2nd_TravelTide_Analysis_Sebastian_Gurgel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Importing the libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "SuwZGNctMlkX"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "from datetime import timedelta\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NO_Hmf4GRAH0"
   },
   "source": [
    "# **Loading the joined Data from SQL:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "HacZ3dgAQGHS"
   },
   "outputs": [],
   "source": [
    "#selecting the path for the dataframe\n",
    "file_path = \"//Users/sebastiangurgel/Desktop/Masteryproject1/Not used/query_results-2023-08-25_40326.csv\"\n",
    "\n",
    "df = pd.read_csv(file_path, low_memory=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Checking the Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 216
    },
    "id": "GAmX1w_WQFuH",
    "outputId": "418466b9-e7ed-42c0-bc91-8619f8be5ef5"
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>session_id</th>\n",
       "      <th>user_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>hotel_booked</th>\n",
       "      <th>page_clicks</th>\n",
       "      <th>cancellation</th>\n",
       "      <th>user_id.1</th>\n",
       "      <th>birthdate</th>\n",
       "      <th>gender</th>\n",
       "      <th>married</th>\n",
       "      <th>has_children</th>\n",
       "      <th>home_country</th>\n",
       "      <th>home_city</th>\n",
       "      <th>home_airport</th>\n",
       "      <th>home_airport_lat</th>\n",
       "      <th>home_airport_lon</th>\n",
       "      <th>sign_up_date</th>\n",
       "      <th>trip_id.1</th>\n",
       "      <th>origin_airport</th>\n",
       "      <th>destination</th>\n",
       "      <th>destination_airport</th>\n",
       "      <th>seats</th>\n",
       "      <th>return_flight_booked</th>\n",
       "      <th>departure_time</th>\n",
       "      <th>return_time</th>\n",
       "      <th>checked_bags</th>\n",
       "      <th>trip_airline</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>365825-7b348567fdae44498d5e8a0f0a5b1713</td>\n",
       "      <td>365825</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-01-04 16:06:00</td>\n",
       "      <td>2023-01-04 16:06:14</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>365825</td>\n",
       "      <td>1979-05-10</td>\n",
       "      <td>F</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>usa</td>\n",
       "      <td>los angeles</td>\n",
       "      <td>LAX</td>\n",
       "      <td>33.942</td>\n",
       "      <td>-118.408</td>\n",
       "      <td>2022-10-16</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",
       "      <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>385689-5a95f307608c4b5ab0d664fe6e9413b4</td>\n",
       "      <td>385689</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-01-04 13:21:00</td>\n",
       "      <td>2023-01-04 13:21:24</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>385689</td>\n",
       "      <td>1974-10-08</td>\n",
       "      <td>F</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>usa</td>\n",
       "      <td>nashville</td>\n",
       "      <td>BNA</td>\n",
       "      <td>36.124</td>\n",
       "      <td>-86.678</td>\n",
       "      <td>2022-10-29</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",
       "      <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>388767-304d033927fe4bf79333ccdf03753dcf</td>\n",
       "      <td>388767</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-01-04 21:27:00</td>\n",
       "      <td>2023-01-04 21:27:43</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>388767</td>\n",
       "      <td>1979-09-13</td>\n",
       "      <td>F</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>usa</td>\n",
       "      <td>tallahassee</td>\n",
       "      <td>TLH</td>\n",
       "      <td>30.396</td>\n",
       "      <td>-84.350</td>\n",
       "      <td>2022-10-31</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",
       "      <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",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                                session_id  user_id trip_id  \\\n",
       "0  365825-7b348567fdae44498d5e8a0f0a5b1713   365825     NaN   \n",
       "1  385689-5a95f307608c4b5ab0d664fe6e9413b4   385689     NaN   \n",
       "2  388767-304d033927fe4bf79333ccdf03753dcf   388767     NaN   \n",
       "\n",
       "         session_start          session_end  flight_discount  hotel_discount  \\\n",
       "0  2023-01-04 16:06:00  2023-01-04 16:06:14            False           False   \n",
       "1  2023-01-04 13:21:00  2023-01-04 13:21:24            False           False   \n",
       "2  2023-01-04 21:27:00  2023-01-04 21:27:43            False           False   \n",
       "\n",
       "   flight_discount_amount  hotel_discount_amount  flight_booked  hotel_booked  \\\n",
       "0                     NaN                    NaN          False         False   \n",
       "1                     NaN                    NaN          False         False   \n",
       "2                     NaN                    NaN          False         False   \n",
       "\n",
       "   page_clicks  cancellation  user_id.1   birthdate gender  married  \\\n",
       "0            2         False     365825  1979-05-10      F    False   \n",
       "1            3         False     385689  1974-10-08      F    False   \n",
       "2            6         False     388767  1979-09-13      F     True   \n",
       "\n",
       "   has_children home_country    home_city home_airport  home_airport_lat  \\\n",
       "0         False          usa  los angeles          LAX            33.942   \n",
       "1          True          usa    nashville          BNA            36.124   \n",
       "2          True          usa  tallahassee          TLH            30.396   \n",
       "\n",
       "   home_airport_lon sign_up_date trip_id.1 origin_airport destination  \\\n",
       "0          -118.408   2022-10-16       NaN            NaN         NaN   \n",
       "1           -86.678   2022-10-29       NaN            NaN         NaN   \n",
       "2           -84.350   2022-10-31       NaN            NaN         NaN   \n",
       "\n",
       "  destination_airport  seats return_flight_booked departure_time return_time  \\\n",
       "0                 NaN    NaN                  NaN            NaN         NaN   \n",
       "1                 NaN    NaN                  NaN            NaN         NaN   \n",
       "2                 NaN    NaN                  NaN            NaN         NaN   \n",
       "\n",
       "   checked_bags trip_airline  destination_airport_lat  \\\n",
       "0           NaN          NaN                      NaN   \n",
       "1           NaN          NaN                      NaN   \n",
       "2           NaN          NaN                      NaN   \n",
       "\n",
       "   destination_airport_lon  base_fare_usd trip_id.2 hotel_name  nights  rooms  \\\n",
       "0                      NaN            NaN       NaN        NaN     NaN    NaN   \n",
       "1                      NaN            NaN       NaN        NaN     NaN    NaN   \n",
       "2                      NaN            NaN       NaN        NaN     NaN    NaN   \n",
       "\n",
       "  check_in_time check_out_time  hotel_per_room_usd  \n",
       "0           NaN            NaN                 NaN  \n",
       "1           NaN            NaN                 NaN  \n",
       "2           NaN            NaN                 NaN  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the first 3 rows\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 320
    },
    "id": "kPF8DpoARc9M",
    "outputId": "1252ee29-3626-44ed-d225-3a05a3bd5e11"
   },
   "outputs": [
    {
     "data": {
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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>flight_discount_amount</th>\n",
       "      <th>hotel_discount_amount</th>\n",
       "      <th>page_clicks</th>\n",
       "      <th>user_id.1</th>\n",
       "      <th>home_airport_lat</th>\n",
       "      <th>home_airport_lon</th>\n",
       "      <th>seats</th>\n",
       "      <th>checked_bags</th>\n",
       "      <th>destination_airport_lat</th>\n",
       "      <th>destination_airport_lon</th>\n",
       "      <th>base_fare_usd</th>\n",
       "      <th>nights</th>\n",
       "      <th>rooms</th>\n",
       "      <th>hotel_per_room_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>49211.000000</td>\n",
       "      <td>8282.000000</td>\n",
       "      <td>6205.000000</td>\n",
       "      <td>49211.000000</td>\n",
       "      <td>49211.000000</td>\n",
       "      <td>49211.000000</td>\n",
       "      <td>49211.000000</td>\n",
       "      <td>14270.000000</td>\n",
       "      <td>14270.000000</td>\n",
       "      <td>14270.000000</td>\n",
       "      <td>14270.000000</td>\n",
       "      <td>14270.000000</td>\n",
       "      <td>14726.000000</td>\n",
       "      <td>14726.000000</td>\n",
       "      <td>14726.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>545282.694946</td>\n",
       "      <td>0.139864</td>\n",
       "      <td>0.112192</td>\n",
       "      <td>17.588791</td>\n",
       "      <td>545282.694946</td>\n",
       "      <td>38.427222</td>\n",
       "      <td>-94.183762</td>\n",
       "      <td>1.215207</td>\n",
       "      <td>0.592922</td>\n",
       "      <td>38.610024</td>\n",
       "      <td>-88.947203</td>\n",
       "      <td>525.901390</td>\n",
       "      <td>3.672484</td>\n",
       "      <td>1.194554</td>\n",
       "      <td>177.937390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>64640.047648</td>\n",
       "      <td>0.083914</td>\n",
       "      <td>0.062119</td>\n",
       "      <td>21.495987</td>\n",
       "      <td>64640.047648</td>\n",
       "      <td>6.185962</td>\n",
       "      <td>18.065868</td>\n",
       "      <td>0.585891</td>\n",
       "      <td>0.677641</td>\n",
       "      <td>6.949739</td>\n",
       "      <td>33.090373</td>\n",
       "      <td>818.296468</td>\n",
       "      <td>3.806717</td>\n",
       "      <td>0.499344</td>\n",
       "      <td>118.334569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>23557.000000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>23557.000000</td>\n",
       "      <td>21.316000</td>\n",
       "      <td>-157.927000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-37.008000</td>\n",
       "      <td>-157.927000</td>\n",
       "      <td>2.410000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>517119.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>517119.000000</td>\n",
       "      <td>33.818000</td>\n",
       "      <td>-112.383000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.942000</td>\n",
       "      <td>-112.383000</td>\n",
       "      <td>200.432500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>540308.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>540308.000000</td>\n",
       "      <td>39.103000</td>\n",
       "      <td>-90.035000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>39.872000</td>\n",
       "      <td>-87.752000</td>\n",
       "      <td>381.765000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>148.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>573922.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>573922.000000</td>\n",
       "      <td>42.238000</td>\n",
       "      <td>-79.370000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>42.409000</td>\n",
       "      <td>-75.241000</td>\n",
       "      <td>600.632500</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>222.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>844489.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>566.000000</td>\n",
       "      <td>844489.000000</td>\n",
       "      <td>61.251000</td>\n",
       "      <td>-63.499000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>55.972000</td>\n",
       "      <td>174.792000</td>\n",
       "      <td>21548.040000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1376.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             user_id  flight_discount_amount  hotel_discount_amount  \\\n",
       "count   49211.000000             8282.000000            6205.000000   \n",
       "mean   545282.694946                0.139864               0.112192   \n",
       "std     64640.047648                0.083914               0.062119   \n",
       "min     23557.000000                0.050000               0.050000   \n",
       "25%    517119.000000                0.100000               0.050000   \n",
       "50%    540308.000000                0.100000               0.100000   \n",
       "75%    573922.000000                0.200000               0.150000   \n",
       "max    844489.000000                0.600000               0.450000   \n",
       "\n",
       "        page_clicks      user_id.1  home_airport_lat  home_airport_lon  \\\n",
       "count  49211.000000   49211.000000      49211.000000      49211.000000   \n",
       "mean      17.588791  545282.694946         38.427222        -94.183762   \n",
       "std       21.495987   64640.047648          6.185962         18.065868   \n",
       "min        1.000000   23557.000000         21.316000       -157.927000   \n",
       "25%        6.000000  517119.000000         33.818000       -112.383000   \n",
       "50%       13.000000  540308.000000         39.103000        -90.035000   \n",
       "75%       22.000000  573922.000000         42.238000        -79.370000   \n",
       "max      566.000000  844489.000000         61.251000        -63.499000   \n",
       "\n",
       "              seats  checked_bags  destination_airport_lat  \\\n",
       "count  14270.000000  14270.000000             14270.000000   \n",
       "mean       1.215207      0.592922                38.610024   \n",
       "std        0.585891      0.677641                 6.949739   \n",
       "min        1.000000      0.000000               -37.008000   \n",
       "25%        1.000000      0.000000                33.942000   \n",
       "50%        1.000000      1.000000                39.872000   \n",
       "75%        1.000000      1.000000                42.409000   \n",
       "max        8.000000      8.000000                55.972000   \n",
       "\n",
       "       destination_airport_lon  base_fare_usd        nights         rooms  \\\n",
       "count             14270.000000   14270.000000  14726.000000  14726.000000   \n",
       "mean                -88.947203     525.901390      3.672484      1.194554   \n",
       "std                  33.090373     818.296468      3.806717      0.499344   \n",
       "min                -157.927000       2.410000     -2.000000      1.000000   \n",
       "25%                -112.383000     200.432500      1.000000      1.000000   \n",
       "50%                 -87.752000     381.765000      2.000000      1.000000   \n",
       "75%                 -75.241000     600.632500      5.000000      1.000000   \n",
       "max                 174.792000   21548.040000     43.000000      4.000000   \n",
       "\n",
       "       hotel_per_room_usd  \n",
       "count        14726.000000  \n",
       "mean           177.937390  \n",
       "std            118.334569  \n",
       "min             17.000000  \n",
       "25%             99.000000  \n",
       "50%            148.000000  \n",
       "75%            222.000000  \n",
       "max           1376.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking statistical values for each numeric column\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IT-7-iNpEsVP",
    "outputId": "2aa99307-e1f8-4c79-c64d-2c559bb2168d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "session_id                  object\n",
       "user_id                      int64\n",
       "trip_id                     object\n",
       "session_start               object\n",
       "session_end                 object\n",
       "flight_discount               bool\n",
       "hotel_discount                bool\n",
       "flight_discount_amount     float64\n",
       "hotel_discount_amount      float64\n",
       "flight_booked                 bool\n",
       "hotel_booked                  bool\n",
       "page_clicks                  int64\n",
       "cancellation                  bool\n",
       "user_id.1                    int64\n",
       "birthdate                   object\n",
       "gender                      object\n",
       "married                       bool\n",
       "has_children                  bool\n",
       "home_country                object\n",
       "home_city                   object\n",
       "home_airport                object\n",
       "home_airport_lat           float64\n",
       "home_airport_lon           float64\n",
       "sign_up_date                object\n",
       "trip_id.1                   object\n",
       "origin_airport              object\n",
       "destination                 object\n",
       "destination_airport         object\n",
       "seats                      float64\n",
       "return_flight_booked        object\n",
       "departure_time              object\n",
       "return_time                 object\n",
       "checked_bags               float64\n",
       "trip_airline                object\n",
       "destination_airport_lat    float64\n",
       "destination_airport_lon    float64\n",
       "base_fare_usd              float64\n",
       "trip_id.2                   object\n",
       "hotel_name                  object\n",
       "nights                     float64\n",
       "rooms                      float64\n",
       "check_in_time               object\n",
       "check_out_time              object\n",
       "hotel_per_room_usd         float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the datatypes of each row for further manupilation purposes\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UxoHqnDkSJ4V"
   },
   "source": [
    "# **Searching for Outlier**\n",
    "after inspecting the describe():\n",
    "base_fare_usd Max= 21548 seems quite high\n",
    "page clicks 566, hotel_per_room 1376 and nights 43 needs a verification check:\n",
    "1) Visual Inspection\n",
    "2) statistical Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "id": "jC2NTtZyVltX",
    "outputId": "20400ccf-ceb3-4e68-c414-6d271dbb49be"
   },
   "outputs": [],
   "source": [
    "# Plotting a histogram for base_fare_usd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_histogram(data_frame, column_name, bins=100, color='blue', alpha=0.7):\n",
    "    \"\"\"\n",
    "    Plot a histogram for a specific column in a DataFrame.\n",
    "\n",
    "    Args:\n",
    "        data_frame (pd.DataFrame): The DataFrame containing the data.\n",
    "        column_name (str): The name of the column to plot.\n",
    "        bins (int, optional): Number of bins for the histogram. Default is 100.\n",
    "        color (str, optional): Color of the bars in the histogram. Default is 'blue'.\n",
    "        alpha (float, optional): Transparency of the bars in the histogram. Default is 0.7.\n",
    "\n",
    "    Returns:\n",
    "        None\n",
    "    \"\"\"\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.hist(data_frame[column_name], bins=bins, color=color, alpha=alpha)\n",
    "    plt.title(f'Histogram of {column_name}')\n",
    "    plt.xlabel(column_name)\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.grid(True)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_histogram(df, 'base_fare_usd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "id": "6K7a_An2Vljr",
    "outputId": "4530edc6-b95c-4a20-8b74-875d697d8e7a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting a  histogram for page_clicks\n",
    "plot_histogram(df, 'page_clicks', color='green')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_histogram(df, 'hotel_per_room_usd', color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "id": "guuP1MRqA6qU",
    "outputId": "df90d19c-b74a-4c69-fefd-31c6d828cd2d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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sAAAAAFRmfg1bJ0+eVKtWrTR79uwit0+dOlXTp0/X7NmztXXrVnk8HnXr1k3Hjx+35yQmJio5OVmLFy/W+vXrdeLECfXs2VPnzp2z5/Tv31/p6elasWKFVqxYofT0dA0cOND4+QEAAACovPz66Pe7775bd999d5HbLMvSzJkzNX78eN13332SpAULFig6OlqLFi3SkCFDlJOTo7lz5+qdd95RXFycJGnhwoWqV6+eVq5cqe7du2vPnj1asWKFNm3apPbt20uS3nzzTXXo0EF79+7VDTfcUDYnCwAAAKBScezv2dq3b58yMzMVHx9vjwUHB6tz587asGGDhgwZorS0NHm9Xp85MTExatGihTZs2KDu3btr48aNioyMtIOWJN1yyy2KjIzUhg0big1beXl5ysvLs1/n5uZKkrxer7xeb2mfru38vk0eo9S53WV7PD9em3LZn0qE/jgXvXE2+uNs9Me56I2zmezP5e7TsWErMzNTkhQdHe0zHh0drQMHDthzgoKCVL169UJzzr8/MzNTtWrVKrT/WrVq2XOKMnnyZE2cOLHQeEpKikJDQ6/sZEogNTXV+DFKTUJC2R5v+fKyPV4RylV/KiH641z0xtnoj7PRH+eiN85moj+nTp26rHmODVvnuVwun9eWZRUau9CFc4qaf6n9jBs3TqNGjbJf5+bmql69eoqPj1dERMTlln/FvF6vUlNT1a1bN7nLesWopPr1K9vjLVlStsf7hXLZn0qE/jgXvXE2+uNs9Me56I2zmezP+bveLsWxYcvj8Uj6eWWqdu3a9nhWVpa92uXxeJSfn6/s7Gyf1a2srCx17NjRnvPDDz8U2v+PP/5YaNXsl4KDgxUcHFxo3O12l8mHqayOUyrKeuncAdelXPWnEqI/zkVvnI3+OBv9cS5642wm+nO5+3Ps79lq2LChPB6Pz7Jffn6+1q5daweptm3byu12+8zJyMjQzp077TkdOnRQTk6OtmzZYs/ZvHmzcnJy7DkAAAAAUNr8urJ14sQJffPNN/brffv2KT09XTVq1NB1112nxMRETZo0SU2aNFGTJk00adIkhYaGqn///pKkyMhIDR48WKNHj1ZUVJRq1KihMWPGKDY21n46YdOmTXXXXXfpscce0+uvvy5J+sMf/qCePXvyJEIAAAAAxvg1bH3xxRe644477NfnvyOVkJCg+fPna+zYsTp9+rSGDh2q7OxstW/fXikpKQoPD7ffM2PGDAUGBqpv3746ffq0unbtqvnz5ysgIMCe8+677+qJJ56wn1rYu3fvYn+3FwAAAACUBr+GrS5dusiyrGK3u1wuJSUlKSkpqdg5ISEhmjVrlmbNmlXsnBo1amjhwoVXUyoAAAAAXBHHfmcLAAAAAMozwhYAAAAAGEDYAgAAAAADCFsAAAAAYABhCwAAAAAMIGwBAAAAgAGELQAAAAAwgLAFAAAAAAYQtgAAAADAAMIWAAAAABhA2AIAAAAAAwhbAAAAAGAAYQsAAAAADCBsAQAAAIABhC0AAAAAMICwBQAAAAAGELYAAAAAwADCFgAAAAAYQNgCAAAAAAMIWwAAAABgAGELAAAAAAwgbAEAAACAAYQtAAAAADCAsAUAAAAABgT6uwCUE716+bsCAAAAoFxhZQsAAAAADCBsAQAAAIABhC0AAAAAMICwBQAAAAAGELYAAAAAwADCFgAAAAAYQNgCAAAAAAMIWwAAAABgAGELAAAAAAwgbAEAAACAAYQtAAAAADCAsAUAAAAABhC2AAAAAMAAwhYAAAAAGEDYAgAAAAADCFsAAAAAYABhCwAAAAAMIGwBAAAAgAGELQAAAAAwgLAFAAAAAAYQtgAAAADAAMIWAAAAABhA2AIAAAAAAwhbAAAAAGAAYQsAAAAADCBsAQAAAIABhC0AAAAAMICwBQAAAAAGELYAAAAAwIBAfxcAlKpevS6+fdmysqkDAAAAlR4rWwAAAABgAGELAAAAAAwgbAEAAACAAYQtAAAAADCAsAUAAAAABvA0QpQ/l3riIAAAAOAArGwBAAAAgAGELQAAAAAwgLAFAAAAAAYQtgAAAADAAMIWAAAAABhA2AIAAAAAAwhbAAAAAGCAo8PW2bNn9eyzz6phw4aqWrWqGjVqpOeff14FBQX2HMuylJSUpJiYGFWtWlVdunTRrl27fPaTl5enESNGqGbNmgoLC1Pv3r11+PDhsj4dAAAAAJWIo8PWSy+9pNdee02zZ8/Wnj17NHXqVE2bNk2zZs2y50ydOlXTp0/X7NmztXXrVnk8HnXr1k3Hjx+35yQmJio5OVmLFy/W+vXrdeLECfXs2VPnzp3zx2kBAAAAqAQC/V3AxWzcuFH33nuvevToIUlq0KCB3nvvPX3xxReSfl7VmjlzpsaPH6/77rtPkrRgwQJFR0dr0aJFGjJkiHJycjR37ly98847iouLkyQtXLhQ9erV08qVK9W9e/cij52Xl6e8vDz7dW5uriTJ6/XK6/UaO+fz+zZ5jBJxu/1dQem4yuvq2P5AEv1xMnrjbPTH2eiPc9EbZzPZn8vdp8uyLKvUj15KpkyZotdee00pKSm6/vrr9eWXXyo+Pl4zZ87UQw89pO+++06NGzfWtm3b1KZNG/t99957r6pVq6YFCxZo9erV6tq1q44dO6bq1avbc1q1aqU+ffpo4sSJRR47KSmpyG2LFi1SaGho6Z8sAAAAgHLh1KlT6t+/v3JychQREVHsPEevbD399NPKycnRjTfeqICAAJ07d04vvviiHnroIUlSZmamJCk6OtrnfdHR0Tpw4IA9JygoyCdonZ9z/v1FGTdunEaNGmW/zs3NVb169RQfH3/RC3q1vF6vUlNT1a1bN7mdtJrUr5+/KygdS5Zc1dsd2x9Ioj9ORm+cjf44G/1xLnrjbCb7c/6ut0txdNhasmSJFi5cqEWLFql58+ZKT09XYmKiYmJilJCQYM9zuVw+77Msq9DYhS41Jzg4WMHBwYXG3W53mXyYyuo4l62iLI+X0jV1XH/gg/44F71xNvrjbPTHueiNs5noz+Xuz9Fh66mnntIzzzyjBx98UJIUGxurAwcOaPLkyUpISJDH45H08+pV7dq17fdlZWXZq10ej0f5+fnKzs72Wd3KyspSx44dy/BsAAAAAFQmjn4a4alTp1Slim+JAQEB9qPfGzZsKI/Ho9TUVHt7fn6+1q5daweptm3byu12+8zJyMjQzp07CVsAAAAAjHH0ylavXr304osv6rrrrlPz5s21fft2TZ8+XY8++qikn28fTExM1KRJk9SkSRM1adJEkyZNUmhoqPr37y9JioyM1ODBgzV69GhFRUWpRo0aGjNmjGJjY+2nEwIAAABAaXN02Jo1a5b+/Oc/a+jQocrKylJMTIyGDBmi5557zp4zduxYnT59WkOHDlV2drbat2+vlJQUhYeH23NmzJihwMBA9e3bV6dPn1bXrl01f/58BQQE+OO0AAAAAFQCjg5b4eHhmjlzpmbOnFnsHJfLpaSkJCUlJRU7JyQkRLNmzfL5ZcgAAAAAYJKjv7MFAAAAAOWVo1e2UMZ69fJ3BQAAAECFwcoWAAAAABhA2AIAAAAAAwhbAAAAAGAA39lC5XKx76UtW1Z2dQAAAKDCY2ULAAAAAAwgbAEAAACAAYQtAAAAADCAsAUAAAAABhC2AAAAAMAAwhYAAAAAGEDYAgAAAAADCFsAAAAAYABhCwAAAAAMIGwBAAAAgAGELQAAAAAwgLAFAAAAAAYQtgAAAADAAMIWAAAAABhA2AIAAAAAAwhbAAAAAGAAYQsAAAAADCBsAQAAAIABhC0AAAAAMICwBQAAAAAGELYAAAAAwADCFgAAAAAYQNgCAAAAAAMIWwAAAABgAGELAAAAAAwgbAEAAACAAYQtAAAAADCAsAUAAAAABhC2AAAAAMAAwhYAAAAAGEDYAgAAAAADCFsAAAAAYABhCwAAAAAMIGwBAAAAgAGELQAAAAAwgLAFAAAAAAYQtgAAAADAAMIWAAAAABhA2AIAAAAAAwhbAAAAAGAAYQsAAAAADCBsAQAAAIABhC0AAAAAMICwBQAAAAAGELYAAAAAwADCFgAAAAAYEOjvAgDH6NWr+G3LlpVdHQAAAKgQWNkCAAAAAAMIWwAAAABgAGELAAAAAAwgbAEAAACAAYQtAAAAADCAsAUAAAAABhC2AAAAAMAAwhYAAAAAGEDYAgAAAAADrjhseb1e/e53v9N3331noh4AAAAAqBCuOGy53W4lJyebqAUAAAAAKowS3Ub4m9/8RkuXLi3lUgAAAACg4ggsyZt+9atf6YUXXtCGDRvUtm1bhYWF+Wx/4oknSqU4AAAAACivShS2/vu//1vVqlVTWlqa0tLSfLa5XC7CFgAAAIBKr0S3Ee7bt6/Yn9J+cMb333+vAQMGKCoqSqGhoWrdurVPwLMsS0lJSYqJiVHVqlXVpUsX7dq1y2cfeXl5GjFihGrWrKmwsDD17t1bhw8fLtU6AQAAAOCXrurR7/n5+dq7d6/Onj1bWvX4yM7OVqdOneR2u/Wvf/1Lu3fv1ssvv6xq1arZc6ZOnarp06dr9uzZ2rp1qzwej7p166bjx4/bcxITE5WcnKzFixdr/fr1OnHihHr27Klz584ZqRsAAAAAShS2Tp06pcGDBys0NFTNmzfXwYMHJf38Xa0pU6aUWnEvvfSS6tWrp3nz5unXv/61GjRooK5du6px48aSfl7VmjlzpsaPH6/77rtPLVq00IIFC3Tq1CktWrRIkpSTk6O5c+fq5ZdfVlxcnNq0aaOFCxdqx44dWrlyZanVCgAAAAC/VKLvbI0bN05ffvmlPvvsM9111132eFxcnCZMmKBnnnmmVIr76KOP1L17dz3wwANau3at6tSpo6FDh+qxxx6T9PPtjJmZmYqPj7ffExwcrM6dO2vDhg0aMmSI0tLS5PV6febExMSoRYsW2rBhg7p3717ksfPy8pSXl2e/zs3NlfTz7xnzer2lcn5FOb9vk8colttd9scsLy7oi1/6g0uiP85Fb5yN/jgb/XEueuNsJvtzufssUdhaunSplixZoltuuUUul8seb9asmb799tuS7LJI3333nebMmaNRo0bpT3/6k7Zs2aInnnhCwcHBGjRokDIzMyVJ0dHRPu+Ljo7WgQMHJEmZmZkKCgpS9erVC805//6iTJ48WRMnTiw0npKSotDQ0Ks9tUtKTU01foxCEhLK/pjlxfLlPi/90h9cNvrjXPTG2eiPs9Ef56I3zmaiP6dOnbqseSUKWz/++KNq1apVaPzkyZM+4etqFRQUqF27dpo0aZIkqU2bNtq1a5fmzJmjQYMG2fMuPKZlWZes41Jzxo0bp1GjRtmvc3NzVa9ePcXHxysiIqIkp3NZvF6vUlNT1a1bN7nLeqWpX7+yPV55smSJJD/3B5dEf5yL3jgb/XE2+uNc9MbZTPbn/F1vl1KisHXzzTfrk08+0YgRIyT9X9h588031aFDh5Lsski1a9dWs2bNfMaaNm2qDz74QJLk8Xgk/bx6Vbt2bXtOVlaWvdrl8XiUn5+v7Oxsn9WtrKwsdezYsdhjBwcHKzg4uNC42+0ukw9TWR3HB0vgxbugF37pDy4b/XEueuNs9MfZ6I9z0RtnM9Gfy91fiR6QMXnyZI0fP16PP/64zp49q1deeUXdunXT/Pnz9eKLL5Zkl0Xq1KmT9u7d6zP29ddfq379+pKkhg0byuPx+CwN5ufna+3atXaQatu2rdxut8+cjIwM7dy586JhCwAAAACuRonCVseOHfU///M/OnXqlBo3bqyUlBRFR0dr48aNatu2bakV9+STT2rTpk2aNGmSvvnmGy1atEhvvPGGhg0bJunnFbXExERNmjRJycnJ2rlzpx555BGFhoaqf//+kqTIyEgNHjxYo0eP1qpVq7R9+3YNGDBAsbGxiouLK7VaAQAAAOCXSnQboSTFxsZqwYIFpVlLITfffLOSk5M1btw4Pf/882rYsKFmzpyphx9+2J4zduxYnT59WkOHDlV2drbat2+vlJQUhYeH23NmzJihwMBA9e3bV6dPn1bXrl01f/58BQQEGK0fFUivXj//r9v984NE+vX7v9suly3zX10AAABwrBKFrW3btsntdis2NlaS9M9//lPz5s1Ts2bNlJSUpKCgoFIrsGfPnurZs2ex210ul5KSkpSUlFTsnJCQEM2aNUuzZs0qtboAAAAA4GJKdBvhkCFD9PXXX0v6+fHs/fr1U2hoqP7xj39o7NixpVogAAAAAJRHJQpbX3/9tVq3bi1J+sc//qHOnTtr0aJFmj9/vv2kQAAAAACozEoUtizLUkFBgSRp5cqVuueeeyRJ9erV03/+85/Sqw4AAAAAyqkSha127drpL3/5i9555x2tXbtWPXr0kCTt27fP/v1WAAAAAFCZlShszZgxQ2lpaRo+fLjGjx+vX/3qV5Kk999/n99dBQAAAAAq4dMIW7VqpZ07dxYanzZtGo9TBwAAAABd4cpWlSpVFBAQUOinevXquuWWW7R8+XK53W5TtQIAAABAuXFFK1vJyclFjv/000/asmWLBgwYoAULFuiBBx4oleIAAAAAoLy6orB17733FrstISFBzZo103/9138RtgAAAABUeiV6QEZx4uPj7V92DAAAAACVWamGrdOnTyskJKQ0dwkAAAAA5VKphq0333xTbdq0Kc1dAgAAAEC5dEXf2Ro1alSR4zk5Ofriiy/07bffat26daVSGAAAAACUZ1cUtrZv317keEREhO666y4NHTpU9evXL5XCAAAAAKA8u6KwtWbNGlN1AAAAAECFckVhCxVAr17+rgAAAACoFEr1ARkAAAAAgJ8RtgAAAADAAMIWAAAAABhA2AIAAAAAAwhbAAAAAGAAYQsAAAAADCBsAQAAAIABhC0AAAAAMICwBQAAAAAGELYAAAAAwADCFgAAAAAYQNgCAAAAAAMIWwAAAABgAGELAAAAAAwgbAEAAACAAYQtAAAAADCAsAUAAAAABhC2AAAAAMAAwhYAAAAAGEDYAgAAAAADCFsAAAAAYABhCwAAAAAMIGwBAAAAgAGELQAAAAAwgLAFAAAAAAYQtgAAAADAAMIWAAAAABhA2AIAAAAAAwL9XQBQ7vXqVfy2ZcvKrg4AAAA4CitbAAAAAGAAYQsAAAAADCBsAQAAAIABhC0AAAAAMICwBQAAAAAGELYAAAAAwADCFgAAAAAYQNgCAAAAAAP4pcaASfzCYwAAgEqLlS0AAAAAMICwBQAAAAAGELYAAAAAwADCFgAAAAAYQNgCAAAAAAMIWwAAAABgAGELAAAAAAwgbAEAAACAAYQtAAAAADCAsAUAAAAABhC2AAAAAMAAwhYAAAAAGEDYAgAAAAADylXYmjx5slwulxITE+0xy7KUlJSkmJgYVa1aVV26dNGuXbt83peXl6cRI0aoZs2aCgsLU+/evXX48OEyrh4AAABAZVJuwtbWrVv1xhtvqGXLlj7jU6dO1fTp0zV79mxt3bpVHo9H3bp10/Hjx+05iYmJSk5O1uLFi7V+/XqdOHFCPXv21Llz58r6NAAAAABUEuUibJ04cUIPP/yw3nzzTVWvXt0etyxLM2fO1Pjx43XfffepRYsWWrBggU6dOqVFixZJknJycjR37ly9/PLLiouLU5s2bbRw4ULt2LFDK1eu9NcpAQAAAKjgAv1dwOUYNmyYevToobi4OP3lL3+xx/ft26fMzEzFx8fbY8HBwercubM2bNigIUOGKC0tTV6v12dOTEyMWrRooQ0bNqh79+5FHjMvL095eXn269zcXEmS1+uV1+st7VO0nd+3sWO43Wb2W0l4//f6eUvjOhr8c1RZGf/8oMTojbPRH2ejP85Fb5zNZH8ud5+OD1uLFy/Wtm3btHXr1kLbMjMzJUnR0dE+49HR0Tpw4IA9JygoyGdF7Pyc8+8vyuTJkzVx4sRC4ykpKQoNDb3i87hSqampZnackGBmv5VMav/+V7+T5cuvfh8okrHPD64avXE2+uNs9Me56I2zmejPqVOnLmueo8PWoUOHNHLkSKWkpCgkJKTYeS6Xy+e1ZVmFxi50qTnjxo3TqFGj7Ne5ubmqV6+e4uPjFRERcZlncOW8Xq9SU1PVrVs3uU2sQvXrV/r7rES8brdS+/dXt0WL5L7afyVZsqR0ioLN+OcHJUZvnI3+OBv9cS5642wm+3P+rrdLcXTYSktLU1ZWltq2bWuPnTt3Tp9//rlmz56tvXv3Svp59ap27dr2nKysLHu1y+PxKD8/X9nZ2T6rW1lZWerYsWOxxw4ODlZwcHChcbfbXSYfJmPHYZm7VLi93qsPW/yfsjFl9TnFlaM3zkZ/nI3+OBe9cTYT/bnc/Tn6ARldu3bVjh07lJ6ebv+0a9dODz/8sNLT09WoUSN5PB6fpcH8/HytXbvWDlJt27aV2+32mZORkaGdO3deNGwBAAAAwNVw9MpWeHi4WrRo4TMWFhamqKgoezwxMVGTJk1SkyZN1KRJE02aNEmhoaHq/7/fqYmMjNTgwYM1evRoRUVFqUaNGhozZoxiY2MVFxdX5ucEAAAAoHJwdNi6HGPHjtXp06c1dOhQZWdnq3379kpJSVF4eLg9Z8aMGQoMDFTfvn11+vRpde3aVfPnz1dAQIAfKwcAAABQkZW7sPXZZ5/5vHa5XEpKSlJSUlKx7wkJCdGsWbM0a9Yss8UBV6JXr+K3LVtWdnUAAADACEd/ZwsAAAAAyivCFgAAAAAYQNgCAAAAAAMIWwAAAABgAGELAAAAAAwgbAEAAACAAYQtAAAAADCAsAUAAAAABhC2AAAAAMAAwhYAAAAAGEDYAgAAAAADCFsAAAAAYABhCwAAAAAMIGwBAAAAgAGELQAAAAAwgLAFAAAAAAYQtgAAAADAAMIWAAAAABhA2AIAAAAAAwhbAAAAAGBAoL8LAHCFevUqftuyZWVXBwAAAC6KlS0AAAAAMICVLcCJLrZ6BQAAgHKBlS0AAAAAMICwBQAAAAAGELYAAAAAwADCFgAAAAAYQNgCAAAAAAMIWwAAAABgAGELAAAAAAwgbAEAAACAAYQtAAAAADCAsAUAAAAABhC2AAAAAMAAwhYAAAAAGEDYAgAAAAADCFsAAAAAYABhCwAAAAAMIGwBAAAAgAGELQAAAAAwgLAFAAAAAAYQtgAAAADAAMIWAAAAABhA2AIAAAAAAwhbAAAAAGAAYQsAAAAADCBsAQAAAIABhC0AAAAAMICwBQAAAAAGBPq7AABlpFev4rctW1Z2dQAAAFQSrGwBAAAAgAGsbAEVycVWrwAAAFCmWNkCAAAAAAMIWwAAAABgAGELAAAAAAwgbAEAAACAATwgoyLiIQkAAACA37GyBQAAAAAGELYAAAAAwADCFgAAAAAYQNgCAAAAAAMIWwAAAABgAGELAAAAAAwgbAEAAACAAYQtAAAAADCAsAUAAAAABhC2AAAAAMAAwhYAAAAAGODosDV58mTdfPPNCg8PV61atdSnTx/t3bvXZ45lWUpKSlJMTIyqVq2qLl26aNeuXT5z8vLyNGLECNWsWVNhYWHq3bu3Dh8+XJanAgAAAKCScXTYWrt2rYYNG6ZNmzYpNTVVZ8+eVXx8vE6ePGnPmTp1qqZPn67Zs2dr69at8ng86tatm44fP27PSUxMVHJyshYvXqz169frxIkT6tmzp86dO+eP0wIAAABQCQT6u4CLWbFihc/refPmqVatWkpLS9Ptt98uy7I0c+ZMjR8/Xvfdd58kacGCBYqOjtaiRYs0ZMgQ5eTkaO7cuXrnnXcUFxcnSVq4cKHq1aunlStXqnv37kUeOy8vT3l5efbr3NxcSZLX65XX6zVxuvb+f/m/JeJ2l1I1uJD3f6+tt6JdY4N/pstSqXx+YAS9cTb642z0x7nojbOZ7M/l7tNlWZZV6kc35JtvvlGTJk20Y8cOtWjRQt99950aN26sbdu2qU2bNva8e++9V9WqVdOCBQu0evVqde3aVceOHVP16tXtOa1atVKfPn00ceLEIo+VlJRU5LZFixYpNDS09E8OAAAAQLlw6tQp9e/fXzk5OYqIiCh2nqNXtn7JsiyNGjVKt956q1q0aCFJyszMlCRFR0f7zI2OjtaBAwfsOUFBQT5B6/yc8+8vyrhx4zRq1Cj7dW5ururVq6f4+PiLXtCr5fV6lZqaqm7dusld0tWTfv1KtyjYvG63Uvv3V7dFi+SuSP+KtWSJvysoFaXy+YER9MbZ6I+z0R/nojfOZrI/5+96u5RyE7aGDx+ur776SuvXry+0zeVy+by2LKvQ2IUuNSc4OFjBwcGFxt1ud5l8mK7qOBUpBDiU2+utWGGrgv0Hoqw+p7hy9MbZ6I+z0R/nojfOZqI/l7u/chG2RowYoY8++kiff/656tata497PB5JP69e1a5d2x7PysqyV7s8Ho/y8/OVnZ3ts7qVlZWljh07ltEZAA7Xq1fx25YtK7s6AAAAKhBHP43QsiwNHz5cH374oVavXq2GDRv6bG/YsKE8Ho9SU1Ptsfz8fK1du9YOUm3btpXb7faZk5GRoZ07dxK2AAAAABjj6JWtYcOGadGiRfrnP/+p8PBw+ztWkZGRqlq1qlwulxITEzVp0iQ1adJETZo00aRJkxQaGqr+/fvbcwcPHqzRo0crKipKNWrU0JgxYxQbG2s/nRAAAAAASpujw9acOXMkSV26dPEZnzdvnh555BFJ0tixY3X69GkNHTpU2dnZat++vVJSUhQeHm7PnzFjhgIDA9W3b1+dPn1aXbt21fz58xUQEFBWpwIAAACgknF02Lqcp9K7XC4lJSUpKSmp2DkhISGaNWuWZs2aVYrVAZUE3+cCAAAoEUd/ZwsAAAAAyivCFgAAAAAYQNgCAAAAAAMIWwAAAABgAGELAAAAAAxw9NMIATgcTyoEAAAoFitbAAAAAGAAYQsAAAAADCBsAQAAAIABhC0AAAAAMICwBQAAAAAGELYAAAAAwADCFgAAAAAYwO/ZAmAGv4MLAABUcqxsAQAAAIABhC0AAAAAMICwBQAAAAAGELYAAAAAwADCFgAAAAAYwNMIAZQ9nlQIAAAqAVa2AAAAAMAAwhYAAAAAGEDYAgAAAAADCFsAAAAAYABhCwAAAAAMIGwBAAAAgAGELQAAAAAwgLAFAAAAAAYQtgAAAADAAMIWAAAAABhA2AIAAAAAAwL9XQAA+OjVq/hty5aVXR0AAABXiZUtAAAAADCAsAUAAAAABhC2AAAAAMAAvrMFoGLo1Utyu6WEBKlfP8nr/b9tfNcLAAD4AStbAAAAAGAAK1sAyo+LPakQAADAYVjZAgAAAAADCFsAAAAAYABhCwAAAAAMIGwBAAAAgAGELQAAAAAwgKcRAqjcLvaEQ34/FwAAuAqELQAVH4+MBwAAfsBthAAAAABgAGELAAAAAAwgbAEAAACAAYQtAAAAADCAsAUAAAAABvA0wvKKp6sBAAAAjkbYAoCSuNQ/ePA7ugAAqPS4jRAAAAAADGBlCwCKw+26AADgKrCyBQAAAAAGsLIFACZcbFWM73MBAFApELYAoKwRxAAAqBS4jRAAAAAADGBlCwCchFUvAAAqDMIWAFQEJX1yIgEOAABjCFsAUF7wKHoAAMoVvrMFAAAAAAawsgUAlRnfEQMAwBhWtgAAAADAAFa2AADlG6tzAACHImwBAIpW0hBT1Pvcbikh4eprAgCgHCFsAQCcoSKsUFWEcwAAlJpKFbZeffVVTZs2TRkZGWrevLlmzpyp2267zd9lAUD5U9LH0PfrJ3m9ZXc8E/ssT6GJ8AcAflVpwtaSJUuUmJioV199VZ06ddLrr7+uu+++W7t379Z1113n7/IAACY46XeTVYZwBwDwUWnC1vTp0zV48GD9/ve/lyTNnDlTn376qebMmaPJkyf7uToAQLlR1gHO1PF+ud/z36m7nJXHK/2+nkkVPYiev55F9aek585qJ1CmKkXYys/PV1pamp555hmf8fj4eG3YsKHI9+Tl5SkvL89+nZOTI0k6duyYvCW5BeYyeb1enTp1SkePHpXb7TZ2HJSMV/q5P5LojvPQH+eiN852Rf2pCKuFJTV/fvHbHnmk9Pf5v4rsz8XO/TL2WaRLXc+S7vdiSnrdylox527/va1fP7mL+vuhiWtWGVzsz8UVXFOTf68+fvy4JMmyrIvOc1mXmlEBHDlyRHXq1NH//M//qGPHjvb4pEmTtGDBAu3du7fQe5KSkjRx4sSyLBMAAABAOXLo0CHVrVu32O2VYmXrPJfL5fPasqxCY+eNGzdOo0aNsl8XFBTo2LFjioqKKvY9pSE3N1f16tXToUOHFBERYew4KBn642z0x7nojbPRH2ejP85Fb5zNZH8sy9Lx48cVExNz0XmVImzVrFlTAQEByszM9BnPyspSdHR0ke8JDg5WcHCwz1i1atVMlVhIREQEH1oHoz/ORn+ci944G/1xNvrjXPTG2Uz1JzIy8pJzqpT6UR0oKChIbdu2VWpqqs94amqqz22FAAAAAFBaKsXKliSNGjVKAwcOVLt27dShQwe98cYbOnjwoP74xz/6uzQAAAAAFVClCVv9+vXT0aNH9fzzzysjI0MtWrTQ8uXLVb9+fX+X5iM4OFgTJkwodAsjnIH+OBv9cS5642z0x9noj3PRG2dzQn8qxdMIAQAAAKCsVYrvbAEAAABAWSNsAQAAAIABhC0AAAAAMICwBQAAAAAGELYc5tVXX1XDhg0VEhKitm3bat26df4uqcKbPHmybr75ZoWHh6tWrVrq06eP9u7d6zPHsiwlJSUpJiZGVatWVZcuXbRr1y6fOXl5eRoxYoRq1qypsLAw9e7dW4cPHy7LU6nwJk+eLJfLpcTERHuM3vjX999/rwEDBigqKkqhoaFq3bq10tLS7O30x3/Onj2rZ599Vg0bNlTVqlXVqFEjPf/88yooKLDn0J+y8fnnn6tXr16KiYmRy+XS0qVLfbaXVh+ys7M1cOBARUZGKjIyUgMHDtRPP/1k+OzKv4v1x+v16umnn1ZsbKzCwsIUExOjQYMG6ciRIz77oD/mXOrz80tDhgyRy+XSzJkzfcb92R/CloMsWbJEiYmJGj9+vLZv367bbrtNd999tw4ePOjv0iq0tWvXatiwYdq0aZNSU1N19uxZxcfH6+TJk/acqVOnavr06Zo9e7a2bt0qj8ejbt266fjx4/acxMREJScna/HixVq/fr1OnDihnj176ty5c/44rQpn69ateuONN9SyZUufcXrjP9nZ2erUqZPcbrf+9a9/affu3Xr55ZdVrVo1ew798Z+XXnpJr732mmbPnq09e/Zo6tSpmjZtmmbNmmXPoT9l4+TJk2rVqpVmz55d5PbS6kP//v2Vnp6uFStWaMWKFUpPT9fAgQONn195d7H+nDp1Stu2bdOf//xnbdu2TR9++KG+/vpr9e7d22ce/THnUp+f85YuXarNmzcrJiam0Da/9seCY/z617+2/vjHP/qM3XjjjdYzzzzjp4oqp6ysLEuStXbtWsuyLKugoMDyeDzWlClT7DlnzpyxIiMjrddee82yLMv66aefLLfbbS1evNie8/3331tVqlSxVqxYUbYnUAEdP37catKkiZWammp17tzZGjlypGVZ9Mbfnn76aevWW28tdjv98a8ePXpYjz76qM/YfffdZw0YMMCyLPrjL5Ks5ORk+3Vp9WH37t2WJGvTpk32nI0bN1qSrP/3//6f4bOqOC7sT1G2bNliSbIOHDhgWRb9KUvF9efw4cNWnTp1rJ07d1r169e3ZsyYYW/zd39Y2XKI/Px8paWlKT4+3mc8Pj5eGzZs8FNVlVNOTo4kqUaNGpKkffv2KTMz06c3wcHB6ty5s92btLQ0eb1enzkxMTFq0aIF/SsFw4YNU48ePRQXF+czTm/866OPPlK7du30wAMPqFatWmrTpo3efPNNezv98a9bb71Vq1at0tdffy1J+vLLL7V+/Xrdc889kuiPU5RWHzZu3KjIyEi1b9/ennPLLbcoMjKSXpWynJwcuVwuexWf/vhXQUGBBg4cqKeeekrNmzcvtN3f/Qm8qnej1PznP//RuXPnFB0d7TMeHR2tzMxMP1VV+ViWpVGjRunWW29VixYtJMm+/kX15sCBA/acoKAgVa9evdAc+nd1Fi9erG3btmnr1q2FttEb//ruu+80Z84cjRo1Sn/605+0ZcsWPfHEEwoODtagQYPoj589/fTTysnJ0Y033qiAgACdO3dOL774oh566CFJfH6corT6kJmZqVq1ahXaf61atehVKTpz5oyeeeYZ9e/fXxEREZLoj7+99NJLCgwM1BNPPFHkdn/3h7DlMC6Xy+e1ZVmFxmDO8OHD9dVXX2n9+vWFtpWkN/Tv6hw6dEgjR45USkqKQkJCip1Hb/yjoKBA7dq106RJkyRJbdq00a5duzRnzhwNGjTInkd//GPJkiVauHChFi1apObNmys9PV2JiYmKiYlRQkKCPY/+OENp9KGo+fSq9Hi9Xj344IMqKCjQq6++esn59Me8tLQ0vfLKK9q2bdsVX8ey6g+3ETpEzZo1FRAQUCg9Z2VlFfrXLpgxYsQIffTRR1qzZo3q1q1rj3s8Hkm6aG88Ho/y8/OVnZ1d7BxcubS0NGVlZalt27YKDAxUYGCg1q5dq7/+9a8KDAy0ry298Y/atWurWbNmPmNNmza1H+rDZ8e/nnrqKT3zzDN68MEHFRsbq4EDB+rJJ5/U5MmTJdEfpyitPng8Hv3www+F9v/jjz/Sq1Lg9XrVt29f7du3T6mpqfaqlkR//GndunXKysrSddddZ/894cCBAxo9erQaNGggyf/9IWw5RFBQkNq2bavU1FSf8dTUVHXs2NFPVVUOlmVp+PDh+vDDD7V69Wo1bNjQZ3vDhg3l8Xh8epOfn6+1a9favWnbtq3cbrfPnIyMDO3cuZP+XYWuXbtqx44dSk9Pt3/atWunhx9+WOnp6WrUqBG98aNOnToV+jUJX3/9terXry+Jz46/nTp1SlWq+P5nPiAgwH70O/1xhtLqQ4cOHZSTk6MtW7bYczZv3qycnBx6dZXOB61///vfWrlypaKiony20x//GThwoL766iufvyfExMToqaee0qeffirJAf25qsdroFQtXrzYcrvd1ty5c63du3dbiYmJVlhYmLV//35/l1ahPf7441ZkZKT12WefWRkZGfbPqVOn7DlTpkyxIiMjrQ8//NDasWOH9dBDD1m1a9e2cnNz7Tl//OMfrbp161orV660tm3bZt15551Wq1atrLNnz/rjtCqsXz6N0LLojT9t2bLFCgwMtF588UXr3//+t/Xuu+9aoaGh1sKFC+059Md/EhISrDp16lgff/yxtW/fPuvDDz+0atasaY0dO9aeQ3/KxvHjx63t27db27dvtyRZ06dPt7Zv324/za60+nDXXXdZLVu2tDZu3Ght3LjRio2NtXr27Fnm51veXKw/Xq/X6t27t1W3bl0rPT3d5+8JeXl59j7ojzmX+vxc6MKnEVqWf/tD2HKYv/3tb1b9+vWtoKAg66abbrIfPw5zJBX5M2/ePHtOQUGBNWHCBMvj8VjBwcHW7bffbu3YscNnP6dPn7aGDx9u1ahRw6patarVs2dP6+DBg2V8NhXfhWGL3vjXsmXLrBYtWljBwcHWjTfeaL3xxhs+2+mP/+Tm5lojR460rrvuOiskJMRq1KiRNX78eJ+/INKfsrFmzZoi/zuTkJBgWVbp9eHo0aPWww8/bIWHh1vh4eHWww8/bGVnZ5fRWZZfF+vPvn37iv17wpo1a+x90B9zLvX5uVBRYcuf/XFZlmVd3doYAAAAAOBCfGcLAAAAAAwgbAEAAACAAYQtAAAAADCAsAUAAAAABhC2AAAAAMAAwhYAAAAAGEDYAgAAAAADCFsAAAAAYABhCwBwUV26dFFiYmKZHzcpKUmtW7cu8+Pi8j3yyCPq06ePv8sAAMcibAEAjCI0AQAqK8IWAABFsCxLZ8+evaL3eL1eQ9UAAMojwhYA4JIKCgo0duxY1ahRQx6PR0lJSfa2gwcP6t5779U111yjiIgI9e3bVz/88IMkaf78+Zo4caK+/PJLuVwuuVwuzZ8/X5KUk5OjP/zhD6pVq5YiIiJ055136ssvvyxRfedvZ5s4caK9vyFDhig/P9+eY1mWpk6dqkaNGqlq1apq1aqV3n//fXv7Z599JpfLpU8//VTt2rVTcHCw1q1bd9Hjnl+1e+utt9SoUSMFBwfLsqyLXpPz5syZo8aNGysoKEg33HCD3nnnHZ/tLpdLr7/+unr27KnQ0FA1bdpUGzdu1DfffKMuXbooLCxMHTp00LfffntF1+iXEhMT1aVLF/v1+++/r9jYWFWtWlVRUVGKi4vTyZMnJUnnzp3TqFGjVK1aNUVFRWns2LGyLOuyjg0AlRVhCwBwSQsWLFBYWJg2b96sqVOn6vnnn1dqaqosy1KfPn107NgxrV27Vqmpqfr222/Vr18/SVK/fv00evRoNW/eXBkZGcrIyFC/fv1kWZZ69OihzMxMLV++XGlpabrpppvUtWtXHTt2rEQ1rlq1Snv27NGaNWv03nvvKTk5WRMnTrS3P/vss5o3b57mzJmjXbt26cknn9SAAQO0du1an/2MHTtWkydP1p49e9SyZctLHvebb77R3//+d33wwQdKT0+XpIteE0lKTk7WyJEjNXr0aO3cuVNDhgzR7373O61Zs8Zn3y+88IIGDRqk9PR03Xjjjerfv7+GDBmicePG6YsvvpAkDR8+vETX60IZGRl66KGH9Oijj2rPnj367LPPdN9999mB6uWXX9Zbb72luXPnav369Tp27JiSk5NL5dgAUGFZAABcROfOna1bb73VZ+zmm2+2nn76aSslJcUKCAiwDh48aG/btWuXJcnasmWLZVmWNWHCBKtVq1Y+71+1apUVERFhnTlzxme8cePG1uuvv17s+4qTkJBg1ahRwzp58qQ9NmfOHOuaa66xzp07Z504ccIKCQmxNmzY4PO+wYMHWw899JBlWZa1Zs0aS5K1dOnSyzrm+RrdbreVlZVlj13ONenYsaP12GOP+ezrgQcesO655x77tSTr2WeftV9v3LjRkmTNnTvXHnvvvfeskJCQy6o1ISHBuvfee33GRo4caXXu3NmyLMtKS0uzJFn79+8v8v21a9e2pkyZYr/2er1W3bp1C+0TAPB/WNkCAFzShSs8tWvXVlZWlvbs2aN69eqpXr169rZmzZqpWrVq2rNnT7H7S0tL04kTJxQVFaVrrrnG/tm3b99l3xZ3oVatWik0NNR+3aFDB504cUKHDh3S7t27debMGXXr1s3neG+//Xah47Vr1+6Kjlu/fn1de+219uvLuSZ79uxRp06dfPbTqVOnQtfsl9c9OjpakhQbG+szdubMGeXm5l5RzUVp1aqVunbtqtjYWD3wwAN68803lZ2dLennWz4zMjLUoUMHe35gYOAVXysAqGwC/V0AAMD53G63z2uXy6WCggJZliWXy1VofnHj5xUUFKh27dr67LPPCm2rVq3a1Zbr43ytkvTJJ5+oTp06PtuDg4N9XoeFhV3R/i+cf7nX5MI5Rb3vl9f9/Laixs6f38VUqVKl0HesfvlAj4CAAKWmpmrDhg1KSUnRrFmzNH78eG3evFk1atS45P4BAIWxsgUAKLFmzZrp4MGDOnTokD22e/du5eTkqGnTppKkoKAgnTt3zud9N910kzIzMxUYGKhf/epXPj81a9YsUS1ffvmlTp8+bb/etGmTrrnmGtWtW1fNmjVTcHCwDh48WOh4v1yBKg2Xc02aNm2q9evX+7xvw4YN9nYTrr32WmVkZPiMnf+O2Xkul0udOnXSxIkTtX37dgUFBSk5OVmRkZGqXbu2Nm3aZM89e/as0tLSjNULABUBK1sAgBKLi4tTy5Yt9fDDD2vmzJk6e/ashg4dqs6dO9u3mDVo0ED79u1Tenq66tatq/DwcMXFxalDhw7q06ePXnrpJd1www06cuSIli9frj59+pTo9rT8/HwNHjxYzz77rA4cOKAJEyZo+PDhqlKlisLDwzVmzBg9+eSTKigo0K233qrc3Fxt2LBB11xzjRISEsr0mjz11FPq27ev/VCQZcuW6cMPP9TKlStLrY4L3XnnnZo2bZrefvttdejQQQsXLtTOnTvVpk0bSdLmzZu1atUqxcfHq1atWtq8ebN+/PFHOwCOHDlSU6ZMUZMmTdS0aVNNnz5dP/30k7F6AaAiYGULAFBiLpdLS5cuVfXq1XX77bcrLi5OjRo10pIlS+w5999/v+666y7dcccduvbaa/Xee+/J5XJp+fLluv322/Xoo4/q+uuv14MPPqj9+/fb3026Ul27dlWTJk10++23q2/fvurVq5fPI+pfeOEFPffcc5o8ebKaNm2q7t27a9myZWrYsOHVXgYfl3NN+vTpo1deeUXTpk1T8+bN9frrr2vevHk+j2Evbd27d9ef//xnjR07VjfffLOOHz+uQYMG2dsjIiL0+eef65577tH111+vZ599Vi+//LLuvvtuSdLo0aM1aNAgPfLII+rQoYPCw8P1m9/8xli9AFARuKwLb+AGAKCceeSRR/TTTz9p6dKl/i4FAAAbK1sAAAAAYABhCwDgeL98XPuFP+vWrTN23ObNmxd73HfffdfYcUuiPNUKAJUFtxECABzvm2++KXZbnTp1VLVqVSPHPXDggM/j0X8pOjpa4eHhRo5bEuWpVgCoLAhbAAAAAGAAtxECAAAAgAGELQAAAAAwgLAFAAAAAAYQtgAAAADAAMIWAAAAABhA2AIAAAAAAwhbAAAAAGDA/wenGyuqnqDgwQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting a histogram for hotel_per_room\n",
    "plot_histogram(df, 'hotel_per_room_usd', color='brown')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "id": "cziJzKLKoxvr",
    "outputId": "b28f8f8c-11d8-471f-c442-e48f9ccd2796"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting histogram for hotel_per_room\n",
    "plot_histogram(df, 'nights', color='brown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "B8FFht0PbXy7",
    "outputId": "a1bfc35c-5e89-4c22-c563-ac0342d25fa8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # Creating a subplot with boxplots\n",
    "column_names = ['base_fare_usd','page_clicks', 'hotel_per_room_usd', 'nights']\n",
    "\n",
    "fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(16, 8))\n",
    "\n",
    "# Loop through each column and create the corresponding boxplot in the subplot\n",
    "for i, column_name in enumerate(column_names):\n",
    "    row_idx = i // 4\n",
    "    col_idx = i % 4\n",
    "    df.boxplot(column=column_name, ax=axes[row_idx, col_idx])\n",
    "    axes[row_idx, col_idx].set_ylabel(column_name)\n",
    "    axes[row_idx, col_idx].set_title(f'Boxplot of {column_name}')\n",
    "\n",
    "# Remove any empty subplot if the number of columns is not divisible by 4\n",
    "if len(column_names) < 8:\n",
    "    for i in range(len(column_names), 8):\n",
    "        fig.delaxes(axes.flatten()[i])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sN4gk1oaxP3Y"
   },
   "source": [
    "# *The boxplots make our Inspection clearly visible:*\n",
    "\n",
    "\n",
    "1.   base_fare_usd outlier starting around 7500 and thers is value at 21548\n",
    "2.   page clicks outlier starting around 250 and thers is value at 566\n",
    "3.   hotel_per_room outlier starting around 900 and thers is value at 1376\n",
    "4.   nights outlier starting around 10 and there are values at 40\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eZymhA1d713Y"
   },
   "source": [
    "# **Interquartile Range (IQR) method for Outlier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "ftcr_JQwcVsv"
   },
   "outputs": [],
   "source": [
    "# Using the interquartile range method to filter the outlier on statistical values\n",
    "numeric_columns = ['base_fare_usd', 'page_clicks', 'hotel_per_room_usd','nights']\n",
    "\n",
    "df_filtered = pd.DataFrame()\n",
    "\n",
    "# calculating IQR and defining the tresholds  and iterating over every row in the dataset\n",
    "for col in numeric_columns:\n",
    "    Q1 = df[col].quantile(0.25)\n",
    "    Q3 = df[col].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "\n",
    "    if col == 'base_fare_usd':\n",
    "        outlier_threshold = 15 * IQR\n",
    "    elif col == 'page_clicks':\n",
    "        outlier_threshold = 12 * IQR\n",
    "    elif col == 'hotel_per_room_usd':\n",
    "        outlier_threshold = 6 * IQR\n",
    "    else:\n",
    "        outlier_threshold = 8 * IQR\n",
    "\n",
    "    outlier_mask = (df[col] < (Q1 - outlier_threshold)) | (df[col] > (Q3 + outlier_threshold))\n",
    "    df_filtered[col] = df[col][~outlier_mask]\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qveNhwC-ro_1"
   },
   "source": [
    "# **Handling outliers in this Dataset manually**\n",
    "The Interquartile Range (IQR) method, seems quite strict with 5 * IQR, but to get my intended result i decided to manually adjust the threshold for evey possible outlier\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "_psuiJFieka0",
    "outputId": "053a5aa0-1855-41fa-e47e-e07f70d5cb8c"
   },
   "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>base_fare_usd</th>\n",
       "      <th>page_clicks</th>\n",
       "      <th>hotel_per_room_usd</th>\n",
       "      <th>nights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14225.000000</td>\n",
       "      <td>49155.000000</td>\n",
       "      <td>14691.000000</td>\n",
       "      <td>14707.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>494.974915</td>\n",
       "      <td>17.491405</td>\n",
       "      <td>176.780478</td>\n",
       "      <td>3.653906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>580.547117</td>\n",
       "      <td>20.964505</td>\n",
       "      <td>113.818576</td>\n",
       "      <td>3.754886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.410000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>199.830000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>380.840000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>597.900000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6194.480000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>932.000000</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       base_fare_usd   page_clicks  hotel_per_room_usd        nights\n",
       "count   14225.000000  49155.000000        14691.000000  14707.000000\n",
       "mean      494.974915     17.491405          176.780478      3.653906\n",
       "std       580.547117     20.964505          113.818576      3.754886\n",
       "min         2.410000      1.000000           17.000000     -2.000000\n",
       "25%       199.830000      6.000000           99.000000      1.000000\n",
       "50%       380.840000     13.000000          148.000000      2.000000\n",
       "75%       597.900000     22.000000          222.000000      5.000000\n",
       "max      6194.480000    211.000000          932.000000     35.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking statistical values of the outlier dataset\n",
    "df_filtered.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "YC0U-P74jFII",
    "outputId": "e2225b49-14df-4e75-94fe-025569f5731c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #  Creating a subplot with boxplots\n",
    "column_names = ['base_fare_usd','page_clicks','hotel_per_room_usd', 'nights']\n",
    "\n",
    "# Create a 2x4 subplot (2 rows and 4 columns)\n",
    "fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(16, 8))\n",
    "\n",
    "# Loop through each column and create the corresponding boxplot in the subplot\n",
    "for i, column_name in enumerate(column_names):\n",
    "    row_idx = i // 4\n",
    "    col_idx = i % 4\n",
    "    df_filtered.boxplot(column=column_name, ax=axes[row_idx, col_idx])\n",
    "    axes[row_idx, col_idx].set_ylabel(column_name)\n",
    "    axes[row_idx, col_idx].set_title(f'Boxplot of {column_name}')\n",
    "\n",
    "# Remove any empty subplot if the number of columns is not divisible by 4\n",
    "if len(column_names) < 8:\n",
    "    for i in range(len(column_names), 8):\n",
    "        fig.delaxes(axes.flatten()[i])\n",
    "\n",
    "plt.tight_layout()  # Adjust spacing between subplots\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OP7ArXwbMc_h"
   },
   "source": [
    "# **Export to CSV**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1Q1EhjpLy9yZ"
   },
   "source": [
    "Exporte the dataset again to manually filter the outlier and perform the aggregations in SQL/PGadmin4, since it seems to be faster and more visual in SQL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "UB8YUnQgy2Eb"
   },
   "outputs": [],
   "source": [
    " #df_filtered.to_csv('/Users/sebastiangurgel/Desktop/Masteryproject1/raw_data_filtered.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rZ6vBzfFRLzz"
   },
   "source": [
    "# **Loading the aggregated and Outliner cleaned Data from SQL on a user level**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "id": "xHv9_FjbhaAk"
   },
   "outputs": [],
   "source": [
    "url = \"/Users/sebastiangurgel/Desktop/Masteryproject1/Not used/outlier_aggregated_data.csv\"\n",
    "df_clean = pd.read_csv(url, low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['user_id', 'age', 'sign_up_date', 'last_session_start', 'gender',\n",
       "       'married', 'has_children', 'home_city', 'home_country',\n",
       "       'number_of_sessions', 'avg_page_clicks', 'avg_session_length_minutes',\n",
       "       'recent_session', 'number_of_trips', 'number_of_flights',\n",
       "       'number_of_hotels', 'num_cancellations', 'trips_cancellations_ratio',\n",
       "       'avg_checked_bags', 'avg_seats', 'avg_flight_discount_amount',\n",
       "       'discount_flight_proportion', 'avg_base_fare', 'ads',\n",
       "       'avg_hotel_discount_amount', 'avg_flight_total_usd', 'avg_distance_km',\n",
       "       'ads_per_km', 'avg_hotel_total_usd', 'discount_hotel_proportion',\n",
       "       'avg_hotel_length'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 226
    },
    "id": "SLsWWQc9FRYF",
    "outputId": "895184fa-9cee-4e5f-893c-43084e8a6e69"
   },
   "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>age</th>\n",
       "      <th>sign_up_date</th>\n",
       "      <th>last_session_start</th>\n",
       "      <th>gender</th>\n",
       "      <th>married</th>\n",
       "      <th>has_children</th>\n",
       "      <th>home_city</th>\n",
       "      <th>home_country</th>\n",
       "      <th>number_of_sessions</th>\n",
       "      <th>avg_page_clicks</th>\n",
       "      <th>avg_session_length_minutes</th>\n",
       "      <th>recent_session</th>\n",
       "      <th>number_of_trips</th>\n",
       "      <th>number_of_flights</th>\n",
       "      <th>number_of_hotels</th>\n",
       "      <th>num_cancellations</th>\n",
       "      <th>trips_cancellations_ratio</th>\n",
       "      <th>avg_checked_bags</th>\n",
       "      <th>avg_seats</th>\n",
       "      <th>avg_flight_discount_amount</th>\n",
       "      <th>discount_flight_proportion</th>\n",
       "      <th>avg_base_fare</th>\n",
       "      <th>ads</th>\n",
       "      <th>avg_hotel_discount_amount</th>\n",
       "      <th>avg_flight_total_usd</th>\n",
       "      <th>avg_distance_km</th>\n",
       "      <th>ads_per_km</th>\n",
       "      <th>avg_hotel_total_usd</th>\n",
       "      <th>discount_hotel_proportion</th>\n",
       "      <th>avg_hotel_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23557</td>\n",
       "      <td>64</td>\n",
       "      <td>2021-07-22</td>\n",
       "      <td>2023-07-14 12:36:00</td>\n",
       "      <td>F</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>new york</td>\n",
       "      <td>usa</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>1.3</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>19.59</td>\n",
       "      <td>0.250</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>94883</td>\n",
       "      <td>51</td>\n",
       "      <td>2022-02-07</td>\n",
       "      <td>2023-05-28 17:28:00</td>\n",
       "      <td>F</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>kansas city</td>\n",
       "      <td>usa</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>85</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>432.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.10</td>\n",
       "      <td>108.01</td>\n",
       "      <td>1451.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>22.50</td>\n",
       "      <td>0.125</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101486</td>\n",
       "      <td>50</td>\n",
       "      <td>2022-02-17</td>\n",
       "      <td>2023-07-18 09:35:00</td>\n",
       "      <td>F</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>tacoma</td>\n",
       "      <td>usa</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>2.0</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>189.91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.74</td>\n",
       "      <td>995.68</td>\n",
       "      <td>0.00</td>\n",
       "      <td>49.63</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101961</td>\n",
       "      <td>42</td>\n",
       "      <td>2022-02-17</td>\n",
       "      <td>2023-06-22 13:27:00</td>\n",
       "      <td>F</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>boston</td>\n",
       "      <td>usa</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>2.0</td>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>248.53</td>\n",
       "      <td>2.48</td>\n",
       "      <td>0.10</td>\n",
       "      <td>149.74</td>\n",
       "      <td>1366.57</td>\n",
       "      <td>0.02</td>\n",
       "      <td>85.00</td>\n",
       "      <td>0.125</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>106907</td>\n",
       "      <td>44</td>\n",
       "      <td>2022-02-24</td>\n",
       "      <td>2023-03-24 18:09:00</td>\n",
       "      <td>F</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>miami</td>\n",
       "      <td>usa</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>2.8</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  age sign_up_date   last_session_start gender  married  \\\n",
       "0    23557   64   2021-07-22  2023-07-14 12:36:00      F     True   \n",
       "1    94883   51   2022-02-07  2023-05-28 17:28:00      F     True   \n",
       "2   101486   50   2022-02-17  2023-07-18 09:35:00      F     True   \n",
       "3   101961   42   2022-02-17  2023-06-22 13:27:00      F     True   \n",
       "4   106907   44   2022-02-24  2023-03-24 18:09:00      F     True   \n",
       "\n",
       "   has_children    home_city home_country  number_of_sessions  \\\n",
       "0         False     new york          usa                   8   \n",
       "1         False  kansas city          usa                   8   \n",
       "2          True       tacoma          usa                   8   \n",
       "3         False       boston          usa                   8   \n",
       "4          True        miami          usa                   6   \n",
       "\n",
       "   avg_page_clicks  avg_session_length_minutes  recent_session  \\\n",
       "0               10                         1.3              38   \n",
       "1                9                         1.1              85   \n",
       "2               16                         2.0              35   \n",
       "3               16                         2.0              60   \n",
       "4               23                         2.8             150   \n",
       "\n",
       "   number_of_trips  number_of_flights  number_of_hotels  num_cancellations  \\\n",
       "0                2                  0                 2                  0   \n",
       "1                2                  2                 2                  0   \n",
       "2                2                  1                 2                  0   \n",
       "3                5                  5                 5                  0   \n",
       "4                0                  0                 0                  0   \n",
       "\n",
       "   trips_cancellations_ratio  avg_checked_bags  avg_seats  \\\n",
       "0                        0.0               NaN        NaN   \n",
       "1                        0.0               1.0        2.0   \n",
       "2                        0.0               NaN        1.0   \n",
       "3                        0.0               1.0        1.0   \n",
       "4                        0.0               NaN        NaN   \n",
       "\n",
       "   avg_flight_discount_amount  discount_flight_proportion  avg_base_fare  \\\n",
       "0                         NaN                     0.00000            NaN   \n",
       "1                         NaN                     0.00000         432.05   \n",
       "2                        0.08                     0.03125         189.91   \n",
       "3                        0.15                     0.03125         248.53   \n",
       "4                         NaN                     0.00000            NaN   \n",
       "\n",
       "    ads  avg_hotel_discount_amount  avg_flight_total_usd  avg_distance_km  \\\n",
       "0   NaN                       0.18                  0.00              NaN   \n",
       "1   NaN                       0.10                108.01          1451.34   \n",
       "2  0.00                        NaN                 23.74           995.68   \n",
       "3  2.48                       0.10                149.74          1366.57   \n",
       "4   NaN                        NaN                  0.00              NaN   \n",
       "\n",
       "   ads_per_km  avg_hotel_total_usd  discount_hotel_proportion  \\\n",
       "0        0.00                19.59                      0.250   \n",
       "1        0.00                22.50                      0.125   \n",
       "2        0.00                49.63                      0.000   \n",
       "3        0.02                85.00                      0.125   \n",
       "4        0.00                 0.00                      0.000   \n",
       "\n",
       "   avg_hotel_length  \n",
       "0              10.0  \n",
       "1               1.0  \n",
       "2               4.0  \n",
       "3               4.0  \n",
       "4               NaN  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 320
    },
    "id": "gJCKZjBOOr9X",
    "outputId": "92a133ec-45a7-4914-f6dd-fa8851cf927a"
   },
   "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>age</th>\n",
       "      <th>number_of_sessions</th>\n",
       "      <th>avg_page_clicks</th>\n",
       "      <th>avg_session_length_minutes</th>\n",
       "      <th>recent_session</th>\n",
       "      <th>number_of_trips</th>\n",
       "      <th>number_of_flights</th>\n",
       "      <th>number_of_hotels</th>\n",
       "      <th>num_cancellations</th>\n",
       "      <th>trips_cancellations_ratio</th>\n",
       "      <th>avg_checked_bags</th>\n",
       "      <th>avg_seats</th>\n",
       "      <th>avg_flight_discount_amount</th>\n",
       "      <th>discount_flight_proportion</th>\n",
       "      <th>avg_base_fare</th>\n",
       "      <th>ads</th>\n",
       "      <th>avg_hotel_discount_amount</th>\n",
       "      <th>avg_flight_total_usd</th>\n",
       "      <th>avg_distance_km</th>\n",
       "      <th>ads_per_km</th>\n",
       "      <th>avg_hotel_total_usd</th>\n",
       "      <th>discount_hotel_proportion</th>\n",
       "      <th>avg_hotel_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5998.000000</td>\n",
       "      <td>5998.000000</td>\n",
       "      <td>5998.000000</td>\n",
       "      <td>5998.000000</td>\n",
       "      <td>5998.000000</td>\n",
       "      <td>5998.000000</td>\n",
       "      <td>5998.000000</td>\n",
       "      <td>5998.000000</td>\n",
       "      <td>5998.000000</td>\n",
       "      <td>5998.000000</td>\n",
       "      <td>5998.000000</td>\n",
       "      <td>4074.000000</td>\n",
       "      <td>5169.000000</td>\n",
       "      <td>4671.000000</td>\n",
       "      <td>5998.000000</td>\n",
       "      <td>5169.000000</td>\n",
       "      <td>4671.000000</td>\n",
       "      <td>4006.000000</td>\n",
       "      <td>5998.000000</td>\n",
       "      <td>5169.000000</td>\n",
       "      <td>5998.00000</td>\n",
       "      <td>5998.000000</td>\n",
       "      <td>5998.000000</td>\n",
       "      <td>5397.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>545440.695565</td>\n",
       "      <td>40.948816</td>\n",
       "      <td>8.192231</td>\n",
       "      <td>17.577526</td>\n",
       "      <td>3.083211</td>\n",
       "      <td>65.837112</td>\n",
       "      <td>2.772424</td>\n",
       "      <td>2.368123</td>\n",
       "      <td>2.447816</td>\n",
       "      <td>0.098866</td>\n",
       "      <td>0.010053</td>\n",
       "      <td>1.150466</td>\n",
       "      <td>1.214161</td>\n",
       "      <td>0.141250</td>\n",
       "      <td>0.020614</td>\n",
       "      <td>500.206914</td>\n",
       "      <td>16.569657</td>\n",
       "      <td>0.112416</td>\n",
       "      <td>126.250130</td>\n",
       "      <td>2421.883401</td>\n",
       "      <td>0.00979</td>\n",
       "      <td>46.759812</td>\n",
       "      <td>0.138017</td>\n",
       "      <td>4.280341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>64924.749760</td>\n",
       "      <td>12.048380</td>\n",
       "      <td>0.481673</td>\n",
       "      <td>8.624738</td>\n",
       "      <td>3.970954</td>\n",
       "      <td>29.465841</td>\n",
       "      <td>1.570656</td>\n",
       "      <td>1.564530</td>\n",
       "      <td>1.509636</td>\n",
       "      <td>0.306772</td>\n",
       "      <td>0.031955</td>\n",
       "      <td>0.422996</td>\n",
       "      <td>0.479035</td>\n",
       "      <td>0.071354</td>\n",
       "      <td>0.016012</td>\n",
       "      <td>480.941356</td>\n",
       "      <td>46.536804</td>\n",
       "      <td>0.054368</td>\n",
       "      <td>152.243262</td>\n",
       "      <td>2099.448763</td>\n",
       "      <td>0.02150</td>\n",
       "      <td>37.002164</td>\n",
       "      <td>0.121444</td>\n",
       "      <td>2.860509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>23557.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.350000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.450000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>517152.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.012346</td>\n",
       "      <td>279.980000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>30.657500</td>\n",
       "      <td>1473.410000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>18.542500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>540542.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>390.640000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>91.075000</td>\n",
       "      <td>2043.480000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>40.130000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>574146.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>2.475000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>557.770000</td>\n",
       "      <td>15.890000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>166.075000</td>\n",
       "      <td>2729.670000</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>68.227500</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>844489.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>35.600000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.093750</td>\n",
       "      <td>6052.900000</td>\n",
       "      <td>1473.800000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>1613.760000</td>\n",
       "      <td>23057.330000</td>\n",
       "      <td>0.35000</td>\n",
       "      <td>253.630000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             user_id          age  number_of_sessions  avg_page_clicks  \\\n",
       "count    5998.000000  5998.000000         5998.000000      5998.000000   \n",
       "mean   545440.695565    40.948816            8.192231        17.577526   \n",
       "std     64924.749760    12.048380            0.481673         8.624738   \n",
       "min     23557.000000    16.000000            5.000000         4.000000   \n",
       "25%    517152.000000    34.000000            8.000000        12.000000   \n",
       "50%    540542.000000    41.000000            8.000000        16.000000   \n",
       "75%    574146.000000    48.000000            8.000000        20.000000   \n",
       "max    844489.000000    88.000000           12.000000        86.000000   \n",
       "\n",
       "       avg_session_length_minutes  recent_session  number_of_trips  \\\n",
       "count                 5998.000000     5998.000000      5998.000000   \n",
       "mean                     3.083211       65.837112         2.772424   \n",
       "std                      3.970954       29.465841         1.570656   \n",
       "min                      0.500000       24.000000         0.000000   \n",
       "25%                      1.500000       43.000000         2.000000   \n",
       "50%                      1.900000       58.000000         3.000000   \n",
       "75%                      2.475000       81.000000         4.000000   \n",
       "max                     35.600000      195.000000         9.000000   \n",
       "\n",
       "       number_of_flights  number_of_hotels  num_cancellations  \\\n",
       "count        5998.000000       5998.000000        5998.000000   \n",
       "mean            2.368123          2.447816           0.098866   \n",
       "std             1.564530          1.509636           0.306772   \n",
       "min             0.000000          0.000000           0.000000   \n",
       "25%             1.000000          1.000000           0.000000   \n",
       "50%             2.000000          2.000000           0.000000   \n",
       "75%             3.000000          3.000000           0.000000   \n",
       "max             9.000000          9.000000           2.000000   \n",
       "\n",
       "       trips_cancellations_ratio  avg_checked_bags    avg_seats  \\\n",
       "count                5998.000000       4074.000000  5169.000000   \n",
       "mean                    0.010053          1.150466     1.214161   \n",
       "std                     0.031955          0.422996     0.479035   \n",
       "min                     0.000000          1.000000     1.000000   \n",
       "25%                     0.000000          1.000000     1.000000   \n",
       "50%                     0.000000          1.000000     1.000000   \n",
       "75%                     0.000000          1.000000     1.000000   \n",
       "max                     0.300000          5.000000     6.000000   \n",
       "\n",
       "       avg_flight_discount_amount  discount_flight_proportion  avg_base_fare  \\\n",
       "count                 4671.000000                 5998.000000    5169.000000   \n",
       "mean                     0.141250                    0.020614     500.206914   \n",
       "std                      0.071354                    0.016012     480.941356   \n",
       "min                      0.050000                    0.000000       5.350000   \n",
       "25%                      0.100000                    0.012346     279.980000   \n",
       "50%                      0.130000                    0.015625     390.640000   \n",
       "75%                      0.180000                    0.031250     557.770000   \n",
       "max                      0.550000                    0.093750    6052.900000   \n",
       "\n",
       "               ads  avg_hotel_discount_amount  avg_flight_total_usd  \\\n",
       "count  4671.000000                4006.000000           5998.000000   \n",
       "mean     16.569657                   0.112416            126.250130   \n",
       "std      46.536804                   0.054368            152.243262   \n",
       "min       0.000000                   0.050000              0.000000   \n",
       "25%       0.000000                   0.080000             30.657500   \n",
       "50%       0.000000                   0.100000             91.075000   \n",
       "75%      15.890000                   0.150000            166.075000   \n",
       "max    1473.800000                   0.450000           1613.760000   \n",
       "\n",
       "       avg_distance_km  ads_per_km  avg_hotel_total_usd  \\\n",
       "count      5169.000000  5998.00000          5998.000000   \n",
       "mean       2421.883401     0.00979            46.759812   \n",
       "std        2099.448763     0.02150            37.002164   \n",
       "min          27.450000     0.00000             0.000000   \n",
       "25%        1473.410000     0.00000            18.542500   \n",
       "50%        2043.480000     0.00000            40.130000   \n",
       "75%        2729.670000     0.01000            68.227500   \n",
       "max       23057.330000     0.35000           253.630000   \n",
       "\n",
       "       discount_hotel_proportion  avg_hotel_length  \n",
       "count                5998.000000       5397.000000  \n",
       "mean                    0.138017          4.280341  \n",
       "std                     0.121444          2.860509  \n",
       "min                     0.000000         -1.000000  \n",
       "25%                     0.000000          2.000000  \n",
       "50%                     0.125000          4.000000  \n",
       "75%                     0.250000          5.000000  \n",
       "max                     0.750000         30.000000  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# quick check with the descibe() function if the rows are correct and the outlier are removed\n",
    "df_clean.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LZyRFY21nqb2",
    "outputId": "bbb2a4ce-99bb-4d30-f849-a1f12b70cbbc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5998, 31)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the rows\n",
    "df_clean.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ad-uQlK8kxgN"
   },
   "source": [
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oST35tHpkWsP"
   },
   "source": [
    "# **MInMax Scaling the Features needed for Segmentation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "id": "VoyYxVJs3V3C"
   },
   "outputs": [],
   "source": [
    " # Replace NaN values with 0 in the entire DataFrame to prepare the data to bring them on one scale(scaling)\n",
    "df_clean.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting boolean columns into integer\n",
    "#df_clean['gender'] = df_clean['gender'].astype(int) \n",
    "df_clean['married'] = df_clean['married'].astype(int)\n",
    "df_clean['has_children'] = df_clean['has_children'].astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F    43486\n",
      "M     5635\n",
      "O       90\n",
      "Name: gender, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# there are 3 values in the gender column, so there are not easy to convert before deciding what to do with them\n",
    "print(df['gender'].value_counts())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>age</th>\n",
       "      <th>sign_up_date</th>\n",
       "      <th>last_session_start</th>\n",
       "      <th>gender</th>\n",
       "      <th>married</th>\n",
       "      <th>has_children</th>\n",
       "      <th>home_city</th>\n",
       "      <th>home_country</th>\n",
       "      <th>number_of_sessions</th>\n",
       "      <th>avg_page_clicks</th>\n",
       "      <th>avg_session_length_minutes</th>\n",
       "      <th>recent_session</th>\n",
       "      <th>number_of_trips</th>\n",
       "      <th>number_of_flights</th>\n",
       "      <th>number_of_hotels</th>\n",
       "      <th>num_cancellations</th>\n",
       "      <th>trips_cancellations_ratio</th>\n",
       "      <th>avg_checked_bags</th>\n",
       "      <th>avg_seats</th>\n",
       "      <th>avg_flight_discount_amount</th>\n",
       "      <th>discount_flight_proportion</th>\n",
       "      <th>avg_base_fare</th>\n",
       "      <th>ads</th>\n",
       "      <th>avg_hotel_discount_amount</th>\n",
       "      <th>avg_flight_total_usd</th>\n",
       "      <th>avg_distance_km</th>\n",
       "      <th>ads_per_km</th>\n",
       "      <th>avg_hotel_total_usd</th>\n",
       "      <th>discount_hotel_proportion</th>\n",
       "      <th>avg_hotel_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23557</td>\n",
       "      <td>64</td>\n",
       "      <td>2021-07-22</td>\n",
       "      <td>2023-07-14 12:36:00</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>new york</td>\n",
       "      <td>usa</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>1.3</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>19.59</td>\n",
       "      <td>0.250</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>94883</td>\n",
       "      <td>51</td>\n",
       "      <td>2022-02-07</td>\n",
       "      <td>2023-05-28 17:28:00</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>kansas city</td>\n",
       "      <td>usa</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>85</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>432.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>108.01</td>\n",
       "      <td>1451.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>22.50</td>\n",
       "      <td>0.125</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101486</td>\n",
       "      <td>50</td>\n",
       "      <td>2022-02-17</td>\n",
       "      <td>2023-07-18 09:35:00</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>tacoma</td>\n",
       "      <td>usa</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>2.0</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>189.91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>23.74</td>\n",
       "      <td>995.68</td>\n",
       "      <td>0.00</td>\n",
       "      <td>49.63</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101961</td>\n",
       "      <td>42</td>\n",
       "      <td>2022-02-17</td>\n",
       "      <td>2023-06-22 13:27:00</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>boston</td>\n",
       "      <td>usa</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>2.0</td>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>248.53</td>\n",
       "      <td>2.48</td>\n",
       "      <td>0.10</td>\n",
       "      <td>149.74</td>\n",
       "      <td>1366.57</td>\n",
       "      <td>0.02</td>\n",
       "      <td>85.00</td>\n",
       "      <td>0.125</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>106907</td>\n",
       "      <td>44</td>\n",
       "      <td>2022-02-24</td>\n",
       "      <td>2023-03-24 18:09:00</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>miami</td>\n",
       "      <td>usa</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>2.8</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  age sign_up_date   last_session_start gender  married  \\\n",
       "0    23557   64   2021-07-22  2023-07-14 12:36:00      F        1   \n",
       "1    94883   51   2022-02-07  2023-05-28 17:28:00      F        1   \n",
       "2   101486   50   2022-02-17  2023-07-18 09:35:00      F        1   \n",
       "3   101961   42   2022-02-17  2023-06-22 13:27:00      F        1   \n",
       "4   106907   44   2022-02-24  2023-03-24 18:09:00      F        1   \n",
       "\n",
       "   has_children    home_city home_country  number_of_sessions  \\\n",
       "0             0     new york          usa                   8   \n",
       "1             0  kansas city          usa                   8   \n",
       "2             1       tacoma          usa                   8   \n",
       "3             0       boston          usa                   8   \n",
       "4             1        miami          usa                   6   \n",
       "\n",
       "   avg_page_clicks  avg_session_length_minutes  recent_session  \\\n",
       "0               10                         1.3              38   \n",
       "1                9                         1.1              85   \n",
       "2               16                         2.0              35   \n",
       "3               16                         2.0              60   \n",
       "4               23                         2.8             150   \n",
       "\n",
       "   number_of_trips  number_of_flights  number_of_hotels  num_cancellations  \\\n",
       "0                2                  0                 2                  0   \n",
       "1                2                  2                 2                  0   \n",
       "2                2                  1                 2                  0   \n",
       "3                5                  5                 5                  0   \n",
       "4                0                  0                 0                  0   \n",
       "\n",
       "   trips_cancellations_ratio  avg_checked_bags  avg_seats  \\\n",
       "0                        0.0               0.0        0.0   \n",
       "1                        0.0               1.0        2.0   \n",
       "2                        0.0               0.0        1.0   \n",
       "3                        0.0               1.0        1.0   \n",
       "4                        0.0               0.0        0.0   \n",
       "\n",
       "   avg_flight_discount_amount  discount_flight_proportion  avg_base_fare  \\\n",
       "0                        0.00                     0.00000           0.00   \n",
       "1                        0.00                     0.00000         432.05   \n",
       "2                        0.08                     0.03125         189.91   \n",
       "3                        0.15                     0.03125         248.53   \n",
       "4                        0.00                     0.00000           0.00   \n",
       "\n",
       "    ads  avg_hotel_discount_amount  avg_flight_total_usd  avg_distance_km  \\\n",
       "0  0.00                       0.18                  0.00             0.00   \n",
       "1  0.00                       0.10                108.01          1451.34   \n",
       "2  0.00                       0.00                 23.74           995.68   \n",
       "3  2.48                       0.10                149.74          1366.57   \n",
       "4  0.00                       0.00                  0.00             0.00   \n",
       "\n",
       "   ads_per_km  avg_hotel_total_usd  discount_hotel_proportion  \\\n",
       "0        0.00                19.59                      0.250   \n",
       "1        0.00                22.50                      0.125   \n",
       "2        0.00                49.63                      0.000   \n",
       "3        0.02                85.00                      0.125   \n",
       "4        0.00                 0.00                      0.000   \n",
       "\n",
       "   avg_hotel_length  \n",
       "0              10.0  \n",
       "1               1.0  \n",
       "2               4.0  \n",
       "3               4.0  \n",
       "4               0.0  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "id": "7JYrIxTLk78V"
   },
   "outputs": [],
   "source": [
    "#Defining the numerical  to be scaled from the dataset\n",
    "scaled_features = [ 'age', 'has_children', 'married',\n",
    "       'number_of_sessions', 'avg_page_clicks', 'avg_session_length_minutes',\n",
    "       'number_of_trips', 'number_of_flights',\n",
    "       'number_of_hotels', 'num_cancellations', 'trips_cancellations_ratio',\n",
    "       'avg_checked_bags', 'avg_seats', 'avg_flight_discount_amount',\n",
    "       'discount_flight_proportion', 'avg_base_fare', 'ads',\n",
    "       'avg_hotel_discount_amount', 'avg_flight_total_usd', 'avg_distance_km',\n",
    "       'ads_per_km', 'avg_hotel_total_usd', 'discount_hotel_proportion',\n",
    "       'avg_hotel_length']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "id": "ZHtufmdkk7qZ"
   },
   "outputs": [],
   "source": [
    "# Create a new DataFrame with the scaled features for the segmentation\n",
    "scaled_metrics = df_clean[scaled_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "id": "SoBScHx3n4pl"
   },
   "outputs": [],
   "source": [
    "# Scale the features using Min-Max scaling\n",
    "\n",
    "scaled_metrics_scaled = ((scaled_metrics - scaled_metrics.min()) / (scaled_metrics.max() - scaled_metrics.min()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 226
    },
    "id": "Vxhd8bPiohXi",
    "outputId": "9be84f61-88b5-4892-e893-29d425d92f06"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>has_children</th>\n",
       "      <th>married</th>\n",
       "      <th>number_of_sessions</th>\n",
       "      <th>avg_page_clicks</th>\n",
       "      <th>avg_session_length_minutes</th>\n",
       "      <th>number_of_trips</th>\n",
       "      <th>number_of_flights</th>\n",
       "      <th>number_of_hotels</th>\n",
       "      <th>num_cancellations</th>\n",
       "      <th>trips_cancellations_ratio</th>\n",
       "      <th>avg_checked_bags</th>\n",
       "      <th>avg_seats</th>\n",
       "      <th>avg_flight_discount_amount</th>\n",
       "      <th>discount_flight_proportion</th>\n",
       "      <th>avg_base_fare</th>\n",
       "      <th>ads</th>\n",
       "      <th>avg_hotel_discount_amount</th>\n",
       "      <th>avg_flight_total_usd</th>\n",
       "      <th>avg_distance_km</th>\n",
       "      <th>ads_per_km</th>\n",
       "      <th>avg_hotel_total_usd</th>\n",
       "      <th>discount_hotel_proportion</th>\n",
       "      <th>avg_hotel_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.073171</td>\n",
       "      <td>0.022792</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.077238</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.354839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.486111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.060976</td>\n",
       "      <td>0.017094</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.071379</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.066931</td>\n",
       "      <td>0.062945</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.088712</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.064516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.472222</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.146341</td>\n",
       "      <td>0.042735</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.145455</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.031375</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.014711</td>\n",
       "      <td>0.043183</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.195679</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.161290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.361111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.146341</td>\n",
       "      <td>0.042735</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.041060</td>\n",
       "      <td>0.001683</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.092790</td>\n",
       "      <td>0.059268</td>\n",
       "      <td>0.057143</td>\n",
       "      <td>0.335134</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.161290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.388889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.231707</td>\n",
       "      <td>0.065527</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.032258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "        age  has_children  married  number_of_sessions  avg_page_clicks  \\\n",
       "0  0.666667           0.0      1.0            0.428571         0.073171   \n",
       "1  0.486111           0.0      1.0            0.428571         0.060976   \n",
       "2  0.472222           1.0      1.0            0.428571         0.146341   \n",
       "3  0.361111           0.0      1.0            0.428571         0.146341   \n",
       "4  0.388889           1.0      1.0            0.142857         0.231707   \n",
       "\n",
       "   avg_session_length_minutes  number_of_trips  number_of_flights  \\\n",
       "0                    0.022792         0.222222           0.000000   \n",
       "1                    0.017094         0.222222           0.222222   \n",
       "2                    0.042735         0.222222           0.111111   \n",
       "3                    0.042735         0.555556           0.555556   \n",
       "4                    0.065527         0.000000           0.000000   \n",
       "\n",
       "   number_of_hotels  num_cancellations  trips_cancellations_ratio  \\\n",
       "0          0.222222                0.0                        0.0   \n",
       "1          0.222222                0.0                        0.0   \n",
       "2          0.222222                0.0                        0.0   \n",
       "3          0.555556                0.0                        0.0   \n",
       "4          0.000000                0.0                        0.0   \n",
       "\n",
       "   avg_checked_bags  avg_seats  avg_flight_discount_amount  \\\n",
       "0               0.0   0.000000                    0.000000   \n",
       "1               0.2   0.333333                    0.000000   \n",
       "2               0.0   0.166667                    0.145455   \n",
       "3               0.2   0.166667                    0.272727   \n",
       "4               0.0   0.000000                    0.000000   \n",
       "\n",
       "   discount_flight_proportion  avg_base_fare       ads  \\\n",
       "0                    0.000000       0.000000  0.000000   \n",
       "1                    0.000000       0.071379  0.000000   \n",
       "2                    0.333333       0.031375  0.000000   \n",
       "3                    0.333333       0.041060  0.001683   \n",
       "4                    0.000000       0.000000  0.000000   \n",
       "\n",
       "   avg_hotel_discount_amount  avg_flight_total_usd  avg_distance_km  \\\n",
       "0                   0.400000              0.000000         0.000000   \n",
       "1                   0.222222              0.066931         0.062945   \n",
       "2                   0.000000              0.014711         0.043183   \n",
       "3                   0.222222              0.092790         0.059268   \n",
       "4                   0.000000              0.000000         0.000000   \n",
       "\n",
       "   ads_per_km  avg_hotel_total_usd  discount_hotel_proportion  \\\n",
       "0    0.000000             0.077238                   0.333333   \n",
       "1    0.000000             0.088712                   0.166667   \n",
       "2    0.000000             0.195679                   0.000000   \n",
       "3    0.057143             0.335134                   0.166667   \n",
       "4    0.000000             0.000000                   0.000000   \n",
       "\n",
       "   avg_hotel_length  \n",
       "0          0.354839  \n",
       "1          0.064516  \n",
       "2          0.161290  \n",
       "3          0.161290  \n",
       "4          0.032258  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the first 5 rowso f the scaled data\n",
    "scaled_metrics_scaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    " #df_filtered.to_csv('/Users/sebastiangurgel/Desktop/Masteryproject1/scaled.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QOhKf7a8GIWg"
   },
   "source": [
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nSeVQ4uC4GS4"
   },
   "source": [
    "# **Segmenting the Data on Thresholds**\n",
    "\n",
    "\n",
    "\n",
    "*   free hotel meal\n",
    "*   free checked bag\n",
    "*   no cancellation fees\n",
    "*   exclusive discounts\n",
    "*   1 night free hotel with flight\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "id": "22OPf2uX5ny0"
   },
   "outputs": [],
   "source": [
    "# adding a new column\n",
    "df_clean['avg_total_spent'] = df_clean['avg_hotel_total_usd'] + df_clean['avg_flight_total_usd']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2Jfry36TlpRw"
   },
   "source": [
    "\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ycdI1wxJK7Wi"
   },
   "source": [
    "# **1) Free Hotel Meal**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "pakdCndxRgOD",
    "outputId": "61157c64-d86f-4171-81a9-0a8769467dae"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>number_of_hotels</th>\n",
       "      <th>age</th>\n",
       "      <th>avg_hotel_total_usd</th>\n",
       "      <th>rank_free_hotel_meal</th>\n",
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       "      <th>208</th>\n",
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       "      <td>5</td>\n",
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       "      <td>151.79</td>\n",
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       "      <th>398</th>\n",
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       "      <td>6</td>\n",
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       "      <td>125.38</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>204943</td>\n",
       "      <td>7</td>\n",
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      ],
      "text/plain": [
       "     user_id  number_of_hotels  age  avg_hotel_total_usd  rank_free_hotel_meal\n",
       "208   435882                 4   50               112.44              0.588337\n",
       "407   477730                 5   44               151.79              0.554019\n",
       "398   476665                 6   38               125.38              0.474695\n",
       "24    204943                 7   40               167.63              0.448984\n",
       "75    354050                 5   49               109.23              0.389476"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filtering the condition for the Free Hotel Meal Perk:\n",
    "#1) number of hotel bookings >= 1\n",
    "#2) age > 36 Years\n",
    "#3) Hotelprice > 100\n",
    "\n",
    "# Condition 1:\n",
    "condition1 = (df_clean['number_of_hotels'] > 2) & (df_clean['age'] > 36) & (df_clean['avg_hotel_total_usd'] > 100 )\n",
    "\n",
    "perk_label = 'Free Hotel Meal'\n",
    "\n",
    "df_clean.loc[condition1, 'perk_label'] = perk_label\n",
    "\n",
    "# Filter the DataFrame based on the condition\n",
    "filtered_customers = df_clean[condition1]\n",
    "\n",
    "# Initialize a DataFrame to store rank values\n",
    "rank_df = filtered_customers[['user_id', 'number_of_hotels', 'age', 'avg_hotel_total_usd']].copy()\n",
    "\n",
    "# Scale the metrics for rank calculation\n",
    "scaler = MinMaxScaler()\n",
    "scaled_data = scaler.fit_transform(rank_df[['number_of_hotels', 'age', 'avg_hotel_total_usd']])\n",
    "scaled_df_5 = pd.DataFrame(scaled_data, columns=['number_of_hotels_scaled', 'age_scaled', 'avg_hotel_total_usd_scaled'])\n",
    "scaled_df_5['user_id'] = rank_df['user_id']\n",
    "scaled_df_5['rank_free_hotel_meal'] = 0.2 * scaled_df_5['number_of_hotels_scaled'] + 0.3 * scaled_df_5['age_scaled'] + 0.6 * scaled_df_5['avg_hotel_total_usd_scaled']\n",
    "\n",
    "# Sort the scaled DataFrame by rank\n",
    "scaled_df_5 = scaled_df_5.sort_values(by='rank_free_hotel_meal')\n",
    "\n",
    "# Merge the scaled_df DataFrame back into df_clean\n",
    "df_clean = df_clean.merge(scaled_df_5[['user_id', 'rank_free_hotel_meal']], on='user_id', how='left')\n",
    "\n",
    "# Sort the df_clean DataFrame by rank\n",
    "df_clean = df_clean.sort_values(by='rank_free_hotel_meal', ascending=False)\n",
    "\n",
    "# Print the resulting DataFrame with relevant columns\n",
    "df_clean[['user_id', 'number_of_hotels', 'age', 'avg_hotel_total_usd', 'rank_free_hotel_meal']].head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8LSRcRA6lmi9"
   },
   "source": [
    "\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iZVl1xq8l17C"
   },
   "source": [
    "# **2) Free Checked Bags**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "i9bjf1AgRgFM",
    "outputId": "e3391d07-3710-4a73-ef14-092f42520f8e"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>avg_checked_bags</th>\n",
       "      <th>avg_distance_km</th>\n",
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       "      <th>3831</th>\n",
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       "      <td>2972.97</td>\n",
       "      <td>3</td>\n",
       "      <td>0.561746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  avg_checked_bags  avg_distance_km  number_of_flights  \\\n",
       "3831   557236               1.0          3271.53                  4   \n",
       "2453   530810               1.0          2273.56                  3   \n",
       "927    510326               2.0          1533.89                  6   \n",
       "2098   525405               1.0          3664.18                  1   \n",
       "1242   513796               1.0          2972.97                  3   \n",
       "\n",
       "      rank_free_checked_bags  \n",
       "3831                0.716209  \n",
       "2453                0.655153  \n",
       "927                 0.628274  \n",
       "2098                0.600453  \n",
       "1242                0.561746  "
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Filtering the condition for Free Checked Bags Perk:\n",
    "# 1) average checked bags >= 1\n",
    "# 2) average distance >= 500\n",
    "# 3) flight bookings >= 1\n",
    "\n",
    "\n",
    "# Condition 2:\n",
    "condition2 = (df_clean['avg_checked_bags'] >= 1) & \\\n",
    "              (df_clean['avg_distance_km'] >= 500) & \\\n",
    "              (df_clean['number_of_flights'] >= 1)\n",
    "\n",
    "perk_label = 'Free Checked Bags'\n",
    "\n",
    "df_clean.loc[condition2, 'perk_label'] = perk_label\n",
    "\n",
    "# Filter the DataFrame based on the condition\n",
    "filtered_customers = df_clean[condition2]\n",
    "\n",
    "# Initialize a DataFrame to store rank values\n",
    "rank_df = filtered_customers[['user_id', 'avg_checked_bags', 'avg_distance_km', 'number_of_flights']].copy()\n",
    "\n",
    "# Scale the metrics  for rank calculation\n",
    "scaler = MinMaxScaler()\n",
    "scaled_data = scaler.fit_transform(rank_df[['avg_checked_bags', 'avg_distance_km', 'number_of_flights']])\n",
    "scaled_df_6 = pd.DataFrame(scaled_data, columns=['avg_checked_bags_scaled', 'avg_distance_km_scaled', 'number_of_flights_scaled'])\n",
    "scaled_df_6['user_id'] = rank_df['user_id']\n",
    "scaled_df_6['rank_free_checked_bags'] = 0.6 * scaled_df_6['avg_checked_bags_scaled'] + 0.2 * scaled_df_6['avg_distance_km_scaled'] + 0.2 * scaled_df_6['number_of_flights_scaled']\n",
    "\n",
    "# Sort the scaled DataFrame by rank\n",
    "scaled_df_6 = scaled_df_6.sort_values(by='rank_free_checked_bags', ascending=False)\n",
    "\n",
    "# Merge the scaled_df DataFrame back into df_clean\n",
    "df_clean = df_clean.merge(scaled_df_6[['user_id', 'rank_free_checked_bags']], on='user_id', how='left')\n",
    "\n",
    "# Sort the df_clean DataFrame by rank\n",
    "df_clean = df_clean.sort_values(by='rank_free_checked_bags', ascending=False)\n",
    "\n",
    "# Print the resulting DataFrame with relevant columns\n",
    "df_clean[['user_id', 'avg_checked_bags', 'avg_distance_km', 'number_of_flights', 'rank_free_checked_bags']].head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LBslqaQJlike"
   },
   "source": [
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yLmSB9f2mU3b"
   },
   "source": [
    "# **3) No Cancellation Fees**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['user_id', 'age', 'sign_up_date', 'last_session_start', 'gender',\n",
       "       'married', 'has_children', 'home_city', 'home_country',\n",
       "       'number_of_sessions', 'avg_page_clicks', 'avg_session_length_minutes',\n",
       "       'recent_session', 'number_of_trips', 'number_of_flights',\n",
       "       'number_of_hotels', 'num_cancellations', 'trips_cancellations_ratio',\n",
       "       'avg_checked_bags', 'avg_seats', 'avg_flight_discount_amount',\n",
       "       'discount_flight_proportion', 'avg_base_fare', 'ads',\n",
       "       'avg_hotel_discount_amount', 'avg_flight_total_usd', 'avg_distance_km',\n",
       "       'ads_per_km', 'avg_hotel_total_usd', 'discount_hotel_proportion',\n",
       "       'avg_hotel_length', 'avg_total_spent', 'perk_label',\n",
       "       'rank_free_hotel_meal', 'rank_free_checked_bags'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "sWR8bWZTZJIe",
    "outputId": "db7a717a-719f-4ad5-a88f-e206f236ccbd"
   },
   "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>trips_cancellations_ratio</th>\n",
       "      <th>avg_total_spent</th>\n",
       "      <th>avg_session_length_minutes</th>\n",
       "      <th>rank_no_cancellation_fees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>171470</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1055.96</td>\n",
       "      <td>30.7</td>\n",
       "      <td>0.749974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>174997</td>\n",
       "      <td>0.1</td>\n",
       "      <td>431.31</td>\n",
       "      <td>16.5</td>\n",
       "      <td>0.302412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2319</th>\n",
       "      <td>357737</td>\n",
       "      <td>0.1</td>\n",
       "      <td>374.57</td>\n",
       "      <td>18.4</td>\n",
       "      <td>0.249036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2695</th>\n",
       "      <td>411179</td>\n",
       "      <td>0.1</td>\n",
       "      <td>151.53</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0.150282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>458479</td>\n",
       "      <td>0.3</td>\n",
       "      <td>215.38</td>\n",
       "      <td>35.6</td>\n",
       "      <td>0.126321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  trips_cancellations_ratio  avg_total_spent  \\\n",
       "699    171470                        0.3          1055.96   \n",
       "642    174997                        0.1           431.31   \n",
       "2319   357737                        0.1           374.57   \n",
       "2695   411179                        0.1           151.53   \n",
       "296    458479                        0.3           215.38   \n",
       "\n",
       "      avg_session_length_minutes  rank_no_cancellation_fees  \n",
       "699                         30.7                   0.749974  \n",
       "642                         16.5                   0.302412  \n",
       "2319                        18.4                   0.249036  \n",
       "2695                        17.5                   0.150282  \n",
       "296                         35.6                   0.126321  "
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtering the condition for No Cancellation Fees Perk:\n",
    "# 1) trips_cancellations_ratio >= 0.1\n",
    "# 2) session length >= 16\n",
    "# 2) average total spent > 110$\n",
    "\n",
    "# Condition 3: No Cancellation Fees\n",
    "condition3 = (\n",
    "    (df_clean['trips_cancellations_ratio'] >= 0.1) &\n",
    "    (df_clean['avg_session_length_minutes'] >= 16) &  # Adding avg_session_length_minutes condition\n",
    "    (df_clean['avg_total_spent'] > 110)\n",
    ")\n",
    "\n",
    "perk_label = 'No Cancellation Fees'\n",
    "\n",
    "# Filter the DataFrame based on the condition\n",
    "filtered_customers = df_clean[condition3]\n",
    "\n",
    "# Initialize a DataFrame to store rank values\n",
    "rank_df = filtered_customers[['user_id', 'trips_cancellations_ratio', 'avg_total_spent', 'avg_session_length_minutes']].copy()  # Adding avg_session_length_minutes\n",
    "\n",
    "# Scale the metrics for rank calculation\n",
    "scaler = MinMaxScaler()\n",
    "scaled_data = scaler.fit_transform(rank_df[['trips_cancellations_ratio', 'avg_total_spent', 'avg_session_length_minutes']])\n",
    "scaled_df_no_cancellation_fees = pd.DataFrame(scaled_data, columns=['trips_cancellations_ratio_scaled', 'avg_total_spent_scaled', 'avg_session_length_minutes_scaled'])  # Adding avg_session_length_minutes_scaled\n",
    "scaled_df_no_cancellation_fees['user_id'] = rank_df['user_id']\n",
    "scaled_df_no_cancellation_fees['rank_no_cancellation_fees'] = (\n",
    "    0.2 * scaled_df_no_cancellation_fees['trips_cancellations_ratio_scaled'] +\n",
    "    0.4 * scaled_df_no_cancellation_fees['avg_total_spent_scaled'] +\n",
    "    0.4 * scaled_df_no_cancellation_fees['avg_session_length_minutes_scaled']  # Adjusting weights with the added session length\n",
    ")\n",
    "\n",
    "# Sort the scaled DataFrame by rank\n",
    "scaled_df_no_cancellation_fees = scaled_df_no_cancellation_fees.sort_values(by='rank_no_cancellation_fees')\n",
    "\n",
    "# Merge the scaled_df DataFrame back into df_clean\n",
    "df_clean = df_clean.merge(scaled_df_no_cancellation_fees[['user_id', 'rank_no_cancellation_fees']], on='user_id', how='left')\n",
    "\n",
    "# Sort the df_clean DataFrame by rank\n",
    "df_clean = df_clean.sort_values(by='rank_no_cancellation_fees', ascending=False)\n",
    "\n",
    "# Print the resulting DataFrame with relevant columns\n",
    "df_clean[['user_id', 'trips_cancellations_ratio', 'avg_total_spent', 'avg_session_length_minutes', 'rank_no_cancellation_fees']].head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xfGShA07vS1o"
   },
   "source": [
    "\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tWvQE4LvvUvO"
   },
   "source": [
    "# **4) Exclusive Discounts**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "IGhFk2yTvSGA",
    "outputId": "97e26820-c1ec-4165-81b1-f4ddd1e9f6a0"
   },
   "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>discount_flight_proportion</th>\n",
       "      <th>discount_hotel_proportion</th>\n",
       "      <th>ads_per_km</th>\n",
       "      <th>rank_exclusive_discounts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>845</th>\n",
       "      <td>544818</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.599822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>546648</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.512852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>528421</td>\n",
       "      <td>0.046875</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.509822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>474464</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.506970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030</th>\n",
       "      <td>547512</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.498057</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  discount_flight_proportion  discount_hotel_proportion  \\\n",
       "845    544818                    0.015625                      0.125   \n",
       "282    546648                    0.031250                      0.250   \n",
       "1033   528421                    0.046875                      0.125   \n",
       "994    474464                    0.031250                      0.125   \n",
       "1030   547512                    0.031250                      0.250   \n",
       "\n",
       "      ads_per_km  rank_exclusive_discounts  \n",
       "845         0.08                  0.599822  \n",
       "282         0.03                  0.512852  \n",
       "1033        0.11                  0.509822  \n",
       "994         0.08                  0.506970  \n",
       "1030        0.02                  0.498057  "
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtering the condition for Exclusive Discounts Perk:\n",
    "# 1) discount_flight_proportion> 0.01\n",
    "# 2) discount_hotel_proportion > 0.1\n",
    "# 3) dollar saved per km > 0.008\n",
    "\n",
    "# Condition 4: Exclusive Discounts\n",
    "condition4 = ((df_clean['discount_flight_proportion'] > 0.01) &\n",
    "              (df_clean['discount_hotel_proportion'] > 0.1) &\n",
    "              (df_clean['ads_per_km'] > 0.008))\n",
    "\n",
    "perk_label = 'Exclusive Discounts'\n",
    "\n",
    "# Assign the perk label based on condition 4\n",
    "df_clean.loc[condition4, 'perk_label'] = perk_label\n",
    "\n",
    "# Filter the DataFrame based on condition 4\n",
    "filtered_customers = df_clean[condition4]\n",
    "\n",
    "# Initialize a DataFrame to store rank values\n",
    "rank_df = filtered_customers[['user_id', 'discount_flight_proportion', 'discount_hotel_proportion', 'ads_per_km']].copy()\n",
    "\n",
    "\n",
    "# Scale the metrics for rank calculation\n",
    "scaler = MinMaxScaler()\n",
    "scaled_data = scaler.fit_transform(rank_df[['discount_flight_proportion', 'discount_hotel_proportion', 'ads_per_km']])\n",
    "scaled_df_exclusive_discounts = pd.DataFrame(scaled_data, columns=['discount_flight_proportion', 'discount_hotel_proportion', 'ads_per_km'])\n",
    "scaled_df_exclusive_discounts['user_id'] = rank_df['user_id']\n",
    "scaled_df_exclusive_discounts['rank_exclusive_discounts'] = (0.5 * scaled_df_exclusive_discounts['discount_flight_proportion'] +\n",
    "                                                             0.4 * scaled_df_exclusive_discounts['discount_hotel_proportion'] +\n",
    "                                                             0.1 * scaled_df_exclusive_discounts['ads_per_km'])\n",
    "\n",
    "# Sort the scaled DataFrame by rank\n",
    "scaled_df_exclusive_discounts = scaled_df_exclusive_discounts.sort_values(by='rank_exclusive_discounts')\n",
    "\n",
    "# Merge the scaled_df DataFrame back into df_clean\n",
    "df_clean = df_clean.merge(scaled_df_exclusive_discounts[['user_id', 'rank_exclusive_discounts']], on='user_id', how='left')\n",
    "\n",
    "# Sort the df_clean DataFrame by rank\n",
    "df_clean = df_clean.sort_values(by='rank_exclusive_discounts', ascending=False)\n",
    "\n",
    "# Print the resulting DataFrame with relevant columns\n",
    "df_clean[['user_id', 'discount_flight_proportion', 'discount_hotel_proportion', 'ads_per_km', 'rank_exclusive_discounts']].head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_1MZ7kzjwuCY"
   },
   "source": [
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vYpe7CbIwtvk"
   },
   "source": [
    "# **5) One Night Free Hotel**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "meBzu9h-vSBM",
    "outputId": "a8dca0c8-0fd9-48b2-d723-0f521721e275"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>number_of_flights</th>\n",
       "      <th>number_of_hotels</th>\n",
       "      <th>avg_total_spent</th>\n",
       "      <th>rank_one_night_free_hotel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1353</th>\n",
       "      <td>544694</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>128.25</td>\n",
       "      <td>0.653042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>499979</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>367.40</td>\n",
       "      <td>0.585335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>546065</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>143.91</td>\n",
       "      <td>0.533962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>560733</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>278.38</td>\n",
       "      <td>0.519042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>474464</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>173.55</td>\n",
       "      <td>0.513588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  number_of_flights  number_of_hotels  avg_total_spent  \\\n",
       "1353   544694                  2                 2           128.25   \n",
       "773    499979                  5                 4           367.40   \n",
       "603    546065                  3                 3           143.91   \n",
       "638    560733                  3                 4           278.38   \n",
       "3      474464                  3                 2           173.55   \n",
       "\n",
       "      rank_one_night_free_hotel  \n",
       "1353                   0.653042  \n",
       "773                    0.585335  \n",
       "603                    0.533962  \n",
       "638                    0.519042  \n",
       "3                      0.513588  "
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtering the condition for One Night Free Hotel Perk:\n",
    "# 1) flight bookings > 1\n",
    "# 2) hotel_bookings  > 1\n",
    "# 3) average total spent > 100\n",
    "# 4) must be married\n",
    "\n",
    "# Condition 5: One Night Free Hotel\n",
    "condition5 = ((df_clean['number_of_flights'] >= 1) &\n",
    "              (df_clean['number_of_hotels'] >= 1)) & \\\n",
    "              (df_clean['avg_total_spent'] >= 100) & \\\n",
    "              (df_clean['married'] == True)\n",
    "\n",
    "perk_label = 'One Night Free Hotel'\n",
    "\n",
    "df_clean.loc[condition5, 'perk_label'] = perk_label\n",
    "\n",
    "# Filter the DataFrame based on the condition\n",
    "filtered_customers = df_clean[condition5]\n",
    "\n",
    "# Initialize a DataFrame to store rank values\n",
    "rank_df = filtered_customers[['user_id', 'number_of_flights', 'number_of_hotels', 'avg_total_spent']].copy()\n",
    "\n",
    "# Scale the metrics  for rank calculation\n",
    "scaler = MinMaxScaler()\n",
    "scaled_data = scaler.fit_transform(rank_df[['number_of_flights', 'number_of_hotels', 'avg_total_spent']])\n",
    "scaled_df_one_night_free_hotel = pd.DataFrame(scaled_data, columns=['number_of_flights_scaled', 'number_of_hotels_scaled', 'avg_total_spent_scaled'])\n",
    "scaled_df_one_night_free_hotel['user_id'] = rank_df['user_id']\n",
    "scaled_df_one_night_free_hotel['rank_one_night_free_hotel'] = (0.3 * scaled_df_one_night_free_hotel['number_of_flights_scaled'] +\n",
    "                                                              0.3 * scaled_df_one_night_free_hotel['number_of_hotels_scaled'] +\n",
    "                                                              0.4 * scaled_df_one_night_free_hotel['avg_total_spent_scaled'])\n",
    "\n",
    "# Sort the scaled DataFrame by rank\n",
    "scaled_df_one_night_free_hotel = scaled_df_one_night_free_hotel.sort_values(by='rank_one_night_free_hotel')\n",
    "\n",
    "# Merge the scaled_df DataFrame back into df_clean\n",
    "df_clean = df_clean.merge(scaled_df_one_night_free_hotel[['user_id', 'rank_one_night_free_hotel']], on='user_id', how='left')\n",
    "\n",
    "# Sort the df_clean DataFrame by rank\n",
    "df_clean = df_clean.sort_values(by='rank_one_night_free_hotel', ascending=False)\n",
    "\n",
    "# Print the resulting DataFrame with relevant columns\n",
    "df_clean[['user_id', 'number_of_flights', 'number_of_hotels', 'avg_total_spent', 'rank_one_night_free_hotel']].head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 313
    },
    "id": "OjR2FHx2C7h6",
    "outputId": "6fb161cf-bd6c-4487-8cda-429c39239f83"
   },
   "outputs": [
    {
     "data": {
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       "    }\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>age</th>\n",
       "      <th>sign_up_date</th>\n",
       "      <th>last_session_start</th>\n",
       "      <th>gender</th>\n",
       "      <th>married</th>\n",
       "      <th>has_children</th>\n",
       "      <th>home_city</th>\n",
       "      <th>home_country</th>\n",
       "      <th>number_of_sessions</th>\n",
       "      <th>avg_page_clicks</th>\n",
       "      <th>avg_session_length_minutes</th>\n",
       "      <th>recent_session</th>\n",
       "      <th>number_of_trips</th>\n",
       "      <th>number_of_flights</th>\n",
       "      <th>number_of_hotels</th>\n",
       "      <th>num_cancellations</th>\n",
       "      <th>trips_cancellations_ratio</th>\n",
       "      <th>avg_checked_bags</th>\n",
       "      <th>avg_seats</th>\n",
       "      <th>avg_flight_discount_amount</th>\n",
       "      <th>discount_flight_proportion</th>\n",
       "      <th>avg_base_fare</th>\n",
       "      <th>ads</th>\n",
       "      <th>avg_hotel_discount_amount</th>\n",
       "      <th>avg_flight_total_usd</th>\n",
       "      <th>avg_distance_km</th>\n",
       "      <th>ads_per_km</th>\n",
       "      <th>avg_hotel_total_usd</th>\n",
       "      <th>discount_hotel_proportion</th>\n",
       "      <th>avg_hotel_length</th>\n",
       "      <th>avg_total_spent</th>\n",
       "      <th>perk_label</th>\n",
       "      <th>rank_free_hotel_meal</th>\n",
       "      <th>rank_free_checked_bags</th>\n",
       "      <th>rank_no_cancellation_fees</th>\n",
       "      <th>rank_exclusive_discounts</th>\n",
       "      <th>rank_one_night_free_hotel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1353</th>\n",
       "      <td>544694</td>\n",
       "      <td>52</td>\n",
       "      <td>2023-01-21</td>\n",
       "      <td>2023-07-01 01:25:00</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>bakersfield</td>\n",
       "      <td>usa</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>1.4</td>\n",
       "      <td>52</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>368.35</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>92.09</td>\n",
       "      <td>2127.37</td>\n",
       "      <td>0.00</td>\n",
       "      <td>36.16</td>\n",
       "      <td>0.125</td>\n",
       "      <td>2.0</td>\n",
       "      <td>128.25</td>\n",
       "      <td>One Night Free Hotel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.069869</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.653042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>499979</td>\n",
       "      <td>49</td>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>2023-07-02 19:44:00</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>toronto</td>\n",
       "      <td>canada</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>2.3</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>658.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>329.33</td>\n",
       "      <td>2711.48</td>\n",
       "      <td>0.00</td>\n",
       "      <td>38.07</td>\n",
       "      <td>0.100</td>\n",
       "      <td>3.0</td>\n",
       "      <td>367.40</td>\n",
       "      <td>One Night Free Hotel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.114655</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.585335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>546065</td>\n",
       "      <td>46</td>\n",
       "      <td>2023-01-22</td>\n",
       "      <td>2023-06-01 10:18:00</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>omaha</td>\n",
       "      <td>usa</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>2.3</td>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>294.76</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>110.53</td>\n",
       "      <td>1650.73</td>\n",
       "      <td>0.00</td>\n",
       "      <td>33.38</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>143.91</td>\n",
       "      <td>One Night Free Hotel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.156927</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.533962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>560733</td>\n",
       "      <td>52</td>\n",
       "      <td>2023-01-29</td>\n",
       "      <td>2023-07-03 22:21:00</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>indianapolis</td>\n",
       "      <td>usa</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>1.8</td>\n",
       "      <td>49</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>519.68</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.15</td>\n",
       "      <td>194.88</td>\n",
       "      <td>1411.93</td>\n",
       "      <td>0.00</td>\n",
       "      <td>83.50</td>\n",
       "      <td>0.125</td>\n",
       "      <td>3.0</td>\n",
       "      <td>278.38</td>\n",
       "      <td>One Night Free Hotel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.142849</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.519042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>474464</td>\n",
       "      <td>56</td>\n",
       "      <td>2022-12-17</td>\n",
       "      <td>2023-06-04 11:47:00</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>new york</td>\n",
       "      <td>usa</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>1.8</td>\n",
       "      <td>79</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>925.82</td>\n",
       "      <td>150.19</td>\n",
       "      <td>0.10</td>\n",
       "      <td>134.42</td>\n",
       "      <td>2681.22</td>\n",
       "      <td>0.08</td>\n",
       "      <td>39.13</td>\n",
       "      <td>0.125</td>\n",
       "      <td>3.0</td>\n",
       "      <td>173.55</td>\n",
       "      <td>One Night Free Hotel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.088438</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.50697</td>\n",
       "      <td>0.513588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  age sign_up_date   last_session_start gender  married  \\\n",
       "1353   544694   52   2023-01-21  2023-07-01 01:25:00      M        1   \n",
       "773    499979   49   2022-12-30  2023-07-02 19:44:00      F        1   \n",
       "603    546065   46   2023-01-22  2023-06-01 10:18:00      M        1   \n",
       "638    560733   52   2023-01-29  2023-07-03 22:21:00      F        1   \n",
       "3      474464   56   2022-12-17  2023-06-04 11:47:00      F        1   \n",
       "\n",
       "      has_children     home_city home_country  number_of_sessions  \\\n",
       "1353             0   bakersfield          usa                   8   \n",
       "773              0       toronto       canada                  10   \n",
       "603              1         omaha          usa                   8   \n",
       "638              0  indianapolis          usa                   8   \n",
       "3                0      new york          usa                   8   \n",
       "\n",
       "      avg_page_clicks  avg_session_length_minutes  recent_session  \\\n",
       "1353               12                         1.4              52   \n",
       "773                19                         2.3              50   \n",
       "603                18                         2.3              82   \n",
       "638                14                         1.8              49   \n",
       "3                  14                         1.8              79   \n",
       "\n",
       "      number_of_trips  number_of_flights  number_of_hotels  num_cancellations  \\\n",
       "1353                2                  2                 2                  0   \n",
       "773                 5                  5                 4                  0   \n",
       "603                 3                  3                 3                  0   \n",
       "638                 4                  3                 4                  0   \n",
       "3                   3                  3                 2                  0   \n",
       "\n",
       "      trips_cancellations_ratio  avg_checked_bags  avg_seats  \\\n",
       "1353                        0.0               1.0        1.0   \n",
       "773                         0.0               1.0        1.0   \n",
       "603                         0.0               1.0        1.0   \n",
       "638                         0.0               2.0        2.0   \n",
       "3                           0.0               3.0        2.0   \n",
       "\n",
       "      avg_flight_discount_amount  discount_flight_proportion  avg_base_fare  \\\n",
       "1353                        0.00                     0.00000         368.35   \n",
       "773                         0.05                     0.01000         658.65   \n",
       "603                         0.00                     0.00000         294.76   \n",
       "638                         0.13                     0.03125         519.68   \n",
       "3                           0.13                     0.03125         925.82   \n",
       "\n",
       "         ads  avg_hotel_discount_amount  avg_flight_total_usd  \\\n",
       "1353    0.00                       0.10                 92.09   \n",
       "773     0.00                       0.05                329.33   \n",
       "603     0.00                       0.00                110.53   \n",
       "638     0.00                       0.15                194.88   \n",
       "3     150.19                       0.10                134.42   \n",
       "\n",
       "      avg_distance_km  ads_per_km  avg_hotel_total_usd  \\\n",
       "1353          2127.37        0.00                36.16   \n",
       "773           2711.48        0.00                38.07   \n",
       "603           1650.73        0.00                33.38   \n",
       "638           1411.93        0.00                83.50   \n",
       "3             2681.22        0.08                39.13   \n",
       "\n",
       "      discount_hotel_proportion  avg_hotel_length  avg_total_spent  \\\n",
       "1353                      0.125               2.0           128.25   \n",
       "773                       0.100               3.0           367.40   \n",
       "603                       0.000               5.0           143.91   \n",
       "638                       0.125               3.0           278.38   \n",
       "3                         0.125               3.0           173.55   \n",
       "\n",
       "                perk_label  rank_free_hotel_meal  rank_free_checked_bags  \\\n",
       "1353  One Night Free Hotel                   NaN                0.069869   \n",
       "773   One Night Free Hotel                   NaN                0.114655   \n",
       "603   One Night Free Hotel                   NaN                0.156927   \n",
       "638   One Night Free Hotel                   NaN                0.142849   \n",
       "3     One Night Free Hotel                   NaN                0.088438   \n",
       "\n",
       "      rank_no_cancellation_fees  rank_exclusive_discounts  \\\n",
       "1353                        NaN                       NaN   \n",
       "773                         NaN                       NaN   \n",
       "603                         NaN                       NaN   \n",
       "638                         NaN                       NaN   \n",
       "3                           NaN                   0.50697   \n",
       "\n",
       "      rank_one_night_free_hotel  \n",
       "1353                   0.653042  \n",
       "773                    0.585335  \n",
       "603                    0.533962  \n",
       "638                    0.519042  \n",
       "3                      0.513588  "
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sH3GCa8tC8MH"
   },
   "source": [
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8crOr88e1qNI"
   },
   "source": [
    "# **Fuzzy Segmentation**\n",
    "\n",
    "where customers can belong to multiple segments (perks) with varying degrees of affinity.\n",
    "\n",
    "-Ordering and Ranking for Each Perk: For each perk, order the customers by\n",
    " their affinity metric (perk index) for that perk. Assign a rank from 1 to N for N customers, indicating their preference for that perk.\n",
    "\n",
    "-Calculate Minimum Rank Across Perks: For each customer, calculate the minimum  rank across all the perks. This will determine the perk that the customer is most affiliated with.\n",
    "\n",
    "-Assign the Most Preferred Perk: Based on the minimum rank calculated in step\n",
    " 2, assign the customer to the corresponding perk. If there's a tie in ranks, it doesn't matter which perk they are assigned to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
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    },
    "id": "8pk4-tGuTOhC",
    "outputId": "d64c9d8d-0782-44e9-83b4-c1de7222da3d"
   },
   "outputs": [
    {
     "data": {
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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>preferred_perk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>544694</td>\n",
       "      <td>exclusive_discounts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>499979</td>\n",
       "      <td>exclusive_discounts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>546065</td>\n",
       "      <td>exclusive_discounts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>560733</td>\n",
       "      <td>exclusive_discounts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>474464</td>\n",
       "      <td>free_hotel_meal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id       preferred_perk\n",
       "0   544694  exclusive_discounts\n",
       "1   499979  exclusive_discounts\n",
       "2   546065  exclusive_discounts\n",
       "3   560733  exclusive_discounts\n",
       "4   474464      free_hotel_meal"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.fillna(0, inplace=True)  # Replace NaN values with 0 in the entire DataFrame\n",
    "\n",
    "# Create a DataFrame to store the rankings for each perk\n",
    "rankings_df = df_clean[['user_id']].copy()\n",
    "\n",
    "# List of perk labels\n",
    "perk_labels = ['Free Hotel Meal', 'Free Checked Bags', 'No Cancellation Fees', 'Exclusive Discounts', 'One Night Free Hotel']\n",
    "\n",
    "# Calculate and store rankings for each perk\n",
    "for perk_label in perk_labels:\n",
    "    rank_col_name = 'rank_' + perk_label.lower().replace(' ', '_')\n",
    "    rankings_df[rank_col_name] = df_clean.groupby('perk_label')[rank_col_name].rank()\n",
    "\n",
    "# Calculate the minimum rank across all perks for each customer\n",
    "rank_columns = ['rank_' + perk_label.lower().replace(' ', '_') for perk_label in perk_labels]\n",
    "rankings_df['min_rank'] = rankings_df[rank_columns].min(axis=1)\n",
    "\n",
    "# Determine the most preferred perk based on minimum rank\n",
    "preferred_perk_col = 'preferred_perk'\n",
    "rankings_df[preferred_perk_col] = rankings_df[rank_columns].idxmin(axis=1)\n",
    "rankings_df[preferred_perk_col] = rankings_df[preferred_perk_col].apply(lambda x: x.replace('rank_', ''))\n",
    "\n",
    "# Merge the preferred perk information back to the original DataFrame\n",
    "df_clean = df_clean.merge(rankings_df[['user_id', preferred_perk_col]], on='user_id', how='left')\n",
    "\n",
    "# check the resulting DataFrame with preferred perks\n",
    "df_clean[['user_id', preferred_perk_col]].head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SNv2dDpcMMsk",
    "outputId": "8b437bd6-9511-4a2a-9d53-6d1784e10d37"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "free_checked_bags       2017\n",
      "free_hotel_meal         1869\n",
      "no_cancellation_fees     953\n",
      "exclusive_discounts      752\n",
      "one_night_free_hotel     407\n",
      "Name: preferred_perk, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Count the number of customers in each preferred perk category\n",
    "preferred_perk_counts = df_clean['preferred_perk'].value_counts()\n",
    "\n",
    "# Display the count of customers for each preferred perk\n",
    "print(preferred_perk_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 663
    },
    "id": "cOGIg5JZMvjR",
    "outputId": "6014ba63-6585-4e8a-f4e0-3627c7d92a08"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Count of customers for each preferred perk\n",
    "preferred_perk_counts = df_clean['preferred_perk'].value_counts()\n",
    "\n",
    "# Create a bar plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "preferred_perk_counts.plot(kind='bar', color='skyblue')\n",
    "plt.title('Count of Customers for Each Preferred Perk')\n",
    "plt.xlabel('Preferred Perk')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **The Final Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 216
    },
    "id": "LlVUpurQaDwm",
    "outputId": "ff6c258f-25fb-4e26-b428-c179a68d205b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>age</th>\n",
       "      <th>sign_up_date</th>\n",
       "      <th>last_session_start</th>\n",
       "      <th>gender</th>\n",
       "      <th>married</th>\n",
       "      <th>has_children</th>\n",
       "      <th>home_city</th>\n",
       "      <th>home_country</th>\n",
       "      <th>number_of_sessions</th>\n",
       "      <th>avg_page_clicks</th>\n",
       "      <th>avg_session_length_minutes</th>\n",
       "      <th>recent_session</th>\n",
       "      <th>number_of_trips</th>\n",
       "      <th>number_of_flights</th>\n",
       "      <th>number_of_hotels</th>\n",
       "      <th>num_cancellations</th>\n",
       "      <th>trips_cancellations_ratio</th>\n",
       "      <th>avg_checked_bags</th>\n",
       "      <th>avg_seats</th>\n",
       "      <th>avg_flight_discount_amount</th>\n",
       "      <th>discount_flight_proportion</th>\n",
       "      <th>avg_base_fare</th>\n",
       "      <th>ads</th>\n",
       "      <th>avg_hotel_discount_amount</th>\n",
       "      <th>avg_flight_total_usd</th>\n",
       "      <th>avg_distance_km</th>\n",
       "      <th>ads_per_km</th>\n",
       "      <th>avg_hotel_total_usd</th>\n",
       "      <th>discount_hotel_proportion</th>\n",
       "      <th>avg_hotel_length</th>\n",
       "      <th>avg_total_spent</th>\n",
       "      <th>perk_label</th>\n",
       "      <th>rank_free_hotel_meal</th>\n",
       "      <th>rank_free_checked_bags</th>\n",
       "      <th>rank_no_cancellation_fees</th>\n",
       "      <th>rank_exclusive_discounts</th>\n",
       "      <th>rank_one_night_free_hotel</th>\n",
       "      <th>preferred_perk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>544694</td>\n",
       "      <td>52</td>\n",
       "      <td>2023-01-21</td>\n",
       "      <td>2023-07-01 01:25:00</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>bakersfield</td>\n",
       "      <td>usa</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>1.4</td>\n",
       "      <td>52</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>368.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>92.09</td>\n",
       "      <td>2127.37</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.16</td>\n",
       "      <td>0.125</td>\n",
       "      <td>2.0</td>\n",
       "      <td>128.25</td>\n",
       "      <td>One Night Free Hotel</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.069869</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.653042</td>\n",
       "      <td>exclusive_discounts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>499979</td>\n",
       "      <td>49</td>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>2023-07-02 19:44:00</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>toronto</td>\n",
       "      <td>canada</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>2.3</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.01</td>\n",
       "      <td>658.65</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>329.33</td>\n",
       "      <td>2711.48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.07</td>\n",
       "      <td>0.100</td>\n",
       "      <td>3.0</td>\n",
       "      <td>367.40</td>\n",
       "      <td>One Night Free Hotel</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.114655</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.585335</td>\n",
       "      <td>exclusive_discounts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>546065</td>\n",
       "      <td>46</td>\n",
       "      <td>2023-01-22</td>\n",
       "      <td>2023-06-01 10:18:00</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>omaha</td>\n",
       "      <td>usa</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>2.3</td>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>294.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>110.53</td>\n",
       "      <td>1650.73</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.38</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>143.91</td>\n",
       "      <td>One Night Free Hotel</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.156927</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.533962</td>\n",
       "      <td>exclusive_discounts</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  age sign_up_date   last_session_start gender  married  \\\n",
       "0   544694   52   2023-01-21  2023-07-01 01:25:00      M        1   \n",
       "1   499979   49   2022-12-30  2023-07-02 19:44:00      F        1   \n",
       "2   546065   46   2023-01-22  2023-06-01 10:18:00      M        1   \n",
       "\n",
       "   has_children    home_city home_country  number_of_sessions  \\\n",
       "0             0  bakersfield          usa                   8   \n",
       "1             0      toronto       canada                  10   \n",
       "2             1        omaha          usa                   8   \n",
       "\n",
       "   avg_page_clicks  avg_session_length_minutes  recent_session  \\\n",
       "0               12                         1.4              52   \n",
       "1               19                         2.3              50   \n",
       "2               18                         2.3              82   \n",
       "\n",
       "   number_of_trips  number_of_flights  number_of_hotels  num_cancellations  \\\n",
       "0                2                  2                 2                  0   \n",
       "1                5                  5                 4                  0   \n",
       "2                3                  3                 3                  0   \n",
       "\n",
       "   trips_cancellations_ratio  avg_checked_bags  avg_seats  \\\n",
       "0                        0.0               1.0        1.0   \n",
       "1                        0.0               1.0        1.0   \n",
       "2                        0.0               1.0        1.0   \n",
       "\n",
       "   avg_flight_discount_amount  discount_flight_proportion  avg_base_fare  ads  \\\n",
       "0                        0.00                        0.00         368.35  0.0   \n",
       "1                        0.05                        0.01         658.65  0.0   \n",
       "2                        0.00                        0.00         294.76  0.0   \n",
       "\n",
       "   avg_hotel_discount_amount  avg_flight_total_usd  avg_distance_km  \\\n",
       "0                       0.10                 92.09          2127.37   \n",
       "1                       0.05                329.33          2711.48   \n",
       "2                       0.00                110.53          1650.73   \n",
       "\n",
       "   ads_per_km  avg_hotel_total_usd  discount_hotel_proportion  \\\n",
       "0         0.0                36.16                      0.125   \n",
       "1         0.0                38.07                      0.100   \n",
       "2         0.0                33.38                      0.000   \n",
       "\n",
       "   avg_hotel_length  avg_total_spent            perk_label  \\\n",
       "0               2.0           128.25  One Night Free Hotel   \n",
       "1               3.0           367.40  One Night Free Hotel   \n",
       "2               5.0           143.91  One Night Free Hotel   \n",
       "\n",
       "   rank_free_hotel_meal  rank_free_checked_bags  rank_no_cancellation_fees  \\\n",
       "0                   0.0                0.069869                        0.0   \n",
       "1                   0.0                0.114655                        0.0   \n",
       "2                   0.0                0.156927                        0.0   \n",
       "\n",
       "   rank_exclusive_discounts  rank_one_night_free_hotel       preferred_perk  \n",
       "0                       0.0                   0.653042  exclusive_discounts  \n",
       "1                       0.0                   0.585335  exclusive_discounts  \n",
       "2                       0.0                   0.533962  exclusive_discounts  "
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print the final dataset with preferred perk added\n",
    "df_clean.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ebNoVZEMjaqa"
   },
   "source": [
    "# **Export to CSV**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "id": "ILzD3oq4ognt"
   },
   "outputs": [],
   "source": [
    "#df_clean.to_csv('/Users/sebastiangurgel/Desktop/Masteryproject1/traveltide_customer_perks.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4s3DmksiLF8f"
   },
   "source": [
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-odSWF8x7R9W"
   },
   "source": [
    "# **K Means**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jWkOvDWM5oxT",
    "outputId": "29ad9e3e-05d0-46bc-ef4a-8a657453baba"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['user_id', 'age', 'sign_up_date', 'last_session_start', 'gender',\n",
       "       'married', 'has_children', 'home_city', 'home_country',\n",
       "       'number_of_sessions', 'avg_page_clicks', 'avg_session_length_minutes',\n",
       "       'recent_session', 'number_of_trips', 'number_of_flights',\n",
       "       'number_of_hotels', 'num_cancellations', 'trips_cancellations_ratio',\n",
       "       'avg_checked_bags', 'avg_seats', 'avg_flight_discount_amount',\n",
       "       'discount_flight_proportion', 'avg_base_fare', 'ads',\n",
       "       'avg_hotel_discount_amount', 'avg_flight_total_usd', 'avg_distance_km',\n",
       "       'ads_per_km', 'avg_hotel_total_usd', 'discount_hotel_proportion',\n",
       "       'avg_hotel_length', 'avg_total_spent', 'perk_label',\n",
       "       'rank_free_hotel_meal', 'rank_free_checked_bags',\n",
       "       'rank_no_cancellation_fees', 'rank_exclusive_discounts',\n",
       "       'rank_one_night_free_hotel', 'preferred_perk'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading the available columns\n",
    "df_clean.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "# i tried the features I used in my manual Segmentation- but the outcome was not as obvious\n",
    "# selecting the features for K means\n",
    "#selected_features = [\n",
    "    #'avg_distance_km',\n",
    "   #'avg_session_length_minutes',\n",
    "  #  'number_of_flights',\n",
    "  #   'discount_flight_proportion',\n",
    "   # 'discount_hotel_proportion',\n",
    "   #  'avg_hotel_total_usd',\n",
    "   #    'number_of_hotels',\n",
    "   #    'trips_cancellations_ratio',\n",
    "   #   'age',\n",
    "   #   'ads_per_km',\n",
    "   #    'avg_total_spent']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I tried to find the best combination of features for an obvious cluster\n",
    "# selecting the features for K means\n",
    "selected_features = ['age', 'avg_distance_km', 'avg_total_spent','avg_hotel_length', 'number_of_hotels','number_of_flights','avg_checked_bags',\n",
    "'recent_session', 'avg_page_clicks','avg_session_length_minutes',  'trips_cancellations_ratio']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "id": "KNMVF4dtwkgq"
   },
   "outputs": [],
   "source": [
    "# Create a new DataFrame with the selected features\n",
    "behavioral_metrics = df_clean[selected_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "vF4oxidsSKWj",
    "outputId": "1fc493e8-5044-48aa-de04-ea1db497527b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>avg_distance_km</th>\n",
       "      <th>avg_total_spent</th>\n",
       "      <th>avg_hotel_length</th>\n",
       "      <th>number_of_hotels</th>\n",
       "      <th>number_of_flights</th>\n",
       "      <th>avg_checked_bags</th>\n",
       "      <th>recent_session</th>\n",
       "      <th>avg_page_clicks</th>\n",
       "      <th>avg_session_length_minutes</th>\n",
       "      <th>trips_cancellations_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52</td>\n",
       "      <td>2127.37</td>\n",
       "      <td>128.25</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>12</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>2711.48</td>\n",
       "      <td>367.40</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50</td>\n",
       "      <td>19</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46</td>\n",
       "      <td>1650.73</td>\n",
       "      <td>143.91</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>82</td>\n",
       "      <td>18</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52</td>\n",
       "      <td>1411.93</td>\n",
       "      <td>278.38</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>49</td>\n",
       "      <td>14</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>2681.22</td>\n",
       "      <td>173.55</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>79</td>\n",
       "      <td>14</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  avg_distance_km  avg_total_spent  avg_hotel_length  number_of_hotels  \\\n",
       "0   52          2127.37           128.25               2.0                 2   \n",
       "1   49          2711.48           367.40               3.0                 4   \n",
       "2   46          1650.73           143.91               5.0                 3   \n",
       "3   52          1411.93           278.38               3.0                 4   \n",
       "4   56          2681.22           173.55               3.0                 2   \n",
       "\n",
       "   number_of_flights  avg_checked_bags  recent_session  avg_page_clicks  \\\n",
       "0                  2               1.0              52               12   \n",
       "1                  5               1.0              50               19   \n",
       "2                  3               1.0              82               18   \n",
       "3                  3               2.0              49               14   \n",
       "4                  3               3.0              79               14   \n",
       "\n",
       "   avg_session_length_minutes  trips_cancellations_ratio  \n",
       "0                         1.4                        0.0  \n",
       "1                         2.3                        0.0  \n",
       "2                         2.3                        0.0  \n",
       "3                         1.8                        0.0  \n",
       "4                         1.8                        0.0  "
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "behavioral_metrics.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "id": "bx6hkN0UwkSQ"
   },
   "outputs": [],
   "source": [
    "# Scale the features using Min-Max scaling manually\n",
    "behavioral_metrics_scaled = ((behavioral_metrics - behavioral_metrics.min()) / (behavioral_metrics.max() - behavioral_metrics.min()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "QVdQO8fZwkCm",
    "outputId": "673155c4-82a6-4505-a526-60952c90b4fd"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>avg_distance_km</th>\n",
       "      <th>avg_total_spent</th>\n",
       "      <th>avg_hotel_length</th>\n",
       "      <th>number_of_hotels</th>\n",
       "      <th>number_of_flights</th>\n",
       "      <th>avg_checked_bags</th>\n",
       "      <th>recent_session</th>\n",
       "      <th>avg_page_clicks</th>\n",
       "      <th>avg_session_length_minutes</th>\n",
       "      <th>trips_cancellations_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.092264</td>\n",
       "      <td>0.078337</td>\n",
       "      <td>0.096774</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.2</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.458333</td>\n",
       "      <td>0.117597</td>\n",
       "      <td>0.224413</td>\n",
       "      <td>0.129032</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.152047</td>\n",
       "      <td>0.182927</td>\n",
       "      <td>0.051282</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.071592</td>\n",
       "      <td>0.087902</td>\n",
       "      <td>0.193548</td>\n",
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       "      <td>0.333333</td>\n",
       "      <td>0.2</td>\n",
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       "      <td>0.170732</td>\n",
       "      <td>0.051282</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>3</th>\n",
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       "      <td>0.037037</td>\n",
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       "      <td>0.129032</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.321637</td>\n",
       "      <td>0.121951</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  avg_distance_km  avg_total_spent  avg_hotel_length  \\\n",
       "0  0.500000         0.092264         0.078337          0.096774   \n",
       "1  0.458333         0.117597         0.224413          0.129032   \n",
       "2  0.416667         0.071592         0.087902          0.193548   \n",
       "3  0.500000         0.061236         0.170038          0.129032   \n",
       "4  0.555556         0.116285         0.106007          0.129032   \n",
       "\n",
       "   number_of_hotels  number_of_flights  avg_checked_bags  recent_session  \\\n",
       "0          0.222222           0.222222               0.2        0.163743   \n",
       "1          0.444444           0.555556               0.2        0.152047   \n",
       "2          0.333333           0.333333               0.2        0.339181   \n",
       "3          0.444444           0.333333               0.4        0.146199   \n",
       "4          0.222222           0.333333               0.6        0.321637   \n",
       "\n",
       "   avg_page_clicks  avg_session_length_minutes  trips_cancellations_ratio  \n",
       "0         0.097561                    0.025641                        0.0  \n",
       "1         0.182927                    0.051282                        0.0  \n",
       "2         0.170732                    0.051282                        0.0  \n",
       "3         0.121951                    0.037037                        0.0  \n",
       "4         0.121951                    0.037037                        0.0  "
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "behavioral_metrics_scaled.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P301Y0hyUwAC"
   },
   "source": [
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1Zn36PWFhqWL"
   },
   "source": [
    "# **Initialize random centroids**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "id": "yW8kp_V37XqP"
   },
   "outputs": [],
   "source": [
    "# Define the random_centroids function\n",
    "def random_centroids(behavioral_metrics_scaled, k):\n",
    "    centroids = []  # This list will hold the centroids \n",
    "    for i in range(k):  #  k number of centroids \n",
    "        centroid = behavioral_metrics_scaled.apply(lambda x: float(x.sample()))   # randomly picking samples for our centroid\n",
    "        centroids.append(centroid)  # Add the centroid to our list\n",
    "    return pd.concat(centroids, axis=1)  # Return all the centroids together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "id": "w3i28vvU0sMB"
   },
   "outputs": [],
   "source": [
    "# Generating random centroids for K-Means clustering\n",
    "centroids = random_centroids(behavioral_metrics_scaled, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "id": "qSrsZgrB0sHG",
    "outputId": "5f6c06ef-4361-4d6f-c5f5-7fbe4c2b391b"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.291667</td>\n",
       "      <td>0.319444</td>\n",
       "      <td>0.305556</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.069444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_distance_km</th>\n",
       "      <td>0.141721</td>\n",
       "      <td>0.085736</td>\n",
       "      <td>0.092889</td>\n",
       "      <td>0.039999</td>\n",
       "      <td>0.096477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_total_spent</th>\n",
       "      <td>0.193891</td>\n",
       "      <td>0.005900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.049702</td>\n",
       "      <td>0.050655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_hotel_length</th>\n",
       "      <td>0.096774</td>\n",
       "      <td>0.096774</td>\n",
       "      <td>0.129032</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.193548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_of_hotels</th>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_of_flights</th>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_checked_bags</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recent_session</th>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.192982</td>\n",
       "      <td>0.467836</td>\n",
       "      <td>0.140351</td>\n",
       "      <td>0.198830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_page_clicks</th>\n",
       "      <td>0.121951</td>\n",
       "      <td>0.158537</td>\n",
       "      <td>0.146341</td>\n",
       "      <td>0.182927</td>\n",
       "      <td>0.146341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_session_length_minutes</th>\n",
       "      <td>0.025641</td>\n",
       "      <td>0.025641</td>\n",
       "      <td>0.039886</td>\n",
       "      <td>0.085470</td>\n",
       "      <td>0.056980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trips_cancellations_ratio</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   0         1         2         3         4\n",
       "age                         0.291667  0.319444  0.305556  0.250000  0.069444\n",
       "avg_distance_km             0.141721  0.085736  0.092889  0.039999  0.096477\n",
       "avg_total_spent             0.193891  0.005900  0.000000  0.049702  0.050655\n",
       "avg_hotel_length            0.096774  0.096774  0.129032  0.032258  0.193548\n",
       "number_of_hotels            0.555556  0.333333  0.444444  0.333333  0.333333\n",
       "number_of_flights           0.444444  0.000000  0.222222  0.000000  0.444444\n",
       "avg_checked_bags            0.000000  0.000000  0.200000  0.200000  0.000000\n",
       "recent_session              0.105263  0.192982  0.467836  0.140351  0.198830\n",
       "avg_page_clicks             0.121951  0.158537  0.146341  0.182927  0.146341\n",
       "avg_session_length_minutes  0.025641  0.025641  0.039886  0.085470  0.056980\n",
       "trips_cancellations_ratio   0.000000  0.000000  0.000000  0.333333  0.000000"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centroids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kSfeKqw11kI_"
   },
   "source": [
    "# **Get labels for each data point**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "id": "35LGGexX0sBu"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.513527</td>\n",
       "      <td>0.379725</td>\n",
       "      <td>0.435229</td>\n",
       "      <td>0.504448</td>\n",
       "      <td>0.549407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.318728</td>\n",
       "      <td>0.658034</td>\n",
       "      <td>0.535365</td>\n",
       "      <td>0.722992</td>\n",
       "      <td>0.504268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.447912</td>\n",
       "      <td>0.446131</td>\n",
       "      <td>0.258179</td>\n",
       "      <td>0.565087</td>\n",
       "      <td>0.441861</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.487839</td>\n",
       "      <td>0.590166</td>\n",
       "      <td>0.473416</td>\n",
       "      <td>0.606187</td>\n",
       "      <td>0.627287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.780675</td>\n",
       "      <td>0.754467</td>\n",
       "      <td>0.563906</td>\n",
       "      <td>0.738387</td>\n",
       "      <td>0.802925</td>\n",
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       "          0         1         2         3         4\n",
       "0  0.513527  0.379725  0.435229  0.504448  0.549407\n",
       "1  0.318728  0.658034  0.535365  0.722992  0.504268\n",
       "2  0.447912  0.446131  0.258179  0.565087  0.441861\n",
       "3  0.487839  0.590166  0.473416  0.606187  0.627287\n",
       "4  0.780675  0.754467  0.563906  0.738387  0.802925"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate distances of data points from centroids\n",
    "distances = centroids.apply(lambda x: np.sqrt(((behavioral_metrics_scaled - x) ** 2).sum(axis=1)))\n",
    "distances.head()\n",
    "\n",
    "# 'centroids' contains the randomly generated centroids \n",
    "# 'behavioral_metrics_scaled' contains the scaled data\n",
    "\n",
    "# The code calculates the Euclidean distance between each customer and each centroid .\n",
    "\n",
    "\n",
    "# Here, 'x' represents a single centroid from the 'centroids' list.\n",
    "# The expression ((behavioral_metrics_scaled - x) ** 2) calculates the squared differences between each customer\n",
    "# and the qualities of the current centroid 'x'.\n",
    "\n",
    "# The '.sum(axis=1)' part sums up the squared differences for each customer along the rows, resulting in a series of sums.\n",
    "\n",
    "# Finally, the 'np.sqrt(...)' function calculates the square root of these sums, which gives the Euclidean distance\n",
    "# between each customer and the current centroid 'x'.\n",
    "\n",
    "# The result is a DataFrame 'distances' where each column corresponds to a centroid ,\n",
    "# and each row contains the Euclidean distances of each customer from the respective centroid.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "id": "RGSmqHsH0ruZ"
   },
   "outputs": [],
   "source": [
    "def get_labels(behavioral_metrics_scaled, centroids):\n",
    "    # Calculate distances of customer from centroids\n",
    "    distances = centroids.apply(lambda x: np.sqrt(((behavioral_metrics_scaled - x) ** 2).sum(axis=1)))\n",
    "\n",
    "    # For each customer , find the index (cluster label) of the centroid that has the minimum distance\n",
    "    # This assigns a cluster to each customer\n",
    "    return distances.idxmin(axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "id": "XTTFUpmM19vx"
   },
   "outputs": [],
   "source": [
    "# Assign clusters (labels) to the data points using the previously defined 'get_labels' function\n",
    "labels = get_labels(behavioral_metrics_scaled, centroids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4xD2_gsB19m_",
    "outputId": "fbf60d30-9803-474a-ecb8-f7faa0e7003a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2086\n",
       "2    1723\n",
       "0    1448\n",
       "4     470\n",
       "3     271\n",
       "dtype: int64"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L_xLZAU52X2T"
   },
   "source": [
    "---\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "id": "u3hLp12N3Hgl"
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA  # Importing the PCA class from the sklearn.decomposition module\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import clear_output # Importing the clear_output function from the IPython.display module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "id": "p1Iwwh5y19dX"
   },
   "outputs": [],
   "source": [
    "# Calculating the geometric mean of each feature to update the centroids and find the center of the cluster\n",
    "\n",
    "# Defining a function called 'new_centroids' that takes three arguments:\n",
    "# - behavioral_metrics_scaled: Scaled behavioral metrics data\n",
    "# - labels: Cluster labels assigned to each customer\n",
    "# - k: Number of clusters\n",
    "\n",
    "def new_centroids(behavioral_metrics_scaled, labels, k):\n",
    "    # Using the 'groupby' function on 'behavioral_metrics_scaled' with 'labels'\n",
    "    # and applying the 'mean' function after transforming the data using logarithm\n",
    "    # and exponentiation operations to calculate the geometric mean\n",
    "    centroids = behavioral_metrics_scaled.groupby(labels).apply(lambda x: np.exp(np.log(x).mean())).T\n",
    "    \n",
    "    # Returning the updated centroids\n",
    "    return centroids\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "id": "_HmRibNM2vxu"
   },
   "outputs": [],
   "source": [
    "# Plotting the clusters in 2D and iterating over the rows\n",
    "\n",
    "# Defining a function called 'plot_clusters' that takes four arguments:\n",
    "# - behavioral_metrics_scaled: Scaled behavioral metrics data\n",
    "# - labels: Cluster labels assigned to each customer\n",
    "# - centroids: Centroids of the clusters\n",
    "# - iteration: Current iteration number\n",
    "\n",
    "def plot_clusters(behavioral_metrics_scaled, labels, centroids, iteration):\n",
    "    # Creating an instance of PCA with 2 components to reduce data to 2D\n",
    "    pca = PCA(n_components=2)\n",
    "    \n",
    "    # Transforming the scaled behavioral metrics data to 2D using PCA\n",
    "    behavioral_metrics_scaled_2d = pca.fit_transform(behavioral_metrics_scaled)\n",
    "    \n",
    "    # Transforming the centroids to 2D using PCA\n",
    "    centroids_2d = pca.transform(centroids.T)\n",
    "    \n",
    "    # Clearing previous plot and setting the title including the current iteration\n",
    "    clear_output(wait=True)\n",
    "    plt.title(f'Iteration {iteration}')\n",
    "    \n",
    "    # Creating a scatter plot of customer data points in 2D, coloring them by cluster labels\n",
    "    plt.scatter(x=behavioral_metrics_scaled_2d[:, 0], y=behavioral_metrics_scaled_2d[:, 1], c=labels)\n",
    "    \n",
    "    # Creating a scatter plot of centroids in 2D\n",
    "    plt.scatter(x=centroids_2d[:, 0], y=centroids_2d[:, 1])\n",
    "    \n",
    "    # Displaying the plot\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 452
    },
    "id": "u33u1amw2vta",
    "outputId": "c68e4ab8-acf1-4c7a-ee64-49aaeed37aab"
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setting the maximum number of iterations and the count of centroids\n",
    "max_iterations = 250\n",
    "centroid_count = 5\n",
    "\n",
    "# Initializing centroids using the random_centroids function\n",
    "centroids = random_centroids(behavioral_metrics_scaled, centroid_count)\n",
    "\n",
    "# Initializing an empty DataFrame to store old centroids for comparison\n",
    "old_centroids = pd.DataFrame()\n",
    "\n",
    "# Initializing the iteration counter\n",
    "iteration = 1\n",
    "\n",
    "# Loop to perform clustering iterations\n",
    "while iteration < max_iterations and not centroids.equals(old_centroids):\n",
    "    # Updating old centroids with the current centroids\n",
    "    old_centroids = centroids\n",
    "\n",
    "    # Assigning labels to each customer using the get_labels function\n",
    "    labels = get_labels(behavioral_metrics_scaled, centroids)\n",
    "    \n",
    "    # Updating centroids using the new_centroids function\n",
    "    centroids = new_centroids(behavioral_metrics_scaled, labels, centroid_count)\n",
    "    \n",
    "    # Plotting the clusters using the plot_clusters function\n",
    "    plot_clusters(behavioral_metrics_scaled, labels, centroids, iteration)\n",
    "    \n",
    "    # Incrementing the iteration counter\n",
    "    iteration += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "id": "HbmsKl452vo2",
    "outputId": "544f1885-181a-4e19-d7fb-a571a2e8d92e"
   },
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.47881</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_distance_km</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_total_spent</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.123358</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.175441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_hotel_length</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.156853</td>\n",
       "      <td>0.097270</td>\n",
       "      <td>0.119457</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_of_hotels</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.389079</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_of_flights</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_checked_bags</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recent_session</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.175142</td>\n",
       "      <td>0.528039</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_page_clicks</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.167312</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.450973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_session_length_minutes</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.051020</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.462028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trips_cancellations_ratio</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.346395</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  0         1         2         3         4\n",
       "age                         0.47881  0.000000  0.000000  0.000000  0.000000\n",
       "avg_distance_km             0.00000  0.000000  0.000000  0.000000  0.000000\n",
       "avg_total_spent             0.00000  0.123358  0.000000  0.000000  0.175441\n",
       "avg_hotel_length            0.00000  0.156853  0.097270  0.119457  0.000000\n",
       "number_of_hotels            0.00000  0.389079  0.000000  0.000000  0.000000\n",
       "number_of_flights           0.00000  0.000000  0.000000  0.000000  0.000000\n",
       "avg_checked_bags            0.00000  0.000000  0.000000  0.000000  0.000000\n",
       "recent_session              0.00000  0.000000  0.175142  0.528039  0.000000\n",
       "avg_page_clicks             0.00000  0.167312  0.000000  0.000000  0.450973\n",
       "avg_session_length_minutes  0.00000  0.051020  0.000000  0.000000  0.462028\n",
       "trips_cancellations_ratio   0.00000  0.000000  0.000000  0.000000  0.346395"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centroids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c-l20yor_MIl"
   },
   "source": [
    "# **Assigning a Label to each customer based on the clustering**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "id": "ucYp5SLo2vjG"
   },
   "outputs": [],
   "source": [
    "# Creating separate DataFrames for each segment (cluster)\n",
    "seg1_df=df_clean[labels == 0][[\"user_id\"] + selected_features]\n",
    "seg2_df=df_clean[labels == 1][[\"user_id\"] + selected_features]\n",
    "seg3_df=df_clean[labels == 2][[\"user_id\"] + selected_features]\n",
    "seg4_df=df_clean[labels == 3][[\"user_id\"] + selected_features]\n",
    "seg5_df=df_clean[labels == 4][[\"user_id\"] + selected_features]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Checking visual and statisical values to get more insights of each cluster**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "o7htbY5m2vbV",
    "outputId": "1a317fd9-7e76-4534-e1e6-ddf8fc6a4494"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>age</th>\n",
       "      <th>avg_distance_km</th>\n",
       "      <th>avg_total_spent</th>\n",
       "      <th>avg_hotel_length</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>544694</td>\n",
       "      <td>52</td>\n",
       "      <td>2127.37</td>\n",
       "      <td>128.25</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>474464</td>\n",
       "      <td>56</td>\n",
       "      <td>2681.22</td>\n",
       "      <td>173.55</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>79</td>\n",
       "      <td>14</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>530213</td>\n",
       "      <td>48</td>\n",
       "      <td>1681.53</td>\n",
       "      <td>126.08</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>74</td>\n",
       "      <td>14</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>550400</td>\n",
       "      <td>72</td>\n",
       "      <td>5212.32</td>\n",
       "      <td>380.36</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>127</td>\n",
       "      <td>30</td>\n",
       "      <td>16.4</td>\n",
       "      <td>0.1</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>501125</td>\n",
       "      <td>45</td>\n",
       "      <td>1107.08</td>\n",
       "      <td>146.50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>57</td>\n",
       "      <td>13</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id  age  avg_distance_km  avg_total_spent  avg_hotel_length  \\\n",
       "0    544694   52          2127.37           128.25               2.0   \n",
       "4    474464   56          2681.22           173.55               3.0   \n",
       "9    530213   48          1681.53           126.08               3.0   \n",
       "13   550400   72          5212.32           380.36               8.0   \n",
       "16   501125   45          1107.08           146.50               5.0   \n",
       "\n",
       "    number_of_hotels  number_of_flights  avg_checked_bags  recent_session  \\\n",
       "0                  2                  2               1.0              52   \n",
       "4                  2                  3               3.0              79   \n",
       "9                  3                  3               1.0              74   \n",
       "13                 1                  3               1.0             127   \n",
       "16                 2                  2               1.0              57   \n",
       "\n",
       "    avg_page_clicks  avg_session_length_minutes  trips_cancellations_ratio  \n",
       "0                12                         1.4                        0.0  \n",
       "4                14                         1.8                        0.0  \n",
       "9                14                         1.8                        0.0  \n",
       "13               30                        16.4                        0.1  \n",
       "16               13                         1.6                        0.0  "
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "seg1_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9Bnpktk_-Ail",
    "outputId": "ed7fe7b6-1953-4959-9f36-59b81aa20b5d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1798, 12)"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seg1_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>age</th>\n",
       "      <th>avg_distance_km</th>\n",
       "      <th>avg_total_spent</th>\n",
       "      <th>avg_hotel_length</th>\n",
       "      <th>number_of_hotels</th>\n",
       "      <th>number_of_flights</th>\n",
       "      <th>avg_checked_bags</th>\n",
       "      <th>recent_session</th>\n",
       "      <th>avg_page_clicks</th>\n",
       "      <th>avg_session_length_minutes</th>\n",
       "      <th>trips_cancellations_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1798.000000</td>\n",
       "      <td>1798.000000</td>\n",
       "      <td>1798.000000</td>\n",
       "      <td>1798.000000</td>\n",
       "      <td>1798.000000</td>\n",
       "      <td>1798.000000</td>\n",
       "      <td>1798.000000</td>\n",
       "      <td>1798.000000</td>\n",
       "      <td>1798.000000</td>\n",
       "      <td>1798.000000</td>\n",
       "      <td>1798.000000</td>\n",
       "      <td>1798.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>554073.328699</td>\n",
       "      <td>51.572859</td>\n",
       "      <td>1859.181669</td>\n",
       "      <td>115.146101</td>\n",
       "      <td>3.489433</td>\n",
       "      <td>1.759177</td>\n",
       "      <td>1.780311</td>\n",
       "      <td>0.710790</td>\n",
       "      <td>58.142380</td>\n",
       "      <td>14.429366</td>\n",
       "      <td>1.889321</td>\n",
       "      <td>0.001669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>68759.277263</td>\n",
       "      <td>9.146734</td>\n",
       "      <td>1684.073267</td>\n",
       "      <td>98.548704</td>\n",
       "      <td>3.044769</td>\n",
       "      <td>1.031697</td>\n",
       "      <td>1.180962</td>\n",
       "      <td>0.645006</td>\n",
       "      <td>20.873757</td>\n",
       "      <td>4.397635</td>\n",
       "      <td>1.236555</td>\n",
       "      <td>0.012812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>23557.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>519792.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>882.777500</td>\n",
       "      <td>42.105000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>548229.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>1777.490000</td>\n",
       "      <td>102.015000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>586600.750000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>2499.965000</td>\n",
       "      <td>163.160000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>811077.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>22272.190000</td>\n",
       "      <td>739.540000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>16.900000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "             user_id          age  avg_distance_km  avg_total_spent  \\\n",
       "count    1798.000000  1798.000000      1798.000000      1798.000000   \n",
       "mean   554073.328699    51.572859      1859.181669       115.146101   \n",
       "std     68759.277263     9.146734      1684.073267        98.548704   \n",
       "min     23557.000000    32.000000         0.000000         0.000000   \n",
       "25%    519792.000000    45.000000       882.777500        42.105000   \n",
       "50%    548229.000000    50.000000      1777.490000       102.015000   \n",
       "75%    586600.750000    56.000000      2499.965000       163.160000   \n",
       "max    811077.000000    88.000000     22272.190000       739.540000   \n",
       "\n",
       "       avg_hotel_length  number_of_hotels  number_of_flights  \\\n",
       "count       1798.000000       1798.000000        1798.000000   \n",
       "mean           3.489433          1.759177           1.780311   \n",
       "std            3.044769          1.031697           1.180962   \n",
       "min           -1.000000          0.000000           0.000000   \n",
       "25%            2.000000          1.000000           1.000000   \n",
       "50%            3.000000          2.000000           2.000000   \n",
       "75%            5.000000          2.000000           3.000000   \n",
       "max           28.000000          5.000000           6.000000   \n",
       "\n",
       "       avg_checked_bags  recent_session  avg_page_clicks  \\\n",
       "count       1798.000000     1798.000000      1798.000000   \n",
       "mean           0.710790       58.142380        14.429366   \n",
       "std            0.645006       20.873757         4.397635   \n",
       "min            0.000000       24.000000         4.000000   \n",
       "25%            0.000000       42.000000        11.000000   \n",
       "50%            1.000000       54.000000        14.000000   \n",
       "75%            1.000000       70.000000        17.000000   \n",
       "max            5.000000      154.000000        41.000000   \n",
       "\n",
       "       avg_session_length_minutes  trips_cancellations_ratio  \n",
       "count                 1798.000000                1798.000000  \n",
       "mean                     1.889321                   0.001669  \n",
       "std                      1.236555                   0.012812  \n",
       "min                      0.500000                   0.000000  \n",
       "25%                      1.400000                   0.000000  \n",
       "50%                      1.700000                   0.000000  \n",
       "75%                      2.100000                   0.000000  \n",
       "max                     16.900000                   0.100000  "
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seg1_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "EVPlyql59j8J",
    "outputId": "192d9d88-29f4-496b-8dba-928c427f5d04"
   },
   "outputs": [
    {
     "data": {
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       "\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>age</th>\n",
       "      <th>avg_distance_km</th>\n",
       "      <th>avg_total_spent</th>\n",
       "      <th>avg_hotel_length</th>\n",
       "      <th>number_of_hotels</th>\n",
       "      <th>number_of_flights</th>\n",
       "      <th>avg_checked_bags</th>\n",
       "      <th>recent_session</th>\n",
       "      <th>avg_page_clicks</th>\n",
       "      <th>avg_session_length_minutes</th>\n",
       "      <th>trips_cancellations_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>499979</td>\n",
       "      <td>49</td>\n",
       "      <td>2711.48</td>\n",
       "      <td>367.40</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50</td>\n",
       "      <td>19</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>546065</td>\n",
       "      <td>46</td>\n",
       "      <td>1650.73</td>\n",
       "      <td>143.91</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>82</td>\n",
       "      <td>18</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>560733</td>\n",
       "      <td>52</td>\n",
       "      <td>1411.93</td>\n",
       "      <td>278.38</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>49</td>\n",
       "      <td>14</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>290123</td>\n",
       "      <td>40</td>\n",
       "      <td>2533.38</td>\n",
       "      <td>692.90</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39</td>\n",
       "      <td>21</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>523442</td>\n",
       "      <td>53</td>\n",
       "      <td>1181.95</td>\n",
       "      <td>166.74</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33</td>\n",
       "      <td>23</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  age  avg_distance_km  avg_total_spent  avg_hotel_length  \\\n",
       "1   499979   49          2711.48           367.40               3.0   \n",
       "2   546065   46          1650.73           143.91               5.0   \n",
       "3   560733   52          1411.93           278.38               3.0   \n",
       "5   290123   40          2533.38           692.90               3.0   \n",
       "6   523442   53          1181.95           166.74               4.0   \n",
       "\n",
       "   number_of_hotels  number_of_flights  avg_checked_bags  recent_session  \\\n",
       "1                 4                  5               1.0              50   \n",
       "2                 3                  3               1.0              82   \n",
       "3                 4                  3               2.0              49   \n",
       "5                 6                  7               2.0              39   \n",
       "6                 4                  3               1.0              33   \n",
       "\n",
       "   avg_page_clicks  avg_session_length_minutes  trips_cancellations_ratio  \n",
       "1               19                         2.3                        0.0  \n",
       "2               18                         2.3                        0.0  \n",
       "3               14                         1.8                        0.0  \n",
       "5               21                         2.6                        0.0  \n",
       "6               23                         2.8                        0.0  "
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seg2_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iKT3t0UY9-nB",
    "outputId": "c97b8bbb-6df0-4588-9a43-dfe91e206c0c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2247, 12)"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seg2_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>age</th>\n",
       "      <th>avg_distance_km</th>\n",
       "      <th>avg_total_spent</th>\n",
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       "      <th>count</th>\n",
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       "      <td>39.013351</td>\n",
       "      <td>2227.189315</td>\n",
       "      <td>238.382377</td>\n",
       "      <td>4.281264</td>\n",
       "      <td>3.672452</td>\n",
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       "      <td>2.605429</td>\n",
       "      <td>0.006409</td>\n",
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       "      <th>std</th>\n",
       "      <td>60722.523448</td>\n",
       "      <td>7.959425</td>\n",
       "      <td>1489.844379</td>\n",
       "      <td>143.283005</td>\n",
       "      <td>2.389522</td>\n",
       "      <td>1.114729</td>\n",
       "      <td>1.340701</td>\n",
       "      <td>0.540512</td>\n",
       "      <td>17.924004</td>\n",
       "      <td>5.290433</td>\n",
       "      <td>1.859970</td>\n",
       "      <td>0.024496</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
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       "      <td>1.000000</td>\n",
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       "      <th>25%</th>\n",
       "      <td>516817.500000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1536.810000</td>\n",
       "      <td>146.235000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>537818.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>2020.420000</td>\n",
       "      <td>210.370000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>569952.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>2610.765000</td>\n",
       "      <td>297.405000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>max</th>\n",
       "      <td>785107.000000</td>\n",
       "      <td>63.000000</td>\n",
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       "      <td>1558.450000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>18.700000</td>\n",
       "      <td>0.100000</td>\n",
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      "text/plain": [
       "             user_id          age  avg_distance_km  avg_total_spent  \\\n",
       "count    2247.000000  2247.000000      2247.000000      2247.000000   \n",
       "mean   542690.671117    39.013351      2227.189315       238.382377   \n",
       "std     60722.523448     7.959425      1489.844379       143.283005   \n",
       "min    101961.000000    16.000000         0.000000         7.000000   \n",
       "25%    516817.500000    35.000000      1536.810000       146.235000   \n",
       "50%    537818.000000    39.000000      2020.420000       210.370000   \n",
       "75%    569952.000000    44.000000      2610.765000       297.405000   \n",
       "max    785107.000000    63.000000     23057.330000      1558.450000   \n",
       "\n",
       "       avg_hotel_length  number_of_hotels  number_of_flights  \\\n",
       "count       2247.000000       2247.000000        2247.000000   \n",
       "mean           4.281264          3.672452           3.408990   \n",
       "std            2.389522          1.114729           1.340701   \n",
       "min            1.000000          1.000000           0.000000   \n",
       "25%            3.000000          3.000000           3.000000   \n",
       "50%            4.000000          4.000000           3.000000   \n",
       "75%            5.000000          4.000000           4.000000   \n",
       "max           21.000000          9.000000           9.000000   \n",
       "\n",
       "       avg_checked_bags  recent_session  avg_page_clicks  \\\n",
       "count       2247.000000     2247.000000      2247.000000   \n",
       "mean           0.971518       54.287494        18.570538   \n",
       "std            0.540512       17.924004         5.290433   \n",
       "min            0.000000       24.000000         8.000000   \n",
       "25%            1.000000       40.000000        15.000000   \n",
       "50%            1.000000       50.000000        18.000000   \n",
       "75%            1.000000       65.000000        21.000000   \n",
       "max            5.000000      139.000000        55.000000   \n",
       "\n",
       "       avg_session_length_minutes  trips_cancellations_ratio  \n",
       "count                 2247.000000                2247.000000  \n",
       "mean                     2.605429                   0.006409  \n",
       "std                      1.859970                   0.024496  \n",
       "min                      1.000000                   0.000000  \n",
       "25%                      1.900000                   0.000000  \n",
       "50%                      2.200000                   0.000000  \n",
       "75%                      2.600000                   0.000000  \n",
       "max                     18.700000                   0.100000  "
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seg2_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "YhAYBK259jxq",
    "outputId": "bf66c2ee-454d-43a4-b604-bcba641dc9b6"
   },
   "outputs": [
    {
     "data": {
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       "      <th>14</th>\n",
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       "      <td>1.0</td>\n",
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       "      <th>675</th>\n",
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       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>82</td>\n",
       "      <td>11</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id  age  avg_distance_km  avg_total_spent  avg_hotel_length  \\\n",
       "14    498857   36          1964.98           166.30               4.0   \n",
       "42    528612   38          2356.40           138.23               1.0   \n",
       "387   512999   33          2606.84           227.29               2.0   \n",
       "411   556314   38         11243.92           822.33               7.0   \n",
       "675   523090   35          2787.78           115.80               4.0   \n",
       "\n",
       "     number_of_hotels  number_of_flights  avg_checked_bags  recent_session  \\\n",
       "14                  2                  3               1.0              75   \n",
       "42                  2                  2               1.0              81   \n",
       "387                 2                  3               1.0              83   \n",
       "411                 1                  2               1.0              79   \n",
       "675                 2                  2               1.0              82   \n",
       "\n",
       "     avg_page_clicks  avg_session_length_minutes  trips_cancellations_ratio  \n",
       "14                17                         2.1                        0.0  \n",
       "42                12                         1.4                        0.0  \n",
       "387               14                         1.7                        0.0  \n",
       "411               12                         1.6                        0.0  \n",
       "675               11                         1.4                        0.0  "
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seg3_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZgxAfq5E99Ba",
    "outputId": "f1197e05-5f4b-4caa-c3fc-3b7d88d1df36"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(679, 12)"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seg3_df.shape"
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  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>age</th>\n",
       "      <th>avg_distance_km</th>\n",
       "      <th>avg_total_spent</th>\n",
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       "      <th>number_of_hotels</th>\n",
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       "      <td>679.000000</td>\n",
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       "      <td>679.000000</td>\n",
       "      <td>679.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>552578.312224</td>\n",
       "      <td>25.907216</td>\n",
       "      <td>1287.113476</td>\n",
       "      <td>56.212901</td>\n",
       "      <td>3.365243</td>\n",
       "      <td>0.874816</td>\n",
       "      <td>0.787923</td>\n",
       "      <td>0.381443</td>\n",
       "      <td>57.754050</td>\n",
       "      <td>12.777614</td>\n",
       "      <td>1.614580</td>\n",
       "      <td>0.001031</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>68674.489524</td>\n",
       "      <td>7.097629</td>\n",
       "      <td>2085.214087</td>\n",
       "      <td>77.469743</td>\n",
       "      <td>3.822658</td>\n",
       "      <td>0.693798</td>\n",
       "      <td>0.840934</td>\n",
       "      <td>0.564702</td>\n",
       "      <td>14.683983</td>\n",
       "      <td>4.235929</td>\n",
       "      <td>0.642825</td>\n",
       "      <td>0.010108</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>133058.000000</td>\n",
       "      <td>16.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.000000</td>\n",
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       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>520474.500000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>546664.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>569.100000</td>\n",
       "      <td>34.210000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>586717.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>2114.910000</td>\n",
       "      <td>79.605000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>844489.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>20599.210000</td>\n",
       "      <td>822.330000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>0.100000</td>\n",
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       "             user_id         age  avg_distance_km  avg_total_spent  \\\n",
       "count     679.000000  679.000000       679.000000       679.000000   \n",
       "mean   552578.312224   25.907216      1287.113476        56.212901   \n",
       "std     68674.489524    7.097629      2085.214087        77.469743   \n",
       "min    133058.000000   16.000000         0.000000         0.000000   \n",
       "25%    520474.500000   20.000000         0.000000         0.000000   \n",
       "50%    546664.000000   25.000000       569.100000        34.210000   \n",
       "75%    586717.000000   32.000000      2114.910000        79.605000   \n",
       "max    844489.000000   42.000000     20599.210000       822.330000   \n",
       "\n",
       "       avg_hotel_length  number_of_hotels  number_of_flights  \\\n",
       "count        679.000000        679.000000         679.000000   \n",
       "mean           3.365243          0.874816           0.787923   \n",
       "std            3.822658          0.693798           0.840934   \n",
       "min            0.000000          0.000000           0.000000   \n",
       "25%            0.000000          0.000000           0.000000   \n",
       "50%            3.000000          1.000000           1.000000   \n",
       "75%            5.000000          1.000000           1.000000   \n",
       "max           30.000000          2.000000           4.000000   \n",
       "\n",
       "       avg_checked_bags  recent_session  avg_page_clicks  \\\n",
       "count        679.000000      679.000000       679.000000   \n",
       "mean           0.381443       57.754050        12.777614   \n",
       "std            0.564702       14.683983         4.235929   \n",
       "min            0.000000       28.000000         4.000000   \n",
       "25%            0.000000       45.000000        10.000000   \n",
       "50%            0.000000       59.000000        12.000000   \n",
       "75%            1.000000       70.000000        15.000000   \n",
       "max            3.000000       84.000000        44.000000   \n",
       "\n",
       "       avg_session_length_minutes  trips_cancellations_ratio  \n",
       "count                  679.000000                 679.000000  \n",
       "mean                     1.614580                   0.001031  \n",
       "std                      0.642825                   0.010108  \n",
       "min                      0.500000                   0.000000  \n",
       "25%                      1.200000                   0.000000  \n",
       "50%                      1.500000                   0.000000  \n",
       "75%                      1.900000                   0.000000  \n",
       "max                      7.200000                   0.100000  "
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seg3_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "4bqQyn-p9jnD",
    "outputId": "ca329c32-d4fc-4c76-bd8e-74b9a2f3e756"
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   "outputs": [
    {
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       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>528411</td>\n",
       "      <td>43</td>\n",
       "      <td>949.20</td>\n",
       "      <td>184.03</td>\n",
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       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>531789</td>\n",
       "      <td>40</td>\n",
       "      <td>3180.30</td>\n",
       "      <td>266.90</td>\n",
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       "    <tr>\n",
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       "      <td>53</td>\n",
       "      <td>2381.96</td>\n",
       "      <td>240.52</td>\n",
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       "      <td>126</td>\n",
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       "      <td>2.2</td>\n",
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       "      <th>53</th>\n",
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       "      <td>2972.29</td>\n",
       "      <td>244.36</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>545758</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>103</td>\n",
       "      <td>14</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "    user_id  age  avg_distance_km  avg_total_spent  avg_hotel_length  \\\n",
       "35   528411   43           949.20           184.03               2.0   \n",
       "37   531789   40          3180.30           266.90               4.0   \n",
       "38   513259   53          2381.96           240.52               5.0   \n",
       "53   523657   35          2972.29           244.36               2.0   \n",
       "56   545758   30          2712.56           111.61               4.0   \n",
       "\n",
       "    number_of_hotels  number_of_flights  avg_checked_bags  recent_session  \\\n",
       "35                 3                  4               1.0             135   \n",
       "37                 2                  3               1.0             125   \n",
       "38                 2                  3               1.0             126   \n",
       "53                 2                  2               1.0             142   \n",
       "56                 2                  2               1.0             103   \n",
       "\n",
       "    avg_page_clicks  avg_session_length_minutes  trips_cancellations_ratio  \n",
       "35               14                         1.8                        0.0  \n",
       "37               15                         1.8                        0.0  \n",
       "38               18                         2.2                        0.0  \n",
       "53               12                         1.4                        0.0  \n",
       "56               14                         1.7                        0.0  "
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seg4_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "w75tfikI-GUU",
    "outputId": "a7f5e09f-5282-4225-a7ef-b051b8bd567f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(898, 12)"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seg4_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>count</th>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
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       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>530632.158129</td>\n",
       "      <td>36.670379</td>\n",
       "      <td>1889.200111</td>\n",
       "      <td>119.667595</td>\n",
       "      <td>3.703786</td>\n",
       "      <td>1.730512</td>\n",
       "      <td>1.683742</td>\n",
       "      <td>0.678174</td>\n",
       "      <td>116.851893</td>\n",
       "      <td>14.610245</td>\n",
       "      <td>1.954009</td>\n",
       "      <td>0.002339</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>50934.958589</td>\n",
       "      <td>10.797953</td>\n",
       "      <td>2049.076978</td>\n",
       "      <td>115.817065</td>\n",
       "      <td>3.246679</td>\n",
       "      <td>1.114806</td>\n",
       "      <td>1.223234</td>\n",
       "      <td>0.695576</td>\n",
       "      <td>22.290504</td>\n",
       "      <td>4.717385</td>\n",
       "      <td>1.337000</td>\n",
       "      <td>0.015121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>106907.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>513967.750000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>623.392500</td>\n",
       "      <td>36.147500</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>528391.500000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1716.970000</td>\n",
       "      <td>96.515000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>553967.250000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>2522.090000</td>\n",
       "      <td>167.585000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>720819.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>20215.030000</td>\n",
       "      <td>1164.090000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>16.700000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             user_id         age  avg_distance_km  avg_total_spent  \\\n",
       "count     898.000000  898.000000       898.000000       898.000000   \n",
       "mean   530632.158129   36.670379      1889.200111       119.667595   \n",
       "std     50934.958589   10.797953      2049.076978       115.817065   \n",
       "min    106907.000000   16.000000         0.000000         0.000000   \n",
       "25%    513967.750000   29.000000       623.392500        36.147500   \n",
       "50%    528391.500000   38.000000      1716.970000        96.515000   \n",
       "75%    553967.250000   44.000000      2522.090000       167.585000   \n",
       "max    720819.000000   72.000000     20215.030000      1164.090000   \n",
       "\n",
       "       avg_hotel_length  number_of_hotels  number_of_flights  \\\n",
       "count        898.000000        898.000000         898.000000   \n",
       "mean           3.703786          1.730512           1.683742   \n",
       "std            3.246679          1.114806           1.223234   \n",
       "min            0.000000          0.000000           0.000000   \n",
       "25%            2.000000          1.000000           1.000000   \n",
       "50%            3.000000          2.000000           2.000000   \n",
       "75%            5.000000          2.000000           3.000000   \n",
       "max           23.000000          6.000000           5.000000   \n",
       "\n",
       "       avg_checked_bags  recent_session  avg_page_clicks  \\\n",
       "count        898.000000      898.000000       898.000000   \n",
       "mean           0.678174      116.851893        14.610245   \n",
       "std            0.695576       22.290504         4.717385   \n",
       "min            0.000000       80.000000         4.000000   \n",
       "25%            0.000000       99.000000        11.000000   \n",
       "50%            1.000000      113.000000        14.000000   \n",
       "75%            1.000000      132.000000        17.000000   \n",
       "max            5.000000      195.000000        39.000000   \n",
       "\n",
       "       avg_session_length_minutes  trips_cancellations_ratio  \n",
       "count                  898.000000                 898.000000  \n",
       "mean                     1.954009                   0.002339  \n",
       "std                      1.337000                   0.015121  \n",
       "min                      0.500000                   0.000000  \n",
       "25%                      1.400000                   0.000000  \n",
       "50%                      1.700000                   0.000000  \n",
       "75%                      2.100000                   0.000000  \n",
       "max                     16.700000                   0.100000  "
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seg4_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "YLyPiP0O9qwp",
    "outputId": "ed7179b3-9f13-4530-84d8-4a66ef43e3b3"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>age</th>\n",
       "      <th>avg_distance_km</th>\n",
       "      <th>avg_total_spent</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>359072</td>\n",
       "      <td>50</td>\n",
       "      <td>1051.14</td>\n",
       "      <td>160.31</td>\n",
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       "      <td>4</td>\n",
       "      <td>4</td>\n",
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       "      <td>114</td>\n",
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       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>513875</td>\n",
       "      <td>34</td>\n",
       "      <td>2090.50</td>\n",
       "      <td>504.83</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>44</td>\n",
       "      <td>39</td>\n",
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       "    <tr>\n",
       "      <th>165</th>\n",
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       "      <td>49</td>\n",
       "      <td>2415.27</td>\n",
       "      <td>141.68</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
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       "      <td>59</td>\n",
       "      <td>41</td>\n",
       "      <td>17.0</td>\n",
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       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>517131</td>\n",
       "      <td>67</td>\n",
       "      <td>21441.18</td>\n",
       "      <td>1037.30</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>170</th>\n",
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       "      <td>35</td>\n",
       "      <td>41</td>\n",
       "      <td>17.7</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "     user_id  age  avg_distance_km  avg_total_spent  avg_hotel_length  \\\n",
       "28    359072   50          1051.14           160.31               2.0   \n",
       "46    513875   34          2090.50           504.83               4.0   \n",
       "165   498500   49          2415.27           141.68               4.0   \n",
       "169   517131   67         21441.18          1037.30               7.0   \n",
       "170   306819   59           491.64           145.77               1.0   \n",
       "\n",
       "     number_of_hotels  number_of_flights  avg_checked_bags  recent_session  \\\n",
       "28                  4                  4               1.0             114   \n",
       "46                  3                  4               2.0              44   \n",
       "165                 4                  3               1.0              59   \n",
       "169                 4                  2               1.0              27   \n",
       "170                 3                  3               1.0              35   \n",
       "\n",
       "     avg_page_clicks  avg_session_length_minutes  trips_cancellations_ratio  \n",
       "28                40                        16.4                        0.1  \n",
       "46                39                        16.8                        0.1  \n",
       "165               41                        17.0                        0.1  \n",
       "169               30                        16.6                        0.1  \n",
       "170               41                        17.7                        0.1  "
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seg5_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2wFaXqsH2vRU",
    "outputId": "862ac63f-ce40-44a8-f1e7-8b6abfa74fdc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(376, 12)"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seg5_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>age</th>\n",
       "      <th>avg_distance_km</th>\n",
       "      <th>avg_total_spent</th>\n",
       "      <th>avg_hotel_length</th>\n",
       "      <th>number_of_hotels</th>\n",
       "      <th>number_of_flights</th>\n",
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       "      <th>avg_session_length_minutes</th>\n",
       "      <th>trips_cancellations_ratio</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>376.000000</td>\n",
       "      <td>376.000000</td>\n",
       "      <td>376.000000</td>\n",
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       "      <td>376.000000</td>\n",
       "      <td>376.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>543072.226064</td>\n",
       "      <td>39.093085</td>\n",
       "      <td>4257.873723</td>\n",
       "      <td>397.355532</td>\n",
       "      <td>4.244681</td>\n",
       "      <td>2.976064</td>\n",
       "      <td>3.446809</td>\n",
       "      <td>0.952128</td>\n",
       "      <td>64.412234</td>\n",
       "      <td>42.452128</td>\n",
       "      <td>16.996543</td>\n",
       "      <td>0.106649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>83137.260641</td>\n",
       "      <td>12.270044</td>\n",
       "      <td>4559.033924</td>\n",
       "      <td>329.313555</td>\n",
       "      <td>3.396268</td>\n",
       "      <td>1.675537</td>\n",
       "      <td>1.371069</td>\n",
       "      <td>0.702639</td>\n",
       "      <td>29.303429</td>\n",
       "      <td>10.343067</td>\n",
       "      <td>3.137092</td>\n",
       "      <td>0.033995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>171470.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.250000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>513604.500000</td>\n",
       "      <td>30.750000</td>\n",
       "      <td>1626.245000</td>\n",
       "      <td>171.292500</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>16.100000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>544545.500000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>2477.635000</td>\n",
       "      <td>290.005000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>56.500000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>16.800000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>585753.250000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>4539.627500</td>\n",
       "      <td>495.940000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>770252.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>22098.000000</td>\n",
       "      <td>1637.160000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>35.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "             user_id         age  avg_distance_km  avg_total_spent  \\\n",
       "count     376.000000  376.000000       376.000000       376.000000   \n",
       "mean   543072.226064   39.093085      4257.873723       397.355532   \n",
       "std     83137.260641   12.270044      4559.033924       329.313555   \n",
       "min    171470.000000   16.000000         0.000000        20.250000   \n",
       "25%    513604.500000   30.750000      1626.245000       171.292500   \n",
       "50%    544545.500000   40.000000      2477.635000       290.005000   \n",
       "75%    585753.250000   47.000000      4539.627500       495.940000   \n",
       "max    770252.000000   80.000000     22098.000000      1637.160000   \n",
       "\n",
       "       avg_hotel_length  number_of_hotels  number_of_flights  \\\n",
       "count        376.000000        376.000000         376.000000   \n",
       "mean           4.244681          2.976064           3.446809   \n",
       "std            3.396268          1.675537           1.371069   \n",
       "min           -1.000000          0.000000           0.000000   \n",
       "25%            2.000000          2.000000           2.000000   \n",
       "50%            3.000000          3.000000           3.000000   \n",
       "75%            6.000000          4.000000           4.000000   \n",
       "max           20.000000          7.000000           8.000000   \n",
       "\n",
       "       avg_checked_bags  recent_session  avg_page_clicks  \\\n",
       "count        376.000000      376.000000       376.000000   \n",
       "mean           0.952128       64.412234        42.452128   \n",
       "std            0.702639       29.303429        10.343067   \n",
       "min            0.000000       24.000000        13.000000   \n",
       "25%            1.000000       41.000000        36.000000   \n",
       "50%            1.000000       56.500000        42.000000   \n",
       "75%            1.000000       85.000000        48.000000   \n",
       "max            5.000000      162.000000        86.000000   \n",
       "\n",
       "       avg_session_length_minutes  trips_cancellations_ratio  \n",
       "count                  376.000000                 376.000000  \n",
       "mean                    16.996543                   0.106649  \n",
       "std                      3.137092                   0.033995  \n",
       "min                      7.000000                   0.100000  \n",
       "25%                     16.100000                   0.100000  \n",
       "50%                     16.800000                   0.100000  \n",
       "75%                     18.100000                   0.100000  \n",
       "max                     35.600000                   0.300000  "
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seg5_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Putting the most important statistical values in one Dataframe**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>avg_distance_km_Median</th>\n",
       "      <th>avg_total_spent_Min</th>\n",
       "      <th>avg_total_spent_Max</th>\n",
       "      <th>avg_total_spent_Mean</th>\n",
       "      <th>avg_total_spent_Median</th>\n",
       "      <th>avg_hotel_length_Min</th>\n",
       "      <th>avg_hotel_length_Max</th>\n",
       "      <th>avg_hotel_length_Mean</th>\n",
       "      <th>avg_hotel_length_Median</th>\n",
       "      <th>number_of_hotels_Min</th>\n",
       "      <th>number_of_hotels_Max</th>\n",
       "      <th>number_of_hotels_Mean</th>\n",
       "      <th>number_of_hotels_Median</th>\n",
       "      <th>number_of_flights_Min</th>\n",
       "      <th>number_of_flights_Max</th>\n",
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       "      <th>number_of_flights_Median</th>\n",
       "      <th>avg_checked_bags_Min</th>\n",
       "      <th>avg_checked_bags_Max</th>\n",
       "      <th>avg_checked_bags_Mean</th>\n",
       "      <th>avg_checked_bags_Median</th>\n",
       "      <th>recent_session_Min</th>\n",
       "      <th>recent_session_Max</th>\n",
       "      <th>recent_session_Mean</th>\n",
       "      <th>recent_session_Median</th>\n",
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       "      <th>avg_page_clicks_Max</th>\n",
       "      <th>avg_page_clicks_Mean</th>\n",
       "      <th>avg_page_clicks_Median</th>\n",
       "      <th>avg_session_length_minutes_Min</th>\n",
       "      <th>avg_session_length_minutes_Max</th>\n",
       "      <th>avg_session_length_minutes_Mean</th>\n",
       "      <th>avg_session_length_minutes_Median</th>\n",
       "      <th>trips_cancellations_ratio_Min</th>\n",
       "      <th>trips_cancellations_ratio_Max</th>\n",
       "      <th>trips_cancellations_ratio_Mean</th>\n",
       "      <th>trips_cancellations_ratio_Median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Segment 1</td>\n",
       "      <td>32</td>\n",
       "      <td>88</td>\n",
       "      <td>51.572859</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22272.19</td>\n",
       "      <td>1859.181669</td>\n",
       "      <td>1777.490</td>\n",
       "      <td>0.00</td>\n",
       "      <td>739.54</td>\n",
       "      <td>115.146101</td>\n",
       "      <td>102.015</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>3.489433</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1.759177</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.780311</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.710790</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24</td>\n",
       "      <td>154</td>\n",
       "      <td>58.142380</td>\n",
       "      <td>54.0</td>\n",
       "      <td>4</td>\n",
       "      <td>41</td>\n",
       "      <td>14.429366</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>16.9</td>\n",
       "      <td>1.889321</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.001669</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Segment 2</td>\n",
       "      <td>16</td>\n",
       "      <td>63</td>\n",
       "      <td>39.013351</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23057.33</td>\n",
       "      <td>2227.189315</td>\n",
       "      <td>2020.420</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1558.45</td>\n",
       "      <td>238.382377</td>\n",
       "      <td>210.370</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4.281264</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>3.672452</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>3.408990</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.971518</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24</td>\n",
       "      <td>139</td>\n",
       "      <td>54.287494</td>\n",
       "      <td>50.0</td>\n",
       "      <td>8</td>\n",
       "      <td>55</td>\n",
       "      <td>18.570538</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>2.605429</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.006409</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Segment 3</td>\n",
       "      <td>16</td>\n",
       "      <td>42</td>\n",
       "      <td>25.907216</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20599.21</td>\n",
       "      <td>1287.113476</td>\n",
       "      <td>569.100</td>\n",
       "      <td>0.00</td>\n",
       "      <td>822.33</td>\n",
       "      <td>56.212901</td>\n",
       "      <td>34.210</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3.365243</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.874816</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.787923</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.381443</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28</td>\n",
       "      <td>84</td>\n",
       "      <td>57.754050</td>\n",
       "      <td>59.0</td>\n",
       "      <td>4</td>\n",
       "      <td>44</td>\n",
       "      <td>12.777614</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>7.2</td>\n",
       "      <td>1.614580</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.001031</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Segment 4</td>\n",
       "      <td>16</td>\n",
       "      <td>72</td>\n",
       "      <td>36.670379</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20215.03</td>\n",
       "      <td>1889.200111</td>\n",
       "      <td>1716.970</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1164.09</td>\n",
       "      <td>119.667595</td>\n",
       "      <td>96.515</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3.703786</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.730512</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1.683742</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.678174</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80</td>\n",
       "      <td>195</td>\n",
       "      <td>116.851893</td>\n",
       "      <td>113.0</td>\n",
       "      <td>4</td>\n",
       "      <td>39</td>\n",
       "      <td>14.610245</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>16.7</td>\n",
       "      <td>1.954009</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.002339</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Segment 5</td>\n",
       "      <td>16</td>\n",
       "      <td>80</td>\n",
       "      <td>39.093085</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22098.00</td>\n",
       "      <td>4257.873723</td>\n",
       "      <td>2477.635</td>\n",
       "      <td>20.25</td>\n",
       "      <td>1637.16</td>\n",
       "      <td>397.355532</td>\n",
       "      <td>290.005</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.244681</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2.976064</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3.446809</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.952128</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24</td>\n",
       "      <td>162</td>\n",
       "      <td>64.412234</td>\n",
       "      <td>56.5</td>\n",
       "      <td>13</td>\n",
       "      <td>86</td>\n",
       "      <td>42.452128</td>\n",
       "      <td>42.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>35.6</td>\n",
       "      <td>16.996543</td>\n",
       "      <td>16.8</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.106649</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Cluster  age_Min  age_Max   age_Mean  age_Median  avg_distance_km_Min  \\\n",
       "0  Segment 1       32       88  51.572859        50.0                  0.0   \n",
       "1  Segment 2       16       63  39.013351        39.0                  0.0   \n",
       "2  Segment 3       16       42  25.907216        25.0                  0.0   \n",
       "3  Segment 4       16       72  36.670379        38.0                  0.0   \n",
       "4  Segment 5       16       80  39.093085        40.0                  0.0   \n",
       "\n",
       "   avg_distance_km_Max  avg_distance_km_Mean  avg_distance_km_Median  \\\n",
       "0             22272.19           1859.181669                1777.490   \n",
       "1             23057.33           2227.189315                2020.420   \n",
       "2             20599.21           1287.113476                 569.100   \n",
       "3             20215.03           1889.200111                1716.970   \n",
       "4             22098.00           4257.873723                2477.635   \n",
       "\n",
       "   avg_total_spent_Min  avg_total_spent_Max  avg_total_spent_Mean  \\\n",
       "0                 0.00               739.54            115.146101   \n",
       "1                 7.00              1558.45            238.382377   \n",
       "2                 0.00               822.33             56.212901   \n",
       "3                 0.00              1164.09            119.667595   \n",
       "4                20.25              1637.16            397.355532   \n",
       "\n",
       "   avg_total_spent_Median  avg_hotel_length_Min  avg_hotel_length_Max  \\\n",
       "0                 102.015                  -1.0                  28.0   \n",
       "1                 210.370                   1.0                  21.0   \n",
       "2                  34.210                   0.0                  30.0   \n",
       "3                  96.515                   0.0                  23.0   \n",
       "4                 290.005                  -1.0                  20.0   \n",
       "\n",
       "   avg_hotel_length_Mean  avg_hotel_length_Median  number_of_hotels_Min  \\\n",
       "0               3.489433                      3.0                     0   \n",
       "1               4.281264                      4.0                     1   \n",
       "2               3.365243                      3.0                     0   \n",
       "3               3.703786                      3.0                     0   \n",
       "4               4.244681                      3.0                     0   \n",
       "\n",
       "   number_of_hotels_Max  number_of_hotels_Mean  number_of_hotels_Median  \\\n",
       "0                     5               1.759177                      2.0   \n",
       "1                     9               3.672452                      4.0   \n",
       "2                     2               0.874816                      1.0   \n",
       "3                     6               1.730512                      2.0   \n",
       "4                     7               2.976064                      3.0   \n",
       "\n",
       "   number_of_flights_Min  number_of_flights_Max  number_of_flights_Mean  \\\n",
       "0                      0                      6                1.780311   \n",
       "1                      0                      9                3.408990   \n",
       "2                      0                      4                0.787923   \n",
       "3                      0                      5                1.683742   \n",
       "4                      0                      8                3.446809   \n",
       "\n",
       "   number_of_flights_Median  avg_checked_bags_Min  avg_checked_bags_Max  \\\n",
       "0                       2.0                   0.0                   5.0   \n",
       "1                       3.0                   0.0                   5.0   \n",
       "2                       1.0                   0.0                   3.0   \n",
       "3                       2.0                   0.0                   5.0   \n",
       "4                       3.0                   0.0                   5.0   \n",
       "\n",
       "   avg_checked_bags_Mean  avg_checked_bags_Median  recent_session_Min  \\\n",
       "0               0.710790                      1.0                  24   \n",
       "1               0.971518                      1.0                  24   \n",
       "2               0.381443                      0.0                  28   \n",
       "3               0.678174                      1.0                  80   \n",
       "4               0.952128                      1.0                  24   \n",
       "\n",
       "   recent_session_Max  recent_session_Mean  recent_session_Median  \\\n",
       "0                 154            58.142380                   54.0   \n",
       "1                 139            54.287494                   50.0   \n",
       "2                  84            57.754050                   59.0   \n",
       "3                 195           116.851893                  113.0   \n",
       "4                 162            64.412234                   56.5   \n",
       "\n",
       "   avg_page_clicks_Min  avg_page_clicks_Max  avg_page_clicks_Mean  \\\n",
       "0                    4                   41             14.429366   \n",
       "1                    8                   55             18.570538   \n",
       "2                    4                   44             12.777614   \n",
       "3                    4                   39             14.610245   \n",
       "4                   13                   86             42.452128   \n",
       "\n",
       "   avg_page_clicks_Median  avg_session_length_minutes_Min  \\\n",
       "0                    14.0                             0.5   \n",
       "1                    18.0                             1.0   \n",
       "2                    12.0                             0.5   \n",
       "3                    14.0                             0.5   \n",
       "4                    42.0                             7.0   \n",
       "\n",
       "   avg_session_length_minutes_Max  avg_session_length_minutes_Mean  \\\n",
       "0                            16.9                         1.889321   \n",
       "1                            18.7                         2.605429   \n",
       "2                             7.2                         1.614580   \n",
       "3                            16.7                         1.954009   \n",
       "4                            35.6                        16.996543   \n",
       "\n",
       "   avg_session_length_minutes_Median  trips_cancellations_ratio_Min  \\\n",
       "0                                1.7                            0.0   \n",
       "1                                2.2                            0.0   \n",
       "2                                1.5                            0.0   \n",
       "3                                1.7                            0.0   \n",
       "4                               16.8                            0.1   \n",
       "\n",
       "   trips_cancellations_ratio_Max  trips_cancellations_ratio_Mean  \\\n",
       "0                            0.1                        0.001669   \n",
       "1                            0.1                        0.006409   \n",
       "2                            0.1                        0.001031   \n",
       "3                            0.1                        0.002339   \n",
       "4                            0.3                        0.106649   \n",
       "\n",
       "   trips_cancellations_ratio_Median  \n",
       "0                               0.0  \n",
       "1                               0.0  \n",
       "2                               0.0  \n",
       "3                               0.0  \n",
       "4                               0.1  "
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "# Add the 'Cluster' column to each segment dataframe\n",
    "seg1_df['Cluster'] = 'Segment 1'\n",
    "seg2_df['Cluster'] = 'Segment 2'\n",
    "seg3_df['Cluster'] = 'Segment 3'\n",
    "seg4_df['Cluster'] = 'Segment 4'\n",
    "seg5_df['Cluster'] = 'Segment 5'\n",
    "\n",
    "# Combine all the segment dataframes into a single dataframe\n",
    "combined_df = pd.concat([seg1_df, seg2_df, seg3_df, seg4_df, seg5_df], ignore_index=True)\n",
    "\n",
    "# Create an empty list to store the summary data\n",
    "summary_data = []\n",
    "\n",
    "# Iterate through each cluster\n",
    "for cluster_id in range(5):\n",
    "    cluster_name = f\"Segment {cluster_id + 1}\"\n",
    "    cluster_data = {'Cluster': cluster_name}\n",
    "    \n",
    "    # Iterate through selected features\n",
    "    for feature in selected_features:\n",
    "        feature_data = combined_df[combined_df['Cluster'] == cluster_name][feature]\n",
    "        feature_min = feature_data.min()\n",
    "        feature_max = feature_data.max()\n",
    "        feature_mean = feature_data.mean()\n",
    "        feature_median = feature_data.median()\n",
    "        \n",
    "        # Add feature summary to cluster data\n",
    "        cluster_data[f'{feature}_Min'] = feature_min\n",
    "        cluster_data[f'{feature}_Max'] = feature_max\n",
    "        cluster_data[f'{feature}_Mean'] = feature_mean\n",
    "        cluster_data[f'{feature}_Median'] = feature_median\n",
    "    \n",
    "    # Add cluster data to summary list\n",
    "    summary_data.append(cluster_data)\n",
    "\n",
    "# Create a summary dataframe from the list of summary data\n",
    "summary_df = pd.DataFrame(summary_data)\n",
    "\n",
    "# Print the resulting summary dataframe\n",
    "summary_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "# export to csv to analyse it with a pivot table or in tableau\n",
    "#summary_df.to_csv('/Users/sebastiangurgel/Desktop/Masteryproject1/K_means_summary.csv')"
   ]
  }
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