Potential customer mining application of smart home products based on LightGBM PU learning and Spark ML algorithm practice

06/22/2020
by   Duan Zhihua, et al.
0

This paper studies the case of big data-based intelligent product potential customer mining internal competition in China Telecom Shanghai Company. Huge amounts of data based on big data table, the use of machine Learning and data analysis technology, using the algorithm of LightGBM, PySpark machine Learning algorithms, Positive Unlabeled Learning algorithm, and predict whether customers buy whole house product, precision marketing into artificial intelligence for the customer, large data capacity, promote the development of intelligent products of the company.

READ FULL TEXT

Authors

page 1

page 2

page 3

page 4

04/01/2019

Customer churn prediction in telecom using machine learning and social network analysis in big data platform

Customer churn is a major problem and one of the most important concerns...
03/30/2022

RICON: A ML framework for real-time and proactive intervention to prevent customer churn

We consider the problem of churn prediction in real-time. Because of the...
05/17/2020

Neural Networks for Fashion Image Classification and Visual Search

We discuss two potentially challenging problems faced by the ecommerce i...
03/24/2019

Deep recommender engine based on efficient product embeddings neural pipeline

Predictive analytics systems are currently one of the most important are...
05/01/2021

Commercials Sales Prediction Using Multiple Linear Regression

Commercials have always been one of the most important medium for a comp...
05/30/2017

The Role of Data Analysis in the Development of Intelligent Energy Networks

Data analysis plays an important role in the development of intelligent ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.