Unlocking the Secrets of the Habitable Stay: An AirBnB Data Analysis Project Using Tableau

Introduction

Airbnb has quickly become one of the most popular ways to find lodging for travelers around the world. With its vast array of options and competitive pricing, it has revolutionized the way people think about booking accommodations. However, behind the success of Airbnb lies a massive amount of data. From booking trends to user behavior, this data has the potential to provide valuable insights into the way people travel and what they look for in lodging. In this project, I took a deep dive into Airbnb data to explore the patterns and trends that underlie this popular platform.

Statement of Business Task

To use the insights gained from the data to improve the company's offerings and marketing strategies, ultimately driving more business and revenue.

Data Source and Tools Used

The dataset was gotten from Kaggle and it contains 13 columns and 30,479 rows. A snap shot of the dataset is shown below;

I used Excel to carry out the cleaning and transformation of the dataset and Tableau for the visualizations.

Data Cleaning and Transformation

Data cleaning and transformation are an important aspect in data analysis process as it ensures accuracy and credibility of the insights generated from the data. All the datasets were loaded into Excel for the data cleaning and transformation process.

For this stage, the following steps were undertaken:

-I removed columns unnecessary to my analysis, columns such as the Zip-code were removed from the dataset.

-There are obviously blank entries in the dataset so I proceeded to highlight them using “Find & Select” feature in Excel, Then I removed all rows that were highlighted.

-When the data becomes clean enough, I loaded it into tableau to begin my analysis.

Here is a snapshot of my data after the cleaning and transformation stage;

Exploratory Data Analysis and Visualization

• I created my first worksheet, by creating a shape chart which shows the average price of different house types in different neighborhoods.

- From this chart, we can see that home listings in Manhattan are the most expensive and that lighthouses in Brooklyn are the cheapest on average

• Then I created horizontal bar charts showing the average review scores and average number of beds in different property types.

- These bar charts show that Lighthouses, Castles and Chalet have the highest reviews on average and if you are travelling with your family you might need to avoid castles and chalets as they have the lowest number of beds on average.

• I then showed the number of listings in each neighborhoods using bubble charts.

- Manhattan and Brooklyn have the largest number of listings within the time-frame being considered in the dataset.

Overall, these are the insights that can be gotten from my analysis, interact with the link attached below to interact with the dashboard.

Recommendations

1.   The number of listings in neighborhood such as Staten Island and Bronx is extremely low, marketing strategies that target these geographical locations should be launched.
   

2.    Campers, Dorms and Cabin have the lowest average review ratings, Special attention need to be paid to listings with this property type.
   

3.  Light houses have a high number of beds and review ratings on average, this property type can be recommended to customers more often.
   

Here is a link to the Dashboard