https://github.com/patilsukanya/assignment-15-random_forests-
Company Data & Fraud_check
https://github.com/patilsukanya/assignment-15-random_forests-
Last synced: 7 months ago
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Company Data & Fraud_check
- Host: GitHub
- URL: https://github.com/patilsukanya/assignment-15-random_forests-
- Owner: PatilSukanya
- Created: 2023-02-04T14:31:37.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-02-04T14:35:36.000Z (over 2 years ago)
- Last Synced: 2025-02-23T13:38:07.943Z (8 months ago)
- Language: Jupyter Notebook
- Size: 26.4 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
## Assignment-15-Random_Forests-
### Problem Statement For 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.
### Problem Statement For Fraud Check
Use Random Forest to prepare a model on fraud data
treating those who have taxable_income <= 30000 as "Risky" and others are "Good"