R4DataScience-Nigeria/Customer-Retention-Analysis

Predicting and analyzing customer churn to improve retention strategies and drive business growth. ## 1. Customer Retention Analysis This documentation outlines the development of a robust Machine Learning model that accurately predicts customer churn for banks. ### 1.1 Introduction In today's competitive financial sector, customer retention is crucial for banks. Increasing customer churn is impacting revenue and raising the cost of acquiring new customers. Despite various engagement strategies, banks lack a reliable system to predict and prevent customer attrition. ### 1.2 Aim and Objectives The primary aim of this project is to develop a robust Machine Learning model that accurately predicts customer churn for banks. Leveraging historical customer data, including transaction patterns, product usage, account tenure, and demographic information, the model will identify key factors contributing to customer attrition. - **Aim**: To develop a predictive model that accurately identifies customers who are likely to churn. - **Objectives**: - Collect and preprocess customer data. - Explore and analyze the data to identify key features influencing churn. - Build and evaluate multiple machine learning models. - Deploy the best-performing model in a user-friendly dashboard. - Key Deliverable: - Identify At-Risk Customers: Detect customers who are likely to churn in the near future with high accuracy. - Understand Churn Drivers: Analyze the primary reasons for customer churn and provide actionable insights. - Optimize Retention Strategies: Develop targeted retention strategies and personalized interventions to improve customer satisfaction and reduce churn rates. ## 2. Data Information ### 2.1 Data Source - **Source**: The data-set was obtained from a multinational bank with branches in Nigeria and across Africa. It was generated in 2023. - **Description**: The data-set contains 19 variables and 500,000 rows. It is part of a larger data-set of over 20 million records ### 2.2 Metadata - **Columns**: - **`acct_id`**: A unique identifier …