You must submit your ipynb file between 1pm and 2:30pm. No late submission will be accepted. (See the prior announcement for details about any technical issues you may have.) In this analysis, you will submit a Notebook (ipynb) file that fits a linear regression model to the data found here Download here. You do not need to treat this as a report; you simply need to use SparkML to fit a linear regression model to the data and create a conclusion section that clearly reports on the beta coefficients found and RMSE (root mean squared error) value for the model