House Price Prediction using Core ML
The Housing Price Prediction project addresses the critical need for accurate property valuation in real estate, urban planning, and financial markets. By leveraging machine learning techniques, it enables stakeholders such as agencies, investors, and homebuyers to make data-driven decisions on property investments, pricing strategies, and market trend analysis. The project uses the Kaggle House Prices – Advanced Regression Techniques dataset, with extensive data cleaning, feature engineering, and dimensionality reduction to prepare reliable inputs for predictive modeling.
Client
Personal
Client
Personal
Technology
regression models, ML
Technology
regression models, ML
Timeline
5 weeks
Timeline
5 weeks
The project aims to build and compare multiple regression models for housing price estimation by:
Identifying the most effective predictive algorithm based on accuracy (MSE) and explanatory power (R²).
Reducing high-dimensional data (217 features) into meaningful components using PCA.
Employing clustering (K-Means) to uncover natural market segments and provide insights into housing categories.
Delivering interpretable outputs that improve transparency in property valuation for real-world applications.
Among the tested models (Decision Tree, Linear Regression, KNN, Random Forest), Linear Regression emerged as the optimal choice due to:
Highest R² of 0.85 indicating strong explanatory power.
Reasonably low MSE of approximately 1.19e+09 indicating competitive accuracy.
Simplicity and interpretability which makes it easily understood by non-technical stakeholders.
Additionally, K-Means clustering with k=4 revealed distinct housing categories:
Vintage Essentials: Affordable older homes.
Modern Comforts: Newer, amenity-rich suburban houses.
Classic Fixer-Uppers: Dated homes suitable for renovation.
Suburban Classics: Balanced family homes with moderate pricing.
The project demonstrates that a Linear Regression model combined with clustering insights provides a powerful and interpretable tool for housing price prediction and market segmentation. While PCA and linear methods ensure robustness, limitations include reduced interpretability of original features, inability to capture non-linear trends, and a relatively small dataset of about 1500 instances.
Future improvements may involve testing ensemble models, incorporating non-linear approaches such as XGBoost or Neural Networks, and expanding the dataset for better generalizability.
House Price Prediction using Core ML
The Housing Price Prediction project addresses the critical need for accurate property valuation in real estate, urban planning, and financial markets. By leveraging machine learning techniques, it enables stakeholders such as agencies, investors, and homebuyers to make data-driven decisions on property investments, pricing strategies, and market trend analysis. The project uses the Kaggle House Prices – Advanced Regression Techniques dataset, with extensive data cleaning, feature engineering, and dimensionality reduction to prepare reliable inputs for predictive modeling.
Client
Personal
Client
Personal
Technology
regression models, ML
Technology
regression models, ML
Timeline
5 weeks
Timeline
5 weeks
The project aims to build and compare multiple regression models for housing price estimation by:
Identifying the most effective predictive algorithm based on accuracy (MSE) and explanatory power (R²).
Reducing high-dimensional data (217 features) into meaningful components using PCA.
Employing clustering (K-Means) to uncover natural market segments and provide insights into housing categories.
Delivering interpretable outputs that improve transparency in property valuation for real-world applications.
Among the tested models (Decision Tree, Linear Regression, KNN, Random Forest), Linear Regression emerged as the optimal choice due to:
Highest R² of 0.85 indicating strong explanatory power.
Reasonably low MSE of approximately 1.19e+09 indicating competitive accuracy.
Simplicity and interpretability which makes it easily understood by non-technical stakeholders.
Additionally, K-Means clustering with k=4 revealed distinct housing categories:
Vintage Essentials: Affordable older homes.
Modern Comforts: Newer, amenity-rich suburban houses.
Classic Fixer-Uppers: Dated homes suitable for renovation.
Suburban Classics: Balanced family homes with moderate pricing.
The project demonstrates that a Linear Regression model combined with clustering insights provides a powerful and interpretable tool for housing price prediction and market segmentation. While PCA and linear methods ensure robustness, limitations include reduced interpretability of original features, inability to capture non-linear trends, and a relatively small dataset of about 1500 instances.
Future improvements may involve testing ensemble models, incorporating non-linear approaches such as XGBoost or Neural Networks, and expanding the dataset for better generalizability.
House Price Prediction using Core ML
The Housing Price Prediction project addresses the critical need for accurate property valuation in real estate, urban planning, and financial markets. By leveraging machine learning techniques, it enables stakeholders such as agencies, investors, and homebuyers to make data-driven decisions on property investments, pricing strategies, and market trend analysis. The project uses the Kaggle House Prices – Advanced Regression Techniques dataset, with extensive data cleaning, feature engineering, and dimensionality reduction to prepare reliable inputs for predictive modeling.
Client
Personal
Client
Personal
Technology
regression models, ML
Technology
regression models, ML
Timeline
5 weeks
Timeline
5 weeks
The project aims to build and compare multiple regression models for housing price estimation by:
Identifying the most effective predictive algorithm based on accuracy (MSE) and explanatory power (R²).
Reducing high-dimensional data (217 features) into meaningful components using PCA.
Employing clustering (K-Means) to uncover natural market segments and provide insights into housing categories.
Delivering interpretable outputs that improve transparency in property valuation for real-world applications.
Among the tested models (Decision Tree, Linear Regression, KNN, Random Forest), Linear Regression emerged as the optimal choice due to:
Highest R² of 0.85 indicating strong explanatory power.
Reasonably low MSE of approximately 1.19e+09 indicating competitive accuracy.
Simplicity and interpretability which makes it easily understood by non-technical stakeholders.
Additionally, K-Means clustering with k=4 revealed distinct housing categories:
Vintage Essentials: Affordable older homes.
Modern Comforts: Newer, amenity-rich suburban houses.
Classic Fixer-Uppers: Dated homes suitable for renovation.
Suburban Classics: Balanced family homes with moderate pricing.
The project demonstrates that a Linear Regression model combined with clustering insights provides a powerful and interpretable tool for housing price prediction and market segmentation. While PCA and linear methods ensure robustness, limitations include reduced interpretability of original features, inability to capture non-linear trends, and a relatively small dataset of about 1500 instances.
Future improvements may involve testing ensemble models, incorporating non-linear approaches such as XGBoost or Neural Networks, and expanding the dataset for better generalizability.