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AirBnB Valuation.ipynb
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airbnb-valuation.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/firmai/0a20f90e9e6a8c13c048b9b163cbed8c/airbnb-valuation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Aconh-UHZXEI"
},
"source": [
"**Airbnb Rental Valuation**\n",
"\n",
"Welcome to Airbnb Analysis Corp.! Your task is to set the competitive **daily accomodation rate** for a client's house in Bondi Beach. The owner currently charges $500. We have been tasked to estimate a **fair value** that the owner should be charging. The house has the following characteristics and constraints. While developing this model you came to realise that Airbnb can use your model to estimate the fair value of any property on their database, your are effectively creating a recommendation model for all prospective hosts!\n",
"\n",
"\n",
"1. The owner has been a host since **August 2010**\n",
"1. The location is **lon:151.274506, lat:33.889087**\n",
"1. The current review score rating **95.0**\n",
"1. Number of reviews **53**\n",
"1. Minimum nights **4**\n",
"1. The house can accommodate **10** people.\n",
"1. The owner currently charges a cleaning fee of **370**\n",
"1. The house has **3 bathrooms, 5 bedrooms, 7 beds**.\n",
"1. The house is available for **255 of the next 365 days**\n",
"1. The client is **verified**, and they are a **superhost**.\n",
"1. The cancellation policy is **strict with a 14 days grace period**.\n",
"1. The host requires a security deposit of **$1,500**\n",
"\n",
"\n",
"*All values strictly apply to the month of July 2018*"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "lTnEOeuYZXEK"
},
"outputs": [],
"source": [
"from dateutil import parser\n",
"dict_client = {}\n",
"\n",
"dict_client[\"city\"] = \"Bondi Beach\"\n",
"dict_client[\"longitude\"] = 151.274506\n",
"dict_client[\"latitude\"] = -33.889087\n",
"dict_client[\"review_scores_rating\"] = 95\n",
"dict_client[\"number_of_reviews\"] = 53\n",
"dict_client[\"minimum_nights\"] = 4\n",
"dict_client[\"accommodates\"] = 10\n",
"dict_client[\"bathrooms\"] = 3\n",
"dict_client[\"bedrooms\"] = 5\n",
"dict_client[\"beds\"] = 7\n",
"dict_client[\"security_deposit\"] = 1500\n",
"dict_client[\"cleaning_fee\"] = 370\n",
"dict_client[\"property_type\"] = \"House\"\n",
"dict_client[\"room_type\"] = \"Entire home/apt\"\n",
"dict_client[\"availability_365\"] = 255\n",
"dict_client[\"host_identity_verified\"] = 1 ## 1 for yes, 0 for no\n",
"dict_client[\"host_is_superhost\"] = 1\n",
"dict_client[\"cancellation_policy\"] = \"strict_14_with_grace_period\"\n",
"dict_client[\"host_since\"] = parser.parse(\"01-08-2010\")\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XLqEwDW3ZXEN"
},
"source": [
"# Setup"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "g-5V7ujhZXEO"
},
"source": [
"First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "LsbHVGAqZXEP"
},
"outputs": [],
"source": [
"# To support both python 2 and python 3\n",
"from __future__ import division, print_function, unicode_literals\n",
"# Common imports\n",
"import numpy as np\n",
"import os\n",
"import pandas as pd\n",
"\n",
"# to make this notebook's output stable across runs\n",
"np.random.seed(42)\n",
"\n",
"# To plot pretty figures\n",
"%matplotlib inline\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"plt.rcParams['axes.labelsize'] = 14\n",
"plt.rcParams['xtick.labelsize'] = 12\n",
"plt.rcParams['ytick.labelsize'] = 12\n",
"\n",
"# Where to save the figures\n",
"PROJECT_ROOT_DIR = \".\"\n",
"CHAPTER_ID = \"end_to_end_project\"\n",
"IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
"\n",
"def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
" path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
" print(\"Saving figure\", fig_id)\n",
" if tight_layout:\n",
" plt.tight_layout()\n",
" try:\n",
" plt.savefig(path, format=fig_extension, dpi=resolution)\n",
" except:\n",
" plt.savefig(fig_id + \".\" + fig_extension, format=fig_extension, dpi=resolution)\n",
"\n",
"# Ignore useless warnings (see SciPy issue #5998)\n",
"import warnings\n",
"warnings.filterwarnings(action=\"ignore\", message=\"^internal gelsd\")\n",
"pd.options.display.max_columns = None"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "o6O0qpwJZXEQ"
},
"source": [
"# Get the data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "ACVfMcS3ZXEQ",
"outputId": "8eb32c68-4d06-48b8-97f3-8f6fb550eeec",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Be patient: loading from database (2 minutes)\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"<ipython-input-3-1a096963100c>:15: DtypeWarning: Columns (36,54,55) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" df = pd.read_csv(github_p+'sydney_airbnb.csv')\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Done\n"
]
}
],
"source": [
"import pandas as pd\n",
"## This is simply a bit of importing logic that you don't have ..\n",
"## .. to concern yourself with for now.\n",
"\n",
"from pathlib import Path\n",
"\n",
"github_p = \"https://storage.googleapis.com/public-quant/course//content/\"\n",
"\n",
"my_file = Path(\"sydney_airbnb.csv\") # Defines path\n",
"if my_file.is_file(): # See if file exists\n",
" print(\"Local file found\")\n",
" df = pd.read_csv('sydney_airbnb.csv')\n",
"else:\n",
" print(\"Be patient: loading from database (2 minutes)\")\n",
" df = pd.read_csv(github_p+'sydney_airbnb.csv')\n",
" print(\"Done\")"
]
},
{
"cell_type": "code",
"source": [
"df.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 764
},
"id": "UhaVCzir9pVA",
"outputId": "e4e627b1-8512-4f11-ba47-ea877cf9a5e1"
},
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" id listing_url \\\n",
"0 11156 https://www.airbnb.com/rooms/11156 \n",
"1 12351 https://www.airbnb.com/rooms/12351 \n",
"2 14250 https://www.airbnb.com/rooms/14250 \n",
"3 14935 https://www.airbnb.com/rooms/14935 \n",
"4 14974 https://www.airbnb.com/rooms/14974 \n",
"\n",
" name \\\n",
"0 An Oasis in the City \n",
"1 Sydney City & Harbour at the door \n",
"2 Manly Harbour House \n",
"3 Eco-conscious Travellers: Private Room \n",
"4 Eco-conscious Traveller: Sofa Couch \n",
"\n",
" summary \\\n",
"0 Very central to the city which can be reached ... \n",
"1 Come stay with Vinh & Stuart (Awarded as one o... \n",
"2 Beautifully renovated, spacious and quiet, our... \n",
"3 Welcome! This apartment will suit a short term... \n",
"4 Welcome! This apartment will suit a short term... \n",
"\n",
" space \\\n",
"0 Potts Pt. is a vibrant and popular inner-city... \n",
"1 We're pretty relaxed hosts, and we fully appre... \n",
"2 Our home is a thirty minute walk along the sea... \n",
"3 I live upstairs in my own room with my own bat... \n",
"4 Comes with a fully equipped gym and pool - whi... \n",
"\n",
" description \\\n",
"0 Very central to the city which can be reached ... \n",
"1 Come stay with Vinh & Stuart (Awarded as one o... \n",
"2 Beautifully renovated, spacious and quiet, our... \n",
"3 Welcome! This apartment will suit a short term... \n",
"4 Welcome! This apartment will suit a short term... \n",
"\n",
" neighborhood_overview \\\n",
"0 It is very close to everything and everywhere,... \n",
"1 Pyrmont is an inner-city village of Sydney, on... \n",
"2 Balgowlah Heights is one of the most prestigio... \n",
"3 NaN \n",
"4 NaN \n",
"\n",
" notes \\\n",
"0 $150.00 key security deposit, refundable on re... \n",
"1 We've a few reasons for the 6.00pm arrival tim... \n",
"2 NaN \n",
"3 The building can be hard to find, so please en... \n",
"4 I live upstairs in my own room with my own bat... \n",
"\n",
" transit \\\n",
"0 It is 7 minutes walk to the Kings Cross.train ... \n",
"1 Our home is centrally located and an easy walk... \n",
"2 Balgowlah - Manly bus # 131 or #132 (Bus stop... \n",
"3 DIRECTIONS VIA TAXI: Get dropped off at Renwic... \n",
"4 DIRECTIONS VIA TAXI: Get dropped off at Renwic... \n",
"\n",
" access \\\n",
"0 Kitchen & laundry facilities. Shared bathroom. \n",
"1 We look forward to welcoming you just as we wo... \n",
"2 Guests have access to whole house except locke... \n",
"3 I work from home most times - so if I'm home, ... \n",
"4 I work from home most times - so if I'm home, ... \n",
"\n",
" interaction \\\n",
"0 As much as they want. \n",
"1 As much or as little as you like. We live here... \n",
"2 NaN \n",
"3 I'm not a big chatter, so don't get offended i... \n",
"4 I'm not a big chatter, so don't get offended i... \n",
"\n",
" house_rules \\\n",
"0 Be considerate. No showering after 2330h. \n",
"1 We look forward to welcoming you to stay you j... \n",
"2 Standard Terms and Conditions of Temporary Hol... \n",
"3 1. Enjoy and always bring a smile during your ... \n",
"4 1. Enjoy and always bring a smile during your ... \n",
"\n",
" picture_url host_id \\\n",
"0 https://a0.muscache.com/im/pictures/2797669/17... 40855 \n",
"1 https://a0.muscache.com/im/pictures/763ad5c8-c... 17061 \n",
"2 https://a0.muscache.com/im/pictures/56935671/f... 55948 \n",
"3 https://a0.muscache.com/im/pictures/2257353/d3... 58796 \n",
"4 https://a0.muscache.com/im/pictures/2197966/6e... 58796 \n",
"\n",
" host_url host_name host_since \\\n",
"0 https://www.airbnb.com/users/show/40855 Colleen 23/09/09 \n",
"1 https://www.airbnb.com/users/show/17061 Stuart 14/05/09 \n",
"2 https://www.airbnb.com/users/show/55948 Heidi 20/11/09 \n",
"3 https://www.airbnb.com/users/show/58796 Kevin 30/11/09 \n",
"4 https://www.airbnb.com/users/show/58796 Kevin 30/11/09 \n",
"\n",
" host_location \\\n",
"0 Potts Point, New South Wales, Australia \n",
"1 Sydney, New South Wales, Australia \n",
"2 Sydney, New South Wales, Australia \n",
"3 Sydney, New South Wales, Australia \n",
"4 Sydney, New South Wales, Australia \n",
"\n",
" host_about host_response_time \\\n",
"0 Recently retired, I've lived & worked on 4 con... within a day \n",
"1 G'Day from Australia!\\r\\n\\r\\nHe's Vinh, and I'... within an hour \n",
"2 I am a Canadian who has made Australia her hom... within a few hours \n",
"3 I've moved countries twice in the span of 10 y... within an hour \n",
"4 I've moved countries twice in the span of 10 y... within an hour \n",
"\n",
" host_response_rate host_is_superhost \\\n",
"0 67% t \n",
"1 100% f \n",
"2 100% f \n",
"3 100% f \n",
"4 100% f \n",
"\n",
" host_thumbnail_url \\\n",
"0 https://a0.muscache.com/im/users/40855/profile... \n",
"1 https://a0.muscache.com/im/users/17061/profile... \n",
"2 https://a0.muscache.com/im/users/55948/profile... \n",
"3 https://a0.muscache.com/im/users/58796/profile... \n",
"4 https://a0.muscache.com/im/users/58796/profile... \n",
"\n",
" host_picture_url host_neighbourhood \\\n",
"0 https://a0.muscache.com/im/users/40855/profile... Potts Point \n",
"1 https://a0.muscache.com/im/users/17061/profile... Pyrmont \n",
"2 https://a0.muscache.com/im/users/55948/profile... Balgowlah \n",
"3 https://a0.muscache.com/im/users/58796/profile... Redfern \n",
"4 https://a0.muscache.com/im/users/58796/profile... Redfern \n",
"\n",
" host_listings_count host_total_listings_count \\\n",
"0 1.0 1.0 \n",
"1 2.0 2.0 \n",
"2 2.0 2.0 \n",
"3 2.0 2.0 \n",
"4 2.0 2.0 \n",
"\n",
" host_verifications host_has_profile_pic \\\n",
"0 ['email', 'phone', 'reviews'] t \n",
"1 ['email', 'phone', 'manual_online', 'reviews',... t \n",
"2 ['email', 'phone', 'reviews', 'jumio'] t \n",
"3 ['email', 'phone', 'facebook', 'reviews', 'jum... t \n",
"4 ['email', 'phone', 'facebook', 'reviews', 'jum... t \n",
"\n",
" host_identity_verified street neighbourhood \\\n",
"0 f Potts Point, NSW, Australia Potts Point \n",
"1 t Pyrmont, NSW, Australia Pyrmont \n",
"2 t Balgowlah, NSW, Australia Balgowlah \n",
"3 t Redfern, NSW, Australia Redfern \n",
"4 t Redfern, NSW, Australia Redfern \n",
"\n",
" neighbourhood_cleansed neighbourhood_group_cleansed city state \\\n",
"0 Sydney NaN Potts Point NSW \n",
"1 Sydney NaN Pyrmont NSW \n",
"2 Manly NaN Balgowlah NSW \n",
"3 Sydney NaN Redfern NSW \n",
"4 Sydney NaN Redfern NSW \n",
"\n",
" zipcode market smart_location country_code country latitude \\\n",
"0 2011 Sydney Potts Point, Australia AU Australia -33.869168 \n",
"1 2009 Sydney Pyrmont, Australia AU Australia -33.865153 \n",
"2 2093 Sydney Balgowlah, Australia AU Australia -33.800929 \n",
"3 2016 Sydney Redfern, Australia AU Australia -33.890765 \n",
"4 2016 Sydney Redfern, Australia AU Australia -33.889667 \n",
"\n",
" longitude is_location_exact property_type room_type accommodates \\\n",
"0 151.226562 t Apartment Private room 1 \n",
"1 151.191896 t Townhouse Private room 2 \n",
"2 151.261722 t House Entire home/apt 6 \n",
"3 151.200450 t Apartment Private room 2 \n",
"4 151.200896 t Apartment Shared room 1 \n",
"\n",
" bathrooms bedrooms beds bed_type \\\n",
"0 NaN 1.0 1.0 Real Bed \n",
"1 1.0 1.0 1.0 Real Bed \n",
"2 3.0 3.0 3.0 Real Bed \n",
"3 1.0 1.0 1.0 Real Bed \n",
"4 2.0 1.0 1.0 Pull-out Sofa \n",
"\n",
" amenities square_feet price \\\n",
"0 {TV,Kitchen,Elevator,\"Buzzer/wireless intercom... NaN $65.00 \n",
"1 {TV,Internet,Wifi,\"Air conditioning\",\"Paid par... NaN $98.00 \n",
"2 {TV,Wifi,\"Air conditioning\",Kitchen,\"Pets live... NaN $469.00 \n",
"3 {Internet,Wifi,\"Wheelchair accessible\",Pool,Ki... NaN $63.00 \n",
"4 {Internet,Wifi,Pool,Kitchen,Gym,Elevator,\"Buzz... 0.0 $39.00 \n",
"\n",
" weekly_price monthly_price security_deposit cleaning_fee guests_included \\\n",
"0 NaN NaN NaN NaN 1 \n",
"1 $800.00 NaN $0.00 $55.00 2 \n",
"2 $3,000.00 NaN $900.00 $100.00 6 \n",
"3 NaN NaN NaN NaN 1 \n",
"4 NaN NaN NaN NaN 1 \n",
"\n",
" extra_people minimum_nights maximum_nights calendar_updated \\\n",
"0 $0.00 2 180 4 weeks ago \n",
"1 $395.00 2 7 yesterday \n",
"2 $40.00 5 22 4 months ago \n",
"3 $40.00 2 1125 today \n",
"4 $0.00 2 1125 4 days ago \n",
"\n",
" has_availability availability_30 availability_60 availability_90 \\\n",
"0 t 9 39 69 \n",
"1 t 13 30 45 \n",
"2 t 0 0 0 \n",
"3 t 13 31 31 \n",
"4 t 24 50 50 \n",
"\n",
" availability_365 number_of_reviews first_review last_review \\\n",
"0 339 177 5/12/09 1/07/18 \n",
"1 188 468 24/07/10 27/06/18 \n",
"2 168 1 2/01/16 2/01/16 \n",
"3 215 172 28/11/11 26/06/18 \n",
"4 287 147 23/09/11 2/07/18 \n",
"\n",
" review_scores_rating review_scores_accuracy review_scores_cleanliness \\\n",
"0 92.0 9.0 9.0 \n",
"1 95.0 10.0 9.0 \n",
"2 100.0 10.0 10.0 \n",
"3 89.0 9.0 8.0 \n",
"4 90.0 9.0 8.0 \n",
"\n",
" review_scores_checkin review_scores_communication review_scores_location \\\n",
"0 10.0 10.0 10.0 \n",
"1 10.0 10.0 10.0 \n",
"2 10.0 8.0 10.0 \n",
"3 9.0 10.0 9.0 \n",
"4 9.0 9.0 9.0 \n",
"\n",
" review_scores_value instant_bookable cancellation_policy \\\n",
"0 9.0 f moderate \n",
"1 10.0 f strict_14_with_grace_period \n",
"2 10.0 f strict_14_with_grace_period \n",
"3 9.0 f moderate \n",
"4 9.0 f moderate \n",
"\n",
" require_guest_profile_picture require_guest_phone_verification \\\n",
"0 f f \n",
"1 t t \n",
"2 f f \n",
"3 f f \n",
"4 f f \n",
"\n",
" calculated_host_listings_count reviews_per_month \n",
"0 1 1.69 \n",
"1 2 4.83 \n",
"2 2 0.03 \n",
"3 2 2.14 \n",
"4 2 1.78 "
],
"text/html": [
"\n",
" <div id=\"df-fe0718e0-e910-457f-83cf-783d2cb73e0b\" class=\"colab-df-container\">\n",
" <div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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"\n",
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" 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>name</th>\n",
" <th>summary</th>\n",
" <th>space</th>\n",
" <th>description</th>\n",
" <th>neighborhood_overview</th>\n",
" <th>notes</th>\n",
" <th>transit</th>\n",
" <th>access</th>\n",
" <th>interaction</th>\n",
" <th>house_rules</th>\n",
" <th>picture_url</th>\n",
" <th>host_id</th>\n",
" <th>host_url</th>\n",
" <th>host_name</th>\n",
" <th>host_since</th>\n",
" <th>host_location</th>\n",
" <th>host_about</th>\n",
" <th>host_response_time</th>\n",
" <th>host_response_rate</th>\n",
" <th>host_is_superhost</th>\n",
" <th>host_thumbnail_url</th>\n",
" <th>host_picture_url</th>\n",
" <th>host_neighbourhood</th>\n",
" <th>host_listings_count</th>\n",
" <th>host_total_listings_count</th>\n",
" <th>host_verifications</th>\n",
" <th>host_has_profile_pic</th>\n",
" <th>host_identity_verified</th>\n",
" <th>street</th>\n",
" <th>neighbourhood</th>\n",
" <th>neighbourhood_cleansed</th>\n",
" <th>neighbourhood_group_cleansed</th>\n",
" <th>city</th>\n",
" <th>state</th>\n",
" <th>zipcode</th>\n",
" <th>market</th>\n",
" <th>smart_location</th>\n",
" <th>country_code</th>\n",
" <th>country</th>\n",
" <th>latitude</th>\n",
" <th>longitude</th>\n",
" <th>is_location_exact</th>\n",
" <th>property_type</th>\n",
" <th>room_type</th>\n",
" <th>accommodates</th>\n",
" <th>bathrooms</th>\n",
" <th>bedrooms</th>\n",
" <th>beds</th>\n",
" <th>bed_type</th>\n",
" <th>amenities</th>\n",
" <th>square_feet</th>\n",
" <th>price</th>\n",
" <th>weekly_price</th>\n",
" <th>monthly_price</th>\n",
" <th>security_deposit</th>\n",
" <th>cleaning_fee</th>\n",
" <th>guests_included</th>\n",
" <th>extra_people</th>\n",
" <th>minimum_nights</th>\n",
" <th>maximum_nights</th>\n",
" <th>calendar_updated</th>\n",
" <th>has_availability</th>\n",
" <th>availability_30</th>\n",
" <th>availability_60</th>\n",
" <th>availability_90</th>\n",
" <th>availability_365</th>\n",
" <th>number_of_reviews</th>\n",
" <th>first_review</th>\n",
" <th>last_review</th>\n",
" <th>review_scores_rating</th>\n",
" <th>review_scores_accuracy</th>\n",
" <th>review_scores_cleanliness</th>\n",
" <th>review_scores_checkin</th>\n",
" <th>review_scores_communication</th>\n",
" <th>review_scores_location</th>\n",
" <th>review_scores_value</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",
" <th>0</th>\n",
" <td>11156</td>\n",
" <td>https://www.airbnb.com/rooms/11156</td>\n",
" <td>An Oasis in the City</td>\n",
" <td>Very central to the city which can be reached ...</td>\n",
" <td>Potts Pt. is a vibrant and popular inner-city...</td>\n",
" <td>Very central to the city which can be reached ...</td>\n",
" <td>It is very close to everything and everywhere,...</td>\n",
" <td>$150.00 key security deposit, refundable on re...</td>\n",
" <td>It is 7 minutes walk to the Kings Cross.train ...</td>\n",
" <td>Kitchen &amp; laundry facilities. Shared bathroom.</td>\n",
" <td>As much as they want.</td>\n",
" <td>Be considerate. No showering after 2330h.</td>\n",
" <td>https://a0.muscache.com/im/pictures/2797669/17...</td>\n",
" <td>40855</td>\n",
" <td>https://www.airbnb.com/users/show/40855</td>\n",
" <td>Colleen</td>\n",
" <td>23/09/09</td>\n",
" <td>Potts Point, New South Wales, Australia</td>\n",
" <td>Recently retired, I've lived &amp; worked on 4 con...</td>\n",
" <td>within a day</td>\n",
" <td>67%</td>\n",
" <td>t</td>\n",
" <td>https://a0.muscache.com/im/users/40855/profile...</td>\n",
" <td>https://a0.muscache.com/im/users/40855/profile...</td>\n",
" <td>Potts Point</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>['email', 'phone', 'reviews']</td>\n",
" <td>t</td>\n",
" <td>f</td>\n",
" <td>Potts Point, NSW, Australia</td>\n",
" <td>Potts Point</td>\n",
" <td>Sydney</td>\n",
" <td>NaN</td>\n",
" <td>Potts Point</td>\n",
" <td>NSW</td>\n",
" <td>2011</td>\n",
" <td>Sydney</td>\n",
" <td>Potts Point, Australia</td>\n",
" <td>AU</td>\n",
" <td>Australia</td>\n",
" <td>-33.869168</td>\n",
" <td>151.226562</td>\n",
" <td>t</td>\n",
" <td>Apartment</td>\n",
" <td>Private room</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>Real Bed</td>\n",
" <td>{TV,Kitchen,Elevator,\"Buzzer/wireless intercom...</td>\n",
" <td>NaN</td>\n",
" <td>$65.00</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>$0.00</td>\n",
" <td>2</td>\n",
" <td>180</td>\n",
" <td>4 weeks ago</td>\n",
" <td>t</td>\n",
" <td>9</td>\n",
" <td>39</td>\n",
" <td>69</td>\n",
" <td>339</td>\n",
" <td>177</td>\n",
" <td>5/12/09</td>\n",
" <td>1/07/18</td>\n",
" <td>92.0</td>\n",
" <td>9.0</td>\n",
" <td>9.0</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>9.0</td>\n",
" <td>f</td>\n",
" <td>moderate</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>1</td>\n",
" <td>1.69</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>12351</td>\n",
" <td>https://www.airbnb.com/rooms/12351</td>\n",
" <td>Sydney City &amp; Harbour at the door</td>\n",
" <td>Come stay with Vinh &amp; Stuart (Awarded as one o...</td>\n",
" <td>We're pretty relaxed hosts, and we fully appre...</td>\n",
" <td>Come stay with Vinh &amp; Stuart (Awarded as one o...</td>\n",
" <td>Pyrmont is an inner-city village of Sydney, on...</td>\n",
" <td>We've a few reasons for the 6.00pm arrival tim...</td>\n",
" <td>Our home is centrally located and an easy walk...</td>\n",
" <td>We look forward to welcoming you just as we wo...</td>\n",
" <td>As much or as little as you like. We live here...</td>\n",
" <td>We look forward to welcoming you to stay you j...</td>\n",
" <td>https://a0.muscache.com/im/pictures/763ad5c8-c...</td>\n",
" <td>17061</td>\n",
" <td>https://www.airbnb.com/users/show/17061</td>\n",
" <td>Stuart</td>\n",
" <td>14/05/09</td>\n",
" <td>Sydney, New South Wales, Australia</td>\n",
" <td>G'Day from Australia!\\r\\n\\r\\nHe's Vinh, and I'...</td>\n",
" <td>within an hour</td>\n",
" <td>100%</td>\n",
" <td>f</td>\n",
" <td>https://a0.muscache.com/im/users/17061/profile...</td>\n",
" <td>https://a0.muscache.com/im/users/17061/profile...</td>\n",
" <td>Pyrmont</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>['email', 'phone', 'manual_online', 'reviews',...</td>\n",
" <td>t</td>\n",
" <td>t</td>\n",
" <td>Pyrmont, NSW, Australia</td>\n",
" <td>Pyrmont</td>\n",
" <td>Sydney</td>\n",
" <td>NaN</td>\n",
" <td>Pyrmont</td>\n",
" <td>NSW</td>\n",
" <td>2009</td>\n",
" <td>Sydney</td>\n",
" <td>Pyrmont, Australia</td>\n",
" <td>AU</td>\n",
" <td>Australia</td>\n",
" <td>-33.865153</td>\n",
" <td>151.191896</td>\n",
" <td>t</td>\n",
" <td>Townhouse</td>\n",
" <td>Private room</td>\n",
" <td>2</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>Real Bed</td>\n",
" <td>{TV,Internet,Wifi,\"Air conditioning\",\"Paid par...</td>\n",
" <td>NaN</td>\n",
" <td>$98.00</td>\n",
" <td>$800.00</td>\n",
" <td>NaN</td>\n",
" <td>$0.00</td>\n",
" <td>$55.00</td>\n",
" <td>2</td>\n",
" <td>$395.00</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>yesterday</td>\n",
" <td>t</td>\n",
" <td>13</td>\n",
" <td>30</td>\n",
" <td>45</td>\n",
" <td>188</td>\n",
" <td>468</td>\n",
" <td>24/07/10</td>\n",
" <td>27/06/18</td>\n",
" <td>95.0</td>\n",
" <td>10.0</td>\n",
" <td>9.0</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>f</td>\n",
" <td>strict_14_with_grace_period</td>\n",
" <td>t</td>\n",
" <td>t</td>\n",
" <td>2</td>\n",
" <td>4.83</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>14250</td>\n",
" <td>https://www.airbnb.com/rooms/14250</td>\n",
" <td>Manly Harbour House</td>\n",
" <td>Beautifully renovated, spacious and quiet, our...</td>\n",
" <td>Our home is a thirty minute walk along the sea...</td>\n",
" <td>Beautifully renovated, spacious and quiet, our...</td>\n",
" <td>Balgowlah Heights is one of the most prestigio...</td>\n",
" <td>NaN</td>\n",
" <td>Balgowlah - Manly bus # 131 or #132 (Bus stop...</td>\n",
" <td>Guests have access to whole house except locke...</td>\n",
" <td>NaN</td>\n",
" <td>Standard Terms and Conditions of Temporary Hol...</td>\n",
" <td>https://a0.muscache.com/im/pictures/56935671/f...</td>\n",
" <td>55948</td>\n",
" <td>https://www.airbnb.com/users/show/55948</td>\n",
" <td>Heidi</td>\n",
" <td>20/11/09</td>\n",
" <td>Sydney, New South Wales, Australia</td>\n",
" <td>I am a Canadian who has made Australia her hom...</td>\n",
" <td>within a few hours</td>\n",
" <td>100%</td>\n",
" <td>f</td>\n",
" <td>https://a0.muscache.com/im/users/55948/profile...</td>\n",
" <td>https://a0.muscache.com/im/users/55948/profile...</td>\n",
" <td>Balgowlah</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>['email', 'phone', 'reviews', 'jumio']</td>\n",
" <td>t</td>\n",
" <td>t</td>\n",
" <td>Balgowlah, NSW, Australia</td>\n",
" <td>Balgowlah</td>\n",
" <td>Manly</td>\n",
" <td>NaN</td>\n",
" <td>Balgowlah</td>\n",
" <td>NSW</td>\n",
" <td>2093</td>\n",
" <td>Sydney</td>\n",
" <td>Balgowlah, Australia</td>\n",
" <td>AU</td>\n",
" <td>Australia</td>\n",
" <td>-33.800929</td>\n",
" <td>151.261722</td>\n",
" <td>t</td>\n",
" <td>House</td>\n",
" <td>Entire home/apt</td>\n",
" <td>6</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>Real Bed</td>\n",
" <td>{TV,Wifi,\"Air conditioning\",Kitchen,\"Pets live...</td>\n",
" <td>NaN</td>\n",
" <td>$469.00</td>\n",
" <td>$3,000.00</td>\n",
" <td>NaN</td>\n",
" <td>$900.00</td>\n",
" <td>$100.00</td>\n",
" <td>6</td>\n",
" <td>$40.00</td>\n",
" <td>5</td>\n",
" <td>22</td>\n",
" <td>4 months ago</td>\n",
" <td>t</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>168</td>\n",
" <td>1</td>\n",
" <td>2/01/16</td>\n",
" <td>2/01/16</td>\n",
" <td>100.0</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>8.0</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>f</td>\n",
" <td>strict_14_with_grace_period</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>2</td>\n",
" <td>0.03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>14935</td>\n",
" <td>https://www.airbnb.com/rooms/14935</td>\n",
" <td>Eco-conscious Travellers: Private Room</td>\n",
" <td>Welcome! This apartment will suit a short term...</td>\n",
" <td>I live upstairs in my own room with my own bat...</td>\n",
" <td>Welcome! This apartment will suit a short term...</td>\n",
" <td>NaN</td>\n",
" <td>The building can be hard to find, so please en...</td>\n",
" <td>DIRECTIONS VIA TAXI: Get dropped off at Renwic...</td>\n",
" <td>I work from home most times - so if I'm home, ...</td>\n",
" <td>I'm not a big chatter, so don't get offended i...</td>\n",
" <td>1. Enjoy and always bring a smile during your ...</td>\n",
" <td>https://a0.muscache.com/im/pictures/2257353/d3...</td>\n",
" <td>58796</td>\n",
" <td>https://www.airbnb.com/users/show/58796</td>\n",
" <td>Kevin</td>\n",
" <td>30/11/09</td>\n",
" <td>Sydney, New South Wales, Australia</td>\n",
" <td>I've moved countries twice in the span of 10 y...</td>\n",
" <td>within an hour</td>\n",
" <td>100%</td>\n",
" <td>f</td>\n",
" <td>https://a0.muscache.com/im/users/58796/profile...</td>\n",
" <td>https://a0.muscache.com/im/users/58796/profile...</td>\n",
" <td>Redfern</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>['email', 'phone', 'facebook', 'reviews', 'jum...</td>\n",
" <td>t</td>\n",
" <td>t</td>\n",
" <td>Redfern, NSW, Australia</td>\n",
" <td>Redfern</td>\n",
" <td>Sydney</td>\n",
" <td>NaN</td>\n",
" <td>Redfern</td>\n",
" <td>NSW</td>\n",
" <td>2016</td>\n",
" <td>Sydney</td>\n",
" <td>Redfern, Australia</td>\n",
" <td>AU</td>\n",
" <td>Australia</td>\n",
" <td>-33.890765</td>\n",
" <td>151.200450</td>\n",
" <td>t</td>\n",
" <td>Apartment</td>\n",
" <td>Private room</td>\n",
" <td>2</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>Real Bed</td>\n",
" <td>{Internet,Wifi,\"Wheelchair accessible\",Pool,Ki...</td>\n",
" <td>NaN</td>\n",
" <td>$63.00</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>$40.00</td>\n",
" <td>2</td>\n",
" <td>1125</td>\n",
" <td>today</td>\n",
" <td>t</td>\n",
" <td>13</td>\n",
" <td>31</td>\n",
" <td>31</td>\n",
" <td>215</td>\n",
" <td>172</td>\n",
" <td>28/11/11</td>\n",
" <td>26/06/18</td>\n",
" <td>89.0</td>\n",
" <td>9.0</td>\n",
" <td>8.0</td>\n",
" <td>9.0</td>\n",
" <td>10.0</td>\n",
" <td>9.0</td>\n",
" <td>9.0</td>\n",
" <td>f</td>\n",
" <td>moderate</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>2</td>\n",
" <td>2.14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>14974</td>\n",
" <td>https://www.airbnb.com/rooms/14974</td>\n",
" <td>Eco-conscious Traveller: Sofa Couch</td>\n",
" <td>Welcome! This apartment will suit a short term...</td>\n",
" <td>Comes with a fully equipped gym and pool - whi...</td>\n",
" <td>Welcome! This apartment will suit a short term...</td>\n",
" <td>NaN</td>\n",
" <td>I live upstairs in my own room with my own bat...</td>\n",
" <td>DIRECTIONS VIA TAXI: Get dropped off at Renwic...</td>\n",
" <td>I work from home most times - so if I'm home, ...</td>\n",
" <td>I'm not a big chatter, so don't get offended i...</td>\n",
" <td>1. Enjoy and always bring a smile during your ...</td>\n",
" <td>https://a0.muscache.com/im/pictures/2197966/6e...</td>\n",
" <td>58796</td>\n",
" <td>https://www.airbnb.com/users/show/58796</td>\n",
" <td>Kevin</td>\n",
" <td>30/11/09</td>\n",
" <td>Sydney, New South Wales, Australia</td>\n",
" <td>I've moved countries twice in the span of 10 y...</td>\n",
" <td>within an hour</td>\n",
" <td>100%</td>\n",
" <td>f</td>\n",
" <td>https://a0.muscache.com/im/users/58796/profile...</td>\n",
" <td>https://a0.muscache.com/im/users/58796/profile...</td>\n",
" <td>Redfern</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>['email', 'phone', 'facebook', 'reviews', 'jum...</td>\n",
" <td>t</td>\n",
" <td>t</td>\n",
" <td>Redfern, NSW, Australia</td>\n",
" <td>Redfern</td>\n",
" <td>Sydney</td>\n",
" <td>NaN</td>\n",
" <td>Redfern</td>\n",
" <td>NSW</td>\n",
" <td>2016</td>\n",
" <td>Sydney</td>\n",
" <td>Redfern, Australia</td>\n",
" <td>AU</td>\n",
" <td>Australia</td>\n",
" <td>-33.889667</td>\n",
" <td>151.200896</td>\n",
" <td>t</td>\n",
" <td>Apartment</td>\n",
" <td>Shared room</td>\n",
" <td>1</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>Pull-out Sofa</td>\n",
" <td>{Internet,Wifi,Pool,Kitchen,Gym,Elevator,\"Buzz...</td>\n",
" <td>0.0</td>\n",
" <td>$39.00</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>$0.00</td>\n",
" <td>2</td>\n",
" <td>1125</td>\n",
" <td>4 days ago</td>\n",
" <td>t</td>\n",
" <td>24</td>\n",
" <td>50</td>\n",
" <td>50</td>\n",
" <td>287</td>\n",
" <td>147</td>\n",
" <td>23/09/11</td>\n",
" <td>2/07/18</td>\n",
" <td>90.0</td>\n",
" <td>9.0</td>\n",
" <td>8.0</td>\n",
" <td>9.0</td>\n",
" <td>9.0</td>\n",
" <td>9.0</td>\n",
" <td>9.0</td>\n",
" <td>f</td>\n",
" <td>moderate</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>2</td>\n",
" <td>1.78</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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"type": "dataframe",
"variable_name": "df"
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"metadata": {},
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{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "ebrF0xLFZXES"
},
"outputs": [],
"source": [
"### To make this project easier, I will select only a small number of features\n",
"incl = [\"price\",\"city\",\"longitude\",\"latitude\",\"review_scores_rating\",\"number_of_reviews\",\"minimum_nights\",\"security_deposit\",\"cleaning_fee\",\"accommodates\",\"bathrooms\",\"bedrooms\",\"beds\",\"property_type\",\"room_type\",\"availability_365\" ,\"host_identity_verified\", \"host_is_superhost\",\"host_since\",\"cancellation_policy\"]\n",
"df = df[incl]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jWFkhwNdZXET"
},
"source": [
"Lets reformat the price to floats, it is currently a string (object). And lets makes sure the date is in a datetime format."
]
},
{
"cell_type": "code",
"source": [
"df[[\"price\"]].head()"
],
"metadata": {
"id": "oygW0Mptozfq",
"outputId": "ffc3e20b-865c-48ec-cf67-7b9843450fcf",
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"height": 206
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"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
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" price\n",
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"summary": "{\n \"name\": \"df[[\\\"price\\\"]]\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"price\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"$98.00 \",\n \"$39.00 \",\n \"$469.00 \"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
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"metadata": {},
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{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "2Y7AZMImZXEU",
"outputId": "941a6304-2608-477b-f6f3-5f58b01b688c",
"colab": {
"base_uri": "https://localhost:8080/"
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},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"<ipython-input-7-0acd20ed9d9e>:8: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" df['host_since'] = pd.to_datetime(df['host_since'])\n"
]
}
],
"source": [
"import re\n",
"price_list = [\"price\",\"cleaning_fee\",\"security_deposit\"]\n",
"\n",
"for col in price_list:\n",
" df[col] = df[col].fillna(\"0\")\n",
" df[col] = df[col].apply(lambda x: float(re.compile('[^0-9eE.]').sub('', x)) if len(x)>0 else 0)\n",
"\n",
"df['host_since'] = pd.to_datetime(df['host_since'])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "V2_un0LhZXEU",
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"data": {
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"0 65.0\n",
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\n"
},
"metadata": {}
}
],
"source": [
"## Winsorize for high price values, outliers.\n",
"\n",
"df.boxplot(column=\"price\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"id": "0rk7c4VuZXEW",
"outputId": "ecd79759-9f5d-4f92-8fe7-bdefa6b3e181",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"13.808558337216192"
]
},
"metadata": {},
"execution_count": 10
}
],
"source": [
"## this is high, because we have a price we expect it to be high.\n",
"## however, it shouldn't be much above 3.\n",
"df[\"price\"].skew()"
]
},
{
"cell_type": "code",
"source": [
"# df[\"price\"]].clip(low_entry, high_entry)"
],
"metadata": {
"id": "zkRM_IsQpnjy"
},
"execution_count": 11,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df[\"price\"].max()"
],
"metadata": {
"id": "MnGNC0LZpknd",
"outputId": "89e399a3-9c9e-4bb1-e548-d2f5bce388e7",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"12999.0"
]
},
"metadata": {},
"execution_count": 12
}
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"id": "0pHgBvGwZXEX",
"outputId": "263c272d-e49f-4a4b-d8e9-437e1b5b2f01",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"1600.0"
]
},
"metadata": {},
"execution_count": 13
}
],
"source": [
"## This value is still relatively high\n",
"df[\"price\"].quantile(0.995) ## @99.5%"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"id": "W4BY2ErJZXEX"
},
"outputs": [],
"source": [
"df = df[df[\"price\"]<df[\"price\"].quantile(0.995)].reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"id": "6VOcuojRZXEY",
"outputId": "79118e34-5753-4c20-d01f-c7b0bf5defdd",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"2.957872457159033"
]
},
"metadata": {},
"execution_count": 15
}
],
"source": [
"## This would do for now, it might also be worth transforming ..\n",
"## .. the price with a log function at a later stage\n",
"df[\"price\"].skew()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"id": "1m15b3s9ZXEZ",
"outputId": "5c234b8b-2ec0-4dec-87e1-56e67a31d77f",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 711
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"price 0\n",
"city 32\n",
"longitude 0\n",
"latitude 0\n",
"review_scores_rating 7466\n",
"number_of_reviews 0\n",
"minimum_nights 0\n",
"security_deposit 0\n",
"cleaning_fee 0\n",
"accommodates 0\n",
"bathrooms 22\n",
"bedrooms 8\n",
"beds 33\n",
"property_type 0\n",
"room_type 0\n",
"availability_365 0\n",
"host_identity_verified 34\n",
"host_is_superhost 34\n",
"host_since 34\n",
"cancellation_policy 0\n",
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"<class 'pandas.core.frame.DataFrame'>\n",
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" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
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"availability_365\n",
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" price longitude latitude review_scores_rating \\\n",
"count 26931.000000 26931.000000 26931.000000 19465.000000 \n",
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"75% 225.000000 151.264706 -33.832189 100.000000 \n",
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"\n",
" number_of_reviews minimum_nights security_deposit cleaning_fee \\\n",
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"75% 13.000000 5.000000 400.000000 99.000000 \n",
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"\n",
" accommodates bathrooms bedrooms beds \\\n",
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"mean 3.357395 1.340964 1.600787 1.996542 \n",
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"50% 2.000000 1.000000 1.000000 1.000000 \n",
"75% 4.000000 1.500000 2.000000 2.000000 \n",
"max 16.000000 10.000000 46.000000 29.000000 \n",
"std 2.160004 0.638187 1.091213 1.506535 \n",
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" availability_365 host_since \n",
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"mean 101.575916 2015-02-08 18:54:11.604268032 \n",
"min 0.000000 2009-01-10 00:00:00 \n",
"25% 0.000000 2014-01-12 00:00:00 \n",
"50% 32.000000 2015-03-31 00:00:00 \n",
"75% 179.000000 2016-05-01 00:00:00 \n",
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" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-f324cc15-4c0f-4859-92da-3d4b81d9619c button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
"\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"summary": "{\n \"name\": \"df\",\n \"rows\": 8,\n \"fields\": [\n {\n \"column\": \"price\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 9412.879043283101,\n \"min\": 0.0,\n \"max\": 26931.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 196.0654635921429,\n 225.0,\n 26931.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"longitude\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 9475.886085523161,\n \"min\": 0.0794247918478336,\n \"max\": 26931.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 151.21043827135273,\n 151.26470634999998,\n 26931.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"latitude\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 9531.803198219171,\n \"min\": -34.1352122,\n \"max\": 26931.0,\n \"num_unique_values\": 8,\n \"samples\": [\n -33.8626747576295,\n -33.83218909,\n 26931.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"review_scores_rating\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 6856.319814410391,\n \"min\": 9.358515309740715,\n \"max\": 19465.0,\n \"num_unique_values\": 7,\n \"samples\": [\n 19465.0,\n 93.40493192910353,\n 100.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"number_of_reviews\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 9496.187312567057,\n \"min\": 0.0,\n \"max\": 26931.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 14.070030819501689,\n 13.0,\n 26931.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"minimum_nights\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 9476.033540525126,\n \"min\": 1.0,\n \"max\": 26931.0,\n \"num_unique_values\": 7,\n \"samples\": [\n 26931.0,\n 4.482009580037874,\n 1000.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"security_deposit\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 9412.540543654524,\n \"min\": 0.0,\n \"max\": 26931.0,\n \"num_unique_values\": 6,\n \"samples\": [\n 26931.0,\n 293.8702610374661,\n 549.6422023135884\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"cleaning_fee\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 9462.403331600111,\n \"min\": 0.0,\n \"max\": 26931.0,\n \"num_unique_values\": 7,\n \"samples\": [\n 26931.0,\n 65.26868664364487,\n 999.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"accommodates\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 9520.006230353209,\n \"min\": 1.0,\n \"max\": 26931.0,\n \"num_unique_values\": 7,\n \"samples\": [\n 26931.0,\n 3.3573948238089932,\n 16.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"bathrooms\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 9512.98691460894,\n \"min\": 0.0,\n \"max\": 26909.0,\n \"num_unique_values\": 7,\n \"samples\": [\n 26909.0,\n 1.3409639897432086,\n 10.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"bedrooms\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 9516.069565964855,\n \"min\": 0.0,\n \"max\": 26923.0,\n \"num_unique_values\": 7,\n \"samples\": [\n 26923.0,\n 1.6007874308212309,\n 46.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"beds\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 9508.040396383854,\n \"min\": 0.0,\n \"max\": 26898.0,\n \"num_unique_values\": 7,\n \"samples\": [\n 26898.0,\n 1.996542493865715,\n 29.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"availability_365\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 9481.623066311528,\n \"min\": 0.0,\n \"max\": 26931.0,\n \"num_unique_values\": 7,\n \"samples\": [\n 26931.0,\n 101.57591623036649,\n 365.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"host_since\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"1970-01-01 00:00:00.000026897\",\n \"max\": \"2018-12-01 00:00:00\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"26897\",\n \"2015-02-08 18:54:11.604268032\",\n \"2016-05-01 00:00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 19
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"id": "Uy_enZlZZXEd",
"outputId": "037ed6a2-bd11-4a1e-b96a-c5d3fa89c399",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 936
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Saving figure attribute_histogram_plots\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 2000x1500 with 9 Axes>"
],
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},
"metadata": {}
}
],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"\n",
"try:\n",
" df.iloc[:,6:].hist(bins=50, figsize=(20,15))\n",
" save_fig(\"attribute_histogram_plots\")\n",
" plt.show()\n",
"except AttributeError:\n",
" pass\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"id": "GgkH-ZWzZXEe",
"outputId": "407e0742-20d2-4b24-8472-829b8590c612",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 429
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"city\n",
"Bondi Beach 1671\n",
"Manly 958\n",
"Surry Hills 919\n",
"Bondi 785\n",
"Randwick 684\n",
"Sydney 682\n",
"Coogee 675\n",
"Darlinghurst 660\n",
"North Bondi 629\n",
"Newtown 490\n",
"Name: count, dtype: int64"
],
"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>count</th>\n",
" </tr>\n",
" <tr>\n",
" <th>city</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Bondi Beach</th>\n",
" <td>1671</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Manly</th>\n",
" <td>958</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Surry Hills</th>\n",
" <td>919</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bondi</th>\n",
" <td>785</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Randwick</th>\n",
" <td>684</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sydney</th>\n",
" <td>682</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Coogee</th>\n",
" <td>675</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Darlinghurst</th>\n",
" <td>660</td>\n",
" </tr>\n",
" <tr>\n",
" <th>North Bondi</th>\n",
" <td>629</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Newtown</th>\n",
" <td>490</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div><br><label><b>dtype:</b> int64</label>"
]
},
"metadata": {},
"execution_count": 21
}
],
"source": [
"## Even though our customer, sepecifcally wants information about..\n",
"## .. Bondi the addition of other areas will help the final prediction\n",
"\n",
"df[\"city\"].value_counts().head(10)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"id": "OvITLiauZXEf"
},
"outputs": [],
"source": [
"## For this taks we will keep the top 20 Sydney locations\n",
"\n",
"list_of_20 = list(df[\"city\"].value_counts().head(10).index)\n",
"df = df[df[\"city\"].isin(list_of_20)].reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"id": "gbbpk03GZXEf",
"outputId": "b8dea130-5b44-4dad-f32b-e4922081256a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 931
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"property_type\n",
"Apartment 5970\n",
"House 1497\n",
"Townhouse 271\n",
"Condominium 115\n",
"Loft 59\n",
"Guest suite 44\n",
"Other 33\n",
"Hostel 30\n",
"Bed and breakfast 25\n",
"Guesthouse 24\n",
"Serviced apartment 23\n",
"Villa 16\n",
"Bungalow 7\n",
"Boutique hotel 6\n",
"Cottage 6\n",
"Tent 6\n",
"Tiny house 5\n",
"Resort 5\n",
"Hotel 3\n",
"Cabin 2\n",
"Yurt 1\n",
"Camper/RV 1\n",
"Chalet 1\n",
"Aparthotel 1\n",
"Earth house 1\n",
"Houseboat 1\n",
"Name: count, dtype: int64"
],
"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>count</th>\n",
" </tr>\n",
" <tr>\n",
" <th>property_type</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Apartment</th>\n",
" <td>5970</td>\n",
" </tr>\n",
" <tr>\n",
" <th>House</th>\n",
" <td>1497</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Townhouse</th>\n",
" <td>271</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Condominium</th>\n",
" <td>115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Loft</th>\n",
" <td>59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Guest suite</th>\n",
" <td>44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Other</th>\n",
" <td>33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hostel</th>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bed and breakfast</th>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Guesthouse</th>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Serviced apartment</th>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Villa</th>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bungalow</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Boutique hotel</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cottage</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tent</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tiny house</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Resort</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hotel</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cabin</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Yurt</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Camper/RV</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Chalet</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Aparthotel</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Earth house</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Houseboat</th>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div><br><label><b>dtype:</b> int64</label>"
]
},
"metadata": {},
"execution_count": 23
}
],
"source": [
"df[\"property_type\"].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"id": "0TnvlsBUZXEf"
},
"outputs": [],
"source": [
"## Remove rare occurences in categories as is necessary for..\n",
"## .. the eventaul cross validation step, the below step is somewhat ..\n",
"## .. similar for what has been done with cities above\n",
"\n",
"item_counts = df.groupby(['property_type']).size()\n",
"rare_items = list(item_counts.loc[item_counts <= 10].index.values)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"id": "A5W30DNnZXEf"
},
"outputs": [],
"source": [
"df = df[~df[\"property_type\"].isin(rare_items)].reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"id": "kt9-HbbXZXEf"
},
"outputs": [],
"source": [
"# to make this notebook's output identical at every run\n",
"np.random.seed(42)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"id": "lq-WJp8TZXEg"
},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"# For illustration only. Sklearn has train_test_split()\n",
"def split_train_test(data, test_ratio):\n",
" shuffled_indices = np.random.permutation(len(data))\n",
" test_set_size = int(len(data) * test_ratio)\n",
" test_indices = shuffled_indices[:test_set_size]\n",
" train_indices = shuffled_indices[test_set_size:]\n",
" return data.iloc[train_indices], data.iloc[test_indices]"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"id": "5Hrqdh3RZXEg",
"outputId": "8e4a95b4-7e28-44a1-ddb4-3199ab63d540",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"6486 train + 1621 test\n"
]
}
],
"source": [
"train_set, test_set = split_train_test(df, 0.2)\n",
"print(len(train_set), \"train +\", len(test_set), \"test\")"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"id": "79eD-QSnZXEg"
},
"outputs": [],
"source": [
"from zlib import crc32\n",
"\n",
"def test_set_check(identifier, test_ratio):\n",
" return crc32(np.int64(identifier)) & 0xffffffff < test_ratio * 2**32\n",
"\n",
"def split_train_test_by_id(data, test_ratio, id_column):\n",
" ids = data[id_column]\n",
" in_test_set = ids.apply(lambda id_: test_set_check(id_, test_ratio))\n",
" return data.loc[~in_test_set], data.loc[in_test_set]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "J0AASY24ZXEh"
},
"source": [
"The implementation of `test_set_check()` above works fine in both Python 2 and Python 3. In earlier releases, the following implementation was proposed, which supported any hash function, but was much slower and did not support Python 2:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"id": "iC7rt_JuZXEh"
},
"outputs": [],
"source": [
"import hashlib\n",
"\n",
"def test_set_check(identifier, test_ratio, hash=hashlib.md5):\n",
" return hash(np.int64(identifier)).digest()[-1] < 256 * test_ratio"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fRgaKim3ZXEh"
},
"source": [
"If you want an implementation that supports any hash function and is compatible with both Python 2 and Python 3, here is one:"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"id": "V8hHq3PaZXEh"
},
"outputs": [],
"source": [
"def test_set_check(identifier, test_ratio, hash=hashlib.md5):\n",
" return bytearray(hash(np.int64(identifier)).digest())[-1] < 256 * test_ratio"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"id": "c85a4a--ZXEi"
},
"outputs": [],
"source": [
"df_with_id = df.reset_index() # adds an `index` column\n",
"train_set, test_set = split_train_test_by_id(df_with_id, 0.2, \"index\")"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"id": "7a3cLzzMZXEi"
},
"outputs": [],
"source": [
"df_with_id[\"id\"] = df[\"longitude\"] * 1000 + df_with_id[\"latitude\"]\n",
"train_set, test_set = split_train_test_by_id(df_with_id, 0.2, \"id\")"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"id": "RJ4scQdlZXEi",
"outputId": "55730eed-7c1a-40b6-c8d8-34c95188c0b6",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 313
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" index price city longitude latitude review_scores_rating \\\n",
"0 0 111.0 Darlinghurst 151.216541 -33.880455 88.0 \n",
"4 4 130.0 Bondi Beach 151.273084 -33.891846 95.0 \n",
"5 5 111.0 Sydney 151.268865 -33.885690 89.0 \n",
"9 9 990.0 Coogee 151.260116 -33.914816 98.0 \n",
"12 12 202.0 Bondi 151.268418 -33.895158 91.0 \n",
"\n",
" number_of_reviews minimum_nights security_deposit cleaning_fee \\\n",
"0 272 2 0.0 0.0 \n",
"4 119 4 200.0 60.0 \n",
"5 11 4 0.0 100.0 \n",
"9 13 7 3000.0 0.0 \n",
"12 90 1 1000.0 150.0 \n",
"\n",
" accommodates bathrooms bedrooms beds property_type room_type \\\n",
"0 2 1.0 1.0 1.0 Apartment Private room \n",
"4 2 1.0 1.0 1.0 Apartment Entire home/apt \n",
"5 4 1.0 2.0 2.0 Apartment Entire home/apt \n",
"9 12 5.0 6.0 6.0 Villa Entire home/apt \n",
"12 4 1.0 2.0 2.0 Apartment Entire home/apt \n",
"\n",
" availability_365 host_identity_verified host_is_superhost host_since \\\n",
"0 285 t f 2009-03-12 \n",
"4 94 t t 2012-01-18 \n",
"5 14 f f 2010-12-14 \n",
"9 33 t f 2011-10-02 \n",
"12 204 f f 2011-03-31 \n",
"\n",
" cancellation_policy id \n",
"0 strict_14_with_grace_period 151182.660345 \n",
"4 strict_14_with_grace_period 151239.192454 \n",
"5 strict_14_with_grace_period 151234.979210 \n",
"9 strict_14_with_grace_period 151226.201484 \n",
"12 strict_14_with_grace_period 151234.523342 "
],
"text/html": [
"\n",
" <div id=\"df-b3368830-73fe-40b1-bf95-dbb3c6e8c87e\" class=\"colab-df-container\">\n",
" <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>index</th>\n",
" <th>price</th>\n",
" <th>city</th>\n",
" <th>longitude</th>\n",
" <th>latitude</th>\n",
" <th>review_scores_rating</th>\n",
" <th>number_of_reviews</th>\n",
" <th>minimum_nights</th>\n",
" <th>security_deposit</th>\n",
" <th>cleaning_fee</th>\n",
" <th>accommodates</th>\n",
" <th>bathrooms</th>\n",
" <th>bedrooms</th>\n",
" <th>beds</th>\n",
" <th>property_type</th>\n",
" <th>room_type</th>\n",
" <th>availability_365</th>\n",
" <th>host_identity_verified</th>\n",
" <th>host_is_superhost</th>\n",
" <th>host_since</th>\n",
" <th>cancellation_policy</th>\n",
" <th>id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>111.0</td>\n",
" <td>Darlinghurst</td>\n",
" <td>151.216541</td>\n",
" <td>-33.880455</td>\n",
" <td>88.0</td>\n",
" <td>272</td>\n",
" <td>2</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>Apartment</td>\n",
" <td>Private room</td>\n",
" <td>285</td>\n",
" <td>t</td>\n",
" <td>f</td>\n",
" <td>2009-03-12</td>\n",
" <td>strict_14_with_grace_period</td>\n",
" <td>151182.660345</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>130.0</td>\n",
" <td>Bondi Beach</td>\n",
" <td>151.273084</td>\n",
" <td>-33.891846</td>\n",
" <td>95.0</td>\n",
" <td>119</td>\n",
" <td>4</td>\n",
" <td>200.0</td>\n",
" <td>60.0</td>\n",
" <td>2</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>Apartment</td>\n",
" <td>Entire home/apt</td>\n",
" <td>94</td>\n",
" <td>t</td>\n",
" <td>t</td>\n",
" <td>2012-01-18</td>\n",
" <td>strict_14_with_grace_period</td>\n",
" <td>151239.192454</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5</td>\n",
" <td>111.0</td>\n",
" <td>Sydney</td>\n",
" <td>151.268865</td>\n",
" <td>-33.885690</td>\n",
" <td>89.0</td>\n",
" <td>11</td>\n",
" <td>4</td>\n",
" <td>0.0</td>\n",
" <td>100.0</td>\n",
" <td>4</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>Apartment</td>\n",
" <td>Entire home/apt</td>\n",
" <td>14</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>2010-12-14</td>\n",
" <td>strict_14_with_grace_period</td>\n",
" <td>151234.979210</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>9</td>\n",
" <td>990.0</td>\n",
" <td>Coogee</td>\n",
" <td>151.260116</td>\n",
" <td>-33.914816</td>\n",
" <td>98.0</td>\n",
" <td>13</td>\n",
" <td>7</td>\n",
" <td>3000.0</td>\n",
" <td>0.0</td>\n",
" <td>12</td>\n",
" <td>5.0</td>\n",
" <td>6.0</td>\n",
" <td>6.0</td>\n",
" <td>Villa</td>\n",
" <td>Entire home/apt</td>\n",
" <td>33</td>\n",
" <td>t</td>\n",
" <td>f</td>\n",
" <td>2011-10-02</td>\n",
" <td>strict_14_with_grace_period</td>\n",
" <td>151226.201484</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>12</td>\n",
" <td>202.0</td>\n",
" <td>Bondi</td>\n",
" <td>151.268418</td>\n",
" <td>-33.895158</td>\n",
" <td>91.0</td>\n",
" <td>90</td>\n",
" <td>1</td>\n",
" <td>1000.0</td>\n",
" <td>150.0</td>\n",
" <td>4</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>Apartment</td>\n",
" <td>Entire home/apt</td>\n",
" <td>204</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>2011-03-31</td>\n",
" <td>strict_14_with_grace_period</td>\n",
" <td>151234.523342</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <div class=\"colab-df-buttons\">\n",
"\n",
" <div class=\"colab-df-container\">\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b3368830-73fe-40b1-bf95-dbb3c6e8c87e')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
"\n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
" </svg>\n",
" </button>\n",
"\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" .colab-df-buttons div {\n",
" margin-bottom: 4px;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-b3368830-73fe-40b1-bf95-dbb3c6e8c87e button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-b3368830-73fe-40b1-bf95-dbb3c6e8c87e');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
"\n",
"\n",
"<div id=\"df-b0b8062c-0658-4fc7-861c-58ecae9ebe4d\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b0b8062c-0658-4fc7-861c-58ecae9ebe4d')\"\n",
" title=\"Suggest charts\"\n",
" style=\"display:none;\">\n",
"\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <g>\n",
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
" </g>\n",
"</svg>\n",
" </button>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" --bg-color: #E8F0FE;\n",
" --fill-color: #1967D2;\n",
" --hover-bg-color: #E2EBFA;\n",
" --hover-fill-color: #174EA6;\n",
" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
" --fill-color: #D2E3FC;\n",
" --hover-bg-color: #434B5C;\n",
" --hover-fill-color: #FFFFFF;\n",
" --disabled-bg-color: #3B4455;\n",
" --disabled-fill-color: #666;\n",
" }\n",
"\n",
" .colab-df-quickchart {\n",
" background-color: var(--bg-color);\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-b0b8062c-0658-4fc7-861c-58ecae9ebe4d button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
"\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "test_set"
}
},
"metadata": {},
"execution_count": 34
}
],
"source": [
"test_set.head()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"id": "eyyGG0fcZXEj"
},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"train_set, test_set = train_test_split(df, test_size=0.2, random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"id": "v9LZBp0fZXEj",
"outputId": "b121db9b-6fd9-401a-e9cf-989ce044de80",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 313
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" price city longitude latitude review_scores_rating \\\n",
"4084 68.0 North Bondi 151.279684 -33.884092 93.0 \n",
"965 128.0 Surry Hills 151.212610 -33.891416 100.0 \n",
"8100 115.0 Darlinghurst 151.217882 -33.874271 98.0 \n",
"3882 125.0 Sydney 151.204837 -33.875924 NaN \n",
"1010 250.0 North Bondi 151.274298 -33.885652 100.0 \n",
"\n",
" number_of_reviews minimum_nights security_deposit cleaning_fee \\\n",
"4084 3 7 150.0 0.0 \n",
"965 4 5 690.0 99.0 \n",
"8100 8 2 0.0 30.0 \n",
"3882 0 2 150.0 50.0 \n",
"1010 4 2 0.0 80.0 \n",
"\n",
" accommodates bathrooms bedrooms beds property_type room_type \\\n",
"4084 2 2.5 1.0 1.0 House Private room \n",
"965 4 1.0 2.0 2.0 Townhouse Entire home/apt \n",
"8100 3 1.0 1.0 1.0 Apartment Entire home/apt \n",
"3882 4 1.0 1.0 3.0 Other Shared room \n",
"1010 2 1.0 1.0 1.0 Apartment Entire home/apt \n",
"\n",
" availability_365 host_identity_verified host_is_superhost host_since \\\n",
"4084 4 t f 2016-08-18 \n",
"965 173 t t 2014-10-31 \n",
"8100 12 f f 2017-04-02 \n",
"3882 363 f f 2014-12-01 \n",
"1010 363 t f 2012-09-29 \n",
"\n",
" cancellation_policy \n",
"4084 strict_14_with_grace_period \n",
"965 moderate \n",
"8100 moderate \n",
"3882 flexible \n",
"1010 strict_14_with_grace_period "
],
"text/html": [
"\n",
" <div id=\"df-aed13e53-2c6c-4f49-806a-58475196302b\" class=\"colab-df-container\">\n",
" <div>\n",
"<style scoped>\n",
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" 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>price</th>\n",
" <th>city</th>\n",
" <th>longitude</th>\n",
" <th>latitude</th>\n",
" <th>review_scores_rating</th>\n",
" <th>number_of_reviews</th>\n",
" <th>minimum_nights</th>\n",
" <th>security_deposit</th>\n",
" <th>cleaning_fee</th>\n",
" <th>accommodates</th>\n",
" <th>bathrooms</th>\n",
" <th>bedrooms</th>\n",
" <th>beds</th>\n",
" <th>property_type</th>\n",
" <th>room_type</th>\n",
" <th>availability_365</th>\n",
" <th>host_identity_verified</th>\n",
" <th>host_is_superhost</th>\n",
" <th>host_since</th>\n",
" <th>cancellation_policy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4084</th>\n",
" <td>68.0</td>\n",
" <td>North Bondi</td>\n",
" <td>151.279684</td>\n",
" <td>-33.884092</td>\n",
" <td>93.0</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>150.0</td>\n",
" <td>0.0</td>\n",
" <td>2</td>\n",
" <td>2.5</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>House</td>\n",
" <td>Private room</td>\n",
" <td>4</td>\n",
" <td>t</td>\n",
" <td>f</td>\n",
" <td>2016-08-18</td>\n",
" <td>strict_14_with_grace_period</td>\n",
" </tr>\n",
" <tr>\n",
" <th>965</th>\n",
" <td>128.0</td>\n",
" <td>Surry Hills</td>\n",
" <td>151.212610</td>\n",
" <td>-33.891416</td>\n",
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" <td>99.0</td>\n",
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" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>Townhouse</td>\n",
" <td>Entire home/apt</td>\n",
" <td>173</td>\n",
" <td>t</td>\n",
" <td>t</td>\n",
" <td>2014-10-31</td>\n",
" <td>moderate</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8100</th>\n",
" <td>115.0</td>\n",
" <td>Darlinghurst</td>\n",
" <td>151.217882</td>\n",
" <td>-33.874271</td>\n",
" <td>98.0</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>0.0</td>\n",
" <td>30.0</td>\n",
" <td>3</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>Apartment</td>\n",
" <td>Entire home/apt</td>\n",
" <td>12</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>2017-04-02</td>\n",
" <td>moderate</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3882</th>\n",
" <td>125.0</td>\n",
" <td>Sydney</td>\n",
" <td>151.204837</td>\n",
" <td>-33.875924</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>150.0</td>\n",
" <td>50.0</td>\n",
" <td>4</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>Other</td>\n",
" <td>Shared room</td>\n",
" <td>363</td>\n",
" <td>f</td>\n",
" <td>f</td>\n",
" <td>2014-12-01</td>\n",
" <td>flexible</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1010</th>\n",
" <td>250.0</td>\n",
" <td>North Bondi</td>\n",
" <td>151.274298</td>\n",
" <td>-33.885652</td>\n",
" <td>100.0</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>0.0</td>\n",
" <td>80.0</td>\n",
" <td>2</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>Apartment</td>\n",
" <td>Entire home/apt</td>\n",
" <td>363</td>\n",
" <td>t</td>\n",
" <td>f</td>\n",
" <td>2012-09-29</td>\n",
" <td>strict_14_with_grace_period</td>\n",
" </tr>\n",
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"</div>\n",
" <div class=\"colab-df-buttons\">\n",
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"\n",
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" display:flex;\n",
" gap: 12px;\n",
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"\n",
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" fill: #1967D2;\n",
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"\n",
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" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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"\n",
" [theme=dark] .colab-df-convert:hover {\n",
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" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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"\n",
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" const buttonEl =\n",
" document.querySelector('#df-aed13e53-2c6c-4f49-806a-58475196302b button.colab-df-convert');\n",
" buttonEl.style.display =\n",
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"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-aed13e53-2c6c-4f49-806a-58475196302b');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
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"\n",
"\n",
"<div id=\"df-d66e2bc7-264b-47cc-8e95-9882a6b6dcdf\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d66e2bc7-264b-47cc-8e95-9882a6b6dcdf')\"\n",
" title=\"Suggest charts\"\n",
" style=\"display:none;\">\n",
"\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
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"</svg>\n",
" </button>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" --bg-color: #E8F0FE;\n",
" --fill-color: #1967D2;\n",
" --hover-bg-color: #E2EBFA;\n",
" --hover-fill-color: #174EA6;\n",
" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
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" --hover-bg-color: #434B5C;\n",
" --hover-fill-color: #FFFFFF;\n",
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" border: none;\n",
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" display: none;\n",
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" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
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" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-d66e2bc7-264b-47cc-8e95-9882a6b6dcdf button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
"\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "test_set",
"summary": "{\n \"name\": \"test_set\",\n \"rows\": 1622,\n \"fields\": [\n {\n \"column\": \"price\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 180.38478701197207,\n \"min\": 5.0,\n \"max\": 1501.0,\n \"num_unique_values\": 274,\n \"samples\": [\n 97.0,\n 258.0,\n 159.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"city\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 10,\n \"samples\": [\n \"Bondi\",\n \"Surry Hills\",\n \"Coogee\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"longitude\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.03317078961059382,\n \"min\": 151.0858611,\n \"max\": 151.2993822,\n \"num_unique_values\": 1621,\n \"samples\": [\n 151.2589254,\n 151.2107123,\n 151.2719353\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"latitude\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.035030312101295535,\n \"min\": -33.94692212,\n \"max\": -33.7438004,\n \"num_unique_values\": 1622,\n \"samples\": [\n -33.89083358,\n -33.88458531,\n -33.90481042\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"review_scores_rating\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 9.088291826630455,\n \"min\": 20.0,\n \"max\": 100.0,\n \"num_unique_values\": 36,\n \"samples\": [\n 53.0,\n 97.0,\n 78.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"number_of_reviews\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 30,\n \"min\": 0,\n \"max\": 343,\n \"num_unique_values\": 127,\n \"samples\": [\n 7,\n 208,\n 99\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"minimum_nights\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 16,\n \"min\": 1,\n \"max\": 500,\n \"num_unique_values\": 32,\n \"samples\": [\n 28,\n 6,\n 21\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"security_deposit\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 496.99506570040796,\n \"min\": 0.0,\n \"max\": 5000.0,\n \"num_unique_values\": 64,\n \"samples\": [\n 449.0,\n 330.0,\n 150.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"cleaning_fee\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 74.41298753885317,\n \"min\": 0.0,\n \"max\": 495.0,\n \"num_unique_values\": 113,\n \"samples\": [\n 212.0,\n 80.0,\n 350.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"accommodates\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 1,\n \"max\": 12,\n \"num_unique_values\": 12,\n \"samples\": [\n 12,\n 10,\n 2\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"bathrooms\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.5334981406406737,\n \"min\": 0.0,\n \"max\": 5.0,\n \"num_unique_values\": 10,\n \"samples\": [\n 0.5,\n 1.0,\n 3.5\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"bedrooms\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.9230015356784018,\n \"min\": 0.0,\n \"max\": 6.0,\n \"num_unique_values\": 7,\n \"samples\": [\n 1.0,\n 2.0,\n 5.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"beds\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.3014713879778912,\n \"min\": 0.0,\n \"max\": 12.0,\n \"num_unique_values\": 11,\n \"samples\": [\n 0.0,\n 1.0,\n 10.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"property_type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 12,\n \"samples\": [\n \"Serviced apartment\",\n \"Villa\",\n \"House\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"room_type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"Private room\",\n \"Entire home/apt\",\n \"Shared room\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"availability_365\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 120,\n \"min\": 0,\n \"max\": 365,\n \"num_unique_values\": 297,\n \"samples\": [\n 163,\n 346,\n 71\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"host_identity_verified\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"f\",\n \"t\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"host_is_superhost\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"t\",\n \"f\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"host_since\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"2009-03-22 00:00:00\",\n \"max\": \"2018-11-01 00:00:00\",\n \"num_unique_values\": 1049,\n \"samples\": [\n \"2016-06-15 00:00:00\",\n \"2013-12-26 00:00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"cancellation_policy\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"moderate\",\n \"super_strict_60\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 36
}
],
"source": [
"test_set.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HuKh50cyZXEj"
},
"source": [
"The models that would be used in this project can't read textual data, thus we have to turn text categories into numeric categories. The code below will create city codes, this time for the purpose of statified sampeing.\n"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"id": "Z70O8_ZrZXEk"
},
"outputs": [],
"source": [
"from sklearn import preprocessing\n",
"le = preprocessing.LabelEncoder()\n",
"\n",
"for col in [\"city\"]:\n",
" df[col+\"_code\"] = le.fit_transform(df[col])\n"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"id": "oGpC3nWIZXEk"
},
"outputs": [],
"source": [
"## Similar to above encoding, here we encode binary 1, 0 for t and f.\n",
"\n",
"df[\"host_identity_verified\"] = df[\"host_identity_verified\"].apply(lambda x: 1 if x==\"t\" else 0)\n",
"df[\"host_is_superhost\"] = df[\"host_is_superhost\"].apply(lambda x: 1 if x==\"t\" else 0)\n"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"id": "k9feliBNZXEk"
},
"outputs": [],
"source": [
"from sklearn.model_selection import StratifiedShuffleSplit\n",
"\n",
"## we will stratify according to city\n",
"\n",
"split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
"for train_index, test_index in split.split(df, df[\"city_code\"]):\n",
" del df[\"city_code\"]\n",
" strat_train_set = df.loc[train_index]\n",
" strat_test_set = df.loc[test_index]"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"id": "0eDCFB1vZXEl",
"outputId": "b8cba7a6-e723-4213-cfcc-80a5477ccfea",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 429
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"city\n",
"Bondi 198.745223\n",
"Bondi Beach 199.879880\n",
"Coogee 196.574627\n",
"Darlinghurst 184.700000\n",
"Manly 223.447368\n",
"Newtown 117.938776\n",
"North Bondi 248.857143\n",
"Randwick 178.072993\n",
"Surry Hills 175.732240\n",
"Sydney 193.962687\n",
"Name: price, dtype: float64"
],
"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>price</th>\n",
" </tr>\n",
" <tr>\n",
" <th>city</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Bondi</th>\n",
" <td>198.745223</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bondi Beach</th>\n",
" <td>199.879880</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Coogee</th>\n",
" <td>196.574627</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Darlinghurst</th>\n",
" <td>184.700000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Manly</th>\n",
" <td>223.447368</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Newtown</th>\n",
" <td>117.938776</td>\n",
" </tr>\n",
" <tr>\n",
" <th>North Bondi</th>\n",
" <td>248.857143</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Randwick</th>\n",
" <td>178.072993</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Surry Hills</th>\n",
" <td>175.732240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sydney</th>\n",
" <td>193.962687</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div><br><label><b>dtype:</b> float64</label>"
]
},
"metadata": {},
"execution_count": 40
}
],
"source": [
"## Average price per area\n",
"strat_test_set.groupby(\"city\")[\"price\"].mean()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JDojCQEeZXEl"
},
"source": [
"# Discover and visualize the data to gain insights"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"id": "XpoW0SnUZXEl"
},
"outputs": [],
"source": [
"traval = strat_train_set.copy() ##traval - training and validation set"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"id": "a1uLDcx_ZXEl",
"outputId": "cf4ff7de-a7cd-4703-abb9-b5976e92bc23",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 504
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Saving figure bad_visualization_plot\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"traval.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\")\n",
"save_fig(\"bad_visualization_plot\")"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"id": "qIV__NDiZXEm",
"outputId": "e2f51d1b-a577-433e-c6ee-96c83905643d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 504
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Saving figure better_visualization_plot\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"traval.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.1)\n",
"save_fig(\"better_visualization_plot\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nUK4fLMkZXEm"
},
"source": [
"The argument `sharex=False` fixes a display bug (the x-axis values and legend were not displayed). This is a temporary fix (see: https://github.com/pandas-dev/pandas/issues/10611). Thanks to Wilmer Arellano for pointing it out."
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"id": "LmGs6YM9ZXEm"
},
"outputs": [],
"source": [
"traval_co = traval[(traval[\"longitude\"]>151.16)&(traval[\"latitude\"]<-33.75)].reset_index(drop=True)\n",
"\n",
"traval_co = traval_co[traval_co[\"latitude\"]>-33.95].reset_index(drop=True)\n",
"\n",
"traval_co = traval_co[traval_co[\"price\"]<600].reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"id": "texzSnfjZXEn",
"outputId": "018bb894-4c6b-4d1d-aa86-bd7259e0d796",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 724
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Saving figure housing_prices_scatterplot\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x700 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"traval_co.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.5,\n",
" s=traval_co[\"number_of_reviews\"]/2, label=\"Reviews\", figsize=(10,7),\n",
" c=\"price\", cmap=plt.get_cmap(\"jet\"), colorbar=True,\n",
" sharex=False)\n",
"plt.legend()\n",
"save_fig(\"housing_prices_scatterplot\")"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"id": "R27lovILZXEo",
"outputId": "b6a85afa-c1cd-48a5-d6f7-140511ada6fa",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 540
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" price longitude latitude review_scores_rating \\\n",
"price 1.000000 0.157902 0.131160 0.067066 \n",
"longitude 0.157902 1.000000 0.300875 0.046203 \n",
"latitude 0.131160 0.300875 1.000000 -0.006279 \n",
"review_scores_rating 0.067066 0.046203 -0.006279 1.000000 \n",
"number_of_reviews -0.064011 -0.219291 0.005813 0.037707 \n",
"minimum_nights 0.022103 0.008496 0.008439 0.007951 \n",
"security_deposit 0.469423 0.076216 0.071935 0.030690 \n",
"cleaning_fee 0.529834 0.067458 0.060915 0.008525 \n",
"accommodates 0.674368 0.088599 0.073440 -0.034470 \n",
"bathrooms 0.553773 0.014081 0.058784 0.042580 \n",
"bedrooms 0.668963 0.158359 0.046626 0.043310 \n",
"beds 0.582378 0.090013 0.068230 -0.049052 \n",
"availability_365 0.148263 -0.024410 0.067270 -0.031196 \n",
"host_identity_verified 0.048821 0.017378 -0.004305 0.040461 \n",
"host_is_superhost -0.016695 -0.098048 0.016147 0.165590 \n",
"\n",
" number_of_reviews minimum_nights security_deposit \\\n",
"price -0.064011 0.022103 0.469423 \n",
"longitude -0.219291 0.008496 0.076216 \n",
"latitude 0.005813 0.008439 0.071935 \n",
"review_scores_rating 0.037707 0.007951 0.030690 \n",
"number_of_reviews 1.000000 -0.057559 -0.010459 \n",
"minimum_nights -0.057559 1.000000 0.078160 \n",
"security_deposit -0.010459 0.078160 1.000000 \n",
"cleaning_fee 0.027369 0.036996 0.508427 \n",
"accommodates 0.059822 0.009321 0.369833 \n",
"bathrooms -0.055478 0.018838 0.310215 \n",
"bedrooms -0.095475 0.033779 0.353373 \n",
"beds 0.029349 0.018626 0.318991 \n",
"availability_365 0.271525 0.013307 0.127233 \n",
"host_identity_verified 0.081821 -0.018161 0.085009 \n",
"host_is_superhost 0.384543 -0.040309 0.022787 \n",
"\n",
" cleaning_fee accommodates bathrooms bedrooms \\\n",
"price 0.529834 0.674368 0.553773 0.668963 \n",
"longitude 0.067458 0.088599 0.014081 0.158359 \n",
"latitude 0.060915 0.073440 0.058784 0.046626 \n",
"review_scores_rating 0.008525 -0.034470 0.042580 0.043310 \n",
"number_of_reviews 0.027369 0.059822 -0.055478 -0.095475 \n",
"minimum_nights 0.036996 0.009321 0.018838 0.033779 \n",
"security_deposit 0.508427 0.369833 0.310215 0.353373 \n",
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" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-c2d4413c-5be6-422e-b87a-d45ab8e0d070 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
"\n",
" <div id=\"id_2d65b2f1-7327-490a-854e-5b1e3943f841\">\n",
" <style>\n",
" .colab-df-generate {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-generate:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-generate {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-generate:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
" <button class=\"colab-df-generate\" onclick=\"generateWithVariable('corr_matrix')\"\n",
" title=\"Generate code using this dataframe.\"\n",
" style=\"display:none;\">\n",
"\n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
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" </button>\n",
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" (() => {\n",
" const buttonEl =\n",
" document.querySelector('#id_2d65b2f1-7327-490a-854e-5b1e3943f841 button.colab-df-generate');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" buttonEl.onclick = () => {\n",
" google.colab.notebook.generateWithVariable('corr_matrix');\n",
" }\n",
" })();\n",
" </script>\n",
" </div>\n",
"\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "corr_matrix",
"summary": "{\n \"name\": \"corr_matrix\",\n \"rows\": 15,\n \"fields\": [\n {\n \"column\": \"price\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.3247100569276463,\n \"min\": -0.06401086731426675,\n \"max\": 1.0,\n \"num_unique_values\": 15,\n \"samples\": [\n 0.5537725539807253,\n 0.5823782004936213,\n 1.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"longitude\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.2724012275454774,\n \"min\": -0.21929127603231524,\n \"max\": 1.0,\n \"num_unique_values\": 15,\n \"samples\": [\n 0.014081461929539891,\n 0.0900130812953897,\n 0.15790183875484015\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"latitude\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.25309727737664983,\n \"min\": -0.006279427970246807,\n \"max\": 1.0,\n \"num_unique_values\": 15,\n \"samples\": [\n 0.058784365932762546,\n 0.0682295136945938,\n 0.13116040235216517\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"review_scores_rating\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.25656037859153596,\n \"min\": -0.04905197289133972,\n \"max\": 1.0,\n \"num_unique_values\": 15,\n \"samples\": [\n 0.042580354917539066,\n -0.04905197289133972,\n 0.06706593066821302\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"number_of_reviews\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.2894427200376457,\n \"min\": -0.21929127603231524,\n \"max\": 1.0,\n \"num_unique_values\": 15,\n \"samples\": [\n -0.0554780653108212,\n 0.02934873122656267,\n -0.06401086731426675\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"minimum_nights\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.25758624467545876,\n \"min\": -0.05755851504891618,\n \"max\": 1.0,\n \"num_unique_values\": 15,\n \"samples\": [\n 0.01883780528705735,\n 0.01862596053756925,\n 0.02210263733761559\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"security_deposit\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.26884680398063837,\n \"min\": -0.010459309084105863,\n \"max\": 1.0,\n \"num_unique_values\": 15,\n \"samples\": [\n 0.31021543095587645,\n 0.3189911667763665,\n 0.46942251683060554\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"cleaning_fee\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.2858363434339762,\n \"min\": 0.008525327302863726,\n \"max\": 1.0,\n \"num_unique_values\": 15,\n \"samples\": [\n 0.3624233132558887,\n 0.44419661431126534,\n 0.5298343852210934\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"accommodates\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.3555606152551226,\n \"min\": -0.034469602288277536,\n \"max\": 1.0,\n \"num_unique_values\": 15,\n \"samples\": [\n 0.5051666129230334,\n 0.8630462380035905,\n 0.6743684741533621\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"bathrooms\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.3113583441749346,\n \"min\": -0.0554780653108212,\n \"max\": 1.0,\n \"num_unique_values\": 15,\n \"samples\": [\n 1.0,\n 0.4925027379894102,\n 0.5537725539807253\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"bedrooms\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.3562821519508754,\n \"min\": -0.0954749391783975,\n \"max\": 1.0,\n \"num_unique_values\": 15,\n \"samples\": [\n 0.561777912552696,\n 0.731870435832498,\n 0.6689630221985823\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"beds\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.3480609886022178,\n \"min\": -0.04905197289133972,\n \"max\": 1.0,\n \"num_unique_values\": 15,\n \"samples\": [\n 0.4925027379894102,\n 1.0,\n 0.5823782004936213\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"availability_365\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.24915642800908788,\n \"min\": -0.031195567699246005,\n \"max\": 1.0,\n \"num_unique_values\": 15,\n \"samples\": [\n 0.0228769030785698,\n 0.1273145388555082,\n 0.14826271474924402\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"host_identity_verified\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.24851288409337619,\n \"min\": -0.018160783464880877,\n \"max\": 1.0,\n \"num_unique_values\": 15,\n \"samples\": [\n 0.01628744100011867,\n 0.040250618966164754,\n 0.04882096238960308\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"host_is_superhost\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.27362243622351273,\n \"min\": -0.09804820246980865,\n \"max\": 1.0,\n \"num_unique_values\": 15,\n \"samples\": [\n -0.029078666476081098,\n -0.014862120776063019,\n -0.016694965556421526\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 49
}
],
"source": [
"import pandas as pd\n",
"\n",
"# Assume traval is your DataFrame\n",
"numeric_traval = traval.select_dtypes(include='number')\n",
"\n",
"# Compute the correlation matrix\n",
"corr_matrix = numeric_traval.corr()\n",
"\n",
"corr_matrix\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jg5vrs2EZXEo"
},
"outputs": [],
"source": [
"corr_matrix[\"price\"].sort_values(ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "tHoRs0SuZXEo"
},
"outputs": [],
"source": [
"# from pandas.tools.plotting import scatter_matrix # For older versions of Pandas\n",
"from pandas.plotting import scatter_matrix\n",
"\n",
"attributes = [\"price\", \"accommodates\", \"bedrooms\",\n",
" \"cleaning_fee\",\"review_scores_rating\"]\n",
"scatter_matrix(traval[attributes], figsize=(12, 8))\n",
"save_fig(\"scatter_matrix_plot\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "sv2J8-7PZXEp"
},
"outputs": [],
"source": [
"traval.plot(kind=\"scatter\", x=\"accommodates\", y=\"price\",\n",
" alpha=0.1)\n",
"save_fig(\"income_vs_house_value_scatterplot\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "PGPu3Zq_ZXEp"
},
"outputs": [],
"source": [
"traval.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "hcQJ17YIZXEp"
},
"outputs": [],
"source": [
"#### Some Feature Engineering"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "eQMO4aceZXEq"
},
"outputs": [],
"source": [
"traval[\"bedrooms_per_person\"] = traval[\"bedrooms\"]/traval[\"accommodates\"]\n",
"traval[\"bathrooms_per_person\"] = traval[\"bathrooms\"]/traval[\"accommodates\"]\n",
"traval['host_since'] = pd.to_datetime(traval['host_since'])\n",
"traval['days_on_airbnb'] = (pd.to_datetime('today') - traval['host_since']).dt.days"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "i82bqIMTZXEq"
},
"source": [
"# Prepare the data for Machine Learning algorithms"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "f-0iksJhZXEq"
},
"outputs": [],
"source": [
"## Here I will forget about traval and use a more formal way of introducing...\n",
"## ..preprocessin using pipelines"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-WH9zsrXZXEq"
},
"outputs": [],
"source": [
"X = traval.copy().drop(\"price\", axis=1) # drop labels for training set"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "xaXicvUAZXEr"
},
"outputs": [],
"source": [
"sample_incomplete_rows = X[X.isnull().any(axis=1)].head()\n",
"print(sample_incomplete_rows.shape)\n",
"sample_incomplete_rows"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "4XwodyVPZXEr"
},
"outputs": [],
"source": [
"# Rows Remove\n",
"sample_incomplete_rows.dropna(subset=[\"review_scores_rating\"]) # option 1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "eX4eI_xeZXEr"
},
"outputs": [],
"source": [
"# Columns Remove\n",
"sample_incomplete_rows.drop([\"review_scores_rating\"], axis=1) # option 2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "oF5OvJd8ZXEr"
},
"outputs": [],
"source": [
"median = X[\"review_scores_rating\"].median()\n",
"sample_incomplete_rows[\"review_scores_rating\"].fillna(median, inplace=True) # option 3\n",
"\n",
"sample_incomplete_rows"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "LRGC0OEFZXEs"
},
"outputs": [],
"source": [
"from sklearn.impute import SimpleImputer\n",
"imputer = SimpleImputer(missing_values=np.nan, strategy='median')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CLvExC7xZXEs"
},
"source": [
"Remove the text attribute because median can only be calculated on numerical attributes:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mG3Tkpj5ZXEs"
},
"outputs": [],
"source": [
"cat_cols = [\"city\",\"cancellation_policy\",\"host_since\",\"room_type\",\"property_type\",\"host_since\"]\n",
"X_num = X.drop(cat_cols, axis=1)\n",
"# alternatively: X_num = X.select_dtypes(include=[int, float])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "EF1i_7GYZXEs"
},
"outputs": [],
"source": [
"imputer.fit(X_num)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0HhMdYSwZXEs"
},
"outputs": [],
"source": [
"imputer.statistics_"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8FHn3TgDZXEt"
},
"source": [
"Check that this is the same as manually computing the median of each attribute:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "d2IXsugRZXEt"
},
"outputs": [],
"source": [
"X_num.median().values"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "D-DxnF2LZXEt"
},
"source": [
"Transform the training set:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mUAqpg8yZXEu"
},
"outputs": [],
"source": [
"X_num_np = imputer.transform(X_num)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "NYr-ylqvZXEu"
},
"outputs": [],
"source": [
"X_num = pd.DataFrame(X_num_np, columns=X_num.columns,\n",
" index = list(X_num.index.values))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Qjt67ohqZXEu"
},
"outputs": [],
"source": [
"X_num.loc[sample_incomplete_rows.index.values]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "6pHX0HJTZXEu"
},
"outputs": [],
"source": [
"imputer.strategy"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9p6IGSz9ZXEv"
},
"source": [
"Now let's preprocess the categorical input feature, `ocean_proximity`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jt18aFVyZXEv"
},
"outputs": [],
"source": [
"X_cat = X.select_dtypes(include=[object])\n",
"X_cat.head(10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "avVCgxAFZXEv"
},
"outputs": [],
"source": [
"from sklearn.preprocessing import OrdinalEncoder"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "H0KRRktvZXEv"
},
"outputs": [],
"source": [
"X_cat.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "wBwZIFPrZXEw"
},
"outputs": [],
"source": [
"ordinal_encoder = OrdinalEncoder()\n",
"X_cat_enc = ordinal_encoder.fit_transform(X_cat)\n",
"X_cat_enc[:10]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "iz8WYbg6ZXEx"
},
"outputs": [],
"source": [
"ordinal_encoder.categories_"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "VTp9-r3LZXEx"
},
"outputs": [],
"source": [
"from sklearn.preprocessing import OneHotEncoder\n",
"\n",
"cat_encoder = OneHotEncoder()\n",
"X_cat_1hot = cat_encoder.fit_transform(X_cat)\n",
"X_cat_1hot"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FRN0MRzoZXEx"
},
"source": [
"By default, the `OneHotEncoder` class returns a sparse array, but we can convert it to a dense array if needed by calling the `toarray()` method:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ejwzN3QXZXEx"
},
"outputs": [],
"source": [
"X_cat_1hot.toarray()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GQAIib7XZXEy"
},
"source": [
"Alternatively, you can set `sparse=False` when creating the `OneHotEncoder`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "UbmeOaTTZXEy"
},
"outputs": [],
"source": [
"cat_encoder = OneHotEncoder(sparse=False)\n",
"X_cat_1hot = cat_encoder.fit_transform(X_cat)\n",
"X_cat_1hot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "BXYuh9cBZXEy"
},
"outputs": [],
"source": [
"cat_encoder.categories_"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "R__DyD99ZXEz"
},
"source": [
"Let's create a custom transformer to add extra attributes:"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6eUaohqrZXEz"
},
"source": [
"#### **Now let's create a pipeline for preprocessing that is built on the techniques we used up and till now and introduce some new pipeline techniques.**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2lH84PvDZXEz"
},
"outputs": [],
"source": [
"from sklearn.base import BaseEstimator, TransformerMixin\n",
"from datetime import datetime\n",
"numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
"\n",
"# Receive numpy array, convert to pandas for features, convert back to array for output.\n",
"\n",
"class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
" def __init__(self, popularity = True, num_cols=[]): # no *args or **kargs\n",
" self.popularity = popularity\n",
" self.num_cols = num_cols\n",
" def fit(self, X, y=None):\n",
" return self # nothing else to do\n",
" def transform(self, X, y=None):\n",
"\n",
" ### Some feature engineering\n",
" X = pd.DataFrame(X, columns=self.num_cols)\n",
" X[\"bedrooms_per_person\"] = X[\"bedrooms\"]/X[\"accommodates\"]\n",
" X[\"bathrooms_per_person\"] = X[\"bathrooms\"]/X[\"accommodates\"]\n",
"\n",
" global feats\n",
" feats = [\"bedrooms_per_person\",\"bathrooms_per_person\"]\n",
"\n",
" if self.popularity:\n",
" X[\"past_and_future_popularity\"]=X[\"number_of_reviews\"]/(X[\"availability_365\"]+1)\n",
" feats.append(\"past_and_future_popularity\")\n",
"\n",
" return X.values\n",
" else:\n",
" return X.values\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "QZN0uWl0ZXEz"
},
"outputs": [],
"source": [
"from sklearn.pipeline import Pipeline\n",
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"X = strat_train_set.copy().drop(\"price\",axis=1)\n",
"Y = strat_train_set[\"price\"]\n",
"\n",
"num_cols = list(X.select_dtypes(include=numerics).columns)\n",
"cat_cols = list(X.select_dtypes(include=[object]).columns)\n",
"\n",
"num_pipeline = Pipeline([\n",
" ('imputer', SimpleImputer(strategy='median')),\n",
" ('attribs_adder', CombinedAttributesAdder(num_cols=num_cols,popularity=True)),\n",
" ('std_scaler', StandardScaler()),\n",
" ])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "iOIWdl0nZXE0"
},
"outputs": [],
"source": [
"from sklearn.compose import ColumnTransformer\n",
"import itertools\n",
"\n",
"\n",
"numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
"\n",
"mid_pipeline = ColumnTransformer([\n",
" (\"num\", num_pipeline, num_cols),\n",
" (\"cat\", OneHotEncoder(),cat_cols ),\n",
" ])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "lGYW5ngnZXE1"
},
"outputs": [],
"source": [
"mid_pipeline.fit(X) # this one specifically has to be fitted for the cat names\n",
"cat_encoder = mid_pipeline.named_transformers_[\"cat\"]\n",
"sublists = [list(bas) for bas in cat_encoder.categories_]\n",
"one_cols = list(itertools.chain(*sublists))\n",
"\n",
"## In this class, I will be converting numpy back to pandas\n",
"\n",
"class ToPandasDF(BaseEstimator, TransformerMixin):\n",
" def __init__(self, fit_index = [] ): # no *args or **kargs\n",
" self.fit_index = fit_index\n",
" def fit(self, X_df, y=None):\n",
" return self # nothing else to do\n",
" def transform(self, X_df, y=None):\n",
" global cols\n",
" cols = num_cols.copy()\n",
" cols.extend(feats)\n",
" cols.extend(one_cols) # one in place of cat\n",
" X_df = pd.DataFrame(X_df, columns=cols,index=self.fit_index)\n",
"\n",
" return X_df\n",
"\n",
"def pipe(inds):\n",
" return Pipeline([\n",
" (\"mid\", mid_pipeline),\n",
" (\"PD\", ToPandasDF(inds)),\n",
" ])\n",
"\n",
"params = {\"inds\" : list(X.index)}\n",
"\n",
"X_pr = pipe(**params).fit_transform(X) # Now we have done all the preprocessing instead of\n",
" #.. doing it bit by bit. The pipeline becomes\n",
" #.. extremely handy in the cross-validation step."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "q1NzMdZMZXE2"
},
"source": [
"# Select and train a model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "PmMUHQUrZXE3"
},
"outputs": [],
"source": [
"from sklearn.linear_model import LinearRegression\n",
"Y_pr = Y.copy() # just for naming convention, _pr for processed.\n",
"\n",
"lin_reg = LinearRegression()\n",
"lin_reg.fit(X_pr, Y_pr)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Z3Vy7xZrZXE4"
},
"outputs": [],
"source": [
"# let's try the full preprocessing pipeline on a few training instances\n",
"some_data = X.iloc[:5]\n",
"some_labels = Y.iloc[:5]\n",
"some_data_prepared = pipe(inds=list(some_data.index)).transform(some_data)\n",
"\n",
"print(\"Predictions:\", lin_reg.predict(some_data_prepared))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kouA5WArZXE4"
},
"source": [
"Compare against the actual values:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "OSu92IcSZXE4"
},
"outputs": [],
"source": [
"print(\"Labels:\", list(some_labels))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3T4flEmtZXE5"
},
"outputs": [],
"source": [
"## Naturally, these metrics are not that fair, because it is insample.\n",
"## However the first model is linear so overfitting is less likley.\n",
"## We will look at some out of sample validation later on."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "cTdwKQ6QZXE5"
},
"outputs": [],
"source": [
"from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
"\n",
"X_pred = lin_reg.predict(X_pr)\n",
"lin_mse = mean_squared_error(Y, X_pred)\n",
"lin_rmse = np.sqrt(lin_mse)\n",
"lin_rmse"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "cETn4gAkZXE5"
},
"outputs": [],
"source": [
"from sklearn.metrics import mean_absolute_error\n",
"\n",
"lin_mae = mean_absolute_error(Y, X_pred)\n",
"lin_mae"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ZRK9Iu9oZXE6"
},
"outputs": [],
"source": [
"from sklearn.tree import DecisionTreeRegressor\n",
"\n",
"tree_reg = DecisionTreeRegressor(random_state=42)\n",
"tree_reg.fit(X_pr, Y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "vNI9NayEZXE6"
},
"outputs": [],
"source": [
"X_pred = tree_reg.predict(X_pr)\n",
"tree_mse = mean_squared_error(Y, X_pred)\n",
"tree_rmse = np.sqrt(tree_mse)\n",
"tree_rmse ## Model is complex and overfits completely."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "A4PNKzEzZXE6"
},
"source": [
"# Fine-tune your model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Bsd5tpbMZXE7"
},
"outputs": [],
"source": [
"from sklearn.model_selection import cross_val_score\n",
"\n",
"scores = cross_val_score(DecisionTreeRegressor(random_state=42), X_pr, Y,\n",
" scoring=\"neg_mean_squared_error\", cv=10)\n",
"tree_rmse_scores = np.sqrt(-scores)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "QmwGenEsZXE7"
},
"outputs": [],
"source": [
"def display_scores(scores):\n",
" print(\"Scores:\", scores)\n",
" print(\"Mean:\", scores.mean())\n",
" print(\"Standard deviation:\", scores.std())\n",
"\n",
"display_scores(tree_rmse_scores)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "dZdAf0FVZXE7"
},
"outputs": [],
"source": [
"lin_scores = cross_val_score(LinearRegression(), X_pr, Y,\n",
" scoring=\"neg_mean_absolute_error\", cv=10)\n",
"lin_rmse_scores = np.sqrt(-lin_scores)\n",
"display_scores(lin_rmse_scores)\n",
"## bad performance, might need some regularisation."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "bQ87leZjZXE7"
},
"outputs": [],
"source": [
"from sklearn.ensemble import RandomForestRegressor\n",
"\n",
"forest_reg = RandomForestRegressor(random_state=42)\n",
"forest_reg.fit(X_pr, Y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "i3eHPk_FZXE8"
},
"outputs": [],
"source": [
"X_pred = forest_reg.predict(X_pr)\n",
"forest_mse = mean_squared_error(Y, X_pred)\n",
"forest_rmse = np.sqrt(forest_mse)\n",
"forest_rmse"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "a7WxR5raZXE8"
},
"outputs": [],
"source": [
"#might take 40 seconds\n",
"\n",
"from sklearn.model_selection import cross_val_score\n",
"\n",
"forest_scores = cross_val_score(forest_reg, X_pr, Y,\n",
" scoring=\"neg_mean_squared_error\", cv=10)\n",
"forest_rmse_scores = np.sqrt(-forest_scores)\n",
"display_scores(forest_rmse_scores)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "q-ECZNJ2ZXE8"
},
"outputs": [],
"source": [
"scores = cross_val_score(lin_reg, X_pr, Y, scoring=\"neg_mean_squared_error\", cv=10)\n",
"pd.Series(np.sqrt(-scores)).describe()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "XSzo6ZnXZXE9"
},
"outputs": [],
"source": [
"from sklearn.svm import SVR\n",
"\n",
"svm_reg = SVR(kernel=\"linear\")\n",
"svm_reg.fit( X_pr, Y,)\n",
"X_pred = svm_reg.predict(X_pr)\n",
"svm_mse = mean_squared_error(Y, X_pred)\n",
"svm_rmse = np.sqrt(svm_mse)\n",
"svm_rmse"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "GE_D08IFZXE9"
},
"outputs": [],
"source": [
"## 50 Seconds to run this code block.\n",
"from sklearn.model_selection import GridSearchCV\n",
"\n",
"param_grid = [\n",
" # try 12 (3×4) combinations of hyperparameters\n",
" {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},\n",
" # then try 6 (2×3) combinations with bootstrap set as False\n",
" {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]},\n",
" ]\n",
"\n",
"forest_reg = RandomForestRegressor(random_state=42)\n",
"# train across 5 folds, that's a total of (12+6)*5=90 rounds of training\n",
"grid_search = GridSearchCV(forest_reg, param_grid, cv=5,\n",
" scoring='neg_mean_squared_error', return_train_score=True)\n",
"grid_search.fit( X_pr, Y)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2Kkj9tt5ZXE-"
},
"source": [
"The best hyperparameter combination found:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "FOO2uIjyZXE-"
},
"outputs": [],
"source": [
"grid_search.best_params_"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ETCJEzqeZXE_"
},
"outputs": [],
"source": [
"grid_search.best_estimator_"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OxG9iaZ5ZXE_"
},
"source": [
"Let's look at the score of each hyperparameter combination tested during the grid search:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ybvom2NLZXE_"
},
"outputs": [],
"source": [
"cvres = grid_search.cv_results_\n",
"for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
" print(np.sqrt(-mean_score), params)\n",
"\n",
"print(\"\")\n",
"print(\"Best grid-search performance: \", np.sqrt(-cvres[\"mean_test_score\"].max()))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "SpbhLoVfZXFA"
},
"outputs": [],
"source": [
"# Top five results as presented in a dataframe\n",
"pd.DataFrame(grid_search.cv_results_).head(5)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "UVW3yJYXZXFB"
},
"outputs": [],
"source": [
"from sklearn.model_selection import RandomizedSearchCV\n",
"from scipy.stats import randint\n",
"\n",
"param_distribs = {\n",
" 'n_estimators': randint(low=1, high=200),\n",
" 'max_features': randint(low=1, high=8),\n",
" }\n",
"\n",
"forest_reg = RandomForestRegressor(random_state=42)\n",
"rnd_search = RandomizedSearchCV(forest_reg, param_distributions=param_distribs,\n",
" n_iter=5, cv=5, scoring='neg_mean_squared_error', random_state=42)\n",
"rnd_search.fit( X_pr, Y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0Elj0p-3ZXFB"
},
"outputs": [],
"source": [
"cvres = rnd_search.cv_results_\n",
"for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
" print(np.sqrt(-mean_score), params)\n",
"\n",
"print(\"Best grid-search performance: \", np.sqrt(-cvres[\"mean_test_score\"].max()))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "hf382EgvZXFB"
},
"outputs": [],
"source": [
"feature_importances = grid_search.best_estimator_.feature_importances_\n",
"feature_importances"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jND7SmfKZXFC"
},
"outputs": [],
"source": [
"feats = pd.DataFrame()\n",
"feats[\"Name\"] = list(X_pr.columns)\n",
"feats[\"Score\"] = feature_importances"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "LXbIqFlvZXFC"
},
"outputs": [],
"source": [
"feats.sort_values(\"Score\",ascending=False).round(5).head(20)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "WMCMhuiSZXFC"
},
"outputs": [],
"source": [
"strat_test_set.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "RnZVVxx6ZXFC"
},
"outputs": [],
"source": [
"### Now we can test the out of sample performance.\n",
"\n",
"final_model = grid_search.best_estimator_\n",
"\n",
"X_test = strat_test_set.drop(\"price\", axis=1)\n",
"y_test = strat_test_set[\"price\"].copy()\n",
"\n",
"X_test_prepared = pipe(list(X_test.index)).transform(X_test)\n",
"final_predictions = final_model.predict(X_test_prepared)\n",
"\n",
"final_mse = mean_squared_error(y_test, final_predictions)\n",
"final_rmse = np.sqrt(final_mse)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "D-wE7tl0ZXFD"
},
"outputs": [],
"source": [
"final_rmse"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "oBs0b4fpZXFD"
},
"outputs": [],
"source": [
"final_mae = mean_absolute_error(y_test, final_predictions)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Yx0NPbruZXFD"
},
"outputs": [],
"source": [
"final_mae ## not too bad"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0imyjZlhZXFE"
},
"outputs": [],
"source": [
"## Value Estimation for Client"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "tvUezT8LZXFE"
},
"outputs": [],
"source": [
"df_client = pd.DataFrame.from_dict(dict_client, orient='index').T"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "s2SRq3CbZXFE"
},
"outputs": [],
"source": [
"df_client"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "LJlvhehKZXFE"
},
"outputs": [],
"source": [
"df_client = pipe(list(df_client.index)).transform(df_client)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "M57QpQ6QZXFF"
},
"outputs": [],
"source": [
"client_pred = final_model.predict(df_client)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "EqfZb-JHZXFF"
},
"outputs": [],
"source": [
"### Client should be charging about $150 more.\n",
"print('\\x1b[1;31m'+str(client_pred[0])+'\\x1b[0m')\n",
"print('\\x1b[1;31m'+str(-500)+'\\x1b[0m')\n",
"print('\\x1b[1;31m'+\"= \"+str(client_pred[0]-500)+'\\x1b[0m')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4Pk0__NRZXFF"
},
"source": [
"#### We can compute a crude 95% confidence interval for the test RMSE:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "vY9kd2GXZXFF"
},
"outputs": [],
"source": [
"from scipy import stats"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "bSQ2bTtyZXFG"
},
"outputs": [],
"source": [
"y_test.min()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "4HmHsc9ZZXFG"
},
"outputs": [],
"source": [
"## This calculates the RMSE confidence interval\n",
"\n",
"confidence = 0.95\n",
"squared_errors = (final_predictions - y_test) ** 2\n",
"mean = squared_errors.mean()\n",
"m = len(squared_errors)\n",
"\n",
"## MSE\n",
"MSE_int = np.sqrt(stats.t.interval(confidence, m - 1,\n",
" loc=np.mean(squared_errors),\n",
" scale=stats.sem(squared_errors)))\n",
"\n",
"print(\"MSE Interval: \", MSE_int)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HxLsGqt0ZXFG"
},
"source": [
"We could also compute the interval manually like this:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "tIK_sxZKZXFG"
},
"outputs": [],
"source": [
"tscore = stats.t.ppf((1 + confidence) / 2, df=m - 1)\n",
"tmargin = tscore * squared_errors.std(ddof=1) / np.sqrt(m)\n",
"np.sqrt(mean - tmargin), np.sqrt(mean + tmargin)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "F1XBucP5ZXFG"
},
"source": [
"Alternatively, we could use a z-scores rather than t-scores:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "_Eps1ZorZXFG"
},
"outputs": [],
"source": [
"zscore = stats.norm.ppf((1 + confidence) / 2)\n",
"zmargin = zscore * squared_errors.std(ddof=1) / np.sqrt(m)\n",
"np.sqrt(mean - zmargin), np.sqrt(mean + zmargin)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Jh82mE0JZXFH"
},
"outputs": [],
"source": [
"####### What about for MAE\n",
"\n",
"absolute_errors = (final_predictions - y_test).abs()\n",
"mean = absolute_errors.mean()\n",
"m = len(absolute_errors)\n",
"\n",
"MAE_int = stats.t.interval(confidence, m - 1,\n",
" loc=np.mean(absolute_errors),\n",
" scale=stats.sem(absolute_errors))\n",
"\n",
"print(\"MAE Interval: \", MAE_int)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xSyJvcfxZXFH"
},
"source": [
"# Extra material"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0me0PggXZXFH"
},
"source": [
"## You can also include the parameter optimisation in a pipline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "X4P6k25XZXFH"
},
"outputs": [],
"source": [
"X.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ApZFzmykZXFH"
},
"outputs": [],
"source": [
"Y.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "MilKfoaeZXFI"
},
"outputs": [],
"source": [
"from sklearn.model_selection import RandomizedSearchCV\n",
"from scipy.stats import randint\n",
"\n",
"class Optimise(BaseEstimator, TransformerMixin):\n",
" def __init__(self, Y=[] ): # no *args or **kargs\n",
" self.Y = Y\n",
" def fit(self, X_df, y=None):\n",
" return self # nothing else to do\n",
" def transform(self, X_df, y=None):\n",
" param_distribs = {\n",
" 'n_estimators': randint(low=1, high=200),\n",
" 'max_features': randint(low=1, high=8),\n",
" }\n",
"\n",
" forest_reg = RandomForestRegressor(random_state=42)\n",
" rnd_search = RandomizedSearchCV(forest_reg, param_distributions=param_distribs,\n",
" n_iter=5, cv=5, scoring='neg_mean_squared_error', random_state=42)\n",
"\n",
" rnd_search.fit(X_df, self.Y)\n",
"\n",
" return rnd_search.best_estimator_\n",
"\n",
"def pipe_full(inds, Y):\n",
" return Pipeline([\n",
" (\"first\", pipe(inds)),\n",
" (\"opt\", Optimise(Y)),\n",
" ])\n",
"\n",
"params = {\"inds\" : list(X.index),\"Y\" : Y}\n",
"\n",
"modell = pipe_full(**params).fit_transform(X) # Now we have done all the preprocessing instead of\n",
" #.. doing it bit by bit. The pipeline becomes\n",
" #.. extremely handy in the cross-validation step.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "o8QfCuirZXFI"
},
"outputs": [],
"source": [
"X_test_prepared = pipe(list(X_test.index)).transform(X_test)\n",
"X_pred = modell.predict(X_test_prepared)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "K_YsHpVGZXFJ"
},
"outputs": [],
"source": [
"X_pred[:10]"
]
}
],
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