PrediChurn 🚦 – End-to-End Customer Churn Prediction Suite

"Transforming churn risk into retention strategies with advanced ML."
🔍 Powered by: XGBoost, Random Forest, Optuna, SHAP
🧑💻 Engineered by: vishnupriyanpr

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Overview 🚀

PrediChurn is a robust, modular machine learning pipeline for customer churn prediction. Designed for telecom, SaaS, and banking datasets, it automates data wrangling, business-driven feature engineering, model selection, evaluation, and produces clear business insights and analytics dashboards. Its outputs guide retention teams toward targeted, ROI-driven customer strategies.

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Key Features 🧠

• 🔄 Multi-model engine: Logistic Regression, Random Forest, XGBoost—all Optuna-optimized
• 🛠️ Feature engineering: Tenure, ARPU, contract/payment, and behavior features with full NaN/infinite safety
• 🔍 Explainable AI: SHAP for both global and local churn driver visualization
• 📊 Business metrics: Churn rate, “revenue at risk”, “potential revenue saved”, intervention ROI
• 📑 Automated reporting: Executive summaries, actionable recommendations, and visualization outputs

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ML Pipeline Details 🏗️

1. Data Preparation

• Loads raw CSV data
• Cleans missing values and outliers
• Encodes categoricals
• Scales numerical data

2. Feature Engineering

• Generates >10 additional business-focused features (e.g., avg_charges_per_tenure, high_value_customer)
• Handles division-by-zero/NaN/infinite edge cases

3. Modeling and Optimization

• Trains Logistic Regression, Random Forest, and XGBoost models
• Balances training data with SMOTE for rare churn events
• Hyperparameter tuning via Optuna for best ROC-AUC

4. Evaluation

• Measures: accuracy, precision, recall, ROC-AUC
• Generates confusion matrix, ROC, Precision-Recall plots

5. Explainability

• Computes and saves SHAP summary and bar plots
• Ranks top churn features both globally and per-customer

6. Business Analytics

• Calculates "revenue at risk", "potential savings", intervention efficiency
• Generates markdown and visual HTML reports
• Top churn drivers and segment-wise actionable steps

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Workflow 🔁

1. Clone Project & Install

   git clone https://github.com/vishnupriyanpr/churnguard-ai.git
   cd churnguard-ai
   pip install -r requirements.txt
   

2. Prepare Dataset

   • Place your CSV data in data/raw/telco_churn.csv (Kaggle Telco Churn format recommended)

3. Run Pipeline

   python main.py
   

4. View Outputs

   • Metrics, SHAP PNGs, and business report: in reports/
   • Model artifacts: in models/

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Workflow ER Diagram 🗺️

erDiagram

    RAW_DATA {
        string customerID
        string features
        string churn_label
    }
    PROCESSED_DATA {
        string encoded_features
        string target
    }
    ENGINEERED_DATA {
        string new_features
    }
    TRAIN_DATA {
        string balanced_features
        string balanced_target
    }
    MODEL {
        string model_type
        string hyperparameters
        string trained_weights
    }
    METRICS {
        float accuracy
        float precision
        float recall
        float roc_auc
    }
    SHAP_PLOTS {
        string summary_plot
        string feature_importance
    }
    BUSINESS_REPORT {
        string revenue_at_risk
        string recommendations
        string top_drivers
    }

    RAW_DATA ||--o{ PROCESSED_DATA : cleaned_and_preprocessed
    PROCESSED_DATA ||--o{ ENGINEERED_DATA : feature_engineered
    ENGINEERED_DATA ||--o{ TRAIN_DATA : balanced_with_SMOTE
    TRAIN_DATA ||--o{ MODEL : trained_to
    MODEL ||--o{ METRICS : generates
    MODEL ||--o{ SHAP_PLOTS : explains
    METRICS ||--o{ BUSINESS_REPORT : summarized_in
    SHAP_PLOTS ||--o{ BUSINESS_REPORT : visualized_in

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Key Results (Latest Run) 📊

• Accuracy: 78.1%
• Precision: 57.9%
• Recall: 65.0%
• ROC-AUC: 0.822
• Churn Rate: 26.5%
• Revenue at Risk: $374,000
• Potential Revenue Saved: $72,900
• Intervention Efficiency: 57.2%
• Top Churn Drivers:
  • avg_charges_per_tenure (0.132)
  • MonthlyCharges (0.083)
  • charges_trend (0.076)
  • TotalCharges (0.076)
  • price_per_month_ratio (0.075)

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🧾 Business Recommendations

1. Immediate Action: Target high-risk (churn prob > 70%) with retention offers
2. Monitor Medium-Risk: Engage the 30–70% churn probability group
3. Feature Focus: Optimize avg_charges_per_tenure and related drivers
4. Ongoing Scoring: Recompute churn risk monthly for all customers

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Project Structure 📁

churnguard-ai/
├── data/
│ ├── raw/
│ └── processed/
├── models/
├── reports/
├── src/
│ ├── data_loader.py
│ ├── data_preprocessor.py
│ ├── feature_engineer.py
│ ├── model_trainer.py
│ ├── model_evaluator.py
│ └── utils.py
├── main.py
├── requirements.txt
└── README.md

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Output 🖼

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License 📜

MIT License — use, modify, and scale freely!

Credits 🙌

“Tools should disappear into the background and let you build.” — Vishnupriyan P R, caffeinated coder ☕