Beta Bank customers are leaving: little by little, chipping away every month. The bankers figured out it’s cheaper to save the existing customers rather than to attract new ones.

Background

This project is part of the Data Scientist training program from Practicum by Yandex. More info in link below:

https://practicum.yandex.com/data-scientist

Objective

Predict whether a customer will leave the bank soon. Build a model with the maximum possible F1 score. Achieve an F1 score of at least 0.59. Check the F1 for the test set. Additionally, measure the AUC-ROC metric and compare it with the F1.

Data Description

• Features
  • RowNumber — data string index
  • CustomerId — unique customer identifier
  • Surname — surname
  • CreditScore — credit score
  • Geography — country of residence
  • Gender — gender
  • Age — age
  • Tenure — period of maturation for a customer’s fixed deposit (years)
  • Balance — account balance
  • NumOfProducts — number of banking products used by the customer
  • HasCrCard — customer has a credit card
  • IsActiveMember — customer’s activeness
  • EstimatedSalary — estimated salary
• Target
  • Exited — сustomer has left

Libraries Used

• Pandas
• Matplotlib.pyplot
• scipy.stats -numpy
• sklearn

Models Evaluated

• DecisionTreeClassifier
• RandomForestClassifier
• LogisticRegression