A customer segmentation prediction and analysis project with the implementation of RFM analysis and DBSCAN clustering algorithm

This project showcases how to perform Recency, Frequency, and Monetary (RFM) analysis using the powerful Polars DataFrame library in Python.

Customer Segmentation

A RFM model is implemented to relate to customers in each segment this code has been implemented in R

Group Project for Data Science for Business Project (SMU Master of IT in Business)

Personal projects 1 & 2

Neste projeto será realizada uma análise do tipo RFV (Recência, Frequência e Valor) com dados que encontrei neste video no Youtube do canal Jie Jenn.

This project aims to conduct an analysis of costumers behavior and perception of the brand, by implementing different marketing analytics techniques and methods: RFM (recency, frequency, monetary) model, churn classification, MBA (market basket analysis) and sentiment analysis.

Doing RFM analysis on Online retail data using K-means clustering

Projeto de Análise de Dados para Segmentação RFM.

This project employs XGBoost regression and XGBoost classifier model to predict user order and user churn on online travel agency data. Reach 97% prediction accuracy.

In this repository, RFM customer segmentation is applied for a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.

RFM model-based Customer Segmentation using Clustering, Classification and BTYD Models

Tools for Customer Segmentation using RFM Analysis

Recency, Frequency and Monetary (RFM) Segmentation guidance for Amazon Pinpoint.

The project offers RFM segmentation, analyzing Recency, Frequency, and Monetary value. These metrics are vital for understanding customer behavior, influencing both retention and lifetime value.

This project aims to analyze automobile sales data using RFM segmentation combined with machine learning techniques to gain insights into customer behavior and optimize marketing strategies.

The repo focuses on my works in data science

Customer segmentation through their behavior, their habits and their personal data.

Bank data Segmentation (RFM, Kmeans clusters)