Real Estate Price Prediction is the process of estimating or forecasting the future prices of real estate properties, such as houses, apartments, or commercial buildings. The goal is to provide accurate property rates to buyers, sellers, investors, and real estate professionals to make informed decisions about real estate transactions.
The dataset which I used 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.
Steps followed to complete the project:
- Gathered relevant data, Cleaned and prepared the collected data by handling missing values, removing outliers.
- Created new features and transformed existing ones to capture important information that can influence real estate prices.
- Explore and visualize the data to gain insights into its distribution, correlations, and patterns.
- Chose appropriate machine learning algorithm or predictive model for the task.
- Trained the selected model on the training data, optimizing its parameters to make accurate predictions.