The document discusses k-means clustering, an unsupervised learning algorithm used for grouping similar objects into clusters, and highlights the elbow method for determining the optimal number of clusters. It also describes the k-nearest neighbors (k-NN) algorithm, a supervised learning approach that classifies data based on proximity to known instances. The k-NN process involves several steps, including calculating distances and determining the majority class among nearest neighbors.

1. Unsupervised Learning:
K-means
K-Means Clustering is an unsupervised learning algorithm that is
used to solve the clustering problems in machine learning or data
Science.

2. CLUSTERING
• Cluster analysis or clustering is the task of grouping a set of objects
in such a way that objects in the same group (called a cluster) are
more similar (in some sense) to each other than to those in other
groups (clusters)

12. How to choose the value of "K number of
clusters" in K-means Clustering?
Elbow Method
The Elbow method is one of the most popular ways to
find the optimal number of clusters.
This method uses the concept of WCSS value.
WCSS stands for Within Cluster Sum of Squares,

14. Instance Based Learning: KNN
K- Nearest Neighbour is one of only Machine Learning,
algorithms based totally on supervised learning approach.
It is based on the idea that the observations closest to a
given data point are the most "similar" observations in a
data set

15. • K-NN set of rules can be used for regression as well as for
classification
• k-Nearest Neighbors (k-NN) is considered a "lazy learner"
because it doesn't perform any generalization or model
training in advance

19. Steps for KNN
•Step -1 :Select the wide variety of dataset
•Step -2 :Calculate the Euclidean distance of K datasets.
•Step -3 :Take the K nearest neighbors as according to the calculated Euclidean
distance. Step -4 : count the number of the data points in each class.
•Step -5 :Assign the brand new records points to that category for which the
quantity of the neighbor is maximum.
• Step -6 :Our model is ready.

20. • a