Storytelling with Tableau: Analyzing Airbnb Data for Chicago city USA

Overview of Airbnb

Airbnb$$s success lies in its ability to offer diverse and affordable accommodation options, even in areas where traditional hotels are scarce. With a global user base of $150$ million people, Airbnb's growth has been exponential, boasting a $153\%$ growth rate since $2009$ and an estimated revenue of $$8\mathrm{.}5$ billion by $2020.$

Analyzing Chicago's Airbnb Data

Airbnb is recognized as one of the largest hotel chains in the world, despite not owning a single hotel room. The company thrives by connecting travelers needing accommodations with hosts willing to rent out their spaces. Through Airbnb, you can book anything from a shared room in a house to an entire apartment or hotel room.

Since its founding in $2008,$ Airbnb has hosted over $300$ million guests and aims to reach $1$ billion by its $20$th anniversary in $2028.$

The company provides a wealth of its data for free via the Inside Airbnb website, allowing anyone to access extensive information about Airbnb operations in major cities worldwide.

In this project, we will work with a dataset of Chicago properties listed on Airbnb. This dataset includes information about prices, locations, reviews, room types, hosts, and more for over $50,000$ rooms.

Our primary goal is to extract insights from the data, such as the most common room types, popular locations, and how prices vary based on room type and property location etc.

To achieve this, we will follow these steps:

$1.$ Getting and exploring the data

$2.$ Cleaning the data

$3.$ Analyzing the data

We will use Tableau to complete the $3$rd task.

Chicago

Chicago, the third most populous city in the United States, is a major hub of culture, finance, and media. Known for its architectural marvels, vibrant arts scene, and diverse neighborhoods, Chicago attracts millions of visitors each year.

Famous landmarks like Millennium Park, the Willis Tower, Navy Pier, and the Art Institute of Chicago contribute to its appeal. In $2019,$ Chicago welcomed over $60$ million visitors, underscoring its status as a significant Airbnb destination and a compelling subject for our analysis.

Inspiration

$$ What can we learn about different hosts and areas?

$$ What can we learn from predictions? $($ex: locations, prices, reviews, etc$)$

$$ Which hosts are the busiest and why?

$$ Is there any noticeable difference of traffic among different areas and what could be the reason for it$?$

Data Preparation

The initial dataset may be messy and may require extensive cleaning. After cleaning, the datasets from Chicago form the final dataset for visualization. It is as per your requirements.

Exploring the Dataset

Before we perform any analysis, we'll first see what our dataset looks like. These are some of the variables it contains:

$$ id $-$ id number that identifies the property

$$ name $-$ Property name

$$ host$\_$id $-$ id number that identifies the host

$$ host$\_$name $-$ Host name

$$ neighbourhood$\_$group $-$ The main regions of the city

$$ neighbourhood $-$ The neighbourhoods

$$ latitude $-$ Property latitude

$$ longitude $-$ Property longitude

$$ room$\_$type $-$ Type of the room

$$ price $-$ The price for one night

$$ minimum$\_$nights $-$ Minimum amount of nights to book the place

$$ number$\_$of$\_$reviews $-$ Number of reviews received

$$ last$\_$review $-$ Date of the last review

$$ reviews$\_$per$\_$month $-$ Amount of reviews per month

$$ calculated$\_$host$\_$listings$\_$count $-$ Number of properties available on Airbnb owned by the host

Visualization with Tableau

Tableau was chosen for its powerful and flexible visualization capabilities. Using Tableau, you can create interactive dashboards that tell the story of Airbnb in Chicago, focusing on four key areas $($you can modify your questions based on your insightfulness and criteria mentioned in final deliverables$)$:

Section $1$: Popular Neighborhoods

Key Questions:

$$ Which are the popular neighborhoods?

$$ What are their average prices and number of listings?

Visualizations:

$$ Neighborhood Popularity Map: A map highlighting popular neighborhoods in Chicago, showing average prices and the number of listings.

$$ Neighborhood Comparison Bar Chart: A bar chart comparing the average prices and the number of listings in each neighborhood.

Section $2$: Property and Room Types

Key Questions:

$$ What are the percent shares of different property types and room types?

Visualizations:

$$ Property Type Pie Chart: A pie chart showing the distribution of different property types $($e$.$g$.,$ apartments, houses, condos$).$

$$ Room Type Pie Chart: A pie chart illustrating the share of different room types $($e$.$g$.,$ entire home$/$apt$,$ private room, shared room$).$

Section $3$: Pricing Analysis

Key Questions:

$$ How does pricing vary with location, property type, and reviews?

Visualizations:

$$ Price Heatmap: A heatmap showing how prices vary across different locations in Chicago and New Orleans.

$$ Property Type vs$.$ Price Box Plot: A box plot comparing prices across different