Data-Science Supervised Learning Project Solution

Project Description
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.
We need to predict whether a customer will leave the bank soon. You have the data on clients’ past behavior and termination of contracts with the bank.
Build a model with the maximum possible F1 score. To pass the project, you need 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.
Project Instructions
1) Download and prepare the data. Explain the procedure. 2) Examine the balance of classes. Train the model without taking into account the imbalance. Briefly describe your findings. 3) Improve the quality of the model. Make sure you use at least two approaches to fixing class imbalance. Use the training set to pick the best parameters. Train different models on training and validation sets. Find the best one. Briefly describe your findings. 4) Perform the final testing.
Data Description
The data can be found in /datasets/Churn.csv file.
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 — Ñustomer has left