AlmaBetter Verfied Project - Airbnb Bookings Analysis
Since 2008, guests and hosts have used Airbnb to expand on traveling possibilities and present a more unique, personalized way of experiencing the world. Today, Airbnb became one of a kind service that is used and recognized by the whole world. Data analysis on millions of listings provided through Airbnb is a crucial factor for the company. These millions of listings generate a lot of data - data that can be analyzed and used for security, business decisions, understanding of customers' and providers' (hosts) behavior and performance on the platform, guiding marketing initiatives, implementation of innovative additional services and much more.
This dataset has around 49,000 observations in it with 16 columns and it is a mix between categorical and numeric values.
This Project include one colab file where all the python coding is present.
Performed exploratory data analysis using varius visualization plots.
EDA stands for Exploratory Data Analysis. It is a crucial step in the data analysis process where the primary objective is to gain a better understanding of the data before applying any statistical modeling or hypothesis testing. EDA involves examining and visualizing the data through various techniques to uncover patterns, identify outliers, detect missing values, assess data quality, and understand the relationships between variables. By conducting EDA, data analysts can make informed decisions about data preprocessing, feature engineering, and modeling strategies, ultimately leading to more accurate and meaningful insights. EDA plays a fundamental role in data-driven decision-making and helps guide subsequent steps in the data analysis pipeline.
High Level Document(HLD) for Aribnb booking analysis : Word Document .
Airbnb is an American company that operates an online market place for lodging, primarily Homestays for vacation rentals and tourism activities. It’s providing services similar to Indian company like Oyo Rooms. We did data analysis on 49,000 of listings provided through Airbnb is a crucial factor for the company. Our main objective is to find out the key metrics that influence the listing of properties on the platform. For this, we will explore and visualize the dataset from Airbnb in NYC using basic exploratory data analysis (EDA) techniques. We have found out the distribution of every Airbnb listing based on their location, including their price range, room type, listing name, and other related factors. We have analyzed this dataset from different perspectives and have come up with some interesting insights. This can help in making strategic data-driven decisions by the marketing team, finance team and technical team of Airbnb.