In this project, which involves the MongoDB Atlas sample Airbnb dataset, our objective is to conduct Exploratory Data Analysis (EDA) by performing data cleaning. Ultimately, we aim to represent the insightful findings in a visually compelling format through storytelling using Power BI.
It effectively communicates the goals of the project, including analyzing Airbnb data using MongoDB Atlas, conducting data cleaning and preparation, and utilizing geospatial visualizations and dynamic plots to uncover insights related to pricing, availability, and location-based trends. If you have any specific questions or if there's anything else you'd like assistance with regarding this project, feel free to let me know!
The project's objective is to Airbnb data visualization techniques for a comprehensive understanding of the dataset. It aims to leverage Exploratory Data Analysis (EDA) and Business Intelligence tools, such as Power BI, to visually interpret the data and extract meaningful insights.
Modules needed for the project!
a) Pandas b) urllib.parse c) pymongo
- Power BI
The main product for creating data visualizations and reports. It is a Windows desktop application that can connect to a variety of data sources and allows users to create interactive visualizations using a drag-and-drop interface.
Step 1 :
Establish a connection to the MongoDB Atlas database and retrieve the Airbnb dataset.
Step 2 :
Clean the Airbnb dataset by addressing missing values, eliminating duplicates, and adjusting data types as required. Prepare the dataset for Exploratory Data Analysis (EDA) and visualization tasks, ensuring data integrity and consistency throughout the process.
Step 3 :
Utilize the cleaned data to conduct an analysis and visualization of price variations across different locations, property types, and seasons. Develop dynamic plots and charts that empower users to explore trends in pricing, identify outliers, and discern correlations with other variables.
Step 4 :
Leverage Tableau or Power BI to craft a comprehensive dashboard showcasing key insights derived from your analysis. Integrate various visualizations, including maps, charts, and tables, to offer a holistic perspective on the Airbnb dataset and its underlying patterns. Employ analysis techniques such as DAX (Data Analysis Expressions) to create new measures and add columns, enhancing the utility and depth of your analysis. This approach will enable a more nuanced exploration of the dataset, facilitating a richer understanding of trends and relationships within the Airbnb data.
Step 5 :
The final output of my project aims to provide an easily accessible analysis of Airbnb data for clients and users, particularly those interested in tourism and hotel visits. This analysis includes features such as hotel types, average prices, minimum and maximum prices, types of rooms, available facilities, and a map for convenient exploration. By presenting this information in a user-friendly format, we aim to enhance the experience of individuals seeking accommodation, offering valuable insights into various aspects of Airbnb listings to facilitate informed decision-making for their travel plans.
