Astro AI Platform
A production-grade, enterprise AI platform for Telco Customer Churn prediction with full MLOps lifecycle.
Business Problem
Customer churn is one of the most critical challenges in the telecommunications industry. Losing a customer costs 5-25x more than retaining one. This platform leverages machine learning to predict which customers are likely to churn, enabling proactive retention strategies.
Dataset Intelligence
Telco Customer Churn — features, preprocessing, and encoding strategy.
Categorical Features (15)
Numerical Features (4)
Preprocessing
- • Missing TotalCharges → Median imputation
- • Categoricals → Label Encoding
- • Numericals → Standard Scaling
- • Train/Test split: 80/20 stratified
Multi-Model Strategy
Six algorithms compete — the best is automatically crowned Champion.
Logistic Regression
Interpretable baseline
Random Forest
Robust to overfitting
Gradient Boosting
Strong generalization
XGBoost
Speed + performance
LightGBM
Memory efficient
CatBoost
Handles categoricals natively
Champion Selection Strategy
The Champion is selected using a balanced score that weighs multiple metrics:
The model with the highest balanced score is automatically registered as Champion in the MLflow Model Registry and deployed for inference.
Enterprise Stack
Frontend
Backend
MLOps
Deployment
Cloud Architecture
Frontend — Vercel
Next.js app deployed on Vercel's edge network with global CDN and automatic SSL.
Backend — Render
FastAPI backend deployed on Render with auto-scaling, health checks, and managed environment.
MLflow — DagsHub
Experiment tracking and model registry hosted on DagsHub with Git-integrated version control.