Selecting useful information under the background of big data can help enterprises to classify customers more accurately. Outlier data includes important customer information. In order to study customer classification problem based on customer asset outlier data, a customer classification model based on outlier data analysis concerning customer asset is constructed successfully. The model is based on Variables in 4 dimensions including transaction frequency, types of products or services traded, transaction amount and client age. And using clustering before classification to divide twenty-five types of outlier customer data into four categories and corresponding marketing strategies also are put forward according to different classification of outlier customer data of a company.
Data Mining algorithms
• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask,hadoop Frontend :-python Backend:- MYSQL
₹10000 (INR)
2019