Housing Price Prediction Dataset - Dataset Information
RSK World - Free Programming Resources & Source Code
Website: https://rskworld.in
Contact: help@rskworld.in, support@rskworld.in
Phone: +91 93305 39277
Founder: Molla Samser
Designer & Tester: Rima Khatun
Created: 2026

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DATASET INFORMATION
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Dataset Name: Housing Price Prediction Dataset
Category: Tabular Data
Difficulty: Beginner
Total Records: 50 properties
Total Features: 20 columns

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FEATURE DESCRIPTIONS
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id              : Unique identifier for each property
price           : Sale price of the property (target variable)
bedrooms        : Number of bedrooms
bathrooms       : Number of bathrooms (can include half baths like 2.25)
sqft_living     : Square footage of interior living space
sqft_lot        : Square footage of land lot
floors          : Number of floors/levels in the house
waterfront      : Binary indicator (0=no, 1=yes) for waterfront view
view            : Quality of view rating (0-4 scale)
condition       : Overall condition rating (1-5 scale)
grade           : Overall grade/quality rating (1-13 scale)
sqft_above      : Square footage above ground level
sqft_basement   : Square footage of basement
yr_built        : Year the house was originally built
yr_renovated    : Year of last renovation (0 if never renovated)
zipcode         : ZIP code of property location
lat             : Latitude coordinate
long            : Longitude coordinate
sqft_living15   : Average sqft of interior living space of 15 nearest neighbors
sqft_lot15      : Average sqft of land lot of 15 nearest neighbors
neighborhood    : Name of the neighborhood

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USE CASES
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1. Regression Analysis: Predict house prices based on features
2. Feature Engineering: Create new features from existing ones
3. Exploratory Data Analysis: Understand relationships between variables
4. Machine Learning: Train and evaluate regression models
5. Data Visualization: Create charts and plots for insights

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RECOMMENDED MODELS
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- Linear Regression
- Random Forest Regressor
- Gradient Boosting Regressor
- Ridge/Lasso Regression
- Support Vector Regression

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FILE FORMATS
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- CSV: housing_prices.csv (comma-separated values)
- JSON: housing_prices.json (JavaScript Object Notation)

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DEPENDENCIES
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See requirements.txt for full list. Key libraries:
- pandas: Data manipulation and analysis
- numpy: Numerical computing
- scikit-learn: Machine learning algorithms
- matplotlib: Data visualization
- seaborn: Statistical visualization

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