What are the industry applications of the K Nearest Neighbor algorithm?

Some of the other applications of KNN in finance are mentioned below: Forecasting stock market: Predict the price of a stock, on the basis of company performance measures and economic data. Currency exchange rate. Bank bankruptcies.

Where is K nearest neighbor used?

K nearest neighbors is a simple algorithm that stores all available cases and classifies new cases based on a similarity measure (e.g., distance functions). KNN has been used in statistical estimation and pattern recognition already in the beginning of 1970’s as a non-parametric technique.

How useful is KNN?

Usage of KNN The KNN algorithm can compete with the most accurate models because it makes highly accurate predictions. Therefore, you can use the KNN algorithm for applications that require high accuracy but that do not require a human-readable model. The quality of the predictions depends on the distance measure.

Why is KNN bad?

On the negative side, we can say that the knn algorithm becomes very slow as the number of data points increases because the model needs to store all data points. Due to this reason, it is also not memory efficient. Finally, it is sensitive to outliers because outliers also have a vote in decisions.

Why is KNN called a lazy learning algorithm?

KNN algorithm is the Classification algorithm. It is also called as K Nearest Neighbor Classifier. K-NN is a lazy learner because it doesn’t learn a discriminative function from the training data but memorizes the training dataset instead. There is no training time in K-NN.

Can the K-nearest Neighbours technique be used for regression problems?

As we saw above, KNN algorithm can be used for both classification and regression problems. The KNN algorithm uses ‘feature similarity’ to predict the values of any new data points. This means that the new point is assigned a value based on how closely it resembles the points in the training set.