Docker LabIntermediate3 hours
Build a Classification Model with Scikit-Learn
Train, evaluate, and tune a classification model. Compare algorithms, handle imbalanced data, and interpret results.
Part of Data Science (Week 6)
What You'll Build
A customer churn prediction model comparing Logistic Regression, Random Forest, and Gradient Boosting with cross-validation, ROC curves, and feature importance analysis.
Tools Used
PythonScikit-LearnPandasJupyter
Skills Practiced
Model trainingHyperparameter tuningModel evaluation
Prerequisites
- Pandas basics
- Statistics fundamentals
Why This Matters in Real Jobs
Classification is the most common ML task in industry. Interviewers expect you to explain precision/recall trade-offs, handle class imbalance, and justify your algorithm choice.
Access This Lab
This lab is part of the Data Science course. Enrol to get access to all labs, projects, and career support.