This repository contains introductory notebook for clustering techniques like k-means, hierarchical and DB SCAN
Perform clustering (hierarchical,K means clustering and DBSCAN) for the airlines data to obtain optimum number of clusters. Draw the inferences from the clusters obtained.
Data Description:
The file EastWestAirlinescontains information on passengers who belong to an airline’s frequent flier program. For each passenger the data include information on their mileage history and on different ways they accrued or spent miles in the last year. The goal is to try to identify clusters of passengers that have similar characteristics for the purpose of targeting different segments for different types of mileage offers
ID --Unique ID
Balance--Number of miles eligible for award travel
Qual_mile--Number of miles counted as qualifying for Topflight status
cc1_miles -- Number of miles earned with freq. flyer credit card in the past 12 months: cc2_miles -- Number of miles earned with Rewards credit card in the past 12 months: cc3_miles -- Number of miles earned with Small Business credit card in the past 12 months:
1 = under 5,000 2 = 5,000 - 10,000 3 = 10,001 - 25,000 4 = 25,001 - 50,000 5 = over 50,000
Bonus_miles--Number of miles earned from non-flight bonus transactions in the past 12 months
Bonus_trans--Number of non-flight bonus transactions in the past 12 months
Flight_miles_12mo--Number of flight miles in the past 12 months
Flight_trans_12--Number of flight transactions in the past 12 months
Days_since_enrolled--Number of days since enrolled in flier program
Award--whether that person had award flight (free flight) or not
Perform Clustering(Hierarchical, Kmeans & DBSCAN) for the crime data and identify the number of clusters formed and draw inferences.
Data Description:
Murder -- Muder rates in different places of United States
Assualt- Assualt rate in different places of United States
UrbanPop - urban population in different places of United States
Rape - Rape rate in different places of United States