{"id":24449245,"url":"https://github.com/nishumehta/house-sales-analysis","last_synced_at":"2026-01-11T16:49:00.657Z","repository":{"id":273098433,"uuid":"917293335","full_name":"NishuMehta/House-Sales-Analysis","owner":"NishuMehta","description":"House Sales Analysis Dashboard for King County, Washington, built with Tableau. 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It provides insights into sales patterns, price distributions, and property conditions using interactive charts and maps.\n\n## 📊 Overview\nThis dashboard displays trends and distributions for house sales in King County, Washington, using data from **Mo Chen's Tableau End-to-End Portfolio Project** on GitHub and YouTube.\n\n### Key Insights:\n- **Daily Average House Sales Price**: Shows price fluctuations throughout October 2014.  \n- **House Price Distribution**: Visualizes the frequency of sales across different price ranges.  \n- **Bedroom and Bathroom Distribution**: Displays the most common property types by bedrooms and bathrooms.  \n- **View vs. Condition Heatmap**: Analyzes the relationship between house conditions, views, and average prices.  \n- **Geographical Insights**: Uses a map to show house sales patterns across King County neighborhoods.  \n\n---\n\n## 🛠️ Tools \u0026 Technologies Used\n- **Tableau** (for dashboard creation)  \n- **Excel** (for data preparation)  \n- **Python (Pandas)** (for data cleaning - optional)  \n- **GitHub** (for version control and project sharing)  \n\n---\n\n## 📂 Project Files\n- `Dashboard.twbx` – Tableau packaged workbook with the dashboard.  \n- `data.xlsx` – House sales dataset.  \n- `HouseSalesAnalysis.png` – Screenshot of the completed dashboard.  \n- `README.md` – This project documentation.  \n\n---\n\n## 📈 Dataset Information\n- **Source:** [Mo Chen GitHub](https://github.com/mochen862/tableau-end-to-end-portfolio-project) and Mo Chen’s YouTube Channel  \n- **Columns Include:**  \n  - `date` – Sale date  \n  - `price` – House sale price  \n  - `bedrooms`, `bathrooms` – Number of rooms  \n  - `sqft_living`, `sqft_lot` – House and lot area  \n  - `condition`, `view` – Condition ratings and view scores  \n  - `zipcode`, `location` – Geographical details  \n\n---\n\n## 💡 Insights Discovered\n- **Price Trends:** House prices peaked mid-October 2014, with an average around $450K–$500K.  \n- **Most Common House Type:** 3-bedroom houses were the most sold.  \n- **View Impact on Price:** Houses with excellent views and good conditions had the highest average sale prices.  \n- **Regional Sales:** The northern region of King County experienced the highest sales volume.  \n\n---\n\n## 🚀 How to Reproduce This Dashboard\n1. **Download the Dataset:** From Mo Chen’s GitHub repository.  \n2. **Open Tableau Public or Desktop:**  \n3. **Connect to Data:** Import `data.xlsx`.  \n4. **Create Sheets:**  \n   - Line chart for daily sales price.  \n   - Histograms for price and room distributions.  \n   - Heatmap for view vs. condition.  \n   - Map for geographical analysis.  \n5. **Assemble the Dashboard:** Arrange all sheets with filters and tooltips.  \n6. **Publish or Export:** Share via Tableau Public or save as `.twbx` file.  \n\n---\n\n## 💻 Learning Resources\n- **[Mo Chen YouTube Channel](https://www.youtube.com/@mochen)** – End-to-end Tableau tutorials.  \n- **[Tableau Documentation](https://help.tableau.com/)** – Official documentation for best practices.  \n- **[Kaggle](https://www.kaggle.com/)** – Practice with similar datasets.  \n\n---\n\n## 🏷️ Tags\n`Tableau` `Data Analysis` `House Sales` `Portfolio Project` `Real Estate`  \n\n---\n\n## 📢 Author\n- **[Your Name]** – Data Analyst and Tableau Enthusiast  \n- **GitHub:** [Your GitHub Profile]  \n- **LinkedIn:** [Your LinkedIn Profile]  \n\n---\n\n⭐ If you found this project helpful, please consider giving it a star on GitHub! ⭐  \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnishumehta%2Fhouse-sales-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnishumehta%2Fhouse-sales-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnishumehta%2Fhouse-sales-analysis/lists"}