Airbnb Open Data - Exploratory Data Analysis

Python: Pandas | Matplotlib | Seaborn

In this project, I performed an in-depth exploratory data analysis (EDA) on Airbnb Open Data, focusing on uncovering insights and trends within the dataset.

The analysis includes: 
 

• Data Cleaning and Preprocessing: Addressed missing values, handled outliers, and transformed data for better usability.
   

• Descriptive Statistics: Computed various statistical measures to understand the distribution and central tendencies of the data.
   

• Visualization: Utilized different visualization techniques to identify patterns and relationships, including:
   

  • Geographic distribution of listings.

  • Price distribution and influencing factors.

  • Review trends over time.

  • Host activity and listing characteristics.
     

• Correlation Analysis: Explored relationships between different variables to identify potential influencing factors on pricing and popularity.
   

• Insights: Summarized key findings and insights drawn from the data, providing actionable recommendations for stakeholders.