Assignment
About the data: Let’s consider a Company dataset with around 10 variables and 400 records. The attributes are as follows:
- Sales -- Unit sales (in thousands) at each location
- Competitor Price -- 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
- Shelf Location at stores -- 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
The company dataset looks like this:
Problem Statement: A cloth manufacturing company is interested to know about the segment or attributes causes high sale. Approach - A decision tree can be built with target variable Sale (we will first convert it in categorical variable) & all other variable will be independent in the analysis.