Business Scenario: Before a bank offers a home loan, it is critical to conduct a home appraisal. The appraisal confirms the validity of the sales price of the property for the bank.
You have been asked to build and train a regression model that estimates the price of a home, based on several factors including: square feet of home, square feet of lot, number of bedrooms, number of bathrooms, location, and so on. You have been provided data for the following variables:
Variable Data
| Variable | Description |
|---|
| ID | Unique ID for each customer. |
| Date | Sale date of the estate. This variable is to be excluded for this regression model. |
| PRICE | The property's sales price in dollars. Price is the variable that you are trying to predict. |
| BEDROOMS | Number of bedrooms above basement level. |
| BATHROOMS | Number of bathrooms above basement level. |
| SQFT_LIVI | Above ground living area in square feet. |
| SQFT_LOT | Square foot measurement for all floors. |
| FLOORS | Number of floors. |
| WATERFRONT | Is there a waterfront on the property? Yes = 1/No=0 |
| Condition | Overall condition rating. |
| GRADE | Overall grade. |
| YR_BUILT | Original property construction date (year). |
| ZIPCODE | House location |
Task Flow: In this practice exercise, you will:
- Build a regression model.
- Train the regression model.
- Verify the output from the regression model.
