(Houses.csv Data File in the comments)

Solve Using R / R Studio

The dataset houses.csv contains data on houses sold in in a certain city. The dataset includes the following variables: HJU'HU'l-lh- price = selling price of the house date = date house was sold, measured in days since January 1, 2014 bedrooms = number of bedrooms bathrooms 2 number of bathrooms sgfg = interior square footage floors 2 number of floors waterfront = 1 if the house has a view of the waterfront, 0 otherwise 1r built = year the house was built yr renovated = 0 if the house was never renovated, the year the house was renovated if else 000000000 The objective is creating a model to predict a house's selling price. Generate appropriate graphs that will help you in your modeling exercise. Randomly divide the dataset into two subsets. The training set with 80% of the data and the testing set with 20% of the data. Using only the training set, identify the best selection of predictor variables that you will use to predict the price. Specify what criteria and what technique did you use. For your selected choice, run the necessary diagnostics and adopt remedies if required. Fine tune the final model. Write down the equation of your final model and interpret its coefficients. Apply your model on the test data and obtain a prediction for the price of each point in the test data. Now compute the prediction SSE for the test data. Compute also the prediction R square for the test data by computing l-SSEXSST where SSE and SST are computed for the test data. What do you conclude