K-Nearest Neighbors (KNN): A Complete Course for Data Scientists

MODULE 1: Introduction to KNN

1.1 What is KNN?

• Concept of similarity
• KNN as a lazy, non-parametric learning algorithm
• Classification vs Regression tasks

1.2 Real-World Use Cases

• Recommendation systems
• Customer segmentation
• Fraud detection
• Handwritten digit recognition (MNIST)

📊 MODULE 2: Theoretical Foundations

2.1 How KNN Works

• Distance-based voting (classification)
• Distance-weighted averaging (regression)
• Role of the parameter k

2.2 Distance Metrics

• Euclidean Distance
• Manhattan Distance
• Minkowski Distance
• Cosine Similarity (when to use it)

2.3 Curse of Dimensionality (review this again here)

• Impact on KNN
• Importance of feature scaling and dimensionality reduction

⚙️ MODULE 3: Data Preparation for KNN

3.1 Feature Scaling

• Why scaling matters in distance calculations
• Min-Max vs StandardScaler

3.2 Handling Missing Values

• Imputation strategies
• Using KNNImputer

3.3 Feature Selection

• Reducing noise to improve performance

💻 MODULE 4: Implementing KNN in Python

4.1 From Scratch (Step-by-step with NumPy)

• Writing your own KNN classifier and regressor

4.2 With Scikit-learn

• KNeighborsClassifier
• KNeighborsRegressor
• Important parameters: n_neighbors, weights, metric, algorithm

4.3 Evaluating Performance

• Accuracy, precision, recall, F1 (classification)
• MAE, RMSE, R² (regression)
• Confusion matrix

🔍 MODULE 5: Model Selection and Optimization

5.1 Choosing the Right k

• Bias-variance trade-off
• Plotting error vs k

5.2 Cross-Validation

• K-Fold CV to evaluate KNN
• GridSearchCV to tune hyperparameters

5.3 Weighted KNN

• Uniform vs distance weights
• Handling imbalanced datasets

🚀 MODULE 6: Advanced KNN Techniques

6.1 KD-Tree and Ball-Tree

• Efficient neighbors search
• When brute is better

6.2 KNN with Large Datasets

• Sampling
• Approximate Nearest Neighbors (FAISS, Annoy)

6.3 KNN in Unsupervised Learning

• KNN-based anomaly detection (e.g., LOF)
• KNN for density estimation

📦 MODULE 7: Project-Based Learning

7.1 Classification Project

• Customer churn prediction
• Dataset: Telco churn / synthetic

7.2 Regression Project

• House price prediction
• Dataset: Boston Housing / Superstore

7.3 Outlier Detection Project

• Using KNN in PyOD
• Dataset: Credit card fraud or synthetic

🧪 MODULE 8: Evaluation & Comparison

8.1 Compare KNN vs:

• Logistic Regression
• Decision Tree
• SVM
• Random Forest

8.2 When Not to Use KNN

• High-dimensional data
• Need for interpretability

📚 BONUS RESOURCES

• Papers: KNN improvements, real-time nearest neighbor search
• Tools: sklearn, PyOD, Faiss, Annoy, KNNImputer
• Cheat sheet PDF