This project predicts real estate prices using Multiple Linear Regression. Key features like house age, proximity to MRT stations, and the number of stores were analyzed. The model was evaluated using metrics such as Mean Squared Error (MSE) and R-squared to ensure accuracy.
Real estate price prediction is critical for informed decision-making. This study leverages machine learning to build a regression model that uses key property features to predict housing prices.
Data Collection: A real estate dataset was loaded and cleaned.
Feature Selection: Variables influencing price were selected (e.g., house age, MRT distance).
Modeling: Multiple Linear Regression was applied using the training dataset.
Exploratory Data Analysis (EDA) revealed correlations between features.
The dataset was split (80% training, 20% testing) to train the regression model.
Predictions were compared to actual prices for validation.
Coefficients: Indicate the impact of each feature on prices.
Metrics:
Mean Squared Error: [Insert Value]
R-squared: [Insert Value]
Predictions closely matched actual prices, as visualized in the scatter plot.
The Multiple Linear Regression model successfully predicts real estate prices with reasonable accuracy. This approach can assist stakeholders in property valuation and market analysis
