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Explored models such as Logistic Regression (SMOTE), SVM, RandomForest & XGBoost to assess customers’ propensity or risk to churn for a telecom. Performed In-depth EDA & data preprocessing in Python.

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Customer Churn Prediction for TeleCom

In reality, customer retention has been a major use of data mining technologies—especially in telecommunications and finance businesses. These more generally were some of the earliest and widest adopters of data mining technologies.

Motivation

Tens of millions of customers have contracts expiring each month, so each one of them has an increased likelihood of defection in the near future. If we can improve our ability to estimate, for a given customer, how profitable it would be for us to focus on her, we can potentially reap large benefits by applying this ability to the millions of customers in the population.

Methodology

Tech & tools used

  1. Python, pandas,

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Explored models such as Logistic Regression (SMOTE), SVM, RandomForest & XGBoost to assess customers’ propensity or risk to churn for a telecom. Performed In-depth EDA & data preprocessing in Python.

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