Mini-project

A linear regression model to predict house prices based on house sizes. Using a scatterplot of test data, the model will form a relationship between the two variables and then makes predictions. (Data set is attached with it) linear regression is used to model relationships between independent x variables (e.g., house sizes) and a dependent y variable (e.g., house prices) by fitting a linear equation to observed data. Put simply, we are drawing a straight line through a dataset to map out and predict. In data science, linear regression is widely used for tasks such as predicting sales, analyzing economic trends, and understanding the impact of variables on an outcome.