KNN Algorithm
The k-Nearest Neighbors (KNN) algorithm is a simple yet powerful supervised machine learning technique used for classification and regression tasks. The core idea behind KNN is that the input data points can be classified based on the majority class of their closest neighbors in the feature space. KNN is a non-parametric, instance-based learning method, which means that it does not make any assumptions about the underlying distribution of the data and relies on the actual data points to make predictions.
To implement the KNN algorithm, the first step involves selecting the number of nearest neighbors (k) to consider when making predictions. When a new input data point needs to be classified, the algorithm calculates the distance between this point and all other points in the dataset using a distance metric such as Euclidean, Manhattan, or Minkowski distance. Then, the algorithm selects the k nearest data points and determines the majority class among these neighbors. For classification tasks, the predicted class is simply the class with the most votes, while for regression tasks, the predicted value is the average of the k-nearest neighbors' target values. KNN's effectiveness primarily depends on the choice of k and the distance metric, which can be determined using techniques such as cross-validation and grid search.
library(knn)
x <- cbind(x_train,y_train)
# Fitting model
fit <-knn(y_train ~ ., data = x,k=5)
summary(fit)
# Predict Output
predicted= predict(fit,x_test)
