Use Random Forest to prepare a model on fraud data
treating those who have taxable_income <= 30000 as "Risky" and others are "Good"
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 Statemen A cloth manufacturing company is interested to know about the segment or attributes causes high sale. Approach - A Random Forest can be built with target variable Sales (we will first convert it in categorical variable) & all other variable will be independent in the analysis.