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

AI/ML Engineer Portfolio | End-to-end projects (Customer Churn Streamlit, Credit Card Fraud Detection with XGBoost+Docker+SHAP, Japanese Sentiment Analysis with BERT) | Production-ready (Streamlit Cloud + Docker) | Targeting mid-level roles in Japan

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Predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs

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Predict customer churn using ML + generate AI-based retention suggestions.

AI-based customer churn prediction web app using Flask and ML

The Customer Churn Prediction project aims to help businesses anticipate which customers are likely to stop using their services or products. By analyzing historical customer behavior, demographics, and interaction patterns, the project seeks to uncover the key indicators of churn. Using predictive modeling,

Predict customer churn with XGBoost ML model. Analyze churn drivers, segment customers by risk, and get AI-powered retention strategies. Interactive Streamlit dashboard included.

Built ML models to segment customers using behavioral data and predict churn. Applied clustering and supervised learning techniques, with Explainable AI to interpret predictions and identify key drivers of customer churn.

AI Customer Churn Predictor: Predicts customer churn using ML, explains key risk factors, estimates revenue loss, classifies risk levels, provides business recommendations, and supports bulk CSV predictions via an interactive Streamlit dashboard.

End-to-end ML system for telecom customer churn prediction with XGBoost, SHAP explainability, customer segmentation, CLV estimation, survival analysis, and an interactive Streamlit dashboard (ChurnShield AI)

Consulting-style Data & AI project predicting customer churn for a digital bank using Python (EDA, ML, and business insights for retention strategy)

📡 Multimodal AI system for Telecom Customer Churn Prediction using ML, DL + Sentiment Analysis. Includes Business Dashboard, SHAP Explainability, PDF Reports & Batch Processing.

Gen AI powered solution that utilizes ML models as well as Language models to provide customized offers and promotional messages to customers based on their language and banking data, reducing risk of customer churn

Developed an AI-powered dashboard using OpenAI GPT to summarize telecom customer conversations, predict churn risk with ML, and visualize insights via Streamlit — enabling Vodafone UK to proactively address pain points and boost customer satisfaction

An end-to-end ML application that predicts bank customer churn using 9 different models and provides AI-generated retention strategies with Groq LLM. Built with Streamlit for interactive predictions and visualizations.

Customer Churn Prediction using AI/ML

The customer churn, also known as customer attrition, refers to the phenomenon whereby a customer leaves a company. Some studies confirmed that acquiring new customers can cost five times more than satisfying and retaining existing customers. As a matter of fact, there are a lot of benefits that encourage the tracking of the customer churn rate, for example: Marketing costs to acquire new customers are high. Therefore, it is important to retain customers so that the initial investment is not wasted. It has a direct impact on the ability to expand the company. In this project our goal is to predict the probability of a customer is likely to churn using machine learning techniques.

Churn prediction project using Python and Random Forest . This project is focused on predicting customer churn using machine learning techniques in Python

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