A linear regression model built for a Western AI project that predicts house prices based on various features from a .csv file. The data is cleaned and preprocessed, then split into training and testing sets. A linear regression model is trained on the training data and tested on the testing data, with the R-squared value being calculated as a measure of performance.
The model training and testing process is repeated 100 times.
The resulting average R-squared value from running the code is ~77.5%.