K-Nearest Neighbours

 An introduction to the K-Nearest Neighbors (KNN) algorithm is provided. KNN is a classification algorithm used to predict the class of a new data point based on its similarity to existing data points.

The example used in the video involves a telecommunications provider segmenting its customer base into four groups based on service usage patterns. The goal is to predict group membership using demographic data such as age and income.

KNN works by calculating the distance between the new data point and existing data points in the dataset. It then selects the K nearest neighbors and assigns the majority class label among them to the new data point.

The choice of K is crucial in KNN. A low K value may lead to overfitting, capturing noise in the data, while a high K value may result in an overly generalized model. The optimal K value is typically determined using a validation set or cross-validation.

KNN can also be used for regression tasks, where the predicted value for a new data point is the average or median target value of its nearest neighbors.

Overall, KNN is a simple yet powerful algorithm that is widely used in classification and regression tasks across various industries.