Airline membership segmentation, for marketing purposed

In this project, I'm analyzing the characteristics of airline member, based on various category and segmenting the members. The result from this analysis, will help identify the right marketing steps for each segment. An airline can be defined as a company that offers regular services for transporting passengers or goods via the air. These companies are said to make up the airline industry, which is also regarded as a sub-sector of the aviation sector and the wider travel industry.

For the dataset, I'm using flight.csv, and KMeans Clustering method to help clustering the dataset. K-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean.

source source

MEMBER_NO : ID Member FFP_DATE : Frequent Flyer Program Join Date FIRST_FLIGHT_DATE : First flight date GENDER : Member's gender FFP_TIER : Tier of Frequent Flyer Program WORK_CITY : Member's Hometown WORK_PROVINCE : Member's Hometown province WORK_COUNTRY : Member's home country AGE : Member's age LOAD_TIME : Times when data collected FLIGHT_COUNT : Amount of flight taken BP_SUM : Travel plan SUM_YR_1 : Fare Revenue SUM_YR_2 : Votes Prices SEG_KM_SUM : Total miles LAST_FLIGHT_DATE : Last flight date LAST_TO_END : Distance between latest flight with last flight AVG_INTERVAL : Average time distance MAX_INTERVAL : Maximum time distance EXCHANGE_COUNT : Point exchange avg_discount : Member's average discount Points_Sum : Member's point amount Point_NotFlight : Unused point

Python, numpy, pandas, sklearn, seaborn and matplotlib