House Price Prediction Techniques

Using machine learning models to forecast the price of houses based on various property attributes.

A dataset containing information about houses, including size, bedrooms, location, and condition, used for the house price prediction study.

A statistical method for modeling the relationship between a dependent variable (house price) and one or more independent variables (house characteristics).

A machine learning ensemble method that combines multiple weak learning models (like decision trees) to build a powerful prediction model.

A gradient boosting algorithm known for its high efficiency and accuracy in machine learning tasks.

A method used for explaining the predictions of machine learning models by capturing the importance of each feature in the prediction.

The steps involved in cleaning, transforming, and preparing data before feeding it to a machine learning model.

A metric used to evaluate the prediction accuracy of a model, measuring the average absolute difference between predicted and actual house prices.

A statistical measure that represents the proportion of variance in the dependent variable that is predictable from the independent variables.

The process of creating new features from existing ones in a dataset to improve model performance and interpretability.

Average magnitude of errors between predicted and actual house prices, ignoring direction.

Indicates better model performance in predicting house prices.

Coefficient of determination, showing how well a model explains data variance.

Signifies a better model at explaining house price variations.

A simple ML model for predicting house prices.

ML method for complex relationships in house price predictions.

Tree-based ML that improves predictions via advanced tree boosting.

A method to understand how each feature impacts model predictions.

The process of handling missing values and ensuring data consistency in a dataset.

Replacing missing values in a dataset using a method like forward filling.

Identifying extreme values in a dataset using methods like IQR.

Converting categorical data into numerical format to be used in machine learning models.

A simple, interpretable model used for prediction.

An ensemble method that improves predictions by combining multiple decision trees.

A fast and efficient gradient boosting algorithm used for large datasets.

A technique used to understand the importance of different features in a machine learning model.

A method to detect outliers by identifying values outside the interquartile range.

Transforming data to improve model performance.

LightGBM model showed high accuracy in house price prediction, closely aligning predictions with actual values in a scatter plot.

Linear Regression showed broader dispersion in its scatter plot compared to LightGBM, implying its limitations in modelling complex relationships in house price prediction.

SHAP (SHapley Additive exPlanations) analysis identifies the most influential features affecting house prices, offering insights into their impact on model predictions.

LightGBM's SHAP analysis highlighted key features like square footage, location, and house condition as significantly affecting house price predictions.

LightGBM demonstrated superior predictive performance and computational efficiency for house price prediction compared with Linear Regression and Gradient Boosting models.

A metric used to evaluate the model's performance; lower MAE suggests better predictions.

A statistical measure of how well a model fits the data; higher R² indicates a better fit.

Steps taken to prepare the data for modeling; including handling missing values, removing outliers, and encoding categorical features.

A method for identifying and removing outliers using the interquartile range (IQR).

A method for transforming categorical features into numerical representations, suitable for machine learning models.

Using algorithms to predict real estate prices and understand factors influencing prices.

Machine learning models whose predictions can be easily understood by humans.

A method to explain model predictions by showing the importance of each feature.

Local Interpretable Model-Agnostic Explanations, provides explanations for individual model predictions.

A scalable tree boosting algorithm, often used for accurate real estate predictions.

Gradient boosting algorithm known for efficiency and accuracy in predictions.

Ensemble of decision trees used for real estate price prediction.

Assessing the accuracy and performance of machine learning models in real estate price prediction.

A gradient boosting machine learning algorithm known for high efficiency and accuracy, often used for real-world applications like property valuation.

LightGBM is well-suited for dynamic markets, like real estate, where both high accuracy and computational efficiency are needed.

Used to judge how well a model predicts, in this study they combined with interpretability tools to validate model effectiveness and practical value.

Used in this study, techniques like SHAP help understand how different factors, like house features or market trends, impact a model's predictions.

How quickly a model can process data, important for real-world applications with large datasets like in real-estate valuations.

Machine learning models beyond LightGBM, like CatBoost and XGBoost, that can be tested for comparison.

Data outside the core dataset, like economic trends or zoning regulations, to make the property valuation estimations more accurate

Combining LightGBM with deep learning models could improve predictions especially for complex data with lots of intertwined features.

Understanding how a model reaches its predictions. Tools like LIME and global surrogate models offer diverse ways to interpret how features influence outcomes.

Balancing the need for highly accurate property predictions with ease of understanding how the prediction was made. Important in real world applications.