This repository focuses on the ETL (Extract, Transform, Load) process and visualization of the NY Airbnb dataset, obtained from Kaggle. The project provides a structured workflow for cleaning the original dataset, performing exploratory data analysis, and creating insightful visualizations.

• starting_file/: Contains the original dataset from Kaggle.

  • Airbnb_Open_Data.csv: The raw data file downloaded from Kaggle.

• cleaning_starting_file/: Contains Jupyter Notebook for cleaning and transforming the starting file.

  • airbnb_ny_df.ipynb: Jupyter Notebook documenting the process of importing the starting file, cleaning, and analyzing it, and exporting the cleaned dataset.

  • exported_cleaned_starting_file/: Contains the cleaned dataset exported from the ETL process.

  • airbnb_df_cleaned.csv: The cleaned and processed dataset, ready for analysis.

• graphs/: Contains Jupyter Notebook for data analysis and visualizations.

  • graphs.ipynb: Jupyter Notebook documenting the process of cleaning and transforming the starting file, and creating visualizations.

Explore the cleaned dataset and leverage the insights gained from the analysis. The Jupyter Notebooks serve as comprehensive guides, providing transparency into the ETL process and visualization techniques applied to the NY Airbnb dataset.