KNIME_​project assignment

This node is used to load the dataset (Mall Customers) into KNIME for further processing and analysis.

This node selects the relevant numerical features (Age, Annual Income, Spending Score) and removes unnecessary columns such as CustomerID and Gender.

This node standardizes the selected features using Z-score normalization to ensure all variables are on the same scale before applying clustering algorithms.

This node applies hierarchical clustering using Euclidean distance and complete linkage to group similar data points into clusters.

This node assigns different colors to each cluster to improve the visualization and distinguish between clusters.

This node visualizes the clustering results using a two-dimensional scatter plot, where each color represents a different cluster.

This node applies the K-Means clustering algorithm to partition the dataset into three clusters based on similarity between data points.