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ChurnShield: Protecting Customer Relationships Through Predictive Intelligence

Overview

ChurnShield is a predictive intelligence solution developed for Horizon Trust Bank to address customer churn. By analyzing customer demographics, banking behaviors, and historical churn data, ChurnShield identifies potential churners, enabling the bank to implement targeted retention strategies and maintain strong customer relationships.

Key Features

  • Business Understanding ChurnShield emphasizes the significance of predicting customer churn for Horizon Trust Bank's revenue, profitability, and customer relationships. Understanding the cost implications of customer loss and the benefits of retention helps the bank prioritize its resources effectively.

  • Data Understanding Exploration of the dataset, understanding its structure, and identifying key features relevant to churn prediction, including customer demographics, account information, transaction patterns, and historical churn data.

  • Data Cleaning Preparation of data through handling missing values, encoding categorical variables, and removing irrelevant columns ensures that the dataset is suitable for accurate model training.

  • Exploratory Data Analysis (EDA) Analysis of numerical feature distribution, visualization of variable relationships, and identification of trends related to customer churn uncover hidden patterns in the data.

  • Model Building Training and evaluating various machine learning models, including Logistic Regression, Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbors, and Decision Tree, to predict customer churn. Each model is tuned and optimized for the best performance.

  • Model Evaluation Assessment of model performance using metrics such as accuracy, precision, recall, F1 score, and ROC AUC score ensures the selection of the most effective model. These metrics provide a comprehensive understanding of the model's predictive power.

  • Recommendations Based on the model results, actionable insights and recommendations are provided to Horizon Trust Bank, helping to implement targeted interventions and improve customer retention strategies.

  • Results ChurnShield delivers actionable insights and recommendations, enabling Horizon Trust Bank to take proactive measures to address customer churn and strengthen customer relationships. The chosen machine learning models provide high accuracy and reliability, making ChurnShield a valuable tool for the bank.

  • Contributor

    Ronon Kipkirui Henry

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Protecting Customer Relationships Through Predictive Intelligence

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