# New York City Airbnb PCA

## New York City Airbnb Dimensionality Reduction using PCA

When working with a dataset with many features it is extremely difficult to visualize/explore the relationships between features. Not only it makes the EDA process difficult but also affects the machine learning model’s performance since the chances are that you might overfit your model or violate some of the assumptions of the algorithm, like the independence of features in linear regression. This is where dimensionality reduction comes in.

In machine learning, dimensionality reduction is the process of reducing the number of random variables under consideration by obtaining a set of principal variables. By reducing the dimension of your feature space, you have fewer relationships between features to consider which can be explored and visualized easily and also you are less likely to overfit your model. One way to perform Dimensionality reduction is by using Principal Components Analysis.

Principal Component Analysis or PCA is a linear feature extraction technique. It performs a linear mapping of the data to a lower-dimensional space in such a way that the variance of the data in the low-dimensional representation is maximized. It does so by calculating the eigenvectors from the covariance matrix. The eigenvectors that correspond to the largest eigenvalues (the principal components) are used to reconstruct a significant fraction of the variance of the original data. For more details read my post on PCA here: Intro-to-pca

In simpler terms, PCA combines your input features in a specific way that you can drop the least important feature while still retaining the most valuable parts of all of the features. As an added benefit, each of the new features or components created after PCA are all independent of one another.

## Get the data

``````# data managing and display libs
import pandas as pd
import numpy as np
import os
import io

import matplotlib.pyplot as plt
import matplotlib
%matplotlib inline

# sagemaker libraries
import boto3
``````
``````# boto3 client to get S3 data
s3_client = boto3.client('s3')
bucket_name='skuchkula-sagemaker-airbnb'
``````
``````# list the bucket objects
response = s3_client.list_objects(Bucket=bucket_name)

# get list of objects inside the bucket
files = [file['Key'] for file in response['Contents']]
files
``````
``````['clean/airbnb_clean.csv',
'detailed_listings.csv',
'feature_eng/amenities_features.csv',
'feature_eng/description_features.csv',
'feature_eng/host_verification_features.csv',
'feature_eng/merged_features.csv',
'feature_eng/min_max_scaled_final_df.csv',
'feature_eng/scaled_final_df.csv',
'summary_listings.csv']
``````
``````# download the file from s3
def get_data_frame(bucket_name, file_name):
'''
Takes the location of the dataset on S3 and returns a dataframe.
arguments:
bucket_name: the name of the bucket
file_name: the key inside the bucket
returns:
dataframe
'''
# get an S3 object by passing in the bucket and file name
data_object = s3_client.get_object(Bucket=bucket_name, Key=file_name)

# information is in the "Body" of the object

data_stream = io.BytesIO(data_body)

# create a dataframe

return df
``````
``````airbnb_file='feature_eng/min_max_scaled_final_df.csv'
df_airbnb = get_data_frame(bucket_name, airbnb_file)
``````
accommodates bathrooms bedrooms beds guests_included extra_people availability_30 availability_60 availability_90 number_of_reviews ... description_contains_yankee description_contains_yard description_contains_year description_contains_yellow description_contains_yoga description_contains_york description_contains_young description_contains_yummy description_contains_zero description_contains_zone
0 0.04 0.064516 0.000000 0.025 0.066667 0.000000 0.433333 0.283333 0.344444 0.071987 ... 0.0 0.0 0.00000 0.0 0.000000 0.000000 0.0 0.0 0.0 0.0
1 0.04 0.064516 0.071429 0.025 0.066667 0.066667 1.000000 1.000000 1.000000 0.000000 ... 0.0 0.0 0.00000 0.0 0.000000 0.000000 0.0 0.0 0.0 0.0
2 0.00 0.064516 0.071429 0.025 0.000000 0.066667 0.000000 0.000000 0.000000 0.014085 ... 0.0 0.0 0.00000 0.0 0.137645 0.000000 0.0 0.0 0.0 0.0
3 0.04 0.064516 0.071429 0.025 0.066667 0.333333 0.800000 0.550000 0.700000 0.117371 ... 0.0 0.0 0.00000 0.0 0.000000 0.074083 0.0 0.0 0.0 0.0
4 0.04 0.064516 0.071429 0.025 0.000000 0.100000 0.000000 0.000000 0.000000 0.076682 ... 0.0 0.0 0.18384 0.0 0.000000 0.000000 0.0 0.0 0.0 0.0

5 rows × 2201 columns

## PCA analysis

PCA attempts to reduce the number of features within a dataset while retaining the “principal components”, which are defined as weighted, linear combinations of existing features that are designed to be linearly independent and account for the largest possible variability in the data! You can think of this method as taking many features and combining similar or redundant features together to form a new, smaller feature set.

Using sklearn’s PCA implementation, we pass in n_components=50 to produce 50 principal components.

n_components: An integer that defines the number of PCA components to produce.

``````from sklearn.decomposition import PCA
pca = PCA(n_components=50)
pca.fit(df_airbnb)
``````
``````PCA(copy=True, iterated_power='auto', n_components=50, random_state=None,
svd_solver='auto', tol=0.0, whiten=False)
``````
``````features = range(pca.n_components_)
plt.figure(figsize=(18,10))
plt.bar(features, pca.explained_variance_)
plt.xticks(features)
plt.ylabel('variance')
plt.xlabel('PCA feature')
plt.show()
``````

The breakdown of how much variance explained by each PCA feature is shown in the above plot.

``````pca_features = pca.transform(df_airbnb)
pca_features.shape
``````
``````(45605, 50)
``````
``````pca_df = pd.DataFrame(pca_features)
``````
0 1 2 3 4 5 6 7 8 9 ... 40 41 42 43 44 45 46 47 48 49
0 2.658552 -1.455526 0.699093 -0.553412 -0.125384 -0.654187 -0.044713 -0.001357 0.108582 1.333186 ... 0.576904 0.466956 0.331311 0.261779 -0.330193 1.620287 0.867739 -0.798060 -0.576860 -0.254925
1 -1.644174 -0.715321 1.097099 0.893833 0.399013 -0.139751 -1.183842 -1.132572 0.996817 0.151908 ... -0.252328 0.226731 0.269839 -0.211928 0.147831 1.354929 0.801862 -0.292820 -0.804985 -0.202175
2 -1.012114 -1.718006 -0.320889 -0.356113 0.113797 0.392780 -0.399569 -0.819086 -0.133456 -0.200684 ... 0.193168 0.068044 0.015844 0.197295 -0.167786 1.117572 0.749340 0.109282 -0.870940 -0.278948
3 0.067402 -0.520919 -0.263865 0.353879 -0.907122 -0.906052 -1.158888 -0.624668 -0.071571 0.780228 ... 0.416250 -0.057388 0.073780 0.143102 0.198163 1.440193 0.641507 -0.021390 -0.912154 -0.285108
4 -1.666907 1.296202 0.520533 0.659033 1.262238 -0.516450 -0.827510 -0.678685 0.010626 0.180606 ... -0.214280 -0.095810 0.224063 0.083689 -0.192449 0.410582 0.017357 0.246550 0.796558 0.307420

5 rows × 50 columns

## Component make-up

``````# display makeup of first component
num=1
display_component(v, df_airbnb.columns.values, component_num=num, n_weights=20)
``````

``````# display makeup of first component
num=2
display_component(v, df_airbnb.columns.values, component_num=num, n_weights=20)
``````

``````# display makeup of first component
num=3
display_component(v, df_airbnb.columns.values, component_num=num, n_weights=20)
``````

``````# display makeup of first component
num=4
display_component(v, df_airbnb.columns.values, component_num=num, n_weights=20)
``````

## Create price_category to aid in visualization

As of now, the price feature is a continuous variable. We can apply binning to create a discreatized version of the price column. Doing so, we can filter locations based on price category. Ideally, this should be inside the Feature Engineering step, but for now, we will go with it.

``````# Read in the cleaned dataset

``````
``````# let pandas know that you are working with a copy
airbnb_temp = airbnb_detailed.copy()

# get the indices of low, med and high rows
med_indexes = airbnb_temp[(airbnb_temp.adjusted_price >= 50) &
``````
``````# create a new column called 'price_category'
airbnb_temp.loc[low_indexes, 'price_category'] = 'low'
airbnb_temp.loc[med_indexes, 'price_category'] = 'medium'
airbnb_temp.loc[high_indexes, 'price_category'] = 'high'
``````
id name summary description host_listings_count host_total_listings_count host_verifications neighbourhood_cleansed neighbourhood_group_cleansed latitude ... cancellation_policy_strict cancellation_policy_strict_14_with_grace_period cancellation_policy_super_strict_30 cancellation_policy_super_strict_60 require_guest_profile_picture_f require_guest_profile_picture_t require_guest_phone_verification_f require_guest_phone_verification_t adjusted_price price_category
0 2595 Skylit Midtown Castle Find your romantic getaway to this beautiful, ... Find your romantic getaway to this beautiful, ... 5.0 5.0 ['email', 'phone', 'reviews', 'kba', 'work_ema... Midtown Manhattan 40.75362 ... 0 1 0 0 0 1 0 1 225.000000 high
1 3647 THE VILLAGE OF HARLEM....NEW YORK ! NaN WELCOME TO OUR INTERNATIONAL URBAN COMMUNITY T... 1.0 1.0 ['email', 'phone', 'google', 'reviews', 'jumio... Harlem Manhattan 40.80902 ... 0 1 0 0 0 1 0 1 50.000000 medium
2 5022 Entire Apt: Spacious Studio/Loft by central park NaN Loft apartment with high ceiling and wood floo... 1.0 1.0 ['email', 'phone', 'facebook', 'reviews', 'kba'] East Harlem Manhattan 40.79851 ... 0 1 0 0 0 1 0 1 8.000000 low
3 5099 Large Cozy 1 BR Apartment In Midtown East My large 1 bedroom apartment is true New York ... My large 1 bedroom apartment is true New York ... 1.0 1.0 ['email', 'phone', 'reviews', 'jumio', 'govern... Murray Hill Manhattan 40.74767 ... 0 1 0 0 0 1 0 1 66.666667 medium
4 5121 BlissArtsSpace! NaN HELLO EVERYONE AND THANKS FOR VISITING BLISS A... 1.0 1.0 ['email', 'phone', 'facebook', 'reviews', 'off... Bedford-Stuyvesant Brooklyn 40.68688 ... 0 1 0 0 1 0 1 0 1.333333 low

5 rows × 69 columns

``````airbnb_temp.price_category.value_counts()
``````
``````low       25133
medium    18484
high       1988
Name: price_category, dtype: int64
``````

## Merge PCA features with the original dataset

``````# take only cols that you want to display in visualization from main dataset
cols = ['price_category', 'name', 'id', 'price', 'adjusted_price',
'minimum_nights', 'bedrooms', 'bathrooms',
'neighbourhood_group_cleansed', 'neighbourhood_cleansed']

airbnb_reduced = airbnb_temp[cols]

# merge this with PCA features
airbnb_final = pd.concat([airbnb_reduced, pca_df], axis=1)

``````
price_category name id price adjusted_price minimum_nights bedrooms bathrooms neighbourhood_group_cleansed neighbourhood_cleansed ... 40 41 42 43 44 45 46 47 48 49
0 high Skylit Midtown Castle 2595 225.0 225.000000 1 0.0 1.0 Manhattan Midtown ... 0.576904 0.466956 0.331311 0.261779 -0.330193 1.620287 0.867739 -0.798060 -0.576860 -0.254925
1 medium THE VILLAGE OF HARLEM....NEW YORK ! 3647 150.0 50.000000 3 1.0 1.0 Manhattan Harlem ... -0.252328 0.226731 0.269839 -0.211928 0.147831 1.354929 0.801862 -0.292820 -0.804985 -0.202175
2 low Entire Apt: Spacious Studio/Loft by central park 5022 80.0 8.000000 10 1.0 1.0 Manhattan East Harlem ... 0.193168 0.068044 0.015844 0.197295 -0.167786 1.117572 0.749340 0.109282 -0.870940 -0.278948
3 medium Large Cozy 1 BR Apartment In Midtown East 5099 200.0 66.666667 3 1.0 1.0 Manhattan Murray Hill ... 0.416250 -0.057388 0.073780 0.143102 0.198163 1.440193 0.641507 -0.021390 -0.912154 -0.285108
4 low BlissArtsSpace! 5121 60.0 1.333333 45 1.0 1.0 Brooklyn Bedford-Stuyvesant ... -0.214280 -0.095810 0.224063 0.083689 -0.192449 0.410582 0.017357 0.246550 0.796558 0.307420

5 rows × 60 columns

## Save and upload to S3

``````airbnb_final.to_csv('airbnb_final.csv', index=False)
``````
``````import configparser
config = configparser.ConfigParser()

KEY = config.get('AWS','KEY')
SECRET = config.get('AWS','SECRET')
``````
``````import boto3

# Generate the boto3 client for interacting with S3
s3 = boto3.client('s3', region_name='us-east-1',
# Set up AWS credentials
aws_access_key_id=KEY,
aws_secret_access_key=SECRET)
``````
``````s3.upload_file(Bucket='skuchkula-sagemaker-airbnb',
Filename='airbnb_final.csv',
Key='feature/airbnb_final.csv')
``````

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