Real Estate Price Prediction Data

<b>Overview:</b> This dataset was collected and curated to support research on predicting real estate prices using machine learning algorithms, specifically Support Vector Regression (SVR) and Gradient Boosting Machine (GBM). The dataset includes comprehensive information on residential properties, enabling the development and evaluation of predictive models for accurate and transparent real estate appraisals.<b>Data Source:</b> The data was sourced from Department of Lands and Survey real estate listings.<b>Features:</b> The dataset contains the following key attributes for each property:<b>Area (in square meters):</b> The total living area of the property.<b>Floor Number:</b> The floor on which the property is located.<b>Location:</b> Geographic coordinates or city/region where the property is situated.<b>Type of Apartment:</b> The classification of the property, such as studio, one-bedroom, two-bedroom, etc.<b>Number of Bathrooms:</b> The total number of bathrooms in the property.<b>Number of Bedrooms:</b> The total number of bedrooms in the property.<b>Property Age (in years):</b> The number of years since the property was constructed.<b>Property Condition:</b> A categorical variable indicating the condition of the property (e.g., new, good, fair, needs renovation).<b>Proximity to Amenities:</b> The distance to nearby amenities such as schools, hospitals, shopping centers, and public transportation.<b>Market Price (target variable):</b> The actual sale price or listed price of the property.<b>Data Preprocessing:</b><b>Normalization:</b> Numeric features such as area and proximity to amenities were normalized to ensure consistency and improve model performance.<b>Categorical Encoding:</b> Categorical features like property condition and type of apartment were encoded using one-hot encoding or label encoding, depending on the specific model requirements.<b>Missing Values:</b> Missing data points were handled using appropriate imputation techniques or by excluding records with significant missing information.<b>Usage:</b> This dataset was utilized to train and test machine learning models, aiming to predict the market price of residential properties based on the provided attributes. The models developed using this dataset demonstrated improved accuracy and transparency over traditional appraisal methods.<b>Dataset Availability:</b> The dataset is available for public use under the [CC BY 4.0]. Users are encouraged to cite the related publication when using the data in their research or applications.<b>Citation:</b> If you use this dataset in your research, please cite the following publication:<br>[Real Estate Decision-Making: Precision in Price Prediction through Advanced Machine Learning Algorithms].