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customer-churn-prediction-with-machine-learning

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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

  • Updated Jan 23, 2024
  • Jupyter Notebook

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.

  • Updated Aug 20, 2023
  • Python

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.

  • Updated Jul 24, 2024
  • Jupyter Notebook

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.

  • Updated Jul 24, 2024
  • Jupyter Notebook
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