Data_Analysis_airbnb.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AirBnB Seattle Data Analysis\n",
    "\n",
    "In this project, there are three bussiness questions which will be tried to answered using the Seattle Data provided by AirBnB website. \n",
    "These questions are as following: \n",
    "\n",
    "1. What features most influence on the price of the properties?\n",
    "\n",
    "2. How many properties are available during the Year? How prices change during the calendar period?\n",
    "\n",
    "3. which streets in Seattle host more guests than other streets? What is the average price of these popular streets? \n",
    " \n",
    "In order to answer the above questions, the **CRISP-DM(Cross Industry Standard Process for Data Mining)** is followed while analysing data. \n",
    "\n",
    "The steps for this project are as following:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We import the required modules and libraries and also read our three csv data files which are **claendar**, **reviews** and **listings** for the city Seattle:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "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",
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       "\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>241032</td>\n",
       "      <td>https://www.airbnb.com/rooms/241032</td>\n",
       "      <td>20160104002432</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>Stylish Queen Anne Apartment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Make your self at home in this charming one-be...</td>\n",
       "      <td>Make your self at home in this charming one-be...</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>f</td>\n",
       "      <td>moderate</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>2</td>\n",
       "      <td>4.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>953595</td>\n",
       "      <td>https://www.airbnb.com/rooms/953595</td>\n",
       "      <td>20160104002432</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>Bright &amp; Airy Queen Anne Apartment</td>\n",
       "      <td>Chemically sensitive? We've removed the irrita...</td>\n",
       "      <td>Beautiful, hypoallergenic apartment in an extr...</td>\n",
       "      <td>Chemically sensitive? We've removed the irrita...</td>\n",
       "      <td>none</td>\n",
       "      <td>Queen Anne is a wonderful, truly functional vi...</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>f</td>\n",
       "      <td>strict</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>6</td>\n",
       "      <td>1.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3308979</td>\n",
       "      <td>https://www.airbnb.com/rooms/3308979</td>\n",
       "      <td>20160104002432</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>New Modern House-Amazing water view</td>\n",
       "      <td>New modern house built in 2013.  Spectacular s...</td>\n",
       "      <td>Our house is modern, light and fresh with a wa...</td>\n",
       "      <td>New modern house built in 2013.  Spectacular s...</td>\n",
       "      <td>none</td>\n",
       "      <td>Upper Queen Anne is a charming neighborhood fu...</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>f</td>\n",
       "      <td>strict</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>2</td>\n",
       "      <td>1.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>7421966</td>\n",
       "      <td>https://www.airbnb.com/rooms/7421966</td>\n",
       "      <td>20160104002432</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>Queen Anne Chateau</td>\n",
       "      <td>A charming apartment that sits atop Queen Anne...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A charming apartment that sits atop Queen Anne...</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>f</td>\n",
       "      <td>flexible</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>278830</td>\n",
       "      <td>https://www.airbnb.com/rooms/278830</td>\n",
       "      <td>20160104002432</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>Charming craftsman 3 bdm house</td>\n",
       "      <td>Cozy family craftman house in beautiful neighb...</td>\n",
       "      <td>Cozy family craftman house in beautiful neighb...</td>\n",
       "      <td>Cozy family craftman house in beautiful neighb...</td>\n",
       "      <td>none</td>\n",
       "      <td>We are in the beautiful neighborhood of Queen ...</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>f</td>\n",
       "      <td>strict</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 92 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                           listing_url       scrape_id last_scraped  \\\n",
       "0   241032   https://www.airbnb.com/rooms/241032  20160104002432   2016-01-04   \n",
       "1   953595   https://www.airbnb.com/rooms/953595  20160104002432   2016-01-04   \n",
       "2  3308979  https://www.airbnb.com/rooms/3308979  20160104002432   2016-01-04   \n",
       "3  7421966  https://www.airbnb.com/rooms/7421966  20160104002432   2016-01-04   \n",
       "4   278830   https://www.airbnb.com/rooms/278830  20160104002432   2016-01-04   \n",
       "\n",
       "                                  name  \\\n",
       "0         Stylish Queen Anne Apartment   \n",
       "1   Bright & Airy Queen Anne Apartment   \n",
       "2  New Modern House-Amazing water view   \n",
       "3                   Queen Anne Chateau   \n",
       "4       Charming craftsman 3 bdm house   \n",
       "\n",
       "                                             summary  \\\n",
       "0                                                NaN   \n",
       "1  Chemically sensitive? We've removed the irrita...   \n",
       "2  New modern house built in 2013.  Spectacular s...   \n",
       "3  A charming apartment that sits atop Queen Anne...   \n",
       "4  Cozy family craftman house in beautiful neighb...   \n",
       "\n",
       "                                               space  \\\n",
       "0  Make your self at home in this charming one-be...   \n",
       "1  Beautiful, hypoallergenic apartment in an extr...   \n",
       "2  Our house is modern, light and fresh with a wa...   \n",
       "3                                                NaN   \n",
       "4  Cozy family craftman house in beautiful neighb...   \n",
       "\n",
       "                                         description experiences_offered  \\\n",
       "0  Make your self at home in this charming one-be...                none   \n",
       "1  Chemically sensitive? We've removed the irrita...                none   \n",
       "2  New modern house built in 2013.  Spectacular s...                none   \n",
       "3  A charming apartment that sits atop Queen Anne...                none   \n",
       "4  Cozy family craftman house in beautiful neighb...                none   \n",
       "\n",
       "                               neighborhood_overview  ... review_scores_value  \\\n",
       "0                                                NaN  ...                10.0   \n",
       "1  Queen Anne is a wonderful, truly functional vi...  ...                10.0   \n",
       "2  Upper Queen Anne is a charming neighborhood fu...  ...                10.0   \n",
       "3                                                NaN  ...                 NaN   \n",
       "4  We are in the beautiful neighborhood of Queen ...  ...                 9.0   \n",
       "\n",
       "  requires_license license jurisdiction_names instant_bookable  \\\n",
       "0                f     NaN         WASHINGTON                f   \n",
       "1                f     NaN         WASHINGTON                f   \n",
       "2                f     NaN         WASHINGTON                f   \n",
       "3                f     NaN         WASHINGTON                f   \n",
       "4                f     NaN         WASHINGTON                f   \n",
       "\n",
       "  cancellation_policy  require_guest_profile_picture  \\\n",
       "0            moderate                              f   \n",
       "1              strict                              t   \n",
       "2              strict                              f   \n",
       "3            flexible                              f   \n",
       "4              strict                              f   \n",
       "\n",
       "  require_guest_phone_verification calculated_host_listings_count  \\\n",
       "0                                f                              2   \n",
       "1                                t                              6   \n",
       "2                                f                              2   \n",
       "3                                f                              1   \n",
       "4                                f                              1   \n",
       "\n",
       "  reviews_per_month  \n",
       "0              4.07  \n",
       "1              1.48  \n",
       "2              1.15  \n",
       "3               NaN  \n",
       "4              0.89  \n",
       "\n",
       "[5 rows x 92 columns]"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score, mean_squared_error,mean_absolute_error\n",
    "import seaborn as sns\n",
    "from collections import defaultdict\n",
    "import itertools\n",
    "%matplotlib inline\n",
    "\n",
    "df = pd.read_csv(\"listings_Seattle.csv\")\n",
    "df_calendar = pd.read_csv(\"calendar_Seattle.csv\")\n",
    "df_review = pd.read_csv(\"reviews_Seattle.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Understand Data:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the number of colums and raws of the listings:  (3818, 92)\n",
      "the number of the columns and raws of the calendar data is:  (1308890, 4)\n"
     ]
    }
   ],
   "source": [
    "print(\"the number of colums and raws of the listings: \",df.shape)\n",
    "print(\"the number of the columns and raws of the calendar data is: \", df_calendar.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3818 entries, 0 to 3817\n",
      "Data columns (total 92 columns):\n",
      "id                                  3818 non-null int64\n",
      "listing_url                         3818 non-null object\n",
      "scrape_id                           3818 non-null int64\n",
      "last_scraped                        3818 non-null object\n",
      "name                                3818 non-null object\n",
      "summary                             3641 non-null object\n",
      "space                               3249 non-null object\n",
      "description                         3818 non-null object\n",
      "experiences_offered                 3818 non-null object\n",
      "neighborhood_overview               2786 non-null object\n",
      "notes                               2212 non-null object\n",
      "transit                             2884 non-null object\n",
      "thumbnail_url                       3498 non-null object\n",
      "medium_url                          3498 non-null object\n",
      "picture_url                         3818 non-null object\n",
      "xl_picture_url                      3498 non-null object\n",
      "host_id                             3818 non-null int64\n",
      "host_url                            3818 non-null object\n",
      "host_name                           3816 non-null object\n",
      "host_since                          3816 non-null object\n",
      "host_location                       3810 non-null object\n",
      "host_about                          2959 non-null object\n",
      "host_response_time                  3295 non-null object\n",
      "host_response_rate                  3295 non-null object\n",
      "host_acceptance_rate                3045 non-null object\n",
      "host_is_superhost                   3816 non-null object\n",
      "host_thumbnail_url                  3816 non-null object\n",
      "host_picture_url                    3816 non-null object\n",
      "host_neighbourhood                  3518 non-null object\n",
      "host_listings_count                 3816 non-null float64\n",
      "host_total_listings_count           3816 non-null float64\n",
      "host_verifications                  3818 non-null object\n",
      "host_has_profile_pic                3816 non-null object\n",
      "host_identity_verified              3816 non-null object\n",
      "street                              3818 non-null object\n",
      "neighbourhood                       3402 non-null object\n",
      "neighbourhood_cleansed              3818 non-null object\n",
      "neighbourhood_group_cleansed        3818 non-null object\n",
      "city                                3818 non-null object\n",
      "state                               3818 non-null object\n",
      "zipcode                             3811 non-null object\n",
      "market                              3818 non-null object\n",
      "smart_location                      3818 non-null object\n",
      "country_code                        3818 non-null object\n",
      "country                             3818 non-null object\n",
      "latitude                            3818 non-null float64\n",
      "longitude                           3818 non-null float64\n",
      "is_location_exact                   3818 non-null object\n",
      "property_type                       3817 non-null object\n",
      "room_type                           3818 non-null object\n",
      "accommodates                        3818 non-null int64\n",
      "bathrooms                           3802 non-null float64\n",
      "bedrooms                            3812 non-null float64\n",
      "beds                                3817 non-null float64\n",
      "bed_type                            3818 non-null object\n",
      "amenities                           3818 non-null object\n",
      "square_feet                         97 non-null float64\n",
      "price                               3818 non-null object\n",
      "weekly_price                        2009 non-null object\n",
      "monthly_price                       1517 non-null object\n",
      "security_deposit                    1866 non-null object\n",
      "cleaning_fee                        2788 non-null object\n",
      "guests_included                     3818 non-null int64\n",
      "extra_people                        3818 non-null object\n",
      "minimum_nights                      3818 non-null int64\n",
      "maximum_nights                      3818 non-null int64\n",
      "calendar_updated                    3818 non-null object\n",
      "has_availability                    3818 non-null object\n",
      "availability_30                     3818 non-null int64\n",
      "availability_60                     3818 non-null int64\n",
      "availability_90                     3818 non-null int64\n",
      "availability_365                    3818 non-null int64\n",
      "calendar_last_scraped               3818 non-null object\n",
      "number_of_reviews                   3818 non-null int64\n",
      "first_review                        3191 non-null object\n",
      "last_review                         3191 non-null object\n",
      "review_scores_rating                3171 non-null float64\n",
      "review_scores_accuracy              3160 non-null float64\n",
      "review_scores_cleanliness           3165 non-null float64\n",
      "review_scores_checkin               3160 non-null float64\n",
      "review_scores_communication         3167 non-null float64\n",
      "review_scores_location              3163 non-null float64\n",
      "review_scores_value                 3162 non-null float64\n",
      "requires_license                    3818 non-null object\n",
      "license                             0 non-null float64\n",
      "jurisdiction_names                  3818 non-null object\n",
      "instant_bookable                    3818 non-null object\n",
      "cancellation_policy                 3818 non-null object\n",
      "require_guest_profile_picture       3818 non-null object\n",
      "require_guest_phone_verification    3818 non-null object\n",
      "calculated_host_listings_count      3818 non-null int64\n",
      "reviews_per_month                   3191 non-null float64\n",
      "dtypes: float64(17), int64(13), object(62)\n",
      "memory usage: 2.7+ MB\n"
     ]
    }
   ],
   "source": [
    "# overview of the listing data and understand how to clean it. \n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Clean Data\n",
    "\n",
    "#### In order to clean listings data:\n",
    "- The listings data has 5 columns which are numerical but they are needed to be converted from object type to numericals. \n",
    "\n",
    "- There are some boolean value columns which are determined by t and f for **True** and **False** respectively. we replace them with  associated **0** and **1** values. \n",
    "\n",
    "- There is a **street** column which is address of the properties and as we need their street name, we extract their street name and put on a new column **street_name**. \n",
    "\n",
    "- There is a date column **last_review** and as it is object type we convert them to **date time** objects in order to work better with them.\n",
    "\n",
    "- At this step we can extract all the **numercial valued columns** which are **float8* and **integer** types select_dtypes() function. \n",
    "\n",
    "- It is also good to remove columns with a lot of nan values.  So we put the threshold value to be 0.75 and remove all the columns with more than **%75 nan values**. \n",
    "\n",
    "- We then **impute the missing values** with the **mode or mean** of the columns. we fill 4 columns with their mode and fill the rest with mean of the columns\n",
    "\n",
    "- The last step is to use **categorical values*. In order to use them we make them **dummy variables** and add them to our numerical dataframe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_data(df):\n",
    "    \"\"\"\n",
    "    INPUT\n",
    "    df - pandas dataframe \n",
    "    \n",
    "    OUTPUT\n",
    "    (df_numeric,df_cat_numeric) - tuple of the cleaned numerical pandas dataframe which is ready for statistical models.\n",
    "    the first data is cleaned numerical data and second one contains the dummy categorial variables as well. \n",
    "    \n",
    "    \"\"\"\n",
    "    # steps to clean data\n",
    "    \n",
    "    #1. the columns to convert from object types to floats\n",
    "    df['zipcode']=df['zipcode'].replace('99\\n98122','98122')\n",
    "    list_df = ['price','security_deposit','cleaning_fee','extra_people','zipcode']\n",
    "    for obj in list_df:\n",
    "        df[obj] = df[obj].map(lambda x: x.replace('$','').replace(',',''),na_action='ignore')\n",
    "        df[obj] = df[obj].astype(float)\n",
    "        \n",
    "    # 2. convert boolean columns with strings (f and t) inputs  to 0 and 1 integer values \n",
    "    list_boolean =[]\n",
    "    for cols in df.columns:\n",
    "        if len(df[cols].value_counts().index)== 2:\n",
    "            list_boolean.append(cols)\n",
    "    for cols in list_boolean:\n",
    "        df[cols] =  df[cols].replace({'f':0,'t':1})\n",
    "        \n",
    "    # 3. add new column street_name which is classified based on the street name of each property\n",
    "    data = []\n",
    "    string = df['street'].values\n",
    "    for st in string:\n",
    "        y = \"\".join(itertools.takewhile(lambda x: x!=\",\", st))\n",
    "        data.append(y)\n",
    "    df['street_name'] = data    \n",
    "    \n",
    "    # 4. convert last_review column to datetime object  \n",
    "    df['last_review'] =  pd.to_datetime(df['last_review'])\n",
    "\n",
    "    # 5.  select all the columns with float and integer values \n",
    "    df_numeric = df.select_dtypes(include = ['number'])\n",
    "    \n",
    "    # 6. drop all the columns with more than %75 of their data with nan values \n",
    "    df_numeric = df_numeric.dropna(thresh = 0.75,axis=1)\n",
    "    \n",
    "    # 7. fill the missed column values with mode of their associated columns.\n",
    "    fill_mode  = lambda col:col.fillna(col.mode()[0])\n",
    "    mode_fills = ['accommodates','bathrooms','bedrooms','beds']\n",
    "    df_numeric[mode_fills] = df_numeric[mode_fills].apply(fill_mode,axis=0)\n",
    "    \n",
    "    # 8. fill the missed values of the remaining columns with the mean of their associated columns \n",
    "    fill_mean  = lambda col:col.fillna(col.mean())\n",
    "    df_numeric = df_numeric.apply(fill_mean,axis=0)\n",
    "    \n",
    "    # 9. convert categorical variables to dummies and then include the dummies and numerical data in the new data set df_cat_numeric\n",
    "    cat_cols = ['cancellation_policy','room_type','property_type',\n",
    "                'host_identity_verified','host_has_profile_pic',\n",
    "                'neighbourhood','host_response_time',\n",
    "                'street_name','last_review'] # required categorical columns from df\n",
    "    dummy_df = df[cat_cols]\n",
    "    for col in  cat_cols:\n",
    "        dummy_df = pd.concat([dummy_df.drop(col, axis=1), \n",
    "                              pd.get_dummies(dummy_df[col], prefix=col, prefix_sep='_', \n",
    "                                             drop_first=True, dummy_na=False)], axis=1)\n",
    "            \n",
    "    df_cat_numeric = df_numeric.join(dummy_df) #  join dummies and numeric data\n",
    "    df_cat_numeric = df_cat_numeric.drop(['id','scrape_id','host_id'],axis=1) # drop the unnecessary columns  \n",
    "\n",
    "    return df_numeric,df_cat_numeric\n",
    "\n",
    "df_numerical,df_cleaned  = clean_data(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Predict price of the properties\n",
    "We use two models to predict prices:\n",
    "\n",
    "**1. Ridge Regression**.\n",
    "\n",
    "**2. Random Forest Regression**.\n",
    "\n",
    "There are two functions which is used for these predictions:\n",
    "\n",
    "- The data is split to **explanotary** variables(matrix) and **response** variables( vector).\n",
    "- we then split data to train and test parts. \n",
    "- the model is **instantiated** and **fitted** with the **train** data.\n",
    "- the prices are predicted based on the model and **r-square values** are provided using **sklearn** library. \n",
    "\n",
    "The **Function predict_with_Ridge()** split the data to explanatory and response variables and use the Ridge regression model to predict the prices.  \n",
    "On the Other hand, function **predict_with_RandomForest()** predicts the prices using the RandomForest Regressor using sklearn built-in library.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_with_Ridge(df):\n",
    "    \"\"\"\n",
    "    INPUT\n",
    "    df - Pandas Dataframe( the input data set is cleaned data ready for our model)\n",
    "    \n",
    "    OUTPUT\n",
    "    x_train,x_test,y_train,y_test,y_predict_train,y_predict_test - the tuple of the train and test data and predicted \n",
    "    test and train data.\n",
    "    \"\"\"\n",
    "    #Split into explanatory and response variables\n",
    "    X = df.drop('price',axis=1)\n",
    "    y = df['price']\n",
    "\n",
    "    #Split into train and test\n",
    "    x_train,x_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state=42)\n",
    "    \n",
    "    lm_model = Ridge()  # Instantiate with Ridge model\n",
    "    lm_model.fit(x_train,y_train) # fit\n",
    "    \n",
    "    #Predict the model and then return scores for the model. \n",
    "    y_predict_train = lm_model.predict(x_train)\n",
    "    y_predict_test = lm_model.predict(x_test) \n",
    "    \n",
    "    return x_train,x_test,y_train,y_test,y_predict_train,y_predict_test\n",
    "\n",
    "x_train,x_test,y_train,y_test,y_predict_train,y_predict_test = predict_with_Ridge(df_cleaned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge Regression: The r2_score between train  and predicted train is 0.8178805550761346 and test  with predicted test is 0.5934633580752056\n"
     ]
    }
   ],
   "source": [
    "print(\"Ridge Regression: The r2_score between train  and predicted train is {} and test  with predicted test is {}\"\n",
    "      .format(r2_score(y_train,y_predict_train),r2_score(y_test,y_predict_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Results For Ridge Regression :\n",
    "\n",
    "The plot below compares the distribution of the two predicted and actual values using Ridge regression model. It is clear that they don't match in shape and new model is needed to be tested on the data.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the plotting steps using seaborn library\n",
    "plt.figure(figsize=(12,8)) # plot size\n",
    "sns.distplot(a=y_test, bins=40, hist=False, color='orange' ,label = \"Actual\") # distribution plot for y_test\n",
    "sns.distplot(a=y_predict_test, bins=40, hist=False, color='gray', label = \"Predicted\"); # distribution plot for y_predict_test\n",
    "plt.title(\"Distribution for Actual and Predicted prices\") # title\n",
    "plt.ylabel(\"Frequency\") # xlabel\n",
    "plt.savefig(\"plot1.png\") # saving the plot as png format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_with_RandomForest(df):\n",
    "    \n",
    "    #Split into explanatory and response variables\n",
    "    X = df.drop('price',axis=1)\n",
    "    y = df['price']\n",
    "\n",
    "    #Split into train and test\n",
    "    x_train,x_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state=42)\n",
    "    \n",
    "    model_rf = RandomForestRegressor(n_estimators=100, random_state=0)  # Instantiate with RandomForestRegressor \n",
    "\n",
    "    model_rf.fit(x_train, y_train) # fit\n",
    "    \n",
    "    #Predict wth Random Forest Regressor and return the scores for the model\n",
    "    preds_rf_test = model_rf.predict(x_test)\n",
    "    preds_rf_train = model_rf.predict(x_train)\n",
    "\n",
    "    return x_train,x_test,y_train,y_test,preds_rf_train,preds_rf_test\n",
    "\n",
    "x_train,x_test,y_train,y_test,preds_rf_train,preds_rf_test = predict_with_RandomForest(df_cleaned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForest Regression: the r2_score between train  and predicted train is 0.9387925470664666 and test  with predicted test is 0.6359849204918233\n"
     ]
    }
   ],
   "source": [
    "print(\"RandomForest Regression: the r2_score between train  and predicted train is {} and test  with predicted test is {}\"\n",
    "      .format(r2_score(y_train,preds_rf_train),r2_score(y_test,preds_rf_test)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Results For RandomForest Regression :\n",
    "\n",
    "The plot below compares the distribution of the two predicted and actual values using RandomForest regressor model. It is clear that they  match much better in shape and this model can be used for improving predictions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the plotting steps using seaborn library\n",
    "plt.figure(figsize=(12,8)) # plot size\n",
    "sns.distplot(a=y_test, bins=40, hist=False, color='orange' ,label = \"Actual\") # distribution plot for y_test\n",
    "sns.distplot(a=preds_rf_test, bins=40, hist=False, color='gray', label = \"Predicted\"); # distribution plot for y_predict_test\n",
    "plt.title(\"Distribution for Actual and Predicted prices\") # title\n",
    "plt.ylabel(\"Frequency\") # xlabel\n",
    "plt.savefig(\"plot2.png\") # saving the plot as png format."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **In order to get better predictions, the size of the data must be larger as our categorical variables are large and in order to arrive at better accuracy we might need larger sample of data.** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Answer to question 1:\n",
    "\n",
    "**What features most influence on the price of the properties?**\n",
    "\n",
    "In order to find out how different **features** affect the **price** of the properties we use their **correlation** with **price** column:\n",
    "\n",
    "- The correlation between column **price** and all other columns of the cleaned numerical data is measured and sorted.\n",
    "\n",
    "- The heatmap from **seaborn** library represents the top 10 highest features which influence on the property price. Not surprisingly, **cleaning_fee** , **accommodates**(number of people who are accepted), **bedrooms**, **beds**, **size** and number of **bathrooms** have highest correlations with price. As there are not very negative correlations(close to -1), we only considered postive ones. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr = []  # list to save calculated correlations\n",
    "features = [] # Coresponding features to save in a list \n",
    "for col in df_numerical.columns:\n",
    "    corr.append(df_numerical[['price',col]].corr().iloc[0].iloc[1]) \n",
    "    features.append(col)\n",
    "    \n",
    "# build a data frame to match the correlation of price with coresponding feature \n",
    "corr_with_price = pd.DataFrame({\"correlation\": corr}, index = features) \n",
    "corr_with_price = corr_with_price.dropna()\n",
    "corr_with_price = corr_with_price.sort_values(ascending = False, by = 'correlation').drop('price')# sorted values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " **The correlation of price with itself is obviously 1 and we don't need it. The other 9 features show how they are correlated with price.** \n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,10))  \n",
    "sns.heatmap(corr_with_price.head(10), annot=True, fmt=\".2f\", linewidth = 0.5, ax =ax);\n",
    "fig.savefig('plot3.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   #### Answer to question 2:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "**How many properties are available during the Year?? How prices change during the calendar period??**\n",
    "\n",
    "To answer  this question we need to use **calendar** data. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uVHF5ld/lAAAAoAVocaFbkm4/d4j2lVXpyY83+10KAAAAWoAWGbrTe3ZURnqKnvp4kwpKK/wuBwAAAFGuRYZuSbrlnCE6UFWjhz/c6HcpAAAAiHItNnQP7NZe3xrbUy8s3qpd+w74XQ4AAACiWIsN3ZJ009cHSU564P0sv0sBAABAFGvRobtn5wRdelJv/X3ZDm3KK/W7HAAAAESpFh26JemGrw1U67gY3Ttvg9+lAAAAIEq1+NCdnNha3z+ln/61Mkerdxb5XQ4AAACiUIsP3ZJ0zen91bFtK93z7nq/SwEAAEAUInRL6ti2la49Y4Dmr8/T51sK/S4HAAAAUYbQHXTlpL5KTmytP/97nZxzfpcDAACAKELoDmobH6sbzxqoz7fs1Ycb8vwuBwAAAFGE0B3iovG91Supre6eu161tax2AwAAoGkQukPEx8XoJ18frDW7ijVndY7f5QAAACBKELoPMWN0mgZ3b697392g6ppav8sBAABAFCB0HyI2xnTbuUO0KX+/Xvtih9/lAAAAIAoQuutxzrDuGt2rk+5/L0vlVTV+lwMAAIAIR+iuh5npp5OHKKeoXC99ts3vcgAAABDhCN2HMWlgV50ysItmzc9WaUW13+UAAAAgghG6j+D2yUNVuL9STy/c7HcpAAAAiGCE7iMY3auTzh3WXU8s2KS9+yv9LgcAAAARitB9FLdNHqLSymo9+tFGv0sBAABAhCJ0H8Xg7on6xug0PfvpFu0uKve7HAAAAEQgQncD/OScwap1Tg9+kOV3KQAAAIhAhO4G6JWUoIvH99bfPt+urQX7/S4HAAAAEYbQ3UA/Pmug4mJN983b4HcpAAAAiDCE7gbq1qGNrpzUT2+u2KV1u4v9LgcAAAARhNB9DK49o7/at47T3XNZ7QYAAEDDEbqPQaeEeP3w9P56L3OPlm3d63c5AAAAiBCE7mN01Sn91LV9vO6au07OOb/LAQAAQAQgdB+jdq3j9KOvDdTiTYVamJ3vdzkAAACIAITuRrjkpN5K69RWd81dz2o3AAAAjorQ3Qit42J189cHaeWOIs1ds9vvcgAAABDmCN2N9I0xaRqQ3E53v7tBNbWsdgMAAODwCN2NFBcbo9vOHaLs3FK98eVOv8sBAABAGCN0H4cpI1KUntZR983boIrqGr/LAQAAQJgidB8HM9Ptk4do574DemXJdr/LAQAAQJgidB+n0wZ11Un9kvTgB9kqq6z2uxwAAACEIc9Ct5n1MrP5ZpZpZmvM7KZD9t9mZs7MugYfm5k9YGbZZrbSzMZ6VVtTMjP9dMoQ5ZdW6JlPtvhdDgAAAMKQlyvd1ZJudc6dIGmipBvMbJgUCOSSzpG0LeT4qZIGBW8zJT3iYW1N6sQ+STp7aDc99tFGFZVV+V0OAAAAwoxnods5l+Oc+yJ4v0RSpqS04O77JP1UUuisvRmSnncBiyV1MrNUr+prardNHqLi8mo9tmCj36UAAAAgzDRLT7eZ9ZU0RtJnZjZd0k7n3IpDDkuTFPppxB36T0gPfa6ZZrbUzJbm5eV5VPGxOyG1g6aP6qFnPtmi3JJyv8sBAABAGPE8dJtZe0mvSbpZgZaTX0n6dX2H1rPtK1edcc497pwb55wbl5yc3KS1Hq9bzhmsqppazfog2+9SAAAAEEY8Dd1m1kqBwP2Sc+51SQMk9ZO0wsy2SOop6QszS1FgZbtXyOk9Je3ysr6m1rdrO31nfC+9vGSbtheW+V0OAAAAwoSX00tM0lOSMp1z90qSc26Vc66bc66vc66vAkF7rHNut6S3JF0enGIyUVKRcy7Hq/q8cuNZgxRjpvvfy/K7FAAAAIQJL1e6T5F0maSzzGx58JZxhOPnSNokKVvSE5Ku97A2z6R0bKMrJvXVG1/uUNaeEr/LAQAAQBjwcnrJQuecOedGOudGB29zDjmmr3MuP3jfOeducM4NcM6lO+eWelWb1649Y4AS4uN097vr/S4FAAAAYYArUnogqV28rjmtv+au2aMV2/f5XQ4AAAB8Ruj2yA9O66ekdvG6ay6r3QAAAC0dodsj7VvH6fozB2hhdr4+zc73uxwAAAD4iNDtoe9N7KPUjm3057nr5dxXRo4DAACghSB0e6hNq1jddPYgLd++T+9l5vpdDgAAAHxC6PbYt0/sqX5d2+nuuetVU8tqNwAAQEtE6PZYXGyMbjlnsNbvKdHbKyLqApsAAABoIoTuZjAtPVXDUjvo3nkbVFld63c5AAAAaGaE7mYQE2O6ffIQbSss06tLt/tdDgAAAJoZobuZnDkkWeP7dtYD72fpQGWN3+UAAACgGRG6m4mZ6fbJQ5VbUqHnF23xuxwAAAA0I0J3M5rQL0lnDknWIx9tVHF5ld/lAAAAoJkQupvZbecO0b6yKj25YJPfpQAAAKCZELqb2Yi0jpo2MlVPLtys/NIKv8sBAABAMyB0++CWcwarorpWs+Zn+10KAAAAmgGh2wcDktvr22N76qXF27Rz3wG/ywEAAIDHCN0+ufHrgyRJf3lvg8+VAAAAwGuEbp+kdWqr703so38s26GNeaV+lwMAAAAPEbp9dP3XBqhNq1jd+y6r3QAAANGM0O2jru1b6+pT+2n2qhyt3lnkdzkAAADwCKHbZ1ef3l+dElrprrnr/S4FAAAAHiF0+6xDm1a67owB+mhDnj7bVOB3OQAAAPAAoTsMXDGpr7p3aK275q6Xc87vcgAAANDECN1hoE2rWP34rEFaunWvPlyf53c5AAAAaGKE7jBx0fhe6p2UoD/PXa/aWla7AQAAogmhO0y0io3RLecMVmZOsWavyvG7HAAAADQhQncYmT6qh4amJOreeRtUVVPrdzkAAABoIoTuMBITY7r13CHanL9fry3b4Xc5AAAAaCKE7jDz9RO6aUzvTvrL+1kqr6rxuxwAAAA0AUJ3mDEz3T55iHKKyvXi4q1+lwMAAIAmQOgOQ5MGdNXE/kl6btEW5nYDAABEAUJ3mPrmmJ7aXnhAq3cW+10KAAAAjhOhO0ydO7y74mKM8YEAAABRgNAdpjolxGvSwK6asyqHFhMAAIAIR+gOY9PSU7StsExrdtFiAgAAEMkI3WHs3GEpiqXFBAAAIOIRusNY53bxmjSgCy0mAAAAEY7QHeampadqawEtJgAAAJGM0B3mzh0eaDGZQ4sJAABAxCJ0h7kkWkwAAAAiHqE7AmSkp2pLQZnW5tBiAgAAEIkI3RFgMi0mAAAAEY3QHQGS2sXr5P5dNGfVblpMAAAAIhChO0JMTU/R5vz9yswp8bsUAAAAHCNCd4SYPDxFMSZaTAAAACIQoTtCdG3fWhP7M8UEAAAgEnkWus2sl5nNN7NMM1tjZjcFt//ezFaa2XIze9fMegS3m5k9YGbZwf1jvaotUmWkp2pT/n6t202LCQAAQCTxcqW7WtKtzrkTJE2UdIOZDZN0l3NupHNutKR/Sfp18PipkgYFbzMlPeJhbRFpyghaTAAAACKRZ6HbOZfjnPsieL9EUqakNOdc6LDpdpLqeiVmSHreBSyW1MnMUr2qLxJ1bd9aJ/Xrotm0mAAAAESUZunpNrO+ksZI+iz4+E4z2y7pu/rPSneapO0hp+0Ibjv0uWaa2VIzW5qXl+dl2WEpY2SqNuXt1/o9tJgAAABECs9Dt5m1l/SapJvrVrmdc79yzvWS9JKkH9UdWs/pX1nOdc497pwb55wbl5yc7FXZYWtK3RSTlbSYAAAARApPQ7eZtVIgcL/knHu9nkNelvSt4P0dknqF7OspaZeX9UWi5MTWmtAvSXNW7/a7FAAAADSQl9NLTNJTkjKdc/eGbB8Ucth0SeuC99+SdHlwislESUXOOZZz6zEtPVXZuaXaQIsJAABARPBypfsUSZdJOis4HnC5mWVI+l8zW21mKyWdK+mm4PFzJG2SlC3pCUnXe1hbRJs8IkVm0mxaTAAAACJCnFdP7JxbqPr7tOcc5ngn6Qav6okm3RLbaELfJM1ZlaOfnDPY73IAAABwFFyRMkJNG5mqrNxSZdFiAgAAEPYI3RFqSl2LCRfKAQAACHuE7gjVLbGNxgdbTAAAABDeCN0RbFp6qjbsKVV2Li0mAAAA4YzQHcGmHpxiwsxuAACAcEbojmDdOrTR+D60mAAAAIQ7QneEy0hP0fo9JcrOLfW7FAAAABwGoTvCTU1PlZlY7QYAAAhjhO4I171DG43r05nQDQAAEMYI3VEgIz1V63aXaGMeLSYAAADhiNAdBaaOSJUkzVnJajcAAEA4InRHgZSOgRYTrk4JAAAQngjdUaKuxWQTLSYAAABhh9AdJaamp0hiigkAAEA4InRHidSObXVin86avYqrUwIAAIQbQncUyUhPVWZOsTbn7/e7FAAAAIQgdEeRDFpMAAAAwhKhO4qkdmyrsb07aTajAwEAAMIKoTvKZKSnam1OsbbQYgIAABA2CN1RJiM9cKEcZnYDAACED0J3lOnRqa3G9O5EXzcAAEAYIXRHoWnpqVqzq1hbC2gxAQAACAeE7ig0lRYTAACAsELojkJpndpqdC9aTAAAAMIFoTtKTUtP1eqdxdpWUOZ3KQAAAC0eoTtKTQ1eKIcWEwAAAP8RuqNUz84JGkWLCQAAQFggdEexaekpWrWziBYTAAAAnxG6o9jUEYEpJnNWs9oNAADgJ0J3FOuVlKBRPTvSYgIAAOAzQneUy0hP1codRdpeSIsJAACAXwjdUS4jeKEcVrsBAAD8Q+iOcr2SEjSSFhMAAABfEbpbgIz0VK2gxQQAAMA3hO4WYFqwxeQdppgAAAD4gtDdAvRKSlB6WkfNXrXb71IAAABaJEJ3C5GRnqoV2/dpx15aTAAAAJobobuFONhiwmo3AABAsyN0txC9uyRoRFoHzWaKCQAAQLMjdLcgGempWr59n3buO+B3KQAAAC0KobsF+U+LCavdAAAAzYnQ3YL06dJOw3vQYgIAANDcCN0tTEZ6qr7ctk+7aDEBAABoNoTuFqauxYTLwgMAADQfQncL07drOw1L7UDoBgAAaEaE7hZo2shUfUGLCQAAQLPxLHSbWS8zm29mmWa2xsxuCm6/y8zWmdlKM3vDzDqFnPMLM8s2s/VmNtmr2lq6jLopJqu5UA4AAEBz8HKlu1rSrc65EyRNlHSDmQ2TNE/SCOfcSEkbJP1CkoL7LpY0XNIUSQ+bWayH9bVY/bq20wm0mAAAADQbz0K3cy7HOfdF8H6JpExJac65d51z1cHDFkvqGbw/Q9IrzrkK59xmSdmSJnhVX0s3LT1Fy7buVU4RLSYAAABea5aebjPrK2mMpM8O2fV9Se8E76dJ2h6yb0dw26HPNdPMlprZ0ry8vKYvtoU42GKyihYTAAAAr3keus2svaTXJN3snCsO2f4rBVpQXqrbVM/p7isbnHvcOTfOOTcuOTnZi5JbhP7J7TU0JZEWEwAAgGbgaeg2s1YKBO6XnHOvh2y/QtJ5kr7rnKsL1jsk9Qo5vaekXV7W19JlpKdq6da92l1U7ncpAAAAUc3L6SUm6SlJmc65e0O2T5H0M0nTnXNlIae8JeliM2ttZv0kDZK0xKv6EDrFhNVuAAAAL3m50n2KpMsknWVmy4O3DEkPSUqUNC+47VFJcs6tkfSqpLWS/i3pBudcjYf1tXgDu7XXkO60mAAAAHgtzqsnds4tVP192nOOcM6dku70qiZ8VUZ6qu5/f4P2FJere4c2fpcDAAAQlbgiZQs3bWSKnJPeYbUbAADAM4TuFm5gt0QN7t5ecxgdCAAA4JljCt1m1s6rQuCfjPRUfb61ULnFTDEBAADwQoNCt5lNMrO1ClxVUmY2yswe9rQyNJtp6alyTvr3Gla7AQAAvNDQle77JE2WVCBJzrkVkk73qig0r0HdEzWoW3vNXklfNwAAgBca3F7inNt+yCbG+UWRjPRULdlSqNwSWkwAAACaWkND93YzmyTJmVm8md2mYKsJosO0kYEWk7mraTEBAABoag0N3ddKukFSmgKXax8dfIwoMbh7ogZ2a6/ZjA4EAABocg26OI5zLl/Sdz2uBT7LSE/VQx9kKa+kQsmJrf0uBwAAIGo0dHrJc2bWKeRxZzN72ruy4Idp6amqZYoJAABAk2toe8lI59y+ugfOuablRxsAACAASURBVL2SxnhTEvwyuHt7DUhupzlMMQEAAGhSDQ3dMWbWue6BmSWpga0piBxmpmnpqfpsc4HySyv8LgcAACBqNDR03yPpUzP7vZn9XtKnkv7sXVnwS8bIYIsJU0wAAACaTINCt3PueUnfkrRHUq6kbzrnXvCyMPhjSPdE9U9upzlMMQEAAGgyRwzdZtYh+N8kSbslvSzpJUm7g9sQZepaTBZvosUEAACgqRxtpfvl4H+XSVoacqt7jCiUEZxiMpcpJgAAAE3iiKHbOXeemZmkM5xz/UNu/Zxz/ZupRjSzoSmJ6t+VFhMAAICmctSebueck/RGM9SCMGFmykhP1aKNBSqgxQQAAOC4NXR6yWIzG+9pJQgr/2kx2eN3KQAAABGvoaH7awoE741mttLMVpnZSi8Lg79OSE1UP1pMAAAAmkRDL3Az1dMqEHYCLSYpevSjTSrcX6mkdvF+lwQAABCxjjYysI2Z3SzpdklTJO10zm2tuzVLhfBNRnqqamodU0wAAACO09HaS56TNE7SKgVWu+/xvCKEjWGpHdS3SwItJgAAAMfpaO0lw5xz6ZJkZk9JWuJ9SQgXdVNMHltAiwkAAMDxONpKd1XdHedctce1IAzVtZi8S4sJAABAox0tdI8ys+LgrUTSyLr7ZlbcHAXCX8N7dFCfLgmaTYsJAABAox3tipSxzrkOwVuicy4u5H6H5ioS/qlrMfl0Y4H27q/0uxwAAICI1NA53WjBptW1mKylxQQAAKAxCN04quE9Oqh3UoJmryJ0AwAANAahG0d1sMUkO1/7ymgxAQAAOFaEbjTItPRUVdc6vbtmj9+lAAAARBxCNxpkRFoH9UpqyxQTAACARiB0o0HqWkw+ocUEAADgmBG60WAHW0zW0mICAABwLAjdaLD0tI7q2bmt5tBiAgAAcEwI3WgwM9O0YItJUVmV3+UAAABEDEI3jklGeqqqarhQDgAAwLEgdOOYjOxJiwkAAMCxInTjmNRNMVmYna+iA7SYAAAANAShG8esrsVkHlNMAAAAGoTQjWM2qmdHpXWixQQAAKChCN04ZoEWkxR9nJVHiwkAAEADELrRKHUtJu/RYgIAAHBUhG40yuhenWgxAQAAaCBCNxrFzDR1RIo+zspXcTktJgAAAEdC6EajZYxMVWVNLS0mAAAAR+FZ6DazXmY238wyzWyNmd0U3H5h8HGtmY075JxfmFm2ma03s8le1YamMaZXJ/Xo2IYWEwAAgKPwcqW7WtKtzrkTJE2UdIOZDZO0WtI3JS0IPTi472JJwyVNkfSwmcV6WB+Ok5lpanqqFmygxQQAAOBIPAvdzrkc59wXwfslkjIlpTnnMp1z6+s5ZYakV5xzFc65zZKyJU3wqj40jYz0QIvJ+5m0mAAAABxOs/R0m1lfSWMkfXaEw9IkbQ95vCO47dDnmmlmS81saV5eXlOWiUYY06uTUju20eyVu/0uBQAAIGx5HrrNrL2k1yTd7JwrPtKh9WxzX9ng3OPOuXHOuXHJyclNVSYaKSbGNHVEqhZk5amEFhMAAIB6eRq6zayVAoH7Jefc60c5fIekXiGPe0ra5VVtaDrTRqaosrpW72fm+l0KAABAWPJyeolJekpSpnPu3gac8paki82stZn1kzRI0hKv6kPTGdOrs1I6tNFsppgAAADUy8uV7lMkXSbpLDNbHrxlmNk3zGyHpJMlzTazuZLknFsj6VVJayX9W9INzrkaD+tDE4mJMU1NT9FHG2gxAQAAqE+cV0/snFuo+vu0JemNw5xzp6Q7vaoJ3pmWnqpnPtmiD9blasbor3z+FQAAoEXjipRoEmN7B1tMVtJiAgAAcChCN5pETIxpyogUfbghT6UV1X6XAwAAEFYI3Wgy00amBqeYcKEcAACAUIRuNJkTe3dWt8TWmsMUEwAAgP9C6EaTCVwoJ0Ufrs/TflpMAAAADiJ0o0llpKeqorpW76/jQjkAAAB1CN1oUuP6Jik5sbXmMMUEAADgIEI3mlRssMVk/vpcWkwAAACCCN1ocnUtJh/QYgIAACCJ0A0PjO+bpK7tmWICAABQh9CNJhfaYlJWSYsJAAAAoRueyEhPVXlVreavy/O7FAAAAN8RuuGJCf1oMQEAAKhD6IYnYmNMU0Z01wfrcnWgssbvcgAAAHxF6IZnMtJTdaCqRvPXM8UEAAC0bIRueOakfl3UtX28ZtNiAgAAWjhCNzwTG2OaPDxFH2TSYgIAAFo2Qjc8NS3YYvIhLSYAAKAFI3TDUxP6JalLO1pMAABAy0bohqfiYmM0eUSKPliXq/IqWkwAAEDLROiG56alp6qskhYTAADQchG64bmT+iUpqV28Zq/a7XcpAAAAviB0w3NxsTGaPDxF72fuocUEAAC0SIRuNIv/tJjk+V0KAABAsyN0o1lM7B9oMZnDFBMAANACEbrRLAItJt1pMQEAAC0SoRvNJiM9Vfsra/TRBlpMAABAy0LoRrM5uX8XdU5oRYsJAABocQjdaDb/mWLChXIAAEDLQuhGs8pIT1VpRbUW0GICAABakDi/C0DLcvKALuoUbDE5d3iK3+UAAIAGqq6pVWFZpQpKK1W4v1IF+ytVUFpx8P7oXp104Yk9ZWZ+lxqWCN1oVq1iYzR5WIpmr8pReVWN2rSK9bskAABapKOF6P++X6miA1X1Pk+MSe1ax+nlz7Zp5Y59uuP84YqLpZniUIRuNLuMkan629Lt+jgrX+cM6+53OQAARIWmDNGdE+KV1C5eXdrH64SUDgfvd2kXr6R2rUPux6tTQrxM0v/9e50eW7BJ2wsP6KFLxyixTavm/QKEOUI3mt2kAV2UnNhav317jYb16KC0Tm39LgkAgLBTF6ILgyG5YH+lCksrAqHZ4xAdG3PsLSK/yDhBfbu20//8c7UufHSRnr5yvHrwb/xB5pzzu4ZGGzdunFu6dKnfZaARVu0o0qVPLlbnhHj97YcTldqR/1ECAKLb0UJ0YWmlCvYHHhfur9S+soaF6C7tWnsSohvr46w8Xf/iF2obH6unrhiv9J4dm+21vWRmy5xz4xp9PqEbflm+fZ8ue/IzdU1srVdmTlT3Dm38LgkAgCa3raBMf5i9VvMy96i+2BXuIboxNuwp0VXPfK7C/ZX6y8Wjo2J4AqGb0B3Rlm3dq8uf+kwpHdvolZknKzmxtd8lAQDQJMoqq/Xw/I16/ONNahVjuvSk3uqdlBCRIboxckvKdc1zS7VyZ5F+lXGCfnBqv4iebELoJnRHvCWbC3XF00vUs3NbvTJzorq0J3gDACKXc07/WpmjP87JVE5Rub4xJk0/nzq0Rf5F90BljW55dbneWb1b35vYO6Inmxxv6I7Md42oMqFfkp6+cry27y3Td5/8THv3V/pdEgAAjZKZU6yLH1+sH//1S3VOiNc/rj1Z9100ukUGbklqGx+rWZeO1Q/P6K8XF2/TD55bqpLy+nvVox0r3QgbC7Py9YPnPteA5PZ6+ZqT1Ckh3u+SAABokH1llbpv3ga9sHirOrZtpdsmD9HF43tHZdtIY/11yTb9zz9Xa1C39nrqyvERN72M9hJCd1T5aEOernluqYakJOrFq09Sx7bM+AQAhK+aWqe/fb5dd81dp6IDVfrexD665ZzBLBwdxsKsfF334jK1iY/VU1eM08ienfwuqcFoL0FUOWNwsh69bKzW7S7WFU8vabF/ggIAhL9lWws1Y9ZC/fKNVRrUPVH/+vFp+t2MEQTuIzh1UFe9dv0kxcfG6DuPLdLcNbv9LqnZELoRds4a2l2zLh2r1TuLdOUzn6u0otrvkgAAOCi3uFw/+dtyfeuRRcovqdQDl4zR32ZO1LAeHfwuLSIM7p6of95wioakdNC1Ly7Tkx9vUiR3XjQU7SUIW++sytGP/vqlTuzdWc9+f7wS4rmAKgDAP5XVtXrmk8164P0sVdU4zTy9v67/2gD+fWqk0Mkm3z2pt347Pbwnm9Begqg1NT1V9180Wku3FuoHzy7Vgcoav0sCALRQ89fnasr9C/Snd9bp5AFdNO+W03Xb5CEE7uNQN9nk2jMG6KXPtun7UT7ZhNCNsHb+qB665zujtHhzgWa+sFTlVQRvAEDz2ZK/X1c/97mueuZzSdIzV43Xk1eMV58u7XyuLDrExJh+PnWo/veb6fo0O18XPrpIO/cd8LssT3gWus2sl5nNN7NMM1tjZjcFtyeZ2Twzywr+t3Nwu5nZA2aWbWYrzWysV7UhsnxjTE/9+VsjtTA7Xz98YZkqqgneAABvlVVW666563TufQu0aGOBfjF1qP598+n62pBufpcWlS6e0FvPXjVBO/cd0AWzPtHKHfv8LqnJebnSXS3pVufcCZImSrrBzIZJ+rmk951zgyS9H3wsSVMlDQreZkp6xMPaEGEuHNdLf/pGuj7akKfrX/xCldW1fpcEAIhCzjm9tWKXzrr7I82av1HnjUzVB7edqR+eMUDxcTQIeOnUQV31+nWT1DouMNnk36uja7KJZz89zrkc59wXwfslkjIlpUmaIem54GHPSbogeH+GpOddwGJJncws1av6EHkuntBbv79ghN5fl6sf//ULVdUQvAEATWftrmJd9Phi3fjXL9U1MXA1yXtb8NUk/TCoe6LeuP4UDU3poOteWqYnFkTPZJNm+ZXNzPpKGiPpM0ndnXM5UiCYS6r7O02apO0hp+0Ibjv0uWaa2VIzW5qXl+dl2QhDl03sozvOH6a5a/bo5leWq5rgDQA4TvvKKvX//rla5z34sbL2lOiP30jXmzecqnF9k/wurUVKTmytV2ZO1NQRKbpzTqZ+9c/VUfHvvecfuTWz9pJek3Szc67Y7LCXQ61vx1d+tXHOPS7pcSkwMrCp6kTkuPKUfqqudfrD7EzFxpjuu2g0l9kFAByzmlqnVz7fprvnrlfRgSpdfnJf/eTrg9Uxgash+61Nq1g9dMlY3dVlvR75cKN27D2gWZeOUWKbyP3eeBq6zayVAoH7Jefc68HNe8ws1TmXE2wfyQ1u3yGpV8jpPSXt8rI+RK6rT+uvqhqn//v3OsXFmO66cBTBGwDQYJ9vKdRv3lyjtTnFOqlfkn47Y7iGpnBxm3ASE2P62ZSh6tslQb96Y7W+/cgiPXXlOPXsnOB3aY3i5fQSk/SUpEzn3L0hu96SdEXw/hWS3gzZfnlwislESUV1bShAfa47c4BuPWewXv9yp37+2krV1vKHDwDAke0pLtfNr3ypCx9dpL1llXro0jF6ZeZEAncYu2h8bz33/QnaVXRAF8z6VCu2R+ZkE8+uSGlmp0r6WNIqSXWNOL9UoK/7VUm9JW2TdKFzrjAY0h+SNEVSmaSrnHNHvNwkV6SEJN07b4MeeD9Ll0zorTsvGKEYVrwBAIeoqK7R0wu36MEPslRd6/TD0/vrujO5mmQkydpToque/Vz5pRW6/6IxmjIipVlf/3ivSMll4BHxnHO6a+56PfzhRl02sY9+N2O4jvDZAQBACzN/Xa5+96+12py/X+cM667/N22YeneJzBaFli6vpELXPL9UK3bs0y+mDtU1p/Vvtn/zjzd08+sdIp6Z6fbJQ1RT6/TYgk2KizX9+rxhBG8AaOG25O/X7/+1Vu+vy1X/5HZ69qrxOpOL20S0uskmt766Qn+cs05bCsr02+nD1So2/GeoE7oRFcwCl5GtqnF6+pPNahUbo19MHUrwBoAWaH9FtWbNz9aTH29Wq1jTLzOG6spJ/bi4TZRo0ypWD14yRn26JOjhDzdqe2GZZn13rDqE+WQTQjeihpnp/513gmpqa/X4gk2KiwmsgBO8AaBlqLua5J/mrNPu4nJ9c2yafj5lqLpxcZuoExNj+umUoerbpZ1++cYqffuRT/X0lePDerIJoRtRxcx0x/Thqqp1evjDjYqLjdEt5wz2uywAgMfW7irWHW+t0ZIthUpP66hZ3x2rE/t09rsseOw743sprXNbXfviMl0w61M9dcU4jerVye+y6sXfWRB1zEx/mDFCF43rpQfez9KD72f5XRIAwCN791fqf/65Suc9+LGy80r1v99M1z9vOIXA3YKcMrCr3rh+ktrGx+iixxfp36vDc+I0K92ISjExpj99M11VtbW6Z94GxcXG6LozB/hdFgCgidTUOr28ZJvueXe9SsqruZpkCzewW6LeuP4UXfP8Ul330hfNPtmkIQjdiFoxMaa7vj1KNbWBK1e2ijVdfVp/v8sCABynJZsL9Zu31igzp1gT+yfpjulcTRJS1/at9ddrJurWvwcmm2zOL9PvZoTPZBNCN6JabIzpngtHqbrW6Q+zMxUbY7rqlH5+lwUAaITdReX60zuZenP5LvXo2EazLh2rjPSUsFrNhL/atIrVgxePUd8uCZo1f6N27A2fySaEbkS9uNgY3X/RaNXUOP327bWKi43RZRP7+F0WAA/sr6hW67gYxYXJyhaaRkV1jZ5auFkPfZCt6lqnG88aqOvOHKi28bF+l4YwFBNjun3yUPXp0k6/fD18JptwRUq0GJXVtbr+pWV6LzNXf/pmui6Z0NvvkgA0odySck29/2NJ0rSRqZo+qofG9u6smBhWQSPZB+v26Hdvr9WWgjKdO6y7/t95w9QrKXzHwiG8fJqdr2tfXKb4uFg9ecU4jT6OySZcBp7QjWNQUV2ja19Ypg835OnP3xqpC8f18rskAE3AOadrX1ym+evzdPbQbvpgXa4qqmuV1qmtzh/VQzNG99DQlETaECLI5uDVJD9Yl6sBye30m/OH6/TByX6XhQiUnVuqq55dorySCt33ndGamp7aqOchdBO6cYzKq2p0zfNLtTA7X/d+Z5S+Maan3yUBOE6zV+bohpe/0M+mDNV1Zw5QaUW15q3drTeX79LHWfmqqXUa1K29Zozuoemj0tS7Cyul4Wp/RbUe/CBbTy3cpNZxsbr564N0+cl9uZokjktBaYWueX6pvti2T7+YOlQzTz/2ySaEbkI3GqG8qkbff/ZzLd5UoPsvHqPpo3r4XRKARircX6lz7v1IPTq11RvXT/pKP3dBaYXmrN6tt5bv1Odb9kqSRvfqpOmjeui8UanqlsjVCsNBdU2t/rUyR396J1N7iiv07RN76qdThvD9QZMpr6rRbX9foX+tzNElE3rpdzNGHNNkE0I3oRuNVFZZrSuf+VzLtu7Vg5eMUUYj/9wEwF83vfKl5qzK0ds/PvWoY+N27jugt1fs0lvLd2ltTrFiTJo0oKumj+qhySNS1LGt/xMOWpKd+w5owYY8LdiQp4XZ+Sopr9bInh11x/ThGtubi9ug6dXWOt0zb71mzd+o0wZ1PabJJoRuQjeOw/6Kal3x9BIt375Ps747VpOHp/hdEoBj8N7aPbr6+aW6+euDdPPXBx/Tudm5JXpr+S69uWKXthaUKT42RmcOSdaM0Wk6+4RuatOKyRhNrbyqRp9tLtRH6/O0ICtP2bmlkqTUjm10xuBkfW1oN51zQnc+/ArPvbp0u375+ir169pOT185vkEfziV0E7pxnErKq3T500u0emeRHv3eiTr7hO5+lwSgAYoOVOnc+z5S54R4vfWjUxvd8+uc08odRXpz+S79a+Uu5ZZUqF18rCYPT9H00T10ysCuYXNxjUjjnFN2bqk+2pCnjzbkacnmQlVU1yo+LkYn9UvSGYOTdcbgZA3s1p4PuaLZfboxX9e+sEzxcTF68orxR51sQugmdKMJFJdX6XtPfqZ1OSV6/PITdeaQbn6XBOAofvaPlfrHFzv0z+tPUXrPjk3ynDW1Tp9tKtBbK3ZpzqocFZdXK6ldvKalp2r66B46kRGER1V0oEqfZOcfbBvZVVQuSRqQ3E5nDO6m0wd31Un9ujBjG2EhO7dU33/2c+0pLtf9Fx15sgmhm9CNJlJUVqVLn1ysrNxSPXXFOJ02iNFUQLj6OCtPlz21RNedOUA/mzLUk9eoqK7Rgg35enP5Tr2XuUflVYERhOeNStWMUWk6IZURhFLgF5VVO4u0ILiavXz7PtXUOiW2jtMpA7vqjCHJOn1wstI6tfW7VKBeoZNNfj51qH54mMkmhG5CN5rQ3v2VuuSJxdqcv1/PXDVekwZ09bskAIcorajW5PsWqHWrGM258bRm6b0urajWe2v36M3lO/VxVr6qa50GdmuvGaN6aProHurTpZ3nNYST3OJyfbQhTwuy8vVxVp72lVXJTBqZ1lGnB1tGRvfqxJVBETFCJ5tcPL6Xfn/BVyebELoJ3WhiBaUVuuSJxdpeeEDPXjVeJ/Xv4ndJAEL8+s3VemHxVv3j2pN1Yp+kZn/9wv2VmrMqR2+t2KUlmwslSaOCIwjPH5mqbh2ib8RdRXWNlm3Ze7A3e93uEklScmJrnT4oWacP7qrTBiUrqV28z5UCjVdb63TvvA16aH62Th3YVQ9/778nmxC6Cd3wQF5JhS5+fJFyisr1wg8m+PIPO4Cv+mxTgS56fLGuOqWvfnP+cL/L0a66EYQrdmnNrsAIwpMHdNH0UT00ZXiqOiZE5ghC55y2FJQdbBlZtLFAB6pq1CrWNK5P0sHVbFpsEI3+vnS7fvnGKvXt8t+TTQjdhG54JLe4XBc9vlh5JRV64QcTNIaZsYCvDlTWaOpfFqjGOc29+XQlxMf5XdJ/yc4t1Vsrdumt5Tu1JTiC8IwhyZoxuofOHto97D84WFpRrU+z87UgK08LNuRrW2GZJKlPlwSdMThZpw9K1skDuqhd6/D6ugNeWLSxQD98Yani42L0xOXjNKZ3Z0I3oRteyik6oIseW6y9ZZV66eqTNLLnkccJAfDOnbPX6omPN+vlq0/SpIHh+3kL5wIfLKwbQbinODCC8NzgCMJTw2QEYW2t09qcYi3IytNH6/O0bOteVdc6JcTHatKALoGgPTi5xfWrA3U25pXqqmcCk03uu2i0po3sQegGvLRz3wFd9NgilZRX66WrT9KItKYZTQag4b7ctlffeuRTXTyht/74jXS/y2mwmlqnzzYX6O0VuzRn1W4VHahS54RWmjYyVdNHpWlcn+YdQVhQWqGPs4Lj/LLylV9aIUkaltpBpw8O9GaP65PU6JnnQLQpKK3QzBeWadnWvdr6f+cRugGvbS8s00WPLdKBqhr9debEo15qGkDTqaiu0XkPLFRpRbXe/cnpSmzgJZvDTWV1rRZsyNObK3bpvbV7dKCqRj06ttH5wQkow1I7NHl/dFVNrb7ctu9gb/bqXUVyTuqc0EqnDQr0ZZ82uKu6JUbfhz+BplJeVaOfv7ZSf7lkLKEbaA5bC/broscWq6qmVq/MnKhB3RP9LgloEe55d70e/CBbz1w1Xl+LkgtX7a+o1nuZe/Tm8l1asCFP1bVOA5LbacboNE0f1UN9uza+pWN7YVmwLztPn2YXqKSiWrExprG9OwUnjSRrRFpHxXKRH+CY0NNN6EYz2pRXqosfX6xaJ70yc6IGdmvvd0lAVFu9s0gzZn2iC0an6Z7vjPK7HE/s3V+pOatz9NbyXfqsbgRhz446f1QPnT+qh7ofZQThgcoaLd5coI/W52lBVp425e2XJKV1ahucMtJVJw/oqo5tI/MvBEC4IHQTutHMsnNLdfHjixRjpr/98GT1O44VKQCHV1VTqxkPfaK80grN+8np6pQQ/TOgc4oO6F8rcvTmip1avbNYZtLJ/QMjCKeOCIwgdM5pw57Sgy0jS7YUqrK6Vq3jYjSxf5eD4/wGJLdjnB/QhAjdhG74YMOeEl38+GK1jovR32aerN5dEvwuCYg6D32Qpbvf3aBHv3eipoxI8bucZrcxr1RvLQ/MAN+cv1+tYk0T+iVpY+5+7S4ulyQN6tb+4JSRCf2SmuXqnEBLRegmdMMna3cV69InF6tdfJxemTnx4PB8AMdvw54SnffAQp0zvLtmXTrW73J85ZzT6p3FenP5Ti3IytPAYNA+bVCyenRq63d5QItB6CZ0w0erdxbp0icWq2NCK/1t5sn8Awg0gZpap28+8qm2FezXvFvO+P/t3XmcVXX9x/HXZ1ZgNpZhFVDZHGRVkMWssLLcVwrQn6mpmFn9zB+ZmWX6+7mktmhaAoqoFa5YWJaYaZqCiig7sivIsC/DDDDr5/fHOQPjOCDL3HvuvfN+Ph485sw5517fX79z7/3c7/mecyjMzY46kojIYRfduhCnyGHoe0QBj18+lG1llYyZOJN123dHHUkk6U36z0rmrN7Gz8/uo4JbRFKGim6RwzSgS0sevXwIm0sruHDiTDaUqPAWOVQrN5Vxz/QP+Erv9pw9oFPUcUREGo2KbpFGcHzXVky+7ATWlezmwofeYuOO8qgjiSSdmhrnR8/OJSsjjdvO66srb4hISlHRLdJIBh/VmkmXnsCarTv5r4feYnOpCm+Rg/HHtz7k7ZVb+OmZx37mtalFRJKNim6RRjSsWxsmXXICqzaXMXrCTDbs0FQTkQOxestO7vj7Yj7fs5CvD+ocdRwRkUanolukkZ3Yo5DJlw3h4227GD1+JsXbd0UdSSShuTs3PjcPA+44v5+mlYhISlLRLRIDw7u34fHLh7BxRznfGD+D1Vt2Rh1JJGE9PWsNry/dxA2nFdG5la53LyKpSUW3SIwMOrI1f7hiKNt3VjJq/AxWbSqLOpJIwlm3fTf/+7eFDDm6NRcNPTLqOCIiMaOiWySGBnRpyZSxw9hdVcM3xs9g2YYdUUcSSRjuzk+em0dldQ13XdCftDRNKxGR1KWiWyTG+nQq4Imxw6hxGDV+JouKS6KOJJIQps1Zy8uLNzDuq8dwVGFO1HFERGJKRbdIHPRqn8dTVw0jMz2NMRNnMv/j7VFHEonUptJyfj5tAQO7tOSyzx0ddRwRkZhT0S0SJ93a5vLUVcPJycpgzMSZzP5oa9SRRCJz87QFlJVXc/fI/qRrWomINAEqukXiqGubFjz17eG0zsni4ofe4u2VW6KOJBJ3/5hfzN/mFvPfX+lJz/Z5UccRaoGDDQAAG2VJREFUEYkLFd0icXZEy+Y8ddVwOhQ045JJb/PGsk1RRxKJm207K7jpzwvo0ymfsV/oFnUcEZG4UdEtEoH2+c148qrhHNmmBZdNfodXPtgQdSSRuLj1rwvZtrOCu0b2JzNdH0Ei0nTE7B3PzCaZ2QYzm19n3QAzm2Fm88zseTPLr7Ptx2a2zMw+MLOvxSqXSKIozM1mypXD6NU+l7GPzWL6gnVRRxKJqVcWb2Dq7I+5ekR3+nQqiDqOiEhcxXKYYTJwar11DwE3uHs/4DnghwBmdiwwGugTPuZ3ZpYew2wiCaFVThZ/vGIYfToV8J0/zuavc9dGHUkkJkp2V3Ljc/Po2S6X736pR9RxRETiLmZFt7u/BtQ/S+wY4LVw+SXggnD5HOAJdy9395XAMmBIrLKJJJKC5pn84YqhHN+1Fd+f8h5TZ6+JOpJIo7vjhcWsL9nNXSP7k52hMRURaXriPaFuPnB2uPx1oEu4fASwus5+a8J1Ik1CbnYGk791AsO6teF/np7DE29/FHUkkUbz5rJNTHn7I674fDeO69oq6jgiIpGId9H9LeAaM3sXyAMqwvUNXaTVG3oCMxtrZrPMbNbGjRtjFFMk/lpkZTDp0hP4Yq+23DB1Ho/NWBV1JJHDtrOiih9NncvRhTlcd0qvqOOIiEQmrkW3uy9296+6+yBgCrA83LSGvaPeAJ2BBie3uvsEdx/s7oPbtm0b28AicdYsM53xFw/ilGPb87O/LGDiayuijiRyWO5+8QNWb9nFLy7oT7NMTSsRkaYrrkW3mbULf6YBNwEPhpumAaPNLNvMjgZ6Am/HM5tIosjOSOd3Fx3PGf06ctsLi7j/X0ujjiRySGat2sLkN1dxyfAjGXJ066jjiIhEKiNWT2xmU4ARQKGZrQFuBnLN7Jpwl6nAIwDuvsDMngIWAlXANe5eHatsIokuMz2Ne0cPJDsjjXumL6G8qobrTumFmW6XLclhd2U11z8zl04Fzbn+1KKo44iIRC5mRbe7j9nHpnv3sf9twG2xyiOSbDLS07j76wPIykjjt/9aRnlVDT8+rUiFtySF3/xzKSs2lfH45UPIyY7ZR42ISNLQO6FIAktPM24/rx9ZGWlMeG0F5ZXV3HxWH9LSVHhL4pqzehsTXlvOqMFd+HxPnXsjIgIqukUSXlqaccvZfcjOSGPi6yupqK7htnP7qfCWhFRRVcP1z8ylbV42N57RO+o4IiIJQ0W3SBIwM248vTfZGenc/8oyyitruGtkfzLS433VT5H9e+CVZXywfgcPXzKYguaZUccREUkYKrpFkoSZMe5rx5CdkcYvX1pCRXUNvx41kEwV3pIgFhWX8MAryzh3YCe+3Lt91HFERBKKim6RJPO9L/ckOzON219YTEVVDb+98DjdVlsiV1UdTCtp2SKTm8/qE3UcEZGEoyEykSQ09gvdueXsPkxfuJ6rHn+X3ZW6wqZEa+LrK5n38XZuPacvrXKyoo4jIpJwVHSLJKlLTjyKO87vx7+XbOSKR2exs6Iq6kjSRC3bUMqv/7mE0/p24PR+HaOOIyKSkFR0iySxMUO6cs/IAby5fBOXTnqH0nIV3hJf1TXO9c/MoUVWOreco2klIiL7oqJbJMldMKgz944+jnc/2srFD7/F9l2VUUeSJuTRN1cx+6Nt/OzMY2mX1yzqOCIiCUtFt0gKOGtAJx648Hjmf7ydix6aydayiqgjSRPw4eYy7npxMScf05bzjjsi6jgiIglNRbdIiji1bwcmXDyYJetLGTNxJptKy6OOJCmspsa54dl5ZKalcfv5/TDTzZpERPZHRbdICjm5qB2TLjmBVZvLGDV+ButLdkcdSVLUlHc+YsaKzdx4Rm86FjSPOo6ISMJT0S2SYk7qWcijlw1h3fbdjBo/g7XbdkUdSVLM2m27uOOFxXyuRxtGn9Al6jgiIklBRbdIChrarQ2PXzGUzWUVfGP8DFZv2Rl1JEkR7s6Nz82jusa58/z+mlYiInKAVHSLpKjju7biT1cMo7S8im+Mn8GKjaVRR5IUMHX2x7z6wUZ+dOoxdGndIuo4IiJJQ0W3SArr17mAKVcOo6KqhlETZrJ0/Y6oI0kS21Cym1ueX8DgI1vxzeFHRR1HRCSpqOgWSXG9O+bz5FXDMGDUhJksXFsSdSRJQu7OTX+ez+6qGn4xsj9paZpWIiJyMFR0izQBPdrl8eRVw2mWkcaYiTOZu2Zb1JEkyfxtXjHTF67nulN60b1tbtRxRESSjopukSbi6MIcnrxqOPnNM7ho4lu8++GWqCNJkthcWs7Nf1lA/84FXHHS0VHHERFJSiq6RZqQLq1b8OTY4RTmZXPxw28zc8XmqCNJErjl+YWU7K7krpH9yUjXx4aIyKHQu6dIE9OpZXOeHDuMI1o259JH3ub1pRujjiQJbPqCdUybs5bvntyTog75UccREUlaKrpFmqB2+c14Yuwwji7M5fJHZ/HyovVRR5IEtH1XJTf9eT5FHfK4ekT3qOOIiCQ1Fd0iTVSb3GymXDmUog55fPsP7/KP+cVRR5IEc9vfFrK5rIK7Rw4gK0MfFyIih0PvoiJNWMsWWfzhiqH0O6KAa/70HtPmrI06kiSI15Zs5KlZa7jqC93o17kg6jgiIklPRbdIE5ffLJPHLh/K4CNbce0T7/HMu2uijiQRKy2v4sdT59G9bQ7f/3LPqOOIiKQEFd0iQm52BpMvG8LnehQy7uk5/Omtj6KOJBH6xd8Xs3b7Lu4aOYBmmelRxxERSQkqukUEgOZZ6Uz85mC+VNSOG5+bxyNvrIw6kkRg5orNPD7zQy478WgGHdkq6jgiIilDRbeI7NEsM50H/2sQX+vTnlueX8iD/14edSSJo10V1fzo2bl0bd2CcV/rFXUcEZGUoqJbRD4hKyON+y88nrMGdOLOvy/m3n8uxd2jjiVx8MvpH/Dh5p3ceUE/WmRlRB1HRCSl6F1VRD4lMz2N34waSHZGGr/+5xIqqqsZ99VjMLOoo0mMzP5oKw+/sZKLhnblxO6FUccREUk5KrpFpEHpacZdF/QnMz2NB15Zzu7KGm46o7cK7xRUXlXN9c/MpWN+M244rSjqOCIiKUlFt4jsU1qacft5fcnOSOPh/6ykvKqaW8/uS1qaCu9U8tuXl7FsQymTLzuBvGaZUccREUlJKrpFZL/MjJvPOpbszDTG/3sFlVXO7ef3I12Fd0qY//F2fv/v5Ywc1JkRx7SLOo6ISMpS0S0in8nMuOHUIpplpHPvy0spLa9izJCuFHXMozA3O+p4cogqq2v44TNzaZ2TxU/PODbqOCIiKU1Ft4gcEDPjB6f0Ijszjbtf/IC/zSsGoG1eNkUd8ji2Yz5FHfPo3TGfboW5ZGXo4kiJ7sFXl7OouITxFw+ioIWmlYiIxJKKbhE5KN8Z0YPRJ3RlcXEJC4tLWLxuB4uKS3jkjVVUVNcAkJlu9GiXR+8OQRFeW4xrVDxxLFm/g/v+tZQz+3fka306RB1HRCTlqegWkYPWOieLE3sUcmKPvZeWq6yuYeWmMhYVl7CoeAeL15XwxvJNTH3v4z37FOZm0zsswHt3zKOoQz7d22pUPN6qqmv44dNzyGuWyS1n94k6johIk6CiW0QaRWZ6Gr3a59GrfR7nDNy7fktZBYuLS1gUjogvXlfC5DdXUVG1d1S8e9vcT0xPKeqQT9s8jYrHyqQ3VjJnzXbuG3McbXT0QUQkLlR0i0hMNTQqXhWOitednvLm8s37HBUvCqepaFT88K3YWMovpy/hlGPbc1b/jlHHERFpMlR0i0jcZaSn0bN9Hj3b53FOnfVbyipYvC6cnlJcwqJ6o+IZaUaPdrmfKMSLOubRLq9ZNA1JADU1zrZdlWwpK2dTaQWbSyv2LpeVs6WsIlwfLG/dWUl+swz+79y+utGRiEgcqegWkYTROieLE7sXfuI25LWj4numpxSXMGP5Zp77xKh4FkUd9s4T790xnx7tknNU3N3ZUV7F5rBQ3lxW8cnlsnC5NFjeurOC6hpv8LlatcikdU4WbXKzOaZDXrCck80px7anfX7T/aIiIhIFc2/4zToZDB482GfNmhV1DBGJwNayChatK2Fxce1c8R18sH7Hp0bF946IB0V529zsuI/w7qwIiuhN4Wjz5tIKNpWVsyUsnOuu31xWTmV1w+/Lec0yaBMW0cHPoIhuk5tF65wsCnP3LrdukUVGevJ96RARSVRm9q67Dz7kx6voFpFUUVVdw6rNZSysnZ4SFuPF23fv2adNTtanpqf0aJdLdkb6Af93dldWs6WsIpy6UR5O6QgK6drlzaXBFI8tZRXsqqxu8HmaZ6YHhXNtEV2/oK6z3Don66AyiohI41LRraJbRD7D1rKKPSds1s4Zrz8q3r1tbjA9pWM+HfKbBYVzvTnRm8sq2FJawY7yqgb/O1npaXsK5Da52RTuKZiDEejC2uVwfYsszfATEUkWKrpVdIvIITiQUfH0NAvnQe+dyhFM4wiK6j3LOdm0zs0iLztDJyeKiKSowy26NcwiIk1SRnoaPdrl0aNdHmcP6LRn/badwch2m5wsCppnkpamIlpERA6fim4RkTpatsiiZYusqGOIiEiKidmp7WY2ycw2mNn8OusGmtlMM3vfzGaZ2ZBwvZnZfWa2zMzmmtnxscolIiIiIhJvsbye1GTg1Hrr7gJucfeBwM/C3wFOA3qG/8YCv49hLhERERGRuIpZ0e3urwFb6q8G8sPlAmBtuHwO8JgHZgItzUz3JxYRERGRlBDvOd3XAi+a2T0EBf+J4fojgNV19lsTriuu/wRmNpZgNJyuXbvGNKyIiIiISGOI9+3KrgZ+4O5dgB8AD4frG7o8QIPXMnT3Ce4+2N0Ht23bNkYxRUREREQaT7yL7kuAqeHy08CQcHkN0KXOfp3ZO/VERERERCSpxbvoXgt8MVz+ErA0XJ4GfDO8iskwYLu7f2pqiYiIiIhIMorZnG4zmwKMAArNbA1wM3AlcK+ZZQC7CedmAy8ApwPLgJ3AZbHKJSIiIiISbzErut19zD42DWpgXweuiVUWEREREZEoxXt6iYiIiIhIk6OiW0REREQkxlR0i4iIiIjEmIpuEREREZEYU9EtIiIiIhJjKrpFRERERGJMRbeIiIiISIyp6BYRERERiTEL7kuTnMxsO3tvJZ+KCoDtUYeIIbUveRUCm6IOEUOp3Heg9iW7VG5fKrcN1L5k19PdCw71wTG7I2WcPOnuYz97t+RkZhPUvuSVyu0zs1nuPjjqHLGSyn0Hal+yS+X2pXLbQO1LdmY24XAen+zTS56POkCMqX3JLdXbl8pSve/UvuSWyu1L5baB2pfsDqt9ST29RESikeoj3SIiIo0t2Ue6RSQah3WITUREpKnRSLeIiIiISIwl/Ei3mf3EzBaY2Vwze9/MhkadqTGZWWcz+4uZLTWz5WZ2r5ll7Wf/a82sRTwzHgozczP7ZZ3fx5nZzyOM1KjMrDr8e1xgZnPM7DozS/jXkwTq9F/tv6P2s+8IM/tr/NIdvvD193id3zPMbGOytWN/zOy8sJ1FUWdpLE2h32qZWWnUGeLhs9ppZq+aWdJM1UvF111dsa45E7pIMLPhwJnA8e7eH/gKsDraVI3HzAyYCvzZ3XsCvYBc4Lb9POxaIOGLbqAcON/MCqMOEiO73H2gu/cBTgFOB26OOJMcuNr+q/23KupAjawM6GtmzcPfTwE+PpgnMLNEv7rVGOA/wOiDeZCZpccmTqM47H4TibFDet0lg3jUnAlddAMdgU3uXg7g7pvcfa2ZDTKzf5vZu2b2opl1hD3fGH9jZm+a2XwzGxJp+s/2JWC3uz8C4O7VwA+Ab5lZjpndY2bzwm9c3zOz7wOdgFfM7JUIcx+IKoJ5vz+ov8HMjjSzl8N2vWxmXc2swMxW1Y4Wm1kLM1ttZpnxDn6w3H0DMBb4rgXSzexuM3snbONVtfua2fVhn84xszujS31gmspoFATF2L76Dcg3s+fMbKGZPZgkRzX+DpwRLo8BptRuMLMh4fvke+HPY8L1l5rZ02b2PDA9/pEPjJnlAp8DLif88A+PSLzWUD+ZWamZ3WpmbwHDo0t+QA6l3143s4F19nvDzPrHNfUhqH8UyczuN7NLw+VVZnaLmc0O3zOTdmR1f+1MJvt53e2rD083s8Vm9h8zuy8JjtjEvOZM9A+O6UAXM1tiZr8zsy+GRdhvgZHuPgiYxCdHhnPc/UTgO+G2RNYHeLfuCncvAT4CrgCOBo4Lv3H90d3vA9YCJ7v7yfEOewgeAC4ys/oXkr8feKy2XcB97r4dmAN8MdznLOBFd6+MW9rD4O4rCF5P7QjekLa7+wnACcCVZna0mZ0GnAsMdfcBwF2RBZbmtndqyXPhugb7Ldw2BPgfoB/QHTg/7okP3hPAaDNrBvQH3qqzbTHwBXc/DvgZcHudbcOBS9z9S3FLevDOBf7h7kuALWZ2fLh+X/2UA8x396Hu/p+4pz04h9JvDwGXAphZLyDb3efGLXHsbHL344HfA+OiDiP7fN19Svj3Ox44zd1PAtrGKePhiHnNmdBFt7uXAoMIRhE3Ak8CVwF9gZfM7H3gJqBznYdNCR/7GsHoVMu4hj44BjR0JqsBXwAedPcqAHffEs9gjSH8AvEY8P16m4YDfwqXHwdOCpefBEaFy6PD35OJhT+/Cnwz/Pt8C2gD9CQ4VPWIu++E5OlTM8sNj0jUjjidE64/yswWmdlEC+bATbe9h8UTXd3pJeeF6/bVbwBvu/uK8GjUFPb+zSassOg6imC09IV6mwuAp81sPvBrggGAWi8lwd/mGILilPDnmHB5X/1UDTwb34iH5hD77WngzLBA+BYwOS5hY29q+PNdgv8nEq19ve4aUgSscPeV4e9T9rNvQohHzZnoc/Zqp1y8CrxqZvOAa4AF7r6vQ4T1i9hEvjzLAuCCuivMLB/oAqwgsbMfqN8As4FH9rNPbTunAXeYWWuCP/x/xThbozGzbgQf7BsIiu/vufuL9fY5leTs093Aee5eYsEc/ZlmNi3c1hMY4+5XmtlTBH/Pf4gq6GHaV7+NILneV+qaBtwDjCD4ElHrf4FX3P08C04ifbXOtrI4ZTskZtaGYGpeXzNzIJ2gP15g3/20O/wsSRYH1W/uvtPMXgLOAb4BJMuJeVV8cvCvWb3t5eHPapKgXtmPz2pnwtvP624aDbfNSEKxrjkTeqTbzI4xs551Vg0EFgFtLZjwjpllmlndUZpR4fqTCA4Vb49b4IP3MtDCzL4Je07w+SXBKMV04NsWnswUFqIAO4C8+Ec9NOGI2VMEh+5rvcnekzAuIjgpo/Zb5tvAvcBfk+VD0szaAg8C93twDc4XgavDUSfMrJeZ5RD06bcsvPpMnT5NdAbcbmZzgX8CRwDtw20r3f39cDnZR6P21W8AQ8IpQmkE7zGJPkWh1iTgVnefV299AXtP0Ls0rokO30iC6WlHuvtR7t4FWEkwqp2s/VTfofTbQ8B9wDtJcKSi1ofAsWaWHU5D/HLUgWIkFdq5r9cdNNy2xUA323tlqFEkuHjUnIn+zTEX+G04XF8FLCMY9p8A3Bd2cAbBaOqC8DFbzexNIJ/gMFvCcnc3s/OA35nZTwm+BL0A3Ejwzb4XMNfMKoGJBHOhJwB/N7PiJJnXDcEXie/W+f37wCQz+yHBIZzL6mx7kuBQ6Yi4pTs0zcNDTZkEf5uPA78Ktz1EUHzONjMjaOO57v4PC052mmVmFezt60R3EcF8vEHuXmlmq9g7mlFeZ79qIFmmlzSkwX4Lt80A7iSYK/wa8FxDT5Bo3H0NwZfY+u4CHjWz60iiI0qhMQR9UdezwNUkaT/Vdyj95u7vmlkJ+z+qmBDCwaRyd18dHiGbCywF3os2WeNKsXbu63V3IcHA2ifa5u67zOw7wD/MbBPBgFqii3nNmVI3xzGzV4Fx7j4r6iwiqcCCq5f8BOjh7t8zs5MJPuxrTzD8q7v3DfcdB+S6+88jCStNWjgNaJy7nxl1liiYWSeCw+JF7l4TcZz9MrMBwER3T/QrjB2WptLOfTGzXHcvDQcxHgCWuvuvo87VWA6l5kzo6SUiEp3aURqCK8wMNrNZBKPeiyMNJiKfEE5RfAv4SRIU3N8mOPnspqizxFJTaednuDI8IryAYGrU+IjzRC6lRrpFpPE09VEaERGRxqSRbhH5FI3SiIiINC6NdIuIiIiIxJhGukVEREREYkxFt4hgZl3M7JXwDpMLzOy/w/WtzewlM1sa/mwVri8ysxlmVh5etaT2eY6xvbdXf9/MSszs2qjaJSIikig0vUREMLOOQEd3n21meQQ3ujmX4AYcW9z9TjO7AWjl7j8ys3bAkeE+W939ngaeM53gRh5D3f3DeLVFREQkEWmkW0Rw92J3nx0u7yC4C9cRBLeVfjTc7VHCm8W4+wZ3fweo3M/TfhlYroJbRERERbeI1BPetvc4guv+tnf3YggKc6DdQTzVaIIroIiIiDR5KrpFZA8zyyW4te+17l5yGM+TBZwNPN1Y2URERJKZim4RAcDMMgkK7j+6+9Rw9fpwvnftvO8NB/h0pwGz3X194ycVERFJPiq6RQQzM+BhYJG7/6rOpmnAJeHyJcBfDvApx6CpJSIiInvo6iUigpmdBLwOzANqwtU3EszrfgroCnwEfN3dt5hZB2AWkB/uXwoc6+4lZtYCWA10c/ft8W2JiIhIYlLRLSIiIiISY5peIiIiIiISYyq6RURERERiTEW3iIiIiEiMqegWEREREYkxFd0iIiIiIjGmoltEJAWYWbWZvW9mC8xsjpldZ2b7fY83s6PM7MJ4ZRQRacpUdIuIpIZd7j7Q3fsApwCnAzd/xmOOAlR0i4jEga7TLSKSAsys1N1z6/zeDXgHKASOBB4HcsLN33X3N81sJtAbWAk8CtwH3AmMALKBB9x9fNwaISKSwlR0i4ikgPpFd7huK1AE7ABq3H23mfUEprj7YDMbAYxz9zPD/ccC7dz9/8wsG3iD4C6kK+PaGBGRFJQRdQAREYkZC39mAveb2UCgGui1j/2/CvQ3s5Hh7wVAT4KRcBEROQwqukVEUlA4vaQa2EAwt3s9MIDgXJ7d+3oY8D13fzEuIUVEmhCdSCkikmLMrC3wIHC/B3MIC4Bid68BLgbSw113AHl1HvoicLWZZYbP08vMchARkcOmkW4RkdTQ3MzeJ5hKUkVw4uSvwm2/A541s68DrwBl4fq5QJWZzQEmA/cSXNFktpkZsBE4N14NEBFJZTqRUkREREQkxjS9REREREQkxlR0i4iIiIjEmIpuEREREZEYU9EtIiIiIhJjKrpFRERERGJMRbeIiIiISIyp6BYRERERibH/B0a6Lj3ZMa0lAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the price column should be converted to float type\n",
    "df_calendar['price'] = df_calendar['price'].map(lambda x: x.replace('$','').replace(',',''),na_action='ignore')\n",
    "df_calendar['price'] = df_calendar['price'].astype(float)\n",
    "\n",
    "# convert boolean columns with strings (f and t) inputs  to 0 and 1 integer values \n",
    "df_calendar['available'] = df_calendar['available'].replace({'f':0,'t':1})\n",
    "\n",
    "# the index is set to be date column \n",
    "df_calendar.index = df_calendar['date']\n",
    "df_calendar = df_calendar.drop('date',axis=1)\n",
    "\n",
    "# the date index is object type, so it should be converted to date_time index\n",
    "df_calendar.index = pd.to_datetime(df_calendar.index)\n",
    "\n",
    "# the data is resampled from daily to monthly data in order to get better plot from price change\n",
    "df1 = df_calendar.resample('M').mean() #the average price of the whole properties over each month\n",
    "\n",
    "# plot\n",
    "df1['price'].plot(figsize = (12,8))\n",
    "plt.title(\"Price Change Over Calendar Period\")\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel(\"Price\")\n",
    "plt.savefig(\"plot4.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can bee seen that the average **price** is changing over time and it depends on the season and holidays. In summer time specially **September** it is more expensive as **demand** is high and it **drops** as we go to autumn and **winter**. In **April** it goes up again as we have Easter holiday and demand is again becomes high and during **summer** time on average the price of the properties are higher. \n",
    "\n",
    "The price average range is between **180 to 250** USD. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The whole number of the properties available during the whole data period in 2016 and 2017. \n",
    "date_availabilty = df_calendar.groupby('date')['available'].sum().sort_values(ascending = False) # availability based on date \n",
    "date_availabilty.resample('M').mean().plot(figsize = (12,8),color = 'b') # plot\n",
    "plt.savefig(\"plot5.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " The number of the properties  are available during the period is totally different. In sum up, There are less properties available in Summer as it is the busiest time of the AirBnB. On **January and February**, The number of properties are the highest and they are close to **2000 properties**, but in September it is  less than that. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Answer to  question 3:\n",
    "\n",
    "- **Which streets in Seattle host more guests than other streets?** \n",
    "- **What is the average price of these popular streets?** \n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "street_values = df['street_name'].value_counts().head(20) # the number of properties in Streets(top 20)\n",
    "street_values.plot(kind='bar',figsize = (10,8),color ='r')\n",
    "plt.title(\"Number of Properties in Seattle Streets-Top 20\")\n",
    "plt.xlabel(\"Street name\")\n",
    "plt.ylabel(\"The Number of Properties\")\n",
    "plt.savefig(\"plot6.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " **It can be seen that 1st Avenue, 2nd Avenue and Western Avenue are the most popular streets in hosting people on AirBnB.**\n",
    "\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_price_st = df.groupby('street_name')['price'].mean().sort_values(ascending =False).head(20) # Price of the Properties in streets- top 20\n",
    "df_price_st.plot(kind = 'bar',color = 'y',figsize  =(10,6)) # plot\n",
    "plt.title(\"Average Price of The streets-Top 20 \")\n",
    "plt.xlabel(\"Street name\")\n",
    "plt.ylabel(\"The Average price\")\n",
    "plt.savefig(\"plot7.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The price of the 20 top expensive streets is represented in the plot above. West Lee Street is the most expensive with close to 1000 USD value for one day stay and 33rd Avenue is the least expensive in this plot. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17.7579884756417 % of properties are available for the whole year\n"
     ]
    }
   ],
   "source": [
    "# availabality of the the properties during the 365 days period(1 year).  \n",
    "df_availability = df.groupby('id')['availability_365'].sum()/365  # vailabilty of each property in 365 days\n",
    "\n",
    "# we calculate that how many properties are available during the whole year. \n",
    "print(len(df_availability[df_availability == 1])/len(df_availability)*100,\"% of properties are available for the whole year\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Based on the above analysis, only 17.5% of the properties in Seattle  are available for the whole year. In other words, they are full-time working on the AirBnB platform. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}