OroCRM - an open-source Customer Relationship Management application.

An implementation of the shifted-beta-geometric (sBG) model from Fader and Hardie's "How to Project Customer Retention" (2006)

Data Science & Machine Learning Internship at Flip Robo Technologies

Comprehensive Power BI dashboards showcasing insights on Call Centre Trends, Customer Retention, and Diversity & Inclusion to drive business impact.

Retainful Website

Abandoned Cart Recovery Email and Next Order Coupon Plugin for WooCommerce. Easily recover abandoned carts with a single click and drive repeat purchases with Retainful

Loyalty Bridge is a web-based loyalty management system that enables businesses to track and incentivize customer loyalty.

Predicts which telecom customers are likely to churn with 95% accuracy using real-world data features from usage, billing, and support data. Implements Sturges-based binning, one-hot encoding, stratified 80/20 train-test split, and a two-level ensemble pipeline with soft voting. Achieves 94.60% accuracy, 0.8968 AUC, 0.8675 precision, 0.7423 recall.

LoyalPyME: Integrated digital loyalty (LCo) and hospitality service (LC) platform for SMEs. Boost customer retention and streamline operations with points, tiers, rewards, digital menus, QR codes, and advanced customer management. (React, Node.js, PostgreSQL)

This demo repository demonstrates how to analyze customer reviews with Azure OpenAI Service (AOAI). I leveraged "ASOS Customer Review" from Kaggle to obtain valuable insight from the customer review content.

Assessed brand loyalty patterns and price elasticity metrics to provide insights for brand’s market growth. Recommended strategies for the top 3 brands based on competitor analysis, customer profiling and customer retention through RFM analysis and multinomial logit.

ChurnShield – AI-powered Flask web app predicting customer churn and generating personalized retention strategies with a Random Forest ML pipeline and admin dashboard.

The Bank Churn Classification project predicts customer churn in the banking sector using machine learning algorithms and EDA. It features a user-friendly interface built with HTML and CSS, with model deployment via Flask. This helps banks identify churn patterns and implement strategies to retain customers.

A collection of applied AI use cases for the telecom retail industry. Includes ready-to-use demos for customer churn prediction, referral-based growth engines, customer segmentation, and more, designed to help telecom operators retain customers and drive acquisition using machine learning and predictive analytics.

This project predicts Customer Lifetime Value (CLV) for e-commerce. It aims at forecasting the revenue a business can expect from a customer over time. I did an explatory analysis. From Linear Regression to Neural Networks, explore how different models perform in predicting CLV.

PwC Switzerland Power BI in Data Analytics Virtual Case Experience helps build foundation in data analysis and visualization with Power Bi

This project uses machine learning to predict customer churn in the banking sector. It covers the end-to-end process, from data ingestion, validation, and transformation to model training and deployment using FastAPI. The system includes real-time predictions and provides an API for customer churn analysis.

Customer Churn Prediction System using XGBoost Regressor, built on Telecom Industry dataset

Creation of a MultiLayer Perceptron using Back Propagation Algorithm. It was trained to efficiently classify the data into two sets:exit and stay. This was able to predict whether a customer might stay with the bank or leave it in future.

Customer churn analysis project using Excel and Power BI. This project investigates the exit patterns of banking customers using various demographics and behavior indicators such as age, gender, credit card status, geography, and credit score. Insights help identify key drivers of churn and guide retention strategies.