Datasets:
Tasks:
Tabular Regression
Modalities:
Tabular
Formats:
csv
Languages:
English
Size:
10K - 100K
License:
| language: | |
| - en | |
| license: mit | |
| task_categories: | |
| - tabular-regression | |
| pretty_name: Synthetic Housing Price Dataset | |
| size_categories: | |
| - 10K<n<100K | |
| tags: | |
| - regression | |
| - tabular | |
| - synthetic | |
| - machine-learning | |
| - housing | |
| - education | |
| # 🏠 Synthetic Housing Price Dataset | |
| A synthetic tabular dataset designed for **machine learning regression** tasks. | |
| The dataset contains **10,000** randomly generated houses with prices computed using a deterministic rule-based pricing model. It is intended for experimentation, benchmarking, and educational purposes. | |
| > **Note:** This dataset is entirely synthetic and does not represent real-world housing market data. | |
| --- | |
| # Dataset Summary | |
| - **Rows:** 10,000 | |
| - **Features:** 6 | |
| - **Target:** `price` | |
| - **Task:** Regression | |
| - **License:** MIT | |
| --- | |
| # Dataset Structure | |
| | Feature | Type | Description | | |
| |----------|------|-------------| | |
| | `rooms` | Integer | Number of rooms | | |
| | `area` | Integer | House area in square feet | | |
| | `road_rating` | Float | Road quality rating (0.0–1.0) | | |
| | `water_electricity` | Float | Water & electricity availability rating (0.0–1.0) | | |
| | `police` | Integer | Nearby police station (0 = No, 1 = Yes) | | |
| | `education` | Integer | Nearby educational institution (0 = No, 1 = Yes) | | |
| | `price` | Integer | House price in USD (target variable) | | |
| --- | |
| # Data Generation | |
| The dataset was generated using **NumPy**. | |
| Generation process: | |
| - Random integer generation for `rooms` and `area` | |
| - Uniform random values between **0** and **1** for infrastructure ratings | |
| - Binary indicators for nearby police and educational facilities | |
| - House prices computed from a rule-based pricing function using: | |
| - Base price per square foot | |
| - Infrastructure quality | |
| - Available amenities | |
| - Total house area | |
| Because the underlying generation process is known, this dataset is useful for validating regression algorithms and benchmarking implementations. | |
| --- | |
| # Example | |
| | rooms | area | road_rating | water_electricity | police | education | price | | |
| |------:|-----:|------------:|------------------:|--------:|----------:|------:| | |
| | 3 | 516 | 0.635397 | 0.113835 | 1 | 0 | 114036 | | |
| | 3 | 516 | 0.708022 | 0.708022 | 0 | 0 | 114552 | | |
| | 3 | 516 | 0.486341 | 0.087613 | 1 | 1 | 122292 | | |
| --- | |
| # Intended Use | |
| This dataset is suitable for: | |
| - Regression | |
| - Machine Learning education | |
| - Model benchmarking | |
| - Feature engineering | |
| - Data visualization | |
| - Testing custom ML libraries | |
| - Algorithm comparison | |
| --- | |
| # Limitations | |
| - Synthetic data only | |
| - Not representative of any real housing market | |
| - Should not be used for real-world property valuation or economic analysis | |
| --- | |
| # Citation | |
| If you use this dataset in a project, please cite or reference this repository. | |
| --- | |
| ## Author | |
| Created by **ItzRustam**. |