Data Analysis Project – Airbnb Analysis EDA

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Data Analysis Project – Airbnb Analysis EDA

This article presents a data analysis project focused on performing exploratory data analysis (EDA) on Airbnb listings. It explores various aspects of the analysis, including the use of data visualization and statistical techniques to gain insights from the Airbnb dataset. The article aims to provide college students with a user-friendly understanding of the project and its significance in the field of data analysis. You can find this code on GitHub also.

Data Analysis Project: Introduction

Data analysis projects play a crucial role in college education, particularly for students pursuing degrees in fields such as data science, business analytics, and statistics. These projects offer practical experience in applying data analysis techniques to real-world datasets, helping students develop essential skills for their future careers. In this comprehensive guide, we will explore the significance of data analysis projects and provide useful tips for college students to excel in their endeavors.

Airbnb, a popular online marketplace for short-term rentals, generates a vast amount of data. Analyzing this data can provide valuable insights into pricing patterns, neighborhood preferences, and customer reviews. In this data analysis project, we will explore the process of conducting an Exploratory Data Analysis (EDA) on Airbnb listings. By leveraging techniques such as data visualization and statistical analysis, we aim to uncover meaningful information that can benefit hosts, guests, and the Airbnb platform itself.

Data Analysis Project: Understanding Exploratory Data Analysis (EDA)

Exploratory Data Analysis (EDA) is a crucial step in any data analysis project. It involves examining and visualizing data to discover patterns, relationships, and trends. By using EDA techniques, we can gain a comprehensive understanding of the Airbnb dataset, identify key variables, and extract meaningful insights. EDA allows us to explore data distribution, spot outliers, handle missing values, and perform initial statistical analyses.

Exploratory Data Analysis (EDA) involves visualizing and summarizing the dataset to identify patterns, relationships, and outliers. Techniques such as data visualization, descriptive statistics, and correlation analysis help students understand the characteristics of the dataset and guide further analysis.

Gathering and Preparing Airbnb Data

Choosing the appropriate dataset is a critical first step in any data analysis project. It is essential to consider factors such as relevance to your field of study, data quality, and availability. Accessing publicly available datasets from reliable sources can be a good starting point for college students. Websites like Kaggle, UCI Machine Learning Repository, and Data.gov offer a wide range of datasets across various domains.

To perform the Airbnb Analysis EDA, we first need to gather the relevant Airbnb dataset. This dataset typically contains information about listings, hosts, guests, prices, amenities, and reviews. Once we have the dataset, we must ensure its cleanliness and quality. Data cleaning techniques, such as handling missing values and removing duplicates, are applied to ensure the integrity of the analysis. Additionally, we may need to perform data transformation or feature engineering to enhance the dataset\’s usefulness for analysis.

Exploring Pricing Patterns and Neighborhood Preferences

One key aspect of the Airbnb Analysis EDA is exploring pricing patterns and understanding neighborhood preferences. By analyzing the dataset, we can identify factors that influence the pricing of listings, such as location, property type, and amenities. We can also uncover popular neighborhoods based on booking frequency, guest reviews, and host ratings. This information can be valuable for both hosts and guests in making informed decisions.

Visualizing Data and Extracting Insights

Data visualization plays a vital role in the Airbnb Analysis EDA. Through visual representations such as charts, graphs, and maps, we can effectively communicate complex patterns and trends in the data. Visualizations enable us to identify occupancy trends, seasonality effects, and correlations between variables. Furthermore, statistical analysis techniques can be applied to quantify relationships and validate hypotheses, allowing us to extract meaningful insights from the data.

In conclusion, the Airbnb Analysis EDA project offers college students a valuable opportunity to explore the field of data analysis. By delving into the Airbnb dataset and applying EDA techniques, students can gain hands-on experience in data exploration, visualization, and statistical analysis. This project provides insights into pricing patterns, neighborhood preferences, and the overall dynamics of the Airbnb marketplace. Understanding the significance of EDA in data analysis is essential for leveraging data-driven insights to make informed decisions.

Data Analysis Project: FAQs (Frequently Asked Questions)

  1. Q: What is Exploratory Data Analysis (EDA), and why is it important in the Airbnb project?
    • A: EDA involves exploring and visualizing data to uncover patterns and trends. In the Airbnb project, EDA helps us gain valuable insights into pricing, neighborhood preferences, and other crucial factors.
  2. Q: Can college students with little data analysis experience participate in this Airbnb Analysis EDA project?
    • A: Absolutely! This project is designed for college students and provides an accessible introduction to data analysis concepts, making it suitable for beginners.
  3. Q: How can data visualization techniques enhance the understanding of Airbnb data?
    • A: Data visualization techniques, such as charts and graphs, help present complex data in a visually appealing manner. This enables students to identify trends and patterns more easily.
  4. Q: What role does statistical analysis play in the Airbnb Analysis EDA project?
    • A: Statistical analysis allows us to validate hypotheses, quantify relationships between variables, and make data-driven conclusions about the Airbnb dataset.
  5. Q: Can the insights gained from the Airbnb Analysis EDA project be applied to other real estate platforms?
    • A: While the focus is on Airbnb, the insights and skills acquired in this project can be transferred to other real estate platforms, providing a foundation for analyzing similar datasets.

Here is the for this Data Analysis Project.

# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here\'s several helpful packages to load in 

#This notebook is inpired from the work done by: Erik Bruin in the following link
#https://www.kaggle.com/erikbruin/airbnb-the-amsterdam-story-with-interactive-maps
#Also Please upvote the great work done by Erik 

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
import folium
from folium.plugins import MarkerCluster
from folium import plugins
from folium.plugins import FastMarkerCluster
from folium.plugins import HeatMap
from scipy.stats import pearsonr

#Loading of Files
dataset = pd.read_csv(\'../AB_NYC_2019.csv/\')
dataset.head()

# Now we need to check how many rows and columns the dataset is having. It is also necessary to check how many unique values are present for each column. For this we will also use the nunique.
# So we have the 48895 rows and 16 columns.

print(\'\\nRows : \',dataset.shape[0])
print(\'\\nColumns :\', dataset.shape[1])
print(\'\\nColumns:\',dataset.columns.to_list())
print(\' \\nUnique:\\n\',dataset.nunique())

#Since according to the dataset we have been provided with the latitude and longitude so we will use folium libraries to present the data into a map. This map can be zoomed and the individual locations can be viewed in detail. I have used the zoom start to 9 so that the clusters can be visible.
Long=-73.80
Lat=40.80
locations = list(zip(dataset.latitude, dataset.longitude))

map1 = folium.Map(location=[Lat,Long], zoom_start=9)
FastMarkerCluster(data=locations).add_to(map1)
map1

# For Nan Values in column reviews_per_month,replacing Nan with 0
dataset.fillna({\'reviews_per_month\':0},inplace=True)

# Scatter Plot to show the neighbourhood group based on Latitude and Longitude
# We will plot the same latitude and longitude in a scatter plot to have the cluster of the location, the same we did with the folium maps.

plt.figure(figsize=(12,8))
sns.scatterplot(x=dataset.longitude,y=dataset.latitude,hue=dataset.neighbourhood_group)
plt.show()

# Unique Values
# It is very important to understand and analyze the Unique values, this gives a lot of insight to the data and the user preference for a particular choice. We will take Room Type and Neighbourhood group.
# From the below, we have 3 types of room in the dataset and 5 different neighbourhood group. In the next section we will explore more on these two categories to understand the user distribution.

print(\'Unique value for room_type are :\',dataset.room_type.unique())
print(\'Unique value for neighbourhood_group are :\',dataset.neighbourhood_group.unique())

# Room Types and Neighbourhood Group
# We will first check the distribution of the room type by grouping the data. From the below its clear the Apartment and Private data is more than that of shared rooms. In general, Shared rooms costs less and can be very useful for travellers who moves from one city to another city quite frequently. Though the shared rooms data is less, we will still try to uncover as much details as we can.
dataset[\'room_type\'].value_counts().plot(kind=\'bar\',color=[\'r\',\'b\',\'y\'])
plt.show()

# Top 10 Apartment listings
apt = dataset[dataset[\'room_type\']==\'Entire home/apt\']
list_apt = apt.groupby([\'host_id\',\'host_name\',\'neighbourhood\',\'neighbourhood_group\']).size().reset_index(name=\'apartment\').sort_values(by=[\'apartment\'],ascending=False)
list_apt.head(10)

# Lets see the Sonder (NYC)
sonder_data = dataset[dataset[\'host_name\']==\'Sonder (NYC)\']
sonder_data_by = sonder_data[[\'host_id\',\'host_name\',\'neighbourhood\',\'latitude\',\'longitude\']]
sonder_data_by.head(5)

# Top 10 Private room
private = dataset[dataset[\'room_type\']==\'Private room\']
list_private = private.groupby([\'host_id\',\'host_name\',\'neighbourhood\']).size().reset_index(name=\'private\').sort_values(by=[\'private\'],ascending=False)
list_private.head(10)

# Location wise Private room
private_data = dataset[dataset[\'host_name\']==\'John\']
private_data_by = private_data[[\'host_id\',\'host_name\',\'neighbourhood\',\'latitude\',\'longitude\']]
private_data_by.head()

# Shared Room Exploration
private = dataset[dataset[\'room_type\']==\'Shared room\']
list_private = private.groupby([\'host_id\',\'host_name\',\'neighbourhood\']).size().reset_index(name=\'shared\').sort_values(by=[\'shared\'],ascending=False)
list_private.head(10)

# Exploration of Neighbourhood Group
dataset[\'neighbourhood_group\'].value_counts().plot(kind=\'bar\',color=[\'r\',\'b\',\'y\',\'g\',\'m\'])
plt.show()
private = dataset[dataset[\'neighbourhood_group\']==\'Manhattan\']
list_private = private.groupby([\'host_id\',\'host_name\',\'neighbourhood\',\'neighbourhood_group\']).size().reset_index(name=\'count\').sort_values(by=[\'count\'],ascending=False)
list_private.head(10)

# Price Exploration
dataset.price.isna().sum()

dataset[\'price\'].describe()
figsize=(12,8)
sns.boxenplot(x=\'price\',data=dataset)

# Average room rent for locality
dataset.head()
plt.figure(figsize=(12,8))
df = dataset[dataset[\'minimum_nights\']==1]
df1 = df.groupby([\'room_type\',\'neighbourhood_group\'])[\'price\'].mean().sort_values(ascending=True)
df1.plot(kind=\'bar\')
plt.title(\'Average Price for rooms in neighbourhood group\')
plt.ylabel(\'Average Daily Price\')
plt.xlabel(\'Neighbourhood Group\')
plt.show()
print(\'List of Average Price per night based on the neighbourhood group\')
pd.DataFrame(df1).sort_values(by=\'room_type\')

# Expensive Neighbourhood
print(\'Top 20 most expensive locality in Airbnb listing are :\')
df4 = dataset.dropna(subset=[\"price\"]).groupby(\"neighbourhood\")[[\"neighbourhood\", \"price\"]].agg(\"mean\").sort_values(by=\"price\",
                                                                                                              ascending=False).rename(index=str, columns={\"price\": \"Average price per night based on neighbourhood\"}).head(15)

df4.plot(kind=\'bar\')
plt.show()
pd.DataFrame(df4)

print(\'Least expensive neighbourhood according to Airbnb listing are\')
df4 = dataset.dropna(subset=[\"price\"]).groupby(\"neighbourhood\")[[\"neighbourhood\", \"price\"]].agg(\"mean\").sort_values(by=\"price\",
                                                                                                              ascending=False).rename(index=str, columns={\"price\": \"Average price per night based on neighbourhood\"}).tail(15)

df4.plot(kind=\'bar\')
plt.show()
pd.DataFrame(df4)

# Most number of locality listed
df5 = dataset.groupby(\'neighbourhood\')[[\'neighbourhood\',\'host_name\']].agg([\'count\']
                                                                   )[\'host_name\'].sort_values(by=\'count\',ascending=False).rename(index=str,columns={\'Count\':\'Listing Count\'})

df5.head(15).plot(kind=\'barh\')
plt.show()
pd.DataFrame(df5.head(15))

# Location and Review Score
fig = plt.figure(figsize=(12,4))
review_50 = dataset[dataset[\'number_of_reviews\']>=50]
df2 = review_50[\'neighbourhood_group\'].value_counts()
df2.plot(kind=\'bar\',color=[\'r\',\'b\',\'g\',\'y\',\'m\'])
plt.title(\'Location and Review Score(Min of 50)\')
plt.ylabel(\'Number of Review\')
plt.xlabel(\'Neighbourhood Group\')
plt.show()
print(\' Count of Review v/s neighbourhood group\')
pd.DataFrame(df2)

# Top 5 host
map1=folium.Map([40.7128,-74.0080],zoom_start=9.8)
location = [\'latitude\',\'longitude\']
df = review_50[location]
HeatMap(df.dropna(),radius=8,gradient={.4: \'blue\', .65: \'lime\', 1: \'red\'}).add_to(map1)
map1
plt.figure(figsize=(12,6))
review_50.head(2)
df1 = review_50[\'host_name\'].value_counts()[:5].plot(kind=\'bar\',color=[\'r\',\'b\',\'g\',\'y\',\'m\'])
#sns.barplot(x=df1.index,y=df1.values)

# Plot Price based on the Availability 365
plt.figure(figsize=(15,8))
sns.scatterplot(y=dataset[\'price\'],x=dataset[\'availability_365\'])

# Average Listing for each Neighbourhood group
df6 = review_50.groupby([\'neighbourhood_group\',\'room_type\']).mean()
df6 = df6.drop([\'id\',\'calculated_host_listings_count\',\'reviews_per_month\'],axis=1)
pd.DataFrame(df6).sort_values(\'neighbourhood_group\')

Analyzing Airbnb Data: Unveiling Insights from a Wealth of Information

In the era of data-driven decision-making, analyzing vast datasets has become essential for gaining valuable insights across various industries. One such area of interest is the analysis of Airbnb data. With millions of listings worldwide, Airbnb provides a treasure trove of information that can be leveraged to understand trends, preferences, and patterns in the global hospitality market. In this article, we will explore the process of analyzing Airbnb data and uncovering hidden knowledge that can inform strategic decisions.

Analyze Airbnb Data: Collecting Airbnb Data

To commence the analysis of Airbnb data, it is crucial to acquire the necessary dataset. Fortunately, Airbnb provides access to a rich collection of data through its public APIs and open data initiatives. Researchers, data enthusiasts, and analysts can obtain information on listings, reviews, pricing, availability, and more. Additionally, various third-party platforms and data providers offer pre-processed and curated datasets for analysis.

Airbnb Data Analysis: Understanding Data Structure

Once the Airbnb data is obtained, the next step is to comprehend its structure and variables. Typical Airbnb datasets include features such as listing details (e.g., location, property type, amenities), host information, guest reviews, pricing information, availability, and booking history. Familiarizing oneself with the dataset\’s structure helps identify relevant variables for analysis and enables the formulation of insightful research questions.

Airbnb Data Analytics: Data Cleaning and Preparation

Before diving into the analysis, it is essential to perform data cleaning and preparation. This involves handling missing values, removing duplicates, correcting inconsistencies, and transforming data into a suitable format. Cleaning the Airbnb data ensures the accuracy and reliability of subsequent analysis steps, reducing the risk of erroneous insights.

Analyze Airbnb Data: Exploratory Data Analysis (EDA)

Exploratory Data Analysis is a crucial phase in the analysis of Airbnb data. EDA involves descriptive statistical analysis, data visualization, and uncovering initial patterns and trends. By using Python libraries such as Pandas, matplotlib, and Seaborn, analysts can generate summary statistics, create visualizations, and explore relationships between variables. Through EDA, one can gain an understanding of the distribution of listings, pricing dynamics, seasonal patterns, and factors influencing guest reviews.

Airbnb Data Analysis: Advanced Analytics and Modeling

To delve deeper into Airbnb data, advanced analytics, and modeling techniques can be applied. Machine learning algorithms can help predict pricing, occupancy rates, and customer satisfaction based on historical data. Clustering algorithms can identify distinct groups of listings or guests, allowing for targeted marketing strategies. Sentiment analysis can extract insights from guest reviews to gauge the quality and satisfaction levels of different properties.

Analyze Airbnb Data: Deriving Actionable Insights

The primary objective of analyzing Airbnb data is to derive actionable insights that can drive business strategies or inform individual decisions. By combining the findings from exploratory analysis and advanced modeling, analysts can identify market trends, optimize pricing strategies, improve listing descriptions, and enhance the overall guest experience. These insights empower stakeholders, including hosts, property managers, and policymakers, to make informed decisions in a competitive hospitality landscape.

Airbnb Data Analytics: Conclusion

Analyzing Airbnb data provides a valuable opportunity to unlock a wealth of information about the global hospitality market. By collecting, cleaning, and exploring the dataset, analysts can gain insights into market trends, pricing dynamics, guest preferences, and more. Advanced analytics techniques further deepen the understanding, enabling predictive modeling and sentiment analysis. Leveraging the power of data, stakeholders in the Airbnb ecosystem can make informed decisions that enhance customer satisfaction, drive revenue growth, and shape the future of the industry. So dive into the world of Airbnb data analysis and unlock the hidden potential it holds.

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