churn_predictor.py source

"""E-Commerce Customer Churn Prediction

Stacking ensemble that predicts which e-commerce customers will churn, deployed with a live app.
"""

from portfolio import Metric, Project


class ChurnPredictor(Project):
    status = "live"
    featured = True
    stack = [
        "Python",
        "Scikit-Learn",
        "Imbalanced-Learn",
        "XGBoost",
        "FastAPI",
        "React",
        "Docker",
    ]
    metrics = [Metric("Accuracy", "96%"), Metric("Macro F1", "0.93")]
    links = {
        "repo": "https://github.com/maaz7409/customer-churn-predictor",
        "live": "https://ecommerce-customer-churn-predictor.vercel.app/",
        "api": "https://customer-churn-predictor-backend.vercel.app",
    }
    demo = "churn"

    highlights = [
        "Preprocessing: KNN imputation, winsorization, one-hot encoding, and SMOTE for class imbalance.",
        "Model: a stacking ensemble (KNN, SVM, XGBoost, Random Forest) with a Logistic Regression meta-model.",
        "Results: 96% accuracy and 0.93 macro F1.",
        "Deployment: FastAPI backend and React frontend, both on Vercel.",
    ]


if __name__ == "__main__":
    ChurnPredictor().run()  # ▶ live demo