{"id":18223968,"url":"https://github.com/sushant-suresh/airbnb-listings-eda-data-viz-using-python","last_synced_at":"2026-05-02T03:31:15.952Z","repository":{"id":260905329,"uuid":"882662871","full_name":"Sushant-Suresh/Airbnb-Listings-EDA-Data-Viz-Using-Python","owner":"Sushant-Suresh","description":"EDA on NY Airbnb data to uncover trends and patterns in rental listings.","archived":false,"fork":false,"pushed_at":"2024-11-03T14:59:17.000Z","size":1789,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-08T02:51:55.408Z","etag":null,"topics":["data-analytics","jupyter-notebook","python"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Sushant-Suresh.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-11-03T12:36:28.000Z","updated_at":"2024-11-03T15:02:16.000Z","dependencies_parsed_at":"2024-11-03T14:26:39.472Z","dependency_job_id":"e4ae48db-8f4d-4b5b-8f3a-dc3f2480c9ed","html_url":"https://github.com/Sushant-Suresh/Airbnb-Listings-EDA-Data-Viz-Using-Python","commit_stats":null,"previous_names":["sushant-suresh/python-eda-data-viz-project-airbnb-listings-in-new-york","sushant-suresh/airbnb-listings-eda-data-viz-using-python"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Sushant-Suresh%2FAirbnb-Listings-EDA-Data-Viz-Using-Python","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Sushant-Suresh%2FAirbnb-Listings-EDA-Data-Viz-Using-Python/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Sushant-Suresh%2FAirbnb-Listings-EDA-Data-Viz-Using-Python/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Sushant-Suresh%2FAirbnb-Listings-EDA-Data-Viz-Using-Python/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Sushant-Suresh","download_url":"https://codeload.github.com/Sushant-Suresh/Airbnb-Listings-EDA-Data-Viz-Using-Python/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247767250,"owners_count":20992541,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-analytics","jupyter-notebook","python"],"created_at":"2024-11-04T01:05:38.568Z","updated_at":"2026-05-02T03:31:15.902Z","avatar_url":"https://github.com/Sushant-Suresh.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Airbnb Listings EDA \u0026 Data Viz Project: New York\n\n![Renting-out-on-Air-BnB-980x655](https://github.com/user-attachments/assets/13abc58f-626a-4c0c-837c-16ecf451f186)\n\n## Project Overview\nThis project performs **Exploratory Data Analysis (EDA)** on New York Airbnb data to uncover trends and patterns in rental listings using libraries like **Pandas, Numpy, Matplotlib, Seaborn** for cleaning, visualization, and analysis. \n\n## Objective\nThe goal of this project is to:\n1. Detect and remove potential **outliers** in rental prices.\n2. Analyze **price distribution and average price per bed** across different neighborhoods.\n3. Analyze the **ratings** distribution for all rental listings.\n4. Find relationship between **price** and **rating** of the rental listings.\n\n## Dataset\nThe dataset contains **20,770 rows and 22 fields**, including:\n- `id`: Unique identifier for each listing  \n- `name`: Title of the Airbnb listing  \n- `host_name`: Name of the host  \n- `neighborhood_group`: Area where the listing is located  \n- `latitude/longitude`: Geolocation of listings  \n- `price`: Per night rental price  \n- `room_type`: Type of accommodation (e.g., entire home, private room)  \n- `reviews_per_month`: Average monthly reviews for the listing  \n- `availability_365`: Number of available days in the year\n\n## Workflow\n\n### 1. Data Cleaning\n- **Handle missing data**: Dropped all NULL values from the dataframe.\n- **Handle duplicate entries**: Dropped all duplicate entries from the dataframe.\n- **Remove outliers**: Listings with **price \u003e $2,000** were capped to avoid skewed visualizations.\n\n### 2. EDA \u0026 Data Visualization\n1. **Price distribution**: \n   - Visualized the price distribution after removing potential outliers using histogram.\n   - Identified **rent \u003c 250$** as the most common rental price range.\n\n2. **Rating Distribution**:\n   - Analyzed ratings given by guests.\n   - **Ratings \u003e 4** had the max. frequency.\n\n3. **Price Distribution Across Neighbourhood**:\n   - Used barplot to show rent across differnet areas in New York.\n   - Further categorized it based on room types.\n\n4. **Price/Bed Distribution Across Neighbourhood**:\n   - Used barplot to analyze rent per bed across differnet areas in New York.\n   - Further categorized it based on room types.\n\n5. **Relationship between Price and Ratings**:\n   - Used scatterplot to show the relationship between rental price and ratings given by guests.\n   - **Ratings \u003e 4** were common for listings with high rental.\n\n6. **Geographic Distribution of Airbnb Listings**:\n   - Used scatterplot to vizualize Airbnb locations using their longitude and latitude values.\n   - Found out Hotel Rooms were very less and majority of the room types were either Private or Entire Home.\n\n## Key Findings and Insights\n1. Manhattan has the most expensive listings (mostly Hotel Rooms), followed by Brooklyn.\n2. Majority of the listings have guest ratings \u003e 4.\n3. Few listings priced at $10,000+ were detected and were filtered out.\n4. There are high chances of guests getting a good stay under 250$.\n5. Entire homes/apartments cost significantly more than private or shared rooms.\n\n## Conclusion\nThis project offers valuable insights into the New York Airbnb listings, helping both guests and hosts make informed decisions. By using **EDA techniques**, we identified key trends and developed actionable recommendations. Future improvements can involve advanced analytics and predictive modeling to further enhance the findings.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsushant-suresh%2Fairbnb-listings-eda-data-viz-using-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsushant-suresh%2Fairbnb-listings-eda-data-viz-using-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsushant-suresh%2Fairbnb-listings-eda-data-viz-using-python/lists"}