Project Description: Predicting House Prices with Random Forest Regression Introduction: The goal of this project is to predict house prices using a Random Forest Regression model. The dataset used contains various features of residential homes and their corresponding sale prices.

Data: The dataset consists of two files: train.csv and test.csv. train.csv contains the training data, including both the features and the target variable (SalePrice). test.csv contains only the features, and the goal is to predict the SalePrice for these observations.