/Simple-ANN-implementation-leave-bank-ChurnModelling

Classification customer leave bank or not with ANN

Primary LanguageJupyter Notebook

Simple-ANN-implementation-leave-bank-ChurnModelling

Project Description

This is just the simple implementation of Artificial Neural Network classification on customer will LEAVE (1) the bank or NOT LEAVE (0).

Dataset

The dataset is provided in the repository as csv file. The features in the dataset -

  • RowNumber
  • CustomerId
  • Surname
  • CreditScore
  • Geography
  • Gender
  • Age
  • Tenure
  • Balance
  • NumOfProducts
  • HasCrCard
  • IsActiveMember
  • EstimatedSalary
  • Exited

ANN Architecture and its parameter

  • We used 3-Lyer ANN architecutrue with with units=6 and activation='relu'.

Confusion and Accuracy Score while prediction on 20% data of X_test

The confusion matrix and accuracy score is as follow:

  • Actual Leave & Predicted Leave -> 1534
  • Actual Leave & Predicted not Leave -> 66
  • Actual not Leave & Predicted Leave -> 224
  • Actual not Leave & Predicted not Leave -> 176
  • Accuracy score is 0.855 ann1

Future work

  • Need to improve the accuracy score

Please check the Notebook!