RSK World - Housing Price Prediction Dataset | Real Estate ML | Price Prediction AI | Housing Price Deep Learning | RSK World

Housing Price Prediction Dataset - RSK World

Price Prediction Property Data Machine Learning XGBoost & LightGBM Python Regression Ready

This project features a comprehensive Housing Price Prediction dataset designed for professional real estate price prediction, property valuation, and housing market analytics. The dataset includes property records with features like bedrooms, bathrooms, square footage, location coordinates, neighborhood data, and sale prices. Includes powerful Python scripts: data_analysis.py for data analysis, feature_engineering.py for feature engineering, advanced_models.py for model training, hyperparameter_tuning.py for optimization, model_comparison.py for evaluation, predict_price.py for price predictions, and data_visualization.py for visualization. The package includes interactive demo website, comprehensive README.md, and MIT License. Perfect for data scientists, researchers, students, and developers working on real estate analytics, price prediction, property valuation, and machine learning projects.

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Dataset Overview

Complete housing price prediction dataset with property features, location data, and sale prices for real estate ML models.

Property records with comprehensive features
Location data (coordinates, zipcode, neighborhood)
Price labels (regression target)
50 property records with 20 features
Bedrooms, bathrooms, square footage features
Property quality and condition information
Pre-labeled with sale prices
Ready for regression models
Advanced feature engineering included
Multiple ML models support
Perfect for price prediction & ML training

Dataset Structure & Files

Well-organized project structure with property data, Python scripts for ML training, evaluation, and prediction pipelines.

housing_prices.csv - Main dataset file
data_analysis.py - Data analysis script
feature_engineering.py - Feature engineering
advanced_models.py - Model training script
hyperparameter_tuning.py - Hyperparameter optimization
model_comparison.py - Model comparison and evaluation
predict_price.py - Price prediction utility
data_visualization.py - Data visualization script
housing_price_prediction.ipynb - Jupyter notebook
Consistent naming convention
Easy to load with pandas
Scikit-learn/XGBoost/LightGBM ready format

Machine Learning Training

Complete training pipeline with support for price prediction models, feature engineering, and multiple ML frameworks.

Random Forest regressor
XGBoost gradient boosting
LightGBM fast gradient boosting
Linear Regression and Ridge/Lasso
Scikit-learn compatibility
Advanced feature engineering
Hyperparameter tuning support
Cross-validation support
Model evaluation metrics
Regression analysis integration
Performance metrics
Model training examples
Price prediction utilities

Multiple File Formats

Dataset available in CSV format for maximum compatibility with different data science tools and ML frameworks.

CSV format (comma-separated values)
Excel compatible
Pandas DataFrame ready
NumPy array compatible
Scikit-learn compatible
XGBoost/LightGBM ready
Standard data science formats
Easy to import and process
Compatible with all ML libraries
Jupyter Notebook ready
Python pandas ready

Analysis & Visualization

Comprehensive analysis tools with visualization capabilities and interactive housing data explorer.

Interactive Housing Data Explorer
Price distribution charts
Property feature visualization
Dataset statistics
Feature importance analysis
Price range filtering
Performance benchmarking
Model evaluation metrics
Regression visualization
Dataset analysis tools
Interactive Demo Website

Compatible Frameworks

Works with all major machine learning and data science frameworks out of the box.

Scikit-learn ML library
XGBoost gradient boosting
LightGBM fast boosting
SHAP model explainability
NumPy numerical computing
pandas data manipulation
matplotlib visualization
Jupyter Notebook support
Price prediction models
Regression models
Ensemble methods

What You Get

Complete package with all files needed for professional housing price prediction projects.

Property records with sale prices
Python utility scripts
data_analysis.py - Data analysis
feature_engineering.py - Feature engineering
advanced_models.py - Model training
hyperparameter_tuning.py - Hyperparameter optimization
model_comparison.py - Model comparison
predict_price.py - Price prediction
data_visualization.py - Visualization
Interactive demo website
Complete documentation (README, ADVANCED_FEATURES)
MIT License

Interactive Demo Website

Beautiful demo website with housing data explorer, property analytics dashboard, and comprehensive guide.

Modern animated design
Interactive Housing Data Explorer
Property Analytics Dashboard
Model Performance Metrics
Filter by price range
Property feature visualization
Price distribution charts
Dataset statistics display
Step-by-step usage guide
Dark theme with gradients
Fully responsive layout

Python Scripts Included

Professional Python scripts for price prediction, feature engineering, and model training.

data_analysis.py - Analyze housing dataset
feature_engineering.py - Advanced feature creation
advanced_models.py - Train ML models (RF, XGBoost, LightGBM)
hyperparameter_tuning.py - Optimize model parameters
model_comparison.py - Comprehensive model comparison
predict_price.py - Price prediction utility
data_visualization.py - Data visualization
validate_data.py - Dataset verification
Batch processing support
Feature engineering utilities
Model training tools
Complete code examples

Dataset Features

Comprehensive housing price prediction dataset with property records and various property features.

Property records - Bedrooms, bathrooms, square footage, location
Location features - Coordinates, zipcode, neighborhood
Price labels - Regression target (sale prices)
50 property records - Balanced dataset
Advanced features - Quality ratings, renovation year, waterfront
High-quality property data
Accurate price labels
Ready for ML training
Feature engineering utilities
Easy to extend dataset
Organized project structure

Credits & Acknowledgments

This dataset is provided for educational and research purposes. Core technologies and libraries are credited below.

Python 3.8+ - Programming language (PSF License)
Scikit-learn - Machine learning library (BSD License)
XGBoost - Gradient boosting framework (Apache 2.0)
NumPy - Numerical computing (BSD License)
pandas - Data manipulation (BSD License)
matplotlib - Data Visualization (PSF License)
RSK World - Dataset creator and provider
GitHub Repository - Source code and releases
Author: Molla Samser | Designer: Rima Khatun
MIT License - Free for learning & research

Support & Contact

For commercial use, custom datasets, or integration help, please contact us.

Email: help@rskworld.in
Phone: +91 93305 39277
Website: RSKWORLD.in
Location: Nutanhat, Mongolkote, West Bengal, India
Author: Molla Samser
Designer & Tester: Rima Khatun
GitHub: Coming Soon
Housing Price Prediction Dataset Documentation
Technical Support Available
Custom Dataset Requests Welcome

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Housing Price Prediction

Dataset Overview

Dataset Structure & Files

Machine Learning Training

Multiple File Formats

Analysis & Visualization

Compatible Frameworks

What You Get

Interactive Demo Website

Python Scripts Included

Dataset Features

Credits & Acknowledgments

Support & Contact

Featured Content

Additional Sponsored Content

Download Free Source Code

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