https://github.com/gandhasiri-rahul-mohan/random-forests-q2-company_data

About the data: Let’s consider a Company dataset with around 10 variables and 400 records.
https://github.com/gandhasiri-rahul-mohan/random-forests-q2-company_data

data-science machine-learning numpy pandas python random-forest seaborn-plots

Last synced: about 1 month ago
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About the data: Let’s consider a Company dataset with around 10 variables and 400 records.

• Host: GitHub
• URL: https://github.com/gandhasiri-rahul-mohan/random-forests-q2-company_data
• Owner: Gandhasiri-Rahul-Mohan
• Created: 2023-01-03T16:44:58.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2023-01-03T16:47:20.000Z (almost 2 years ago)
• Last Synced: 2023-08-16T19:50:56.307Z (over 1 year ago)
• Topics: data-science, machine-learning, numpy, pandas, python, random-forest, seaborn-plots
• Language: Python
• Homepage:
• Size: 9.77 KB
• Stars: 2
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Random-Forests-Q2-Company_Data

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.