A Streamlit App for Customer Segmentation Project using Kmeans Clustering (Best Choice)

📊🎯✨ Harness the power of the RFM (Recency, Frequency, Monetary) method to cluster customers based on their purchase behavior! Gain valuable insights into distinct customer segments, enabling you to optimize marketing strategies and drive business growth. 📈💡🚀

This Program is for Clustering Customer Data On the Basis of their Spending, Income,Family and Children.

 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.

analyze the shopping behaviors and demographic profiles of customers visiting a mall using various clustering techniques.

Customer Segmentation Python Project

Deploying clustering machine learning algorithms to segment survey respondents

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.

Data quality assessment and insights generation