Telco Churn Analysis and Modeling is a comprehensive project focused on understanding and predicting customer churn in the telecommunications industry. Utilizing advanced data analysis and machine learning techniques, this project aims to provide insights into customer behavior and help develop effective strategies for customer

An end-to-end machine learning project predicting bank customer churn with a Gradient Boosting Classifier. It features a complete pipeline for data processing, model training, and real-time predictions via a Flask API. SMOTE is used for handling imbalanced data, and MLflow is integrated for model tracking.

Derive insights of factors contributing to customer churn in the Telecom Industry.

Customer Churn Prediction with Machine Learning

Machine Learning based bank customer churn prediction

In this project, we embark on an exciting journey to explore and analyze customer churn within the Telecom network service using the CRISP-DM (Cross-Industry Standard Process for Data Mining) framework.

Customer Churn Prediction is a machine learning project that analyzes telecom customer data to predict which users are likely to stop using the service. By identifying the key factors that lead to churn and comparing different models, this project helps businesses take proactive steps to retain customers and reduce revenue loss.

Tree methods for customer churn prediction. Creating a model to predict whether or not a customer will Churn .

Graduation Project Repository - Bogazici University IE 492 - Spring 2024

Developed an end-to-end machine learning model to predict credit card customer churn. (All stages including ingestion, EDA, feature engineering, normalization, and scaling, train-validation-split & deployment)

This repository contains my mentorness internship codes and project resources.

This repository contains all the tasks, code, and documentation completed during the BCG Data Science job simulation on The Forage platform. The simulation focused on analyzing customer churn, building predictive models, and presenting insights for a major utility company.

This project predicts customer churn using a real-world Telco dataset. It combines data cleaning, EDA, and advanced models (Logistic Regression, Random Forest, XGBoost) to uncover key churn drivers. An interactive Streamlit app lets you simulate churn risk in real time and explore data-driven strategies to improve customer retention.