Mapping_of_Features.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "import nltk\n",
    "from nltk import word_tokenize\n",
    "from collections import Counter\n",
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
    "import random\n",
    "from random import randint\n",
    "from nltk.corpus import stopwords \n",
    "from nltk.tokenize import word_tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the Data\n",
    "Hotels = pd.ExcelFile(\"E:/RAAM/GSU MSA/Data Management for Analytics/Final Project/Final.xlsx\")\n",
    "Hotels_List = Hotels.sheet_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['room', 'clean', 'in/check', 'aircon', 'unit', 'hall', 'work', 'noisy', 'heat', 'bedroom', 'door', 'apartment', 'style', 'room', 'hall', 'bedroom', 'door', 'weather', 'cold', 'imagine', 'heat', 'room', 'summer', 'lobby', 'interesting', 'game', 'room', 'area', 'wood', 'chat', 'restaurant', 'ground', 'floor', 'hotel', 'hotel', 'door', 'conference', 'room', 'shower', 'stall', 'bathroom', 'floor', 'elevator', 'floor', 'office', 'building', 'room', 'work', 'room', 'return', 'day', 'hotel', 'hotel', 'trip', 'visit', 'area', 'walk', 'train', 'station', 'mind', 'tiring', 'day', 'walk', 'get', 'room', 'level', 'ceiling', 'bed', 'size', 'use', 'half', 'glass', 'water', 'floor', 'shower', 'warehouse', 'floor', 'room', 'bar', 'lobby', 'beer', 'area', 'pier', 'bar', 'trailer', 'winter', 'hotel', 'stock', 'visit', 'area', 'hotel', 'convenient', 'location', 'station', 'accessibility', 'cafes', 'room', 'room', 'area', 'shower', 'room', 'didnt', 'control', 'doesnt', 'hotel', 'call', 'change', 'room', 'room', 'phone', 'didnt', 'work', 'repair', 'check', 'ground', 'floor', 'level', 'round', 'table', 'afternoon', 'tea', 'reservation', 'bar', 'stool', 'mushroom', 'fettucine', 'tea', 'afternoon', 'tea', 'king', 'room', 'person', 'shopping', 'family', 'member', 'room', 'cramped', 'table', 'bench', 'touch', 'space', 'part', 'wardrobe', 'space', 'table', 'bathroom', 'day', 'shopping', 'walk', 'cafes', 'size', 'room', 'floor', 'way', 'room', 'reception', 'check', 'in/check', 'gripe', 'airconditioning', 'way', 'temperature', 'mood', 'temperature', 'front', 'entrance', 'shopping', 'access', 'cafes', 'supermarket', 'walk', 'subway', 'hotel', 'acessibility', 'humidifier', 'room', 'buffet', 'breakfast', 'spread', 'night', 'onsen', 'impression', 'area', 'food', 'dinner', 'breakfast', 'breathtaking', 'service', 'bath', 'pool', 'floor', 'area', 'morning', 'bathe', 'bath', 'level', 'gorge', 'walk', 'gorge', 'town', 'evening', 'pearl', 'powder', 'bath', 'skin', 'soak', 'lake', 'furnishing', 'isnt', 'room', 'room', 'quiet', 'spread', 'service', 'attentive', 'hotel', 'subway', 'walk', 'convenient', 'subway', 'way', 'airport', 'morning', 'hour', 'way', 'cab', 'pool', 'lifeguard', 'fancy', 'swim', 'walk', 'hotel', 'pitstop', 'lobby', 'service', 'attentive', 'room', 'bathroom', 'floor', 'get', 'soak', 'day', 'shopping', 'spa', 'table', 'room', 'comfy', 'sofa', 'tv', 'wardrobe', 'housekeeping', 'location', 'convenient', 'walk', 'trams', 'room', 'bed', 'size', 'bathtub/shower', 'curtain/shower', 'combo', 'destination', 'fee', 'night', 'card', 'water', 'list', 'night', 'landing', 'airport', 'evening', 'hotel', 'shuttle', 'hall', 'end', 'terminal', 'wait', 'hotel', 'burger', 'place', 'hotel', 'door', 'shower', 'bed', 'room', 'size', 'area', 'glass', 'stall', 'water', 'shuttle', 'airport', 'convenient', 'conference', 'choice', 'water', 'floor', 'room', 'conference', 'day', 'shower', 'change', 'room', 'hotel', 'pack/unpack', 'airconditioning', 'night', 'issue', 'set', 'design', 'concept', 'get', 'look', 'lobby', 'insist', 'noisy', 'lobby', 'chatter', 'inconsistent', 'year', 'conference', 'hotel', 'water', 'conference', 'week', 'size', 'balcony', 'studio', 'room', 'party', 'room', 'living', 'room', 'pantry', 'hotel', 'room', 'business', 'business', 'district', 'quarter', 'cab', 'car', 'front', 'desk', 'staff', 'style', 'bar', 'pool', 'promotion', 'issue', 'room', 'evening', 'hotel', 'everyone', 'night', 'day', 'wifi', 'plan', 'nothing', 'office', 'area', 'plenty', 'shopping', 'hotel', 'bathroom', 'buffet', 'spread', 'selection', 'tourist', 'place', 'lobby', 'serve', 'tea', 'coffee', 'pastry/cake', 'basement', 'bell', 'staff', 'duty', 'check', 'check', 'luggage', 'standard', 'ole', 'dame', 'conference', 'resort', 'conference', 'resort', 'room', 'living', 'room', 'modernization', 'front', 'desk', 'suite', 'suite', 'bedroom', 'room', 'area', 'balcony', 'pool', 'beach', 'pool', 'area', 'lit', 'night', 'staff', 'time', 'sleep', 'resort', 'issue', 'night', 'conference', 'party', 'area', 'loud', 'band', 'resort', 'rest', 'singing', 'evening', 'night', 'music', 'tune', 'noise', 'peace', 'issue', 'night', 'conference', 'party', 'area', 'loud', 'band', 'resort', 'rest', 'singing', 'evening', 'night', 'music', 'tune', 'noise', 'peace', 'hotel', 'bedroom/bathroom', 'shower', 'work', 'desk', 'wifi', 'mall', 'business', 'holiday']\n"
     ]
    }
   ],
   "source": [
    "# Finding Nouns used by Customer\n",
    "cust_review = pd.read_excel(\"E:/RAAM/GSU MSA/Data Management for Analytics/Final Project/TravelBug0082.xlsx\",sheet_name = 'Sheet1').iloc[0,0]\n",
    "\n",
    "# Removing Stopwords\n",
    "stop_words = set(stopwords.words('english')) \n",
    "x = word_tokenize(cust_review) \n",
    "x = [w for w in x if not w in stop_words]\n",
    "\n",
    "\n",
    "\n",
    "y = nltk.pos_tag(x)\n",
    "\n",
    "m = []\n",
    "for i in range(len(y)):\n",
    "    if y[i][1] == \"NN\":\n",
    "        m.append(y[i][0])  \n",
    "    else:\n",
    "        pass\n",
    "    \n",
    "m.remove('”')\n",
    "print(m)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting Score of each noun used by customer for every hotel\n",
    "p = []\n",
    "for i in range(len(Hotels_List)):\n",
    "    l = []\n",
    "    for j in range(len(m)):\n",
    "        if m[j] in Hotels.parse(i)[\"Noun\"].to_list():\n",
    "            x = Hotels.parse(i).index[Hotels.parse(i)[\"Noun\"] == m[j]].to_list()[0]\n",
    "            l.append(Hotels.parse(0).loc[x,\"Compound\"])\n",
    "        else:\n",
    "            l.append(0)\n",
    "    p.append(l)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making a Dataframe\n",
    "d = {}\n",
    "for i in range(len(Hotels_List)):\n",
    "    d.update({Hotels_List[i]:p[i]})\n",
    "X = pd.DataFrame(d,index = m)\n",
    "\n",
    "# Rolling upto Unique words\n",
    "X = X.groupby(X.index).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Aston Islander on the Beach</th>\n",
       "      <th>Coconut Waikiki Hotel</th>\n",
       "      <th>La_Croix</th>\n",
       "      <th>OHANA Waikiki Malia Outrigger</th>\n",
       "      <th>Otani Hotel</th>\n",
       "      <th>Ramada Plaza by Wyndham Waikiki</th>\n",
       "      <th>Sheraton Princess Hotel</th>\n",
       "      <th>Aqua Pacific Monarch</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>access</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.599669</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>afternoon</th>\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",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>aircon</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>...</th>\n",
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       "      <th>wifi</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.427747</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>winter</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>wood</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <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>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.603800</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
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       "<p>215 rows × 8 columns</p>\n",
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      ],
      "text/plain": [
       "               Aston Islander on the Beach  Coconut Waikiki Hotel  La_Croix  \\\n",
       "access                                 0.0               0.000000       0.0   \n",
       "accessibility                          0.0               0.000000       0.0   \n",
       "acessibility                           0.0               0.000000       0.0   \n",
       "afternoon                              0.0               1.155167       0.0   \n",
       "aircon                                 0.0               0.000000       0.0   \n",
       "...                                    ...                    ...       ...   \n",
       "wifi                                   0.0               0.000000       0.0   \n",
       "winter                                 0.0               0.000000       0.0   \n",
       "wood                                   0.0               0.000000       0.0   \n",
       "work                                   0.0               0.000000       0.0   \n",
       "year                                   0.0               0.000000       0.0   \n",
       "\n",
       "               OHANA Waikiki Malia Outrigger  Otani Hotel  \\\n",
       "access                              0.599669     0.412929   \n",
       "accessibility                       0.000000     0.000000   \n",
       "acessibility                        0.000000     0.000000   \n",
       "afternoon                           0.000000     0.000000   \n",
       "aircon                              0.000000     0.000000   \n",
       "...                                      ...          ...   \n",
       "wifi                                0.000000     0.000000   \n",
       "winter                              0.000000     0.000000   \n",
       "wood                                0.000000     0.000000   \n",
       "work                                0.000000     0.000000   \n",
       "year                                0.000000     0.000000   \n",
       "\n",
       "               Ramada Plaza by Wyndham Waikiki  Sheraton Princess Hotel  \\\n",
       "access                                     0.0                 0.000000   \n",
       "accessibility                              0.0                 0.000000   \n",
       "acessibility                               0.0                 0.000000   \n",
       "afternoon                                  0.0                 0.000000   \n",
       "aircon                                     0.0                 0.000000   \n",
       "...                                        ...                      ...   \n",
       "wifi                                       0.0                 1.427747   \n",
       "winter                                     0.0                 0.000000   \n",
       "wood                                       0.0                 0.000000   \n",
       "work                                       0.0                 0.000000   \n",
       "year                                       0.0                 0.603800   \n",
       "\n",
       "               Aqua Pacific Monarch  \n",
       "access                          0.0  \n",
       "accessibility                   0.0  \n",
       "acessibility                    0.0  \n",
       "afternoon                       0.0  \n",
       "aircon                          0.0  \n",
       "...                             ...  \n",
       "wifi                            0.0  \n",
       "winter                          0.0  \n",
       "wood                            0.0  \n",
       "work                            0.0  \n",
       "year                            0.0  \n",
       "\n",
       "[215 rows x 8 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ramada Plaza by Wyndham Waikiki</th>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aqua Pacific Monarch</th>\n",
       "      <td>99.630602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OHANA Waikiki Malia Outrigger</th>\n",
       "      <td>76.210716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sheraton Princess Hotel</th>\n",
       "      <td>56.826213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coconut Waikiki Hotel</th>\n",
       "      <td>53.611913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aston Islander on the Beach</th>\n",
       "      <td>53.063778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Otani Hotel</th>\n",
       "      <td>32.678532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>La_Croix</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      Score\n",
       "Ramada Plaza by Wyndham Waikiki  100.000000\n",
       "Aqua Pacific Monarch              99.630602\n",
       "OHANA Waikiki Malia Outrigger     76.210716\n",
       "Sheraton Princess Hotel           56.826213\n",
       "Coconut Waikiki Hotel             53.611913\n",
       "Aston Islander on the Beach       53.063778\n",
       "Otani Hotel                       32.678532\n",
       "La_Croix                           0.000000"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ranking and Recommending the Hotel\n",
    "Scores_sum = X.sum()\n",
    "Ranking = pd.DataFrame(Scores_sum.sort_values(ascending = False), columns= [\"Score\"])\n",
    "Ranking\n",
    "# Applying Min-Max Scaling\n",
    "Max = Ranking['Score'].max(axis = 0)\n",
    "Min = Ranking['Score'].min(axis = 0)\n",
    "Ranking['Score'] = Ranking['Score'].apply(lambda x:(x - Min)/(Max - Min)*100)\n",
    "Ranking"
   ]
  }
 ],
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