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 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.
Business Impact - My model predict an accuracy of 99%