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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
The core purpose of this study is to find the impact of Sentiment Analysis in predicting customer churn for the e-commerce industry by employing different predictive models. Furthermore, the study is also focused on observing which model is best in a more accurate prediction for determining the churn rate of customers.
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.
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)
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.
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.
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