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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
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.
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, I utilized survival analysis models to assess how the likelihood of customer churn changes over time and to calculate customer Lifetime Value (LTV). Additionally, I implemented a Random Forest model to predict customer churn and deployed this model using a Flask web application.
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)
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