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Project - Bank Churn Customer Prediction

Skills - Python, Machine Learning Algos (Naive Bayes, Decision Tree, Adaboost)

  1. Analysis of various customer-related data such as transaction history, demographics, account activity, and customer interactions, the model aims to predict which customers are most likely to churn in the near future.

  2. In Business Context, the percentage of FN is important. A FN predicts a customer will not churn when they actually do. So, FN should be minimized.

  3. The best model selected is AdaBoost with an average recall-FN for the testing data of 0.998333. 👍

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