ChurnGuard

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  Customer Prediction using ML

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  Customer Management

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  Telemetry Logs

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  Dashboard Overview

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  Feedback Analytics

ChurnGuard: Inspiration Addressing the critical pain point of customer churn for small businesses lacking resources for advanced analytics. Goal: democratize churn prediction and customer insights.

What it does A comprehensive web application for customer retention:

Churn Prediction: ML (RandomForest) identifies at-risk customers with probability and risk levels.

Customer Management: Centralized profiles, risk assessment, AI-powered retention recommendations.

Feedback Collection: Intuitive forms with star ratings, document upload simulation for auto-filling data, and sentiment analysis.

Analytics Dashboard: Visualizes churn trends, revenue, and risk factors for data-driven decisions.

High-Risk Identification: Automatically flags customers needing immediate attention.

Telemetry Logging: Tracks system interactions for deeper analysis.

How we built it Backend: Python 3.x, Flask for APIs and ML serving.

Machine Learning: Scikit-learn (RandomForest), Pandas, NumPy for prediction and data processing.

Frontend: HTML5, CSS3, JavaScript, Bootstrap 5 for responsive UI, Font Awesome for icons.

Data Storage: CSV files (initial, scalable to databases).

Architecture: Classic client-server.

Challenges we ran into Simulating realistic churn data.

Optimizing ML model integration and performance in Flask.

Conceptually implementing "auto-filling from document" (simulated).

Ensuring complex responsive design across all devices.

Designing an effective user feedback loop.

Accomplishments that we're proud of Successfully integrated ML for accessible churn prediction.

Created an intuitive, responsive user experience.

Developed an innovative, simulated feedback mechanism.

Built a comprehensive, insightful analytics dashboard.

Delivered a tailored solution for small businesses.

What we learned User-centric design is paramount.

ML deployment can be simple for prototypes.

Data visualization is key for actionable insights.

Iterative development is highly effective.

Scalability considerations are vital from the start.

What's next for ChurnGuard Database integration (e.g., Firestore).

Enhanced ML (time-series, XAI).

Automated retention workflows (email/SMS).

CRM integration.

User authentication and roles.

Advanced NLP for feedback.

Subscription management integration.

Built With

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