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A Streamlit App for Customer Segmentation Project using Kmeans Clustering (Best Choice)
CLV PULSE - A DYNAMIC CUSTOMER LIFETIME VALUE PREDICTOR MODEL USING MACHINE LEARNING
This is a basic workflow with CrewAI agents working with sales transactions to draw business insights and marketing recommendations. The agents will work on everything from the execution plan to the business insights report. It works with local LLM via Ollama (I'm using llama3:8B but you can easily change it).
Segmenting customers using RFM model
Customer Segmentation
CRM Analysis of a E commerce company.
Hotel Customer Segmentation and Behavioral Analysis
Credit risk classification and loan portfolio analysis using Python and Excel.
This Program is for Clustering Customer Data On the Basis of their Spending, Income,Family and Children.
analyze the shopping behaviors and demographic profiles of customers visiting a mall using various clustering techniques.
End-to-end telecom data analysis with 9 ML models for churn prediction and K-Means customer segmentation with PCA visualization
Credit card customer segmentation, churn prediction, and revenue analytics with Power BI dashboard
In this project, a RFM model is implemented to relate to customers in each segment. Assessed the Data Quality, performed EDA using Python and created Dashboard using Tableau.
The goal of segmenting customers is to decide how to relate to customers in each segment in order to maximize the value of each customer to the business. The purpose is to understand customer response to different offers in order to come up with better approaches to sending customers specific promotional deals.
RFM segmentation analysis exploring customer purchase frequency, repeat behavior and engagement patterns.
Consumer segmentation & brand loyalty prediction for 600 profiles using K-Means clustering, Logistic Regression & Random Forest. Built for AXANTEUS market research agency. Built in R.
This repository contains the data, code, and documentation for a project to analyze and predict churn in PowerCo's SME customer segment. The project includes data exploration, cleaning, and transformation, as well as the development and evaluation of a machine learning model to predict churn based on price sensitivity and other relevant factors.
This project is an exploratory data analysis EDA of the Superstore dataset, examining sales, profit, customer, and regional patterns. The goal is to gain data-driven insights to support business decisions
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