{"id":27646183,"url":"https://github.com/fbarffmann/home_sales","last_synced_at":"2026-05-06T19:09:13.841Z","repository":{"id":287743063,"uuid":"863186118","full_name":"fbarffmann/Home_Sales","owner":"fbarffmann","description":"Analyzed 25,000+ home sales using PySpark and SparkSQL. Identified pricing trends by year built, home features, and view rating. Optimized query run-time by 70% using caching.","archived":false,"fork":false,"pushed_at":"2025-04-13T17:38:08.000Z","size":2604,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-13T17:39:27.321Z","etag":null,"topics":["aws","big-data","data-analysis","home-sales","parquet","pyspark","python","spark","spark-sql","sql"],"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/fbarffmann.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,"zenodo":null}},"created_at":"2024-09-25T21:37:33.000Z","updated_at":"2025-04-13T17:38:11.000Z","dependencies_parsed_at":"2025-04-13T17:49:33.673Z","dependency_job_id":null,"html_url":"https://github.com/fbarffmann/Home_Sales","commit_stats":null,"previous_names":["fbarffmann/home_sales"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fbarffmann%2FHome_Sales","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fbarffmann%2FHome_Sales/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fbarffmann%2FHome_Sales/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fbarffmann%2FHome_Sales/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/fbarffmann","download_url":"https://codeload.github.com/fbarffmann/Home_Sales/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":250540920,"owners_count":21447428,"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":["aws","big-data","data-analysis","home-sales","parquet","pyspark","python","spark","spark-sql","sql"],"created_at":"2025-04-24T01:17:22.243Z","updated_at":"2026-05-06T19:09:13.795Z","avatar_url":"https://github.com/fbarffmann.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Home Sales Analysis with PySpark\n\nBuilt a scalable data analysis pipeline using PySpark to explore pricing trends in home sales across King County, Washington. Leveraged SparkSQL for querying and partitioned the dataset to optimize performance on large-scale data.\n\n## Tools \u0026 Technologies Used\n\n- Python\n- PySpark\n- SparkSQL\n- Parquet File Partitioning\n- AWS S3 (Data Source)\n- Jupyter Notebooks\n\n## File Structure\n\n```text\n.\n├── Home_Sales.ipynb                     # PySpark analysis notebook\n├── home_sales_partitioned/              # Partitioned parquet files by year built\n└── Resources/\n    └── home_sales.csv                   # Raw home sales dataset\n```\n\n## Skills Demonstrated\n\n- Distributed data processing with PySpark\n- SQL querying within Spark\n- Data partitioning and caching for optimized performance\n- Handling large real-world datasets\n- Identifying pricing trends from structured data\n\n## Key Findings\n\n- Analyzed over 25,000 home sales in King County, WA.\n- 4-bedroom homes sold for an average price between $300,263 and $306,910 per year.\n- Homes with 3 beds, 3 baths, 2 floors, and 2,000+ sqft averaged over $600,000 after 2015.\n- Homes with a view rating of 4 or higher had an average sale price exceeding $350,000.\n- Partitioning data by year built improved query performance by over 70%.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffbarffmann%2Fhome_sales","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffbarffmann%2Fhome_sales","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffbarffmann%2Fhome_sales/lists"}