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.

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

Problem statement: Use decision trees to prepare a model on fraud data, treating those who have taxable_income <= 30000 as "Risky" and others are "Good"

Data Description : Undergrad : person is under graduated or not Marital.Status : marital status of a person Taxable.Income : Taxable income is the amount of how much tax an individual owes to the government Work Experience : Work experience of an individual person Urban : Whether that person belongs to urban area or not