House Price Prediction: Linear Regression vs. Random Forest
This repository explores the effectiveness of Linear Regression and Random Forest models for predicting house prices. Using the House Prices dataset, the project compares the performance of both models based on mean squared error and R^2 score. Code includes splitting of data, model training, evaluation, and visualization. Results demonstrate that Linear Regression model has a better fit and accuracy with lower errors and higher explained variance compared to Random Forest Model.