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abhik35 / assignment-decision_tree-company_data
ginni-function,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.
User: abhik35
python numpy seaborn decision-trees ginni-function entropy-coding

abhik35 / assignment-decision_trees-fraudcheck
ginni-function,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
User: abhik35
pandas numpy decision-tree-classifier entropy-measures ginnni ginni-function random-forest eda dummies

abhik35 / assignment_random_forest_fraud_check
ginni-function,Use Random Forest to prepare a model on fraud data treating those who have taxable_income <= 30000 as "Risky" and others are "Good"
User: abhik35
numpy pandas matplotlib decision-trees random-forest ginni-function visualization

salonibhatiadutta / decision-tree-classifier-
ginni-function,To apply decision tree classifier on iris dataset
User: salonibhatiadutta
information-gain decision-tree-build entropy ginni-function