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model-evaluation-and-selection

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This project involves predicting customer churn in a telecommunications company using machine learning techniques, exploring various features' impact, optimizing models, and identifying key factors influencing churn.

  • Updated Aug 25, 2023
  • Jupyter Notebook

This project explores the predictive modeling workflow using the Kaggle competition "Titanic - Machine Learning from Disaster." It emphasizes key stages like data analysis and model evaluation, aiming to identify the optimal model. Through a real-world approach, we enhance our understanding of the workflow and emphasize rigorous model evaluation.

  • Updated Jul 3, 2023
  • Jupyter Notebook

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