https://github.com/moindalvs/assignment_decision_tree_1
https://github.com/moindalvs/assignment_decision_tree_1
cost-complexity-pruning data-science decision decision-tree-classifier hyper-parameter-optimization hyperparameter-tuning post-pruning pruning-optimization
Last synced: about 2 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/moindalvs/assignment_decision_tree_1
- Owner: MoinDalvs
- Created: 2022-05-31T16:41:19.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2022-06-01T05:21:59.000Z (almost 3 years ago)
- Last Synced: 2025-01-28T00:45:33.447Z (4 months ago)
- Topics: cost-complexity-pruning, data-science, decision, decision-tree-classifier, hyper-parameter-optimization, hyperparameter-tuning, post-pruning, pruning-optimization
- Language: Jupyter Notebook
- Homepage:
- Size: 15 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Decision Tree
Assignment
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 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.