ginni-function

There are 0 repository under ginni-function topic.

• Abhik35 / Assignment-Decision_Tree-company_data

  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.

  pythonnumpyseaborndecision-treesginni-functionentropy-coding
  Language:Jupyter Notebook 2

• salonibhatiadutta / decision-tree-classifier-

  To apply decision tree classifier on iris dataset

  information-gaindecision-tree-buildentropyginni-function
  Language:Jupyter Notebook 2

• Abhik35 / Assignment-decision_trees-fraudcheck

  Use decision trees to prepare a model on fraud data treating those who have taxable_income <= 30000 as "Risky" and others are "Good" Data Description : Undergrad : person is under graduated or not Marital.Status : marital status of a person Taxable.Income : Taxable income is the amount of how much tax an individual owes to the government Work Experience : Work experience of an individual person Urban : Whether that person belongs to urban area or not

  pandasnumpydecision-tree-classifierentropy-measuresginnniginni-functionrandom-forestedadummies
  Language:Jupyter Notebook 1

• Abhik35 / Assignment_Random_Forest_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"

  numpypandasmatplotlibdecision-treesrandom-forestginni-functionvisualization
  Language:Jupyter Notebook 1