/Churn_Prediction

Primary LanguageJupyter Notebook

Churn_Modelling

In this project we are going to consider a Dataset of 10000 customers of a bank that contains following features:

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

We are going to use these features and we are going to build an Artificial Neural Network (ANN) and train in order to find out wether the customer is going to exit the bank or not.

The complete project is divided into four parts

Part 1 Data Preprocessing

  • Importing Libraries
  • Importing Dataset
  • Encoding Categorical Data
  • Splitting the Dataset into Training Set and Test Set
  • Applying Feature Scaling

Part 2 Building the ANN

  • Initiallizing the ANN
  • Adding the input layer and the first hidden Layer
  • Adding the Second hidden layer
  • Adding the Output Layer

Part 3 Training the ANN

  • Compiling the ANN
  • Training the ANN on the training Set

Part 4 Making the Prediction and Evaluating the Model

  • Predicting the result of a Single Observation
  • Predicting the Test Result
  • Making the confusion matrix

Parameters used for Part 1 of the Problem

Use the ANN model to predict if the customer with the following informations will leave the bank: Geography: France Credit Score: 600 Gender: Male Age: 40 years old Tenure: 3 years Balance: $ 60000 Number of Products: 2 Does this customer have a credit card? Yes Is this customer an Active Member: Yes Estimated Salary: $ 50000 So, should we say goodbye to that customer?

Conclusion The ANN model predicted the results with accuracy of 86.05% with 279 errors. 79 errors were of type-I 200 errors were of type-II