This project aims to profile e-commerce customers based on transaction activity or how frequently they shop and the amount spent using RFM-T
-
Updated
Jun 28, 2024 - Jupyter Notebook
This project aims to profile e-commerce customers based on transaction activity or how frequently they shop and the amount spent using RFM-T
This project involves predicting phishing URLs by extracting 17 features across three categories. It entails training and testing machine learning models using a dataset sourced from Phishtank.
Customer segmentation by using the RFM method and K-Means clustering
This project showcases how to perform Recency, Frequency, and Monetary (RFM) analysis using the powerful Polars DataFrame library in Python.
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。
Customer segmentation through their behavior, their habits and their personal data.
Aplicação de técnica RFM e K-Means com linguagem R
Kodi music addon : Get web metadata information of webradio, like fip radio.
Recency, frequency, monetary value Model ,Customer Segmentation
CRM-RFM-Analysis
Fast rainflow counting written in C (C99). Including wrappers for MATLAB and Python
RFM (Recency, Frequency, Monetary) analysis
CLTV_customer-lifetime-value-analysis
Customer segmentation using RFMC analysis
FLO, as an online shoe store, aims to segment its customers and define marketing strategies based on these segments. To achieve this, they will analyze customer behavior and create groups according to the segmentation within these behaviors.
This project involves performing customer segmentation and RFM (Recency, Frequency, Monetary) analysis on customer data from a retail company. The primary goal is to categorize customers into segments based on their buying behavior and identify potential target groups for marketing campaigns.
Add a description, image, and links to the rfm topic page so that developers can more easily learn about it.
To associate your repository with the rfm topic, visit your repo's landing page and select "manage topics."