Open-source customer engagement. Automate transactional and marketing messages across email, SMS, mobile push, WhatsApp, Slack, and more 📨

This project uses K-Nearest Neighbors (KNN) to classify customers of a telecommunication company into four groups based on features such as region, tenure, age, and marital status. It includes Exploratory Data Analysis (EDA) with visualizations and evaluates model performance to find the best value of k.

Customer churn is a critical issue for banks, as retaining customers is more cost-effective than acquiring new ones. This project aims to analyse customer churn in a bank and develop a predictive model to identify customers who are likely to leave, and the responsible factors.

The repo focuses on my works in data science

This repository contains our solution for the "Classifying Customers into Segments" challenge originally published on Kaggle.

Website Portfolio

This project demonstrates customer segmentation using K-Means clustering, a popular machine learning technique. By analyzing customer data, we group customers into distinct segments to better understand their behaviors and preferences. This segmentation can help businesses tailor their marketing strategies and improve customer satisfaction.

This project aims to analyze customer churn data to identify key drivers and suggest actionable strategies to improve retention.

This notebook provides a comprehensive example of how to perform customer segmentation using K-Means clustering, including data preprocessing, visualization, standardization, one-hot encoding, model training, evaluation, and saving/loading the model.

This repository contains project materials for the Spring 2024 STAT 208 class, specifically for Team 8. All materials are the property of Team 8, University of California, Riverside, A. Gary Anderson School of Management. Thank you for viewing our repository.

Tools for Customer Segmentation using RFM Analysis

Customer Segmentation using R

Skills: Python (Pandas, Numpy, Matplotlib, Seaborn)

This project aims to identify and present the target personas for a new Marketing campaign.

In-depth analysis of sales data for Everyday Essentials Mart, covering customer demographics, purchasing patterns, and product preferences. Features data cleaning, EDA, and visualizations using Python (Pandas, NumPy, Matplotlib, Seaborn).

Real world cooperate Project I will be conducting analysis on client's transaction dataset and identify customer purchasing behaviours to generate insights and provide commercial recommendations.

Analysing customer purchasing behaviour against the customer's gender and various other factors to help the business make better decisions.

Virtual Internship Program : Full-stack Data Analytics - RevoU X Telkom Indonesia