Real Estate Price Prediction
Overview
Real Estate Price prediction plays a crucial role in both the real estate market and the economy at large. For investors, developers, and homeowners, accurate price predictions facilitate informed decision-making, investment planning, and risk management.
The provided dataset contains 414 entries with detailed information on real estate transactions. It encompasses several features that are typically influential in real estate pricing:
- Transaction date: Date of the property transaction.
- House age: Age of the property in years.
- Distance to the nearest MRT station: Proximity to the nearest Mass Rapid Transit station in meters, is a key factor considering convenience and accessibility.
- Number of convenience stores: Count of convenience stores in the vicinity, indicating the property’s accessibility to basic amenities.
- Latitude and Longitude: Geographical coordinates of the property, reflecting its location.
- House price of unit area: The target variable, represents the house price per unit area.
The dataset is well-rounded, featuring a mix of continuous and categorical variables. It lacks missing values, making it a robust foundation for predictive modelling.
Objectives
The primary objective is to develop a predictive model that accurately forecasts the house price per unit area based on various features like the property’s age, its proximity to key amenities (MRT stations and convenience stores), and its geographical location.