJWVpTFjxkiScnJyNGjQIN18880qKSmR1+vVPffco4KCgrOexQEAAGhN2K7Sas3vfvc7RUZGauLEifL5fMrNzdWjjz7qrO/SpYs2bNigadOmKSsrS127dlV+fr7mzZvXFtMF0A71m/HieduXq4tRyehT79/7Oi/3fbBgQghnBeBcnJfC88orrwTcjo2N1bJly7Rs2bKg9+nbt2+bXIUBAADsw3dpAQAA61F4AACA9Sg8AADAehQeAABgPQoPAACwHoUHAABYj8IDAACsR+EBAADWo/AAAADrUXgAAID1KDwAAMB6FB4AAGA9Cg8AALAehQcAAFiPwgMAAKxH4QEAANaj8AAAAOtReAAAgPUoPAAAwHoUHgAAYD0KDwAAsB6FBwAAWI/CAwAArEfhAQAA1qPwAAAA61F4AACA9Sg8AADAehQeAABgPQoPAACwHoUHAABYL6qtJwAAnUW/GS+29RTO2QcLJrT1FICQ4AwPAACwHoUHAABYj8IDAACsR+EBAADWo/AAAADrUXgAAID1KDwAAMB6FB4AAGA9Cg8AALAehQcAAFiPwgMAAKxH4QEAANaj8AAAAOtReAAAgPWiQr3B4uJiPfvss/rrX/+quLg4XXrppVq4cKH69+/vjDl+/LjuuOMOrVu3Tj6fT7m5uXr00UeVnJzsjDl48KCmTZuml19+WRdccIHy8/NVXFysqKiQTxkAEES/GS9+4RhXF6OS0dKQOZvka4w4D7Nq3QcLJrT1FNAOhfwMz9atW1VQUKA333xTHo9Hfr9fOTk5Onr0qDPm9ttv1wsvvKBnnnlGW7du1SeffKIf/vCHzvrGxkZNmDBBJ06c0BtvvKEnnnhCq1ev1uzZs0M9XQAA0AmE/HRJaWlpwO3Vq1erV69eqqqq0pVXXqlDhw7pD3/4g9auXavvfe97kqRVq1Zp4MCBevPNNzVmzBiVlZVpz549+stf/qLk5GSNGDFC8+fP19133605c+YoJiYm1NMGAAAWC/vrQ4cOHZIkJSUlSZKqqqrk9/uVnZ3tjBkwYID69OmjiooKjRkzRhUVFRo6dGjAS1y5ubmaNm2adu/erZEjR7bYj8/nk8/nc243NDRIkvx+v/x+f0iPydXFhHR7oeaKNAF/41/IJjiyCY5sWtfe8gn1c/7X0TyX9jSn9iJYNuHKKqyFp6mpSdOnT9dll12mIUOGSJK8Xq9iYmKUmJgYMDY5OVler9cZc3rZaV7fvO5siouLNXfu3BbLy8rKFB8f/3UPJUDJ6JBuLmzmZzS19RTaLbIJjmyCI5vWtZd8Nm7c2NZTaMHj8bT1FNqtM7M5duxYWPYT1sJTUFCgXbt26bXXXgvnbiRJM2fOVFFRkXO7oaFBaWlpysnJkdvtDum+hszZFNLthZor0mh+RpNm7YiUr6nt30DYnpBNcGQTHNm0rr3ls2tObltPweH3++XxeDRu3DhFR0e39XTalWDZNL9CE2phKzyFhYXasGGDysvLdeGFFzrLU1JSdOLECdXX1wec5ampqVFKSoozZtu2bQHbq6mpcdadjcvlksvlarE8Ojo65A+y9nAVwpfha4roMHM938gmOLIJjmxa117yaY/FIhy/i2xxZjbhyinkV2kZY1RYWKjnnntOW7ZsUXp6esD6UaNGKTo6Wps3b3aW7du3TwcPHlRWVpYkKSsrSzt37lRtba0zxuPxyO12a9CgQaGeMgAAsFzIz/AUFBRo7dq1+vOf/6xu3bo577lJSEhQXFycEhISNHnyZBUVFSkpKUlut1u/+tWvlJWVpTFjxkiScnJyNGjQIN18880qKSmR1+vVPffco4KCgrOexQEAAGhNyAvP8uXLJUlXXXVVwPJVq1bp5z//uSTpd7/7nSIjIzVx4sSADx5s1qVLF23YsEHTpk1TVlaWunbtqvz8fM2bNy/U0wUAAJ1AyAuPMV98WWJsbKyWLVumZcuWBR3Tt2/fdvlOewAA0PHwXVoAAMB6FB4AAGA9Cg8AALAehQcAAFiPwgMAAKxH4QEAANYL+7elAwBwPvWb8WJbT8Hh6mJUMvrUdzC29rUbHyyYcB5n1TlxhgcAAFiPwgMAAKxH4QEAANaj8AAAAOtReAAAgPUoPAAAwHoUHgAAYD0KDwAAsB6FBwAAWI9PWgYAoI21p0+H/rI62qdDc4YHAABYj8IDAACsR+EBAADWo/AAAADrUXgAAID1KDwAAMB6FB4AAGA9Cg8AALAehQcAAFiPwgMAAKxH4QEAANaj8AAAAOtReAAAgPUoPAAAwHoUHgAAYD0KDwAAsB6FBwAAWI/CAwAArEfhAQAA1qPwAAAA61F4AACA9Sg8AADAehQeAABgPQoPAACwHoUHAABYj8IDAACsR+EBAADWo/AAAADrtevCs2zZMvXr10+xsbHKzMzUtm3b2npKAACgA2q3hefpp59WUVGR7r33Xr311lsaPny4cnNzVVtb29ZTAwAAHUy7LTyLFi3SlClTdMstt2jQoEFasWKF4uPj9fjjj7f11AAAQAcT1dYTOJsTJ06oqqpKM2fOdJZFRkYqOztbFRUVZ72Pz+eTz+dzbh86dEiSVFdXJ7/fH9L5RZ08GtLthVpUk9GxY02K8keqsSmirafTrpBNcGQTHNm0jnyCszmbTz/99Gvd3+/369ixY/r0008VHR3tLD98+LAkyRjztbZ/pnZZeP75z3+qsbFRycnJAcuTk5P117/+9az3KS4u1ty5c1ssT09PD8sc27uftPUE2jGyCY5sgiOb1pFPcLZm0/O34d3+4cOHlZCQELLttcvC81XMnDlTRUVFzu2mpibV1dWpR48eioiwq1V/kYaGBqWlpemjjz6S2+1u6+m0K2QTHNkERzatI5/gyCa4YNkYY3T48GGlpqaGdH/tsvD07NlTXbp0UU1NTcDympoapaSknPU+LpdLLpcrYFliYmK4ptghuN1u/oEFQTbBkU1wZNM68gmObII7WzahPLPTrF2+aTkmJkajRo3S5s2bnWVNTU3avHmzsrKy2nBmAACgI2qXZ3gkqaioSPn5+crIyNDo0aO1ePFiHT16VLfccktbTw0AAHQw7bbw3HjjjfrHP/6h2bNny+v1asSIESotLW3xRma05HK5dO+997Z4iQ9k0xqyCY5sWkc+wZFNcOc7mwgT6uu+AAAA2pl2+R4eAACAUKLwAAAA61F4AACA9Sg8AADAehQeCy1btkz9+vVTbGysMjMztW3btraeUkiVl5fr2muvVWpqqiIiIrR+/fqA9cYYzZ49W71791ZcXJyys7O1f//+gDF1dXXKy8uT2+1WYmKiJk+erCNHjgSMeffdd3XFFVcoNjZWaWlpKikpCfehfW3FxcX6zne+o27duqlXr166/vrrtW/fvoAxx48fV0FBgXr06KELLrhAEydObPEhnwcPHtSECRMUHx+vXr166c4779TJkycDxrzyyiu65JJL5HK5dNFFF2n16tXhPryvZfny5Ro2bJjzIWdZWVl66aWXnPWdNZezWbBggSIiIjR9+nRnWWfNZ86cOYqIiAj4M2DAAGd9Z82l2ccff6yf/vSn6tGjh+Li4jR06FDt2LHDWd+uno8NrLJu3ToTExNjHn/8cbN7924zZcoUk5iYaGpqatp6aiGzceNG81//9V/m2WefNZLMc889F7B+wYIFJiEhwaxfv96888475gc/+IFJT083n3/+uTPm6quvNsOHDzdvvvmmefXVV81FF11kbrrpJmf9oUOHTHJyssnLyzO7du0yTz31lImLizO///3vz9dhfiW5ublm1apVZteuXaa6utpcc801pk+fPubIkSPOmFtvvdWkpaWZzZs3mx07dpgxY8aYSy+91Fl/8uRJM2TIEJOdnW3efvtts3HjRtOzZ08zc+ZMZ8z7779v4uPjTVFRkdmzZ49ZunSp6dKliyktLT2vx3sunn/+efPiiy+a9957z+zbt8/85je/MdHR0WbXrl3GmM6by5m2bdtm+vXrZ4YNG2Zuu+02Z3lnzefee+81gwcPNn//+9+dP//4xz+c9Z01F2OMqaurM3379jU///nPTWVlpXn//ffNpk2bzN/+9jdnTHt6PqbwWGb06NGmoKDAud3Y2GhSU1NNcXFxG84qfM4sPE1NTSYlJcU8+OCDzrL6+nrjcrnMU089ZYwxZs+ePUaS2b59uzPmpZdeMhEREebjjz82xhjz6KOPmu7duxufz+eMufvuu03//v3DfEShVVtbaySZrVu3GmNOZREdHW2eeeYZZ8zevXuNJFNRUWGMOVUoIyMjjdfrdcYsX77cuN1uJ4+77rrLDB48OGBfN954o8nNzQ33IYVU9+7dzX//93+Ty/93+PBhc/HFFxuPx2P+/d//3Sk8nTmfe++91wwfPvys6zpzLsacek68/PLLg65vb8/HvKRlkRMnTqiqqkrZ2dnOssjISGVnZ6uioqINZ3b+HDhwQF6vNyCDhIQEZWZmOhlUVFQoMTFRGRkZzpjs7GxFRkaqsrLSGXPllVcqJibGGZObm6t9+/bps88+O09H8/UdOnRIkpSUlCRJqqqqkt/vD8hnwIAB6tOnT0A+Q4cODfiQz9zcXDU0NGj37t3OmNO30TymozzOGhsbtW7dOh09elRZWVnk8v8VFBRowoQJLY6hs+ezf/9+paam6lvf+pby8vJ08OBBSeTy/PPPKyMjQz/60Y/Uq1cvjRw5Uo899pizvr09H1N4LPLPf/5TjY2NLT6NOjk5WV6vt41mdX41H2drGXi9XvXq1StgfVRUlJKSkgLGnG0bp++jvWtqatL06dN12WWXaciQIZJOzT0mJqbFF+uemc8XHXuwMQ0NDfr888/DcTghsXPnTl1wwQVyuVy69dZb9dxzz2nQoEGdPhdJWrdund566y0VFxe3WNeZ88nMzNTq1atVWlqq5cuX68CBA7riiit0+PDhTp2LJL3//vtavny5Lr74Ym3atEnTpk3Tr3/9az3xxBOS2t/zcbv9agkAX09BQYF27dql1157ra2n0m70799f1dXVOnTokP7nf/5H+fn52rp1a1tPq8199NFHuu222+TxeBQbG9vW02lXxo8f7/w8bNgwZWZmqm/fvvrTn/6kuLi4NpxZ22tqalJGRoYeeOABSdLIkSO1a9curVixQvn5+W08u5Y4w2ORnj17qkuXLi2uEKipqVFKSkobzer8aj7O1jJISUlRbW1twPqTJ0+qrq4uYMzZtnH6PtqzwsJCbdiwQS+//LIuvPBCZ3lKSopOnDih+vr6gPFn5vNFxx5sjNvtbte/BGJiYnTRRRdp1KhRKi4u1vDhw/Xwww93+lyqqqpUW1urSy65RFFRUYqKitLWrVu1ZMkSRUVFKTk5uVPnc7rExER9+9vf1t/+9rdO/7jp3bu3Bg0aFLBs4MCBzkt+7e35mMJjkZiYGI0aNUqbN292ljU1NWnz5s3Kyspqw5mdP+np6UpJSQnIoKGhQZWVlU4GWVlZqq+vV1VVlTNmy5YtampqUmZmpjOmvLxcfr/fGePxeNS/f3917979PB3NuTPGqLCwUM8995y2bNmi9PT0gPWjRo1SdHR0QD779u3TwYMHA/LZuXNnwJOQx+OR2+12ntyysrICttE8pqM9zpqamuTz+Tp9LmPHjtXOnTtVXV3t/MnIyFBeXp7zc2fO53RHjhzR//3f/6l3796d/nFz2WWXtfjYi/fee099+/aV1A6fj8/pLc5o99atW2dcLpdZvXq12bNnj5k6dapJTEwMuEKgozt8+LB5++23zdtvv20kmUWLFpm3337bfPjhh8aYU5dBJiYmmj//+c/m3XffNdddd91ZL4McOXKkqaysNK+99pq5+OKLAy6DrK+vN8nJyebmm282u3btMuvWrTPx8fHt/rL0adOmmYSEBPPKK68EXEZ77NgxZ8ytt95q+vTpY7Zs2WJ27NhhsrKyTFZWlrO++TLanJwcU11dbUpLS803vvGNs15Ge+edd5q9e/eaZcuWtfvLaGfMmGG2bt1qDhw4YN59910zY8YMExERYcrKyowxnTeXYE6/SsuYzpvPHXfcYV555RVz4MAB8/rrr5vs7GzTs2dPU1tba4zpvLkYc+ojDKKiosz9999v9u/fb9asWWPi4+PNH//4R2dMe3o+pvBYaOnSpaZPnz4mJibGjB492rz55pttPaWQevnll42kFn/y8/ONMacuhZw1a5ZJTk42LpfLjB071uzbty9gG59++qm56aabzAUXXGDcbre55ZZbzOHDhwPGvPPOO+byyy83LpfLfPOb3zQLFiw4X4f4lZ0tF0lm1apVzpjPP//c/PKXvzTdu3c38fHx5oYbbjB///vfA7bzwQcfmPHjx5u4uDjTs2dPc8cddxi/3x8w5uWXXzYjRowwMTEx5lvf+lbAPtqjX/ziF6Zv374mJibGfOMb3zBjx451yo4xnTeXYM4sPJ01nxtvvNH07t3bxMTEmG9+85vmxhtvDPicmc6aS7MXXnjBDBkyxLhcLjNgwACzcuXKgPXt6fk4whhjvvz5IAAAgI6H9/AAAADrUXgAAID1KDwAAMB6FB4AAGA9Cg8AALAehQcAAFiPwgMAAKxH4QEAANaj8AAAAOtReAAAgPUoPAAAwHoUHgAAYL3/B0lQySGynq1yAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_filtered['total_hotel_spent'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bb0dd294",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x22c7366ad50>,\n",
       "  <matplotlib.lines.Line2D at 0x22c7366b990>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x22c73674510>,\n",
       "  <matplotlib.lines.Line2D at 0x22c73675050>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x22c7366a110>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x22c73675b90>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x22c73873910>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(df_filtered['total_hotel_spent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9d638ce7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5644.000000\n",
       "mean     1526.987420\n",
       "std      1389.714068\n",
       "min      -575.000000\n",
       "25%       421.500000\n",
       "50%      1157.000000\n",
       "75%      2293.000000\n",
       "max      5770.000000\n",
       "Name: total_hotel_spent, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered['total_hotel_spent'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6f73c1c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      user_id  total_hotel_spent free_meal\n",
      "2675   534594            28114.0       yes\n",
      "5077   596863            27489.0       yes\n",
      "780    507805            27241.0       yes\n",
      "2954   539710            22619.0       yes\n",
      "5818   669329            22356.0       yes\n",
      "...       ...                ...       ...\n",
      "3244   544926             -349.0        no\n",
      "4595   577534             -398.0        no\n",
      "5064   596320             -477.0        no\n",
      "2961   539868             -562.0        no\n",
      "1393   515835             -575.0        no\n",
      "\n",
      "[5998 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "# Create conditional DataFrame column by np.select function.\n",
    "# giving free meal perks\n",
    "conditions = [new_df['total_hotel_spent'] > 2293]\n",
    "\n",
    "choices = ['yes']\n",
    "new_df['free_meal'] = np.select(conditions, choices, default='no')\n",
    "print(new_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7de8cacf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id              1764\n",
       "total_hotel_spent    1764\n",
       "free_meal            1764\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df[new_df['free_meal'] == 'yes'].count() # total users that got the perk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b5287302",
   "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>user_id</th>\n",
       "      <th>free_meal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2675</th>\n",
       "      <td>534594</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5077</th>\n",
       "      <td>596863</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780</th>\n",
       "      <td>507805</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2954</th>\n",
       "      <td>539710</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5818</th>\n",
       "      <td>669329</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3244</th>\n",
       "      <td>544926</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4595</th>\n",
       "      <td>577534</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5064</th>\n",
       "      <td>596320</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2961</th>\n",
       "      <td>539868</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1393</th>\n",
       "      <td>515835</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5998 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id free_meal\n",
       "2675   534594       yes\n",
       "5077   596863       yes\n",
       "780    507805       yes\n",
       "2954   539710       yes\n",
       "5818   669329       yes\n",
       "...       ...       ...\n",
       "3244   544926        no\n",
       "4595   577534        no\n",
       "5064   596320        no\n",
       "2961   539868        no\n",
       "1393   515835        no\n",
       "\n",
       "[5998 rows x 2 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "segmented_df = new_df[['user_id', 'free_meal']] # new dataframe for segmented dataset\n",
    "segmented_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df4c46d5",
   "metadata": {},
   "source": [
    "# Free Checked Bag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "90021f66",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = df[['user_id', 'checked_bags']] # getting the related column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "aea70f87",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate total checked bags for each user\n",
    "# bag_counts = df.groupby('user_id')['checked_bags'].sum()\n",
    "\n",
    "new_df = new_df.groupby('user_id', as_index=False).sum('checked_bags').sort_values('checked_bags', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e540f70a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5998.000000\n",
       "mean        1.410637\n",
       "std         1.447175\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%         1.000000\n",
       "75%         2.000000\n",
       "max        17.000000\n",
       "Name: checked_bags, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.checked_bags.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "70181880",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x22c736b4710>,\n",
       "  <matplotlib.lines.Line2D at 0x22c736bf190>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x22c736abc90>,\n",
       "  <matplotlib.lines.Line2D at 0x22c736ab450>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x22c73878050>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x22c736aa7d0>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x22c736a9010>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(new_df.checked_bags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "688b2dc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original DataFrame:\n",
      "      user_id  checked_bags\n",
      "1435   516283          17.0\n",
      "2372   529602          12.0\n",
      "1244   513807          11.0\n",
      "5403   618157          11.0\n",
      "3483   549450          10.0\n",
      "...       ...           ...\n",
      "3227   544536           0.0\n",
      "3233   544647           0.0\n",
      "3238   544698           0.0\n",
      "3240   544749           0.0\n",
      "5997   844489           0.0\n",
      "\n",
      "[5998 rows x 2 columns]\n",
      "\n",
      "DataFrame after removing outliers:\n",
      "      user_id  checked_bags\n",
      "2962   539883           5.0\n",
      "5110   598911           5.0\n",
      "154    413658           5.0\n",
      "4114   563896           5.0\n",
      "1439   516301           5.0\n",
      "...       ...           ...\n",
      "3227   544536           0.0\n",
      "3233   544647           0.0\n",
      "3238   544698           0.0\n",
      "3240   544749           0.0\n",
      "5997   844489           0.0\n",
      "\n",
      "[5913 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "# Calculate Q1, Q3, and IQR\n",
    "Q1 = new_df['checked_bags'].quantile(0.25)\n",
    "Q3 = new_df['checked_bags'].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# Define bounds for outliers\n",
    "lower_bound = Q1 - 1.5 * IQR\n",
    "upper_bound = Q3 + 1.5 * IQR\n",
    "\n",
    "# Remove outliers\n",
    "df_filtered = new_df[(new_df['checked_bags'] >= lower_bound) & (new_df['checked_bags'] <= upper_bound)]\n",
    "\n",
    "print(\"Original DataFrame:\")\n",
    "print(new_df)\n",
    "print(\"\\nDataFrame after removing outliers:\")\n",
    "print(df_filtered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "045e28e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x22c7390ce90>,\n",
       "  <matplotlib.lines.Line2D at 0x22c7390d850>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x22c7390e290>,\n",
       "  <matplotlib.lines.Line2D at 0x22c7390ebd0>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x22c738f8f50>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x22c7390f4d0>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x22c7390ff90>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(df_filtered.checked_bags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1463b851",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5913.000000\n",
       "mean        1.325385\n",
       "std         1.249760\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%         1.000000\n",
       "75%         2.000000\n",
       "max         5.000000\n",
       "Name: checked_bags, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered.checked_bags.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "12e19e7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id         1101\n",
       "checked_bags    1101\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df[new_df['checked_bags'] > 2].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ade34fe7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      user_id  checked_bags free_checked_bags\n",
      "1435   516283          17.0               yes\n",
      "2372   529602          12.0               yes\n",
      "1244   513807          11.0               yes\n",
      "5403   618157          11.0               yes\n",
      "3483   549450          10.0               yes\n",
      "...       ...           ...               ...\n",
      "3227   544536           0.0                no\n",
      "3233   544647           0.0                no\n",
      "3238   544698           0.0                no\n",
      "3240   544749           0.0                no\n",
      "5997   844489           0.0                no\n",
      "\n",
      "[5998 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "# Create conditional DataFrame column by np.select function.\n",
    "# giving the perk who has more than 2 checked bags\n",
    "conditions = [new_df['checked_bags'] > 2]\n",
    "\n",
    "choices = ['yes']\n",
    "new_df['free_checked_bags'] = np.select(conditions, choices, default='no')\n",
    "print(new_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2c4a4f3f",
   "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>user_id</th>\n",
       "      <th>free_meal</th>\n",
       "      <th>free_checked_bags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>534594</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>596863</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>507805</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>539710</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>669329</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5993</th>\n",
       "      <td>544926</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5994</th>\n",
       "      <td>577534</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5995</th>\n",
       "      <td>596320</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5996</th>\n",
       "      <td>539868</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5997</th>\n",
       "      <td>515835</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5998 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id free_meal free_checked_bags\n",
       "0      534594       yes                no\n",
       "1      596863       yes               yes\n",
       "2      507805       yes               yes\n",
       "3      539710       yes                no\n",
       "4      669329       yes                no\n",
       "...       ...       ...               ...\n",
       "5993   544926        no                no\n",
       "5994   577534        no                no\n",
       "5995   596320        no                no\n",
       "5996   539868        no                no\n",
       "5997   515835        no                no\n",
       "\n",
       "[5998 rows x 3 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merging to main segmented dataset\n",
    "segmented_df = pd.merge(segmented_df, new_df[['user_id', 'free_checked_bags']], on='user_id', how='inner')\n",
    "segmented_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b114cbd1",
   "metadata": {},
   "source": [
    "# No Cancellation Fees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fef5a841",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>user_id</th>\n",
       "      <th>session_id</th>\n",
       "      <th>trip_id</th>\n",
       "      <th>session_start</th>\n",
       "      <th>session_end</th>\n",
       "      <th>flight_discount</th>\n",
       "      <th>hotel_discount</th>\n",
       "      <th>flight_discount_amount</th>\n",
       "      <th>hotel_discount_amount</th>\n",
       "      <th>flight_booked</th>\n",
       "      <th>...</th>\n",
       "      <th>destination_airport_lon</th>\n",
       "      <th>base_fare_usd</th>\n",
       "      <th>trip_id.2</th>\n",
       "      <th>hotel_name</th>\n",
       "      <th>nights</th>\n",
       "      <th>rooms</th>\n",
       "      <th>check_in_time</th>\n",
       "      <th>check_out_time</th>\n",
       "      <th>hotel_per_room_usd</th>\n",
       "      <th>total_hotel_spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>447951</td>\n",
       "      <td>447951-dc80e75ea3234c0398a9911953386d90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-01-04 20:35:00</td>\n",
       "      <td>2023-01-04 20:35:52</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>449980</td>\n",
       "      <td>449980-e740c9ba5e3945db803565a5239b6d2e</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-01-04 22:48:00</td>\n",
       "      <td>2023-01-04 22:50:10</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>485785</td>\n",
       "      <td>485785-b77e273846f34973993dae383568a47c</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-01-05 18:24:00</td>\n",
       "      <td>2023-01-05 18:24:47</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>487352</td>\n",
       "      <td>487352-58491d5ab9d84a34ae58e6ccb3ade0c3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-01-05 09:01:00</td>\n",
       "      <td>2023-01-05 09:03:29</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>489949</td>\n",
       "      <td>489949-a3eec794414a4e13a5929e73c7157f1b</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-01-05 20:58:00</td>\n",
       "      <td>2023-01-05 20:59:23</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49203</th>\n",
       "      <td>551990</td>\n",
       "      <td>551990-572e2a6219bc40d8a6c1f583820533f0</td>\n",
       "      <td>551990-fbb5ba1d6f1f43b58c6bc56e6b6dd288</td>\n",
       "      <td>2023-07-06 15:26:00</td>\n",
       "      <td>2023-07-06 15:27:54</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>551990-fbb5ba1d6f1f43b58c6bc56e6b6dd288</td>\n",
       "      <td>Starwood  - chicago</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-07-16 11:00:00</td>\n",
       "      <td>2023-08-02 11:00:00</td>\n",
       "      <td>175.0</td>\n",
       "      <td>2975.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49204</th>\n",
       "      <td>435231</td>\n",
       "      <td>435231-9f9d9e0971564942a7c8775c50812598</td>\n",
       "      <td>435231-203cc75819b846d59b96b38788174dd5</td>\n",
       "      <td>2023-07-12 19:13:00</td>\n",
       "      <td>2023-07-12 19:16:08</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-83.010</td>\n",
       "      <td>52.88</td>\n",
       "      <td>435231-203cc75819b846d59b96b38788174dd5</td>\n",
       "      <td>Marriott  - detroit</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-07-18 16:24:16.785</td>\n",
       "      <td>2023-07-21 11:00:00</td>\n",
       "      <td>83.0</td>\n",
       "      <td>166.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49208</th>\n",
       "      <td>534295</td>\n",
       "      <td>534295-0a137c45cac34b1e834f23ecab4233fb</td>\n",
       "      <td>534295-3e20b84b40c946b1a8124395231d4412</td>\n",
       "      <td>2023-07-10 18:55:00</td>\n",
       "      <td>2023-07-10 18:57:18</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-117.215</td>\n",
       "      <td>3205.63</td>\n",
       "      <td>534295-3e20b84b40c946b1a8124395231d4412</td>\n",
       "      <td>Aman Resorts - san diego</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2023-07-20 20:54:53.1</td>\n",
       "      <td>2023-07-29 11:00:00</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49209</th>\n",
       "      <td>518416</td>\n",
       "      <td>518416-fd45c188d94a4d30a7e4468056701f1e</td>\n",
       "      <td>518416-cca5cea3fac04cdf8d069e13891f016a</td>\n",
       "      <td>2023-07-15 07:38:00</td>\n",
       "      <td>2023-07-15 07:42:04</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-98.279</td>\n",
       "      <td>103.01</td>\n",
       "      <td>518416-cca5cea3fac04cdf8d069e13891f016a</td>\n",
       "      <td>InterContinental  - san antonio</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2023-07-22 15:22:38.37</td>\n",
       "      <td>2023-07-24 11:00:00</td>\n",
       "      <td>80.0</td>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49210</th>\n",
       "      <td>510093</td>\n",
       "      <td>510093-2d3e14a9e10b4a0a942a4312b914e557</td>\n",
       "      <td>510093-e71475580a7c4948ba8f13114a60fa30</td>\n",
       "      <td>2023-07-18 17:06:00</td>\n",
       "      <td>2023-07-18 17:08:42</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-97.032</td>\n",
       "      <td>381.22</td>\n",
       "      <td>510093-e71475580a7c4948ba8f13114a60fa30</td>\n",
       "      <td>Marriott  - winnipeg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-07-30 11:45:27.81</td>\n",
       "      <td>2023-07-30 11:00:00</td>\n",
       "      <td>109.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48601 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id                               session_id  \\\n",
       "0       447951  447951-dc80e75ea3234c0398a9911953386d90   \n",
       "1       449980  449980-e740c9ba5e3945db803565a5239b6d2e   \n",
       "2       485785  485785-b77e273846f34973993dae383568a47c   \n",
       "3       487352  487352-58491d5ab9d84a34ae58e6ccb3ade0c3   \n",
       "4       489949  489949-a3eec794414a4e13a5929e73c7157f1b   \n",
       "...        ...                                      ...   \n",
       "49203   551990  551990-572e2a6219bc40d8a6c1f583820533f0   \n",
       "49204   435231  435231-9f9d9e0971564942a7c8775c50812598   \n",
       "49208   534295  534295-0a137c45cac34b1e834f23ecab4233fb   \n",
       "49209   518416  518416-fd45c188d94a4d30a7e4468056701f1e   \n",
       "49210   510093  510093-2d3e14a9e10b4a0a942a4312b914e557   \n",
       "\n",
       "                                       trip_id        session_start  \\\n",
       "0                                          NaN  2023-01-04 20:35:00   \n",
       "1                                          NaN  2023-01-04 22:48:00   \n",
       "2                                          NaN  2023-01-05 18:24:00   \n",
       "3                                          NaN  2023-01-05 09:01:00   \n",
       "4                                          NaN  2023-01-05 20:58:00   \n",
       "...                                        ...                  ...   \n",
       "49203  551990-fbb5ba1d6f1f43b58c6bc56e6b6dd288  2023-07-06 15:26:00   \n",
       "49204  435231-203cc75819b846d59b96b38788174dd5  2023-07-12 19:13:00   \n",
       "49208  534295-3e20b84b40c946b1a8124395231d4412  2023-07-10 18:55:00   \n",
       "49209  518416-cca5cea3fac04cdf8d069e13891f016a  2023-07-15 07:38:00   \n",
       "49210  510093-e71475580a7c4948ba8f13114a60fa30  2023-07-18 17:06:00   \n",
       "\n",
       "               session_end  flight_discount  hotel_discount  \\\n",
       "0      2023-01-04 20:35:52            False           False   \n",
       "1      2023-01-04 22:50:10            False           False   \n",
       "2      2023-01-05 18:24:47             True           False   \n",
       "3      2023-01-05 09:03:29            False           False   \n",
       "4      2023-01-05 20:59:23            False           False   \n",
       "...                    ...              ...             ...   \n",
       "49203  2023-07-06 15:27:54            False           False   \n",
       "49204  2023-07-12 19:16:08            False           False   \n",
       "49208  2023-07-10 18:57:18            False           False   \n",
       "49209  2023-07-15 07:42:04            False           False   \n",
       "49210  2023-07-18 17:08:42             True           False   \n",
       "\n",
       "       flight_discount_amount  hotel_discount_amount  flight_booked  ...  \\\n",
       "0                         NaN                    NaN          False  ...   \n",
       "1                         NaN                    NaN          False  ...   \n",
       "2                        0.15                    NaN          False  ...   \n",
       "3                         NaN                    NaN          False  ...   \n",
       "4                         NaN                    NaN          False  ...   \n",
       "...                       ...                    ...            ...  ...   \n",
       "49203                     NaN                    NaN          False  ...   \n",
       "49204                     NaN                    NaN           True  ...   \n",
       "49208                     NaN                    NaN           True  ...   \n",
       "49209                     NaN                    NaN           True  ...   \n",
       "49210                    0.20                    NaN           True  ...   \n",
       "\n",
       "       destination_airport_lon  base_fare_usd  \\\n",
       "0                          NaN            NaN   \n",
       "1                          NaN            NaN   \n",
       "2                          NaN            NaN   \n",
       "3                          NaN            NaN   \n",
       "4                          NaN            NaN   \n",
       "...                        ...            ...   \n",
       "49203                      NaN            NaN   \n",
       "49204                  -83.010          52.88   \n",
       "49208                 -117.215        3205.63   \n",
       "49209                  -98.279         103.01   \n",
       "49210                  -97.032         381.22   \n",
       "\n",
       "                                     trip_id.2  \\\n",
       "0                                          NaN   \n",
       "1                                          NaN   \n",
       "2                                          NaN   \n",
       "3                                          NaN   \n",
       "4                                          NaN   \n",
       "...                                        ...   \n",
       "49203  551990-fbb5ba1d6f1f43b58c6bc56e6b6dd288   \n",
       "49204  435231-203cc75819b846d59b96b38788174dd5   \n",
       "49208  534295-3e20b84b40c946b1a8124395231d4412   \n",
       "49209  518416-cca5cea3fac04cdf8d069e13891f016a   \n",
       "49210  510093-e71475580a7c4948ba8f13114a60fa30   \n",
       "\n",
       "                            hotel_name nights  rooms            check_in_time  \\\n",
       "0                                  NaN    NaN    NaN                      NaN   \n",
       "1                                  NaN    NaN    NaN                      NaN   \n",
       "2                                  NaN    NaN    NaN                      NaN   \n",
       "3                                  NaN    NaN    NaN                      NaN   \n",
       "4                                  NaN    NaN    NaN                      NaN   \n",
       "...                                ...    ...    ...                      ...   \n",
       "49203              Starwood  - chicago   17.0    1.0      2023-07-16 11:00:00   \n",
       "49204              Marriott  - detroit    2.0    1.0  2023-07-18 16:24:16.785   \n",
       "49208         Aman Resorts - san diego    8.0    4.0    2023-07-20 20:54:53.1   \n",
       "49209  InterContinental  - san antonio    1.0    2.0   2023-07-22 15:22:38.37   \n",
       "49210             Marriott  - winnipeg    0.0    1.0   2023-07-30 11:45:27.81   \n",
       "\n",
       "            check_out_time hotel_per_room_usd total_hotel_spent  \n",
       "0                      NaN                NaN               NaN  \n",
       "1                      NaN                NaN               NaN  \n",
       "2                      NaN                NaN               NaN  \n",
       "3                      NaN                NaN               NaN  \n",
       "4                      NaN                NaN               NaN  \n",
       "...                    ...                ...               ...  \n",
       "49203  2023-08-02 11:00:00              175.0            2975.0  \n",
       "49204  2023-07-21 11:00:00               83.0             166.0  \n",
       "49208  2023-07-29 11:00:00               85.0            2720.0  \n",
       "49209  2023-07-24 11:00:00               80.0             160.0  \n",
       "49210  2023-07-30 11:00:00              109.0               0.0  \n",
       "\n",
       "[48601 rows x 44 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# exclude cancel trip\n",
    "no_cancel = df[df['cancellation'] != True]\n",
    "no_cancel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "746b55c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = no_cancel[['user_id', 'trip_id']] # get the related column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fc5bd8b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the total trip by each user\n",
    "new_df = new_df.groupby('user_id', as_index=False)['trip_id'].count().sort_values('trip_id', ascending=False)\n",
    "new_df['no_cancelled_trip'] = new_df['trip_id'] # converting to a meaningful column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "eb3e26d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>trip_id</th>\n",
       "      <th>no_cancelled_trip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>485544</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>824</th>\n",
       "      <td>509115</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>1519</th>\n",
       "      <td>517491</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4438</th>\n",
       "      <td>572554</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1190</th>\n",
       "      <td>513236</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2656</th>\n",
       "      <td>534082</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2674</th>\n",
       "      <td>534586</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>509001</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>508964</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5997</th>\n",
       "      <td>844489</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5998 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  trip_id  no_cancelled_trip\n",
       "469    485544        8                  8\n",
       "824    509115        8                  8\n",
       "1519   517491        8                  8\n",
       "4438   572554        8                  8\n",
       "1190   513236        8                  8\n",
       "...       ...      ...                ...\n",
       "2656   534082        0                  0\n",
       "2674   534586        0                  0\n",
       "817    509001        0                  0\n",
       "815    508964        0                  0\n",
       "5997   844489        0                  0\n",
       "\n",
       "[5998 rows x 3 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "49575e64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5998.000000\n",
       "mean        2.682894\n",
       "std         1.521283\n",
       "min         0.000000\n",
       "25%         2.000000\n",
       "50%         3.000000\n",
       "75%         4.000000\n",
       "max         8.000000\n",
       "Name: no_cancelled_trip, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['no_cancelled_trip'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fbdce9f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id              710\n",
       "trip_id              710\n",
       "no_cancelled_trip    710\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df[new_df['no_cancelled_trip'] > 4].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8cbe7180",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      user_id  trip_id  no_cancelled_trip no_cancellation_fees\n",
      "469    485544        8                  8                  yes\n",
      "824    509115        8                  8                  yes\n",
      "1519   517491        8                  8                  yes\n",
      "4438   572554        8                  8                  yes\n",
      "1190   513236        8                  8                  yes\n",
      "...       ...      ...                ...                  ...\n",
      "2656   534082        0                  0                   no\n",
      "2674   534586        0                  0                   no\n",
      "817    509001        0                  0                   no\n",
      "815    508964        0                  0                   no\n",
      "5997   844489        0                  0                   no\n",
      "\n",
      "[5998 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "# Create conditional DataFrame column by np.select function.\n",
    "# giving the perk who has more than 4 trip\n",
    "conditions = [new_df['trip_id'] > 4]\n",
    "\n",
    "choices = ['yes']\n",
    "new_df['no_cancellation_fees'] = np.select(conditions, choices, default='no')\n",
    "print(new_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bb7d55d1",
   "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>user_id</th>\n",
       "      <th>free_meal</th>\n",
       "      <th>free_checked_bags</th>\n",
       "      <th>no_cancellation_fees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>534594</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>596863</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>507805</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>539710</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>669329</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5993</th>\n",
       "      <td>544926</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5994</th>\n",
       "      <td>577534</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5995</th>\n",
       "      <td>596320</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5996</th>\n",
       "      <td>539868</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5997</th>\n",
       "      <td>515835</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5998 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id free_meal free_checked_bags no_cancellation_fees\n",
       "0      534594       yes                no                   no\n",
       "1      596863       yes               yes                   no\n",
       "2      507805       yes               yes                  yes\n",
       "3      539710       yes                no                   no\n",
       "4      669329       yes                no                   no\n",
       "...       ...       ...               ...                  ...\n",
       "5993   544926        no                no                   no\n",
       "5994   577534        no                no                   no\n",
       "5995   596320        no                no                   no\n",
       "5996   539868        no                no                   no\n",
       "5997   515835        no                no                   no\n",
       "\n",
       "[5998 rows x 4 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merging\n",
    "segmented_df = pd.merge(segmented_df, new_df[['user_id', 'no_cancellation_fees']], on='user_id', how='inner')\n",
    "segmented_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c1d0b2a",
   "metadata": {},
   "source": [
    "# Exclusive Discounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "16d92cc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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>user_id</th>\n",
       "      <th>session_id</th>\n",
       "      <th>trip_id</th>\n",
       "      <th>session_start</th>\n",
       "      <th>session_end</th>\n",
       "      <th>flight_discount</th>\n",
       "      <th>hotel_discount</th>\n",
       "      <th>flight_discount_amount</th>\n",
       "      <th>hotel_discount_amount</th>\n",
       "      <th>flight_booked</th>\n",
       "      <th>...</th>\n",
       "      <th>destination_airport_lon</th>\n",
       "      <th>base_fare_usd</th>\n",
       "      <th>trip_id.2</th>\n",
       "      <th>hotel_name</th>\n",
       "      <th>nights</th>\n",
       "      <th>rooms</th>\n",
       "      <th>check_in_time</th>\n",
       "      <th>check_out_time</th>\n",
       "      <th>hotel_per_room_usd</th>\n",
       "      <th>total_hotel_spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>548859</td>\n",
       "      <td>548859-9b011308c86d46b0a97af20320b19a73</td>\n",
       "      <td>548859-b080407b3cdc4692a6c4e9fe4ef85627</td>\n",
       "      <td>2023-01-23 21:03:00</td>\n",
       "      <td>2023-01-23 21:09:52</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.10</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-114.020</td>\n",
       "      <td>525.80</td>\n",
       "      <td>548859-b080407b3cdc4692a6c4e9fe4ef85627</td>\n",
       "      <td>Best Western - calgary</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-01-29 19:05:46.995</td>\n",
       "      <td>2023-02-06 11:00:00</td>\n",
       "      <td>269.0</td>\n",
       "      <td>1883.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>827</th>\n",
       "      <td>532279</td>\n",
       "      <td>532279-1a179e2189424b53bb56f8956d7b6ba5</td>\n",
       "      <td>532279-e8348444247b469eb9bc915beffff053</td>\n",
       "      <td>2023-02-08 13:45:00</td>\n",
       "      <td>2023-02-08 13:47:34</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>532279-e8348444247b469eb9bc915beffff053</td>\n",
       "      <td>NH Hotel  - new york</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2023-02-15 11:00:00</td>\n",
       "      <td>2023-02-27 11:00:00</td>\n",
       "      <td>208.0</td>\n",
       "      <td>4992.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1226</th>\n",
       "      <td>586936</td>\n",
       "      <td>586936-6918ccd72d374640a8b6a04da632d737</td>\n",
       "      <td>586936-99d45d8801504ec39c1309a62b837ed0</td>\n",
       "      <td>2023-02-22 19:11:00</td>\n",
       "      <td>2023-02-22 19:14:25</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.10</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>586936-99d45d8801504ec39c1309a62b837ed0</td>\n",
       "      <td>Conrad  - new york</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-02-27 11:00:00</td>\n",
       "      <td>2023-03-17 11:00:00</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1224.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2293</th>\n",
       "      <td>514980</td>\n",
       "      <td>514980-10bf88c8c7b34ebe926b42cecd1afda0</td>\n",
       "      <td>514980-d924669a4328486f9145dbd416f16fcd</td>\n",
       "      <td>2023-05-07 19:06:00</td>\n",
       "      <td>2023-05-07 19:09:14</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.15</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-123.182</td>\n",
       "      <td>540.78</td>\n",
       "      <td>514980-d924669a4328486f9145dbd416f16fcd</td>\n",
       "      <td>Extended Stay  - vancouver</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-05-13 12:50:40.29</td>\n",
       "      <td>2023-05-16 11:00:00</td>\n",
       "      <td>133.0</td>\n",
       "      <td>266.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2414</th>\n",
       "      <td>550796</td>\n",
       "      <td>550796-c5e819fdf64740b0bbe89f5c07c1ff21</td>\n",
       "      <td>550796-b32bbe55506b47faaedc93a21342984a</td>\n",
       "      <td>2023-05-21 16:22:00</td>\n",
       "      <td>2023-05-21 16:23:17</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-122.302</td>\n",
       "      <td>614.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49194</th>\n",
       "      <td>543968</td>\n",
       "      <td>543968-3f85a193b98a4cc899b984bc12e9f5a3</td>\n",
       "      <td>543968-04460895f11c475d9f0e40384d23eac4</td>\n",
       "      <td>2023-04-23 15:26:00</td>\n",
       "      <td>2023-04-23 15:28:16</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.10</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.752</td>\n",
       "      <td>494.52</td>\n",
       "      <td>543968-04460895f11c475d9f0e40384d23eac4</td>\n",
       "      <td>Radisson  - chicago</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-05-02 12:34:27.795</td>\n",
       "      <td>2023-05-04 11:00:00</td>\n",
       "      <td>115.0</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49196</th>\n",
       "      <td>549623</td>\n",
       "      <td>549623-c6c4e43755264612b0575e08a07c3f91</td>\n",
       "      <td>549623-e4b934d312c34607a5d9ce4ee70a74ac</td>\n",
       "      <td>2023-04-30 19:57:00</td>\n",
       "      <td>2023-04-30 19:59:34</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.20</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-95.159</td>\n",
       "      <td>245.61</td>\n",
       "      <td>549623-e4b934d312c34607a5d9ce4ee70a74ac</td>\n",
       "      <td>Shangri-La - houston</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-05-07 14:37:17.175</td>\n",
       "      <td>2023-05-09 11:00:00</td>\n",
       "      <td>319.0</td>\n",
       "      <td>319.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49197</th>\n",
       "      <td>578872</td>\n",
       "      <td>578872-14c8bf7b96024f969ce8c16f7a69a62a</td>\n",
       "      <td>578872-d69b6c5a4d0441dab7c64ae1185995d2</td>\n",
       "      <td>2023-06-07 12:21:00</td>\n",
       "      <td>2023-06-07 12:23:04</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-71.005</td>\n",
       "      <td>214.13</td>\n",
       "      <td>578872-d69b6c5a4d0441dab7c64ae1185995d2</td>\n",
       "      <td>Crowne Plaza - boston</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-06-16 19:29:05.055</td>\n",
       "      <td>2023-06-19 11:00:00</td>\n",
       "      <td>403.0</td>\n",
       "      <td>806.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49198</th>\n",
       "      <td>614311</td>\n",
       "      <td>614311-0b76a67ce41843d78389a29dd23d1d79</td>\n",
       "      <td>614311-d21df59cc4ab4640add93d76b450f37c</td>\n",
       "      <td>2023-06-07 15:37:00</td>\n",
       "      <td>2023-06-07 15:38:06</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>105.806</td>\n",
       "      <td>4912.79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49210</th>\n",
       "      <td>510093</td>\n",
       "      <td>510093-2d3e14a9e10b4a0a942a4312b914e557</td>\n",
       "      <td>510093-e71475580a7c4948ba8f13114a60fa30</td>\n",
       "      <td>2023-07-18 17:06:00</td>\n",
       "      <td>2023-07-18 17:08:42</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-97.032</td>\n",
       "      <td>381.22</td>\n",
       "      <td>510093-e71475580a7c4948ba8f13114a60fa30</td>\n",
       "      <td>Marriott  - winnipeg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-07-30 11:45:27.81</td>\n",
       "      <td>2023-07-30 11:00:00</td>\n",
       "      <td>109.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4231 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id                               session_id  \\\n",
       "402     548859  548859-9b011308c86d46b0a97af20320b19a73   \n",
       "827     532279  532279-1a179e2189424b53bb56f8956d7b6ba5   \n",
       "1226    586936  586936-6918ccd72d374640a8b6a04da632d737   \n",
       "2293    514980  514980-10bf88c8c7b34ebe926b42cecd1afda0   \n",
       "2414    550796  550796-c5e819fdf64740b0bbe89f5c07c1ff21   \n",
       "...        ...                                      ...   \n",
       "49194   543968  543968-3f85a193b98a4cc899b984bc12e9f5a3   \n",
       "49196   549623  549623-c6c4e43755264612b0575e08a07c3f91   \n",
       "49197   578872  578872-14c8bf7b96024f969ce8c16f7a69a62a   \n",
       "49198   614311  614311-0b76a67ce41843d78389a29dd23d1d79   \n",
       "49210   510093  510093-2d3e14a9e10b4a0a942a4312b914e557   \n",
       "\n",
       "                                       trip_id        session_start  \\\n",
       "402    548859-b080407b3cdc4692a6c4e9fe4ef85627  2023-01-23 21:03:00   \n",
       "827    532279-e8348444247b469eb9bc915beffff053  2023-02-08 13:45:00   \n",
       "1226   586936-99d45d8801504ec39c1309a62b837ed0  2023-02-22 19:11:00   \n",
       "2293   514980-d924669a4328486f9145dbd416f16fcd  2023-05-07 19:06:00   \n",
       "2414   550796-b32bbe55506b47faaedc93a21342984a  2023-05-21 16:22:00   \n",
       "...                                        ...                  ...   \n",
       "49194  543968-04460895f11c475d9f0e40384d23eac4  2023-04-23 15:26:00   \n",
       "49196  549623-e4b934d312c34607a5d9ce4ee70a74ac  2023-04-30 19:57:00   \n",
       "49197  578872-d69b6c5a4d0441dab7c64ae1185995d2  2023-06-07 12:21:00   \n",
       "49198  614311-d21df59cc4ab4640add93d76b450f37c  2023-06-07 15:37:00   \n",
       "49210  510093-e71475580a7c4948ba8f13114a60fa30  2023-07-18 17:06:00   \n",
       "\n",
       "               session_end  flight_discount  hotel_discount  \\\n",
       "402    2023-01-23 21:09:52            False            True   \n",
       "827    2023-02-08 13:47:34             True           False   \n",
       "1226   2023-02-22 19:14:25            False            True   \n",
       "2293   2023-05-07 19:09:14            False            True   \n",
       "2414   2023-05-21 16:23:17             True           False   \n",
       "...                    ...              ...             ...   \n",
       "49194  2023-04-23 15:28:16            False            True   \n",
       "49196  2023-04-30 19:59:34            False            True   \n",
       "49197  2023-06-07 12:23:04             True           False   \n",
       "49198  2023-06-07 15:38:06             True           False   \n",
       "49210  2023-07-18 17:08:42             True           False   \n",
       "\n",
       "       flight_discount_amount  hotel_discount_amount  flight_booked  ...  \\\n",
       "402                       NaN                   0.10           True  ...   \n",
       "827                      0.15                    NaN          False  ...   \n",
       "1226                      NaN                   0.10          False  ...   \n",
       "2293                      NaN                   0.15           True  ...   \n",
       "2414                     0.20                    NaN           True  ...   \n",
       "...                       ...                    ...            ...  ...   \n",
       "49194                     NaN                   0.10           True  ...   \n",
       "49196                     NaN                   0.20           True  ...   \n",
       "49197                    0.15                    NaN           True  ...   \n",
       "49198                    0.05                    NaN           True  ...   \n",
       "49210                    0.20                    NaN           True  ...   \n",
       "\n",
       "       destination_airport_lon  base_fare_usd  \\\n",
       "402                   -114.020         525.80   \n",
       "827                        NaN            NaN   \n",
       "1226                       NaN            NaN   \n",
       "2293                  -123.182         540.78   \n",
       "2414                  -122.302         614.63   \n",
       "...                        ...            ...   \n",
       "49194                  -87.752         494.52   \n",
       "49196                  -95.159         245.61   \n",
       "49197                  -71.005         214.13   \n",
       "49198                  105.806        4912.79   \n",
       "49210                  -97.032         381.22   \n",
       "\n",
       "                                     trip_id.2                  hotel_name  \\\n",
       "402    548859-b080407b3cdc4692a6c4e9fe4ef85627      Best Western - calgary   \n",
       "827    532279-e8348444247b469eb9bc915beffff053        NH Hotel  - new york   \n",
       "1226   586936-99d45d8801504ec39c1309a62b837ed0          Conrad  - new york   \n",
       "2293   514980-d924669a4328486f9145dbd416f16fcd  Extended Stay  - vancouver   \n",
       "2414                                       NaN                         NaN   \n",
       "...                                        ...                         ...   \n",
       "49194  543968-04460895f11c475d9f0e40384d23eac4         Radisson  - chicago   \n",
       "49196  549623-e4b934d312c34607a5d9ce4ee70a74ac        Shangri-La - houston   \n",
       "49197  578872-d69b6c5a4d0441dab7c64ae1185995d2       Crowne Plaza - boston   \n",
       "49198                                      NaN                         NaN   \n",
       "49210  510093-e71475580a7c4948ba8f13114a60fa30        Marriott  - winnipeg   \n",
       "\n",
       "      nights  rooms            check_in_time       check_out_time  \\\n",
       "402      7.0    1.0  2023-01-29 19:05:46.995  2023-02-06 11:00:00   \n",
       "827     12.0    2.0      2023-02-15 11:00:00  2023-02-27 11:00:00   \n",
       "1226    18.0    1.0      2023-02-27 11:00:00  2023-03-17 11:00:00   \n",
       "2293     2.0    1.0   2023-05-13 12:50:40.29  2023-05-16 11:00:00   \n",
       "2414     NaN    NaN                      NaN                  NaN   \n",
       "...      ...    ...                      ...                  ...   \n",
       "49194    1.0    1.0  2023-05-02 12:34:27.795  2023-05-04 11:00:00   \n",
       "49196    1.0    1.0  2023-05-07 14:37:17.175  2023-05-09 11:00:00   \n",
       "49197    2.0    1.0  2023-06-16 19:29:05.055  2023-06-19 11:00:00   \n",
       "49198    NaN    NaN                      NaN                  NaN   \n",
       "49210    0.0    1.0   2023-07-30 11:45:27.81  2023-07-30 11:00:00   \n",
       "\n",
       "      hotel_per_room_usd total_hotel_spent  \n",
       "402                269.0            1883.0  \n",
       "827                208.0            4992.0  \n",
       "1226                68.0            1224.0  \n",
       "2293               133.0             266.0  \n",
       "2414                 NaN               NaN  \n",
       "...                  ...               ...  \n",
       "49194              115.0             115.0  \n",
       "49196              319.0             319.0  \n",
       "49197              403.0             806.0  \n",
       "49198                NaN               NaN  \n",
       "49210              109.0               0.0  \n",
       "\n",
       "[4231 rows x 44 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all the trip with flight and hotel booking and have flight or hotel discount with no cancellation\n",
    "new_df = df[\n",
    "    ((df['trip_id.1'].notnull()) |\n",
    "    (df['trip_id.2'].notnull())) &\n",
    "    (df['cancellation'] == False) &\n",
    "    ((df['flight_discount'] == True) | (df['hotel_discount'] == True))\n",
    "]\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "50419271",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>user_id</th>\n",
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       "      <th>402</th>\n",
       "      <td>548859</td>\n",
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       "      <td>532279</td>\n",
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       "      <th>1226</th>\n",
       "      <td>586936</td>\n",
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       "      <th>2293</th>\n",
       "      <td>514980</td>\n",
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       "      <th>2414</th>\n",
       "      <td>550796</td>\n",
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       "      <td>...</td>\n",
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       "      <th>49194</th>\n",
       "      <td>543968</td>\n",
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       "      <td>549623</td>\n",
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       "      <th>49197</th>\n",
       "      <td>578872</td>\n",
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       "      <th>49198</th>\n",
       "      <td>614311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49210</th>\n",
       "      <td>510093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4231 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id\n",
       "402     548859\n",
       "827     532279\n",
       "1226    586936\n",
       "2293    514980\n",
       "2414    550796\n",
       "...        ...\n",
       "49194   543968\n",
       "49196   549623\n",
       "49197   578872\n",
       "49198   614311\n",
       "49210   510093\n",
       "\n",
       "[4231 rows x 1 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = new_df[['user_id']]\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a77d6dfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>count</th>\n",
       "      <th>discounted_trip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2572</th>\n",
       "      <td>589489</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>514980</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1072</th>\n",
       "      <td>524226</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1919</th>\n",
       "      <td>551804</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1064</th>\n",
       "      <td>524043</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1228</th>\n",
       "      <td>528706</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1229</th>\n",
       "      <td>528744</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1230</th>\n",
       "      <td>528777</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1232</th>\n",
       "      <td>528828</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3105</th>\n",
       "      <td>801660</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3106 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  count  discounted_trip\n",
       "2572   589489      6                6\n",
       "708    514980      5                5\n",
       "1072   524226      4                4\n",
       "1919   551804      4                4\n",
       "1064   524043      4                4\n",
       "...       ...    ...              ...\n",
       "1228   528706      1                1\n",
       "1229   528744      1                1\n",
       "1230   528777      1                1\n",
       "1232   528828      1                1\n",
       "3105   801660      1                1\n",
       "\n",
       "[3106 rows x 3 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# geting the total discounted trip by each user\n",
    "new_df = new_df.groupby('user_id', as_index=False).value_counts().sort_values('count', ascending=False)\n",
    "new_df['discounted_trip'] = new_df['count']\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "12495ed5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3106.000000\n",
       "mean        1.362202\n",
       "std         0.606864\n",
       "min         1.000000\n",
       "25%         1.000000\n",
       "50%         1.000000\n",
       "75%         2.000000\n",
       "max         6.000000\n",
       "Name: discounted_trip, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['discounted_trip'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "20277415",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x22c741a2850>,\n",
       "  <matplotlib.lines.Line2D at 0x22c741a30d0>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x22c741a3910>,\n",
       "  <matplotlib.lines.Line2D at 0x22c741b0210>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x22c741a1fd0>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x22c741b0b50>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x22c741b1290>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(new_df['discounted_trip'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "663663d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id            163\n",
       "count              163\n",
       "discounted_trip    163\n",
       "dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df[new_df['discounted_trip'] > 2].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "7e9dceb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      user_id  count  discounted_trip exclusive_discounts\n",
      "2572   589489      6                6                 yes\n",
      "708    514980      5                5                 yes\n",
      "1072   524226      4                4                 yes\n",
      "1919   551804      4                4                 yes\n",
      "1064   524043      4                4                 yes\n",
      "...       ...    ...              ...                 ...\n",
      "1228   528706      1                1                  no\n",
      "1229   528744      1                1                  no\n",
      "1230   528777      1                1                  no\n",
      "1232   528828      1                1                  no\n",
      "3105   801660      1                1                  no\n",
      "\n",
      "[3106 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "# Create conditional DataFrame column by np.select function.\n",
    "# giving the perk who has more than 2 trip\n",
    "conditions = [new_df['discounted_trip'] > 2]\n",
    "\n",
    "choices = ['yes']\n",
    "new_df['exclusive_discounts'] = np.select(conditions, choices, default='no')\n",
    "print(new_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "1342a1b7",
   "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>user_id</th>\n",
       "      <th>free_meal</th>\n",
       "      <th>free_checked_bags</th>\n",
       "      <th>no_cancellation_fees</th>\n",
       "      <th>exclusive_discounts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>534594</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>596863</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>507805</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>539710</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>669329</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5993</th>\n",
       "      <td>544926</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5994</th>\n",
       "      <td>577534</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5995</th>\n",
       "      <td>596320</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5996</th>\n",
       "      <td>539868</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5997</th>\n",
       "      <td>515835</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5998 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id free_meal free_checked_bags no_cancellation_fees  \\\n",
       "0      534594       yes                no                   no   \n",
       "1      596863       yes               yes                   no   \n",
       "2      507805       yes               yes                  yes   \n",
       "3      539710       yes                no                   no   \n",
       "4      669329       yes                no                   no   \n",
       "...       ...       ...               ...                  ...   \n",
       "5993   544926        no                no                   no   \n",
       "5994   577534        no                no                   no   \n",
       "5995   596320        no                no                   no   \n",
       "5996   539868        no                no                   no   \n",
       "5997   515835        no                no                   no   \n",
       "\n",
       "     exclusive_discounts  \n",
       "0                    NaN  \n",
       "1                    NaN  \n",
       "2                     no  \n",
       "3                     no  \n",
       "4                    NaN  \n",
       "...                  ...  \n",
       "5993                  no  \n",
       "5994                  no  \n",
       "5995                  no  \n",
       "5996                  no  \n",
       "5997                 NaN  \n",
       "\n",
       "[5998 rows x 5 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merging\n",
    "segmented_df = pd.merge(segmented_df, new_df[['user_id', 'exclusive_discounts']], on='user_id', how='left')\n",
    "segmented_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "39242bd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id                    0\n",
       "free_meal                  0\n",
       "free_checked_bags          0\n",
       "no_cancellation_fees       0\n",
       "exclusive_discounts     2892\n",
       "dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "segmented_df.isna().sum() # calculating the total NA cell"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b845516",
   "metadata": {},
   "source": [
    "# 1 night free hotel with flight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "8a07d868",
   "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>user_id</th>\n",
       "      <th>session_id</th>\n",
       "      <th>trip_id</th>\n",
       "      <th>session_start</th>\n",
       "      <th>session_end</th>\n",
       "      <th>flight_discount</th>\n",
       "      <th>hotel_discount</th>\n",
       "      <th>flight_discount_amount</th>\n",
       "      <th>hotel_discount_amount</th>\n",
       "      <th>flight_booked</th>\n",
       "      <th>...</th>\n",
       "      <th>destination_airport_lon</th>\n",
       "      <th>base_fare_usd</th>\n",
       "      <th>trip_id.2</th>\n",
       "      <th>hotel_name</th>\n",
       "      <th>nights</th>\n",
       "      <th>rooms</th>\n",
       "      <th>check_in_time</th>\n",
       "      <th>check_out_time</th>\n",
       "      <th>hotel_per_room_usd</th>\n",
       "      <th>total_hotel_spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>548859</td>\n",
       "      <td>548859-9b011308c86d46b0a97af20320b19a73</td>\n",
       "      <td>548859-b080407b3cdc4692a6c4e9fe4ef85627</td>\n",
       "      <td>2023-01-23 21:03:00</td>\n",
       "      <td>2023-01-23 21:09:52</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-114.020</td>\n",
       "      <td>525.80</td>\n",
       "      <td>548859-b080407b3cdc4692a6c4e9fe4ef85627</td>\n",
       "      <td>Best Western - calgary</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-01-29 19:05:46.995</td>\n",
       "      <td>2023-02-06 11:00:00</td>\n",
       "      <td>269.0</td>\n",
       "      <td>1883.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>575774</td>\n",
       "      <td>575774-7484f82c3fcc4338a6ee98f37dfe4733</td>\n",
       "      <td>575774-c1f81bbaea8841c8a13619fec71042e5</td>\n",
       "      <td>2023-02-08 16:27:00</td>\n",
       "      <td>2023-02-08 16:29:19</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-80.943</td>\n",
       "      <td>340.63</td>\n",
       "      <td>575774-c1f81bbaea8841c8a13619fec71042e5</td>\n",
       "      <td>Fairmont - charlotte</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-02-15 13:35:23.46</td>\n",
       "      <td>2023-02-21 11:00:00</td>\n",
       "      <td>134.0</td>\n",
       "      <td>670.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947</th>\n",
       "      <td>548504</td>\n",
       "      <td>548504-c1f4807f7ced41a7ae4c268416b98870</td>\n",
       "      <td>548504-a5d29c872e0549b488ff615f1aef614b</td>\n",
       "      <td>2023-02-12 22:35:00</td>\n",
       "      <td>2023-02-12 22:37:23</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-75.241</td>\n",
       "      <td>425.40</td>\n",
       "      <td>548504-a5d29c872e0549b488ff615f1aef614b</td>\n",
       "      <td>Aman Resorts - philadelphia</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-02-19 14:39:43.56</td>\n",
       "      <td>2023-02-20 11:00:00</td>\n",
       "      <td>497.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1072</th>\n",
       "      <td>555770</td>\n",
       "      <td>555770-ea2787aad15a434da2893e8ed54bfe79</td>\n",
       "      <td>555770-a75973f7da3e4e1c8345b378058696bb</td>\n",
       "      <td>2023-02-17 22:20:00</td>\n",
       "      <td>2023-02-17 22:23:15</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.896</td>\n",
       "      <td>116.83</td>\n",
       "      <td>555770-a75973f7da3e4e1c8345b378058696bb</td>\n",
       "      <td>Accor - milwaukee</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-02-26 10:43:32.25</td>\n",
       "      <td>2023-03-07 11:00:00</td>\n",
       "      <td>125.0</td>\n",
       "      <td>1125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1165</th>\n",
       "      <td>524861</td>\n",
       "      <td>524861-3d7d2c5ddd2a49038fa07e91f1f3f749</td>\n",
       "      <td>524861-4fad7e1eb0d443349fa80347bdc3c7a5</td>\n",
       "      <td>2023-02-20 21:47:00</td>\n",
       "      <td>2023-02-20 21:50:09</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.752</td>\n",
       "      <td>430.78</td>\n",
       "      <td>524861-4fad7e1eb0d443349fa80347bdc3c7a5</td>\n",
       "      <td>Wyndham - chicago</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-02-28 12:48:32.13</td>\n",
       "      <td>2023-03-02 11:00:00</td>\n",
       "      <td>106.0</td>\n",
       "      <td>106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49205</th>\n",
       "      <td>438551</td>\n",
       "      <td>438551-f1073b8a5a844032a33256666ee6c47f</td>\n",
       "      <td>438551-1a9fcfc16bd8487ea23e820957d23279</td>\n",
       "      <td>2023-05-31 21:03:37</td>\n",
       "      <td>2023-05-31 23:03:37</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>77.961</td>\n",
       "      <td>13645.96</td>\n",
       "      <td>438551-1a9fcfc16bd8487ea23e820957d23279</td>\n",
       "      <td>Crowne Plaza - agra</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2023-10-10 00:55:04.08</td>\n",
       "      <td>2023-10-23 11:00:00</td>\n",
       "      <td>143.0</td>\n",
       "      <td>5577.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49207</th>\n",
       "      <td>480313</td>\n",
       "      <td>480313-f46ac4d965a2410e84ddf400ede116ad</td>\n",
       "      <td>480313-129a85cc3c5244b5962afe10c7932cda</td>\n",
       "      <td>2023-02-09 22:01:16</td>\n",
       "      <td>2023-02-10 00:01:16</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-121.929</td>\n",
       "      <td>83.44</td>\n",
       "      <td>480313-129a85cc3c5244b5962afe10c7932cda</td>\n",
       "      <td>Shangri-La - san jose</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-02-13 09:37:10.74</td>\n",
       "      <td>2023-02-15 11:00:00</td>\n",
       "      <td>174.0</td>\n",
       "      <td>348.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49208</th>\n",
       "      <td>534295</td>\n",
       "      <td>534295-0a137c45cac34b1e834f23ecab4233fb</td>\n",
       "      <td>534295-3e20b84b40c946b1a8124395231d4412</td>\n",
       "      <td>2023-07-10 18:55:00</td>\n",
       "      <td>2023-07-10 18:57:18</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-117.215</td>\n",
       "      <td>3205.63</td>\n",
       "      <td>534295-3e20b84b40c946b1a8124395231d4412</td>\n",
       "      <td>Aman Resorts - san diego</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2023-07-20 20:54:53.1</td>\n",
       "      <td>2023-07-29 11:00:00</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49209</th>\n",
       "      <td>518416</td>\n",
       "      <td>518416-fd45c188d94a4d30a7e4468056701f1e</td>\n",
       "      <td>518416-cca5cea3fac04cdf8d069e13891f016a</td>\n",
       "      <td>2023-07-15 07:38:00</td>\n",
       "      <td>2023-07-15 07:42:04</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-98.279</td>\n",
       "      <td>103.01</td>\n",
       "      <td>518416-cca5cea3fac04cdf8d069e13891f016a</td>\n",
       "      <td>InterContinental  - san antonio</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2023-07-22 15:22:38.37</td>\n",
       "      <td>2023-07-24 11:00:00</td>\n",
       "      <td>80.0</td>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49210</th>\n",
       "      <td>510093</td>\n",
       "      <td>510093-2d3e14a9e10b4a0a942a4312b914e557</td>\n",
       "      <td>510093-e71475580a7c4948ba8f13114a60fa30</td>\n",
       "      <td>2023-07-18 17:06:00</td>\n",
       "      <td>2023-07-18 17:08:42</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-97.032</td>\n",
       "      <td>381.22</td>\n",
       "      <td>510093-e71475580a7c4948ba8f13114a60fa30</td>\n",
       "      <td>Marriott  - winnipeg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-07-30 11:45:27.81</td>\n",
       "      <td>2023-07-30 11:00:00</td>\n",
       "      <td>109.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12294 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id                               session_id  \\\n",
       "402     548859  548859-9b011308c86d46b0a97af20320b19a73   \n",
       "869     575774  575774-7484f82c3fcc4338a6ee98f37dfe4733   \n",
       "947     548504  548504-c1f4807f7ced41a7ae4c268416b98870   \n",
       "1072    555770  555770-ea2787aad15a434da2893e8ed54bfe79   \n",
       "1165    524861  524861-3d7d2c5ddd2a49038fa07e91f1f3f749   \n",
       "...        ...                                      ...   \n",
       "49205   438551  438551-f1073b8a5a844032a33256666ee6c47f   \n",
       "49207   480313  480313-f46ac4d965a2410e84ddf400ede116ad   \n",
       "49208   534295  534295-0a137c45cac34b1e834f23ecab4233fb   \n",
       "49209   518416  518416-fd45c188d94a4d30a7e4468056701f1e   \n",
       "49210   510093  510093-2d3e14a9e10b4a0a942a4312b914e557   \n",
       "\n",
       "                                       trip_id        session_start  \\\n",
       "402    548859-b080407b3cdc4692a6c4e9fe4ef85627  2023-01-23 21:03:00   \n",
       "869    575774-c1f81bbaea8841c8a13619fec71042e5  2023-02-08 16:27:00   \n",
       "947    548504-a5d29c872e0549b488ff615f1aef614b  2023-02-12 22:35:00   \n",
       "1072   555770-a75973f7da3e4e1c8345b378058696bb  2023-02-17 22:20:00   \n",
       "1165   524861-4fad7e1eb0d443349fa80347bdc3c7a5  2023-02-20 21:47:00   \n",
       "...                                        ...                  ...   \n",
       "49205  438551-1a9fcfc16bd8487ea23e820957d23279  2023-05-31 21:03:37   \n",
       "49207  480313-129a85cc3c5244b5962afe10c7932cda  2023-02-09 22:01:16   \n",
       "49208  534295-3e20b84b40c946b1a8124395231d4412  2023-07-10 18:55:00   \n",
       "49209  518416-cca5cea3fac04cdf8d069e13891f016a  2023-07-15 07:38:00   \n",
       "49210  510093-e71475580a7c4948ba8f13114a60fa30  2023-07-18 17:06:00   \n",
       "\n",
       "               session_end  flight_discount  hotel_discount  \\\n",
       "402    2023-01-23 21:09:52            False            True   \n",
       "869    2023-02-08 16:29:19            False           False   \n",
       "947    2023-02-12 22:37:23            False           False   \n",
       "1072   2023-02-17 22:23:15            False           False   \n",
       "1165   2023-02-20 21:50:09            False           False   \n",
       "...                    ...              ...             ...   \n",
       "49205  2023-05-31 23:03:37             True            True   \n",
       "49207  2023-02-10 00:01:16             True            True   \n",
       "49208  2023-07-10 18:57:18            False           False   \n",
       "49209  2023-07-15 07:42:04            False           False   \n",
       "49210  2023-07-18 17:08:42             True           False   \n",
       "\n",
       "       flight_discount_amount  hotel_discount_amount  flight_booked  ...  \\\n",
       "402                       NaN                    0.1           True  ...   \n",
       "869                       NaN                    NaN           True  ...   \n",
       "947                       NaN                    NaN           True  ...   \n",
       "1072                      NaN                    NaN           True  ...   \n",
       "1165                      NaN                    NaN           True  ...   \n",
       "...                       ...                    ...            ...  ...   \n",
       "49205                     NaN                    NaN           True  ...   \n",
       "49207                     NaN                    NaN           True  ...   \n",
       "49208                     NaN                    NaN           True  ...   \n",
       "49209                     NaN                    NaN           True  ...   \n",
       "49210                     0.2                    NaN           True  ...   \n",
       "\n",
       "       destination_airport_lon  base_fare_usd  \\\n",
       "402                   -114.020         525.80   \n",
       "869                    -80.943         340.63   \n",
       "947                    -75.241         425.40   \n",
       "1072                   -87.896         116.83   \n",
       "1165                   -87.752         430.78   \n",
       "...                        ...            ...   \n",
       "49205                   77.961       13645.96   \n",
       "49207                 -121.929          83.44   \n",
       "49208                 -117.215        3205.63   \n",
       "49209                  -98.279         103.01   \n",
       "49210                  -97.032         381.22   \n",
       "\n",
       "                                     trip_id.2  \\\n",
       "402    548859-b080407b3cdc4692a6c4e9fe4ef85627   \n",
       "869    575774-c1f81bbaea8841c8a13619fec71042e5   \n",
       "947    548504-a5d29c872e0549b488ff615f1aef614b   \n",
       "1072   555770-a75973f7da3e4e1c8345b378058696bb   \n",
       "1165   524861-4fad7e1eb0d443349fa80347bdc3c7a5   \n",
       "...                                        ...   \n",
       "49205  438551-1a9fcfc16bd8487ea23e820957d23279   \n",
       "49207  480313-129a85cc3c5244b5962afe10c7932cda   \n",
       "49208  534295-3e20b84b40c946b1a8124395231d4412   \n",
       "49209  518416-cca5cea3fac04cdf8d069e13891f016a   \n",
       "49210  510093-e71475580a7c4948ba8f13114a60fa30   \n",
       "\n",
       "                            hotel_name nights  rooms            check_in_time  \\\n",
       "402             Best Western - calgary    7.0    1.0  2023-01-29 19:05:46.995   \n",
       "869               Fairmont - charlotte    5.0    1.0   2023-02-15 13:35:23.46   \n",
       "947        Aman Resorts - philadelphia    0.0    1.0   2023-02-19 14:39:43.56   \n",
       "1072                 Accor - milwaukee    9.0    1.0   2023-02-26 10:43:32.25   \n",
       "1165                 Wyndham - chicago    1.0    1.0   2023-02-28 12:48:32.13   \n",
       "...                                ...    ...    ...                      ...   \n",
       "49205              Crowne Plaza - agra   13.0    3.0   2023-10-10 00:55:04.08   \n",
       "49207            Shangri-La - san jose    2.0    1.0   2023-02-13 09:37:10.74   \n",
       "49208         Aman Resorts - san diego    8.0    4.0    2023-07-20 20:54:53.1   \n",
       "49209  InterContinental  - san antonio    1.0    2.0   2023-07-22 15:22:38.37   \n",
       "49210             Marriott  - winnipeg    0.0    1.0   2023-07-30 11:45:27.81   \n",
       "\n",
       "            check_out_time hotel_per_room_usd total_hotel_spent  \n",
       "402    2023-02-06 11:00:00              269.0            1883.0  \n",
       "869    2023-02-21 11:00:00              134.0             670.0  \n",
       "947    2023-02-20 11:00:00              497.0               0.0  \n",
       "1072   2023-03-07 11:00:00              125.0            1125.0  \n",
       "1165   2023-03-02 11:00:00              106.0             106.0  \n",
       "...                    ...                ...               ...  \n",
       "49205  2023-10-23 11:00:00              143.0            5577.0  \n",
       "49207  2023-02-15 11:00:00              174.0             348.0  \n",
       "49208  2023-07-29 11:00:00               85.0            2720.0  \n",
       "49209  2023-07-24 11:00:00               80.0             160.0  \n",
       "49210  2023-07-30 11:00:00              109.0               0.0  \n",
       "\n",
       "[12294 rows x 44 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all the trip with flight and hotel booking\n",
    "new_df = df[(df['trip_id.1'].notnull()) & (df['trip_id.2'].notnull())]\n",
    "\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b1831f4d",
   "metadata": {
    "scrolled": true
   },
   "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>user_id</th>\n",
       "      <th>session_id</th>\n",
       "      <th>trip_id</th>\n",
       "      <th>session_start</th>\n",
       "      <th>session_end</th>\n",
       "      <th>flight_discount</th>\n",
       "      <th>hotel_discount</th>\n",
       "      <th>flight_discount_amount</th>\n",
       "      <th>hotel_discount_amount</th>\n",
       "      <th>flight_booked</th>\n",
       "      <th>...</th>\n",
       "      <th>destination_airport_lon</th>\n",
       "      <th>base_fare_usd</th>\n",
       "      <th>trip_id.2</th>\n",
       "      <th>hotel_name</th>\n",
       "      <th>nights</th>\n",
       "      <th>rooms</th>\n",
       "      <th>check_in_time</th>\n",
       "      <th>check_out_time</th>\n",
       "      <th>hotel_per_room_usd</th>\n",
       "      <th>total_hotel_spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>548859</td>\n",
       "      <td>548859-9b011308c86d46b0a97af20320b19a73</td>\n",
       "      <td>548859-b080407b3cdc4692a6c4e9fe4ef85627</td>\n",
       "      <td>2023-01-23 21:03:00</td>\n",
       "      <td>2023-01-23 21:09:52</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-114.020</td>\n",
       "      <td>525.80</td>\n",
       "      <td>548859-b080407b3cdc4692a6c4e9fe4ef85627</td>\n",
       "      <td>Best Western - calgary</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-01-29 19:05:46.995</td>\n",
       "      <td>2023-02-06 11:00:00</td>\n",
       "      <td>269.0</td>\n",
       "      <td>1883.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947</th>\n",
       "      <td>548504</td>\n",
       "      <td>548504-c1f4807f7ced41a7ae4c268416b98870</td>\n",
       "      <td>548504-a5d29c872e0549b488ff615f1aef614b</td>\n",
       "      <td>2023-02-12 22:35:00</td>\n",
       "      <td>2023-02-12 22:37:23</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-75.241</td>\n",
       "      <td>425.40</td>\n",
       "      <td>548504-a5d29c872e0549b488ff615f1aef614b</td>\n",
       "      <td>Aman Resorts - philadelphia</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-02-19 14:39:43.56</td>\n",
       "      <td>2023-02-20 11:00:00</td>\n",
       "      <td>497.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1165</th>\n",
       "      <td>524861</td>\n",
       "      <td>524861-3d7d2c5ddd2a49038fa07e91f1f3f749</td>\n",
       "      <td>524861-4fad7e1eb0d443349fa80347bdc3c7a5</td>\n",
       "      <td>2023-02-20 21:47:00</td>\n",
       "      <td>2023-02-20 21:50:09</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.752</td>\n",
       "      <td>430.78</td>\n",
       "      <td>524861-4fad7e1eb0d443349fa80347bdc3c7a5</td>\n",
       "      <td>Wyndham - chicago</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-02-28 12:48:32.13</td>\n",
       "      <td>2023-03-02 11:00:00</td>\n",
       "      <td>106.0</td>\n",
       "      <td>106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1574</th>\n",
       "      <td>553955</td>\n",
       "      <td>553955-0774378740e2437ca0fbeb6f9524e0a7</td>\n",
       "      <td>553955-cf601b19c9694254887fe22a95254212</td>\n",
       "      <td>2023-03-11 19:47:00</td>\n",
       "      <td>2023-03-11 19:48:59</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-77.037</td>\n",
       "      <td>359.64</td>\n",
       "      <td>553955-cf601b19c9694254887fe22a95254212</td>\n",
       "      <td>Accor - washington</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-03-17 11:45:18.315</td>\n",
       "      <td>2023-03-20 11:00:00</td>\n",
       "      <td>127.0</td>\n",
       "      <td>381.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1907</th>\n",
       "      <td>389434</td>\n",
       "      <td>389434-74521e4046444bec81eddbd6cbb3305f</td>\n",
       "      <td>389434-cf10e38f0d464b81ace3f3be9f689833</td>\n",
       "      <td>2023-03-26 17:02:00</td>\n",
       "      <td>2023-03-26 17:04:47</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-79.935</td>\n",
       "      <td>105.77</td>\n",
       "      <td>389434-cf10e38f0d464b81ace3f3be9f689833</td>\n",
       "      <td>Wyndham - hamilton</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-04-06 09:42:33.345</td>\n",
       "      <td>2023-04-06 11:00:00</td>\n",
       "      <td>107.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49205</th>\n",
       "      <td>438551</td>\n",
       "      <td>438551-f1073b8a5a844032a33256666ee6c47f</td>\n",
       "      <td>438551-1a9fcfc16bd8487ea23e820957d23279</td>\n",
       "      <td>2023-05-31 21:03:37</td>\n",
       "      <td>2023-05-31 23:03:37</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>77.961</td>\n",
       "      <td>13645.96</td>\n",
       "      <td>438551-1a9fcfc16bd8487ea23e820957d23279</td>\n",
       "      <td>Crowne Plaza - agra</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2023-10-10 00:55:04.08</td>\n",
       "      <td>2023-10-23 11:00:00</td>\n",
       "      <td>143.0</td>\n",
       "      <td>5577.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49207</th>\n",
       "      <td>480313</td>\n",
       "      <td>480313-f46ac4d965a2410e84ddf400ede116ad</td>\n",
       "      <td>480313-129a85cc3c5244b5962afe10c7932cda</td>\n",
       "      <td>2023-02-09 22:01:16</td>\n",
       "      <td>2023-02-10 00:01:16</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-121.929</td>\n",
       "      <td>83.44</td>\n",
       "      <td>480313-129a85cc3c5244b5962afe10c7932cda</td>\n",
       "      <td>Shangri-La - san jose</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-02-13 09:37:10.74</td>\n",
       "      <td>2023-02-15 11:00:00</td>\n",
       "      <td>174.0</td>\n",
       "      <td>348.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49208</th>\n",
       "      <td>534295</td>\n",
       "      <td>534295-0a137c45cac34b1e834f23ecab4233fb</td>\n",
       "      <td>534295-3e20b84b40c946b1a8124395231d4412</td>\n",
       "      <td>2023-07-10 18:55:00</td>\n",
       "      <td>2023-07-10 18:57:18</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-117.215</td>\n",
       "      <td>3205.63</td>\n",
       "      <td>534295-3e20b84b40c946b1a8124395231d4412</td>\n",
       "      <td>Aman Resorts - san diego</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2023-07-20 20:54:53.1</td>\n",
       "      <td>2023-07-29 11:00:00</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49209</th>\n",
       "      <td>518416</td>\n",
       "      <td>518416-fd45c188d94a4d30a7e4468056701f1e</td>\n",
       "      <td>518416-cca5cea3fac04cdf8d069e13891f016a</td>\n",
       "      <td>2023-07-15 07:38:00</td>\n",
       "      <td>2023-07-15 07:42:04</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-98.279</td>\n",
       "      <td>103.01</td>\n",
       "      <td>518416-cca5cea3fac04cdf8d069e13891f016a</td>\n",
       "      <td>InterContinental  - san antonio</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2023-07-22 15:22:38.37</td>\n",
       "      <td>2023-07-24 11:00:00</td>\n",
       "      <td>80.0</td>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49210</th>\n",
       "      <td>510093</td>\n",
       "      <td>510093-2d3e14a9e10b4a0a942a4312b914e557</td>\n",
       "      <td>510093-e71475580a7c4948ba8f13114a60fa30</td>\n",
       "      <td>2023-07-18 17:06:00</td>\n",
       "      <td>2023-07-18 17:08:42</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-97.032</td>\n",
       "      <td>381.22</td>\n",
       "      <td>510093-e71475580a7c4948ba8f13114a60fa30</td>\n",
       "      <td>Marriott  - winnipeg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-07-30 11:45:27.81</td>\n",
       "      <td>2023-07-30 11:00:00</td>\n",
       "      <td>109.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11708 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id                               session_id  \\\n",
       "402     548859  548859-9b011308c86d46b0a97af20320b19a73   \n",
       "947     548504  548504-c1f4807f7ced41a7ae4c268416b98870   \n",
       "1165    524861  524861-3d7d2c5ddd2a49038fa07e91f1f3f749   \n",
       "1574    553955  553955-0774378740e2437ca0fbeb6f9524e0a7   \n",
       "1907    389434  389434-74521e4046444bec81eddbd6cbb3305f   \n",
       "...        ...                                      ...   \n",
       "49205   438551  438551-f1073b8a5a844032a33256666ee6c47f   \n",
       "49207   480313  480313-f46ac4d965a2410e84ddf400ede116ad   \n",
       "49208   534295  534295-0a137c45cac34b1e834f23ecab4233fb   \n",
       "49209   518416  518416-fd45c188d94a4d30a7e4468056701f1e   \n",
       "49210   510093  510093-2d3e14a9e10b4a0a942a4312b914e557   \n",
       "\n",
       "                                       trip_id        session_start  \\\n",
       "402    548859-b080407b3cdc4692a6c4e9fe4ef85627  2023-01-23 21:03:00   \n",
       "947    548504-a5d29c872e0549b488ff615f1aef614b  2023-02-12 22:35:00   \n",
       "1165   524861-4fad7e1eb0d443349fa80347bdc3c7a5  2023-02-20 21:47:00   \n",
       "1574   553955-cf601b19c9694254887fe22a95254212  2023-03-11 19:47:00   \n",
       "1907   389434-cf10e38f0d464b81ace3f3be9f689833  2023-03-26 17:02:00   \n",
       "...                                        ...                  ...   \n",
       "49205  438551-1a9fcfc16bd8487ea23e820957d23279  2023-05-31 21:03:37   \n",
       "49207  480313-129a85cc3c5244b5962afe10c7932cda  2023-02-09 22:01:16   \n",
       "49208  534295-3e20b84b40c946b1a8124395231d4412  2023-07-10 18:55:00   \n",
       "49209  518416-cca5cea3fac04cdf8d069e13891f016a  2023-07-15 07:38:00   \n",
       "49210  510093-e71475580a7c4948ba8f13114a60fa30  2023-07-18 17:06:00   \n",
       "\n",
       "               session_end  flight_discount  hotel_discount  \\\n",
       "402    2023-01-23 21:09:52            False            True   \n",
       "947    2023-02-12 22:37:23            False           False   \n",
       "1165   2023-02-20 21:50:09            False           False   \n",
       "1574   2023-03-11 19:48:59            False           False   \n",
       "1907   2023-03-26 17:04:47            False           False   \n",
       "...                    ...              ...             ...   \n",
       "49205  2023-05-31 23:03:37             True            True   \n",
       "49207  2023-02-10 00:01:16             True            True   \n",
       "49208  2023-07-10 18:57:18            False           False   \n",
       "49209  2023-07-15 07:42:04            False           False   \n",
       "49210  2023-07-18 17:08:42             True           False   \n",
       "\n",
       "       flight_discount_amount  hotel_discount_amount  flight_booked  ...  \\\n",
       "402                       NaN                    0.1           True  ...   \n",
       "947                       NaN                    NaN           True  ...   \n",
       "1165                      NaN                    NaN           True  ...   \n",
       "1574                      NaN                    NaN           True  ...   \n",
       "1907                      NaN                    NaN           True  ...   \n",
       "...                       ...                    ...            ...  ...   \n",
       "49205                     NaN                    NaN           True  ...   \n",
       "49207                     NaN                    NaN           True  ...   \n",
       "49208                     NaN                    NaN           True  ...   \n",
       "49209                     NaN                    NaN           True  ...   \n",
       "49210                     0.2                    NaN           True  ...   \n",
       "\n",
       "       destination_airport_lon  base_fare_usd  \\\n",
       "402                   -114.020         525.80   \n",
       "947                    -75.241         425.40   \n",
       "1165                   -87.752         430.78   \n",
       "1574                   -77.037         359.64   \n",
       "1907                   -79.935         105.77   \n",
       "...                        ...            ...   \n",
       "49205                   77.961       13645.96   \n",
       "49207                 -121.929          83.44   \n",
       "49208                 -117.215        3205.63   \n",
       "49209                  -98.279         103.01   \n",
       "49210                  -97.032         381.22   \n",
       "\n",
       "                                     trip_id.2  \\\n",
       "402    548859-b080407b3cdc4692a6c4e9fe4ef85627   \n",
       "947    548504-a5d29c872e0549b488ff615f1aef614b   \n",
       "1165   524861-4fad7e1eb0d443349fa80347bdc3c7a5   \n",
       "1574   553955-cf601b19c9694254887fe22a95254212   \n",
       "1907   389434-cf10e38f0d464b81ace3f3be9f689833   \n",
       "...                                        ...   \n",
       "49205  438551-1a9fcfc16bd8487ea23e820957d23279   \n",
       "49207  480313-129a85cc3c5244b5962afe10c7932cda   \n",
       "49208  534295-3e20b84b40c946b1a8124395231d4412   \n",
       "49209  518416-cca5cea3fac04cdf8d069e13891f016a   \n",
       "49210  510093-e71475580a7c4948ba8f13114a60fa30   \n",
       "\n",
       "                            hotel_name nights  rooms            check_in_time  \\\n",
       "402             Best Western - calgary    7.0    1.0  2023-01-29 19:05:46.995   \n",
       "947        Aman Resorts - philadelphia    0.0    1.0   2023-02-19 14:39:43.56   \n",
       "1165                 Wyndham - chicago    1.0    1.0   2023-02-28 12:48:32.13   \n",
       "1574                Accor - washington    3.0    1.0  2023-03-17 11:45:18.315   \n",
       "1907                Wyndham - hamilton    0.0    1.0  2023-04-06 09:42:33.345   \n",
       "...                                ...    ...    ...                      ...   \n",
       "49205              Crowne Plaza - agra   13.0    3.0   2023-10-10 00:55:04.08   \n",
       "49207            Shangri-La - san jose    2.0    1.0   2023-02-13 09:37:10.74   \n",
       "49208         Aman Resorts - san diego    8.0    4.0    2023-07-20 20:54:53.1   \n",
       "49209  InterContinental  - san antonio    1.0    2.0   2023-07-22 15:22:38.37   \n",
       "49210             Marriott  - winnipeg    0.0    1.0   2023-07-30 11:45:27.81   \n",
       "\n",
       "            check_out_time hotel_per_room_usd total_hotel_spent  \n",
       "402    2023-02-06 11:00:00              269.0            1883.0  \n",
       "947    2023-02-20 11:00:00              497.0               0.0  \n",
       "1165   2023-03-02 11:00:00              106.0             106.0  \n",
       "1574   2023-03-20 11:00:00              127.0             381.0  \n",
       "1907   2023-04-06 11:00:00              107.0               0.0  \n",
       "...                    ...                ...               ...  \n",
       "49205  2023-10-23 11:00:00              143.0            5577.0  \n",
       "49207  2023-02-15 11:00:00              174.0             348.0  \n",
       "49208  2023-07-29 11:00:00               85.0            2720.0  \n",
       "49209  2023-07-24 11:00:00               80.0             160.0  \n",
       "49210  2023-07-30 11:00:00              109.0               0.0  \n",
       "\n",
       "[11708 rows x 44 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# booked return flight\n",
    "new_df = new_df[new_df['return_flight_booked'] == True]\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "8af9082c",
   "metadata": {},
   "outputs": [
    {
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       "       user_id\n",
       "402     548859\n",
       "947     548504\n",
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       "1574    553955\n",
       "1907    389434\n",
       "...        ...\n",
       "49205   438551\n",
       "49207   480313\n",
       "49208   534295\n",
       "49209   518416\n",
       "49210   510093\n",
       "\n",
       "[11708 rows x 1 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = new_df[['user_id']]\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "9a0aa72c",
   "metadata": {},
   "outputs": [
    {
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       "      <th>808</th>\n",
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       "      <th>3609</th>\n",
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       "    <tr>\n",
       "      <th>4864</th>\n",
       "      <td>811077</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4865 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  count  trip_with_return_flight\n",
       "808    510841      9                        9\n",
       "4554   633725      8                        8\n",
       "394    485544      7                        7\n",
       "2255   535007      7                        7\n",
       "2227   534351      7                        7\n",
       "...       ...    ...                      ...\n",
       "3612   570713      1                        1\n",
       "3611   570697      1                        1\n",
       "3610   570693      1                        1\n",
       "3609   570692      1                        1\n",
       "4864   811077      1                        1\n",
       "\n",
       "[4865 rows x 3 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# counting the total trip by each user\n",
    "new_df = new_df.groupby('user_id', as_index=False).value_counts().sort_values('count', ascending=False)\n",
    "new_df['trip_with_return_flight'] = new_df['count']\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "393224d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4865.000000\n",
       "mean        2.406578\n",
       "std         1.229920\n",
       "min         1.000000\n",
       "25%         1.000000\n",
       "50%         2.000000\n",
       "75%         3.000000\n",
       "max         9.000000\n",
       "Name: trip_with_return_flight, dtype: float64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['trip_with_return_flight'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "03fba1b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id                    897\n",
       "count                      897\n",
       "trip_with_return_flight    897\n",
       "dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df[new_df['trip_with_return_flight'] > 3].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "efa8b6b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      user_id  count  trip_with_return_flight free_hotel_night\n",
      "808    510841      9                        9              yes\n",
      "4554   633725      8                        8              yes\n",
      "394    485544      7                        7              yes\n",
      "2255   535007      7                        7              yes\n",
      "2227   534351      7                        7              yes\n",
      "...       ...    ...                      ...              ...\n",
      "3612   570713      1                        1               no\n",
      "3611   570697      1                        1               no\n",
      "3610   570693      1                        1               no\n",
      "3609   570692      1                        1               no\n",
      "4864   811077      1                        1               no\n",
      "\n",
      "[4865 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "# Create conditional DataFrame column by np.select function.\n",
    "conditions = [new_df['trip_with_return_flight'] > 3]\n",
    "# giving the perk who trip at least 3 times\n",
    "choices = ['yes']\n",
    "new_df['free_hotel_night'] = np.select(conditions, choices, default='no')\n",
    "print(new_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "244ddabe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>free_meal</th>\n",
       "      <th>free_checked_bags</th>\n",
       "      <th>no_cancellation_fees</th>\n",
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       "      <td>yes</td>\n",
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       "      <th>2</th>\n",
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       "      <td>no</td>\n",
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       "      <th>4</th>\n",
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       "    <tr>\n",
       "      <th>5993</th>\n",
       "      <td>544926</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5994</th>\n",
       "      <td>577534</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5995</th>\n",
       "      <td>596320</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5996</th>\n",
       "      <td>539868</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5997</th>\n",
       "      <td>515835</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5998 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id free_meal free_checked_bags no_cancellation_fees  \\\n",
       "0      534594       yes                no                   no   \n",
       "1      596863       yes               yes                   no   \n",
       "2      507805       yes               yes                  yes   \n",
       "3      539710       yes                no                   no   \n",
       "4      669329       yes                no                   no   \n",
       "...       ...       ...               ...                  ...   \n",
       "5993   544926        no                no                   no   \n",
       "5994   577534        no                no                   no   \n",
       "5995   596320        no                no                   no   \n",
       "5996   539868        no                no                   no   \n",
       "5997   515835        no                no                   no   \n",
       "\n",
       "     exclusive_discounts free_hotel_night  \n",
       "0                     no               no  \n",
       "1                     no               no  \n",
       "2                     no              yes  \n",
       "3                     no               no  \n",
       "4                     no               no  \n",
       "...                  ...              ...  \n",
       "5993                  no               no  \n",
       "5994                  no               no  \n",
       "5995                  no               no  \n",
       "5996                  no               no  \n",
       "5997                  no               no  \n",
       "\n",
       "[5998 rows x 6 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merging\n",
    "segmented_df = pd.merge(segmented_df, new_df[['user_id', 'free_hotel_night']], on='user_id', how='left')\n",
    "segmented_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "1d7f330f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id                 0\n",
       "free_meal               0\n",
       "free_checked_bags       0\n",
       "no_cancellation_fees    0\n",
       "exclusive_discounts     0\n",
       "free_hotel_night        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filling all the NA\n",
    "segmented_df['exclusive_discounts'] = segmented_df['exclusive_discounts'].fillna('no')\n",
    "segmented_df['free_hotel_night'] = segmented_df['free_hotel_night'].fillna('no')\n",
    "segmented_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "6f6bfefb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the dataset\n",
    "segmented_df.to_csv('travelTide_customer_segmented_dataset.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "8b74648f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt # for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "8ac622a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "new = segmented_df.iloc[:, 1:] # all the column except user_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "e35be4ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count 'yes' values in each column\n",
    "yes_counts = new.eq('yes').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "a79a9cea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OKpUK69at01qvUqmS/Js0aZJWuc2bN8PHxwfGxsawsLBAQEBAko/3/Plz2NvbQ6VS4dWrV+mzU0RERASAwS7biY6OhqenJ2bOnJnk+sePH2v9LViwACqVCk2bNlXK/Pvvv2jXrh06duyIc+fO4fDhw2jdunWS2+vcuTM8PDzSZV+IiIhIG0/FZjP+/v7w9/dPdr2NjY3W7fXr16Nq1aooXLgwACAuLg59+/bFpEmT0LlzZ6Wcm5tbom3Nnj0br169wvDhw7F169Y02gMiIiJKDlvsKFlPnjzB5s2btQLcmTNn8PDhQ+jp6cHLywu2trbw9/fHxYsXte57+fJljBo1CosXL/7spdlERESUNviNS8latGgRTE1N0aRJE2XZrVu3AADBwcEYOnQoNm3aBAsLC1SpUgUvXrwAAMTExKBVq1aYNGkSHB0dM6XuRERE2RGDHSVrwYIFaNOmDXLmzKksU6vVAIAhQ4agadOm8Pb2xsKFC6FSqbBq1SoAQFBQEFxdXdG2bdtMqTcREVF2xWBHSTp48CBCQ0PRpUsXreW2trYAtPvUGRkZoXDhwrh37x4AYM+ePVi1ahUMDAxgYGCA6tWrAwDy5s2LESNGZNAeEBERZT+8eIKSNH/+fHh7e8PT01Nrube3N4yMjBAaGoqKFSsCAGJjY3Hnzh04OTkBSLhq9u3bt8p9Tp48iU6dOuHgwYNwdnbOuJ0gIiLKZhjsspmoqCjcuHFDuX379m2EhITA0tJS6Q8XGRmJVatW4bfffkt0fzMzM/To0QMjRoyAg4MDnJyclDHuvv32WwBIFN6ePXsGAHB1dUWePHnSY7eIiIgIDHbZzqlTp1C1alXl9oABAwAAgYGB+OuvvwAAy5cvh4igVatWSW5j0qRJMDAwQLt27fD27Vv4+Phgz549sLCwSPf6ExERUfJUIiKZXYmsLjIyEubm5oiIiOCUYkRERJShUpNDMvXiibSY3qpgwYKJ1o8fP15rO+fPn0elSpWQM2dOODg4YOLEiRmxe0REREQZKlODXVpMbwUAo0aN0ir3/fffK+siIyNRq1YtODk54fTp05g0aRKCg4Mxd+7cdN03IiIiooyWqX3s/uv0VhqmpqaJymosW7YM79+/x4IFC2BoaAh3d3eEhIRg8uTJ6Nat23/fiTRUcPDmzK6Czrgzvl5mV4GIiCjDfTXj2CU1vZXG+PHjYWVlBS8vL0yaNAlxcXHKuqNHj8LPzw+GhobKstq1ayM0NBQvX77MkLoTERERZYSv5qrYpKa3AoA+ffqgdOnSsLS0xJEjRxAUFITHjx9j8uTJAICwsDAUKlRI6z758+dX1iV1JWdMTAxiYmKU25GRkWm9O0RERERp7qsJdklNbwX8b7gOAPDw8IChoSG6d++OcePGwcjI6Isea9y4cRg5cuR/qi8RERFRRvsqTsUmN71VUnx8fBAXF4c7d+4ASOin9+TJE60ymtvJ9csLCgpCRESE8nf//v3/tgNEREREGeCrCHbJTW+VlJCQEOjp6cHa2hoA4OvriwMHDiA2NlYps3PnTri4uCQ7oK6RkRHMzMy0/oiIiIiyukwNdlFRUQgJCUFISAiA/01vpZlMHvjf9FZJtdYdPXoUU6dOxblz53Dr1i0sW7YM/fv3R9u2bZXQ1rp1axgaGqJz5864dOkSVqxYgWnTpmmdwiUiIiLSBZnax+6/Tm9lZGSE5cuXIzg4GDExMShUqBD69++vFdrMzc2xY8cO9OrVC97e3sibNy+GDx+e5YY6ISIiIvqvOKVYCmTUlGIcxy7tcBw7IiLSFV/NlGJERERElHYY7IiIiIh0BIMdERERkY5gsCMiIiLSEQx2RERERDqCwY6IiIhIRzDYEREREekIBjsiIiIiHcFgR0RERKQjGOyIiIiIdASDHREREZGOYLAjIiIi0hEMdkREREQ6gsGOiIiISEcw2BERERHpCAY7IiIiIh3BYEdERESkIxjsiIiIiHQEgx0RERGRjmCwIyIiItIRDHZEREREOoLBjoiIiEhHMNgRERER6QgGOyIiIiIdwWBHREREpCMY7IiIiIh0BIMdERERkY5gsCMiIiLSEQx2RERERDqCwY6IiIhIRzDYEREREekIBjsiIiIiHcFgR0RERKQjGOyIiIiIdASDHREREZGOyNRgd+DAATRo0AB2dnZQqVRYt26d1voOHTpApVJp/dWpU0erzIsXL9CmTRuYmZkhT5486Ny5M6KiorTKnD9/HpUqVULOnDnh4OCAiRMnpveuEREREWW4TA120dHR8PT0xMyZM5MtU6dOHTx+/Fj5++eff7TWt2nTBpcuXcLOnTuxadMmHDhwAN26dVPWR0ZGolatWnBycsLp06cxadIkBAcHY+7cuem2X0RERESZwSAzH9zf3x/+/v6fLGNkZAQbG5sk1125cgXbtm3DyZMnUaZMGQDAjBkzULduXfz666+ws7PDsmXL8P79eyxYsACGhoZwd3dHSEgIJk+erBUAiYiIiL52Wb6P3b59+2BtbQ0XFxd89913eP78ubLu6NGjyJMnjxLqAKBGjRrQ09PD8ePHlTJ+fn4wNDRUytSuXRuhoaF4+fJlko8ZExODyMhIrT8iIiKirC5LB7s6depg8eLF2L17NyZMmID9+/fD398f8fHxAICwsDBYW1tr3cfAwACWlpYICwtTyuTPn1+rjOa2pszHxo0bB3Nzc+XPwcEhrXeNiIiIKM1l6qnYz2nZsqXy/5IlS8LDwwPOzs7Yt28fqlevnm6PGxQUhAEDBii3IyMjGe6IiIgoy8vSLXYfK1y4MPLmzYsbN24AAGxsbBAeHq5VJi4uDi9evFD65dnY2ODJkydaZTS3k+u7Z2RkBDMzM60/IiIioqzuqwp2Dx48wPPnz2FrawsA8PX1xatXr3D69GmlzJ49e6BWq+Hj46OUOXDgAGJjY5UyO3fuhIuLCywsLDJ2B4iIiIjSUaYGu6ioKISEhCAkJAQAcPv2bYSEhODevXuIiorCwIEDcezYMdy5cwe7d+9Go0aNUKRIEdSuXRsA4Orqijp16qBr1644ceIEDh8+jN69e6Nly5aws7MDALRu3RqGhobo3LkzLl26hBUrVmDatGlap1qJiIiIdEGmBrtTp07By8sLXl5eAIABAwbAy8sLw4cPh76+Ps6fP4+GDRuiWLFi6Ny5M7y9vXHw4EEYGRkp21i2bBmKFy+O6tWro27duqhYsaLWGHXm5ubYsWMHbt++DW9vb/zwww8YPnw4hzohIiIinaMSEcnsSmR1kZGRMDc3R0RERLr2tys4eHO6bTu7uTO+XmZXgYiIKE2kJod8VX3siIiIiCh5DHZEREREOoLBjoiIiEhHMNgRERER6QgGOyIiIiIdwWBHREREpCMY7IiIiIh0BIMdERERkY5gsCMiIiLSEQx2RERERDqCwY6IiIhIRzDYEREREekIBjsiIiIiHcFgR0RERKQjGOyIiIiIdASDHREREZGOYLAjIiIi0hEMdkREREQ6gsGOiIiISEcw2BERERHpCAY7IiIiIh3BYEdERESkIxjsiIiIiHQEgx0RERGRjmCwIyIiItIRDHZEREREOoLBjoiIiEhHMNgRERER6QgGOyIiIiIdwWBHREREpCMY7IiIiIh0BIMdERERkY5gsCMiIiLSEQx2RERERDoiU4PdgQMH0KBBA9jZ2UGlUmHdunXKutjYWAwaNAglS5aEiYkJ7Ozs0L59ezx69EhrGwULFoRKpdL6Gz9+vFaZ8+fPo1KlSsiZMyccHBwwceLEjNg9IiIiogyVqcEuOjoanp6emDlzZqJ1b968wZkzZzBs2DCcOXMGa9asQWhoKBo2bJio7KhRo/D48WPl7/vvv1fWRUZGolatWnBycsLp06cxadIkBAcHY+7cuem6b0REREQZzSAzH9zf3x/+/v5JrjM3N8fOnTu1lv3+++/45ptvcO/ePTg6OirLTU1NYWNjk+R2li1bhvfv32PBggUwNDSEu7s7QkJCMHnyZHTr1i3tdoaIiIgok31VfewiIiKgUqmQJ08ereXjx4+HlZUVvLy8MGnSJMTFxSnrjh49Cj8/PxgaGirLateujdDQULx8+TLJx4mJiUFkZKTWHxEREVFWl6ktdqnx7t07DBo0CK1atYKZmZmyvE+fPihdujQsLS1x5MgRBAUF4fHjx5g8eTIAICwsDIUKFdLaVv78+ZV1FhYWiR5r3LhxGDlyZDruDREREVHa+yqCXWxsLJo3bw4RwezZs7XWDRgwQPm/h4cHDA0N0b17d4wbNw5GRkZf9HhBQUFa242MjISDg8OXVZ6IiIgog2T5YKcJdXfv3sWePXu0WuuS4uPjg7i4ONy5cwcuLi6wsbHBkydPtMpobifXL8/IyOiLQyERERFRZsnSfew0oe769evYtWsXrKysPnufkJAQ6OnpwdraGgDg6+uLAwcOIDY2Vimzc+dOuLi4JHkaloiIiOhrlaktdlFRUbhx44Zy+/bt2wgJCYGlpSVsbW3RrFkznDlzBps2bUJ8fDzCwsIAAJaWljA0NMTRo0dx/PhxVK1aFaampjh69Cj69++Ptm3bKqGtdevWGDlyJDp37oxBgwbh4sWLmDZtGqZMmZIp+0xERESUXlQiIpn14Pv27UPVqlUTLQ8MDERwcHCiix409u7diypVquDMmTPo2bMnrl69ipiYGBQqVAjt2rXDgAEDtE6lnj9/Hr169cLJkyeRN29efP/99xg0aFCK6xkZGQlzc3NERER89lTwf1Fw8OZ023Z2c2d8vcyuAhERUZpITQ7J1GD3tWCw+/ow2BERka5ITQ7J0n3siIiIiCjlGOyIiIiIdASDHREREZGOYLAjIiIi0hEMdkREREQ6gsGOiIiISEcw2BERERHpCAY7oizkwIEDaNCgAezs7KBSqbBu3Tqt9WvWrEGtWrVgZWUFlUqFkJCQRNt49+4devXqBSsrK+TOnRtNmzZNNF9ynz594O3tDSMjI5QqVSr9doiIiDIUgx1RFhIdHQ1PT0/MnDkz2fUVK1bEhAkTkt1G//79sXHjRqxatQr79+/Ho0eP0KRJk0TlOnXqhBYtWqRZ3YmIKPNl6lyxRKTN398f/v7+ya5v164dAODOnTtJro+IiMD8+fPx999/o1q1agCAhQsXwtXVFceOHUO5cuUAANOnTwcAPH36FOfPn0/DPSAioszEFjsiHXL69GnExsaiRo0ayrLixYvD0dERR48ezcSaERFRRmCwI9IhYWFhMDQ0RJ48ebSW58+fH2FhYZlTKSIiyjAMdkREREQ6gsGOSIfY2Njg/fv3ePXqldbyJ0+ewMbGJnMqRUREGYbBjkiHeHt7I0eOHNi9e7eyLDQ0FPfu3YOvr28m1oyIiDICr4olykKioqJw48YN5fbt27cREhICS0tLODo64sWLF7h37x4ePXoEICG0AQktdTY2NjA3N0fnzp0xYMAAWFpawszMDN9//z18fX2VK2IB4MaNG4iKikJYWBjevn2rjIfn5uYGQ0PDjNthIiJKUwx2RFnIqVOnULVqVeX2gAEDAACBgYH466+/sGHDBnTs2FFZ37JlSwDAiBEjEBwcDACYMmUK9PT00LRpU8TExKB27dqYNWuW1uN06dIF+/fvV257eXkBSAiSBQsWTI9dIyKiDKASEcnsSmR1kZGRMDc3R0REBMzMzNLtcQoO3pxu285u7oyvl9lVICIiShOpySHsY0dERESkI1IV7EQE9+7dw7t379KrPkRERET0hVLVx05EUKRIEVy6dAlFixZNrzoRZUk8VZ42eJqciCj9pKrFTk9PD0WLFsXz58/Tqz5ERERE9IVS3cdu/PjxGDhwIC5evJge9SEiIiKiL5Tq4U7at2+PN2/ewNPTE4aGhjA2NtZa/+LFizSrHBERERGlXKqD3dSpU9OhGkRERET0X6U62AUGBqZHPYiIiIjoP/qicexu3ryJoUOHolWrVggPDwcAbN26FZcuXUrTyhERERFRyqU62O3fvx8lS5bE8ePHsWbNGkRFRQEAzp07hxEjRqR5BYmIiIgoZVId7AYPHozRo0dj586dWpOFV6tWDceOHUvTyhERERFRyqU62F24cAGNGzdOtNza2hrPnj1Lk0oRERERUeqlOtjlyZMHjx8/TrT87NmzKFCgQJpUioiIiIhSL9XBrmXLlhg0aBDCwsKgUqmgVqtx+PBh/Pjjj2jfvn161JGIiIiIUiDVwW7s2LEoXrw4HBwcEBUVBTc3N/j5+aF8+fIYOnRoetSRiIiIiFIg1ePYGRoa4s8//8Tw4cNx4cIFREVFwcvLC0WLFk2P+hERERFRCn3ROHYA4ODggLp166Jp06aIjo7Gy5cvU72NAwcOoEGDBrCzs4NKpcK6deu01osIhg8fDltbWxgbG6NGjRq4fv26VpkXL16gTZs2MDMzQ548edC5c2dlCBaN8+fPo1KlSsiZMyccHBwwceLEVNeViIiIKKtLdbDr168f5s+fDwCIj49H5cqVUbp0aTg4OGDfvn2p2lZ0dDQ8PT0xc+bMJNdPnDgR06dPx5w5c3D8+HGYmJigdu3aePfunVKmTZs2uHTpEnbu3IlNmzbhwIED6Natm7I+MjIStWrVgpOTE06fPo1JkyYhODgYc+fOTe2uExEREWVpqT4Vu3r1arRt2xYAsHHjRty6dQtXr17FkiVLMGTIEBw+fDjF2/L394e/v3+S60QEU6dOxdChQ9GoUSMAwOLFi5E/f36sW7cOLVu2xJUrV7Bt2zacPHkSZcqUAQDMmDEDdevWxa+//go7OzssW7YM79+/x4IFC2BoaAh3d3eEhIRg8uTJWgGQiIiI6GuX6ha7Z8+ewcbGBgCwZcsWNG/eHMWKFUOnTp1w4cKFNKvY7du3ERYWhho1aijLzM3N4ePjg6NHjwIAjh49ijx58iihDgBq1KgBPT09HD9+XCnj5+enNZhy7dq1ERoa+kWnj4mIiIiyqlQHu/z58+Py5cuIj4/Htm3bULNmTQDAmzdvoK+vn2YVCwsLUx7v48fXrAsLC4O1tbXWegMDA1haWmqVSWobHz7Gx2JiYhAZGan1R0RERJTVpTrYdezYEc2bN0eJEiWgUqmUFrXjx4+jePHiaV7BzDBu3DiYm5srfw4ODpldJSIiIqLPSnWwCw4Oxrx589CtWzccPnwYRkZGAAB9fX0MHjw4zSqmOd375MkTreVPnjxR1tnY2CA8PFxrfVxcHF68eKFVJqltfPgYHwsKCkJERITyd//+/f++Q0RERETpLNUXTwBAs2bNEi0LDAz8z5X5UKFChWBjY4Pdu3ejVKlSABKucD1+/Di+++47AICvry9evXqF06dPw9vbGwCwZ88eqNVq+Pj4KGWGDBmC2NhY5MiRAwCwc+dOuLi4wMLCIsnHNjIyUgIrERER0dci1cFu1KhRn1w/fPjwFG8rKioKN27cUG7fvn0bISEhsLS0hKOjI/r164fRo0ejaNGiKFSoEIYNGwY7OzsEBAQAAFxdXVGnTh107doVc+bMQWxsLHr37o2WLVvCzs4OANC6dWuMHDkSnTt3xqBBg3Dx4kVMmzYNU6ZMSe2uExEREWVpqQ52a9eu1bodGxuL27dvw8DAAM7OzqkKdqdOnULVqlWV2wMGDACQ0Pr3119/4aeffkJ0dDS6deuGV69eoWLFiti2bRty5syp3GfZsmXo3bs3qlevDj09PTRt2hTTp09X1pubm2PHjh3o1asXvL29kTdvXgwfPpxDnRAREZHOUYmI/NeNREZGokOHDmjcuDHatWuXFvXKUiIjI2Fubo6IiAiYmZml2+MUHLw53bad3dwZXy/Nt8njkzbS49gQ0dcjPj4ewcHBWLp0KcLCwmBnZ4cOHTpg6NChUKlUABL6wg8aNAg7duzAq1ev4OfnhxkzZijTl965cweFChVKcvsrV67Et99+m2H7kxFSk0O+eEqxD5mZmWHkyJEYNmxYWmyOiIiIdNSECRMwe/Zs/P7777hy5QomTJiAiRMnYsaMGQASJigICAjArVu3sH79epw9exZOTk6oUaMGoqOjASRMa/r48WOtv5EjRyJ37tzJTnyQXXzRxRNJ0VxBSkRERJScI0eOoFGjRqhXL6H1vmDBgvjnn39w4sQJAMD169dx7NgxXLx4Ee7u7gCA2bNnw8bGBv/88w+6dOkCfX39RCNbrF27Fs2bN0fu3LkzdoeymFQHuw/7rwEJyfrx48dYsmRJtk/JRERE9Gnly5fH3Llzce3aNRQrVgznzp3DoUOHMHnyZAAJkwQA0OpPr6enByMjIxw6dAhdunRJtM3Tp08jJCQk2bnns5NUB7uPrybV09NDvnz5EBgYiKCgoDSrGBEREemewYMHIzIyEsWLF4e+vj7i4+MxZswYtGnTBgBQvHhxODo6IigoCH/88QdMTEwwZcoUPHjwAI8fP05ym/Pnz4erqyvKly+fkbuSJaU62N2+fTs96kFERETZwMqVK7Fs2TL8/fffcHd3R0hICPr16wc7OzsEBgYiR44cWLNmDTp37gxLS0vo6+ujRo0a8Pf3R1LXe759+xZ///03+/n/vzTrY0dERET0OQMHDsTgwYPRsmVLAEDJkiVx9+5djBs3TpnswNvbGyEhIYiIiMD79++RL18++Pj4oEyZMom2t3r1arx58wbt27fP0P3IqtLkqlgiIiKilHjz5g309LTjh76+PtRqdaKy5ubmyJcvH65fv45Tp06hUaNGicrMnz8fDRs2RL58+dKtzl8TttgRERFRhmnQoAHGjBkDR0dHuLu74+zZs5g8eTI6deqklFm1ahXy5csHR0dHXLhwAX379kVAQABq1aqlta0bN27gwIED2LJlS0bvRpbFYEdEREQZZsaMGRg2bBh69uyJ8PBw2NnZoXv37lozVz1+/BgDBgzAkydPYGtri/bt2yfZh27BggWwt7dPFPiyszSZeULXceaJrw9nnsi6OPMEEVHqpCaHpKjFbsOGDSl+8IYNG6a4LBERERGlnRQFu4CAgBRtTKVSIT4+/r/Uh4iIiIi+UIqCXVJXqhAREdHXjV1M0kZW6mLC4U6IiIiIdMQXXRUbHR2N/fv34969e3j//r3Wuj59+qRJxYiIiIgodVId7M6ePYu6devizZs3iI6OhqWlJZ49e4ZcuXLB2tqawY6IiIgok6T6VGz//v3RoEEDvHz5EsbGxjh27Bju3r0Lb29v/Prrr+lRRyIiIiJKgVQHu5CQEPzwww/Q09ODvr4+YmJi4ODggIkTJ+Lnn39OjzoSERERUQqkOtjlyJFDmePN2toa9+7dA5Awn9v9+/fTtnZERERElGKp7mPn5eWFkydPomjRoqhcuTKGDx+OZ8+eYcmSJShRokR61JGIiIiIUiDVLXZjx46Fra0tAGDMmDGwsLDAd999h6dPn+KPP/5I8woSERERUcqkusWuTJkyyv+tra2xbdu2NK0QEREREX2ZVLfYVatWDa9evUq0PDIyEtWqVUuLOhERERHRF0h1sNu3b1+iQYkB4N27dzh48GCaVIqIiIiIUi/Fp2LPnz+v/P/y5csICwtTbsfHx2Pbtm0oUKBA2taOiIiIiFIsxcGuVKlSUKlUUKlUSZ5yNTY2xowZM9K0ckRERESUcikOdrdv34aIoHDhwjhx4gTy5cunrDM0NIS1tTX09fXTpZJERERE9HkpDnZOTk4AALVanW6VISIiIqIvl+rhTgDg5s2bmDp1Kq5cuQIAcHNzQ9++feHs7JymlSMiIiKilEv1VbHbt2+Hm5sbTpw4AQ8PD3h4eOD48eNwd3fHzp0706OORERERJQCqW6xGzx4MPr374/x48cnWj5o0CDUrFkzzSpHRERERCmX6ha7K1euoHPnzomWd+rUCZcvX06TShERERFR6qU62OXLlw8hISGJloeEhMDa2jot6kREREREXyDFwW7UqFF48+YNunbtim7dumHChAk4ePAgDh48iPHjx6N79+7o2rVretaViCjLKFiwoDK254d/vXr1AgDMnTsXVapUgZmZGVQqVZJTMb548QJt2rSBmZkZ8uTJg86dOyMqKiqD94SIdEmK+9iNHDkSPXr0wLBhw2BqaorffvsNQUFBAAA7OzsEBwejT58+6VZRIqKs5OTJk4iPj1duX7x4ETVr1sS3334LAHjz5g3q1KmDOnXqKJ+VH2vTpg0eP36MnTt3IjY2Fh07dkS3bt3w999/Z8g+EJHuSXGLnYgAAFQqFfr3748HDx4gIiICERERePDgAfr27QuVSpXmFfzcr+IqVaokWtejRw+tbdy7dw/16tVDrly5YG1tjYEDByIuLi7N60pE2Ue+fPlgY2Oj/G3atAnOzs6oXLkyAKBfv34YPHgwypUrl+T9r1y5gm3btmHevHnw8fFBxYoVMWPGDCxfvhyPHj3KyF0hIh2SqqtiPw5upqamaVqZpHzuVzEAdO3aFaNGjVJu58qVS/l/fHw86tWrBxsbGxw5cgSPHz9G+/btkSNHDowdOzbd609Euu/9+/dYunQpBgwYkOIfuEePHkWePHlQpkwZZVmNGjWgp6eH48ePo3HjxulVXSLSYakKdsWKFfvsh9aLFy/+U4U+9uHUZQAwfvx4rV/FQEKQs7GxSfL+O3bswOXLl7Fr1y7kz58fpUqVwi+//IJBgwYhODgYhoaGaVpfIsp+1q1bh1evXqFDhw4pvk9YWFiiC84MDAxgaWmJsLCwNK4hEWUXqQp2I0eOhLm5eXrV5bOS+1W8bNkyLF26FDY2NmjQoAGGDRumtNodPXoUJUuWRP78+ZXytWvXxnfffYdLly7By8sr0ePExMQgJiZGuR0ZGZmOe0VEX7v58+fD398fdnZ2mV0VIsrmUhXsWrZsmalDmiT1q7h169ZwcnKCnZ0dzp8/j0GDBiE0NBRr1qwBkPCr+MNQB0C5ndyv4nHjxmHkyJHpsxNEpFPu3r2LXbt2KZ85KWVjY4Pw8HCtZXFxcXjx4kWyZyCIiD4nxcEuPS6MSK2kfhV369ZN+X/JkiVha2uL6tWr4+bNm188d21QUBAGDBig3I6MjISDg8OXV5yIdNbChQthbW2NevXqpep+vr6+ePXqFU6fPg1vb28AwJ49e6BWq+Hj45MeVSWibCDFwU5zVWxmSemvYs0H4o0bN+Ds7AwbGxucOHFCq8yTJ08AINlfxUZGRjAyMkqDWhORLlOr1Vi4cCECAwNhYKD9cRoWFoawsDDcuHEDAHDhwgWYmprC0dERlpaWcHV1RZ06ddC1a1fMmTMHsbGx6N27N1q2bMlTukT0xVI83Ilarc7U07Ap/VWsmRXD1tYWQMKv4gsXLmid8ti5cyfMzMzg5uaWbvUlIt23a9cu3Lt3D506dUq0bs6cOfDy8lIGbvfz84OXlxc2bNiglFm2bBmKFy+O6tWro27duqhYsSLmzp2bYfUnIt2Tqj52mSW5X8U3b97E33//jbp168LKygrnz59H//794efnBw8PDwBArVq14Obmhnbt2mHixIkICwvD0KFD0atXL7bKEdF/UqtWrWTPZgQHByM4OPiT97e0tORgxESUpr6KYJfcr2JDQ0Ps2rULU6dORXR0NBwcHNC0aVMMHTpUKaOvr49Nmzbhu+++g6+vL0xMTBAYGKg17h0RERGRLvgqgl1yv4odHBywf//+z97fyckJW7ZsSY+qEREREWUZX0WwIyL6lIKDN2d2FXTGnfGpu7qXiLKWFF88QURERERZG4MdERERkY5gsCMiIiLSEQx2RERERDqCwY6IiIhIRzDYEREREekIBjsiIiIiHcFgR0RERKQjGOyIiIiIdASDHREREZGOYLAjIiIi0hEMdkREREQ6gsGOiIiISEcw2BERERHpCAY7IiIiIh3BYEdERESkIxjsiIiIiHQEgx0RERGRjmCwIyIiItIRDHZEREREOoLBjoiIiEhHMNgRERER6QgGOyIiIiIdwWBHREREpCMY7IiIiIh0BIMdERERkY5gsCMiIiLSEQx2RERERDqCwY6IiIhIRzDYEREREekIBjsiIiIiHcFgR0RERKQjGOyIiIiIdESWDnbBwcFQqVRaf8WLF1fWv3v3Dr169YKVlRVy586Npk2b4smTJ1rbuHfvHurVq4dcuXLB2toaAwcORFxcXEbvChEREVG6M8jsCnyOu7s7du3apdw2MPhflfv374/Nmzdj1apVMDc3R+/evdGkSRMcPnwYABAfH4969erBxsYGR44cwePHj9G+fXvkyJEDY8eOzfB9ISIiIkpPWT7YGRgYwMbGJtHyiIgIzJ8/H3///TeqVasGAFi4cCFcXV1x7NgxlCtXDjt27MDly5exa9cu5M+fH6VKlcIvv/yCQYMGITg4GIaGhhm9O0RERETpJkufigWA69evw87ODoULF0abNm1w7949AMDp06cRGxuLGjVqKGWLFy8OR0dHHD16FABw9OhRlCxZEvnz51fK1K5dG5GRkbh06VKyjxkTE4PIyEitPyIiIqKsLksHOx8fH/z111/Ytm0bZs+ejdu3b6NSpUp4/fo1wsLCYGhoiDx58mjdJ3/+/AgLCwMAhIWFaYU6zXrNuuSMGzcO5ubmyp+Dg0Pa7hgRERFROsjSp2L9/f2V/3t4eMDHxwdOTk5YuXIljI2N0+1xg4KCMGDAAOV2ZGQkwx0RERFleVm6xe5jefLkQbFixXDjxg3Y2Njg/fv3ePXqlVaZJ0+eKH3ybGxsEl0lq7mdVL89DSMjI5iZmWn9EREREWV1X1Wwi4qKws2bN2Frawtvb2/kyJEDu3fvVtaHhobi3r178PX1BQD4+vriwoULCA8PV8rs3LkTZmZmcHNzy/D6ExEREaWnLH0q9scff0SDBg3g5OSER48eYcSIEdDX10erVq1gbm6Ozp07Y8CAAbC0tISZmRm+//57+Pr6oly5cgCAWrVqwc3NDe3atcPEiRMRFhaGoUOHolevXjAyMsrkvSMiIiJKW1k62D148ACtWrXC8+fPkS9fPlSsWBHHjh1Dvnz5AABTpkyBnp4emjZtipiYGNSuXRuzZs1S7q+vr49Nmzbhu+++g6+vL0xMTBAYGIhRo0Zl1i4RERERpZssHeyWL1/+yfU5c+bEzJkzMXPmzGTLODk5YcuWLWldNSIiIqIs56vqY0dEREREyWOwIyIiItIRDHZEREREOoLBjoiIiEhHMNgRERER6QgGOyIiIiIdwWBHREREpCMY7IiIiIh0BIMdERERkY5gsCMiIiLSEQx2RERERDqCwY6IiIhIRzDYEREREekIBjsiIiIiHcFgR0RERKQjGOyIiIiIdASDHREREZGOYLAjIiIi0hEMdkREREQ6gsGOiIiISEcw2BERERHpCAY7IiIiIh3BYEdERESkIxjsiIiIiHQEgx0RERGRjmCwIyIiItIRDHZEREREOoLBjoiIiEhHMNgRERER6QgGOyIiIiIdwWBHREREpCMY7IiIiIh0BIMdERERkY5gsCMiIiLSEVk62I0bNw5ly5aFqakprK2tERAQgNDQUK0yVapUgUql0vrr0aOHVpl79+6hXr16yJUrF6ytrTFw4EDExcVl5K4QERERpTuDzK7Ap+zfvx+9evVC2bJlERcXh59//hm1atXC5cuXYWJiopTr2rUrRo0apdzOlSuX8v/4+HjUq1cPNjY2OHLkCB4/foz27dsjR44cGDt2bIbuDxEREVF6ytLBbtu2bVq3//rrL1hbW+P06dPw8/NTlufKlQs2NjZJbmPHjh24fPkydu3ahfz586NUqVL45ZdfMGjQIAQHB8PQ0DBd94GIiIgoo2TpU7Efi4iIAABYWlpqLV+2bBny5s2LEiVKICgoCG/evFHWHT16FCVLlkT+/PmVZbVr10ZkZCQuXbqUMRUnIiIiygBZusXuQ2q1Gv369UOFChVQokQJZXnr1q3h5OQEOzs7nD9/HoMGDUJoaCjWrFkDAAgLC9MKdQCU22FhYUk+VkxMDGJiYpTbkZGRab07RERERGnuqwl2vXr1wsWLF3Ho0CGt5d26dVP+X7JkSdja2qJ69eq4efMmnJ2dv+ixxo0bh5EjR/6n+hIRERFltK/iVGzv3r2xadMm7N27F/b29p8s6+PjAwC4ceMGAMDGxgZPnjzRKqO5nVy/vKCgIERERCh/9+/f/6+7QERERJTusnSwExH07t0ba9euxZ49e1CoUKHP3ickJAQAYGtrCwDw9fXFhQsXEB4erpTZuXMnzMzM4ObmluQ2jIyMYGZmpvVHRERfjwMHDqBBgwaws7ODSqXCunXrEpW5cuUKGjZsCHNzc5iYmKBs2bK4d++esr579+5wdnaGsbEx8uXLh0aNGuHq1asZuBdEqZelg12vXr2wdOlS/P333zA1NUVYWBjCwsLw9u1bAMDNmzfxyy+/4PTp07hz5w42bNiA9u3bw8/PDx4eHgCAWrVqwc3NDe3atcO5c+ewfft2DB06FL169YKRkVFm7h4REaWT6OhoeHp6YubMmUmuv3nzJipWrIjixYtj3759OH/+PIYNG4acOXMqZby9vbFw4UJcuXIF27dvh4igVq1aiI+Pz6jdIEq1LN3Hbvbs2QASBiH+0MKFC9GhQwcYGhpi165dmDp1KqKjo+Hg4ICmTZti6NChSll9fX1s2rQJ3333HXx9fWFiYoLAwECtce+IiEi3+Pv7w9/fP9n1Q4YMQd26dTFx4kRl2cf9sj/sw12wYEGMHj0anp6euHPnzhf34SZKb1k62InIJ9c7ODhg//79n92Ok5MTtmzZklbVIiKir5harcbmzZvx008/oXbt2jh79iwKFSqEoKAgBAQEJHmf6OhoLFy4EIUKFYKDg0PGVpgoFbL0qVgiIqK0Fh4ejqioKIwfPx516tTBjh070LhxYzRp0iRRY8GsWbOQO3du5M6dG1u3bsXOnTs5sD1laQx2RESUrajVagBAo0aN0L9/f5QqVQqDBw9G/fr1MWfOHK2ybdq0wdmzZ7F//34UK1YMzZs3x7t37zKj2kQpwmBHRETZSt68eWFgYJBoZARXV1etq2IBwNzcHEWLFoWfnx9Wr16Nq1evYu3atRlZXaJUYbAjIqJsxdDQEGXLlkVoaKjW8mvXrsHJySnZ+4kIRERrZiKirCZLXzxBRET0JaKiopSB6gHg9u3bCAkJgaWlJRwdHTFw4EC0aNECfn5+qFq1KrZt24aNGzdi3759AIBbt25hxYoVqFWrFvLly4cHDx5g/PjxMDY2Rt26dTNpr4g+j8GOiIh0zqlTp1C1alXl9oABAwAAgYGB+Ouvv9C4cWPMmTMH48aNQ58+feDi4oJ///0XFStWBADkzJkTBw8exNSpU/Hy5Uvkz58ffn5+OHLkCKytrTNln4hSgsGOiIh0TpUqVT47ZFanTp3QqVOnJNfZ2dlxmCz6KrGPHREREZGOYLAjIiIi0hE8FUtEROmm4ODNmV0FnXFnfL3MrgJ9BdhiR0RERKQjGOyIiIiIdASDHREREZGOYLAjIiIi0hEMdkREREQ6gsGOiIiISEcw2BERERHpCAY7IiIiIh3BYEdERESkIxjsiIiIiHQEgx0RERGRjmCwIyIiItIRDHZEREREOoLBjoiIiEhHMNgRERER6QgGOyIiIiIdwWBHREREpCMY7IiIiIh0BIMdERERkY5gsCMiIiLSEQx2RERERDqCwY6IiIhIRzDYEREREekIBjsiIiIiHcFgR0RERKQjslWwmzlzJgoWLIicOXPCx8cHJ06cyOwqEREREaWZbBPsVqxYgQEDBmDEiBE4c+YMPD09Ubt2bYSHh2d21YiIiIjSRLYJdpMnT0bXrl3RsWNHuLm5Yc6cOciVKxcWLFiQ2VUjIiIiShMGmV2BjPD+/XucPn0aQUFByjI9PT3UqFEDR48eTVQ+JiYGMTExyu2IiAgAQGRkZLrWUx3zJl23n52kx7Hi8UkbPDZZW1ofHx6btMP3TtaV3vlAs30R+WzZbBHsnj17hvj4eOTPn19ref78+XH16tVE5ceNG4eRI0cmWu7g4JBudaS0ZT41s2tAyeGxydp4fLIuHpusK6OOzevXr2Fubv7JMtki2KVWUFAQBgwYoNxWq9V48eIFrKysoFKpMrFmmS8yMhIODg64f/8+zMzMMrs69AEem6yLxyZr4/HJunhsEogIXr9+DTs7u8+WzRbBLm/evNDX18eTJ0+0lj958gQ2NjaJyhsZGcHIyEhrWZ48edKzil8dMzOzbP0my8p4bLIuHpusjccn6+KxwWdb6jSyxcUThoaG8Pb2xu7du5VlarUau3fvhq+vbybWjIiIiCjtZIsWOwAYMGAAAgMDUaZMGXzzzTeYOnUqoqOj0bFjx8yuGhEREVGayDbBrkWLFnj69CmGDx+OsLAwlCpVCtu2bUt0QQV9mpGREUaMGJHoVDVlPh6brIvHJmvj8cm6eGxSTyUpuXaWiIiIiLK8bNHHjoiIiCg7YLAjIiIi0hEMdkREREQ6gsGOvgrsCkpERF8LtVqdaY/NYEdZkojgn3/+QdeuXREXF5ftZ/ygBGq1miH/K8RjRtmJiEBPTw/v379HeHi41vKMwGBHWZJKpcLVq1dx6tQpHDt2DAC/HLKz+Ph45cOSIf/rERcXBxHhMcsi4uPjER8fn9nV0HkqlQqPHj1C4cKFsXHjRgAJP0rfvXuXIY/PYEdZjqYJu169esiTJw927NiRyTWizKavrw+VSoXz589jyJAhWLlyZYZ9SNKXMzAwgEqlwsWLF7F582a8fPkys6uUrenr60NfXx9xcXE4ePAgnj9/ntlV+qo9ffoUQEKjg4go311qtRp2dnZwdXXFP//8g5o1a8LAwABLly7NkHox2FGmExHExcUlWl66dGkUKlQIp06dQkREBH/1ZwPJtcqeOHECPj4+qFy5Ms6cOYPIyEg8e/Ysg2tHyUmqP1FsbCzmzJkDZ2dnVKpUCatXr8b169czoXbZh+Y4hIaGaoVozfJTp06hUaNGyJcvH3744QecPXs2yc9e+rzg4GA0btwY4eHhUKlUUKlU0NNLiFR6eno4d+4cLl68iL1798Lc3BwXL15E165dM6RuDHaU6VQqFQwMEiZBef78ufLmMDAwQLly5fDixQvs378fAE/H6irN6SFNeFer1bhw4QIA4P3791i5ciVsbGzw6NEjbN26FW3btoW9vX2m1ZcSaI6b5j37oTNnzmDmzJn44YcfcOvWLUycOBGurq4ZXcVsRU9PD5cuXYKrqyvu3LkDALh06RJiYmIQHR2Nn3/+GcbGxjhw4ABWrFiBEiVKKJ+9lDKakGxra4v4+Hilq9Djx48xf/58bNq0CQBQtGhRLFmyBPb29qhatSrc3NwyrI4MdpTpbt++jT59+qBw4cL49ttvMWLECISFhQEAKlSoADMzM+zatQsA2Gqno/T19QEAu3fvxurVq/Hzzz+jYsWKAIC3b99i8+bNKFu2LAwMDBAaGoo3b97g3bt3DPqZTHPctm/fjhEjRuDIkSPKKfLFixcjX7586Nq1KywsLGBiYgJTU9PMrK5O+vCCIrVaDXd3d9jb2+P777+Hvb09PD09cenSJWzbtg2XL1/G2LFjUbJkSdja2sLGxiaTa/91UavVyndQjRo1YGZmhp07d2LdunXw9PTE77//jsDAQAwdOhRv3rxBjRo18M0332Dr1q24ffs2gIxpnGBUp3Sn+eDRfAl86PTp0+jZsycKFCiAsWPHIjIyEnPmzMHz58/x+++/w93dHcWLF8fFixdx//59ODg4ZMIeUFpRq9VJtu6EhoaicePGePXqFfz9/XHo0CG8fv0ap0+fhre3N/z9/bF48WLMnDkT3t7eePToEUQEP/30E1q1apXsdiltxMfHJ/n+PXbsGHr27ImwsDCUK1cOf/zxBxo1aoQ//vgDPj4+OHjwICpVqgQnJyeYmpri8ePHaN++PVq0aJEJe6E7NOHgw9N/QEKL3bJly/DgwQO8ePECo0ePRo8ePZAzZ05YWFggOjoagwcPRq5cuWBhYYFXr16hWrVqaNy4MXLnzp1Zu5PlfPh5EhcXp/Tx/fC5dnZ2hqurK06cOIE7d+5g1apVqFy5MkaNGoXNmzejYMGC6NKlC9q0aYN+/frh0qVLKFSoUIZcTMRPQkp3enp6ypfC6dOntfp+FCpUCAEBAVizZg1atmyJjh07olixYti4caPSxO3r64s3b95g9+7dAHg69msjIp88ZQcAs2bNgqWlJc6cOYP58+dj3LhxKFCgAObMmQMAGDFiBGbPno2lS5eiU6dOGDJkCHx9ffHLL7/g/fv3DHVpIDY2FvPnz8fy5csBQKsz+MehTtMva9GiRXB1dcWjR4+wZs0abNq0CYcPH8aQIUPQvn17DB8+HOXLl0fZsmVhb2+PnDlzYvjw4cp7m76Mpk8XAGzduhXDhg3D+vXrAQBNmzbFmTNn8ObNG7i6uiJnzpxQq9VwdnbG/PnzYWhoiPz580NPTw8PHz7ElClTMH/+fADZ+7N11apVaNy4MQDtM0OaC4AeP36MEydOoGbNmsrp1sqVK+PBgwfK/wGgR48eKFiwIDZv3gwACAgIgLGxMU6ePIk3b95kzGeVEKWzhw8fSocOHcTU1FQ8PT2lRIkSsmbNGomJiREREbVaLefOnZMGDRqIhYWFuLm5iYuLi3z//fciInL37l1p0KCBdOzYMTN3g/6juLg4Wbx4sQQFBcmRI0ckKipKWVetWjXp0aOHVtmxY8eKhYWFqNXqJLf3/fffS926deXZs2fpXnddd/nyZXFxcZHChQvL5s2btZ7zuLg4mT9/vnz//feycuVKefLkiXIfd3d3iYqKkvDwcBk9erSUKFFCLC0tZfz48cr7W0QkNjZWREQOHTokRYoUkXXr1mXsDn7l4uPjtf69e/euHDx4UIKCgsTBwUHq1KkjhoaGMn36dImMjBQRkVKlSknbtm213mcamuMbExMjJUqUkNGjR2fQnmRdW7ZskUWLFmktU6vVsmLFCnFwcJCyZctKQECA5MiRQwYOHCgiIo8fPxZ/f3+pVauW1v0mTJggpUuXllOnTomISL9+/cTDw0OWLl0qixcvliFDhojI/45nWmOwo/9MrVZ/8gX6888/S61ateTw4cPy9u1bCQ4OlurVq8uCBQtEJCH41apVSzp27CghISGiVqulefPm4uXlJe/evVO2UaJECTl9+nSG7BOlzK1bt+TAgQMikvAhFRcXl6hMVFSU/Pbbb5I/f35xd3eXWrVqibOzs3Tt2lVERMLCwqRevXpawU5E5NixY2JgYCCbNm0SEZELFy7IypUrZdeuXdK9e3dxdnaWJUuWpPMeZg8jR44UX19fJYBpnDhxQooVKybFihWTzp07i5ubm1SpUkXUarW8fPlSDAwMpGTJkmJmZia+vr4ya9Ysefz4sXL/+Ph4OX78uISHh8vx48elWbNm0rhx4yRfJ/RpmsAmIlK5cmUpWrSotG7dWh49eiQiCeGhQoUKsnnzZhERmT17tlhZWcmVK1dE5H8h4vbt2xIeHi7h4eEyefJk8fLykkuXLmXw3nwdbt68KSVKlJAhQ4bInTt3ZMWKFVKkSBHx8PCQ169fi4jI8OHDpWLFinLmzBnlfvv375dKlSrJqFGjRETk+vXr0qdPHylQoIA4OjpKcHBwutabwY6+SHx8fKIw9+LFC9m8ebOEh4cry0JDQ8XX11eOHDkiIiL379+X7777TvT09KRbt24iIrJ+/XrJnTu3XLhwQUQSWggqVKgguXLlklWrVomIyMaNG6VPnz4SGhqaEbtHKVSlShWpV6+e0oqj8WGLz7179+THH3+UNWvWKMv27NkjKpVK+dLp3r27NGrUSK5du6aUOXXqlBgaGkrbtm1FRCQkJESqVKkihQoVkoCAACVQ0pfTBKzly5eLra2t7NixQ/r27SuXL1+WuLg4qVOnjgwePFgpHxYWJiqVSsaNGyfh4eFSu3Zt8fLykoiICK3t3rhxQ86dOyfx8fHy7bffiqenp5iZmUnLli3l8uXLGbqPumD16tXi7u4u+/btExGRWbNmSe7cuWX48OFKmWvXromfn5/SmhQbGyuGhobKD2iN0aNHS9myZcXS0lJcXFzk77//lvj4+GRbxnVRXFxcov2Nj4+XxYsXS6lSpZRly5cvF2tra7l165aybP78+eLi4iJLly4VEZHt27dLhQoVZPz48UqZyMhICQwMFG9vb+V78u3bt1o/etITO6ZQin04VpWenp5WX4FJkybB3t4e3bt3R5MmTZRBhV+/fo3z58/j4MGD8PDwgLu7O27fvo2VK1di0qRJSpkiRYrg+PHjAIAFCxbA2toa5cqVU/ri1K9fH9OmTUOxYsUyanfpEzR95gICAvDy5UtcunQJIoJhw4ahXr16mDhxojJ4p729Pdq3b4/GjRvj2rVr6Nu3Lzp06AAAmDdvHoCEwagfPHiAhQsXKo9x+vRpFCxYEKtWrUJMTAw8PT0xZ84c3Lp1C2vXrkWlSpUydqd1kL6+PuLj43H06FGEhYWhQYMGuHbtGkxNTREZGYmoqCg0bdoU9+/fx3fffQcfHx/kyZMHefPmhYmJCSpXroyrV6/i6dOniImJAQBcv34dY8eOxZkzZ6Cnp4c+ffrgt99+Q0REBP755x8OefIB+aAf44fOnTuHW7duKbetra1hZWWljA5QvXp1FC5cGG/evFHKFC1aFEWLFsWlS5dw48YNGBgYoF69epgxYwYWLFiAnj17YuLEiejWrRs6deqEo0eP4urVq2jVqlW2mNFFPuozqpkdQnMVt56eHoyMjBAaGoozZ84AAEJCQuDu7g4jIyNlOxUrVoSTk5Myo0S5cuVQsGBBnDp1Cu/fvwcAmJqaomLFiqhXrx6ioqIAADlz5oSNjU2y47am9c4SpdrevXulU6dO8ttvv8mxY8ekbdu2EhoaKleuXJEqVaqIv7+/vH37Vt69eyf29vZSsGBBmT17tty/f1/Zxrt37+T169fy9OlT6dq1q9jY2EjevHnF2dlZNmzYIK9evUr0uOnVJ4FSR/Nr99q1a/LNN9/IyJEjpWvXrlKpUiX58ccfJW/evFK3bl2lRU5EZMWKFVK0aFFp1KiRrF+/XoYPHy62trby+vVreffunSxYsEBUKpUEBARIrVq1xNPTUzZs2CC2traJ+mTFxcXxtZBG7t69K126dJEiRYpIkyZNlOUHDx6UEiVKiJ2dnZibm0ujRo1kzZo1Wn223r9/L1WrVpUCBQpIu3btxN/fXywsLKRBgwZy9uzZRI/F45YgqTMeGkePHhV3d3cJCgpSlkVHR0u3bt2kWrVqSveUli1bSpMmTeTOnTtKuaVLl0r58uVl8eLFIiJy8eJF6dmzpzg6Okq9evXk0KFDWo+lVquz3Wnx+Ph4+ffff6VYsWJiZWUlzZo1U84o3bhxQ3x9fZWzSWvXrpV8+fLJ0aNHtbZRr149KVSokDx8+FBERMaMGSOFCxeWXbt2ZezOJIPBjlLs2bNnMmPGDDl27Ji4urpK06ZNxcHBQYoUKSIjR45Uyq1atUrc3d3l33//FRGRgIAA8fPz09rW8+fP5bffftPqI7VhwwblVMOHstMpgqzg9evXMmPGDFm2bJmIfD5Mt27dWpydncXf31+5kOHIkSNSoUIFad26tYgknI51d3eXIUOGSHR0tIiIzJ07V1QqlWzbtk3Z1rZt26RPnz7Ss2dPuX//voSEhIiDg4PyRUUpk5ovbM3xXbdunZiZmcnVq1dFJCG0eXh4SN26dbXCg0hC/0dNSHj8+LFs2bJF+vXrJwMHDtQK8x/WhxILCwuTGTNmyF9//SVPnz4VkYQQFxgYKAEBAcp7RUTkzz//FC8vL9mwYYOIiCxYsEC8vb2V7ioiCceiXLly0qpVK2XZmzdvknxsXTommrD7OR07dpTu3btL3759Zf78+bJp0ybx9fWVUqVKSXx8vMTExMiIESPEzs5OuY+9vb0MGjRI+UHz4sUL8fHxEVNTU/n9999FROT8+fOyZs0aefv2rdbjfa7/eXphsKMU279/v6hUKvHw8JC9e/eKSMKXgZWVlYwbN04pd+PGDWnYsKHypX7mzBnJly+fVK5cWf755x+ZM2eO+Pn5ibe3t+zevTvJF352+xWZlTx8+FAqVqwozZs3/2Q5zTFavXq15MqVS7mKWSQhLMyfP19y5swp8fHxEhUVJQYGBrJ9+3YRSfggbtWqlahUKqlbt26yXzLBwcHi4eGRYX1TdMGXfmG/fftWTExM5I8//lAuoujbt6+ULl1aVqxYoWz3zJkz0qZNG5k0adIn68D3cML74OMLUjTLBw0aJCYmJlK2bFlxcXGRYsWKyeHDh0VE5Pfff5eyZcvK1q1blfucO3dO/Pz8pFevXiKSEAorVaokP/30k9Zn6OTJk2XdunWJPlfj4uJ07pi8fftW7Ozs5K+//kpR+TFjxohKpdIaYeHOnTuip6cnGzduFBGRffv2iZWVlaxfv15ERCZOnCju7u7SpEkT2bp1q3Tv3l06duwoHTp0kBYtWqT9TqUB9rHL5iQV4xa5ubmhdu3aMDQ0RLly5QAAVatWRePGjbFy5UqlXOHChfHNN9/g3LlzePToEby8vLB8+XIULVoUs2bNwuTJk1GtWjVs27YN1apV0+qrp6lPUoOhUvpSq9WIjY2FnZ0datasiUePHiEkJERZ9zHNcatSpQpKlCiBqKgoxMbGKutKly4NMzMz7NixAyYmJqhWrRq6dOmCvn37okaNGrC0tMSePXvQrVs3rf49c+bMQf/+/VGkSBHMnz8fAwcO5Aj5qaBSqfDs2TOMGzcOQUFBOHv27Gfvo1arkTNnTjRs2BD//PMPoqOjAQA//PADqlWrhjZt2qBp06YoW7Ys/Pz8oFKp0KBBg0TbiY+PV0bn53s44X2gmbLr5s2byufbiRMnsGbNGsydOxcnTpzAxo0b4erqin79+iEiIgI1atSAiYmJ0qcOADw8PBATE4NTp07h+fPnyJ8/P+zt7bF//37cuHFDKde/f380atQo0Xhp+vr6OnVM4uPjkTNnThQoUABbtmxR+rJ9SuvWrZEjRw6lf66IwMnJCb6+vli2bBkAwNXVFWXLlsWiRYsAAD179sS4cePw6tUrBAYGIjw8HOPGjUNERASMjIyUPnqp+S5Nd5kaKynTJHVV0Oeo1WqZMGGCWFlZaf0aXL16tZiamiqncEREdu7cKSVLlpRff/1VaxvPnz9PtE3KetasWSN16tRRrvRK7nSC5vj17NlTqlatqnXJ/759+6Rw4cJKK93du3clODhY/Pz8ZPTo0YmupNRsa926ddKlSxdZsGABXx/JSK6P1rp16+TKlStSvnx55c/MzCxR36qPaZ7nvXv3ipGRkVb/uPj4eDl27JiMGTNGpk6dmug9TAk+PB6a5/Pu3bvy3XffSYECBcTPz085DqNGjZKiRYuKyP9avq9evSoWFhayYsUKERHp06ePlC9fXu7duyciCS12JUqUkDx58sisWbNEROTs2bMSEhLyybroGs1zq3ne/vnnHzEzM1NGVficSpUqSZs2bbS29ddff0mePHnk7t27olarZcaMGaKvr691GvvD06xv376VwoULa/WDzEoY7LK569evy/79+5O8UCEp58+fFyMjI+XLWiShA32ZMmVk0KBByrIHDx5IYGCgTJw4UVn24Zd0bGwsv7SzmNDQUGnQoIEUL15c2rZtK8WLF5fGjRvL+/fvk72P5gtk+/btYm9vL61bt5aHDx+KWq2WUaNGibu7u9ZrK6kvHF3+EvqvXrx4ISL/+xL71LAUkZGRolKpxNnZWSZPniwiIk+fPpUGDRpI9erVE/X/SU7evHll0KBBn3x/fqrzvy5LaoiM5E5vhoeHS8OGDaVGjRqyYcMGOXz4sDJe3LRp06RAgQJKWc3p2pIlS0r//v1FROTAgQNSsmRJqVChgkyaNEnq168vQ4cOlalTp8qxY8fSY/eytE+dRs6ZM6fMnDkzRa/J+fPni4WFhVb3jjdv3kju3Lnljz/+EBGR48ePS82aNbXG97t3756sWLFC5s+fL97e3lK5cmUldGc1DHY67uMPog9/mXt6eoqpqamUL19eihcv/tlf9SIJHXtr1qypdfXcmzdvpH///mJsbPzJx6bM9bk+Ns2bN5fq1avL0aNHZdmyZVKqVClxcHBQXhef+tB8//691KxZU0xMTKRBgwZSunRpMTY2lj///DPJ8gz2nxYRESFdu3aVpk2bJrn+/PnzMnz4cFm+fLnWleYDBgyQnDlzysmTJ5Vlhw4dEgMDA61lSdEc38GDB8vQoUOTfK1kt/HOPjR16lSpWbNmkjOdxMfHy6pVq2TlypXKslmzZomlpaXW2Iwa69atkyJFisjOnTtFRJRZOpo2bap8tsbFxcnRo0elVatW4u3tLRMmTEgynOvK8Ujpfhw6dEjWrFkjL168UF6jTZo0kcqVK6eoNfnZs2diYmKiXBymedzq1atL8+bNRa1WJ1mX8PBw8fX1lZIlS8rIkSOzdMs1g52OSupDWfNijY6Oltq1a8tPP/0k4eHholarpW/fvuLn56f8EvzUl/iiRYskd+7cWldsHTlyRGbOnJmoo3B2/iLIqp49eybr16+XJ0+eKMfm7Nmz4uTkpNUJ+dChQ+Lh4SE///yziCT/wat5rfTt21eqV68uK1askB07dvC4/0cXLlxI1Fr68uVL6dSpk+TOnVsqV64srq6u4u7urkxdtGfPHtHT01NOiWuOgYODgwwbNuyTx0TXOtb/V69evZJJkyYpz+2hQ4eUkKZ5zYeHh0vHjh0ld+7c4urqKt26dVOunmzcuLEyuLaG5njeunVL6tWrJzVr1lQ+M69evSpFihRRTrNqfHzFZ2ZdaZkRnjx5ItevX0/ytbhq1Sqxt7cXW1tbKVu2rPj4+CifV7t375YcOXIkGpYkOU2aNJFy5cppPY8vX75MVE5zbDTvmw8H38/KGOx03JEjR2TZsmVaL9rVq1dLtWrVlBftli1bxNvbW4yNjVN0ddH169dFpVLJ8uXL06valEpnzpyR3bt3f7LMtWvXpH79+mJiYiLu7u7i6emphLZ9+/ZJvnz5tPqpvHv3Ttq0aSM1a9ZUTgkmRfPhGBYWpjXtkciX9eXMrm7dupXkl/iHfVc3b94sBQoUkBMnTohIQh+uKlWqSK1atZSWJAcHB2XKIs1zHxQUJCVKlEjU2qRWq5NsPdXl8JBSx44dEzc3N+nXr5/W8rCwMOX/kydPlipVqsj+/ftFJKELiqb1LTAwUPz8/JIdbuT48eNiYWEhZcqUkV69eknRokWlatWqyc59nNTVtbqmcOHC0qFDBxFJCFqaVuabN29KpUqVZPr06SKS0MI5efJkMTU1VYJ03rx5ZcSIEZ/sOqKxceNG8ff3/+Q8ul8zBruvTFRUlAwfPlyaNWsmr169SrbZeO3atVK4cGGxtbWV0qVLi6enp8yZM0dEEi7fdnR0lC5duoiFhYUULlxYBg4cKOfPn09RHWJjY5OcFii5ulD6UqvVUrZsWWnatGmiCxJCQkKU8bGCgoKkfv36cvfuXRFJOB1vbGwsa9eulbdv34qRkZGsXr1a2aaIyJAhQyRfvnzKsAspGUKBr4PU69OnjxQpUkS5rQnYXbp0kRIlSijLv//+e6lZs6ZWa/mqVau0Wi9+/PFHKV68uFaZa9euiUqlki1btohI0gMFnz9/XsaNGyfHjx9P+x38imgClKaLiY+Pj/J6/u2338TLy0s5Pj4+PtKuXTsRSWgJ//BU6a5du8TAwEAZ/FbjyJEjSv+uM2fOyLhx46RZs2Yyb968dN+3rOTDzwnND5rJkyeLtbW1lCxZUlQqlXTv3l1EEn7Q+Pr6ikhCS+rUqVPFzc1NVCqVMihwv379pESJEsrcuck9ZnZomWaw+4rExsZKs2bNxMPDQ2bOnKm17vXr1zJt2jQ5ceKEvH//XmrXrq010fD8+fPF2NhYbt68KadOnRKVSiXNmjWTPXv2KL8wRRKamjVB4MMvZ80ve8paNMdk7NixUrlyZaXlQERk0qRJUqxYMQkLC5OnT5+Kra2t0tl3586d0rNnT1GpVMpE1Q0aNJA6deoox19EpHPnzmJqaqr8ihZJOsw9efJEaUWiz/v4yr7bt2+LoaGh1K9fXzmtFx8fL3/88Ye4u7sr4aBFixZSq1Ytefv2rbKNO3fuSK1atZSWpYsXL4qRkZEcPHhQ67GGDRsmN27c0KpHeHi4jBkzRooXLy65c+eWli1bJiqTnS1ZskS8vLyUMc1Wrlwpvr6+yunSiRMnSp48eaRs2bLSpEkTqVevnpQtW1Z57suVKyeVKlWShQsXyuPHj2Xjxo3SoEEDZfD2pOh6S2ly3XMiIyPF1tZW9PT0pGnTplqDYg8YMEAqVKgglStXlty5c0vZsmVl+vTpWv1Lr127JgYGBrJjx45E206uf3FKWve+Rgx2Xwm1Wi2PHz8Wa2tr2bx5s7LswoUL8u+//0rdunWlTJkycvjwYdm3b5/y6z8iIkKmTp0qZcqUkRw5csiOHTvk0aNHSivdh86ePSuDBw9WwoHm183Hb8Jz584pfQ3YMpPxkroSLyQkREqXLq0MT/Lw4UOxs7NTfs2eO3dOXFxcpFWrVlKwYEHJnz+/dOzYUfbu3auEw6NHj0qhQoWkTp06cuTIEZk9e7Y0a9ZMOnToIIMHD0502kKtVsvixYvFz89PVCqVdO3aNQP2/uuj+aL+1BWUjRs3FpVKJUWLFlVmFhAROX36tPj5+cmAAQNEJKF1zsTERK5fv651/+LFi8tvv/2m3C5QoMAnj8e6devE19dXTE1NpUKFCjJnzhx58uTJF++jLtB8ll24cEHq1q0r5cqVk44dO4q9vb0yKPCTJ0+kcePG0rJlS+V+f/75p0yePFnmzp0rM2fOlNq1a4uvr6+8ePFCrl+/Lh07dhQXFxexs7MTS0tLGTJkSJKnxLNDS9KHLl++LGPHjpU1a9YoPybfvHkjlStXlubNm2u1OK9fv15UKpV069ZNq2uCZjuaz6Z8+fJJ69atlZbTjxsjnj17JhMmTNAawUEXMdh9RWJiYqROnTrKrA4lSpQQDw8PcXFxkZ9//ln5Apk1a5Z4enpK5cqVxczMTLy9veW3337T+gW0aNEisbOzkypVqsjSpUulV69e4uzsLM2aNZNbt24lCmzXrl2T/v37i7W1tZQqVSrFnVQpfR08eFD5AGzSpIk0bdpUnj59KuPGjZN69eopxzEkJEQqVKggzs7OsnnzZq0+cy9evFD6YO7cuVNq1aoltra2Ym9vL+vWrUvU72vr1q3y7bffirm5uRQvXlyGDx8uoaGhGbPDX4GYmBiZNWuWBAQEJLlerVbL5s2b5ezZs/L69WsREbl//74MHjxYihQpotVBOzo6Wvr37y9lypRRvvjz5csnffr0Ud7Px44dkwIFCmi1Ap07dy7JIYw02wgKCpKRI0fyuH3k1atXUqtWLWnQoIHs3r1bpk6dKtbW1uLq6qq8R0aNGiW+vr6JPgM1n78LFy4UW1tbreN4/vz5bNGirZkSUvMjJqnWx5cvX0qHDh0kV65c4ufnJwULFpTq1asrV98vW7ZMcufOLefOnVPuEx8fLxYWFjJkyBCtM0zHjx+XTp06yYEDB0QkYT7qD38YaSxdulQqVaokpqam4uPjI8uWLdPpllEGu6/I9OnTxdDQUPT19aV9+/YSGhoqgYGBkjNnTmUqGpGEX/VOTk7SqFEjuXnzptYvwYsXLyr/3759u3Tv3l18fX3F399faQnUiIyMlAkTJkjJkiXF3Nxc6tWrJ8uXL0/UQZ4y1tWrV6VFixaSJ08eKVGihHLcZs+eLX5+fjJ79mypV6+e1imJN2/eSOfOnaVUqVJa23r16pV89913WvNNvnr1Sh48eKBVTtMf5s6dO+Lo6CidO3eWw4cPZ7tWhuQ8f/5cRo8erdweM2aMODs7K60Lmueua9eukidPHnFychI3Nzdp1KiR8hw+fPhQVCqVrF27Vmvb//zzj7i6usq6detEJGEw1RIlSoinp6e0b99erKyspF27dsl20v8QW9g/7ciRI2JiYqL1eTp37lwpUqSILFiwQEQSrjyuWLGi/PLLLyKScOz3798vN27ckLlz54qPj4+MGDEi2cfQ1aF+Nm/eLCqVKtmx3TRBasWKFVKwYEHlauNjx45JnTp1pFKlSkpZY2NjmTp1qtYp1KlTp0rx4sWlXLlyMnnyZGncuLHY2tpK9+7dtU7Jajx48EDatWsnJiYmUrx4cRk6dGiSfcN1EYPdV2TLli3yyy+/SKFChZQP+YULF0rhwoVlyZIlSrl79+5JpUqVEs1jd+HCBaldu7bWlY/J/aqKiIiQFi1aSJkyZWTatGny8OHDdNor+tinxpuLjo6Wtm3bSp06deT48eNy5coVuXXrlogkXCFZpkwZsbe3F5VKJYsWLdL6dXvkyBFxdXWVkiVLyrhx42TkyJFSokQJKVu2rHLK9kPJ9atM6YTb2cnu3btFpVIpAfnAgQPi6+urfPm/fftWFi1aJC1atFBaJu7cuSMWFhYyY8YMpdW1dOnS0rZtW63T3levXpX69etLp06dRCShX9D169dl1KhR0qVLF9m2bVtG7upX41OnvpMzY8YM8fX1lZs3byrLNHNfN27cWEQS3oPt2rWTgIAAefv2rYSGhkqrVq2kQIECUqRIERk/frzWaUQNXQxzH3r8+LGy/yIJ74nAwEAZNmyYEqhiYmLkxx9/lMqVK2vdd82aNeLo6Ki8lps3by5VqlRJ1D3gwIEDMmTIEKlatap069YtyQv+NJ9ZJ0+elNGjR8u+ffuyXf9wBruvTHh4uAQEBEj9+vVFJOFXiY+PT6I+A6tWrZICBQpIuXLlZPTo0dK8eXMpUKCAtGnTRgkCH0oqTCR32T1ljJcvX8r58+e1PpTWrVsn+vr6SU4jJJJwsUOVKlUkICBAGUzz999/V1pZr1y5IiNGjJCaNWtK5cqVZd68eTp9SiK9ab6sHz9+LAEBAdKwYUMRSZjxoXv37lKxYkWl7PHjx5Ur9i5evCjjx48XlUol/v7+yhfUH3/8IdbW1lotC3FxcTJ48GDJly/fJwdF5XFM3ueeG8363bt3i5WVVaLB2lu1aiVOTk7K6e/JkyeLs7OzMnH8sWPHsv1FJ/Hx8dK/f39xdXWVM2fOiLu7u7Rq1Uq8vLzE0dFRGSambt260rZtW60r+K9fvy5Vq1ZVLgA6fPiw5MqVS37++WeZNGmSVK1aNdkWaQ7NkxiD3VcmPj5epk2bJvb29sol8126dBF/f/9EzcxHjhyRsWPHSs2aNaVz585a83hS5khqPsmPbx87dkyqVKmi9AcpX7683L59W0QSTvGVKVNGaTXTfKhpwt/KlSulTJkysmbNGomIiJDff/9dChcuLAULFpRhw4Ypj/9xqxs/GFPuw+dbIy4uTmbNmiV58uRR+s3NmzdPXFxclP4/IgndG7p06SJ2dnbSqFEjCQ4OFktLS2V+0Pfv34uZmZn07NlT1q1bJy1atJBr165JSEiILFmyRKsFVlMXHjvtwZU176ObN29Kr169xNvbW/r37y+bNm1K0bYcHR1l8ODBSgd8tVottWvXFn19fRkzZoyI/G+Yko9/JCc3LmB2cejQITE0NBQvLy9lVo1Lly6Ji4uL9OnTR0QShowpWrSo1pmjN2/eiJeXl0ybNk1ZNn36dKlQoYKULVtWGQXiw9d6cmebiMHuq3To0CFxc3OTKVOmiEhCM/Y333yjjGOV0l+nlHGOHTsm9evXVzq4J3eZfUxMjNStW1e6desm165dk7CwMGnUqJEEBATIw4cPZcqUKeLt7a30T/n4CyQyMlKqVKkiPXv2VMJbfHy8zJkzR2lRSMlVmpQyHwatkydPiqOjozIUxsmTJ6Vq1arSt29fEUl4vufNmyceHh7KcBgiImZmZtKnTx+lhXzevHnKBSxt2rTRGhCXUubChQtSqlQpadiwocyePVt69OghNjY2WnNcf0zzXpg4caKULFlSOnXqJKGhoTJ9+nRp2rSptG/fXpnHNTtJTUh9+vSp1KhRQ0qXLq314/HXX38Va2trEUnok2hnZyc//fSTckHKsWPHxNLSUgmDIgnvl5T0G6XEGOy+Qk+fPpVOnTpJlSpVRCRh0GI3Nzfp0qVLol/0Gvx1kzk+nLKrQoUK8sMPPyjrrl27Jrt27dJq/Vm8eLFUrVpVuX3hwgWpVKmS5M6dWzZu3ChXrlyR4sWLy9ixY7Ue5/Dhw8oVjp06dZLatWtrXQVNXyapFtbbt29Lr169xM3NTTp16iQLFy4UkYSLTtq1ayc+Pj4ikvC+HDhwoHh5eSlfctWrV5c2bdoofeiWLl0quXLl0gp7cXFxSZ5yza4DP+/YsUOr1TOpHyNXr16VwMBAEUk4Zt27d5f27dtrlbGzs5OaNWsm2dFe5H/H982bN7J06VLx9PQUCwsLKVq0qGzatCnJz1Zd/kz9kllj1Gq1TJgwQaysrLTGq7tx44YYGRkpV6z+8ccfUrBgQalUqZJ07NhR8ufPL126dElyJgj+AE09Bruv1J9//ikGBgZKX6t9+/Z9ctonyngffyB9//33UqNGDTl27Jg0b95cjI2NxdHRUVq1aiWXLl0SEZHRo0dLsWLFZPTo0VKgQAHJnz+/tG/fXg4ePKh8iQwYMEDs7e1l4MCBcvToUVm0aJHW6Yrw8PAkv3B0+UsoLX3qi+TWrVtSoUIFqV+/vvz1118ybNgwcXR0lLlz54pIwhWsuXPnVk7RrVixQtzc3JRTrUFBQWJjYyMTJ06UxYsXS/PmzWX27NnJ9n2NjY3N9setefPmWqfokrJ161bJlSuX0urp6uoq69evl127dkmFChXE3NxcypYtKytWrNCaHeJT7t+/r3SB0MiO4fr69euyf//+JIfPScr58+fFyMhIq3U0Pj5e6tSpI82aNVOWnTp1SoYPHy5t27ZNNCID/TcMdl+pCxcuyJQpUyQsLCzbfdBkdR9/EWtuL1myRCpXrixNmjSRIUOGyIsXL2T16tXi4eEh33//vYgkfEHlyJFDatasKatXr9aa4/fVq1dKq8GkSZOkYsWKUqBAASlYsKCMHz8+0TA02T0QpIW1a9fKP//8IyIJz+eYMWOkTp06WmXKlCkjLi4ucufOHbl9+7a4u7vLkCFDRCRh8NSGDRsqk8E/ffpUgoKCpGjRolKkSBH59ddf2RqRSmPGjJE//vhDK6DNnDlTKleuLLdv35bo6Ghp1KiRqFQqcXFxkUGDBmkN8/QpyU2Rp8vHKLm+vnv37hVPT08xNTWV8uXLS/HixRNdVJKU6OhoqVmzpjRp0kRrewsWLBCVSqU1s83n6kJfhsGOKJ3s3btXAgMDJSgoSGJjY+Xhw4dSs2ZNsbCwUFroRESCg4PF29tbbt68Kbdu3ZICBQoow2RonD9/XoYPH670rRNJCHqaeV8p9ZJrfXn9+rX88MMPkjt3bnFzc5PmzZsrF0SULVtW/vzzT9m+fbtUrFhRzMzMlP6t79+/l9evX8uAAQOkWLFiIpIQBkeOHCn58+fXGjLo4y83XQ8PKZVca+mff/4pS5cuFZGEeXU9PDxkxowZyvqVK1eKra2tiCScSh0yZIg4ODgk6qO1f/9++ffff7WWJ3UxTHaQ1POseT9ER0dL7dq15aeffpLw8HBRq9XSt29f8fPzk2PHjonIp384Llq0SHLnzq017MuzZ89k5syZSV64xdd+2tIDfdXUanVmV4E+8uLFCzRo0AAtWrSASqWClZUV7t+/Dzs7O5QrVw7GxsbIkSOHUr58+fIwMDDAxo0bUahQIfTv3x+//fYbOnTogJ07d+KXX35Bu3btcO7cOVhZWSn3Mzc3h6OjI0QEcXFxEJHM2N2vklqthkqlgkqlSrTu33//xcGDB7Fs2TJcunQJEydORM6cOQEApUqVQrdu3TBgwACUL18eR44cwfHjxxEYGIgcOXIgd+7cqFmzJh4/foxDhw5BT08P33zzDbp06QI9vf993ObNm1c5bgCgUqmgr6+fMTufhenp6SnPw7lz5/D8+XMAwJYtWzBq1CgAwNixY9GuXTsMGTIEFy5cAABYWFhApVLhyZMnMDY2Ru3atREfH49BgwbhzJkziI6OxubNmzFmzBicPXsWBgYGyntGT08PBgYGAIB9+/ahYcOG+OWXXxAdHZ0Jz0DG0TzPR48exd9//41Xr14p74etW7ciNjYWY8aMQb58+bBt2zYcOnQIJ0+exNWrVwFA6/X8sfLlyyM6OhobN25UlllZWaFnz54wMjLSKvvhMac0krm5kkj3jBkzRjw9PZUpcT5sDdi6dav4+vpq9Rl6/vy5tG3bVhmbUCRhtPsmTZpIyZIlpUyZMrJw4UKeWk1jL168kCVLlmiNFfj69WupWLGiMrj3hy0JmquLjYyMEo3xeOrUKVmwYIFERETInTt35JtvvpFJkyZl3M58hZIaouTs2bPSrFkzMTc3l9q1ayt9r44cOSKGhoZa00zVqlVLatWqJdevX5c1a9ZI+fLlteYRXbNmjbi7u0uJEiXE0dFRLCws5Keffko02PrVq1elT58+YmdnJ/b29tKmTRutx/naREVFyfDhw6VZs2by6tWrZFum165dK4ULFxZbW1spXbq0eHp6ypw5c0Qk4cpgR0dH6dKli1hYWChziyc1IHBSYmNjk53lgadb0x+DHdFnpPSDSFOuQoUK0qNHDxH5X6jThLIXL15Is2bN5Ntvv9Xa7vTp08XV1VX27t2rLHv79m2ifnP8UEy55J6re/fuyYoVKyR37tzi6OgoLi4u8tNPP4lIwmwQRYsWVYYs0dCEkPv370uhQoXk22+/lW3btsnLly9l8+bNUqdOHenTp4+8fv06yWEaOIhq8jTPy8OHD+Wbb75RLha6efOmcvFCfHy8FClSROm7KJIw/3H9+vWlcePGsmbNGrGzs0t0sUlcXJzs3Lkzyb5hp06dkqJFi4qFhYU0bNhQVq9erZxy/1rFxsZKs2bNxMPDQ7mYSuP169cybdo0OXHihLx//15q164twcHByvr58+eLsbGx3Lx5U06dOiUqlUqaNWsme/bs0boiODw8XOlK8OF7LLmZaijjMdgRJeNLLvd/9OiRVKlSRbkYIqn7jx8/XipWrChHjhxRlh08eFACAgJky5YticqzD0rqJHfc4uPjpU+fPmJoaCg9e/aUM2fOyKtXr+SXX34RPT09ZRgMZ2dn+fHHH5VQrQkKmmOwZ88eqV69uri5uUnBggXFwsJCfvjhh0TDaGTnIHf69GkJCgqSa9euiYgk+YUfEREh3t7eytWT8+bNEzc3N2UaqY/Hehw6dKg4Oztrha+bN29KgQIFpFKlSmJiYiLh4eHKuqReA7GxscpxfPLkicybN08Z6P1rp1ar5fHjx2Jtba20dKrVarlw4YL8+++/UrduXSlTpowcPnxY9u3bJ0WKFBGRhOMwdepUKVOmjOTIkUN27Nghjx49UlrpPnT27FkZPHiw7N+/X9l+Uu+3c+fOKceCP0YzHoMd0Wek9nL/Fi1aSPPmzeXBgwci8r9AoPmVe+zYMfHw8NAa044ffmnv9u3bcuLECa1lu3btkly5cknnzp2VsBEdHS0uLi4yYMAAERH55ZdfpHjx4rJ7927lfs+ePZPJkydrbevYsWNy9OjRdN6Lr4vmdbxlyxYxMTFRBuTW0AzJpClXuHBh6dq1q4gkjOH4zTffSMuWLaVv374ybNgwGTBggLKN0NBQ0dPTU1q1P7za3M7OTmrUqKFM2ZZcvXRdTEyM1KlTRxo2bCitW7eWEiVKiIeHh7i4uMjPP/+sPGezZs0ST09PqVy5spiZmYm3t7f89ttvWmNfLlq0SOzs7KRKlSqydOlS6dWrlzg7O0uzZs3k1q1biZ7Ta9euSf/+/cXa2lpKlSrF90YmYrCjbC+tLvfXfGj+/fff4unpKSNHjlTWhYaGysiRIyUsLExiYmJk/Pjxyq/eD7FlLuWSasl8//69LFiwQNzc3CR//vzi4uIiLVq0UMaICwsLkxIlSmjNBiEiMmrUKLG3txeRhFNNjRs3FktLSxk9erRMmzZNqlatKmXLlk12cNvsPI3Uhz48HqVKlZIffvhBaWHz9fWV1q1bi8j/3mO///67WFtby9OnT+Xdu3eydOlSqV69uvTs2VO6d+8u5cuXl3z58snhw4dFRMTHx0c6dOig9Zjv3r1LNFl8djV9+nQxNDQUfX19ad++vYSGhkpgYKDkzJlTeQ5FEuYSd3JykkaNGsnNmze1jtuHQ8Ns375dunfvLr6+vuLv759ovLnIyEiZMGGClCxZUszNzaVevXqyfPnyRF1IKGMx2FG2ldaX+2vuGxUVJXPnzhWVSiVVq1YVf39/sbKyEn9//2SDAaVcUgHqw5aihg0byqRJk+T58+dy/PhxqVKlirRs2VJEEo7ZsGHDxMHBQev+ly5dkhw5ciitQRERETJ8+HCpV6+euLu7y4gRI5Icf4thLvmuAj/99JP4+PjI2bNnRSRh7EUHBwetWTWeP38uBgYGygDOGpo+XZpTguPHjxeRhE79tra2SQYHDhmT8Pr/5ZdfpFChQrJu3ToREVm4cKEULlxYlixZopS7d++eVKpUSblISOPChQtSu3ZtrXlck5u1KCIiQlq0aCFlypSRadOmJboohTIPgx1le0eOHJFly5ZpDQa8evVqqVatmnK6bsuWLeLt7S3GxsbKnLyfc+zYMRk9erT88MMPcubMmUTrs3MfrLRw4MAB5YtF00/r9OnTWtNPbdiwQUqUKCFWVlZKf69Tp06Jvr6+VgtGbGys+Pr6SkBAgNZjfNyZnkEueS9fvpS5c+fKokWLRCShn5WdnZ3yfnn06JHkyJFD1q5dKyL/e/3XqFFDGjdurFxw8ujRI3n9+rVERUXJmDFjpGLFisopwnfv3mX78PY54eHhEhAQoFxl/+DBA/Hx8ZFBgwZplVu1apUUKFBAypUrJ6NHj5bmzZtLgQIFkp0FJanBmz++OpyyBo5jRzonOjoaI0aMwLfffouIiAhIwg+YROXWrVsHZ2dnNG3aFL/99huqVKmCP/74AwBw69Yt3LhxA9999x0sLS3Ru3dvVKtWTRmz7HNEBD4+PhgyZAh+/fVXeHl5QUS0xh381DhQ9GkTJ05E8+bNYW5ujq5duyImJgYAULp0aVSqVAlz585FsWLF0K9fP9SpUwfx8fFYt24dAMDFxQXly5fHvHnzACSMaWdgYIBOnTohX758Wscod+7cAKCMeZbUuHfZyYfvI83/nz17hvbt28PW1hbz58/Hvn378P79e3h4eKBQoUI4cOAAnj59CltbW1SpUgVLly5Vxo8DgPr16+PQoUN48OABYmNjMWHCBLRu3Rr29vZYvHgxevToAUdHRwCAkZER9PX1OX7nJ1hZWaFq1aoICQlBWFgYChQogJIlS+L8+fO4cuWKUq5Zs2ZYtWoVGjZsiP3798PU1BQbN27E0qVLUahQoUTb1dfXTzTe3IfjalIWknmZkijtZcXL/ePj49k6l4bOnj0rVlZWWqeWPnTy5Enx9vaWiRMnKpOKN2zYUCpWrKhMQ/Xbb7+JSqVi608KJNVK+eGMJxMnTpQKFSooLaCPHj1S3gdTpkwRd3d3Zd2KFSskZ86ccv36dRFJOOXapEkTUalUysUpO3fulBkzZnzVY8lltkOHDombm5tMmTJFRBLG9NPMkCLy+bMF/Lz6urHJgHSGiODZs2c4cOAAxo0bh549e0JEcPHiRaxZswYtWrTAkiVLEBsbiyNHjuDmzZsYMWIEIiMjMW3aNMyePRtxcXG4efMm7OzsUKhQIRQqVAhVq1aFoaEhACAkJASTJ0/G5cuXlceNj49XWnM0I9ifP38eT58+BZAwqwBb55InqZwx4+TJk8iXLx9q1qypdX/NLA5Hjx7F3bt30bdvX5iYmODatWs4f/48Dh8+jDNnzgAAmjZtiunTpyM+Pl5r22q1mq1BH/m4lbJ58+bo168fYmJi8PLlSyxduhSlS5dG+fLlER8fD1tbW+V90KpVK0REROD06dNQq9Vo3rw58ufPj06dOmHDhg2YNm0aSpUqhZYtW+LVq1cAgBo1aqB3797w8PCAiCQ6RvR5Li4uKFeuHNavXw8AqFWrFqKionDo0CG8f/8+yc+j+Ph45bXPz6uvG48e6QyVSgVLS0uULl0af/zxB9q0aQMPDw+0adMGP//8M0qVKoXjx4+jfPnyuHz5MkxMTFClShU4ODhgyZIlaNWqFa5fv46aNWvC1tYWI0aMwLJly1C1alUsW7YMvXv3RrNmzXDjxg04ODgoYU5fXx8qlQrXr1/HgAEDkD9/fgQGBuLmzZtKvSixDwNxSmi+dMLCwqCnp4fXr19rrdeECS8vLzx//hxz587F4cOHMXXqVPTr1w8VK1ZUwraTkxN69+6tBHYNPT29bPulplartUKUiODt27dYuXIlrl+/DgCIiopCaGgoatSoASMjIzx58gQRERH45ptvAPxvmir5/+4P+fPnR+nSpXHgwAHcu3cPADB37lw4OjqiS5cu+Oeff1C1alX8/fffGDlypNZjf/j+otTJmzcvfH19cejQIZw7dw4mJiaYNWsWJk6cmOg1r6Gvr59tX/s6J1PaCYnSCS/3//pcu3ZNli1bJvfv31dO+yV1Kkiz7NKlS5IrV65EY6Q9fPhQGSdtwIABUqpUKcmZM6cEBARonTr8EC+GSJpmaq69e/eKu7u7FCxYULZu3SrHjx8XY2Nj5UpxERE7OzsJDg5WTntrjpNmAvi///5bChQoIOvXr1e2//btW62LlUS+bEBwSt6FCxdkypQpEhYWxuc1m2GwI53Cy/2znuSGw7h8+bJUrlxZjI2Nxc3NTYoVKyZDhw795LY0X1A1a9aUqlWryubNm+X9+/cSGRkp/fv3V/pVxsTEyO3btxP1eeSUR4lpntM7d+5I27ZtJU+ePFKmTBmpXr26Mjfo4MGDxdTUVKpVqyYBAQFag3UPHjxYPD09Zd++fcqyu3fvKkOUvHv3TsqWLSs7duxI9NicVYUo7THYkc7h5f6ZL7m5UTXTRMXHx8uPP/4oFSpUkCdPnsijR49k5syZolKpZNOmTcluV/P8X7x4UVq1aiWWlpZSsWJFMTMzEy8vrySnZEsumJO2rl27SqNGjWTv3r1y7949ad26tfj7+yvjNv7zzz9iaGgoRYsWla1btyr3u3//vjRr1kxMTU0lODhYhg8fLl5eXlKlShUOHJwF8LWf/TDYkc6Jj4+XadOmib29vTIPZJcuXcTf318uX76sVfbIkSMyduxYqVmzpnTu3DnJ8ebovwkNDZUmTZqIt7e39O3bV16+fCnv3r0TCwsLWbhwoYj8r9WoRo0a0qxZsxQFgrdv38qBAwdkypQpyiC49GWOHz8uXl5ecvPmTRFJ+MESGBgoKpVKRo8eLSIJrdzm5ubSuXNnyZkzp8yYMUNrUO7Ro0dLo0aNpHz58jJ79myldVRThi1zRBnDILP7+BGlNT09PXh7e8PMzAzLly9Hv379ULduXYwfPx4nTpyAq6sr1Go19PT04OvrC19fXwQFBWltQ7OevoyIYOHChYiIiEBoaCgsLS1RvXp1DBo0CDExMejRo4fWmHHv3r2DsbExOnXqhB9++AFRUVGwtrZOdvtqtRo5c+ZEpUqVUKlSJeUx1Wo1O9t/gcuXLyM6Ohq7du3CrFmzcPv2bfj4+GD58uWoW7cuAGDv3r2wtbXFr7/+ijJlymDSpElYunQpgoODUadOHQwZMgTv37/X6pz/4fuIx4UoY/Cbi3QSL/fPXCqVCiEhIRg+fDhiYmIwa9Ys9OzZE9OnT8fFixexY8cOVK5cGRs3bgQAJQz4+voqV65+TESUIU0+PD6aQMcrKL9cuXLlcOPGDcyYMQOBgYHKMWrevLlybA4ePAhXV1fkyZMHPXr0wMaNG9GgQQO4u7sr2zE0NNQaooTvI6KMx3cd6SRe7p/5+vTpg1y5cqFo0aLIkSMHAKBevXrInTs3rl27Bi8vLxw4cABXrlxRAtn8+fPh6uqqHCNNSJCPxgk8fPgwDh8+DIDjBKYFW1tbFC9eHDVr1kT//v3h4OAAAHjw4AFmzZqFhQsXYvv27WjQoIFyHzc3NwwZMkQpq8GATZS5eCqWdFa5cuUwadIk2NjYQERQuXLlzK5StlK4cGEULVoUjx8/Vk7RWVtbo0SJEjh//jxatmyJmjVrolq1amjbti3UajVWrFiBQYMGwd7eHoB2SLhx4wZmzZqFf//9F8+ePcPs2bPh4+OjhD36cubm5vjxxx/x008/4cmTJ+jevTtu3bqFRYsWQa1WY+7cuZg0aRK+/fZbrfvJ/483x2BNlHWoRFI57DsRUQrNnDkT69evx6+//goPDw8AwK5duzB8+HB06dIF7du3x5w5c7B+/XoYGhqiV69eSp8uEcG7d+8wc+ZMLF68GPfu3UOFChUQGBiIOnXqwMzMLDN3TSctW7YMf//9Nx49eoSXL1+iffv26N279yf7OxJR1sJgRzqPF0JknkePHqF+/fr47rvv0LVrVwAJF0rUrFkTNjY2WLx4MYyNjRMdI83tdevWYcyYMWjbti2aNWuGAgUKZNauZBvv3r3Dy5cvYWtrqyyTVMwQQkSZi8GOiNJVo0aNYGFhgenTpyutbMePH0exYsVgYWGhhDhNh3v2z8o64uLilCnziOjrwGYMIkpXLVu2xPPnz/H8+XNlmY+PDywsLABAazgMhrqsxcDAgKGO6CvDFjsiSlfx8fEMbEREGYQtdkSUrjShTjNGIBERpR+22BERERHpCLbYEREREekIBjsiIiIiHcFgR0RERKQjGOyIiIiIdASDHREREZGOYLAjIiIi0hEMdkREmaBDhw4ICAjI7GoQkY5hsCMi+owOHTpApVJBpVLB0NAQRYoUwahRoxAXF5fZVSMi0mKQ2RUgIvoa1KlTBwsXLkRMTAy2bNmCXr16IUeOHAgKCkrVduLj4zn/KhGlG7bYERGlgJGREWxsbODk5ITvvvsONWrUwIYNGxATE4Mff/wRBQoUgImJCXx8fLBv3z7lfn/99Rfy5MmDDRs2wM3NDUZGRrh3716i7Z88eRL58uXDhAkTAADnzp1D1apVYWpqCjMzM3h7e+PUqVMZtbtE9JViix0R0RcwNjbG8+fP0bt3b1y+fBnLly+HnZ0d1q5dizp16uDChQsoWrQoAODNmzeYMGEC5s2bBysrK1hbW2tta8+ePWjSpAkmTpyIbt26AQDatGkDLy8vzJ49G/r6+ggJCUGOHDkyfD+J6OvCYEdElAoigt27d2P79u1o1aoVFi5ciHv37sHOzg4A8OOPP2Lbtm1YuHAhxo4dCwCIjY3FrFmz4OnpmWh7a9euRfv27TFv3jy0aNFCWX7v3j0MHDgQxYsXBwAlJBIRfQqDHRFRCmzatAm5c+dGbGws1Go1WrdujWbNmuGvv/5CsWLFtMrGxMTAyspKuW1oaAgPD49E2zx+/Dg2bdqE1atXJ7pCdsCAAejSpQuWLFmCGjVq4Ntvv4Wzs3O67BsR6Q72sSMiSoGqVasiJCQE169fx9u3b7Fo0SJERUVBX18fp0+fRkhIiPJ35coVTJs2TbmvsbFxkhdMODs7o3jx4liwYAFiY2O11gUHB+PSpUuoV68e9uzZAzc3N6xduzbd95OIvm4MdkREKWBiYoIiRYrA0dERBgYJJzu8vLwQHx+P8PBwFClSROvPxsbms9vMmzcv9uzZgxs3bqB58+aJwl2xYsXQv39/7NixA02aNMHChQvTZd+ISHcw2BERfaFixYqhTZs2aN++PdasWYPbt2/jxIkTGDduHDZv3pyibVhbW2PPnj24evUqWrVqhbi4OLx9+xa9e/fGvn37cPfuXRw+fBgnT56Eq6trOu8REX3tGOyIiP6DhQsXon379vjhhx/g4uKCgIAAnDx5Eo6Ojineho2NDfbs2YMLFy6gTZs20NPTw/Pnz9G+fXsUK1YMzZs3h7+/P0aOHJmOe0JEukAlIpLZlSAiIiKi/44tdkREREQ6gsGOiIiISEcw2BERERHpCAY7IiIiIh3BYEdERESkIxjsiIiIiHQEgx0RERGRjmCwIyIiItIRDHZEREREOoLBjoiIiEhHMNgRERER6QgGOyIiIiId8X/XeRsIuJPVqQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar chart\n",
    "plt.bar(yes_counts.index, yes_counts.values) # bar plot\n",
    "plt.xlabel('Perks') # plot x label\n",
    "plt.ylabel('Total user') # plot y label\n",
    "plt.title('Total users in each perks') # plot title\n",
    "plt.xticks(rotation=20) # rorate x tickes\n",
    "\n",
    "# Add value labels on top of bars\n",
    "for i, value in enumerate(yes_counts.values):\n",
    "    plt.text(i, value + 0.1, str(value), ha='center', va='bottom', fontsize=10)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show() # show the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "3d8c1882",
   "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>user_id</th>\n",
       "      <th>free_meal</th>\n",
       "      <th>free_checked_bags</th>\n",
       "      <th>no_cancellation_fees</th>\n",
       "      <th>exclusive_discounts</th>\n",
       "      <th>free_hotel_night</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1770</th>\n",
       "      <td>521748</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1772</th>\n",
       "      <td>530468</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1773</th>\n",
       "      <td>565565</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1774</th>\n",
       "      <td>652737</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1775</th>\n",
       "      <td>425632</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5993</th>\n",
       "      <td>544926</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5994</th>\n",
       "      <td>577534</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5995</th>\n",
       "      <td>596320</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5996</th>\n",
       "      <td>539868</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5997</th>\n",
       "      <td>515835</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3307 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id free_meal free_checked_bags no_cancellation_fees  \\\n",
       "1770   521748        no                no                   no   \n",
       "1772   530468        no                no                   no   \n",
       "1773   565565        no                no                   no   \n",
       "1774   652737        no                no                   no   \n",
       "1775   425632        no                no                   no   \n",
       "...       ...       ...               ...                  ...   \n",
       "5993   544926        no                no                   no   \n",
       "5994   577534        no                no                   no   \n",
       "5995   596320        no                no                   no   \n",
       "5996   539868        no                no                   no   \n",
       "5997   515835        no                no                   no   \n",
       "\n",
       "     exclusive_discounts free_hotel_night  \n",
       "1770                  no               no  \n",
       "1772                  no               no  \n",
       "1773                  no               no  \n",
       "1774                  no               no  \n",
       "1775                  no               no  \n",
       "...                  ...              ...  \n",
       "5993                  no               no  \n",
       "5994                  no               no  \n",
       "5995                  no               no  \n",
       "5996                  no               no  \n",
       "5997                  no               no  \n",
       "\n",
       "[3307 rows x 6 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total number of no perks user\n",
    "\n",
    "segmented_df[(segmented_df['free_meal'] == 'no') & \n",
    "          (segmented_df['free_checked_bags'] == 'no') & \n",
    "          (segmented_df['no_cancellation_fees'] == 'no') & \n",
    "          (segmented_df['free_hotel_night'] == 'no') & \n",
    "          (segmented_df['exclusive_discounts'] == 'no')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd665b65",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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