The clustering technique is prevalent in many fields, so many algorithms exist to perform it. K-means is one of them! K-means is an unsupervised learning technique used to partition the data into pre-defined K distinct and non-overlapping partitions. These partitions are called clusters, and the value of K depends upon the user's choice.

Principle Component Analysis (PCA) is an unsupervised learning technique to reduce data dimensionality consisting of many inter-related attributes. The PCA algorithm transforms data attributes into a newer set of attributes called Principal Components (PCs).

Customer Segmentation is splitting the organization's customer base into smaller groups that reflect similarities in their behavior. It helps businesses develop customer-focused strategies, make segment-wise decisions, and maximize the value of each customer to the company. In this blog, we explore the potential of clustering algorithms to accomplish the above task.

Machine learning technologies are now able to predict the individual's personality based on their social media usage. Personality-based communications are highly used in dating apps and recommendation systems.

Based on the nature of input that we provide to a machine learning algorithm, machine learning can be classified into 4 major categories - Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, Reinforcement Learning.