tour.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tourism Spot Recommendation System\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Recommendation systems have become widely popular in today's world, with applications in virtually every sector ranging from e-commerce to banking.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Imprt necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "sns.set_style('dark')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will be making use of three datasets gotten from kaggle. The datasets are dummy datasets created to mimick real life data collected from tourists for a destination. \n",
    "\n",
    "* The first dataset contains users and ratings for 23 different spots which may be restaurants,beaches and the likes.\n",
    "\n",
    "* The second contains information about the users; users' age, gender, region,job etc\n",
    "\n",
    "* The third dataset contains information about the destination spots and their classifications and quality on a scale of 5."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Starting with the destination dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "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>itemId</th>\n",
       "      <th>ItemName</th>\n",
       "      <th>Category</th>\n",
       "      <th>Quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Restaurant Fake</td>\n",
       "      <td>['Gastro']</td>\n",
       "      <td>1.647351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Fiction Nightclub</td>\n",
       "      <td>['Nightlf']</td>\n",
       "      <td>1.188559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Random Shopping Mall</td>\n",
       "      <td>['Shop', 'Relax']</td>\n",
       "      <td>2.948170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Bogus Waterpark</td>\n",
       "      <td>['Themeprk']</td>\n",
       "      <td>1.830793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Unknown Nature Route</td>\n",
       "      <td>['Nature', 'Relax']</td>\n",
       "      <td>2.782113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Some Sport Event</td>\n",
       "      <td>['Sports', 'Events']</td>\n",
       "      <td>4.476363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Never Happened Festival</td>\n",
       "      <td>['Events', 'Culture']</td>\n",
       "      <td>1.836556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>False Tavern</td>\n",
       "      <td>['Gastro', 'Culture']</td>\n",
       "      <td>1.365270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>MakeBelieve Pub</td>\n",
       "      <td>['Gastro', 'Nightlf']</td>\n",
       "      <td>1.957996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Another Sport Event</td>\n",
       "      <td>['Sports', 'Events']</td>\n",
       "      <td>2.133209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>Surprise Concert</td>\n",
       "      <td>['Events', 'Culture']</td>\n",
       "      <td>1.264327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>Museum of Fake History</td>\n",
       "      <td>['Culture']</td>\n",
       "      <td>3.281484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>Fake BTT Route</td>\n",
       "      <td>['Sports', 'Nature']</td>\n",
       "      <td>1.633559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>Random Surfing Lessons</td>\n",
       "      <td>['Sports', 'Nature']</td>\n",
       "      <td>4.649384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>Fake Brands Boutique</td>\n",
       "      <td>['Shop']</td>\n",
       "      <td>3.611481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>Best Imaginary Restaurant</td>\n",
       "      <td>['Gastro']</td>\n",
       "      <td>2.730529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>Bogus Spa</td>\n",
       "      <td>['Relax']</td>\n",
       "      <td>4.598459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>Random Cultural Tour</td>\n",
       "      <td>['Culture']</td>\n",
       "      <td>4.984842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>Nonexisting Zipline</td>\n",
       "      <td>['Sports']</td>\n",
       "      <td>1.853823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>Fake Klub</td>\n",
       "      <td>['Nightlf']</td>\n",
       "      <td>4.636606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>Random Golf Lessons</td>\n",
       "      <td>['Sports']</td>\n",
       "      <td>1.452114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>Secret Beach</td>\n",
       "      <td>['Beach']</td>\n",
       "      <td>1.239977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>Fake Beach</td>\n",
       "      <td>['Beach']</td>\n",
       "      <td>4.736056</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    itemId                   ItemName               Category   Quality\n",
       "0        0            Restaurant Fake             ['Gastro']  1.647351\n",
       "1        1          Fiction Nightclub            ['Nightlf']  1.188559\n",
       "2        2       Random Shopping Mall      ['Shop', 'Relax']  2.948170\n",
       "3        3            Bogus Waterpark           ['Themeprk']  1.830793\n",
       "4        4       Unknown Nature Route    ['Nature', 'Relax']  2.782113\n",
       "5        5           Some Sport Event   ['Sports', 'Events']  4.476363\n",
       "6        6    Never Happened Festival  ['Events', 'Culture']  1.836556\n",
       "7        7               False Tavern  ['Gastro', 'Culture']  1.365270\n",
       "8        8            MakeBelieve Pub  ['Gastro', 'Nightlf']  1.957996\n",
       "9        9        Another Sport Event   ['Sports', 'Events']  2.133209\n",
       "10      10           Surprise Concert  ['Events', 'Culture']  1.264327\n",
       "11      11     Museum of Fake History            ['Culture']  3.281484\n",
       "12      12             Fake BTT Route   ['Sports', 'Nature']  1.633559\n",
       "13      13     Random Surfing Lessons   ['Sports', 'Nature']  4.649384\n",
       "14      14       Fake Brands Boutique               ['Shop']  3.611481\n",
       "15      15  Best Imaginary Restaurant             ['Gastro']  2.730529\n",
       "16      16                  Bogus Spa              ['Relax']  4.598459\n",
       "17      17       Random Cultural Tour            ['Culture']  4.984842\n",
       "18      18        Nonexisting Zipline             ['Sports']  1.853823\n",
       "19      19                  Fake Klub            ['Nightlf']  4.636606\n",
       "20      20        Random Golf Lessons             ['Sports']  1.452114\n",
       "21      21               Secret Beach              ['Beach']  1.239977\n",
       "22      22                 Fake Beach              ['Beach']  4.736056"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read in dataset\n",
    "destination_spots=pd.read_csv(r'C:\\Users\\jessica.ogwu\\Desktop\\Tourism spot recommendation system\\tourism\\tourism datasets\\destination_info.csv')\n",
    "destination_spots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23, 4)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "destination_spots.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset contains 23 destination spots ranging from restaurants to beaches to sports. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>itemId</th>\n",
       "      <th>Quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>23.00000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.00000</td>\n",
       "      <td>2.732131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.78233</td>\n",
       "      <td>1.349089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.188559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.50000</td>\n",
       "      <td>1.640455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11.00000</td>\n",
       "      <td>2.133209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16.50000</td>\n",
       "      <td>4.043922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>22.00000</td>\n",
       "      <td>4.984842</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "         itemId    Quality\n",
       "count  23.00000  23.000000\n",
       "mean   11.00000   2.732131\n",
       "std     6.78233   1.349089\n",
       "min     0.00000   1.188559\n",
       "25%     5.50000   1.640455\n",
       "50%    11.00000   2.133209\n",
       "75%    16.50000   4.043922\n",
       "max    22.00000   4.984842"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "destination_spots.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 23 entries, 0 to 22\n",
      "Data columns (total 4 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   itemId    23 non-null     int64  \n",
      " 1   ItemName  23 non-null     object \n",
      " 2   Category  23 non-null     object \n",
      " 3   Quality   23 non-null     float64\n",
      "dtypes: float64(1), int64(1), object(2)\n",
      "memory usage: 864.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "#Get information about dataset\n",
    "destination_spots.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no null values in the dataset as seen from the above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([\"['Gastro']\", \"['Nightlf']\", \"['Shop', 'Relax']\", \"['Themeprk']\",\n",
       "       \"['Nature', 'Relax']\", \"['Sports', 'Events']\",\n",
       "       \"['Events', 'Culture']\", \"['Gastro', 'Culture']\",\n",
       "       \"['Gastro', 'Nightlf']\", \"['Culture']\", \"['Sports', 'Nature']\",\n",
       "       \"['Shop']\", \"['Relax']\", \"['Sports']\", \"['Beach']\"], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Explore catgories of destinations available\n",
    "destination_spots['Category'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "destination_spots['Category'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 15 unique categories according to the dataset. Although some restaurants have a combination of 2 categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(destination_spots['Category'].unique()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define a function to count the number of individual categories for the destinations\n",
    "def count_categories(category):\n",
    "    count=0\n",
    "    for i in destination_spots['Category']:\n",
    "        if category in i:\n",
    "            count+=1\n",
    "    return count\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 4 destinations under the gastro category\n",
      "There are 3 destinations under the nightlife category\n",
      "There are 2 destinations under the shop category\n",
      "There are 3 destinations under the relax category\n",
      "There are 4 destinations under the events category\n",
      "There are 5 destinations under the culture category\n",
      "There are 6 destinations under the sports category\n",
      "There are 2 destinations under the beach category\n",
      "There are 3 destinations under the nature category\n",
      "There are 1 destinations under the themepark category\n"
     ]
    }
   ],
   "source": [
    "print(f'There are {count_categories(\"Gastro\")} destinations under the gastro category')\n",
    "print(f'There are {count_categories(\"Nightlf\")} destinations under the nightlife category')\n",
    "print(f'There are {count_categories(\"Shop\")} destinations under the shop category')\n",
    "print(f'There are {count_categories(\"Relax\")} destinations under the relax category')\n",
    "print(f'There are {count_categories(\"Events\")} destinations under the events category')\n",
    "print(f'There are {count_categories(\"Culture\")} destinations under the culture category')\n",
    "print(f'There are {count_categories(\"Sports\")} destinations under the sports category')\n",
    "print(f'There are {count_categories(\"Beach\")} destinations under the beach category')\n",
    "print(f'There are {count_categories(\"Nature\")} destinations under the nature category')\n",
    "print(f'There are {count_categories(\"Themeprk\")} destinations under the themepark category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>Category</th>\n",
       "      <th>Quality</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>Random Cultural Tour</td>\n",
       "      <td>['Culture']</td>\n",
       "      <td>4.984842</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    itemId              ItemName     Category   Quality\n",
       "17      17  Random Cultural Tour  ['Culture']  4.984842"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Getting the spot with the highest quality\n",
    "max_quality=destination_spots['Quality'].max()\n",
    "destination_spots[destination_spots[\"Quality\"]==max_quality]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Fiction Nightclub</td>\n",
       "      <td>['Nightlf']</td>\n",
       "      <td>1.188559</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   itemId           ItemName     Category   Quality\n",
       "1       1  Fiction Nightclub  ['Nightlf']  1.188559"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Getting the sot with the lowest quality\n",
    "min_quality=destination_spots['Quality'].min()\n",
    "destination_spots[destination_spots[\"Quality\"]==min_quality]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## User Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Age</th>\n",
       "      <th>AcDeg</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Accom</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Job</th>\n",
       "      <th>Region</th>\n",
       "      <th>GroupComp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>white collar</td>\n",
       "      <td>South Europe</td>\n",
       "      <td>2Adlt+Child</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>North Europe</td>\n",
       "      <td>2Adlt+Child</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>South America</td>\n",
       "      <td>1Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>South Europe</td>\n",
       "      <td>2Adlt+Child</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>North Europe</td>\n",
       "      <td>1Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>white collar</td>\n",
       "      <td>South Europe</td>\n",
       "      <td>1Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>white collar</td>\n",
       "      <td>South America</td>\n",
       "      <td>2Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>South Europe</td>\n",
       "      <td>1Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>white collar</td>\n",
       "      <td>North Europe</td>\n",
       "      <td>2Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>white collar</td>\n",
       "      <td>Middle East</td>\n",
       "      <td>2Adlt+Child</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>South Europe</td>\n",
       "      <td>1Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>North Europe</td>\n",
       "      <td>2Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>North Europe</td>\n",
       "      <td>1Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>white collar</td>\n",
       "      <td>South Europe</td>\n",
       "      <td>1Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>South Europe</td>\n",
       "      <td>2Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>white collar</td>\n",
       "      <td>Middle East</td>\n",
       "      <td>2Adlt+Child</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>East Europe</td>\n",
       "      <td>1Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>white collar</td>\n",
       "      <td>North America</td>\n",
       "      <td>2Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>white collar</td>\n",
       "      <td>North America</td>\n",
       "      <td>2Adlt+Child</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>white collar</td>\n",
       "      <td>North Europe</td>\n",
       "      <td>2Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>South Europe</td>\n",
       "      <td>2Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>white collar</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>white collar</td>\n",
       "      <td>North Europe</td>\n",
       "      <td>2Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>white collar</td>\n",
       "      <td>South Europe</td>\n",
       "      <td>2Adlt+Child</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>South Europe</td>\n",
       "      <td>1Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>white collar</td>\n",
       "      <td>North America</td>\n",
       "      <td>GrpFriends</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>white collar</td>\n",
       "      <td>North Europe</td>\n",
       "      <td>2Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2Adlt+Child</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>South Europe</td>\n",
       "      <td>2Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>white collar</td>\n",
       "      <td>South America</td>\n",
       "      <td>2Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>North Europe</td>\n",
       "      <td>2Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>white collar</td>\n",
       "      <td>North Europe</td>\n",
       "      <td>2Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>South Europe</td>\n",
       "      <td>GrpFriends</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>white collar</td>\n",
       "      <td>North Europe</td>\n",
       "      <td>1Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>white collar</td>\n",
       "      <td>South Europe</td>\n",
       "      <td>2Adlt+Child</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>North Europe</td>\n",
       "      <td>2Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>Middle East</td>\n",
       "      <td>2Adlt+Child</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>white collar</td>\n",
       "      <td>North Europe</td>\n",
       "      <td>2Adlt+Child</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>white collar</td>\n",
       "      <td>South Europe</td>\n",
       "      <td>2Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>white collar</td>\n",
       "      <td>South Europe</td>\n",
       "      <td>2Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>white collar</td>\n",
       "      <td>East Europe</td>\n",
       "      <td>2Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>Middle East</td>\n",
       "      <td>GrpFriends</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>white collar</td>\n",
       "      <td>North America</td>\n",
       "      <td>2Adlt+Child</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>white collar</td>\n",
       "      <td>South America</td>\n",
       "      <td>1Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>North America</td>\n",
       "      <td>1Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>white collar</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2Adlt+Child</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>white collar</td>\n",
       "      <td>North Europe</td>\n",
       "      <td>2Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>East Europe</td>\n",
       "      <td>2Adlt+Child</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age  AcDeg  Budget  Accom  Gender           Job         Region  \\\n",
       "userId                                                                   \n",
       "0         1      3       1      1  Female  white collar   South Europe   \n",
       "1         2      3       2      2    Male   blue collar   North Europe   \n",
       "2         2      3       2      2    Male   blue collar  South America   \n",
       "3         5      3       2      2  Female   blue collar   South Europe   \n",
       "4         3      1       1      2    Male   blue collar   North Europe   \n",
       "5         1      3       1      1  Female  white collar   South Europe   \n",
       "6         2      2       1      2  Female   blue collar         Africa   \n",
       "7         5      4       3      2    Male  white collar  South America   \n",
       "8         4      3       2      2    Male   blue collar   South Europe   \n",
       "9         2      3       2      2    Male  white collar   North Europe   \n",
       "10        3      4       2      2  Female  white collar    Middle East   \n",
       "11        3      3       2      2    Male   blue collar   South Europe   \n",
       "12        3      4       2      2    Male   blue collar   North Europe   \n",
       "13        4      3       2      2  Female   blue collar   North Europe   \n",
       "14        4      2       1      2    Male  white collar   South Europe   \n",
       "15        4      4       2      2    Male   blue collar   South Europe   \n",
       "16        3      4       2      2    Male  white collar    Middle East   \n",
       "17        4      4       2      2  Female   blue collar    East Europe   \n",
       "18        1      3       1      1    Male  white collar  North America   \n",
       "19        2      1       1      1    Male   blue collar           Asia   \n",
       "20        2      4       2      2  Female  white collar  North America   \n",
       "21        5      4       3      4    Male  white collar   North Europe   \n",
       "22        4      4       2      2    Male   blue collar   South Europe   \n",
       "23        3      3       1      1    Male  white collar         Africa   \n",
       "24        5      3       2      2    Male  white collar   North Europe   \n",
       "25        3      4       2      2  Female  white collar   South Europe   \n",
       "26        2      3       1      1  Female   blue collar   South Europe   \n",
       "27        3      4       3      4    Male  white collar  North America   \n",
       "28        5      4       2      3  Female  white collar   North Europe   \n",
       "29        4      1       1      1  Female   blue collar         Africa   \n",
       "30        4      3       2      2  Female   blue collar   South Europe   \n",
       "31        4      4       2      2    Male  white collar  South America   \n",
       "32        3      3       1      1    Male   blue collar   North Europe   \n",
       "33        5      4       3      4  Female  white collar   North Europe   \n",
       "34        3      3       1      2    Male   blue collar   South Europe   \n",
       "35        5      4       1      2    Male  white collar   North Europe   \n",
       "36        3      2       1      1  Female  white collar   South Europe   \n",
       "37        1      3       1      1    Male   blue collar   North Europe   \n",
       "38        3      4       2      2    Male   blue collar    Middle East   \n",
       "39        5      3       2      4  Female  white collar   North Europe   \n",
       "40        5      3       2      2  Female  white collar   South Europe   \n",
       "41        4      4       2      2    Male  white collar   South Europe   \n",
       "42        3      3       2      2  Female  white collar    East Europe   \n",
       "43        3      3       2      2  Female   blue collar    Middle East   \n",
       "44        3      3       2      3  Female  white collar  North America   \n",
       "45        1      3       2      2    Male  white collar  South America   \n",
       "46        1      3       1      1    Male   blue collar  North America   \n",
       "47        4      3       2      2  Female  white collar         Africa   \n",
       "48        5      4       2      2    Male  white collar   North Europe   \n",
       "49        4      3       2      2    Male   blue collar    East Europe   \n",
       "\n",
       "          GroupComp  \n",
       "userId               \n",
       "0       2Adlt+Child  \n",
       "1       2Adlt+Child  \n",
       "2             1Adlt  \n",
       "3       2Adlt+Child  \n",
       "4             1Adlt  \n",
       "5             1Adlt  \n",
       "6             2Adlt  \n",
       "7             2Adlt  \n",
       "8             1Adlt  \n",
       "9             2Adlt  \n",
       "10      2Adlt+Child  \n",
       "11            1Adlt  \n",
       "12            2Adlt  \n",
       "13            1Adlt  \n",
       "14            1Adlt  \n",
       "15            2Adlt  \n",
       "16      2Adlt+Child  \n",
       "17            1Adlt  \n",
       "18            2Adlt  \n",
       "19            2Adlt  \n",
       "20      2Adlt+Child  \n",
       "21            2Adlt  \n",
       "22            2Adlt  \n",
       "23            1Adlt  \n",
       "24            2Adlt  \n",
       "25      2Adlt+Child  \n",
       "26            1Adlt  \n",
       "27       GrpFriends  \n",
       "28            2Adlt  \n",
       "29      2Adlt+Child  \n",
       "30            2Adlt  \n",
       "31            2Adlt  \n",
       "32            2Adlt  \n",
       "33            2Adlt  \n",
       "34       GrpFriends  \n",
       "35            1Adlt  \n",
       "36      2Adlt+Child  \n",
       "37            2Adlt  \n",
       "38      2Adlt+Child  \n",
       "39      2Adlt+Child  \n",
       "40            2Adlt  \n",
       "41            2Adlt  \n",
       "42            2Adlt  \n",
       "43       GrpFriends  \n",
       "44      2Adlt+Child  \n",
       "45            1Adlt  \n",
       "46            1Adlt  \n",
       "47      2Adlt+Child  \n",
       "48            2Adlt  \n",
       "49      2Adlt+Child  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read in user features dataset\n",
    "user_features=pd.read_csv(r'C:\\Users\\jessica.ogwu\\Desktop\\Tourism spot recommendation system\\tourism\\tourism datasets\\user_features.csv', index_col=0)\n",
    "user_features.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100000, 8)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get the shape of the dataset\n",
    "user_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'AcDeg', 'Budget', 'Accom', 'Gender', 'Job', 'Region',\n",
       "       'GroupComp'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_features.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Age</th>\n",
       "      <th>AcDeg</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Accom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.083250</td>\n",
       "      <td>3.113020</td>\n",
       "      <td>1.807570</td>\n",
       "      <td>2.046990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.268643</td>\n",
       "      <td>0.750421</td>\n",
       "      <td>0.622868</td>\n",
       "      <td>0.673251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Age          AcDeg         Budget          Accom\n",
       "count  100000.000000  100000.000000  100000.000000  100000.000000\n",
       "mean        3.083250       3.113020       1.807570       2.046990\n",
       "std         1.268643       0.750421       0.622868       0.673251\n",
       "min         1.000000       1.000000       1.000000       1.000000\n",
       "25%         2.000000       3.000000       1.000000       2.000000\n",
       "50%         3.000000       3.000000       2.000000       2.000000\n",
       "75%         4.000000       4.000000       2.000000       2.000000\n",
       "max         5.000000       4.000000       3.000000       4.000000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_features.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 100000 entries, 0 to 99999\n",
      "Data columns (total 8 columns):\n",
      " #   Column     Non-Null Count   Dtype \n",
      "---  ------     --------------   ----- \n",
      " 0   Age        100000 non-null  int64 \n",
      " 1   AcDeg      100000 non-null  int64 \n",
      " 2   Budget     100000 non-null  int64 \n",
      " 3   Accom      100000 non-null  int64 \n",
      " 4   Gender     100000 non-null  object\n",
      " 5   Job        100000 non-null  object\n",
      " 6   Region     100000 non-null  object\n",
      " 7   GroupComp  100000 non-null  object\n",
      "dtypes: int64(4), object(4)\n",
      "memory usage: 6.9+ MB\n"
     ]
    }
   ],
   "source": [
    "user_features.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 100000 users whose ratings were generated for the sake of the tourist recommendation system. Because the dataset is synthetically generated, it is necessary to carry out some data cleaning we will begin with the age column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 5, 3, 4], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check how many categories are in the age category\n",
    "user_features['Age'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let each age level represent an age range \n",
    "\n",
    "* 1:20-30\n",
    "* 2:31-40\n",
    "* 3:41-50\n",
    "* 4:51-60\n",
    "* 5:61 and above"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dropping the AcDeg and Accom columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Age</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Job</th>\n",
       "      <th>Region</th>\n",
       "      <th>GroupComp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>white collar</td>\n",
       "      <td>South Europe</td>\n",
       "      <td>2Adlt+Child</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>North Europe</td>\n",
       "      <td>2Adlt+Child</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>South America</td>\n",
       "      <td>1Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>South Europe</td>\n",
       "      <td>2Adlt+Child</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>North Europe</td>\n",
       "      <td>1Adlt</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age  Budget  Gender           Job         Region    GroupComp\n",
       "userId                                                               \n",
       "0         1       1  Female  white collar   South Europe  2Adlt+Child\n",
       "1         2       2    Male   blue collar   North Europe  2Adlt+Child\n",
       "2         2       2    Male   blue collar  South America        1Adlt\n",
       "3         5       2  Female   blue collar   South Europe  2Adlt+Child\n",
       "4         3       1    Male   blue collar   North Europe        1Adlt"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_features=user_features.drop(columns=['AcDeg', 'Accom'])\n",
    "user_features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we have the budget column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3], dtype=int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_features['Budget'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create three budget levels\n",
    "\n",
    "* 1: $5000-$10000\n",
    "* 2: $11000-$20000\n",
    "* 3: $21000-above\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Female', 'Male'], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_features['Gender'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 49976 male tourists\n",
      "There are 50024 female tourists\n"
     ]
    }
   ],
   "source": [
    "#Getting the number of male and female tourists\n",
    "print(f'There are {user_features[\"Gender\"][user_features[\"Gender\"]==\"Male\"].count()} male tourists')\n",
    "print(f'There are {user_features[\"Gender\"][user_features[\"Gender\"]==\"Female\"].count()} female tourists')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "total=float(len(user_features['Gender']))\n",
    "sen=sns.countplot(x='Gender', data=user_features)\n",
    "for g in sen.patches:\n",
    "    height=g.get_height()\n",
    "    sen.text(g.get_x()+g.get_width()/2, height+3, '{:1.2f}%'.format((height/total)*100), ha='center')\n",
    "plt.title('Gender')\n",
    "plt.xlabel('Gender')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of male and female tourists is fairly evenly distributed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['white collar', 'blue collar'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_features['Job'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 63912 white collar tourists\n",
      "There are 36088 blue collar tourists\n"
     ]
    }
   ],
   "source": [
    "#Getting the number of white collar and blue collar tourists\n",
    "print(f'There are {user_features[\"Job\"][user_features[\"Job\"]==\"white collar\"].count()} white collar tourists')\n",
    "print(f'There are {user_features[\"Job\"][user_features[\"Job\"]==\"blue collar\"].count()} blue collar tourists')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "total=float(len(user_features['Job']))\n",
    "sen=sns.countplot(x='Job', data=user_features)\n",
    "for g in sen.patches:\n",
    "    height=g.get_height()\n",
    "    sen.text(g.get_x()+g.get_width()/2, height+3, '{:1.2f}%'.format((height/total)*100), ha='center')\n",
    "plt.title('Job')\n",
    "plt.xlabel('Job')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['South Europe', 'North Europe', 'South America', 'Africa',\n",
       "       'Middle East', 'East Europe', 'North America', 'Asia'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_features['Region'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "total=float(len(user_features['Region']))\n",
    "sen=sns.countplot(x='Region', data=user_features)\n",
    "for g in sen.patches:\n",
    "    height=g.get_height()\n",
    "    sen.text(g.get_x()+g.get_width()/2, height+3, '{:1.2f}%'.format((height/total)*100), ha='center')\n",
    "plt.title('Region')\n",
    "plt.xlabel('Region')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A bulk of the tourists seem to come from South Europe and North Europe with South America having the lowest representation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2Adlt+Child', '1Adlt', '2Adlt', 'GrpFriends'], dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_features['GroupComp'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "total=float(len(user_features['GroupComp']))\n",
    "sen=sns.countplot(x='GroupComp', data=user_features)\n",
    "for g in sen.patches:\n",
    "    height=g.get_height()\n",
    "    sen.text(g.get_x()+g.get_width()/2, height+3, '{:1.2f}%'.format((height/total)*100), ha='center')\n",
    "plt.title('GroupComp')\n",
    "plt.xlabel('GroupComp')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most tourists travelled in pairs of 2 with some travelling with a child."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## User Ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>userId</th>\n",
       "      <th>itemId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1.533462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>1.283205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>1.506836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1.260289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>3.203168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>2.645116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>1.326969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>4.293818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>1.640807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>2.807194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>3.699715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>2.159490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>2.034535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>1.515189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "      <td>3.714210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "      <td>2.264020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>1.281077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>104</td>\n",
       "      <td>0</td>\n",
       "      <td>1.678843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "      <td>2.466961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "      <td>2.027684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>117</td>\n",
       "      <td>0</td>\n",
       "      <td>2.544509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>134</td>\n",
       "      <td>0</td>\n",
       "      <td>1.450163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>142</td>\n",
       "      <td>0</td>\n",
       "      <td>3.547333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>143</td>\n",
       "      <td>0</td>\n",
       "      <td>2.553208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>145</td>\n",
       "      <td>0</td>\n",
       "      <td>3.582924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>154</td>\n",
       "      <td>0</td>\n",
       "      <td>1.253207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>163</td>\n",
       "      <td>0</td>\n",
       "      <td>2.852365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "      <td>2.282818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "      <td>1.389594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>194</td>\n",
       "      <td>0</td>\n",
       "      <td>1.321719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>196</td>\n",
       "      <td>0</td>\n",
       "      <td>1.360779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>211</td>\n",
       "      <td>0</td>\n",
       "      <td>3.338572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>220</td>\n",
       "      <td>0</td>\n",
       "      <td>2.004203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>222</td>\n",
       "      <td>0</td>\n",
       "      <td>1.225404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>230</td>\n",
       "      <td>0</td>\n",
       "      <td>2.664722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>235</td>\n",
       "      <td>0</td>\n",
       "      <td>1.867680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>3.297055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>254</td>\n",
       "      <td>0</td>\n",
       "      <td>3.694071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>255</td>\n",
       "      <td>0</td>\n",
       "      <td>4.026249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>269</td>\n",
       "      <td>0</td>\n",
       "      <td>2.404548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>1.516032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>274</td>\n",
       "      <td>0</td>\n",
       "      <td>2.231053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>277</td>\n",
       "      <td>0</td>\n",
       "      <td>2.288567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>278</td>\n",
       "      <td>0</td>\n",
       "      <td>1.719130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>279</td>\n",
       "      <td>0</td>\n",
       "      <td>2.043469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>288</td>\n",
       "      <td>0</td>\n",
       "      <td>3.825260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>324</td>\n",
       "      <td>0</td>\n",
       "      <td>1.923241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>326</td>\n",
       "      <td>0</td>\n",
       "      <td>2.879517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>328</td>\n",
       "      <td>0</td>\n",
       "      <td>2.025179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>330</td>\n",
       "      <td>0</td>\n",
       "      <td>1.651601</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    userId  itemId    rating\n",
       "0       13       0  1.533462\n",
       "1       15       0  1.283205\n",
       "2       17       0  1.506836\n",
       "3       19       0  1.260289\n",
       "4       23       0  3.203168\n",
       "5       24       0  2.645116\n",
       "6       29       0  1.326969\n",
       "7       44       0  4.293818\n",
       "8       47       0  1.640807\n",
       "9       59       0  2.807194\n",
       "10      64       0  3.699715\n",
       "11      73       0  2.159490\n",
       "12      76       0  2.034535\n",
       "13      79       0  1.515189\n",
       "14      81       0  3.714210\n",
       "15      85       0  2.264020\n",
       "16      90       0  1.281077\n",
       "17     104       0  1.678843\n",
       "18     112       0  2.466961\n",
       "19     116       0  2.027684\n",
       "20     117       0  2.544509\n",
       "21     134       0  1.450163\n",
       "22     142       0  3.547333\n",
       "23     143       0  2.553208\n",
       "24     145       0  3.582924\n",
       "25     154       0  1.253207\n",
       "26     163       0  2.852365\n",
       "27     182       0  2.282818\n",
       "28     185       0  1.389594\n",
       "29     194       0  1.321719\n",
       "30     196       0  1.360779\n",
       "31     211       0  3.338572\n",
       "32     220       0  2.004203\n",
       "33     222       0  1.225404\n",
       "34     230       0  2.664722\n",
       "35     235       0  1.867680\n",
       "36     236       0  3.297055\n",
       "37     254       0  3.694071\n",
       "38     255       0  4.026249\n",
       "39     269       0  2.404548\n",
       "40     272       0  1.516032\n",
       "41     274       0  2.231053\n",
       "42     277       0  2.288567\n",
       "43     278       0  1.719130\n",
       "44     279       0  2.043469\n",
       "45     288       0  3.825260\n",
       "46     324       0  1.923241\n",
       "47     326       0  2.879517\n",
       "48     328       0  2.025179\n",
       "49     330       0  1.651601"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_ratings=pd.read_csv(r'C:\\Users\\jessica.ogwu\\Desktop\\Tourism spot recommendation system\\tourism\\tourism datasets\\user_ratings.csv')\n",
    "user_ratings.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(345368, 3)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_ratings.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are over 300000 ratings provided by tourists. This is expected because one tourist would not provide ratings for all 23 destinations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we will join the user_ratings table with the dest_spots table to carry out some more EDA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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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>userId</th>\n",
       "      <th>itemId</th>\n",
       "      <th>rating</th>\n",
       "      <th>ItemName</th>\n",
       "      <th>Category</th>\n",
       "      <th>Quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1.533462</td>\n",
       "      <td>Restaurant Fake</td>\n",
       "      <td>['Gastro']</td>\n",
       "      <td>1.647351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>1.283205</td>\n",
       "      <td>Restaurant Fake</td>\n",
       "      <td>['Gastro']</td>\n",
       "      <td>1.647351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>1.506836</td>\n",
       "      <td>Restaurant Fake</td>\n",
       "      <td>['Gastro']</td>\n",
       "      <td>1.647351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1.260289</td>\n",
       "      <td>Restaurant Fake</td>\n",
       "      <td>['Gastro']</td>\n",
       "      <td>1.647351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>3.203168</td>\n",
       "      <td>Restaurant Fake</td>\n",
       "      <td>['Gastro']</td>\n",
       "      <td>1.647351</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  itemId    rating         ItemName    Category   Quality\n",
       "0      13       0  1.533462  Restaurant Fake  ['Gastro']  1.647351\n",
       "1      15       0  1.283205  Restaurant Fake  ['Gastro']  1.647351\n",
       "2      17       0  1.506836  Restaurant Fake  ['Gastro']  1.647351\n",
       "3      19       0  1.260289  Restaurant Fake  ['Gastro']  1.647351\n",
       "4      23       0  3.203168  Restaurant Fake  ['Gastro']  1.647351"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_ratings=pd.merge(user_ratings, destination_spots, on='itemId')\n",
    "new_ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we can count the ratings we got for each restaurant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ItemName\n",
       "Fiction Nightclub            15264\n",
       "Never Happened Festival      15180\n",
       "Fake Brands Boutique         15177\n",
       "Random Cultural Tour         15123\n",
       "Secret Beach                 15112\n",
       "Fake BTT Route               15111\n",
       "Another Sport Event          15082\n",
       "Fake Beach                   15072\n",
       "Random Surfing Lessons       15042\n",
       "Nonexisting Zipline          15027\n",
       "Random Golf Lessons          15014\n",
       "Unknown Nature Route         15012\n",
       "Some Sport Event             15008\n",
       "Bogus Waterpark              14992\n",
       "False Tavern                 14990\n",
       "Restaurant Fake              14945\n",
       "Surprise Concert             14945\n",
       "Random Shopping Mall         14935\n",
       "Best Imaginary Restaurant    14897\n",
       "MakeBelieve Pub              14892\n",
       "Museum of Fake History       14888\n",
       "Fake Klub                    14857\n",
       "Bogus Spa                    14803\n",
       "Name: rating, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_ratings.groupby('ItemName')['rating'].count().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On an average, about 15% of respondents provided responses for each destination."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us get an idea of the mean rating for each restaurant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ItemName\n",
       "Random Cultural Tour         3.427827\n",
       "Random Shopping Mall         3.351523\n",
       "Bogus Spa                    3.339242\n",
       "Random Surfing Lessons       3.116446\n",
       "Fake Beach                   2.995566\n",
       "Fake Brands Boutique         2.974881\n",
       "Some Sport Event             2.972978\n",
       "Fake Klub                    2.877170\n",
       "Unknown Nature Route         2.875325\n",
       "Museum of Fake History       2.854905\n",
       "Best Imaginary Restaurant    2.718406\n",
       "False Tavern                 2.577721\n",
       "MakeBelieve Pub              2.462013\n",
       "Never Happened Festival      2.314689\n",
       "Restaurant Fake              2.222544\n",
       "Surprise Concert             2.141511\n",
       "Fake BTT Route               1.944467\n",
       "Another Sport Event          1.933442\n",
       "Nonexisting Zipline          1.792523\n",
       "Random Golf Lessons          1.730683\n",
       "Secret Beach                 1.659638\n",
       "Bogus Waterpark              1.621705\n",
       "Fiction Nightclub            1.554146\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_ratings.groupby('ItemName')['rating'].mean().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It would be better to visualize the table with tourists as rows and destinations as columns with ratings as values. To do this, we could pivot the table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>6</th>\n",
       "      <th>7</th>\n",
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       "      <th>10</th>\n",
       "      <th>11</th>\n",
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       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
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       "      <th>19</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2.828157</td>\n",
       "      <td>NaN</td>\n",
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       "      <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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.709697</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>1.341966</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>3</th>\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>2.596720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.658896</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>2.661253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.373965</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.714443</td>\n",
       "      <td>1.636043</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.531573</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.277358</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.804309</td>\n",
       "      <td>1.182217</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.188851</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.323312</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>1.195162</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.596352</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.701678</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.568016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.852247</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.544382</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\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>1.982568</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.992974</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\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>1.794025</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>9</th>\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>1.886728</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.001374</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>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.528033</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>3.207126</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>12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.762828</td>\n",
       "      <td>2.872261</td>\n",
       "      <td>1.428011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.537247</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.063268</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>13</th>\n",
       "      <td>1.533462</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.034984</td>\n",
       "      <td>1.295660</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>2.662960</td>\n",
       "      <td>3.002312</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.795524</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>4.676342</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.314946</td>\n",
       "      <td>1.270593</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.283205</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.028177</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>1.380288</td>\n",
       "      <td>1.681407</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.381641</td>\n",
       "      <td>4.194527</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.471570</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>4.664980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.818496</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.812226</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.225410</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.506836</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.960695</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.560703</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.739492</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.657939</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>18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.582564</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>2.879124</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>1.196622</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.260289</td>\n",
       "      <td>1.486301</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>1.720157</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.807101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.500000</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>20</th>\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>1.720048</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>1.594902</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",
       "</div>"
      ],
      "text/plain": [
       "itemId        0         1         2         3         4         5         6   \\\n",
       "userId                                                                         \n",
       "0            NaN  1.500420       NaN       NaN       NaN       NaN       NaN   \n",
       "1            NaN       NaN       NaN       NaN       NaN  4.156108       NaN   \n",
       "2            NaN       NaN  2.709697       NaN       NaN       NaN       NaN   \n",
       "3            NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4            NaN       NaN  1.373965       NaN       NaN  4.714443  1.636043   \n",
       "5            NaN       NaN       NaN  1.188851       NaN  1.323312       NaN   \n",
       "6            NaN       NaN       NaN  1.596352       NaN       NaN       NaN   \n",
       "7            NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "8            NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "9            NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "10           NaN  1.528033       NaN       NaN       NaN       NaN       NaN   \n",
       "12           NaN  1.762828  2.872261  1.428011       NaN       NaN       NaN   \n",
       "13      1.533462       NaN  3.034984  1.295660       NaN       NaN       NaN   \n",
       "14           NaN       NaN  1.795524       NaN       NaN       NaN       NaN   \n",
       "15      1.283205       NaN  2.028177       NaN       NaN       NaN       NaN   \n",
       "16           NaN       NaN       NaN       NaN  2.471570       NaN       NaN   \n",
       "17      1.506836       NaN       NaN       NaN  2.960695       NaN       NaN   \n",
       "18           NaN       NaN       NaN  1.582564       NaN       NaN       NaN   \n",
       "19      1.260289  1.486301       NaN       NaN       NaN       NaN       NaN   \n",
       "20           NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "itemId        7         8         9         10        11  12        13  \\\n",
       "userId                                                                   \n",
       "0            NaN       NaN       NaN       NaN       NaN NaN       NaN   \n",
       "1       1.845351       NaN  3.968529       NaN       NaN NaN  4.705281   \n",
       "2            NaN       NaN       NaN  1.341966       NaN NaN       NaN   \n",
       "3            NaN       NaN       NaN       NaN  2.596720 NaN  2.658896   \n",
       "4            NaN       NaN       NaN  1.531573       NaN NaN       NaN   \n",
       "5            NaN       NaN       NaN       NaN       NaN NaN       NaN   \n",
       "6            NaN  1.701678       NaN  1.568016       NaN NaN       NaN   \n",
       "7            NaN       NaN       NaN       NaN       NaN NaN       NaN   \n",
       "8            NaN       NaN       NaN       NaN       NaN NaN       NaN   \n",
       "9            NaN       NaN  1.886728       NaN       NaN NaN       NaN   \n",
       "10           NaN       NaN       NaN       NaN       NaN NaN  3.207126   \n",
       "12      3.537247       NaN       NaN       NaN  4.063268 NaN       NaN   \n",
       "13           NaN       NaN       NaN       NaN       NaN NaN       NaN   \n",
       "14           NaN       NaN       NaN       NaN       NaN NaN       NaN   \n",
       "15           NaN       NaN       NaN  1.380288  1.681407 NaN       NaN   \n",
       "16           NaN       NaN       NaN       NaN  4.664980 NaN       NaN   \n",
       "17      1.560703       NaN  1.739492       NaN       NaN NaN  3.657939   \n",
       "18           NaN       NaN       NaN       NaN  2.879124 NaN       NaN   \n",
       "19           NaN  1.720157       NaN       NaN       NaN NaN  2.807101   \n",
       "20      1.720048       NaN       NaN       NaN       NaN NaN       NaN   \n",
       "\n",
       "itemId        14        15        16        17        18        19        20  \\\n",
       "userId                                                                         \n",
       "0            NaN       NaN  2.667926  2.828157       NaN       NaN       NaN   \n",
       "1            NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2            NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3            NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4            NaN       NaN  2.277358       NaN       NaN       NaN  3.804309   \n",
       "5            NaN       NaN       NaN       NaN       NaN       NaN  1.195162   \n",
       "6            NaN       NaN  2.852247       NaN       NaN       NaN       NaN   \n",
       "7            NaN  1.982568       NaN       NaN       NaN       NaN       NaN   \n",
       "8       1.794025       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "9            NaN  3.001374       NaN       NaN       NaN       NaN       NaN   \n",
       "10           NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "12           NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "13           NaN       NaN       NaN       NaN  2.662960  3.002312       NaN   \n",
       "14           NaN  4.676342       NaN       NaN       NaN       NaN  1.314946   \n",
       "15           NaN       NaN       NaN  2.381641  4.194527       NaN       NaN   \n",
       "16      2.818496       NaN  2.812226       NaN  1.225410       NaN       NaN   \n",
       "17           NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "18           NaN       NaN       NaN       NaN  1.196622       NaN       NaN   \n",
       "19           NaN       NaN       NaN  3.500000       NaN       NaN       NaN   \n",
       "20      1.594902       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "itemId        21        22  \n",
       "userId                      \n",
       "0       1.632696       NaN  \n",
       "1            NaN       NaN  \n",
       "2            NaN       NaN  \n",
       "3            NaN  2.661253  \n",
       "4       1.182217       NaN  \n",
       "5            NaN       NaN  \n",
       "6       1.544382       NaN  \n",
       "7       2.992974       NaN  \n",
       "8            NaN       NaN  \n",
       "9            NaN       NaN  \n",
       "10           NaN       NaN  \n",
       "12           NaN       NaN  \n",
       "13           NaN       NaN  \n",
       "14      1.270593       NaN  \n",
       "15           NaN       NaN  \n",
       "16           NaN       NaN  \n",
       "17           NaN       NaN  \n",
       "18           NaN       NaN  \n",
       "19           NaN       NaN  \n",
       "20           NaN       NaN  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_ratings=user_ratings.pivot_table(index='userId', columns='itemId', values='rating')\n",
    "users_ratings.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected there a lot of NaN values becaus most user will not rate all destinations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building The Recommendation System"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "There are three types of recommender systems:\n",
    "* Content based filtering recommender systems\n",
    "* Collaborative filtering recommender sysytems\n",
    "* Hybrid recommender systems\n",
    "\n",
    "We will be building a hybrid recommender system which combines the features of both content based and collaborative recommender systems."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The collaborative based filtering technique will be applied to the user_rating table while content based filtering will be applied to both the dest spots and user features table. It is expected that a combination of these techniques with these tables would create a high performing tourist recommender system."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collaborative filtering\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.705281</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.000000</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",
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       "    <tr>\n",
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       "      <td>2.709697</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>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.341966</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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      "text/plain": [
       "itemId   0        1         2    3    4         5         6         7    8   \\\n",
       "userId                                                                        \n",
       "0       0.0  1.50042  0.000000  0.0  0.0  0.000000  0.000000  0.000000  0.0   \n",
       "1       0.0  0.00000  0.000000  0.0  0.0  4.156108  0.000000  1.845351  0.0   \n",
       "2       0.0  0.00000  2.709697  0.0  0.0  0.000000  0.000000  0.000000  0.0   \n",
       "3       0.0  0.00000  0.000000  0.0  0.0  0.000000  0.000000  0.000000  0.0   \n",
       "4       0.0  0.00000  1.373965  0.0  0.0  4.714443  1.636043  0.000000  0.0   \n",
       "\n",
       "itemId        9         10       11   12        13   14   15        16  \\\n",
       "userId                                                                   \n",
       "0       0.000000  0.000000  0.00000  0.0  0.000000  0.0  0.0  2.667926   \n",
       "1       3.968529  0.000000  0.00000  0.0  4.705281  0.0  0.0  0.000000   \n",
       "2       0.000000  1.341966  0.00000  0.0  0.000000  0.0  0.0  0.000000   \n",
       "3       0.000000  0.000000  2.59672  0.0  2.658896  0.0  0.0  0.000000   \n",
       "4       0.000000  1.531573  0.00000  0.0  0.000000  0.0  0.0  2.277358   \n",
       "\n",
       "itemId        17   18   19        20        21        22  \n",
       "userId                                                    \n",
       "0       2.828157  0.0  0.0  0.000000  1.632696  0.000000  \n",
       "1       0.000000  0.0  0.0  0.000000  0.000000  0.000000  \n",
       "2       0.000000  0.0  0.0  0.000000  0.000000  0.000000  \n",
       "3       0.000000  0.0  0.0  0.000000  0.000000  2.661253  \n",
       "4       0.000000  0.0  0.0  3.804309  1.182217  0.000000  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#First we handle the NANs present in the table, to do this we could either fill with zero or use some feature scaling technique\n",
    "#like standardization or normalization, we will be filling with zero.\n",
    "\n",
    "users_ratings=users_ratings.fillna(0)\n",
    "users_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 1.50042029, 0.        , ..., 0.        , 1.63269589,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 2.70969684, ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
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       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        2.89870291]])"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a numpy array of the users_ratings\n",
    "user_item_matrix=np.array(users_ratings)\n",
    "user_item_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform matrix factorization using SVD\n",
    "def matrix_factorization(R, K=2, steps=10, alpha=0.01, beta=0.02):\n",
    "    # R: user-item matrix\n",
    "    # K: number of latent features\n",
    "    # steps: number of iterations\n",
    "    # alpha: learning rate\n",
    "    # beta: regularization parameter\n",
    "    \n",
    "    N, M = R.shape\n",
    "    P = np.random.rand(N, K)\n",
    "    Q = np.random.rand(M, K)\n",
    "    # N by M= N by K multiplied by k by M\n",
    "    \n",
    "    for step in range(steps):\n",
    "        for i in range(N):\n",
    "            for j in range(M):\n",
    "                if R[i, j] > 0:\n",
    "                    eij = R[i, j] - np.dot(P[i, :], Q[j, :])\n",
    "                    for k in range(K):\n",
    "                        P[i, k] += alpha * (2 * eij * Q[j, k] - beta * P[i, k])\n",
    "                        Q[j, k] += alpha * (2 * eij * P[i, k] - beta * Q[j, k])\n",
    "        \n",
    "        # Calculate RMSE\n",
    "        rmse = 0\n",
    "        for i in range(N):\n",
    "            for j in range(M):\n",
    "                if R[i, j] > 0:\n",
    "                    rmse += (R[i, j] - np.dot(P[i, :], Q[j, :])) ** 2\n",
    "                    for k in range(K):\n",
    "                        rmse += (beta / 2) * (P[i, k] ** 2 + Q[j, k] ** 2)\n",
    "        rmse = np.sqrt(rmse / (N * M))\n",
    "        if step % 1000 == 0:\n",
    "            print(\"Step:\", step, \"RMSE:\", rmse)\n",
    "    \n",
    "    return P, Q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step: 0 RMSE: 0.33249958991344536\n"
     ]
    }
   ],
   "source": [
    "# Perform matrix factorization\n",
    "latent_features = 3\n",
    "P, Q = matrix_factorization(user_item_matrix, K=latent_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate predicted ratings\n",
    "predicted_collaborative_ratings = np.dot(P, Q.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final RMSE: 0.6135994457746267\n",
      "[[1.87332383 1.30637358 2.9036437  ... 1.43449288 1.3528996  2.56017733]\n",
      " [2.92470708 2.17306908 4.83535927 ... 2.34630926 2.22331943 4.19653955]\n",
      " [1.74551856 1.1602068  2.48346585 ... 1.24330241 1.19741372 2.06414491]\n",
      " ...\n",
      " [1.58800498 1.21406869 2.65666067 ... 1.27763193 1.22783928 2.21264718]\n",
      " [1.2449827  0.99855209 2.24020941 ... 1.06552192 1.01023861 1.93569984]\n",
      " [2.14718703 1.48419412 3.2607577  ... 1.61439248 1.53337857 2.8193439 ]]\n"
     ]
    }
   ],
   "source": [
    "# Calculate RMSE\n",
    "rmse = np.sqrt(mean_squared_error(user_item_matrix[user_item_matrix > 0], predicted_collaborative_ratings[user_item_matrix > 0]))\n",
    "print(\"Final RMSE:\", rmse)\n",
    "print(predicted_collaborative_ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(97605, 23)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_collaborative_ratings.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Content Based Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>North Europe</td>\n",
       "      <td>1Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>white collar</td>\n",
       "      <td>South Europe</td>\n",
       "      <td>1Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>white collar</td>\n",
       "      <td>South America</td>\n",
       "      <td>2Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>South Europe</td>\n",
       "      <td>1Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>white collar</td>\n",
       "      <td>North Europe</td>\n",
       "      <td>2Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>white collar</td>\n",
       "      <td>Middle East</td>\n",
       "      <td>2Adlt+Child</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>South Europe</td>\n",
       "      <td>1Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>North Europe</td>\n",
       "      <td>2Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>North Europe</td>\n",
       "      <td>1Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>white collar</td>\n",
       "      <td>South Europe</td>\n",
       "      <td>1Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>South Europe</td>\n",
       "      <td>2Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>white collar</td>\n",
       "      <td>Middle East</td>\n",
       "      <td>2Adlt+Child</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>East Europe</td>\n",
       "      <td>1Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>white collar</td>\n",
       "      <td>North America</td>\n",
       "      <td>2Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>white collar</td>\n",
       "      <td>North America</td>\n",
       "      <td>2Adlt+Child</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>white collar</td>\n",
       "      <td>North Europe</td>\n",
       "      <td>2Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>South Europe</td>\n",
       "      <td>2Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>white collar</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>white collar</td>\n",
       "      <td>North Europe</td>\n",
       "      <td>2Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>white collar</td>\n",
       "      <td>South Europe</td>\n",
       "      <td>2Adlt+Child</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>South Europe</td>\n",
       "      <td>1Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>white collar</td>\n",
       "      <td>North America</td>\n",
       "      <td>GrpFriends</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>white collar</td>\n",
       "      <td>North Europe</td>\n",
       "      <td>2Adlt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>blue collar</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2Adlt+Child</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age  Budget  Gender           Job         Region    GroupComp\n",
       "userId                                                               \n",
       "0         1       1  Female  white collar   South Europe  2Adlt+Child\n",
       "1         2       2    Male   blue collar   North Europe  2Adlt+Child\n",
       "2         2       2    Male   blue collar  South America        1Adlt\n",
       "3         5       2  Female   blue collar   South Europe  2Adlt+Child\n",
       "4         3       1    Male   blue collar   North Europe        1Adlt\n",
       "5         1       1  Female  white collar   South Europe        1Adlt\n",
       "6         2       1  Female   blue collar         Africa        2Adlt\n",
       "7         5       3    Male  white collar  South America        2Adlt\n",
       "8         4       2    Male   blue collar   South Europe        1Adlt\n",
       "9         2       2    Male  white collar   North Europe        2Adlt\n",
       "10        3       2  Female  white collar    Middle East  2Adlt+Child\n",
       "11        3       2    Male   blue collar   South Europe        1Adlt\n",
       "12        3       2    Male   blue collar   North Europe        2Adlt\n",
       "13        4       2  Female   blue collar   North Europe        1Adlt\n",
       "14        4       1    Male  white collar   South Europe        1Adlt\n",
       "15        4       2    Male   blue collar   South Europe        2Adlt\n",
       "16        3       2    Male  white collar    Middle East  2Adlt+Child\n",
       "17        4       2  Female   blue collar    East Europe        1Adlt\n",
       "18        1       1    Male  white collar  North America        2Adlt\n",
       "19        2       1    Male   blue collar           Asia        2Adlt\n",
       "20        2       2  Female  white collar  North America  2Adlt+Child\n",
       "21        5       3    Male  white collar   North Europe        2Adlt\n",
       "22        4       2    Male   blue collar   South Europe        2Adlt\n",
       "23        3       1    Male  white collar         Africa        1Adlt\n",
       "24        5       2    Male  white collar   North Europe        2Adlt\n",
       "25        3       2  Female  white collar   South Europe  2Adlt+Child\n",
       "26        2       1  Female   blue collar   South Europe        1Adlt\n",
       "27        3       3    Male  white collar  North America   GrpFriends\n",
       "28        5       2  Female  white collar   North Europe        2Adlt\n",
       "29        4       1  Female   blue collar         Africa  2Adlt+Child"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_features.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                      3\n",
       "Budget                   3\n",
       "Gender                Male\n",
       "Job           white collar\n",
       "Region       North America\n",
       "GroupComp       GrpFriends\n",
       "Name: 27, dtype: object"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_features.iloc[27,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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",
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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>Budget</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Job</th>\n",
       "      <th>Region</th>\n",
       "      <th>GroupComp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age  Budget  Gender  Job  Region  GroupComp\n",
       "userId                                             \n",
       "0         0       0       0    1       7          2\n",
       "1         1       1       1    0       5          2\n",
       "2         1       1       1    0       6          0\n",
       "3         4       1       0    0       7          2\n",
       "4         2       0       1    0       5          0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Label encode user features \n",
    "lb=LabelEncoder()\n",
    "users_features=user_features.apply(LabelEncoder().fit_transform)\n",
    "users_features.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we one hot encode our category feature to carry out content based filtering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>itemId</th>\n",
       "      <th>ItemName</th>\n",
       "      <th>Category</th>\n",
       "      <th>Quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Restaurant Fake</td>\n",
       "      <td>['Gastro']</td>\n",
       "      <td>1.647351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Fiction Nightclub</td>\n",
       "      <td>['Nightlf']</td>\n",
       "      <td>1.188559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Random Shopping Mall</td>\n",
       "      <td>['Shop', 'Relax']</td>\n",
       "      <td>2.948170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Bogus Waterpark</td>\n",
       "      <td>['Themeprk']</td>\n",
       "      <td>1.830793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Unknown Nature Route</td>\n",
       "      <td>['Nature', 'Relax']</td>\n",
       "      <td>2.782113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Some Sport Event</td>\n",
       "      <td>['Sports', 'Events']</td>\n",
       "      <td>4.476363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Never Happened Festival</td>\n",
       "      <td>['Events', 'Culture']</td>\n",
       "      <td>1.836556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>False Tavern</td>\n",
       "      <td>['Gastro', 'Culture']</td>\n",
       "      <td>1.365270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>MakeBelieve Pub</td>\n",
       "      <td>['Gastro', 'Nightlf']</td>\n",
       "      <td>1.957996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Another Sport Event</td>\n",
       "      <td>['Sports', 'Events']</td>\n",
       "      <td>2.133209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>Surprise Concert</td>\n",
       "      <td>['Events', 'Culture']</td>\n",
       "      <td>1.264327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>Museum of Fake History</td>\n",
       "      <td>['Culture']</td>\n",
       "      <td>3.281484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>Fake BTT Route</td>\n",
       "      <td>['Sports', 'Nature']</td>\n",
       "      <td>1.633559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>Random Surfing Lessons</td>\n",
       "      <td>['Sports', 'Nature']</td>\n",
       "      <td>4.649384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>Fake Brands Boutique</td>\n",
       "      <td>['Shop']</td>\n",
       "      <td>3.611481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>Best Imaginary Restaurant</td>\n",
       "      <td>['Gastro']</td>\n",
       "      <td>2.730529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>Bogus Spa</td>\n",
       "      <td>['Relax']</td>\n",
       "      <td>4.598459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>Random Cultural Tour</td>\n",
       "      <td>['Culture']</td>\n",
       "      <td>4.984842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>Nonexisting Zipline</td>\n",
       "      <td>['Sports']</td>\n",
       "      <td>1.853823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>Fake Klub</td>\n",
       "      <td>['Nightlf']</td>\n",
       "      <td>4.636606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>Random Golf Lessons</td>\n",
       "      <td>['Sports']</td>\n",
       "      <td>1.452114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>Secret Beach</td>\n",
       "      <td>['Beach']</td>\n",
       "      <td>1.239977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>Fake Beach</td>\n",
       "      <td>['Beach']</td>\n",
       "      <td>4.736056</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    itemId                   ItemName               Category   Quality\n",
       "0        0            Restaurant Fake             ['Gastro']  1.647351\n",
       "1        1          Fiction Nightclub            ['Nightlf']  1.188559\n",
       "2        2       Random Shopping Mall      ['Shop', 'Relax']  2.948170\n",
       "3        3            Bogus Waterpark           ['Themeprk']  1.830793\n",
       "4        4       Unknown Nature Route    ['Nature', 'Relax']  2.782113\n",
       "5        5           Some Sport Event   ['Sports', 'Events']  4.476363\n",
       "6        6    Never Happened Festival  ['Events', 'Culture']  1.836556\n",
       "7        7               False Tavern  ['Gastro', 'Culture']  1.365270\n",
       "8        8            MakeBelieve Pub  ['Gastro', 'Nightlf']  1.957996\n",
       "9        9        Another Sport Event   ['Sports', 'Events']  2.133209\n",
       "10      10           Surprise Concert  ['Events', 'Culture']  1.264327\n",
       "11      11     Museum of Fake History            ['Culture']  3.281484\n",
       "12      12             Fake BTT Route   ['Sports', 'Nature']  1.633559\n",
       "13      13     Random Surfing Lessons   ['Sports', 'Nature']  4.649384\n",
       "14      14       Fake Brands Boutique               ['Shop']  3.611481\n",
       "15      15  Best Imaginary Restaurant             ['Gastro']  2.730529\n",
       "16      16                  Bogus Spa              ['Relax']  4.598459\n",
       "17      17       Random Cultural Tour            ['Culture']  4.984842\n",
       "18      18        Nonexisting Zipline             ['Sports']  1.853823\n",
       "19      19                  Fake Klub            ['Nightlf']  4.636606\n",
       "20      20        Random Golf Lessons             ['Sports']  1.452114\n",
       "21      21               Secret Beach              ['Beach']  1.239977\n",
       "22      22                 Fake Beach              ['Beach']  4.736056"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remember our destination spots\n",
    "destination_spots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Generate columns for the category column\n",
    "destination_spots['Gastro']=destination_spots['Category'].apply(lambda x: 1 if 'Gastro' in x else 0)\n",
    "destination_spots['NightLife']=destination_spots['Category'].apply(lambda x: 1 if 'Nightlf' in x else 0)\n",
    "destination_spots['Shop']=destination_spots['Category'].apply(lambda x: 1 if 'Shop' in x else 0)\n",
    "destination_spots['Relax']=destination_spots['Category'].apply(lambda x: 1 if 'Relax' in x else 0)\n",
    "destination_spots['Events']=destination_spots['Category'].apply(lambda x: 1 if 'Events' in x else 0)\n",
    "destination_spots['Culture']=destination_spots['Category'].apply(lambda x: 1 if 'Culture' in x else 0)\n",
    "destination_spots['Sports']=destination_spots['Category'].apply(lambda x: 1 if 'Sports' in x else 0)\n",
    "destination_spots['Beach']=destination_spots['Category'].apply(lambda x: 1 if 'Beach' in x else 0)\n",
    "destination_spots['Nature']=destination_spots['Category'].apply(lambda x: 1 if 'Nature' in x else 0)\n",
    "destination_spots['Themeprk']=destination_spots['Category'].apply(lambda x: 1 if 'Themeprk' in x else 0)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Themeprk</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Restaurant Fake</td>\n",
       "      <td>['Gastro']</td>\n",
       "      <td>1.647351</td>\n",
       "      <td>1</td>\n",
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       "      <td>1.188559</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Random Shopping Mall</td>\n",
       "      <td>['Shop', 'Relax']</td>\n",
       "      <td>2.948170</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Bogus Waterpark</td>\n",
       "      <td>['Themeprk']</td>\n",
       "      <td>1.830793</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Unknown Nature Route</td>\n",
       "      <td>['Nature', 'Relax']</td>\n",
       "      <td>2.782113</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   itemId              ItemName             Category   Quality  Gastro  \\\n",
       "0       0       Restaurant Fake           ['Gastro']  1.647351       1   \n",
       "1       1     Fiction Nightclub          ['Nightlf']  1.188559       0   \n",
       "2       2  Random Shopping Mall    ['Shop', 'Relax']  2.948170       0   \n",
       "3       3       Bogus Waterpark         ['Themeprk']  1.830793       0   \n",
       "4       4  Unknown Nature Route  ['Nature', 'Relax']  2.782113       0   \n",
       "\n",
       "   NightLife  Shop  Relax  Events  Culture  Sports  Beach  Nature  Themeprk  \n",
       "0          0     0      0       0        0       0      0       0         0  \n",
       "1          1     0      0       0        0       0      0       0         0  \n",
       "2          0     1      1       0        0       0      0       0         0  \n",
       "3          0     0      0       0        0       0      0       0         1  \n",
       "4          0     0      1       0        0       0      0       1         0  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Visualize results\n",
    "destination_spots.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
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      ],
      "text/plain": [
       "   itemId   Quality  Gastro  NightLife  Shop  Relax  Events  Culture  Sports  \\\n",
       "0       0  1.647351       1          0     0      0       0        0       0   \n",
       "1       1  1.188559       0          1     0      0       0        0       0   \n",
       "2       2  2.948170       0          0     1      1       0        0       0   \n",
       "3       3  1.830793       0          0     0      0       0        0       0   \n",
       "4       4  2.782113       0          0     0      1       0        0       0   \n",
       "\n",
       "   Beach  Nature  Themeprk  \n",
       "0      0       0         0  \n",
       "1      0       0         0  \n",
       "2      0       0         0  \n",
       "3      0       0         1  \n",
       "4      0       1         0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop columns not needed for content based filtering\n",
    "dest_spots=destination_spots.drop(columns=['ItemName','Category'])\n",
    "dest_spots.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "   userId  itemId    rating\n",
       "0      13       0  1.533462\n",
       "1      15       0  1.283205\n",
       "2      17       0  1.506836\n",
       "3      19       0  1.260289\n",
       "4      23       0  3.203168"
      ]
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     "execution_count": 50,
     "metadata": {},
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   ],
   "source": [
    "user_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Joining the user_ratings dataset with the dest_spots dataset\n",
    "df=user_ratings.merge(dest_spots, on=\"itemId\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>2.645116</td>\n",
       "      <td>1.647351</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>1.326969</td>\n",
       "      <td>1.647351</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>4.293818</td>\n",
       "      <td>1.647351</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>1.640807</td>\n",
       "      <td>1.647351</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>2.807194</td>\n",
       "      <td>1.647351</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>3.699715</td>\n",
       "      <td>1.647351</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>2.159490</td>\n",
       "      <td>1.647351</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>2.034535</td>\n",
       "      <td>1.647351</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>1.515189</td>\n",
       "      <td>1.647351</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "      <td>3.714210</td>\n",
       "      <td>1.647351</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "      <td>2.264020</td>\n",
       "      <td>1.647351</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>1.281077</td>\n",
       "      <td>1.647351</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>104</td>\n",
       "      <td>0</td>\n",
       "      <td>1.678843</td>\n",
       "      <td>1.647351</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "      <td>2.466961</td>\n",
       "      <td>1.647351</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "      <td>2.027684</td>\n",
       "      <td>1.647351</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    userId  itemId    rating   Quality  Gastro  NightLife  Shop  Relax  \\\n",
       "0       13       0  1.533462  1.647351       1          0     0      0   \n",
       "1       15       0  1.283205  1.647351       1          0     0      0   \n",
       "2       17       0  1.506836  1.647351       1          0     0      0   \n",
       "3       19       0  1.260289  1.647351       1          0     0      0   \n",
       "4       23       0  3.203168  1.647351       1          0     0      0   \n",
       "5       24       0  2.645116  1.647351       1          0     0      0   \n",
       "6       29       0  1.326969  1.647351       1          0     0      0   \n",
       "7       44       0  4.293818  1.647351       1          0     0      0   \n",
       "8       47       0  1.640807  1.647351       1          0     0      0   \n",
       "9       59       0  2.807194  1.647351       1          0     0      0   \n",
       "10      64       0  3.699715  1.647351       1          0     0      0   \n",
       "11      73       0  2.159490  1.647351       1          0     0      0   \n",
       "12      76       0  2.034535  1.647351       1          0     0      0   \n",
       "13      79       0  1.515189  1.647351       1          0     0      0   \n",
       "14      81       0  3.714210  1.647351       1          0     0      0   \n",
       "15      85       0  2.264020  1.647351       1          0     0      0   \n",
       "16      90       0  1.281077  1.647351       1          0     0      0   \n",
       "17     104       0  1.678843  1.647351       1          0     0      0   \n",
       "18     112       0  2.466961  1.647351       1          0     0      0   \n",
       "19     116       0  2.027684  1.647351       1          0     0      0   \n",
       "\n",
       "    Events  Culture  Sports  Beach  Nature  Themeprk  \n",
       "0        0        0       0      0       0         0  \n",
       "1        0        0       0      0       0         0  \n",
       "2        0        0       0      0       0         0  \n",
       "3        0        0       0      0       0         0  \n",
       "4        0        0       0      0       0         0  \n",
       "5        0        0       0      0       0         0  \n",
       "6        0        0       0      0       0         0  \n",
       "7        0        0       0      0       0         0  \n",
       "8        0        0       0      0       0         0  \n",
       "9        0        0       0      0       0         0  \n",
       "10       0        0       0      0       0         0  \n",
       "11       0        0       0      0       0         0  \n",
       "12       0        0       0      0       0         0  \n",
       "13       0        0       0      0       0         0  \n",
       "14       0        0       0      0       0         0  \n",
       "15       0        0       0      0       0         0  \n",
       "16       0        0       0      0       0         0  \n",
       "17       0        0       0      0       0         0  \n",
       "18       0        0       0      0       0         0  \n",
       "19       0        0       0      0       0         0  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>itemId</th>\n",
       "      <th>rating</th>\n",
       "      <th>Quality</th>\n",
       "      <th>Gastro</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>3.034984</td>\n",
       "      <td>2.948170</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>1.295660</td>\n",
       "      <td>1.830793</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13</td>\n",
       "      <td>18</td>\n",
       "      <td>2.662960</td>\n",
       "      <td>1.853823</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>5</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>19</td>\n",
       "      <td>3.002312</td>\n",
       "      <td>4.636606</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   userId  itemId    rating   Quality  Gastro  NightLife  Shop  Relax  Events  \\\n",
       "0      13       0  1.533462  1.647351       1          0     0      0       0   \n",
       "1      13       2  3.034984  2.948170       0          0     1      1       0   \n",
       "2      13       3  1.295660  1.830793       0          0     0      0       0   \n",
       "3      13      18  2.662960  1.853823       0          0     0      0       0   \n",
       "4      13      19  3.002312  4.636606       0          1     0      0       0   \n",
       "\n",
       "   Culture  Sports  Beach  Nature  Themeprk  Age  Budget  Gender  Job  Region  \\\n",
       "0        0       0      0       0         0    3       1       0    0       5   \n",
       "1        0       0      0       0         0    3       1       0    0       5   \n",
       "2        0       0      0       0         1    3       1       0    0       5   \n",
       "3        0       1      0       0         0    3       1       0    0       5   \n",
       "4        0       0      0       0         0    3       1       0    0       5   \n",
       "\n",
       "   GroupComp  \n",
       "0          0  \n",
       "1          0  \n",
       "2          0  \n",
       "3          0  \n",
       "4          0  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Next we join this dataset with the user features dataset\n",
    "final_df=df.merge(users_features, on=\"userId\")\n",
    "final_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we can make use of a Gradient Boosting Regressor model to build for content based filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.53346216, 3.03498449, 1.29566007, ..., 2.32362681, 2.69970416,\n",
       "       2.49230547])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Set ratings to y variable\n",
    "y=final_df.iloc[:, 2].values\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>itemId</th>\n",
       "      <th>Quality</th>\n",
       "      <th>Gastro</th>\n",
       "      <th>NightLife</th>\n",
       "      <th>Shop</th>\n",
       "      <th>Relax</th>\n",
       "      <th>Events</th>\n",
       "      <th>Culture</th>\n",
       "      <th>Sports</th>\n",
       "      <th>Beach</th>\n",
       "      <th>Nature</th>\n",
       "      <th>Themeprk</th>\n",
       "      <th>Age</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Job</th>\n",
       "      <th>Region</th>\n",
       "      <th>GroupComp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1.647351</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>2.948170</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>1.830793</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13</td>\n",
       "      <td>18</td>\n",
       "      <td>1.853823</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>19</td>\n",
       "      <td>4.636606</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  itemId   Quality  Gastro  NightLife  Shop  Relax  Events  Culture  \\\n",
       "0      13       0  1.647351       1          0     0      0       0        0   \n",
       "1      13       2  2.948170       0          0     1      1       0        0   \n",
       "2      13       3  1.830793       0          0     0      0       0        0   \n",
       "3      13      18  1.853823       0          0     0      0       0        0   \n",
       "4      13      19  4.636606       0          1     0      0       0        0   \n",
       "\n",
       "   Sports  Beach  Nature  Themeprk  Age  Budget  Gender  Job  Region  \\\n",
       "0       0      0       0         0    3       1       0    0       5   \n",
       "1       0      0       0         0    3       1       0    0       5   \n",
       "2       0      0       0         1    3       1       0    0       5   \n",
       "3       1      0       0         0    3       1       0    0       5   \n",
       "4       0      0       0         0    3       1       0    0       5   \n",
       "\n",
       "   GroupComp  \n",
       "0          0  \n",
       "1          0  \n",
       "2          0  \n",
       "3          0  \n",
       "4          0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop ratings column\n",
    "final_df=final_df.drop(columns=\"rating\")\n",
    "final_df.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.30000000e+01, 0.00000000e+00, 1.64735146e+00, ...,\n",
       "        0.00000000e+00, 5.00000000e+00, 0.00000000e+00],\n",
       "       [1.30000000e+01, 2.00000000e+00, 2.94817012e+00, ...,\n",
       "        0.00000000e+00, 5.00000000e+00, 0.00000000e+00],\n",
       "       [1.30000000e+01, 3.00000000e+00, 1.83079289e+00, ...,\n",
       "        0.00000000e+00, 5.00000000e+00, 0.00000000e+00],\n",
       "       ...,\n",
       "       [9.88020000e+04, 2.20000000e+01, 4.73605642e+00, ...,\n",
       "        1.00000000e+00, 1.00000000e+00, 2.00000000e+00],\n",
       "       [9.97470000e+04, 2.20000000e+01, 4.73605642e+00, ...,\n",
       "        0.00000000e+00, 5.00000000e+00, 3.00000000e+00],\n",
       "       [9.97500000e+04, 2.20000000e+01, 4.73605642e+00, ...,\n",
       "        1.00000000e+00, 5.00000000e+00, 0.00000000e+00]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=final_df.iloc[:,:].values\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train-test-split\n",
    "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.75560000e+04, 1.60000000e+01, 4.59845876e+00, ...,\n",
       "        0.00000000e+00, 4.00000000e+00, 1.00000000e+00],\n",
       "       [4.66620000e+04, 1.50000000e+01, 2.73052852e+00, ...,\n",
       "        1.00000000e+00, 7.00000000e+00, 1.00000000e+00],\n",
       "       [5.48910000e+04, 2.00000000e+01, 1.45211387e+00, ...,\n",
       "        1.00000000e+00, 5.00000000e+00, 2.00000000e+00],\n",
       "       ...,\n",
       "       [3.10990000e+04, 2.00000000e+00, 2.94817012e+00, ...,\n",
       "        0.00000000e+00, 1.00000000e+00, 1.00000000e+00],\n",
       "       [6.60300000e+04, 2.10000000e+01, 1.23997687e+00, ...,\n",
       "        0.00000000e+00, 7.00000000e+00, 1.00000000e+00],\n",
       "       [7.46600000e+03, 3.00000000e+00, 1.83079289e+00, ...,\n",
       "        0.00000000e+00, 7.00000000e+00, 1.00000000e+00]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.31974321, 3.7604297 , 1.30080909, ..., 4.66220948, 1.48901092,\n",
       "       1.21973055])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingRegressor(random_state=42)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Build gbr model\n",
    "model = GradientBoostingRegressor(random_state=42)\n",
    "model.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_importance=model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0,\n",
       " 0.020736145975052418,\n",
       " 0.627754376964976,\n",
       " 0.07722752428797248,\n",
       " 0.00037247161194110015,\n",
       " 0.021259428622798327,\n",
       " 0.018970871245192214,\n",
       " 0.011109453205515268,\n",
       " 0.03667703345940923,\n",
       " 0.012564593920430862,\n",
       " 0.0,\n",
       " 0.0010707634957211913,\n",
       " 0.0009440660169653547,\n",
       " 0.006855592784355817,\n",
       " 0.0009434325943478854,\n",
       " 0.0012357273218226161,\n",
       " 0.10861703112251957,\n",
       " 0.046022231691910796,\n",
       " 0.007639255679069036]"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lis_features=[]\n",
    "lis_importance=[]\n",
    "for feature, importance in zip(final_df.columns, model_importance):\n",
    "    lis_features.append(feature)\n",
    "    lis_importance.append(importance)\n",
    "\n",
    "lis_importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X=np.array(lis_features)\n",
    "y=np.array(lis_importance)\n",
    "plt.bar(X,y)\n",
    "plt.title('Feature importance')\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "content-based rmse = 0.71419458212431\n"
     ]
    }
   ],
   "source": [
    "#Get predictions\n",
    "preds = model.predict(X_test)\n",
    "rmse = np.sqrt(np.mean((preds- y_test)**2))\n",
    "print(f'content-based rmse = {rmse}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.97899207, 3.18639523, 1.81400146, ..., 2.868805  , 3.11942071,\n",
       "       2.87152218])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make predictions on entire dataset\n",
    "predicted_ratings=model.predict(X)\n",
    "predicted_ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>userId</th>\n",
       "      <th>itemId</th>\n",
       "      <th>Quality</th>\n",
       "      <th>Gastro</th>\n",
       "      <th>NightLife</th>\n",
       "      <th>Shop</th>\n",
       "      <th>Relax</th>\n",
       "      <th>Events</th>\n",
       "      <th>Culture</th>\n",
       "      <th>Sports</th>\n",
       "      <th>Beach</th>\n",
       "      <th>Nature</th>\n",
       "      <th>Themeprk</th>\n",
       "      <th>Age</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Job</th>\n",
       "      <th>Region</th>\n",
       "      <th>GroupComp</th>\n",
       "      <th>new_ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1.647351</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1.978992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>2.948170</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3.186395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>1.830793</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1.814001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13</td>\n",
       "      <td>18</td>\n",
       "      <td>1.853823</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1.965226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>19</td>\n",
       "      <td>4.636606</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3.079262</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  itemId   Quality  Gastro  NightLife  Shop  Relax  Events  Culture  \\\n",
       "0      13       0  1.647351       1          0     0      0       0        0   \n",
       "1      13       2  2.948170       0          0     1      1       0        0   \n",
       "2      13       3  1.830793       0          0     0      0       0        0   \n",
       "3      13      18  1.853823       0          0     0      0       0        0   \n",
       "4      13      19  4.636606       0          1     0      0       0        0   \n",
       "\n",
       "   Sports  Beach  Nature  Themeprk  Age  Budget  Gender  Job  Region  \\\n",
       "0       0      0       0         0    3       1       0    0       5   \n",
       "1       0      0       0         0    3       1       0    0       5   \n",
       "2       0      0       0         1    3       1       0    0       5   \n",
       "3       1      0       0         0    3       1       0    0       5   \n",
       "4       0      0       0         0    3       1       0    0       5   \n",
       "\n",
       "   GroupComp  new_ratings  \n",
       "0          0     1.978992  \n",
       "1          0     3.186395  \n",
       "2          0     1.814001  \n",
       "3          0     1.965226  \n",
       "4          0     3.079262  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Generate new predicted ratings column\n",
    "final_df['new_ratings']=predicted_ratings\n",
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
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       "      <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>3.194513</td>\n",
       "      <td>3.708553</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.693665</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.298662</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.828417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.239698</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.660054</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.951497</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>2.008608</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>3</th>\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>2.601214</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.317315</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>3.183211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.946050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.377384</td>\n",
       "      <td>2.17073</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.013352</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.395396</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.227890</td>\n",
       "      <td>1.882141</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.645509</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.953434</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>1.889969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.513059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.747112</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.793417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.379384</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.629719</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\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>2.955340</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.557311</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\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>2.474323</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>9</th>\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>2.031169</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.832961</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>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.384848</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>2.798924</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>12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.723387</td>\n",
       "      <td>3.229073</td>\n",
       "      <td>1.747089</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.850334</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.665777</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>13</th>\n",
       "      <td>1.978992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.186395</td>\n",
       "      <td>1.814001</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>1.965226</td>\n",
       "      <td>3.079262</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.551132</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>3.115988</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.811559</td>\n",
       "      <td>1.686681</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.986039</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.955334</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>1.950120</td>\n",
       "      <td>2.614535</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.039164</td>\n",
       "      <td>2.045565</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.856755</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>3.079858</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.225943</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.275177</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.594783</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.832100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.893949</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.803067</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.911053</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.193433</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>18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.562240</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>3.077674</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>1.845947</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.635003</td>\n",
       "      <td>1.653727</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>1.819970</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.493381</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.917416</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>20</th>\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>2.840744</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>3.121408</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",
       "</div>"
      ],
      "text/plain": [
       "itemId        0         1         2         3         4         5        6   \\\n",
       "userId                                                                        \n",
       "0            NaN  1.518616       NaN       NaN       NaN       NaN      NaN   \n",
       "1            NaN       NaN       NaN       NaN       NaN  3.298662      NaN   \n",
       "2            NaN       NaN  2.951497       NaN       NaN       NaN      NaN   \n",
       "3            NaN       NaN       NaN       NaN       NaN       NaN      NaN   \n",
       "4            NaN       NaN  2.946050       NaN       NaN  3.377384  2.17073   \n",
       "5            NaN       NaN       NaN  1.645509       NaN  2.953434      NaN   \n",
       "6            NaN       NaN       NaN  1.513059       NaN       NaN      NaN   \n",
       "7            NaN       NaN       NaN       NaN       NaN       NaN      NaN   \n",
       "8            NaN       NaN       NaN       NaN       NaN       NaN      NaN   \n",
       "9            NaN       NaN       NaN       NaN       NaN       NaN      NaN   \n",
       "10           NaN  1.384848       NaN       NaN       NaN       NaN      NaN   \n",
       "12           NaN  1.723387  3.229073  1.747089       NaN       NaN      NaN   \n",
       "13      1.978992       NaN  3.186395  1.814001       NaN       NaN      NaN   \n",
       "14           NaN       NaN  2.551132       NaN       NaN       NaN      NaN   \n",
       "15      1.986039       NaN  2.955334       NaN       NaN       NaN      NaN   \n",
       "16           NaN       NaN       NaN       NaN  2.856755       NaN      NaN   \n",
       "17      1.832100       NaN       NaN       NaN  2.893949       NaN      NaN   \n",
       "18           NaN       NaN       NaN  1.562240       NaN       NaN      NaN   \n",
       "19      1.635003  1.653727       NaN       NaN       NaN       NaN      NaN   \n",
       "20           NaN       NaN       NaN       NaN       NaN       NaN      NaN   \n",
       "\n",
       "itemId        7         8         9         10        11  12        13  \\\n",
       "userId                                                                   \n",
       "0            NaN       NaN       NaN       NaN       NaN NaN       NaN   \n",
       "1       1.828417       NaN  2.239698       NaN       NaN NaN  3.660054   \n",
       "2            NaN       NaN       NaN  2.008608       NaN NaN       NaN   \n",
       "3            NaN       NaN       NaN       NaN  2.601214 NaN  3.317315   \n",
       "4            NaN       NaN       NaN  2.013352       NaN NaN       NaN   \n",
       "5            NaN       NaN       NaN       NaN       NaN NaN       NaN   \n",
       "6            NaN  1.747112       NaN  1.793417       NaN NaN       NaN   \n",
       "7            NaN       NaN       NaN       NaN       NaN NaN       NaN   \n",
       "8            NaN       NaN       NaN       NaN       NaN NaN       NaN   \n",
       "9            NaN       NaN  2.031169       NaN       NaN NaN       NaN   \n",
       "10           NaN       NaN       NaN       NaN       NaN NaN  2.798924   \n",
       "12      1.850334       NaN       NaN       NaN  2.665777 NaN       NaN   \n",
       "13           NaN       NaN       NaN       NaN       NaN NaN       NaN   \n",
       "14           NaN       NaN       NaN       NaN       NaN NaN       NaN   \n",
       "15           NaN       NaN       NaN  1.950120  2.614535 NaN       NaN   \n",
       "16           NaN       NaN       NaN       NaN  3.079858 NaN       NaN   \n",
       "17      1.803067       NaN  1.911053       NaN       NaN NaN  3.193433   \n",
       "18           NaN       NaN       NaN       NaN  3.077674 NaN       NaN   \n",
       "19           NaN  1.819970       NaN       NaN       NaN NaN  3.493381   \n",
       "20      2.840744       NaN       NaN       NaN       NaN NaN       NaN   \n",
       "\n",
       "itemId        14        15        16        17        18        19        20  \\\n",
       "userId                                                                         \n",
       "0            NaN       NaN  3.194513  3.708553       NaN       NaN       NaN   \n",
       "1            NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2            NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3            NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4            NaN       NaN  3.395396       NaN       NaN       NaN  2.227890   \n",
       "5            NaN       NaN       NaN       NaN       NaN       NaN  1.889969   \n",
       "6            NaN       NaN  3.379384       NaN       NaN       NaN       NaN   \n",
       "7            NaN  2.955340       NaN       NaN       NaN       NaN       NaN   \n",
       "8       2.474323       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "9            NaN  2.832961       NaN       NaN       NaN       NaN       NaN   \n",
       "10           NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "12           NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "13           NaN       NaN       NaN       NaN  1.965226  3.079262       NaN   \n",
       "14           NaN  3.115988       NaN       NaN       NaN       NaN  1.811559   \n",
       "15           NaN       NaN       NaN  3.039164  2.045565       NaN       NaN   \n",
       "16      3.225943       NaN  3.275177       NaN  1.594783       NaN       NaN   \n",
       "17           NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "18           NaN       NaN       NaN       NaN  1.845947       NaN       NaN   \n",
       "19           NaN       NaN       NaN  2.917416       NaN       NaN       NaN   \n",
       "20      3.121408       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "itemId        21        22  \n",
       "userId                      \n",
       "0       1.693665       NaN  \n",
       "1            NaN       NaN  \n",
       "2            NaN       NaN  \n",
       "3            NaN  3.183211  \n",
       "4       1.882141       NaN  \n",
       "5            NaN       NaN  \n",
       "6       1.629719       NaN  \n",
       "7       1.557311       NaN  \n",
       "8            NaN       NaN  \n",
       "9            NaN       NaN  \n",
       "10           NaN       NaN  \n",
       "12           NaN       NaN  \n",
       "13           NaN       NaN  \n",
       "14      1.686681       NaN  \n",
       "15           NaN       NaN  \n",
       "16           NaN       NaN  \n",
       "17           NaN       NaN  \n",
       "18           NaN       NaN  \n",
       "19           NaN       NaN  \n",
       "20           NaN       NaN  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content_filter_df=final_df.pivot_table(index=\"userId\", columns=\"itemId\", values='new_ratings')\n",
    "content_filter_df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>itemId</th>\n",
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       "      <th>22</th>\n",
       "    </tr>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.660054</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>2.951497</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "itemId   0         1         2    3    4         5        6         7    8   \\\n",
       "userId                                                                        \n",
       "0       0.0  1.518616  0.000000  0.0  0.0  0.000000  0.00000  0.000000  0.0   \n",
       "1       0.0  0.000000  0.000000  0.0  0.0  3.298662  0.00000  1.828417  0.0   \n",
       "2       0.0  0.000000  2.951497  0.0  0.0  0.000000  0.00000  0.000000  0.0   \n",
       "3       0.0  0.000000  0.000000  0.0  0.0  0.000000  0.00000  0.000000  0.0   \n",
       "4       0.0  0.000000  2.946050  0.0  0.0  3.377384  2.17073  0.000000  0.0   \n",
       "\n",
       "itemId        9         10        11   12        13   14   15        16  \\\n",
       "userId                                                                    \n",
       "0       0.000000  0.000000  0.000000  0.0  0.000000  0.0  0.0  3.194513   \n",
       "1       2.239698  0.000000  0.000000  0.0  3.660054  0.0  0.0  0.000000   \n",
       "2       0.000000  2.008608  0.000000  0.0  0.000000  0.0  0.0  0.000000   \n",
       "3       0.000000  0.000000  2.601214  0.0  3.317315  0.0  0.0  0.000000   \n",
       "4       0.000000  2.013352  0.000000  0.0  0.000000  0.0  0.0  3.395396   \n",
       "\n",
       "itemId        17   18   19       20        21        22  \n",
       "userId                                                   \n",
       "0       3.708553  0.0  0.0  0.00000  1.693665  0.000000  \n",
       "1       0.000000  0.0  0.0  0.00000  0.000000  0.000000  \n",
       "2       0.000000  0.0  0.0  0.00000  0.000000  0.000000  \n",
       "3       0.000000  0.0  0.0  0.00000  0.000000  3.183211  \n",
       "4       0.000000  0.0  0.0  2.22789  1.882141  0.000000  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fill NaNs with zero\n",
    "content_filter_df=content_filter_df.fillna(0)\n",
    "content_filter_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.0</td>\n",
       "      <td>3.968529</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.705281</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.341966</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>2.59672</td>\n",
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       "      <td>1.636043</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.531573</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "itemId   0        1         2    3    4         5         6         7    8   \\\n",
       "userId                                                                        \n",
       "0       0.0  1.50042  0.000000  0.0  0.0  0.000000  0.000000  0.000000  0.0   \n",
       "1       0.0  0.00000  0.000000  0.0  0.0  4.156108  0.000000  1.845351  0.0   \n",
       "2       0.0  0.00000  2.709697  0.0  0.0  0.000000  0.000000  0.000000  0.0   \n",
       "3       0.0  0.00000  0.000000  0.0  0.0  0.000000  0.000000  0.000000  0.0   \n",
       "4       0.0  0.00000  1.373965  0.0  0.0  4.714443  1.636043  0.000000  0.0   \n",
       "\n",
       "itemId        9         10       11   12        13   14   15        16  \\\n",
       "userId                                                                   \n",
       "0       0.000000  0.000000  0.00000  0.0  0.000000  0.0  0.0  2.667926   \n",
       "1       3.968529  0.000000  0.00000  0.0  4.705281  0.0  0.0  0.000000   \n",
       "2       0.000000  1.341966  0.00000  0.0  0.000000  0.0  0.0  0.000000   \n",
       "3       0.000000  0.000000  2.59672  0.0  2.658896  0.0  0.0  0.000000   \n",
       "4       0.000000  1.531573  0.00000  0.0  0.000000  0.0  0.0  2.277358   \n",
       "\n",
       "itemId        17   18   19        20        21        22  \n",
       "userId                                                    \n",
       "0       2.828157  0.0  0.0  0.000000  1.632696  0.000000  \n",
       "1       0.000000  0.0  0.0  0.000000  0.000000  0.000000  \n",
       "2       0.000000  0.0  0.0  0.000000  0.000000  0.000000  \n",
       "3       0.000000  0.0  0.0  0.000000  0.000000  2.661253  \n",
       "4       0.000000  0.0  0.0  3.804309  1.182217  0.000000  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(97605, 23)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create an array for content based filtering \n",
    "content_based_array=np.array(content_filter_df)\n",
    "content_based_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.0, 0.7135212619428793],\n",
       " [0.05, 0.6994545608577163],\n",
       " [0.1, 0.6861159060848985],\n",
       " [0.15000000000000002, 0.6735485527255478],\n",
       " [0.2, 0.6617964427167901],\n",
       " [0.25, 0.650903735374482],\n",
       " [0.30000000000000004, 0.6409142503285823],\n",
       " [0.35000000000000003, 0.6318708271806682],\n",
       " [0.4, 0.6238146123870887],\n",
       " [0.45, 0.6167842908434555],\n",
       " [0.5, 0.6108152868525915],\n",
       " [0.55, 0.6059389658144634],\n",
       " [0.6000000000000001, 0.6021818731494063],\n",
       " [0.65, 0.5995650497027766],\n",
       " [0.7000000000000001, 0.59810346238623],\n",
       " [0.75, 0.5978055846423974],\n",
       " [0.8, 0.5986731535310562],\n",
       " [0.8500000000000001, 0.6007011194478823],\n",
       " [0.9, 0.6038777918345708],\n",
       " [0.9500000000000001, 0.6081851711888361],\n",
       " [1.0, 0.6135994457746267]]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Weighted hybrid model\n",
    "chart_val=[]\n",
    "w=np.linspace(0,1,21)\n",
    "for i in w:\n",
    "  final_pred=predicted_collaborative_ratings[user_item_matrix>0]*i + (content_based_array[user_item_matrix>0]*(1-i))\n",
    "  rmse=np.sqrt(np.mean(np.square(final_pred-user_item_matrix[user_item_matrix>0])))\n",
    "  chart_val.append([i,rmse])\n",
    "  \n",
    "chart_val\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'RMSE')"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart_val_np = np.array(chart_val)\n",
    "plt.plot(chart_val_np[:, 0], chart_val_np[:,1])\n",
    "plt.title('Plot showing rmse against weight')\n",
    "plt.xlabel('weight')\n",
    "plt.ylabel('RMSE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Utilizing model\n",
    "To utilize the model:\n",
    "* User will provide ratings for some locations\n",
    "* Generate ratings for other locations using GBR model \n",
    "* Use collaborative filtering on chosen locations to generate ratings\n",
    "* Combine using hybrid\n",
    "* Aggregate for group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assume a user provides data like:\n",
    "* Age:25\n",
    "* Budget:$15000\n",
    "* Gender:Female\n",
    "* Job:White collar\n",
    "* Region: South Europe\n",
    "* GroupComp: 2Adlts\n",
    "\n",
    "Rates 4 restaurants \n",
    "* Restaurant Fake:4.0\n",
    "* Secret Beach: 2.2\n",
    "* False Tavern: 1.5\n",
    "* Bogus Spa: 3.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get activities user did not rate\n",
    "def get_restaurants_not_rated(restaurants):\n",
    "  list_restaurants_to_predict=[]\n",
    "  for i in destination_spots['ItemName'].unique():\n",
    "    if i not in restaurants:\n",
    "        list_restaurants_to_predict.append(i)\n",
    "  return list_restaurants_to_predict\n",
    "\n",
    "\n",
    "  \n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_user_matrix(userId, age, budget, gender, job, region, groupcomp):\n",
    "    user_df=pd.DataFrame({'userId':userId, 'Age':[age], 'Budget':[budget], 'Gender':[gender], 'Job':[job], 'Region':[region], 'GroupComp':[groupcomp]})\n",
    "    return user_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get activity details for activities not rated by user\n",
    "def get_activity_details(restaurants,userId, age, budget, gender, job, region, groupcomp):\n",
    "    restaurants_to_predict=get_restaurants_not_rated(restaurants=restaurants)\n",
    "    activity_df=destination_spots[destination_spots['ItemName'].isin(restaurants_to_predict)].reset_index()\n",
    "    activity_df=activity_df.drop(columns=['ItemName', 'Category'])\n",
    "    user_df=generate_user_matrix(userId, age, budget, gender, job, region, groupcomp)\n",
    "    new_user_df=pd.concat([user_df]*len(activity_df), ignore_index=True)\n",
    "    concat_user_activity=pd.concat([new_user_df,activity_df], axis=1)\n",
    "    new_order=['userId', 'itemId', 'Quality', 'Gastro', 'NightLife', 'Shop', 'Relax', 'Events', 'Culture', 'Sports', 'Beach', 'Nature', 'Themeprk', 'Age', 'Budget', 'Gender', 'Job', 'Region', 'GroupComp']\n",
    "    final_df=concat_user_activity[new_order]\n",
    "    return final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_content_ratings(user_df):\n",
    "    user_inputs=user_df.iloc[:,:].values\n",
    "    pred_ratings=model.predict(user_inputs)\n",
    "    user_df['predicted_ratings']=pred_ratings\n",
    "    user_df=user_df.pivot_table(index=\"userId\", columns=\"itemId\", values=\"predicted_ratings\")\n",
    "    user_array=np.array(user_df)\n",
    "    return user_array\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Matrix Factorization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_user_ratings(user_ratings):\n",
    "    user_ratings_array=[0]*destination_spots.shape[0]\n",
    "    restaurant_names=user_ratings.keys()\n",
    "    for i in range(destination_spots.shape[0]):\n",
    "        for j in restaurant_names:\n",
    "            dest_id=destination_spots['itemId'][destination_spots['ItemName']==j]\n",
    "            if int(dest_id)==i:\n",
    "                user_ratings_array[i]=user_ratings[j]\n",
    "    return user_ratings_array\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def user_matrix_factorization(user_ratings):\n",
    "    # Assume you have the user's ratings and the list of recommended item indices\n",
    "    new_user_ratings=np.array(build_user_ratings(user_ratings))\n",
    "   \n",
    "\n",
    "\n",
    "    # Update the user matrix with the new user's ratings\n",
    "    user_matrix = np.vstack([user_item_matrix, new_user_ratings])\n",
    "    latent_features = 3\n",
    "    P, Q = matrix_factorization(user_matrix, K=latent_features)\n",
    "\n",
    "    # Generate recommendations for the new user\n",
    "    predicted_user_ratings = np.dot(P[-1], Q.T)\n",
    "    dest_spots=destination_spots['ItemName'].unique()\n",
    "\n",
    "    \n",
    "    # # Exclude rated items from the list of recommended items\n",
    "\n",
    "    ratings=np.argsort(predicted_user_ratings)[::-1]\n",
    "\n",
    "    return  predicted_user_ratings[new_user_ratings==0]\n",
    "   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hybrid_filter(restaurants, userId, age, budget, gender, job, region, groupcomp):\n",
    "    restaurants_rated=restaurants.keys()\n",
    "    list_restaurants_not_rated=get_restaurants_not_rated(restaurants_rated)\n",
    "    user_df=get_activity_details(restaurants_rated,userId, age, budget, gender, job, region, groupcomp)\n",
    "    user_content_array=predict_content_ratings(user_df)\n",
    "    user_collab_array=user_matrix_factorization(restaurants)\n",
    "    final_pred=user_collab_array*0.75 + user_content_array* 0.25\n",
    "    best_eight_activities=np.argsort(final_pred[0])[::-1][:8]\n",
    "    recommended_items=[list_restaurants_not_rated[i] for i in best_eight_activities]\n",
    "    return recommended_items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jessica.ogwu\\AppData\\Local\\Temp\\ipykernel_46516\\1320286131.py:7: FutureWarning: Calling int on a single element Series is deprecated and will raise a TypeError in the future. Use int(ser.iloc[0]) instead\n",
      "  if int(dest_id)==i:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step: 0 RMSE: 0.3318921984757229\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jessica.ogwu\\AppData\\Local\\Temp\\ipykernel_46516\\1320286131.py:7: FutureWarning: Calling int on a single element Series is deprecated and will raise a TypeError in the future. Use int(ser.iloc[0]) instead\n",
      "  if int(dest_id)==i:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step: 0 RMSE: 0.3322276562208685\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jessica.ogwu\\AppData\\Local\\Temp\\ipykernel_46516\\1320286131.py:7: FutureWarning: Calling int on a single element Series is deprecated and will raise a TypeError in the future. Use int(ser.iloc[0]) instead\n",
      "  if int(dest_id)==i:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step: 0 RMSE: 0.3323871079552823\n",
      "['Random Shopping Mall', 'Random Cultural Tour', 'Random Surfing Lessons', 'Fake Brands Boutique', 'Unknown Nature Route', 'Fake Klub', 'Fake Beach', 'Some Sport Event']\n",
      "['Random Shopping Mall', 'Bogus Spa', 'Random Cultural Tour', 'Fake Brands Boutique', 'Random Surfing Lessons', 'Fake Klub', 'Unknown Nature Route', 'Fake Beach']\n",
      "['Bogus Spa', 'Random Shopping Mall', 'Random Cultural Tour', 'Random Surfing Lessons', 'Fake Beach', 'Fake Brands Boutique', 'Museum of Fake History', 'Best Imaginary Restaurant']\n"
     ]
    }
   ],
   "source": [
    "user1_pred=hybrid_filter({'Restaurant Fake':4.0, 'Secret Beach':2.2, 'False Tavern':1.5, 'Bogus Spa': 3.2}, 198673210, 0,2,1,0,5,3)\n",
    "user2_pred=hybrid_filter({'Fiction Nightclub':5.0, 'Best Imaginary Restaurant':2.5, 'Museum of Fake History':1.3}, 198673255, 0,2,0,1,4,3)\n",
    "user3_pred=hybrid_filter({'Some Sport Event':3.7, 'Fake Klub':4.0, 'Unknown Nature Route':4.5}, 198679075, 1,2,0,0,7,3)\n",
    "\n",
    "print(user1_pred)\n",
    "print(user2_pred) \n",
    "print(user3_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Random Surfing Lessons', 'Random Cultural Tour', 'Fake Beach', 'Random Shopping Mall', 'Fake Brands Boutique'}\n"
     ]
    }
   ],
   "source": [
    "lis=[]\n",
    "for i,j,k in zip(user1_pred, user2_pred, user3_pred):\n",
    "    if i in user2_pred and i in user3_pred:\n",
    "        lis.append(i)\n",
    "    if j in user1_pred and j in user3_pred:\n",
    "        lis.append(j)\n",
    "    if k in user1_pred and k in user1_pred:\n",
    "        lis.append(k)\n",
    "\n",
    "print(set(lis))"
   ]
  }
 ],
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