Airbnb prices

by: Chris Cave

Feyn version: 2.1+

Last updated: 23/09/2021

Here we use the QLattice to predict the rental price for Airbnb apartments in New York. The purpose of this tutorial is to show how to use auto_run in a regression problem.

import numpy as np
import pandas as pd

import feyn

Connect to QLattice

ql = feyn.connect_qlattice()
ql.reset(random_seed=42)

Read in a data set

The Airbnb dataset is known from Kaggle. It describes the rental price applied to actual rentals over several years in New York City

data = pd.read_csv('../data/airbnb.csv')
data=data.drop(["name","host_name", "last_review","id","host_id"], axis=1)
data=data[data["price"]<400].dropna()
data.head()

	neighbourhood_group	neighbourhood	latitude	longitude	room_type	price	minimum_nights	number_of_reviews	reviews_per_month	calculated_host_listings_count	availability_365
0	Brooklyn	Kensington	40.64749	-73.97237	Private room	149	1	9	0.21	6	365
1	Manhattan	Midtown	40.75362	-73.98377	Entire home/apt	225	1	45	0.38	2	355
3	Brooklyn	Clinton Hill	40.68514	-73.95976	Entire home/apt	89	1	270	4.64	1	194
4	Manhattan	East Harlem	40.79851	-73.94399	Entire home/apt	80	10	9	0.10	1	0
5	Manhattan	Murray Hill	40.74767	-73.97500	Entire home/apt	200	3	74	0.59	1	129

Split the data

train, test = feyn.tools.split(data, ratio=(3,1), random_state=42)

Declare semantic types

The following columns are categorical:

• neighbourhood_group
• neighbourhood
• room_type

We need to declared this in a dictionary that will then pass to the auto_run function

stypes = {
    'neighbourhood_group': 'c', 
    'neighbourhood': 'c', 
    'room_type': 'c'
}

Use auto_run to obtain fitted models

This function we use to run a simulation on a QLattice and return the 10 best and most diverse models to the data

models = ql.auto_run(
    train,
    output_name='price',
    stypes=stypes
    )

Summary plot

This return the metrics of the model on the train and test set.

best = models[0]

best.plot(train, test)

Training Metrics

Test

Trend

The model captures the trend of prices as we can see from this map of New York City.

pred_test = best.predict(test)
f, ax = plt.subplots(nrows=1, ncols=2, sharey=True, sharex=True, figsize=(14, 6))

# Default colors
ax[0].scatter(test["longitude"],test["latitude"], cmap="viridis", c=test['price'], vmax=200, s=8)
ax[0].set_title('Actual price')
ax[1].scatter(test["longitude"],test["latitude"], cmap="viridis", c=pred_test, vmax=200, s=8)
ax[1].set_title('Predicted price')

plt.show()