{"id":15003141,"url":"https://github.com/nero103/airbnb-destination","last_synced_at":"2026-03-27T02:16:53.766Z","repository":{"id":263313247,"uuid":"702723069","full_name":"Nero103/airbnb-destination","owner":"Nero103","description":"This is and end-to-end project to uncover the ideal destination based on listings and hosts. 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The objective was to answer key business questions through data transformation, visualization, and statistical analysis. The final dashboard, built in Tableau, helps identify market patterns, city-level listing behaviors, and seasonal guest activity.\n\n## Business Goal\nTo uncover meaningful insights from Airbnb data such as:\n\nWhich cities dominate in listings and pricing?\n\nHow do amenities like parking relate to listing popularity?\n\nWhen do guest bookings peak by season?\n\nAre host behaviors (like accept rates) correlated with review scores?\n\n## Tools \u0026 Technologies\nExcel: Initial data exploration, cleaning, and distribution checks\n\nMicrosoft SQL Server: Data modeling, aggregation, conditional logic (e.g., IF/ELSE), statistical querying\n\nTableau: Dashboard creation, KPI visuals, city comparisons, seasonal booking trends\n\n## Data Processing\nCSV files were preprocessed in Excel to inspect distributions and fix date formats\n\nThe cleaned dataset was loaded into SQL Server for further transformation\n\nData modeling included creating time-based logic and segmenting data by city, season, and amenity type\n\nFinal outputs were loaded into Tableau for visualization\n\n## Dashboard Insights\nKey performance indicators (KPIs) include:\n\n- 6.87M total listings\n\n- $608.79 average total listing price\n\n- 93.41 average review score\n\n- 0.87 average host response rate\n\n- 0.83 average host accept rate\n\n- 180,024 active hosts with Superhosts making up 17% of the total\n\n**City-Level Insights**\n- Sydney leads in total listings with over 2.6M\n\n- Cape Town and Bangkok have the highest average listing prices\n\n- New York shows the highest instance of paid parking, while Sydney leads in unpaid parking availability\n\n**Seasonal Trends**\nBooking activity peaked in Autumn 2019 and Winter 2020, suggesting shifting travel behavior potentially impacted by global events\n\nSpring and Summer consistently saw higher bookings until 2020\n\n**Correlation Findings**\nA statistically significant but weak negative correlation was found between host accept rate and review location score (p-value \u003c 0.05)\n\nOther host metrics showed minimal linear relationship with review scores\n\n## Recommendations\nOptimize Listings in High-Performing Cities: Sydney, New York, and Paris dominate in volume and visibility. Capitalize on Peak Seasons: Autumn and Winter appear as key booking periods for marketing campaigns. Improve Parking Options: Listings with unpaid parking saw broader availability—hosts can highlight this as a perk Furthermore, study Weak Correlations, Although weak, the relationship between host behavior and review quality may benefit from deeper exploration with additional data.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnero103%2Fairbnb-destination","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnero103%2Fairbnb-destination","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnero103%2Fairbnb-destination/lists"}