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Telco-Churn-Analysis

  • The factors to be improved upon to reduce churn rate as it is one of the biggest problem in Telecom Industries.
  • Exploratory Data Analysis, replacing missing values by median. Classification models explored Logistic Regression, SVM, Decision Tree, Random Forest, ADA-Boost, XG-Boost, Light GBM.
  • Best performance is measured 81% accuracy, 0.85 AUC for hyperparameter tuned (Randomized Search CV) Light GBM. Conclusions based on SHAP plot and Feature Importance.

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Predicting churn among US telecom customers using Logistic Regression, Random Forest, Support Vector Machine and XG Boost in Python. Hyperparameter tuning using Random Grid search CV. Finding features of importance.

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