Proyecto Integrador M5 AirBnB

Data Analytics Airbnb project?

Module 5: Data Analytics

Instructor: Henry Cristian Castro

This activity (purely educational) is part of my portfolio of practices that allow to improve my skills in Data Science with real world data problems and sets.

Welcome to the Data Analytics project on Airbnb in Buenos Aires! In this project, we will explore the fascinating world of short -term rentals and discover investment opportunities using the data provided by Airbnb.

Aim

The main objective of this project is to apply the concepts and techniques learned in the module to perform an exploratory and descriptive analysis of the Airbnb data. In addition, we will seek to identify investment opportunities in the short -term accommodation market.

Steps to follow

1. Download data: Be sure to obtain data files from the indicated source.
2. Import data: Use tools such as Power BI or Python to import and work with data.
3. Cleaning and validation: identifies and treats possible missing, erroneous or atypical values in the data.
4. Exploratory analysis: Use data display and statistical concepts to describe the variables and their relationships.
5. Answer business questions: addresses the questions about the chosen dataset.
6. Prepare conclusions: Create a report with the conclusions obtained from the analysis.

Data analysis

In our project, we have explored various aspects of the Airbnb business, such as availability by type of property and location, guest preferences, seasonal evolution of prices and occupation over time. We use advanced tools such as Power BI to visualize this data clearly and understandably.

Results and conclusions

1. Type of property: We observe a wide diversity of types of properties, being the apartments, houses, lofts and condominiums the most appropriate for short -term rentals.
2. Location: The location is crucial for the success in short -term rental, with neighborhoods such as Villa Urquiza, Villa Ortuzar and Villa Pueyrredon standing out as the most attractive.
3. Importance of reviews: the comments and reviews of guests are determinants in the demand for a property, but do not always correlate with the price.

Conclusion ?

This project has been an enriching and exciting experience. We learned to extract, transform and load data, as well as to perform a deep exploratory analysis. I am delighted with the results and anxious to continue learning and growing in the world of data analysis!

Recommendations and improvements

I am open to read your recommendations to improve my project and my professional development. You can provide your comments and suggestions directly in the Readme file or contact me to discuss possible improvements.

Interacting with the data on Power Bi Desktop

To explore the data interactively, I recommend downloading the M5.pbix Integrator Project file, which contains an interactive Airbnb data model in Power Bi Desktop.

Conclusion ?

This project has been an enriching and exciting experience. We learned to extract, transform and load data, as well as to perform a deep exploratory analysis. I am delighted with the results and anxious to continue learning and growing in the world of data analysis!

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Thank you for reviewing my project and your valuable recommendations!