https://github.com/gandhasiri-rahul-mohan/decesion-tree-q1-company_data

Problem Statement: A cloth manufacturing company is interested to know about the segment or attributes causes high sale.
https://github.com/gandhasiri-rahul-mohan/decesion-tree-q1-company_data

data-science decision-trees machine-learning python

Last synced: 2 days ago
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Problem Statement: A cloth manufacturing company is interested to know about the segment or attributes causes high sale.

• Host: GitHub
• URL: https://github.com/gandhasiri-rahul-mohan/decesion-tree-q1-company_data
• Owner: Gandhasiri-Rahul-Mohan
• Created: 2023-01-06T15:39:28.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2023-01-06T15:40:51.000Z (almost 2 years ago)
• Last Synced: 2023-08-16T19:50:54.363Z (over 1 year ago)
• Topics: data-science, decision-trees, machine-learning, python
• Language: Python
• Homepage:
• Size: 9.77 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Decesion-Tree-Q1-Company_Data

Problem Statement: 

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:

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.