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

CRM Analysis of a E commerce company.

Customer Segmentation

Segmenting customers using RFM model

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.

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.

No description provided.

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

 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.

Credit card customer segmentation, churn prediction, and revenue analytics with Power BI dashboard

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.

Telecom churn analysis using Excel dashboards to identify customer retention risks and revenue exposure.

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

Understanding customer segments, guide marketing & operational decisions, and ultimately support retention and growth.

Customer Segmentation using Clustering Techniques

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

Deploying clustering machine learning algorithms to segment survey respondents

Customer Segmentation Python Project

This dashboard presents an overview of CUSTOMER data, trends and behaviour to understand customer segments and improve customer satisfaction. Building dashboard to help stakeholders, including salesmanager and executives to analyse Customers data respective to sales and profit and products bought.

"This repository contains a cleaned and preprocessed dataset for my Data Analyst Internship task. The dataset was processed using Excel, with missing values handled, duplicates removed, and data formats standardized. The repository includes the cleaned dataset, a README file explaining the cleaning process, and screenshots of key steps."

Power BI Projects

Determining a store's customer segments based on their behavior

This project 📊 dives into real sales data from Vrinda Store, using the power of Excel to extract insights and trends that matter. From top-selling products 🛍️ to monthly revenue trends 📆, it turns raw data into smart business decisions 💡 — no code required! 🚀 Great for learning Excel-based analytics.