The K Nearest Neighbors (KNN) algorithm is a fundamental machine learning technique that’s surprisingly simple yet powerful for classification and regression tasks. Unlike complex algorithms that require extensive training phases, KNN makes predictions by finding the ‘K’ most similar data points to a query and using their values to make decisions. This lazy learning approach makes it perfect for developers who need quick implementation without deep mathematical backgrounds, and it’s particularly valuable when you’re working with datasets where local patterns matter more than global trends. In this guide, you’ll learn how to implement KNN from scratch in Python, optimize its performance for production environments, and avoid the common pitfalls that can tank your application’s response times.

How KNN Works Under the Hood

KNN operates on a deceptively simple principle: similar things exist in close proximity. When you need to classify a new data point, the algorithm calculates distances between this point and all existing points in your training dataset, then picks the K closest neighbors to make a prediction.

The distance calculation typically uses Euclidean distance, but you can also use Manhattan, Minkowski, or even custom distance metrics depending on your data type. For classification tasks, KNN performs a majority vote among the K neighbors, while regression tasks average the target values of those neighbors.

Here’s the basic workflow:

• Load and normalize your training data
• Choose an appropriate value for K
• For each prediction, calculate distances to all training points
• Sort distances and select K nearest neighbors
• Aggregate neighbor labels/values for final prediction

The algorithm’s simplicity is both its strength and weakness. While it requires no training time and adapts well to new data, it can become computationally expensive with large datasets since every prediction requires distance calculations against the entire training set.

Step-by-Step KNN Implementation in Python

Let’s build a KNN classifier from scratch before diving into scikit-learn’s optimized version. This approach helps you understand the mechanics and gives you flexibility for custom distance metrics or specialized use cases.

Basic KNN Implementation

import numpy as np
from collections import Counter
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

class KNNClassifier:
    def __init__(self, k=3):
        self.k = k
    
    def fit(self, X, y):
        """Store training data - KNN is lazy learning"""
        self.X_train = X
        self.y_train = y
    
    def euclidean_distance(self, x1, x2):
        """Calculate Euclidean distance between two points"""
        return np.sqrt(np.sum((x1 - x2) ** 2))
    
    def predict(self, X):
        """Make predictions for test data"""
        predictions = []
        for x in X:
            # Calculate distances to all training points
            distances = [self.euclidean_distance(x, x_train) 
                        for x_train in self.X_train]
            
            # Get indices of k nearest neighbors
            k_indices = np.argsort(distances)[:self.k]
            
            # Get labels of k nearest neighbors
            k_nearest_labels = [self.y_train[i] for i in k_indices]
            
            # Perform majority vote
            most_common = Counter(k_nearest_labels).most_common(1)
            predictions.append(most_common[0][0])
        
        return np.array(predictions)

# Generate sample dataset
X, y = make_classification(n_samples=1000, n_features=2, n_redundant=0, 
                          n_informative=2, random_state=42, n_clusters_per_class=1)

# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train and test our KNN
knn = KNNClassifier(k=5)
knn.fit(X_train, y_train)
predictions = knn.predict(X_test)

# Calculate accuracy
accuracy = np.mean(predictions == y_test)
print(f"Custom KNN Accuracy: {accuracy:.3f}")

Using Scikit-learn’s Optimized KNN

For production applications, scikit-learn’s implementation offers significant performance improvements through optimized data structures and algorithms:

from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import classification_report, confusion_matrix
import seaborn as sns

# Normalize features for better distance calculations
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Create and train KNN classifier
knn_sklearn = KNeighborsClassifier(
    n_neighbors=5,
    weights='uniform',  # or 'distance' for weighted voting
    algorithm='auto',   # lets sklearn choose best algorithm
    metric='euclidean'
)

knn_sklearn.fit(X_train_scaled, y_train)
sk_predictions = knn_sklearn.predict(X_test_scaled)

# Evaluate performance
print("Scikit-learn KNN Results:")
print(classification_report(y_test, sk_predictions))

# Visualize confusion matrix
cm = confusion_matrix(y_test, sk_predictions)
plt.figure(figsize=(8, 6))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')
plt.title('KNN Confusion Matrix')
plt.ylabel('True Label')
plt.xlabel('Predicted Label')
plt.show()

Optimizing K Value and Performance Tuning

Choosing the right K value is crucial for KNN performance. Too small and you’ll get noisy predictions; too large and you’ll lose local patterns. Here’s how to find the optimal K systematically:

from sklearn.model_selection import cross_val_score
import time

def find_optimal_k(X_train, y_train, max_k=20):
    """Find optimal K value using cross-validation"""
    k_values = range(1, max_k + 1)
    cv_scores = []
    training_times = []
    
    for k in k_values:
        start_time = time.time()
        knn = KNeighborsClassifier(n_neighbors=k)
        
        # Use 5-fold cross-validation
        scores = cross_val_score(knn, X_train, y_train, cv=5, scoring='accuracy')
        cv_scores.append(scores.mean())
        
        training_times.append(time.time() - start_time)
        print(f"K={k}: CV Score = {scores.mean():.3f} (+/- {scores.std()*2:.3f})")
    
    # Find best K
    best_k = k_values[np.argmax(cv_scores)]
    
    # Plot results
    plt.figure(figsize=(12, 4))
    
    plt.subplot(1, 2, 1)
    plt.plot(k_values, cv_scores, 'bo-')
    plt.axvline(x=best_k, color='red', linestyle='--', label=f'Best K={best_k}')
    plt.xlabel('K Value')
    plt.ylabel('Cross-Validation Accuracy')
    plt.title('K Value vs Accuracy')
    plt.legend()
    plt.grid(True)
    
    plt.subplot(1, 2, 2)
    plt.plot(k_values, training_times, 'ro-')
    plt.xlabel('K Value')
    plt.ylabel('Training Time (seconds)')
    plt.title('K Value vs Training Time')
    plt.grid(True)
    
    plt.tight_layout()
    plt.show()
    
    return best_k, max(cv_scores)

# Find optimal K
optimal_k, best_score = find_optimal_k(X_train_scaled, y_train)
print(f"\nOptimal K: {optimal_k} with CV score: {best_score:.3f}")

Real-World Use Cases and Examples

KNN shines in several practical scenarios where you need quick implementation and interpretable results. Here are some production-ready examples:

Recommendation System

import pandas as pd
from sklearn.metrics.pairwise import cosine_similarity

class MovieRecommender:
    def __init__(self, k=5):
        self.k = k
        self.movies_df = None
        self.user_movie_matrix = None
    
    def fit(self, ratings_data):
        """ratings_data should have columns: user_id, movie_id, rating"""
        self.movies_df = ratings_data
        
        # Create user-movie matrix
        self.user_movie_matrix = ratings_data.pivot_table(
            index='user_id', 
            columns='movie_id', 
            values='rating'
        ).fillna(0)
    
    def recommend_movies(self, user_id, n_recommendations=5):
        """Recommend movies for a specific user"""
        if user_id not in self.user_movie_matrix.index:
            return "User not found"
        
        # Get user's ratings
        user_ratings = self.user_movie_matrix.loc[user_id].values.reshape(1, -1)
        
        # Calculate similarity with all users
        similarities = cosine_similarity(user_ratings, self.user_movie_matrix.values)[0]
        
        # Get K most similar users
        similar_users_indices = similarities.argsort()[-self.k-1:-1][::-1]
        
        # Get movies liked by similar users but not watched by target user
        user_watched = set(self.user_movie_matrix.columns[
            self.user_movie_matrix.loc[user_id] > 0
        ])
        
        recommendations = {}
        for idx in similar_users_indices:
            similar_user_id = self.user_movie_matrix.index[idx]
            similar_user_movies = self.user_movie_matrix.loc[similar_user_id]
            
            for movie_id, rating in similar_user_movies.items():
                if rating > 3.5 and movie_id not in user_watched:
                    if movie_id not in recommendations:
                        recommendations[movie_id] = []
                    recommendations[movie_id].append(rating * similarities[idx])
        
        # Average scores and sort
        final_recommendations = {
            movie: np.mean(scores) 
            for movie, scores in recommendations.items()
        }
        
        return sorted(final_recommendations.items(), 
                     key=lambda x: x[1], reverse=True)[:n_recommendations]

# Example usage with sample data
sample_ratings = pd.DataFrame({
    'user_id': [1, 1, 1, 2, 2, 2, 3, 3, 3] * 10,
    'movie_id': list(range(90)),
    'rating': np.random.uniform(1, 5, 90)
})

recommender = MovieRecommender(k=3)
recommender.fit(sample_ratings)
recommendations = recommender.recommend_movies(user_id=1)
print("Recommended movies:", recommendations)

Anomaly Detection for Server Monitoring

This example is particularly relevant for VPS and dedicated server administrators who need to monitor system health:

from sklearn.neighbors import LocalOutlierFactor
import numpy as np
import pandas as pd

class ServerAnomalyDetector:
    def __init__(self, n_neighbors=20, contamination=0.1):
        self.detector = LocalOutlierFactor(
            n_neighbors=n_neighbors,
            contamination=contamination
        )
        self.feature_names = None
    
    def prepare_features(self, server_metrics):
        """Prepare server metrics for anomaly detection"""
        features = []
        self.feature_names = ['cpu_usage', 'memory_usage', 'disk_io', 
                             'network_io', 'connection_count']
        
        for metric in server_metrics:
            features.append([
                metric['cpu_usage'],
                metric['memory_usage'], 
                metric['disk_io'],
                metric['network_io'],
                metric['connection_count']
            ])
        
        return np.array(features)
    
    def detect_anomalies(self, server_metrics):
        """Detect anomalous server behavior"""
        X = self.prepare_features(server_metrics)
        
        # Fit and predict (-1 for anomalies, 1 for normal)
        anomaly_labels = self.detector.fit_predict(X)
        
        # Get anomaly scores (more negative = more anomalous)
        anomaly_scores = self.detector.negative_outlier_factor_
        
        results = []
        for i, (label, score) in enumerate(zip(anomaly_labels, anomaly_scores)):
            if label == -1:  # Anomaly detected
                results.append({
                    'timestamp': server_metrics[i].get('timestamp', i),
                    'anomaly_score': score,
                    'metrics': server_metrics[i],
                    'severity': 'HIGH' if score < -2 else 'MEDIUM'
                })
        
        return results

# Example usage with simulated server data
def generate_server_metrics(n_samples=1000):
    """Generate realistic server metrics with some anomalies"""
    np.random.seed(42)
    
    # Normal server behavior
    metrics = []
    for i in range(n_samples):
        if i % 100 == 0 and i > 0:  # Inject anomalies every 100 samples
            # Simulate server spike
            metric = {
                'timestamp': i,
                'cpu_usage': np.random.uniform(85, 98),
                'memory_usage': np.random.uniform(90, 95),
                'disk_io': np.random.uniform(80, 100),
                'network_io': np.random.uniform(70, 90),
                'connection_count': np.random.uniform(800, 1000)
            }
        else:
            # Normal behavior
            metric = {
                'timestamp': i,
                'cpu_usage': np.random.uniform(20, 60),
                'memory_usage': np.random.uniform(30, 70),
                'disk_io': np.random.uniform(10, 40),
                'network_io': np.random.uniform(15, 35),
                'connection_count': np.random.uniform(50, 200)
            }
        metrics.append(metric)
    
    return metrics

# Test the anomaly detector
server_data = generate_server_metrics(500)
detector = ServerAnomalyDetector(n_neighbors=15, contamination=0.05)
anomalies = detector.detect_anomalies(server_data)

print(f"Detected {len(anomalies)} anomalies:")
for anomaly in anomalies[:5]:  # Show first 5
    print(f"Timestamp: {anomaly['timestamp']}, "
          f"Severity: {anomaly['severity']}, "
          f"Score: {anomaly['anomaly_score']:.3f}")

Algorithm Comparison and Performance Analysis

Understanding when to use KNN versus other algorithms helps you make informed decisions. Here’s a comprehensive comparison:

Algorithm	Training Time	Prediction Time	Memory Usage	Interpretability	Best Use Cases
KNN	O(1)	O(n*d)	High	High	Small datasets, irregular decision boundaries
Decision Tree	O(n*log(n)*d)	O(log(n))	Low	High	Categorical features, rule-based decisions
Random Forest	O(n*log(n)*d*k)	O(log(n)*k)	Medium	Medium	Mixed data types, robust predictions
SVM	O(n²) to O(n³)	O(s*d)	Medium	Low	High-dimensional data, clear margins
Naive Bayes	O(n*d)	O(d)	Low	Medium	Text classification, independent features

Let’s benchmark these algorithms on a real dataset:

from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB
from sklearn.datasets import load_digits
import time

def benchmark_algorithms(X_train, X_test, y_train, y_test):
    """Compare multiple algorithms on the same dataset"""
    algorithms = {
        'KNN': KNeighborsClassifier(n_neighbors=5),
        'Decision Tree': DecisionTreeClassifier(random_state=42),
        'Random Forest': RandomForestClassifier(n_estimators=100, random_state=42),
        'SVM': SVC(random_state=42),
        'Naive Bayes': GaussianNB()
    }
    
    results = []
    
    for name, algo in algorithms.items():
        # Training time
        start_time = time.time()
        algo.fit(X_train, y_train)
        train_time = time.time() - start_time
        
        # Prediction time
        start_time = time.time()
        predictions = algo.predict(X_test)
        predict_time = time.time() - start_time
        
        # Accuracy
        accuracy = np.mean(predictions == y_test)
        
        results.append({
            'Algorithm': name,
            'Training Time (s)': f"{train_time:.4f}",
            'Prediction Time (s)': f"{predict_time:.4f}",
            'Accuracy': f"{accuracy:.3f}"
        })
    
    return pd.DataFrame(results)

# Load a more complex dataset
digits = load_digits()
X_train, X_test, y_train, y_test = train_test_split(
    digits.data, digits.target, test_size=0.2, random_state=42
)

# Run benchmark
benchmark_results = benchmark_algorithms(X_train, X_test, y_train, y_test)
print("Algorithm Benchmark Results:")
print(benchmark_results.to_string(index=False))

Best Practices and Common Pitfalls

After implementing KNN in production environments, here are the critical practices that separate reliable systems from problematic ones:

Data Preprocessing and Feature Scaling

This is where most KNN implementations fail. Different feature scales can completely dominate distance calculations:

from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline

class RobustKNNPipeline:
    def __init__(self, k=5):
        self.k = k
        self.pipeline = None
    
    def create_preprocessing_pipeline(self, numeric_features, categorical_features=None):
        """Create robust preprocessing pipeline"""
        preprocessors = []
        
        # Handle numeric features
        if numeric_features:
            # Use RobustScaler for outlier-resistant scaling
            numeric_transformer = Pipeline([
                ('scaler', RobustScaler()),
                # Could add feature selection here
            ])
            preprocessors.append(('num', numeric_transformer, numeric_features))
        
        # Handle categorical features if present
        if categorical_features:
            from sklearn.preprocessing import OneHotEncoder
            categorical_transformer = Pipeline([
                ('onehot', OneHotEncoder(drop='first', sparse=False))
            ])
            preprocessors.append(('cat', categorical_transformer, categorical_features))
        
        return ColumnTransformer(preprocessors)
    
    def build_pipeline(self, numeric_features, categorical_features=None):
        """Build complete ML pipeline"""
        preprocessor = self.create_preprocessing_pipeline(
            numeric_features, categorical_features
        )
        
        self.pipeline = Pipeline([
            ('preprocessor', preprocessor),
            ('classifier', KNeighborsClassifier(
                n_neighbors=self.k,
                weights='distance',  # Weight by distance
                algorithm='ball_tree',  # Better for high dimensions
                metric='euclidean'
            ))
        ])
        
        return self.pipeline
    
    def fit(self, X, y, numeric_features, categorical_features=None):
        """Fit the complete pipeline"""
        if self.pipeline is None:
            self.build_pipeline(numeric_features, categorical_features)
        
        self.pipeline.fit(X, y)
        return self
    
    def predict(self, X):
        """Make predictions"""
        return self.pipeline.predict(X)
    
    def predict_proba(self, X):
        """Get prediction probabilities"""
        return self.pipeline.predict_proba(X)

# Example with mixed data types
sample_data = pd.DataFrame({
    'age': np.random.randint(18, 80, 1000),
    'income': np.random.normal(50000, 20000, 1000),
    'score': np.random.uniform(0, 100, 1000),
    'category': np.random.choice(['A', 'B', 'C'], 1000),
    'target': np.random.choice([0, 1], 1000)
})

# Identify feature types
numeric_features = ['age', 'income', 'score']
categorical_features = ['category']

# Use robust pipeline
robust_knn = RobustKNNPipeline(k=7)
X = sample_data[numeric_features + categorical_features]
y = sample_data['target']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

robust_knn.fit(X_train, y_train, numeric_features, categorical_features)
predictions = robust_knn.predict(X_test)
probabilities = robust_knn.predict_proba(X_test)

print(f"Robust KNN Accuracy: {np.mean(predictions == y_test):.3f}")

Memory and Performance Optimization

For large datasets, naive KNN becomes unusable. Here’s how to optimize for production:

from sklearn.neighbors import NearestNeighbors
from sklearn.decomposition import PCA
import joblib
from memory_profiler import profile

class OptimizedKNN:
    def __init__(self, k=5, use_pca=True, n_components=10):
        self.k = k
        self.use_pca = use_pca
        self.n_components = n_components
        self.pca = None
        self.nn_model = None
        self.y_train = None
    
    def fit(self, X, y):
        """Optimized fitting with dimensionality reduction"""
        X_processed = X.copy()
        
        # Apply PCA for high-dimensional data
        if self.use_pca and X.shape[1] > self.n_components:
            self.pca = PCA(n_components=self.n_components)
            X_processed = self.pca.fit_transform(X)
            print(f"Reduced dimensions from {X.shape[1]} to {X_processed.shape[1]}")
        
        # Use optimized nearest neighbors search
        self.nn_model = NearestNeighbors(
            n_neighbors=self.k,
            algorithm='ball_tree',  # or 'kd_tree' for low dimensions
            metric='euclidean',
            n_jobs=-1  # Use all CPU cores
        )
        
        self.nn_model.fit(X_processed)
        self.y_train = y
        
        return self
    
    def predict(self, X):
        """Fast prediction with optimized search"""
        X_processed = X.copy()
        
        if self.pca is not None:
            X_processed = self.pca.transform(X)
        
        # Find nearest neighbors
        distances, indices = self.nn_model.kneighbors(X_processed)
        
        predictions = []
        for neighbor_indices in indices:
            # Get labels of neighbors
            neighbor_labels = self.y_train[neighbor_indices]
            
            # Majority vote
            prediction = Counter(neighbor_labels).most_common(1)[0][0]
            predictions.append(prediction)
        
        return np.array(predictions)
    
    def save_model(self, filepath):
        """Save trained model"""
        model_data = {
            'nn_model': self.nn_model,
            'pca': self.pca,
            'y_train': self.y_train,
            'k': self.k
        }
        joblib.dump(model_data, filepath)
    
    def load_model(self, filepath):
        """Load trained model"""
        model_data = joblib.load(filepath)
        self.nn_model = model_data['nn_model']
        self.pca = model_data['pca']
        self.y_train = model_data['y_train']
        self.k = model_data['k']
        return self

# Performance comparison
def compare_knn_implementations(X_train, X_test, y_train, y_test):
    """Compare standard vs optimized KNN"""
    
    print("Testing Standard KNN...")
    start_time = time.time()
    standard_knn = KNeighborsClassifier(n_neighbors=5)
    standard_knn.fit(X_train, y_train)
    standard_pred = standard_knn.predict(X_test)
    standard_time = time.time() - start_time
    standard_acc = np.mean(standard_pred == y_test)
    
    print("Testing Optimized KNN...")
    start_time = time.time()
    optimized_knn = OptimizedKNN(k=5, use_pca=True, n_components=10)
    optimized_knn.fit(X_train, y_train)
    optimized_pred = optimized_knn.predict(X_test)
    optimized_time = time.time() - start_time
    optimized_acc = np.mean(optimized_pred == y_test)
    
    print(f"\nResults:")
    print(f"Standard KNN - Time: {standard_time:.3f}s, Accuracy: {standard_acc:.3f}")
    print(f"Optimized KNN - Time: {optimized_time:.3f}s, Accuracy: {optimized_acc:.3f}")
    print(f"Speedup: {standard_time/optimized_time:.2f}x")

# Test with larger dataset
from sklearn.datasets import make_classification
X_large, y_large = make_classification(
    n_samples=5000, n_features=50, n_informative=30, 
    n_redundant=10, random_state=42
)

X_train_large, X_test_large, y_train_large, y_test_large = train_test_split(
    X_large, y_large, test_size=0.2, random_state=42
)

compare_knn_implementations(X_train_large, X_test_large, y_train_large, y_test_large)

Common Pitfalls and Debugging

Here are the most frequent issues you’ll encounter and how to solve them:

• Curse of Dimensionality: In high dimensions, all points become equidistant. Use PCA or feature selection to reduce dimensions below 20-30 features when possible.
• Imbalanced Classes: KNN can be biased toward majority classes. Use stratified sampling or adjust class weights in your evaluation.
• Choosing Odd vs Even K: Use odd K values for binary classification to avoid ties, but this matters less with weighted voting.
• Memory Issues: KNN stores all training data. For datasets >100MB, consider approximate nearest neighbor libraries like Annoy or Faiss.
• Distance Metric Selection: Euclidean works for continuous features, but consider Manhattan for high dimensions or Hamming for categorical data.

def debug_knn_performance(X_train, X_test, y_train, y_test):
    """Comprehensive KNN debugging function"""
    
    print("=== KNN Performance Debug Report ===\n")
    
    # Check data characteristics
    print(f"Training set shape: {X_train.shape}")
    print(f"Test set shape: {X_test.shape}")
    print(f"Feature ranges:")
    
    feature_stats = pd.DataFrame({
        'min': X_train.min(axis=0),
        'max': X_train.max(axis=0),
        'std': X_train.std(axis=0)
    })
    print(feature_stats.head())
    
    # Check for scaling issues
    range_ratios = (X_train.max(axis=0) - X_train.min(axis=0))
    max_ratio = range_ratios.max() / range_ratios.min()
    print(f"\nMax/Min feature range ratio: {max_ratio:.2f}")
    if max_ratio > 100:
        print("⚠️  WARNING: Large feature scale differences detected!")
        print("   Consider using StandardScaler or RobustScaler")
    
    # Check class balance
    class_counts = Counter(y_train)
    print(f"\nClass distribution: {dict(class_counts)}")
    imbalance_ratio = max(class_counts.values()) / min(class_counts.values())
    if imbalance_ratio > 3:
        print(f"⚠️  WARNING: Class imbalance detected (ratio: {imbalance_ratio:.2f})")
        print("   Consider stratified sampling or weighted KNN")
    
    # Test different K values
    print(f"\nTesting different K values:")
    k_values = [1, 3, 5, 7, 9, 15]
    for k in k_values:
        knn = KNeighborsClassifier(n_neighbors=k)
        knn.fit(X_train, y_train)
        pred = knn.predict(X_test)
        acc = np.mean(pred == y_test)
        print(f"K={k}: Accuracy = {acc:.3f}")
    
    # Memory usage estimation
    memory_mb = (X_train.nbytes + y_train.nbytes) / (1024 * 1024)
    print(f"\nTraining data memory usage: {memory_mb:.2f} MB")
    if memory_mb > 500:
        print("⚠️  WARNING: Large dataset detected")
        print("   Consider using approximate methods or dimensionality reduction")

# Example usage
debug_knn_performance(X_train_large, X_test_large, y_train_large, y_test_large)

Integration with Production Systems

Deploying KNN in production requires careful consideration of latency, memory usage, and scalability. Here’s a production-ready implementation with caching and monitoring:

import redis
import pickle
import hashlib
from functools import lru_cache
import logging

class ProductionKNN:
    def __init__(self, k=5, cache_size=1000, redis_host='localhost', redis_port=6379):
        self.k = k
        self.model = None
        self.scaler = None
        self.cache_size = cache_size
        
        # Setup Redis for distributed caching
        try:
            self.redis_client = redis.Redis(host=redis_host, port=redis_port, decode_responses=False)
            self.redis_client.ping()
            self.use_redis = True
        except:
            self.use_redis = False
            logging.warning("Redis not available, using local cache only")
        
        # Setup logging
        logging.basicConfig(level=logging.INFO)
        self.logger = logging.getLogger(__name__)
    
    def fit(self, X, y):
        """Train the model with preprocessing"""
        # Scale features
        self.scaler = StandardScaler()
        X_scaled = self.scaler.fit_transform(X)
        
        # Train KNN
        self.model = KNeighborsClassifier(
            n_neighbors=self.k,
            weights='distance',
            algorithm='ball_tree',
            n_jobs=-1
        )
        self.model.fit(X_scaled, y)
        
        self.logger.info(f"Model trained with {X.shape[0]} samples, {X.shape[1]} features")
        return self
    
    def _get_cache_key(self, X):
        """Generate cache key for input"""
        return hashlib.md5(X.tobytes()).hexdigest()
    
    def _get_from_cache(self, cache_key):
        """Get prediction from cache"""
        if self.use_redis:
            try:
                cached = self.redis_client.get(f"knn_pred:{cache_key}")
                if cached:
                    return pickle.loads(cached)
            except Exception as e:
                self.logger.warning(f"Redis cache error: {e}")
        return None
    
    def _save_to_cache(self, cache_key, prediction):
        """Save prediction to cache"""
        if self.use_redis:
            try:
                self.redis_client.setex(
                    f"knn_pred:{cache_key}", 
                    3600,  # 1 hour TTL
                    pickle.dumps(prediction)
                )
            except Exception as e:
                self.logger.warning(f"Redis cache save error: {e}")
    
    @lru_cache(maxsize=1000)
    def _cached_predict_single(self, x_tuple):
        """Cached prediction for single sample"""
        x_array = np.array(x_tuple).reshape(1, -1)
        x_scaled = self.scaler.transform(x_array)
        return self.model.predict(x_scaled)[0]
    
    def predict(self, X, use_cache=True):
        """Make predictions with caching"""
        if self.model is None:
            raise ValueError("Model not trained. Call fit() first.")
        
        predictions = []
        cache_hits = 0
        
        for i, x in enumerate(X):
            if use_cache:
                # Try cache first
                cache_key = self._get_cache_key(x)
                cached_pred = self._get_from_cache(cache_key)
                
                if cached_pred is not None:
                    predictions.append(cached_pred)
                    cache_hits += 1
                    continue
            
            # Make prediction
            x_scaled = self.scaler.transform(x.reshape(1, -1))
            pred = self.model.predict(x_scaled)[0]
            predictions.append(pred)
            
            # Cache result
            if use_cache:
                self._save_to_cache(cache_key, pred)
        
        if use_cache:
            cache_hit_rate = cache_hits / len(X) * 100
            self.logger.info(f"Cache hit rate: {cache_hit_rate:.1f}%")
        
        return np.array(predictions)
    
    def predict_with_confidence(self, X):
        """Predict with confidence scores"""
        X_scaled = self.scaler.transform(X)
        
        # Get probabilities
        probabilities = self.model.predict_proba(X_scaled)
        predictions = self.model.predict(X_scaled)
        
        # Calculate confidence as max probability
        confidence_scores = np.max(probabilities, axis=1)
        
        results = []
        for pred, conf, probs in zip(predictions, confidence_scores, probabilities):
            results.append({
                'prediction': pred,
                'confidence': conf,
                'probabilities': dict(zip(self.model.classes_, probs))
            })
        
        return results
    
    def get_model_stats(self):
        """Get model performance statistics"""
        if self.model is None:
            return {"error": "Model not trained"}
        
        return {
            "n_samples": self.model.n_samples_fit_,
            "n_features": self.model.n_features_in_,
            "k_neighbors": self.k,
            "algorithm": self.model.algorithm,
            "cache_info": self._cached_predict_single.cache_info()._asdict() if hasattr(self._cached_predict_single, 'cache_info') else None
        }

# Example deployment
if __name__ == "__main__":
    # Create sample data
    X, y = make_classification(n_samples=1000, n_features=10, random_state=42)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    
    # Train production model
    prod_knn = ProductionKNN(k=5, cache_size=500)
    prod_knn.fit(X_train, y_train)
    
    # Make predictions
    predictions = prod_knn.predict(X_test, use_cache=True)
    confidence_results = prod_knn.predict_with_confidence(X_test[:5])
    
    print(f"Accuracy: {np.mean(predictions == y_test):.3f}")
    print(f"Model stats: {prod_knn.get_model_stats()}")
    print(f"Sample confidence results: {confidence_results[0]}")

The K Nearest Neighbors algorithm provides an excellent entry point into machine learning while remaining powerful enough for production use cases. Its intuitive nature makes it perfect for rapid prototyping, but successful deployment requires attention to preprocessing, performance optimization, and proper system integration. Whether you’re building recommendation systems, anomaly detection for your servers, or classification systems, KNN’s simplicity and interpretability make it a valuable tool in your machine learning toolkit.

For more advanced implementations, consider exploring approximate nearest neighbor libraries like Annoy or Faiss for large-scale applications, and always benchmark your specific use case against alternatives to ensure you’re choosing the right algorithm for your problem domain.

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