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K-Nearest Neighbor (KNN) Algorithm: Use Cases and Tips

July 2, 2025

k nearest neighbor

You don’t need to be an ML expert to forecast effectively. With K-nearest neighbor (KNN), you can bring predictive intelligence into business decisions. Now, businesses want to remain ahead in the AI race by building more supervised ML applications and fine-tuning algorithms. While algorithms can get too technical, there are easy and intuitive techniques, like K-nearest neighbor, that enable data classification and regression to improve your strategic predictions.

Teams using data analytics platforms like Tableau or Power BI often embed KNN-based classifiers to power fraud detection, sales predictions, or churn modeling.

KNN’s flexibility means it powers everything from fraud detection systems to recommendation engines. Here’s how it fits into business workflows.

Since KNN predicts based on proximity rather than internal model weights or parameters, it’s easy to interpret and quick to prototype, making it a go-to algorithm for exploratory data analysis and real-time decision support.

A simple KNN example would be feeding the neural network or NN model a training dataset of cats and dogs and testing it on an input image. Based on the similarity between the two animal groups, the KNN classifier would predict whether the object in the image is a dog or a cat. 

TL;DR: Everything you need to know about K-nearest neighbor

  • What it is: KNN is a simple, supervised machine learning algorithm that makes predictions based on the closest labeled data points, relying on distance rather than prior training to classify or estimate outcomes.
  • How it works: KNN stores all training data and uses distance metrics, like Euclidean, Manhattan, Minkowski, or Hamming, to compute the similarity between a new input and existing data. It assigns the class or value based on the majority or average among the closest k neighbors.
  • Why it matters: KNN is intuitive, non-parametric, and doesn’t require model training, making it ideal for exploratory analysis, data imputation, and use cases where interpretability and simplicity are valued.
  • Where it’s used: From recommendation engines and credit risk modeling to image recognition and missing data handling, KNN powers a range of classification and regression tasks across industries.
  • Strengths and limitations: Easy to understand, no assumptions about data distribution. Computationally expensive with large or high-dimensional datasets and sensitive to irrelevant features.
  • Popular use cases: Recommendation systems (e.g., user-based collaborative filtering), Image classification (e.g., digit recognition in MNIST dataset), Credit scoring (e.g., predicting loan default likelihood), Data imputation (e.g., estimating missing values using nearest neighbors.

Unlike traditional models that require heavy upfront training, KNN takes a more relaxed approach. It stores the data and waits until a prediction is needed. This just-in-time strategy earns it the nickname “lazy learner” and makes it especially useful for tasks like data mining, where real-time analysis of large historical datasets is key

Did you know? The "K" in KNN is a tunable parameter that determines how many neighbors to consult when classifying or predicting. A good value of K balances between noise sensitivity and generalization.

Why is KNN considered non-parametric?

It's considered a non-parametric method because it doesn’t make any assumptions about the underlying data distribution. Simply put, KNN tries to determine what group a data point belongs to by looking at the data points around it.

When you feed training data into KNN, it simply stores the dataset. It doesn’t perform any internal calculations, transformations, or optimizations during this time. The actual "learning" happens at prediction time, when the algorithm compares a new data point to the stored training data.

Because of this deferred computation, KNN is sometimes called an "instance-based learner" or "smart learner". This characteristic makes it a strong fit for data mining, where real-time inference from large, historical datasets is common.

Let’s say you’re trying to classify a new data point. Here’s how KNN does it:
  • It calculates the distance between the new data point and all the examples in the training set.
  • It identifies the ‘K’ closest points, also called its K nearest neighbors.
  • It performs a majority vote: If most of the neighbors belong to Group A, the new point is classified as Group A. If most belong to Group B, the new point is classified as Group B.
  • This local voting mechanism makes KNN especially intuitive and interpretable.
 Unlike K-means, which uncovers structure in unlabeled data, KNN is a memory-based supervised method that shifts computation to prediction time, making it simple but computationally demanding for large datasets.

How do you code a simple KNN example in Python?

Below is a fully commented, end-to-end KNN Python example using Sci-Kit Learn. It shows how to load data, scale features, choose K, and evaluate performance. Paste it into a Jupyter notebook or script to see KNN in action.

# 1. Import required libraries
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import classification_report, confusion_matrix
 
# 2. Load sample data (Iris flower dataset)
iris = load_iris()
X, y = iris.data, iris.target
 
# 3. Split into train/test
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, stratify=y, random_state=42
)
 
# 4. Scale features so no single dimension dominates distance calculations
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled  = scaler.transform(X_test)
 
# 5. Instantiate baseline model with a reasonable default k
knn = KNeighborsClassifier(n_neighbors=5)
 
# 6. Fit the model (lazy learner—stores the training data)
knn.fit(X_train_scaled, y_train)
 
# 7. Predict on the test set
y_pred = knn.predict(X_test_scaled)
 
# 8. Evaluate
print(confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred, target_names=iris.target_names))
 
# 9. Optional: hyperparameter tuning with GridSearchCV
param_grid = {
    "n_neighbors": range(3, 16, 2),           # odd numbers avoid ties
    "weights": ["uniform", "distance"],       # vote weighting
    "p": [1, 2]                               # 1 = Manhattan, 2 = Euclidean
}
grid = GridSearchCV(knn, param_grid, cv=5, n_jobs=-1)
grid.fit(X_train_scaled, y_train)
print("Best params:", grid.best_params_)
print("Best CV accuracy:", grid.best_score_)

This example highlights the practical simplicity of KNN, no training phase, minimal assumptions, and a clear prediction logic rooted in spatial relationships. With just a few lines of Python and scikit-learn, you can quickly prototype a classification model and iterate using different K values, distance metrics, and weighting strategies.

While KNN is beginner-friendly, it rewards thoughtful tuning-especially in terms of feature scaling and hyperparameter selection. 

How does K-nearest-neighbor make predictions?

KNN takes an intuitive approach: it doesn’t learn ahead of time, but it predicts by comparing new data to existing labeled examples. Here’s how it works:

  • Store all labeled training data: KNN begins by storing the entire dataset, including the known input features and corresponding class labels. There is no model-fitting or training phase.
  • Choose the value of K: K refers to the number of nearest neighbors that will be considered during classification. For example, if K = 3, the algorithm looks at the 3 closest data points.
  • Calculate distance between the query point and training points: To determine which data points are “nearest,” the algorithm uses a distance metric (e.g., Euclidean, Manhattan, Minkowski, or Hamming) to compute similarity.
  • Identify the K closest neighbors: After calculating distances, the algorithm ranks the training points and selects the K closest examples to the new, unlabeled data point.
  • Perform majority voting: The final class is assigned based on the majority class among the K nearest neighbors. If 7 out of 10 neighbors belong to class B, the data point is labeled as class B.

How does voting work in KNN?

Let’s illustrate with a practical example using a scatter plot containing two groups: Group A and Group B.

  • Scenario 1: Point X near Group A (K = 1): If a new data point X is located close to Group A and K = 1, the algorithm checks its single nearest neighbor. Since it belongs to Group A, X is classified as Group A.
  • Scenario 2: Point X near Group A (K = 10): Even when K = 10, if all 10 closest neighbors are still from Group A, X remains classified as Group A—the majority vote remains unchanged.
  • Scenario 3: Point Y between Groups A and B (K = 10): Suppose a new data point Y is equidistant between both groups. The algorithm finds its 10 nearest neighbors and counts, i.e, 7 belong to Group B and 3 belong to Group A. Based on the vote, Y is classified as Group B.

This voting mechanism scales smoothly to multi-class problems as well, and whichever class receives the most neighbor votes wins.

K-nearest neighbor algorithm pseudocode

Programming languages like Python and R are used to implement the KNN algorithm. The following is the pseudocode for KNN:

  1. Load the data
  2. Choose K value
  3. For each data point in the data:
  4. Find the Euclidean distance to all training data samples
  5. Store the distances in an ordered list and sort it
  6. Choose the top K entries from the sorted list
  7. Label the test point based on the majority of classes present in the selected points
  8. End

To validate the accuracy of the KNN classification, a confusion matrix is used. Statistical methods, such as the likelihood-ratio test, are also used for validation.

In regression analysis, the majority of steps are the same. Instead of assigning the class with the highest votes, the average of the neighbors’ values is calculated and assigned to the unknown data point.

Geometrical distance metrics in KNN: a quick comparison

K-nearest neighbor (KNN) uses distance metrics to measure similarity between data points and determine the nearest neighbors.

The choice of metric directly affects the model's accuracy, especially in datasets with varying scales, mixed data types, or outliers. Here's how the most common geometrical distance metrics compare:

Metric Formula (conceptual) Best used for Pros Cons
Euclidean (L₂) Square root of the sum of squared differences Continuous, low- to mid-dimensional data Intuitive and widely used Sensitive to scale and irrelevant features
Manhattan (L₁) Sum of absolute differences High-dimensional, sparse datasets More robust to outliers; simple math Less intuitive to visualize
Minkowski (Lₚ) Generalized form that includes L₁ and L₂ Tunable similarity for hybrid datasets Flexible; interpolates between L₁ and L₂ Requires setting and tuning the p parameter
Hamming Count of differing elements Binary or categorical data (e.g., strings) Ideal for text, DNA sequences, and bitwise encoding Not suitable for continuous or numerical variables

Always scale your features (via normalization or standardization) when using distance-based metrics like Euclidean or Minkowski to ensure fair comparisons across features.

Understanding these distance functions sets the foundation for where KNN truly shines and can be commercially used across industries today.

Build smarter KNN models with the right DSML stack

From tuning hyperparameters to deploying in production, building effective KNN models requires the right tools. G2 features real reviews on data science and machine learning platforms (DSML) that support training, validation, and scaling, so you can choose what fits your workflow best.

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What are the real-world use cases of KNN?

Classification is a critical problem in data science and machine learning. The KNN is one of the oldest yet accurate algorithms for pattern classification and text recognition.

Here are some of the areas where the k-nearest neighbor algorithm can be used:

  • Credit rating: The KNN algorithm helps determine an individual's credit rating by comparing them with others with similar characteristics.
  • Loan approval: Similar to credit rating, the k-nearest neighbor algorithm is beneficial in identifying individuals who are more likely to default on loans by comparing their traits with similar individuals.
  • Data preprocessing: Datasets can have many missing values. The KNN algorithm is used for a process called missing data imputation that estimates the missing values.
  • Pattern recognition: The ability of the KNN algorithm to identify patterns creates a wide range of applications. For example, it helps detect patterns in credit card usage and spot unusual patterns. Pattern detection is also useful in identifying patterns in customer purchase behavior.
  • Stock price prediction: Since the KNN algorithm has a flair for predicting the values of unknown entities, it's useful in predicting the future value of stocks based on historical data.
  • Recommendation systems: Since KNN can help find users of similar characteristics, it can be used in recommendation systems. For example, it can be used in an online video streaming platform to suggest content a user is more likely to watch by analyzing what similar users watch.
  • Computer vision: The KNN algorithm is used for image classification. Since it’s capable of grouping similar data points, for example, grouping cats together and dogs in a different class, it’s useful in several computer vision applications.
  • KNN in data mining: The KNN in machine learning is used to identify which cluster a particular data point belongs to by calculating the value of nearby data vectors. Based on the similarities between the two vectors, it classifies the input vector into some value or some predefined variable.

Apart from these applications, KNN is frequently used to determine business trends, revenue forecasts, and strategic investment-based ML models to minimize risk and improve the accuracy of the outcomes.

How to choose the optimal value of K

There isn't a specific way to determine the best K value; in other words, the number of neighbors in KNN. This means you might have to experiment with a few values before deciding which one to go forward with.

One way to do this is by considering (or pretending) that a part of the training samples is "unknown". Then, you can categorize the unknown data in the test set by using the k-nearest neighbor algorithm and analyze how good the new categorization is by comparing it with the information you already have in the training data.

When dealing with a two-class problem, it's better to choose an odd value for K. Otherwise, a scenario can arise where the number of neighbors in each class is the same. Also, the value of K must not be a multiple of the number of classes present.

Another way to choose the optimal value of K is by calculating the sqrt(N), where N denotes the number of samples in the training data set.

However, K with lower values, such as K=1 or K=2, can be noisy and subject to the effects of outliers. The chance of overfitting is also high in such cases.

On the other hand, K with larger values will, in most cases, give rise to smoother decision boundaries, but it shouldn't be too large. Otherwise, groups with fewer data points will always be outvoted by other groups. Plus, a larger K will be computationally expensive.

What are the key advantages of KNN algorithm?

KNN is widely appreciated for its simplicity and flexibility. With minimal configuration, it can be applied to a broad range of real-world problems, especially when accuracy and transparency are priorities over speed or scalability.

  • Easy to understand and implement: KNN’s logic is intuitive—it predicts outcomes based on the closest data points in the feature space, making it ideal for beginners and quick prototyping.
  • No training phase required: As a lazy learning algorithm, KNN doesn’t build a model in advance. It simply stores the data and performs computation only when a prediction is requested.
  • Supports both classification and regression: KNN algorithm can handle both discrete and continuous outputs, allowing it to be used across various types of supervised learning tasks.
  • Makes no assumptions about data distribution: Being non-parametric, KNN doesn't require the data to follow a specific distribution, making it a strong choice for irregular or nonlinear datasets.
  • Handles multi-class problems naturally: Unlike some algorithms that require one-vs-rest strategies, KNN can handle datasets with more than two classes without modification.
  • Performance scales with data quality: When provided with clean and representative data, KNN can yield highly competitive performance, even without complex tuning.

Of course, KNN isn't a perfect machine learning algorithm. Since the KNN predictor calculates everything from the ground up, it might not be ideal for large data sets.

What are the limitations of the KNN algorithm?

Despite its strengths, KNN isn't without limitations. The same simplicity that makes it accessible can lead to performance bottlenecks, especially when dealing with large or high-dimensional data.

  • Computationally expensive at prediction time: Since KNN must calculate distances to all stored data points at inference time, it can be slow, especially with large datasets.'
  • High memory usage: Because it retains the full training set in memory, KNN may not scale well without memory optimization or data compression.
  • Sensitive to irrelevant or noisy features: Irrelevant features can distort distance measurements, reducing prediction accuracy unless proper feature selection or dimensionality reduction is applied.
  • Choosing the right value of K is crucial: A small K may lead to overfitting, while a large K might underfit. Determining the optimal value often requires experimentation
  • Performance degrades in high-dimensional spaces: In high-dimensional datasets, the concept of distance becomes less meaningful ("curse of dimensionality"), making KNN less reliable without prior dimensionality reduction.

While ML is a low-level ML technique, it is still prominently used by data science and machine learning teams to leverage regression analysis for real-world problems.

Why does KNN struggle in high-dimensional datasets?

When you have massive amounts of data at hand, it can be quite challenging to extract quick and straightforward information from it. For that, we can use dimensionality reduction algorithms that, in essence, make the data "get directly to the point".

The term "curse of dimensionality" might evoke the impression that it's from a sci-fi movie. But what it means is that the data has too many features.

If the data has too many features, there's a high risk of overfitting the model, leading to inaccurate models. Too many dimensions also make it harder to group data, as every sample in the dataset will appear equidistant from each other.

The k-nearest neighbor algorithm is highly susceptible to overfitting due to the curse of dimensionality. However, this problem can be resolved with the brute-force implementation of the KNN algorithm, but it isn't practical for large datasets.

KNN doesn't work well if there are too many features. Hence, dimensionality reduction techniques like principal component analysis (PCA) and feature selection must be performed during the data preparation phase.

Where is KNN used in the industry today?

KNN’s adaptability makes it a valuable tool across domains. from personalized recommendations to healthcare diagnostics. 

  • Recommendation systems: KNN helps match users with products or content by identifying similar behavior patterns among peer groups. It's commonly used in e-commerce and streaming platforms for collaborative filtering.
  • Image classification: KNN is ideal for identifying objects or handwriting (e.g., in the MNIST dataset), as it compares pixel patterns to known labeled images using distance-based similarity.
  • Credit risk modeling:  Financial institutions use KNN to classify borrowers as low or high risk by analyzing historical profiles and comparing them to new applicants.
  • Medical diagnosis: KNN can assist in disease prediction by analyzing patient symptoms or biometrics and classifying them based on previously diagnosed cases with similar attributes.
  • Customer segmentation: Marketers use KNN to group users based on behavioral or demographic data, enabling personalized campaigns and better targeting.
  • Data imputation: When datasets contain missing values, KNN can estimate them by averaging or majority-voting values from the most similar (nearest) data entries.
  • Anomaly detection: KNN can flag unusual patterns or outliers, such as fraud or system failure, by identifying points that don't align with their nearest neighbors.

While high-dimensional data can be a hurdle for KNN, the algorithm still thrives in many real-world use cases and achieves a high degree of accuracy with low bandwidth requirements.

K-nearest neighbor: Frequently asked questions (FAQs)

Here are some FAQs to help you learn more about KNN in general.

What is KNN in simple terms?

KNN classifies or predicts outcomes based on the closest data points it can find in its training set. Think of it as asking your neighbors for advice; whoever’s closest gets the biggest say.

How does the KNN algorithm work?

KNN calculates the distance between a new data point and all training data and then assigns a class based on the majority vote among the ‘K’ nearest neighbors.

What are the applications of KNN?

Due to its ease of implementation and versatility, KNN is used in recommendation systems, image classification, credit risk modeling, medical diagnostics, and data imputation.   

What are the limitations of KNN?

KNN can be slow with large datasets, requires high memory, and is sensitive to irrelevant features. It also struggles in high-dimensional spaces without preprocessing.

How do I choose the optimal K value in KNN?

The optimal K is typically chosen using cross-validation. Start with odd values (e.g., 3, 5, 7) and look for the one that minimizes error while avoiding overfitting or underfitting.

KNN: the breezy algorithm that won hearts

Despite earning a reputation as a nonparametric and lazy algorithm, KNN is still one of the most efficient supervised machine learning techniques that are ideally suited for structured and labeled datasets and produce a great degree of efficiency in your overall algorithm manufacturing. That said, KNN isn’t immune to high-dimensional pitfalls. But with careful data preparation, it offers a simple way to surface meaningful patterns and build robust predictions

Discover top-rated machine learning platforms on G2 that empower you to seamlessly build, train, validate, and deploy KNN models at scale.

This article was originally published in 2023. It has been updated with new information.


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