Introduction

Clustering is a method in data mining and machine learning that groups similar data points together. The aim is to split a dataset into groups where data points within a group are more similar to each other than to those in other groups. Clustering is commonly used in tasks like image segmentation, market segmentation, and anomaly detection.

In Python, various libraries, including scikit-learn, pandas, and numpy, enable clustering. To use clustering in Python, you typically start by importing the necessary libraries, loading your dataset, and then defining the clustering algorithm you want to use.

For instance, to apply the K-Means algorithm in scikit-learn, you first import the KMeans class and then create an instance by specifying the desired number of clusters. Once you have your clustering algorithm instance, you can fit it to your data using the fit method.

To assess the performance of your clustering algorithm, you can utilize evaluation metrics such as silhouette score, Davies-Bouldin index, and Calinski-Harabasz index. Additionally, dimensionality reduction techniques like PCA or t-SNE can help visualize clusters in high-dimensional data.

It's important to note that clustering is an unsupervised method, meaning that it doesn't require labeled data to work and the output is not clear as classification, it's a way to explore the data and try to find patterns, so the interpretation of the results is an important step. Let's start with our project!