Which customers are about to leave, and can machine learning identify them before they do?
Welcome to Module 5, Episode 5 of Data Science Ascent: Machine Learning Foundations.
In this episode, we move from predicting numbers to predicting yes-or-no outcomes. You'll build your first classification model using logistic regression and apply it to one of the most important real-world machine learning problems: customer churn prediction.
🚀 What You'll Learn
✅ Understand regression vs. classification
✅ Construct a meaningful churn target from customer behavior
✅ Understand why the label is a model before the model
✅ Identify and handle class imbalance
✅ Learn how logistic regression turns a linear score into a probability
✅ Understand the sigmoid S-curve intuitively
✅ Use train_test_split(X, y, stratify=y) for balanced evaluation
✅ Compare .predict() with .predict_proba()
✅ Build a ranked list of customers most likely to churn
✅ Understand why classification thresholds are business decisions
✅ Interpret logistic regression coefficients without confusing prediction with causation
✅ Discover the dangerous accuracy trap
🎯 Churn Isn't a Column. You Build It.
Before training a model, you have to define exactly what you're predicting.
For this project, we create a business rule such as:
No order in the last 120 days = churned.
Change that threshold to 90 or 180 days and you create a different target, different training data, and ultimately a different model.
That's why professional data scientists document and defend how their target variable was constructed.
🤖 Meet Logistic Regression
Why not simply use linear regression to predict 0 or 1?
Because linear regression can happily produce impossible probabilities such as 1.4 or -0.2.
Logistic regression solves this by taking the familiar linear score and passing it through an S-shaped sigmoid curve, producing a probability between 0 and 1.
Linear Score → Sigmoid → Churn Probability
It's fast, interpretable, and one of the most widely used classifiers in applied machine learning.
📞 Probabilities Beat Verdicts
.predict() might tell you:
Customer will churn: YES
But .predict_proba() might tell you:
Customer A: 51% risk
Customer B: 99% risk
Those are very different business situations.
By ranking customers by churn probability, you can create a "Call These First" retention list, directing your most expensive interventions toward the customers at greatest risk.
🪨 The Accuracy Trap
Your churn model scores:
84% accuracy.
Sounds good.
But imagine 80% of customers don't churn. A rock that predicts "STAY" for everyone would achieve 80% accuracy without learning anything.
So is 84% actually impressive?
That question launches our next episode and introduces one of the most important lessons in applied ML: accuracy alone can be dangerously misleading.
🏔️ Data Science Ascent
Module 5: Machine Learning Foundations
✅ E1: What Machine Learning Actually Is
✅ E2: Linear Regression by Hand
✅ E3: Meet scikit-learn
✅ E4: Train/Test Split
▶️ E5: Classification — Will They Leave?
🔜 E6: Metrics Beyond Accuracy — The Rock That Scores 80%
👍 Join the Ascent
If this episode helped classification click, Like, Subscribe, and continue your Data Science Ascent.
💬 Comment: For a retention team, would you rather receive a simple churn YES/NO or a ranked list of churn probabilities?
📌 Pinned Comment
Three ideas to remember:
🎯 The label is a model before the model.
📉 Logistic regression = linear score → sigmoid → probability.
📞 The probability outranks the verdict.
And never celebrate accuracy until you compare it with the baseline.
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