Lightning ⚡️ fast forecasting with statistical and econometric models.

Cloud Native DataOps & AIOps Platform | 云原生数智运维平台

Unofficial implementation of "Fast RobustSTL: Efficient and Robust Seasonal-Trend Decomposition for Time Series with Complex Patterns"

"Deep Metric Learning Meets DeepClustering: An Novel UnsupervisedApproach for Feature Embedding" (BMVC 2020)

Code for "Robust Multi-Objective Bayesian Optimization Under Input Noise"

Learning based Multi-scale Feature Engineering in Partial Discharge Detection

A digital companion to the research paper "Multistep Multiappliance Load Prediction", by Alona Zharova and Antonia Scherz

基于RFM和决策树模型构建专家推荐系统。融合了RFM模型和决策树模型，结合专业运营人员的业务经营，发掘潜在用户，进行推荐营销召回。

基于用户行为的用户画像项目

1st Place Solution for【2016CCF大数据竞赛 客户画像赛题（用户画像）】

Python Source code and datasets used in my doctoral dissertation - Detection of faults in HVAC systems using tree-based ensemble models and dynamic thresholds

The objective of this project is to segment the bank customers into multiple groups and to analyse the results when no labels are given for features. Customer segmentation is the process of dividin…

Implemented this case study using K-Means Unsupervised Machine Learning in python environment under the academic course of Artificial Intelligence.

Build an unsupervised learning model which can enable your company to analyze their customers via RFM (Recency, Frequency and Monetary value) approach.

Using RFM analysis segment the customer. And machine learning algorithm to find out loyal customer.

RFM Analysis as Customer Segmentation by Unsupervised Learning K-Means Algorithm

Ended. Most Recent Learning: RFM Customer Segmentation Next: K-Means Customer Segmentation

Customer segmentation for e-commerce through traditional RFM and unsupervised machine learning model of K-Means

Built an unsupervised machine learning model for segmentation of customers of an online E-commerce platform using RFM modelling and K-means clustering for making clusters.

Using a dataset from the UCI Machine Learning Repository, I segmented customers of an e-commerce company using an RFM analysis. An RFM analysis classifies customers based on the recency of their la…

Implemented in Python,the project uses Unsupervised learning model to classify the transaction data of customers into clusters based on similarity.The project includes Exploratory Data Analysis,Coh…

This project aims to perform cohort analysis of customers and their behavior during the lifespan of a product/organization. This is achieved using Unsupervised Learning techniques - namely k-Means …

This project emphasizes on how to classify different users into groups, based on recency, frequency and monetary (RFM) analysis on VPA application, by using machine-learning techniques.

Different unsupervised machine learning algorithms such as RFM, K-means, Spectral Clustering, GMM etc are used to classify customers into different meaningful clusters of customers.

The Project aims at building an unsupervised machine learning model using K Means clustering tech- niques which analyse and segment the customers via RFM approch.

Performed cohort analysis to understand customer trends and prepared customer segments. Learned how to calculate customer retention, RFM(Recency, Frequency, Monetary) metrics and utilized K-Means c…

Inspired by the infamous RFM (Recency, Frequency, Monetary) segmentation framework in Marketing, in this repository I decide to deploy an end to end machine learning model used for customer segment…

An online store's customer segmentation based on RFM table. The data set is a transnational which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and regist…