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).
Hotel Customer Segmentation and Behavioral Analysis
Segmenting customers using RFM model
CRM Analysis of a E commerce company.
Customer Segmentation
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 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.
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.
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.
Telecom churn analysis using Excel dashboards to identify customer retention risks and revenue exposure.
Credit card customer segmentation, churn prediction, and revenue analytics with Power BI dashboard
Churn prediction and customer segmentation for a 10,000-customer European bank — Random Forest, K-Means, Python
Customer Segmentation using K-Means Clustering on the Mall Customer Dataset to analyze customer behavior based on annual income and spending score.
End-to-end telecom data analysis with 9 ML models for churn prediction and K-Means customer segmentation with PCA visualization
Segmented 10,000+ customers into 4 behavioral groups using K-Means Clustering | 82% silhouette score | Mapped segments to Customer Lifetime Value for targeted marketing
End-to-end EDA, RFM segmentation & cohort analysis on cosmetics retail sales data
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