https://github.com/gui-sitton/beta-bank
The bankers have discovered that it's cheaper to save existing customers than to attract new ones. We need to predict whether a customer will leave the bank soon. Model with the maximum possible F1 value.
https://github.com/gui-sitton/beta-bank
decision-tree-classifier f1-score logisticregression machine-learning one-hot-encoding python random-forest-classifier
Last synced: about 2 months ago
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The bankers have discovered that it's cheaper to save existing customers than to attract new ones. We need to predict whether a customer will leave the bank soon. Model with the maximum possible F1 value.
- Host: GitHub
- URL: https://github.com/gui-sitton/beta-bank
- Owner: Gui-Sitton
- Created: 2023-08-23T18:44:17.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-09-04T13:37:51.000Z (over 1 year ago)
- Last Synced: 2025-01-24T09:12:28.912Z (3 months ago)
- Topics: decision-tree-classifier, f1-score, logisticregression, machine-learning, one-hot-encoding, python, random-forest-classifier
- Language: Jupyter Notebook
- Homepage:
- Size: 15.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Beta-Bank
Beta Bank's customers are leaving: little by little, slipping away every month. The bankers have discovered that it's cheaper to save existing customers than to attract new ones.
We need to predict whether a customer will leave the bank soon. You have data on past customer behavior and contract terminations with the bank.
Build a model with the maximum possible F1 value. To pass the review, you need an F1 value of at least 0.59 for the test data set.
Also, measure the AUC-ROC metric and compare it with the F1 value.
**Features**
* RowNumber - data string index
* CustomerId - unique customer identifier
* Surname - last name
* CreditScore
* Geography - country of residence
* Gender
* Age
* Tenure - length of service for the customer
* Balance - account balance
* NumOfProducts - number of banking products used by the customer
* HasCrCard - client has a credit card (1 - yes; 0 - no)
* IsActiveMember - active customer (1 - yes; 0 - no)
* EstimatedSalary - estimated salary
**Objective**
* Exited - customer left (1 - yes; 0 - no)