boston.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysing Boston AirBnb Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this project i will follow the following steps using Cross-industry standard process for data mining(CRISP-DM)\n",
    "\n",
    "1. Business Understanding\n",
    "2. Data Understanding\n",
    "3. Prepare data\n",
    "4. Data Modeling\n",
    "5. Evaluate The results\n",
    "6. Deploy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Business Understanding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What kind of answers we get from the data using the below questions\n",
    "\n",
    "1. What time of year is the peak season in Boston?\n",
    "2. Analyze what do people think about AirBnb based on reviews?\n",
    "3. What are the important factors that influence price in boston ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Understanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     /home/prabhumanickavelu/nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython import display\n",
    "%matplotlib inline\n",
    "from os import path, getcwd\n",
    "import seaborn as sns\n",
    "import csv\n",
    "import helper\n",
    "from datetime import datetime\n",
    "from sklearn.impute import SimpleImputer\n",
    "from collections import Counter\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import Imputer, StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.metrics import fbeta_score, accuracy_score\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from scipy.sparse import csr_matrix\n",
    "from PIL import Image\n",
    "from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\n",
    "import string\n",
    "import nltk\n",
    "nltk.downloader.download('vader_lexicon')\n",
    "from nltk.sentiment import SentimentIntensityAnalyzer\n",
    "from langdetect import detect "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# current path\n",
    "cwd = path.join(getcwd(), 'data')\n",
    "\n",
    "# data folders\n",
    "cities = ['boston']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# boston data\n",
    "\n",
    "boston_listings = pd.read_csv(path.join(cwd, cities[0], 'listings.csv'))\n",
    "boston_reviews = pd.read_csv(path.join(cwd, cities[0], 'reviews.csv'))\n",
    "boston_calendar  = pd.read_csv(path.join(cwd, cities[0], 'calendar.csv'))\n",
    "\n",
    "bos_lis = boston_listings\n",
    "bos_rev = boston_reviews\n",
    "bos_cal = boston_calendar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3585, 95), (68275, 6), (1308890, 4))"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print boston rows and colimns\n",
    "\n",
    "bos_lis.shape, bos_rev.shape, bos_cal.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>id</th>\n",
       "      <th>listing_url</th>\n",
       "      <th>scrape_id</th>\n",
       "      <th>last_scraped</th>\n",
       "      <th>name</th>\n",
       "      <th>summary</th>\n",
       "      <th>space</th>\n",
       "      <th>description</th>\n",
       "      <th>experiences_offered</th>\n",
       "      <th>neighborhood_overview</th>\n",
       "      <th>...</th>\n",
       "      <th>review_scores_value</th>\n",
       "      <th>requires_license</th>\n",
       "      <th>license</th>\n",
       "      <th>jurisdiction_names</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>require_guest_profile_picture</th>\n",
       "      <th>require_guest_phone_verification</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>reviews_per_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>12147973</td>\n",
       "      <td>https://www.airbnb.com/rooms/12147973</td>\n",
       "      <td>20160906204935</td>\n",
       "      <td>2016-09-07</td>\n",
       "      <td>Sunny Bungalow in the City</td>\n",
       "      <td>Cozy, sunny, family home.  Master bedroom high...</td>\n",
       "      <td>The house has an open and cozy feel at the sam...</td>\n",
       "      <td>Cozy, sunny, family home.  Master bedroom high...</td>\n",
       "      <td>none</td>\n",
       "      <td>Roslindale is quiet, convenient and friendly. ...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>moderate</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3075044</td>\n",
       "      <td>https://www.airbnb.com/rooms/3075044</td>\n",
       "      <td>20160906204935</td>\n",
       "      <td>2016-09-07</td>\n",
       "      <td>Charming room in pet friendly apt</td>\n",
       "      <td>Charming and quiet room in a second floor 1910...</td>\n",
       "      <td>Small but cozy and quite room with a full size...</td>\n",
       "      <td>Charming and quiet room in a second floor 1910...</td>\n",
       "      <td>none</td>\n",
       "      <td>The room is in Roslindale, a diverse and prima...</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>t</td>\n",
       "      <td>moderate</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>6976</td>\n",
       "      <td>https://www.airbnb.com/rooms/6976</td>\n",
       "      <td>20160906204935</td>\n",
       "      <td>2016-09-07</td>\n",
       "      <td>Mexican Folk Art Haven in Boston</td>\n",
       "      <td>Come stay with a friendly, middle-aged guy in ...</td>\n",
       "      <td>Come stay with a friendly, middle-aged guy in ...</td>\n",
       "      <td>Come stay with a friendly, middle-aged guy in ...</td>\n",
       "      <td>none</td>\n",
       "      <td>The LOCATION: Roslindale is a safe and diverse...</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>moderate</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 95 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id                            listing_url       scrape_id  \\\n",
       "0  12147973  https://www.airbnb.com/rooms/12147973  20160906204935   \n",
       "1   3075044   https://www.airbnb.com/rooms/3075044  20160906204935   \n",
       "2      6976      https://www.airbnb.com/rooms/6976  20160906204935   \n",
       "\n",
       "  last_scraped                               name  \\\n",
       "0   2016-09-07         Sunny Bungalow in the City   \n",
       "1   2016-09-07  Charming room in pet friendly apt   \n",
       "2   2016-09-07   Mexican Folk Art Haven in Boston   \n",
       "\n",
       "                                             summary  \\\n",
       "0  Cozy, sunny, family home.  Master bedroom high...   \n",
       "1  Charming and quiet room in a second floor 1910...   \n",
       "2  Come stay with a friendly, middle-aged guy in ...   \n",
       "\n",
       "                                               space  \\\n",
       "0  The house has an open and cozy feel at the sam...   \n",
       "1  Small but cozy and quite room with a full size...   \n",
       "2  Come stay with a friendly, middle-aged guy in ...   \n",
       "\n",
       "                                         description experiences_offered  \\\n",
       "0  Cozy, sunny, family home.  Master bedroom high...                none   \n",
       "1  Charming and quiet room in a second floor 1910...                none   \n",
       "2  Come stay with a friendly, middle-aged guy in ...                none   \n",
       "\n",
       "                               neighborhood_overview  ... review_scores_value  \\\n",
       "0  Roslindale is quiet, convenient and friendly. ...  ...                 NaN   \n",
       "1  The room is in Roslindale, a diverse and prima...  ...                 9.0   \n",
       "2  The LOCATION: Roslindale is a safe and diverse...  ...                10.0   \n",
       "\n",
       "  requires_license license jurisdiction_names instant_bookable  \\\n",
       "0                f     NaN                NaN                f   \n",
       "1                f     NaN                NaN                t   \n",
       "2                f     NaN                NaN                f   \n",
       "\n",
       "  cancellation_policy require_guest_profile_picture  \\\n",
       "0            moderate                             f   \n",
       "1            moderate                             f   \n",
       "2            moderate                             t   \n",
       "\n",
       "  require_guest_phone_verification calculated_host_listings_count  \\\n",
       "0                                f                              1   \n",
       "1                                f                              1   \n",
       "2                                f                              1   \n",
       "\n",
       "   reviews_per_month  \n",
       "0                NaN  \n",
       "1               1.30  \n",
       "2               0.47  \n",
       "\n",
       "[3 rows x 95 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bos_lis.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['listing_id', 'date', 'available', 'price'], dtype='object')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bos_cal.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['listing_id', 'id', 'date', 'reviewer_id', 'reviewer_name', 'comments'], dtype='object')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bos_rev.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'listing_url', 'scrape_id', 'last_scraped', 'name', 'summary',\n",
       "       'space', 'description', 'experiences_offered', 'neighborhood_overview',\n",
       "       'notes', 'transit', 'access', 'interaction', 'house_rules',\n",
       "       'thumbnail_url', 'medium_url', 'picture_url', 'xl_picture_url',\n",
       "       'host_id', 'host_url', 'host_name', 'host_since', 'host_location',\n",
       "       'host_about', 'host_response_time', 'host_response_rate',\n",
       "       'host_acceptance_rate', 'host_is_superhost', 'host_thumbnail_url',\n",
       "       'host_picture_url', 'host_neighbourhood', 'host_listings_count',\n",
       "       'host_total_listings_count', 'host_verifications',\n",
       "       'host_has_profile_pic', 'host_identity_verified', 'street',\n",
       "       'neighbourhood', 'neighbourhood_cleansed',\n",
       "       'neighbourhood_group_cleansed', 'city', 'state', 'zipcode', 'market',\n",
       "       'smart_location', 'country_code', 'country', 'latitude', 'longitude',\n",
       "       'is_location_exact', 'property_type', 'room_type', 'accommodates',\n",
       "       'bathrooms', 'bedrooms', 'beds', 'bed_type', 'amenities', 'square_feet',\n",
       "       'price', 'weekly_price', 'monthly_price', 'security_deposit',\n",
       "       'cleaning_fee', 'guests_included', 'extra_people', 'minimum_nights',\n",
       "       'maximum_nights', 'calendar_updated', 'has_availability',\n",
       "       'availability_30', 'availability_60', 'availability_90',\n",
       "       'availability_365', 'calendar_last_scraped', 'number_of_reviews',\n",
       "       'first_review', 'last_review', 'review_scores_rating',\n",
       "       'review_scores_accuracy', 'review_scores_cleanliness',\n",
       "       'review_scores_checkin', 'review_scores_communication',\n",
       "       'review_scores_location', 'review_scores_value', 'requires_license',\n",
       "       'license', 'jurisdiction_names', 'instant_bookable',\n",
       "       'cancellation_policy', 'require_guest_profile_picture',\n",
       "       'require_guest_phone_verification', 'calculated_host_listings_count',\n",
       "       'reviews_per_month'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# analyze and remove unwanted columns\n",
    "bos_lis.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transform certain features"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "For calendar dataset these are the columns iam going to drop,modify\n",
    "\n",
    " --Converted host since from date to number of days.\n",
    " --Columns with price amounts having '$' and ',' characters was cleaned."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calendar Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29605.0"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# clean price columns as it is string value because of symbols\n",
    "s1 = '$296,05.00'\n",
    "out = float(str(s1).replace(',', '').split('$')[-1])\n",
    "# out = s1.extra_people.map(lambda str_price: str_price[1:]).str.replace(',', '').astype(float)\n",
    "out\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_price(price_conv):\n",
    "    '''\n",
    "    Converts USD prices from string to numeric format\n",
    "    \n",
    "    Args:\n",
    "        price_conv (string): USD price in string format (e.g., '$123,456.00')\n",
    "    \n",
    "    Returns:\n",
    "        out (float): USD price in numeric format (e.g., 123456.00)\n",
    "    '''\n",
    "    out = float(str(price_conv).replace(',', '').split('$')[-1])\n",
    "    return out\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>listing_id</th>\n",
       "      <th>date</th>\n",
       "      <th>available</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>12147973</td>\n",
       "      <td>2017-09-05</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>12147973</td>\n",
       "      <td>2017-09-04</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id        date available price\n",
       "0    12147973  2017-09-05         f   NaN\n",
       "1    12147973  2017-09-04         f   NaN"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bos_cal.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bool_to_num(s):\n",
    "    '''\n",
    "    Converts \"first letter boolean\" strings to integers\n",
    "    \n",
    "    Args:\n",
    "        bool_string (string): 't', 'f' or other\n",
    "    \n",
    "    Returns:\n",
    "        bool_number (int): 1, 0 or None\n",
    "    '''\n",
    "    \n",
    "    if s == 'f':\n",
    "        return 0\n",
    "    elif s == 't':\n",
    "        return 1\n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a = '2019-08-15'\n",
    "# b = '2018-07-13'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# (date_format(a) - date_format(b)).days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "def days_between(d1, d2):\n",
    "    '''\n",
    "    INPUT\n",
    "    d1, d2 - Start and End dates\n",
    "    OUTPUT\n",
    "    Difference between dates in number of days\n",
    "    '''\n",
    "    #d1 = datetime.strptime(d1, \"%Y-%m-%d\")\n",
    "    d2 = datetime.strptime(d2, \"%Y-%m-%d\")\n",
    "\n",
    "    return abs((d2-d1).dt.days)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "For listings dataset these are the columns iam going to drop, impute, modify\n",
    " -- URL fields except for 'thumbnail_url'.\n",
    " --Text fields except the property 'description'\n",
    " --Fields with redundant information such as availability_[30, 60, 90]. Only availability_365 was retained. ------   --Similarly with weekly and monthly price. Only the 'price' field was retained.\n",
    " --The detailed split of review scores except the final rating.\n",
    " --More granualr aspects of location.\n",
    " --Columns with price amounts having '$' and ',' characters was cleaned.\n",
    " --Cleaned percentage columns with '%' character.\n",
    " --Converted thumnail data as available or not-available.\n",
    " --Converted host since from date to number of days.\n",
    " --Converted amenities column to number of amenities."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Listings dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "listing_df = bos_lis[['description','thumbnail_url','host_since','host_response_time','host_response_rate',\n",
    "                           'host_acceptance_rate','host_has_profile_pic','host_identity_verified',\n",
    "                           'neighbourhood_cleansed','neighbourhood_group_cleansed','property_type','room_type',\n",
    "                           'accommodates','bathrooms','bedrooms','beds','bed_type','amenities','square_feet','price',\n",
    "                           'security_deposit','cleaning_fee','guests_included','extra_people','minimum_nights',\n",
    "                           'maximum_nights','availability_365','number_of_reviews','review_scores_rating',\n",
    "                           'instant_bookable','cancellation_policy','reviews_per_month']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_data(df):\n",
    "    '''\n",
    "    INPUT\n",
    "    df - Raw dataframe\n",
    "    OUTPUT\n",
    "    Cleaned dataframe\n",
    "    '''\n",
    "    data_df = df.copy()\n",
    "\n",
    "    # Clean columns with price amounts having '$' and ',' characters\n",
    "    data_df['price'] = data_df[['price']].apply(lambda x : x.str.replace('$', '')).apply(lambda x : x.str.replace(',', '')).astype(float)\n",
    "    data_df['security_deposit'] = data_df[['security_deposit']].apply(lambda x : x.str.replace('$', '')).apply(lambda x : x.str.replace(',', '')).astype(float)\n",
    "    data_df['cleaning_fee'] = data_df[['cleaning_fee']].apply(lambda x : x.str.replace('$', '')).apply(lambda x : x.str.replace(',', '')).astype(float)\n",
    "    data_df['extra_people'] = data_df[['extra_people']].apply(lambda x : x.str.replace('$', '')).apply(lambda x : x.str.replace(',', '')).astype(float)\n",
    "\n",
    "    # Clean percentage columns with '%' character\n",
    "    data_df['host_response_rate'] = data_df[['host_response_rate']].apply(lambda x : x.str.replace('%', '')).astype(float)\n",
    "    data_df['host_acceptance_rate'] = data_df[['host_acceptance_rate']].apply(lambda x : x.str.replace('%', '')).astype(float)\n",
    "\n",
    "    # Convert thumnail data as available or not-available\n",
    "    data_df['thumbnail_available'] = np.where(data_df['thumbnail_url'].isnull(), 0, 1)\n",
    "    data_df.drop('thumbnail_url', axis = 1, inplace = True)\n",
    "\n",
    "    # Convert host since to number of days\n",
    "    data_df['host_since'] = pd.to_datetime(data_df['host_since'], format='%Y-%m-%d')\n",
    "    data_df['host_since'] = data_df[['host_since']].apply(lambda x : days_between(x,'2016-01-04'))\n",
    "\n",
    "    # Convert amenities column to number of amenities\n",
    "    amen_count = []\n",
    "    for str in data_df.amenities.values:\n",
    "        amenities = list(csv.reader([str[1:-2]]))[0]\n",
    "        amen_count.append(len(amenities))\n",
    "\n",
    "    data_df['amenities_count'] = amen_count\n",
    "    data_df = data_df.drop(['amenities'], axis=1)\n",
    "\n",
    "    return data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston_df = clean_data(listing_df)\n",
    "boston_df.drop('description', axis=1, inplace=True)\n",
    "# boston_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         feature  nan_count\n",
      "8                  property_type          3\n",
      "13                          beds          9\n",
      "12                      bedrooms         10\n",
      "11                     bathrooms         14\n",
      "3           host_acceptance_rate        471\n",
      "2             host_response_rate        471\n",
      "1             host_response_time        471\n",
      "28             reviews_per_month        756\n",
      "25          review_scores_rating        813\n",
      "18                  cleaning_fee       1107\n",
      "17              security_deposit       2243\n",
      "15                   square_feet       3529\n",
      "7   neighbourhood_group_cleansed       3585\n",
      "                         feature  nan_count  percent\n",
      "8                  property_type          3      0.0\n",
      "13                          beds          9      0.0\n",
      "12                      bedrooms         10      0.0\n",
      "11                     bathrooms         14      0.0\n",
      "3           host_acceptance_rate        471     12.0\n",
      "2             host_response_rate        471     12.0\n",
      "1             host_response_time        471     12.0\n",
      "28             reviews_per_month        756     20.0\n",
      "25          review_scores_rating        813     21.0\n",
      "18                  cleaning_fee       1107     29.0\n",
      "17              security_deposit       2243     59.0\n",
      "15                   square_feet       3529     92.0\n",
      "7   neighbourhood_group_cleansed       3585     94.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/prabhumanickavelu/project1/lib/python3.6/site-packages/ipykernel_launcher.py:4: FutureWarning: from_items is deprecated. Please use DataFrame.from_dict(dict(items), ...) instead. DataFrame.from_dict(OrderedDict(items)) may be used to preserve the key order.\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "# Calculate percentage of missing data in columns\n",
    "df_nans = boston_df.isnull().sum()\n",
    "data = [('feature', list(df_nans.index)), ('nan_count', list(df_nans.values))]\n",
    "nan_counts = pd.DataFrame.from_items(data)\n",
    "\n",
    "nan_counts = nan_counts.sort_values(by='nan_count')\n",
    "print(nan_counts[nan_counts['nan_count'] != 0])\n",
    "nan_percent = nan_counts['nan_count'].apply(lambda x : round(x/3818, 2)*100)\n",
    "nan_counts['percent'] = nan_percent\n",
    "print(nan_counts[nan_counts['nan_count'] > 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove columns with relatively high percentage of missing values\n",
    "clean_df = boston_df.copy()\n",
    "clean_df = clean_df[list(nan_counts[nan_counts['percent'] < 14.0]['feature'])]\n",
    "\n",
    "# Split the data into features and target label\n",
    "label_df = np.where(clean_df['price'] > clean_df['price'].median(), 0, 1)\n",
    "feature_df = clean_df.drop(['price'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a function for preprocessing features like imputation, encoding categorical,, feature scaling\n",
    "def process_features(feature_df):\n",
    "    '''\n",
    "    Perform feature encoding, imputes missing values, and scales features\n",
    "    INPUT\n",
    "    feature_df - Feature dataframe\n",
    "    OUTPUT\n",
    "    scaled_df - Perform feature encoding, imputes missing values, and \n",
    "    returns scaled features\n",
    "    '''\n",
    "\n",
    "    # Identify and encode categorical variables\n",
    "    cat_feat = list(feature_df.select_dtypes(include = ['object']).columns)\n",
    "\n",
    "    # Encode categorical variables\n",
    "    feature_df = pd.get_dummies(feature_df, columns = cat_feat)\n",
    "\n",
    "    # Impute missing values\t\n",
    "    fill_NaN = Imputer(missing_values=np.nan, strategy='most_frequent', axis=1)\n",
    "    imputed_df = pd.DataFrame(fill_NaN.fit_transform(feature_df))\n",
    "    imputed_df.columns = feature_df.columns\n",
    "    imputed_df.index = feature_df.index\n",
    "\n",
    "    # Feature scaling\n",
    "    num_feat = list(imputed_df.select_dtypes(include = ['int64','float64']).columns)\n",
    "    scaler = MinMaxScaler()\n",
    "    scaled_df = imputed_df.copy()\n",
    "    scaled_df[num_feat] = scaler.fit_transform(imputed_df[num_feat])\n",
    "\n",
    "    return scaled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/prabhumanickavelu/project1/lib/python3.6/site-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "scaled_df = process_features(feature_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q1) What time of year is the peak season in Boston??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>listing_id</th>\n",
       "      <th>date</th>\n",
       "      <th>available</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>12147973</td>\n",
       "      <td>2017-09-05</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>12147973</td>\n",
       "      <td>2017-09-04</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>12147973</td>\n",
       "      <td>2017-09-03</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>12147973</td>\n",
       "      <td>2017-09-02</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>12147973</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id        date available price\n",
       "0    12147973  2017-09-05         f   NaN\n",
       "1    12147973  2017-09-04         f   NaN\n",
       "2    12147973  2017-09-03         f   NaN\n",
       "3    12147973  2017-09-02         f   NaN\n",
       "4    12147973  2017-09-01         f   NaN"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for this we take calendar data from both cities to analyze\n",
    "bos_cal.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "price_cols = ['price']\n",
    "bool_cols = ['available']\n",
    "bos_cal_1 = bos_cal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# clean price column\n",
    "for col in price_cols:\n",
    "    bos_cal_1[col] = bos_cal_1[col].apply(clean_price)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert available column from bool to num\n",
    "for col in bool_cols:\n",
    "    bos_cal_1[col] = bos_cal_1[col].apply(bool_to_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate mean price for each month to analyze the peak season\n",
    "# boston dataset\n",
    "\n",
    "bos_cal_1['year'] = pd.DatetimeIndex(bos_cal_1['date']).year\n",
    "bos_cal_1['month'] = pd.DatetimeIndex(bos_cal_1['date']).month\n",
    "bos_cal_1['price'] = bos_cal_1['price']\n",
    "# bos_cal_1.groupby(['year','month'])[['price']].mean().plot(kind=\"bar\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa51f3a21d0>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot seattle dataset\n",
    "\n",
    "bos_cal_1.groupby(['year','month'])[['price']].mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above graph it can be seen that in the month of september, the average hotel price is more than other months"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q2) Analyze what do people think about AirBnb based on reviews?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>listing_id</th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>reviewer_id</th>\n",
       "      <th>reviewer_name</th>\n",
       "      <th>comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1178162</td>\n",
       "      <td>4724140</td>\n",
       "      <td>2013-05-21</td>\n",
       "      <td>4298113</td>\n",
       "      <td>Olivier</td>\n",
       "      <td>My stay at islam's place was really cool! Good...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1178162</td>\n",
       "      <td>4869189</td>\n",
       "      <td>2013-05-29</td>\n",
       "      <td>6452964</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>Great location for both airport and city - gre...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1178162</td>\n",
       "      <td>5003196</td>\n",
       "      <td>2013-06-06</td>\n",
       "      <td>6449554</td>\n",
       "      <td>Sebastian</td>\n",
       "      <td>We really enjoyed our stay at Islams house. Fr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1178162</td>\n",
       "      <td>5150351</td>\n",
       "      <td>2013-06-15</td>\n",
       "      <td>2215611</td>\n",
       "      <td>Marine</td>\n",
       "      <td>The room was nice and clean and so were the co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1178162</td>\n",
       "      <td>5171140</td>\n",
       "      <td>2013-06-16</td>\n",
       "      <td>6848427</td>\n",
       "      <td>Andrew</td>\n",
       "      <td>Great location. Just 5 mins walk from the Airp...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id       id        date  reviewer_id reviewer_name  \\\n",
       "0     1178162  4724140  2013-05-21      4298113       Olivier   \n",
       "1     1178162  4869189  2013-05-29      6452964     Charlotte   \n",
       "2     1178162  5003196  2013-06-06      6449554     Sebastian   \n",
       "3     1178162  5150351  2013-06-15      2215611        Marine   \n",
       "4     1178162  5171140  2013-06-16      6848427        Andrew   \n",
       "\n",
       "                                            comments  \n",
       "0  My stay at islam's place was really cool! Good...  \n",
       "1  Great location for both airport and city - gre...  \n",
       "2  We really enjoyed our stay at Islams house. Fr...  \n",
       "3  The room was nice and clean and so were the co...  \n",
       "4  Great location. Just 5 mins walk from the Airp...  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assessing the data\n",
    "bos_rev.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Some preprocessing prices and date from strings to float and datetime\n",
    "def fix_price(x):\n",
    "    \"\"\"\n",
    "    Convert price in string format with $ sign to a float\n",
    "    input: a string including a price with optional $ symbol\n",
    "    output: a float\n",
    "    \"\"\"\n",
    "    if type(x)==str:\n",
    "        x=x.replace('$','')\n",
    "        x=x.replace(',','')\n",
    "        x=float(x)\n",
    "    return x\n",
    "\n",
    "bos_rev['date']=pd.to_datetime(bos_rev['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can use the listings dataset and the scores ratings to have an idea of what to expect from the reviews:\n",
    "\n",
    "# Mostly positive reviews\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.subplot(1,2,1)\n",
    "bos_lis['review_scores_rating'].sort_values().reset_index(drop=True).dropna().hist(color='blue', bins=20)\n",
    "plt.title('Boston Reviews Scores Rating');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the plot above shows that most of the reviews are positives score from scale 0 to 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generating  a wordmap from the comments\n",
    "# Importing classes\n",
    "def gen_wordcloud(reviews, mask_file):\n",
    "    # drop rows without reviews\n",
    "    reviews = reviews.dropna(subset=['comments'])\n",
    "    text = \" \".join(review for review in reviews.comments)\n",
    "    # remove punctation\n",
    "    nopunc = [char for char in text if char not in string.punctuation]\n",
    "    # Join the characters again to form the string.\n",
    "    nopunc = ''.join(nopunc)\n",
    "    # remove stopwords\n",
    "    stopwords = set(STOPWORDS)\n",
    "    # generating wordcloud\n",
    "    # Loading a mask from seattle\n",
    "    \n",
    "    mask = np.array(Image.open(mask_file))\n",
    "    wordcloud = WordCloud(stopwords=stopwords, max_words=100, background_color=\"white\",\n",
    "                     mask=mask, margin=3, scale=2).generate(nopunc)\n",
    "\n",
    "    return wordcloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston_wc = gen_wordcloud(bos_rev,'boston-shape.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa5273e4d68>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2880x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[40,20])\n",
    "plt.subplot(1,3,1)\n",
    "plt.imshow(boston_wc, interpolation='bilinear')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# classify reviews based on text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we will use vader sentiment analyzer that produces four sentiment metrics positive, negative, neutral and final metric compound score normalized sum of all of the lexicon ratings which have been standardised to range between -1 and 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_sentiment_scores(sentence):\n",
    "    \"\"\"\n",
    "    Prints the polarity scores from teh analyser\n",
    "    input: a text sentence\n",
    "    output: a list with the polarity scores (positivity, negativity, neutral, compound) of the sentence\n",
    "    \"\"\"\n",
    "    snt = analyser.polarity_scores(sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Detect language of reviews and drop rows with non-english reviews\n",
    "def safe_detect(text):\n",
    "    \"\"\"\n",
    "    Detect language of the text\n",
    "    input: a string with text\n",
    "    output: the encoding of the language of the text (example 'en' for english), or 'None' if not found \n",
    "    \"\"\"\n",
    "    try:\n",
    "        return(detect(text))\n",
    "    except:\n",
    "        return('None')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will remove all non-english reviews  \n",
    "\n",
    "def review_preprocessing(reviews):\n",
    "    \"\"\"\n",
    "    Preprocessing of reviews airbnb dataframe\n",
    "    input: dataframe of airbnb reviews\n",
    "    output: dataframe with language column added, limited to rows with reviews in english\n",
    "    \"\"\"\n",
    "    \n",
    "    df = reviews.dropna(subset=['comments']).copy()\n",
    "    df['language'] = df['comments'].apply(safe_detect)\n",
    "    return df[df['language']=='en']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This will take some time, better to save the files once done\n",
    "rv_boston = review_preprocessing(bos_rev)\n",
    "rv_boston.to_csv('boston_prep_reviews.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using the model \n",
    "# We add the polarity scores from Vader to the reviews datasets\n",
    "analyser = SentimentIntensityAnalyzer()\n",
    "boston_scores = [[d['neg'], d['neu'], d['pos'], d['compound']] for d in rv_boston.comments.apply(analyser.polarity_scores)]\n",
    "rv_boston = pd.concat([rv_boston, pd.DataFrame(data = boston_scores, columns=['negative', 'neutral', 'positive', 'compound'])], \n",
    "                                           axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# visualization of boston dataset for scores of positive, negative, neutral and compound score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 50000)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distributions for Boston\n",
    "plt.figure(figsize=(15,4))\n",
    "plt.subplot(1,4,1)\n",
    "rv_boston.groupby(pd.cut(rv_boston[\"positive\"], np.arange(0, 1.1, 0.1))).listing_id.count().plot(kind='bar', color='green')\n",
    "plt.title('Positive Sentiment')\n",
    "plt.ylim(0,50000)\n",
    "plt.subplot(1,4,2)\n",
    "rv_boston.groupby(pd.cut(rv_boston[\"neutral\"], np.arange(0, 1.1, 0.1))).listing_id.count().plot(kind='bar', color='grey')\n",
    "plt.title('Neutral Sentiment')\n",
    "plt.ylim(0,50000)\n",
    "plt.subplot(1,4,3)\n",
    "rv_boston.groupby(pd.cut(rv_boston[\"negative\"], np.arange(0, 1.1, 0.1))).listing_id.count().plot(kind='bar', color='red')\n",
    "plt.title('Negative Sentiment')\n",
    "plt.ylim(0,50000)\n",
    "plt.subplot(1,4,4)\n",
    "rv_boston.groupby(pd.cut(rv_boston[\"compound\"], np.arange(-1.0, 1.1, 0.2))).listing_id.count().plot(kind='bar', color='black')\n",
    "plt.title('Compound Sentiment');\n",
    "plt.ylim(0,50000)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.737064786249449\n"
     ]
    }
   ],
   "source": [
    "print(rv_boston[rv_boston[\"compound\"]>0.8].listing_id.count()/rv_boston.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plots show that only just over 6% of the reviews have a strong positive sentiment (>0.5) and the majority (61%) are in the 0.2 - 0.4 range, and most of the reviews are neutral (93%). However the negative sentiment is very low as the majority of comments have negativity < 0.1 The compound score shows that 73% of the guests in Boston give overall positive reviews"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vader compound score VS listings Review Scores rating"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "To check vader algorithim works well or not\n",
    "--we will compute the mean of the compound score from Vader for each listing. \n",
    "-- to compare the two values we will apply min-max normalization. \n",
    "--Then we will plot the distribution of the difference between these two scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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>compound</th>\n",
       "      <th>review_scores_rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>listing_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>3353.0</td>\n",
       "      <td>0.870618</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5506.0</td>\n",
       "      <td>0.938817</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6695.0</td>\n",
       "      <td>0.903248</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6976.0</td>\n",
       "      <td>0.801451</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8792.0</td>\n",
       "      <td>0.815483</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            compound  review_scores_rating\n",
       "listing_id                                \n",
       "3353.0      0.870618                  90.0\n",
       "5506.0      0.938817                  91.0\n",
       "6695.0      0.903248                  95.0\n",
       "6976.0      0.801451                  98.0\n",
       "8792.0      0.815483                  91.0"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mean of the compound score from Vader\n",
    "comp_rev=rv_boston.groupby(['listing_id'])[['compound']].mean().reset_index().merge(\n",
    "    bos_lis[['id', 'review_scores_rating']].dropna(), right_on='id', left_on='listing_id')\n",
    "\n",
    "comp_rev.drop('id', axis=1, inplace=True)\n",
    "comp_rev.set_index('listing_id', drop=True, inplace=True)\n",
    "comp_rev.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>compound</th>\n",
       "      <th>review_scores_rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>listing_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>3353.0</td>\n",
       "      <td>0.935756</td>\n",
       "      <td>0.8750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5506.0</td>\n",
       "      <td>0.970287</td>\n",
       "      <td>0.8875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6695.0</td>\n",
       "      <td>0.952277</td>\n",
       "      <td>0.9375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6976.0</td>\n",
       "      <td>0.900735</td>\n",
       "      <td>0.9750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8792.0</td>\n",
       "      <td>0.907840</td>\n",
       "      <td>0.8875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            compound  review_scores_rating\n",
       "listing_id                                \n",
       "3353.0      0.935756                0.8750\n",
       "5506.0      0.970287                0.8875\n",
       "6695.0      0.952277                0.9375\n",
       "6976.0      0.900735                0.9750\n",
       "8792.0      0.907840                0.8875"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now we normalize two scores so that they can be compared, using min max normalization\n",
    "def normalize(df):\n",
    "    \"\"\"\n",
    "    Normalize the columns in the dataset containing compond score from Vader and rating score from airbnb\n",
    "    input: a dataframe with listing_id, vader compound score (-1, 1) range and airbnb scores rating in (0,10) range\n",
    "    output: a dataframe with normalized scores using min-max normalization\n",
    "    \"\"\"\n",
    "    result = df.copy()\n",
    "    for feature_name in df.columns:\n",
    "        max_value = df[feature_name].max()\n",
    "        min_value = df[feature_name].min()\n",
    "        result[feature_name] = (df[feature_name] - min_value) / (max_value - min_value)\n",
    "    return result\n",
    "\n",
    "normal_comp = normalize(comp_rev)\n",
    "normal_comp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa529b5e400>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now we can compare the two:\n",
    "# Differences, skewwd towards 0.3===> Good!!!\n",
    "\n",
    "normal_comp['diff']=np.abs(normal_comp['compound'] - normal_comp['review_scores_rating'])\n",
    "normal_comp['diff'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "mean of the VADER compound score and the review_scores_rating for most of listings is below 0.1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q3) What are the important factors that influence price in boston ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# model and evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the classifier\n",
    "clf = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(), random_state=42)\n",
    "\n",
    "# Create the parameters list to tune, using a dictionary.\n",
    "parameters = {\"n_estimators\": [10, 50, 100],\n",
    "              \"learning_rate\": [0.005, .01, 0.05, 0.1, 0.5, 1],\n",
    "             'base_estimator__min_samples_split' : [2, 4, 6, 8, 10], \n",
    "             'base_estimator__max_depth' : [2, 4, 6, 8, 10]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/prabhumanickavelu/project1/lib/python3.6/site-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "best_clf, X_train, X_test, y_train, y_test = helper.boost_classifier(clf, parameters, scaled_df, label_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoostClassifier(algorithm='SAMME.R',\n",
      "                   base_estimator=DecisionTreeClassifier(class_weight=None,\n",
      "                                                         criterion='gini',\n",
      "                                                         max_depth=2,\n",
      "                                                         max_features=None,\n",
      "                                                         max_leaf_nodes=None,\n",
      "                                                         min_impurity_decrease=0.0,\n",
      "                                                         min_impurity_split=None,\n",
      "                                                         min_samples_leaf=1,\n",
      "                                                         min_samples_split=2,\n",
      "                                                         min_weight_fraction_leaf=0.0,\n",
      "                                                         presort=False,\n",
      "                                                         random_state=None,\n",
      "                                                         splitter='best'),\n",
      "                   learning_rate=0.05, n_estimators=100, random_state=42)\n"
     ]
    }
   ],
   "source": [
    "print(best_clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score on testing data: 0.8448\n",
      "Accuracy score on training data: 0.9996\n"
     ]
    }
   ],
   "source": [
    "# Accuracy scores for Unoptimized model\n",
    "test_accuracy, train_accuracy = helper.prediction_scores(clf, X_train, X_test, y_train, y_test)\n",
    "helper.print_scores(test_accuracy, train_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score on testing data: 0.8197\n",
      "Accuracy score on training data: 0.8501\n"
     ]
    }
   ],
   "source": [
    "# Accuracy scores for Optimized model\n",
    "test_accuracy, train_accuracy = helper.prediction_scores(best_clf, X_train, X_test, y_train, y_test)\n",
    "helper.print_scores(test_accuracy, train_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# important features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "def feature_plot(importances, X_train, y_train):\n",
    "    '''\n",
    "    INPUTS\n",
    "    importances - Feature importances\n",
    "    X_train, y_train - Training data\n",
    "    OUTPUT\n",
    "    Plot of five most important features\n",
    "    '''\n",
    "\n",
    "    # Displays the five most important features\n",
    "    indices = np.argsort(importances)[::-1]\n",
    "    columns = X_train.columns.values[indices[:5]]\n",
    "    values = importances[indices][:5]\n",
    "\n",
    "    # Creat the plot\n",
    "    fig = plt.figure(figsize = (15,5))\n",
    "    plt.title(\"Normalized Weights for First Five Most Predictive Features\", fontsize = 16)\n",
    "    plt.bar(np.arange(5), values, width = 0.6, align=\"center\", color = '#03d7fc', \\\n",
    "          label = \"Feature Weight\")\n",
    "    plt.bar(np.arange(5) - 0.3, np.cumsum(values), width = 0.2, align = \"center\", color = '#4e03fc', \\\n",
    "          label = \"Cumulative Feature Weight\")\n",
    "    plt.xticks(np.arange(5), columns)\n",
    "    plt.xlim((-0.5, 4.5))\n",
    "    plt.ylabel(\"Weight\", fontsize = 16)\n",
    "    plt.xlabel(\"Feature\", fontsize = 16)\n",
    "\n",
    "    plt.legend(loc = 'upper center')\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Top 10 features by importance\n",
      "------\n",
      "['room_type_Entire home/apt' 'accommodates'\n",
      " 'neighbourhood_cleansed_Dorchester' 'neighbourhood_cleansed_East Boston'\n",
      " 'neighbourhood_cleansed_Jamaica Plain' 'neighbourhood_cleansed_Brighton'\n",
      " 'amenities_count' 'neighbourhood_cleansed_Beacon Hill' 'minimum_nights'\n",
      " 'bathrooms']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract the feature importances using .feature_importances_ \n",
    "importances = best_clf.feature_importances_\n",
    "indices = np.argsort(importances)[::-1]\n",
    "print(\"\\nTop 10 features by importance\\n------\")\n",
    "print(X_train.columns.values[indices[:10]])\n",
    "\n",
    "# Plot\n",
    "feature_plot(importances, X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Can property description serve as an indicator of price?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/prabhumanickavelu/Desktop/project1/helper.py:127: FutureWarning: SparseSeries is deprecated and will be removed in a future version.\n",
      "Use a Series with sparse values instead.\n",
      "\n",
      "    >>> series = pd.Series(pd.SparseArray(...))\n",
      "\n",
      "See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.\n",
      "\n",
      "  text_features = pd.SparseDataFrame([ pd.SparseSeries(text_features[i].toarray().ravel())                               for i in np.arange(text_features.shape[0]) ])\n",
      "/home/prabhumanickavelu/Desktop/project1/helper.py:127: FutureWarning: SparseDataFrame is deprecated and will be removed in a future version.\n",
      "Use a regular DataFrame whose columns are SparseArrays instead.\n",
      "\n",
      "See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.\n",
      "\n",
      "  text_features = pd.SparseDataFrame([ pd.SparseSeries(text_features[i].toarray().ravel())                               for i in np.arange(text_features.shape[0]) ])\n",
      "/home/prabhumanickavelu/project1/lib/python3.6/site-packages/pandas/core/generic.py:3606: FutureWarning: SparseDataFrame is deprecated and will be removed in a future version.\n",
      "Use a regular DataFrame whose columns are SparseArrays instead.\n",
      "\n",
      "See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.\n",
      "\n",
      "  result = self._constructor(new_data).__finalize__(self)\n",
      "/home/prabhumanickavelu/project1/lib/python3.6/site-packages/pandas/core/generic.py:5997: FutureWarning: SparseDataFrame is deprecated and will be removed in a future version.\n",
      "Use a regular DataFrame whose columns are SparseArrays instead.\n",
      "\n",
      "See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.\n",
      "\n",
      "  return self._constructor(data).__finalize__(self)\n",
      "/home/prabhumanickavelu/project1/lib/python3.6/site-packages/pandas/core/frame.py:3456: FutureWarning: SparseSeries is deprecated and will be removed in a future version.\n",
      "Use a Series with sparse values instead.\n",
      "\n",
      "    >>> series = pd.Series(pd.SparseArray(...))\n",
      "\n",
      "See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.\n",
      "\n",
      "  return klass(values, index=self.index, name=items, fastpath=True)\n"
     ]
    }
   ],
   "source": [
    "# to analyze word level features using countervectorizer use description column\n",
    "desc_df = listing_df['description']\n",
    "text_df = helper.create_text_features(desc_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# optimize model using GridSearch CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/prabhumanickavelu/project1/lib/python3.6/site-packages/pandas/core/generic.py:3606: FutureWarning: SparseDataFrame is deprecated and will be removed in a future version.\n",
      "Use a regular DataFrame whose columns are SparseArrays instead.\n",
      "\n",
      "See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.\n",
      "\n",
      "  result = self._constructor(new_data).__finalize__(self)\n",
      "/home/prabhumanickavelu/project1/lib/python3.6/site-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# Initialize the classifier\n",
    "clf = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(), random_state=42)\n",
    "\n",
    "# Create the parameters list to tune, using a dictionary.\n",
    "parameters = {\"n_estimators\": [10, 50, 100],\n",
    "              \"learning_rate\": [.01, 0.05, 0.1, 0.5, 1],\n",
    "             'base_estimator__min_samples_split' : [2, 4, 6, 8, 10], \n",
    "             'base_estimator__max_depth' : [2, 4, 6, 8, 10]}\n",
    "\n",
    "best_clf, X_train, X_test, y_train, y_test = helper.boost_classifier(clf, parameters, text_df, label_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoostClassifier(algorithm='SAMME.R',\n",
      "                   base_estimator=DecisionTreeClassifier(class_weight=None,\n",
      "                                                         criterion='gini',\n",
      "                                                         max_depth=2,\n",
      "                                                         max_features=None,\n",
      "                                                         max_leaf_nodes=None,\n",
      "                                                         min_impurity_decrease=0.0,\n",
      "                                                         min_impurity_split=None,\n",
      "                                                         min_samples_leaf=1,\n",
      "                                                         min_samples_split=2,\n",
      "                                                         min_weight_fraction_leaf=0.0,\n",
      "                                                         presort=False,\n",
      "                                                         random_state=None,\n",
      "                                                         splitter='best'),\n",
      "                   learning_rate=0.5, n_estimators=50, random_state=42)\n"
     ]
    }
   ],
   "source": [
    "print(best_clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score on testing data: 0.7323\n",
      "Accuracy score on training data: 0.9980\n"
     ]
    }
   ],
   "source": [
    "# Accuracy scores for Unoptimized model\n",
    "test_accuracy, train_accuracy = helper.prediction_scores(clf, X_train, X_test, y_train, y_test)\n",
    "helper.print_scores(test_accuracy, train_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score on testing data: 0.7472\n",
      "Accuracy score on training data: 0.8745\n"
     ]
    }
   ],
   "source": [
    "# Accuracy scores for Optimized model\n",
    "test_accuracy, train_accuracy = helper.prediction_scores(best_clf, X_train, X_test, y_train, y_test)\n",
    "helper.print_scores(test_accuracy, train_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_wordcloud(importances, X_train, y_train):\n",
    "    '''\n",
    "    INPUTS\n",
    "    importances - Feature importances\n",
    "    X_train, y_train - Training data\n",
    "    OUTPUT\n",
    "    Word colud of top 50 word features\n",
    "    '''\n",
    "    indices = np.argsort(importances)[::-1]\n",
    "    columns = X_train.columns.values[indices[:50]]\n",
    "    values = importances[indices][:50]\n",
    "    word_dict = dict(zip(columns, values))\n",
    "\n",
    "    # Generate wordcloud using top 50 word features\n",
    "    wordcloud = WordCloud().generate_from_frequencies(word_dict)\n",
    "    plt.figure(figsize=(50, 30))\n",
    "    plt.imshow(wordcloud)\n",
    "    plt.axis(\"off\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['room' 'has' 'my' 'charlestown' 'queen' 'shared' 'marathon' 'amenities'\n",
      " 'level' 'bedrooms' 'very' 'just' 'have' '20' 'families' 'luxury' 'mins'\n",
      " 'fitness' 'end' 'free' 'inquire' 'fluffy' 'master' 'bus' 'newbury'\n",
      " 'ashmont' 'perfectly' 'tvs' 'rooftop' 'allow' 'beacon' 'not' 'steps'\n",
      " 'deck' 'perfect' 'almost' 'steel' 'theaters' 'suite' 'is' 'roommate' 'as'\n",
      " 'these' 'me' 'university' 'luxurious' 'renovated' 'modern' 'brings'\n",
      " 'heart']\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 3600x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract the feature importances using .feature_importances_ \n",
    "importances = best_clf.feature_importances_\n",
    "indices = np.argsort(importances)[::-1]\n",
    "print(X_train.columns.values[indices[:50]])\n",
    "# WordCloud\n",
    "create_wordcloud(importances, X_train, y_train)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "From the Above analysis we can conclude that\n",
    "\n",
    "-- Neighbourhood plays a key role in price determination because lot of people expects it to be cleaner provided with lot of options\n",
    "\n",
    "-- one more feature the people look into is type of house and how many people it can accommodate\n",
    "\n",
    "-- With proper listing, descriptions and key features can be helpful to remain competitive in the market\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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