A decision tree implementation for the carseat sales dataset from Kaggle.
- Sales - Unit sales (in thousands) at each location
- CompPrice - Price charged by competitor at each location
- Income - Community income level (in thousands of dollars)
- Advertising - Local advertising budget for company at each location (in thousands of dollars)
- Population - Population size in region (in thousands)
- Price - Price company charges for car seats at each site
- ShelveLoc - A factor with levels Bad, Good and Medium indicating the quality of the shelving location for the car seats at each site
- Age - Average age of the local population
- Education - Education level at each location
- Urban - A factor with levels No and Yes to indicate whether the store is in an urban or rural location
- US - A factor with levels No and Yes to indicate whether the store is in the US or not
- Recursive binary splitting
- Categorical decision making
- Regression
- Reservoir sampling
- Bagging/ensemble