If you're here to view the code, you can explore it directly on GitHub. For interactive visualizations, please click here to view the notebook on nbviewer: ([https://nbviewer.org/github/Raagini23/EDA-Airbnb-listings-in-Milan/blob/main/Airbnb%20listings%20Milan%20EDA%20.ipynb)]
This project is an exploratory data analysis (EDA) on Airbnb listings in Milan, aimed at extracting actionable insights for property hosts and owners. The analysis focuses on understanding market dynamics, pricing strategies, and identifying opportunities within the Milan Airbnb market.
The dataset used for this analysis was obtained from Inside Airbnb (https://insideairbnb.com/milan/). It contains comprehensive information about listings, including prices, reviews, property types, and availability.
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Data Cleaning:
- Handling missing values by using available data and filling in some missing values manually.
- For specific listings with missing data, I visited the Airbnb Milan website, clicked on the individual listings, and filled in the missing information manually.
- Addressing outliers and other anomalies in the dataset to ensure the analysis is accurate and reliable.
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Data Exploration: Exploring various aspects of the data using statistical summaries and visualizations.
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Insights & Findings: Highlighting the key insights derived from the analysis.
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Conclusion: Summarizing the findings and their implications.
Several visualizations were created to illustrate the trends and patterns in the data, including:
- Correlation of Price with Minimum Nights, No. of Reviews etc
- .Details of High-priced listings
- Property types and their availability.
The project makes use of the following Python libraries:
- pandas
- numpy
- matplotlib
- seaborn
- plotly
- geopandas