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relataly.com

  • AI
    • Simple Regression
    • Classification: Two Class
    • Classification: Multi-Class
    • Clustering
    • Time Series Forecasting
    • Anomaly Detection
    • Natural Language
    • Recommender Systems
    • Reinforcement Learning
    • Responsible AI
  • Use Cases
    • Stock Market Forecasting
    • Algorithmic Trading
    • Sentiment Analysis
    • Churn Prediction
    • Fraud Detection
    • Predictive Maintenance
    • Marketing Automation
    • Customer Segmentation
    • Sales Forecasting
    • ChatBots
    • Fighting Crime
    • Risk Management
    • Image Recognition
  • Algorithms
    • CNNs
    • RNNs (LSTM)
    • Decision Trees
    • Random Decision Forests
    • Random Isolation Forest
    • Local Outlier Factor
    • Gradient Boosting
    • Collaborative Filtering
    • Content-based Filtering
    • K-Nearest Neighbors
    • K-Means
    • Affinity Propagation
    • Agglomerative Clustering
    • Logistic Regression
    • Naive Bayes
    • ARIMA
  • Data Science
    • Exploratory Data Analysis
    • Feature Engineering
    • Hyperparameter Tuning
    • Dimensionality Reduction
    • Model Interpretation
    • Data Visualization
    • Correlation
    • Measuring Performance
    • Cross-Validation
    • Vector Databases
    • SQLite
    • Data Science Environments
      • Anaconda
      • Azure Machine Learning
    • Python Libraries
      • Scikit-Learn
      • Tensorflow
      • Keras
      • Pytorch
      • PySpark
      • Chainer
      • OpenAI Gym
      • Seaborn
      • Fairlearn
      • Facebook Prophet
      • GeoPandas
  • Data & APIs
    • OpenAI API
    • REST APIs
    • NewsAPI
    • Coinmarketcap API
    • Coinbase API
    • Gate.io API
    • Yahoo Finance API
    • Statworx COVID-19 API
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    • Reddit API
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    • Synthetic Data
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K-Nearest Neighbors (KNN)

Here you’ll find everything about k-nearest neighbor (k-NN), including tutorials on implementing the algorithm in Python.

The k-nearest neighbor algorithm is a method for classifying data points based on their similarity to other data points. It is a non-parametric, lazy learning algorithm that is used for both classification and regression. In k-NN, a data point is assigned to the class that is most common among its k nearest neighbors. The number of neighbors is a positive integer (k) that is specified by the user. The algorithm determines the neighbors by using a distance metric, such as Euclidean distance, that measures the similarity between data points. k-NN is known for its simplicity and versatility, but it can be computationally expensive. It also may not work well on high-dimensional data. Application areas include image recognition, speech recognition, and gene expression analysis.

credit card fraud detection python machine learning tutorial cyber criminal neon lights-min

Multivariate Anomaly Detection on Time-Series Data in Python: Using Isolation Forests to Detect Credit Card Fraud

May 27, 2023June 16, 2021

Credit card fraud has become one of the most common use cases for anomaly detection systems. The number of fraud … Read more

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