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Random_Forest_Company_Data_Analysis:

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.

1.Prepare a Random Forest Prediction Model.
2.Do proper EDA.

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

To view Jupyter Notebook click here

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Random Forest Model-- For Predicting Attributes causing High Sales

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