How do you know how stable your model is? K-Fold Cross-Validation.

1. Split data into K folds.
2. Train on K-1, test on 1.
3.Repeat K times.

Get the mean & standard deviation of the scores.

A low std dev means your model's performance is consistent! 😌 #MLZoomcamp #DataTalksClub #LearningInPublic

How does a model predict customer churn? 🤔 With Logistic Regression!

A linear model scores a customer, then the sigmoid function squashes that score into a 0-1 probability. High probability = high churn risk! 🎯

Learn more from Alexey Grigorev!
#MLZoomcamp #DataTalksClub #MachineLearning

This week's #MLZoomcamp project is so practical! We're addressing customer churn by building a model to predict who is likely to leave a telecom service and offer them discounts. It's a classic binary classification problem with a clear business outcome. 🎯 #DataTalksClub #BusinessAnalytics

Data distribution matters! My car price data had a long tail, which is bad news for ML models. A logarithmic transformation was the key to normalizing it. Never underestimate the power of solid data prep! 📊 #MLZoomcamp #DataTalksClub #DataScience

ML projects need a plan! Learning the CRISP-DM lifecycle:
📈 Business Goal -> 📊 Data -> 🧹 Prep -> 🤖 Model -> ✅ Evaluate -> 🚀 Deploy.

It's a cycle, not a straight line! Loving this structured approach.
#MachineLearning #Process #MLZoomcamp #DataTalksClub