Understand how to load and explore a real-world or synthetic dataset suitable for binary classification. Examples might include datasets for predicting customer churn, credit default, or disease presence.
Execute essential data preprocessing steps that are crucial for robust model performance:
- Feature Scaling: Apply appropriate scaling techniques (e.g., StandardScaler from Scikit-learn) to your numerical features. This is critical for KNN (to ensure all features contribute fairly to distance calculations) and often beneficial for Logistic Regression (to speed up convergence of optimization algorithms).
- Address any missing values present in the dataset (e.g., imputation, removal), explaining the rationale behind your chosen method.
Perform the fundamental step of splitting your dataset into distinct training and testing sets. Emphasize the importance of using stratified sampling (e.g., stratify parameter in train_test_split) especially for imbalanced datasets, to ensure that the class proportions in the original dataset are maintained in both the training and testing splits. This prevents scenarios where one split might have very few instances of the minority class.

Initialize and train a Logistic Regression model using a robust machine learning library (e.g., LogisticRegression from sklearn.linear_model).
Explore and understand the role of key parameters:
- solver: Different optimization algorithms used to find the model coefficients.
- C (Regularization Strength): Discuss its purpose in preventing overfitting (a concept that will be formally covered in later modules, but can be briefly introduced as a penalty on large coefficients).
Access and interpret the learned coefficients (weights) and the intercept of the trained Logistic Regression model. Explain how these values define the decision boundary.

Initialize and train a KNN classifier (e.g., KNeighborsClassifier from sklearn.neighbors).
Experiment with the critical hyperparameters of KNN:
- n_neighbors (the 'K' value): Try different values (e.g., 1, 3, 5, 10, 20) and observe their immediate impact.
- metric (distance metric): Use common metrics like Euclidean ('euclidean') and Manhattan ('manhattan').
Discuss how the choice of 'K' influences the model's complexity, its decision boundary, and how this relates to the bias-variance trade-off for KNN (small K = high variance, large K = high bias).

Use both your trained Logistic Regression and KNN models to make class predictions (e.g., 0 or 1) on both the training dataset (to check for learning performance) and, more importantly, on the unseen testing dataset (to assess generalization capability).
For Logistic Regression, also obtain the predicted probabilities for each class (predict_proba method), understanding how these probabilities are then converted into class labels using the 0.5 threshold.

For both Logistic Regression and KNN models (trained on the test set predictions):
- Generate and Visualize the Confusion Matrix: Use a library function (e.g., confusion_matrix from sklearn.metrics) to create the confusion matrix. Present it clearly, perhaps even visually with a heatmap. Explicitly label and identify the counts for True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN).
- Calculate and Interpret Core Metrics: For each model, calculate and present the following metrics, providing a clear interpretation for each:
- Accuracy: Explain its overall predictive correctness and highlight its potential shortcomings with imbalanced datasets.
- Precision: Explain what a high precision value means in terms of "false alarms" and discuss specific real-world scenarios where maximizing precision is the primary goal (e.g., spam filtering, expensive medical tests).
- Recall (Sensitivity): Explain what a high recall value means in terms of "missed opportunities" and discuss specific real-world scenarios where maximizing recall is the primary goal (e.g., detecting fatal diseases, catching all fraud).
- F1-Score: Explain how this metric balances precision and recall, and why it's a preferred single metric for comparison, especially in contexts with imbalanced classes.
- Comparative Analysis: Compare the performance of Logistic Regression versus KNN based on these various metrics. Discuss which model seems more suitable for the given dataset based on its strengths and weaknesses.

Using the confusion matrices generated in the lab, discuss real-world implications of FP and FN errors specific to your chosen dataset. For instance, if predicting disease:
- What does a high FP count mean? (e.g., healthy people getting unnecessary anxiety and tests).
- What does a high FN count mean? (e.g., sick people not getting the treatment they need).
Reiterate why relying solely on accuracy can be deceptive in scenarios with skewed class distributions and how Precision, Recall, and F1-Score offer a much more nuanced and reliable picture of model performance.