K-Means clustering algorithm

See sample notebook here

See article here

This repository contains unsupervised learning models worked on the following datasets:

1) Iris dataset

The attributes are:

1) sepal_length    
2) sepal_width    
3) petal_length    
4) petal_width    
5) species 

The classification algorithm applied are:

1) Hierarchial clustering    
2) K-means

See notebook for Hierarchial clustering here

See notebook for K-means here

2) Loan dataset

Here are what the columns represent:

• credit.policy: 1 if the customer meets the credit underwriting criteria of LendingClub.com, and 0 otherwise.
• purpose: The purpose of the loan (takes values "credit_card", "debt_consolidation", "educational", "major_purchase", "small_business", and "all_other").
• int.rate: The interest rate of the loan, as a proportion (a rate of 11% would be stored as 0.11). Borrowers judged by LendingClub.com to be more risky are assigned higher interest rates.
• installment: The monthly installments owed by the borrower if the loan is funded.
• log.annual.inc: The natural log of the self-reported annual income of the borrower.
• dti: The debt-to-income ratio of the borrower (amount of debt divided by annual income).
• fico: The FICO credit score of the borrower.
• days.with.cr.line: The number of days the borrower has had a credit line.
• revol.bal: The borrower's revolving balance (amount unpaid at the end of the credit card billing cycle).
• revol.util: The borrower's revolving line utilization rate (the amount of the credit line used relative to total credit available).
• inq.last.6mths: The borrower's number of inquiries by creditors in the last 6 months.
• delinq.2yrs: The number of times the borrower had been 30+ days past due on a payment in the past 2 years.
• pub.rec: The borrower's number of derogatory public records (bankruptcy filings, tax liens, or judgments).

The classification algorithm applied are:

1) Hierarchial clustering    
2) K-means

See notebook for Hierarchial clustering here

See notebook for K-means here

3) Mall customers dataset

The attributes are:

1) CustomerID
2) Genre
3) Age
4) Annual Income (k$)
5) Spending Score (1-100)

The classification algorithm applied are:

1) Hierarchial clustering    
2) K-means

See notebook for Hierarchial clustering here

See notebook for K-means here