BankCustomerChurn_EDA-ML_Python

Project: Advanced Business Analytics

Topic: Predictive Analysis of Customer Churn in Banking Industry

Keywords: Business Analytics, Predictive Analysis, Bank Customer Churn, Classification, Exploratory Data Analysis (EDA), Machine Learning, Python

Table of Content

• Project Overview
• Motivation
  • Aim & Objective
• Contents
• Technologies Used
• License
• Credits

Project Overview

• Use data and business analytics to identify which bank customer will churn or not based on features like Age, Sex, EstimatedSalary, CreditScore, etc.
• The dataset was obtained from an unknown company for confidentiality purposes.
• Python is used to assist this project with business analytics by extracting important insights using:
  • Basic Statistics
  • Data Visualization
  • Exploratory Data Analysis
  • Supervised Machine Learning Models (Classification)
    • Logistic Regression
    • Decision Tree Classifier
    • K-Nearest Neighbor Classifier

Motivation

• With the banking industry becoming much more competitive, banks must implement customer retention tactics while striving to improve their market share by attracting new customers.
• It has been proven that increasing the retention rate by 5% can boost a bank's profit by up to 85%. (Nie et al., 2011).
• As a result, banks should implement Machine Learning algorithms to forecast customer churn to preserve their competitive advantage.
• Despite this, many banks with a large customer base that are seeking for a competitive advantage have not seized advantage of the massive amounts of data they have, particularly in addressing one of the most well-known issues, customer churn.

Aim & Objective

• Aim:
  • To identify the various factors and relationships that cause customers churn as well as to evaluate the performance of predictive modelling using various analytical techniques that classifies bank customers into churners and non-churners.
• Objective:
  • To create and select the best Machine Learning model that classifies bank customers into churners or not based on the importance of data variables and models evaluation and assessment (i.e. Accuracy, Recall, AUC, etc.).
  • The insights and variables gained may be utilised to make better choices and adjustments, such as adding or upgrading services to decrease churn and measuring the success of marketing and other customer acquisition methods and techniques.

Contents

(1) BankCustomerChurn_Dataset.csv

• Bank Customer Churn dataset file in CSV format.

(2) BankCustomerChurn_EDA-ML_Python Folder

• Contains the main Python notebook with implementation codes and explanations for the project.

Technologies Used

License

• None (for now)

Credits

• Took inspiration from Kaggle