{"id":50391003,"url":"https://github.com/pydevcasts/churn_modeling_article","last_synced_at":"2026-05-30T18:01:35.376Z","repository":{"id":360560375,"uuid":"1009648439","full_name":"pydevcasts/Churn_modeling_Article","owner":"pydevcasts","description":"customer churn prediction system for banking institutions using advanced feature engineering and ensemble learning techniques. 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The model addresses the challenge of highly imbalanced datasets (10:1 ratio) through advanced feature engineering and ensemble learning techniques.\n\n### Key Highlights\n- **Accuracy**: 92%\n- **AUC Score**: 0.96\n- **Precision**: 0.96\n- **Recall**: 0.87\n- **Outperformed previous best model** (91% accuracy)\n\n---\n\n## 🎯 Problem Statement\n\nCustomer churn is a critical challenge in the banking industry. Identifying customers likely to leave enables proactive retention strategies. The main challenges addressed:\n- **Highly imbalanced dataset** (90% non-churn vs 10% churn)\n- **Complex feature interactions** affecting customer behavior\n- **Need for interpretable** yet powerful predictions\n\n---\n\n## 📊 Dataset\n\nThe dataset contains banking customer information with the following features:\n\n| Feature | Description |\n|---------|-------------|\n| CreditScore | Customer's credit score |\n| Geography | Country (France, Germany, Spain) |\n| Gender | Male/Female |\n| Age | Customer age |\n| Tenure | Years with the bank |\n| Balance | Account balance |\n| NumOfProducts | Number of bank products used |\n| HasCrCard | Credit card ownership (0/1) |\n| IsActiveMember | Active member status (0/1) |\n| EstimatedSalary | Estimated annual salary |\n| Exited | **Target**: Churn status (1 = exited) |\n\n---\n\n## 🛠️ Methodology\n\n### 1. Data Preprocessing\n\n**Outlier Removal:**\n```python\n- CreditScore ≤ 359\n- Age ≥ 71 years\n- NumOfProducts ≥ 4\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpydevcasts%2Fchurn_modeling_article","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpydevcasts%2Fchurn_modeling_article","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpydevcasts%2Fchurn_modeling_article/lists"}