10.8.3: Hands-On Lab Exercise
- Page ID
- 58337
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Hands-On Lab Exercise
Lab: Building and Evaluating Predictive Models
Scenario:
You have a dataset of 2,000 bank customers: age, income, account_balance, num_products, credit_score, years_as_customer, has_credit_card, is_active_member, churned (0/1).
Instructions:
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EDA (15 min): Explore the target variable distribution (churned). Is it balanced? Compute summary stats and create visualizations showing how features relate to churn.
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Data Preparation (10 min): Split data into 80% train / 20% test. Identify which features to use. Handle any preprocessing needs.
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Model Building (25 min): Train three models on the training set: (a) Logistic Regression, (b) Decision Tree (max_depth=5), (c) Random Forest (100 trees). Use scikit-learn.
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Model Evaluation (20 min): For each model on the test set, compute: accuracy, precision, recall, F1 score. Create confusion matrices. Which model performs best, and by which metric?
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Interpretation (20 min): For the best model: what are the most important features predicting churn? For the decision tree, visualize or describe the top decision rules. Write a business recommendation based on the model's findings.
Deliverables:
EDA visualizations, train/test split code, 3 trained models with code, evaluation metrics comparison table, confusion matrices, feature importance analysis, business recommendation memo.