🚀 MLflow – Telco Customer Churn Prediction
This repository contains an MLflow-based Machine Learning experiment tracking system built to predict customer churn for a telecom company. It demonstrates how to train models, track metrics, and manage experiments using MLflow instead of only notebooks.
📌 What this project does
This project helps answer:
Which telecom customers are likely to leave the service?
Using customer data, we train machine learning models and use MLflow to:
Track experiments
Store models
Compare performance
Save results in a database
⚙️ Tech Stack
Python
Pandas, NumPy
Scikit-learn
MLflow
SQLite (mlflow.db)
📂 Repository Files File Purpose eda2.py Performs data analysis and model training mlflow.db MLflow database storing experiments & metrics README.md Project documentation
📊 What is MLflow used for here?
MLflow is used to:
Log model parameters
Log accuracy and other metrics
Save trained models
Track multiple experiments
Compare model performance
This makes the project reproducible and professional.
Install required libraries
pip install mlflow pandas scikit-learn
Run the ML experiment
python eda2.py
Start MLflow UI
mlflow ui
Open in browser
🎯 Output
You will see:
Different ML experiments
Accuracy and metrics
Stored models
Versioned runs
All tracked inside MLflow dashboard.
💡 Why this project is important
Most people only train models in Jupyter notebooks. This project shows how to use MLflow, which is what companies use to:
Track ML experiments
Manage model versions
Monitor performance
Build production-ready ML systems
👨💻 Author
Syed Sadath G Data Scientist | Machine Learning | MLOps