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In this article, we are talking about bank customer churn prediction models. Here we will give you complete information about the bank customer churn prediction model.
Customer churn is a big problem in many banks because it costs a lot more to acquire a new customer than retaining existing ones. Banks can identify churn customers with the help of a bank customer churn prediction model so that the bank can take some action to prevent them from leaving.
Project Idea
The model for bank customer churn prediction has to be trained using a dataset that consists of data such as customerid, name,gender, age, tenure, bank balance etc and other features. This project will require training and testing the data model. After using data visualisation techniques, clean the data and handle the missing values. This project is an excellent means to learn how to build models such as random forest, support vector machine and logistic regression.
Dataset
To build the bank customer churn prediction model we have used a bank loan dataset. The data file churn_modeling.csv contains the information used to create the model. It consists of 10000 rows and 14 columns. The columns represent the variables, while the rows represent the instances.
The Dataset is composed of four concepts.
Data source
Variables
Instances
Missing values
This dataset uses the following 14 variables:
RowNumber : row number index
CustomerId : bank customer id
Surname : surname of bank customer
CreditScore: credit score of bank customer
Geography : country of bank customer
Gender : Gender of bank customer
Age : Age of bank customer
Tenure : how long does a customer have a bank account.
Balance : bank balance of customer
NumOfProducts : number of product
HasCrCard : Whether the customer has a credit card or not.
IsActiveMember : Whether the customer is active or not.
EstimatedSalary : estimated salary of customer
Exited : Is customer churn or not
In our explanation video of Data-driven bank churn prediction models using python, We cover techniques of exploratory analytics, data aggregation and cleansing, feature engineering, more importantly, model building and evaluation. We utilised Random Forest Classifier, Support Vector Machine and Logistic Regression with best parameters possible for getting the best prediction accuracy. All these algorithms are mathematical implementations and we have utilised them with optimal parameters.
The Bank customer churn Prediction Project is described in three videos part 1, part 2 and part 3.
Part 1 : Title : BANK CUSTOMER EXIT PREDICTION Project Part 1 | AI ML Project Series
Description : This is the introduction part of BANK CUSTOMER EXIT PREDICTION Project where we provide the details and procedures of the coming project that we will build in Part2 of this Series and deploy the same in Part 3. This is based on analysis of various parameters of a customer like credit score, balance, location, salary etc. to analyse whether a customer is expected to stay with the bank or will leave soon. The result will be able to tell by using credentials of a person that if they will stay with the bank or will leave it.
Part 2 : Title : BANK CUSTOMER EXIT PREDICTION Project Part 2 | AI ML Project Series
Description : This is the second part of the BANK CUSTOMER EXIT PREDICTION Project where we create a complete project on Kaggle Community Platform regarding prediction of exit chances of customers of a bank based on their credentials. We use data cleaning, Artificial Neural Network from Keras and Sequential models for getting the best prediction accuracy. This is a Deep Learning algorithm that we have used here for prediction of the requirements.
Artificial Neural Networks work like the human brain with many functionalities that can be extended using Keras. In the next part we Deploy the model and the project so look up to that as well.
Part 3 : Title : BANK CUSTOMER EXIT PREDICTION Project Part 3 | AI ML Project Series
Description : This is the third part of the BANK CUSTOMER EXIT PREDICTION Project where we Deploy our project on Google Colaboratory regarding prediction of exit chances of customers of a bank based on their credentials. We use Streamlit and NPX Local Tunnel to create a locally running web portal UI that will take input from the user as GUI and show our output as a Number.
Streamlite is the easiest way to deploy a Python based AI project and this can be done without worrying about installing api or repositories on your local system just by running the application on Google Colab.
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