Task2 - Prediction using Unsupervised ML (Sparks Foundation internship programme)

Task2 - Prediction using Unsupervised ML (Sparks Foundation internship programme)
K- Means Clustering :
k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. It is popular for cluster analysis in data mining. k-means clustering minimizes within-cluster variances (squared Euclidean distances), but not regular Euclidean distances, which would be the more difficult Weber problem: the mean optimizes squared errors, whereas only the geometric median minimizes Euclidean distances. For instance, better Euclidean solutions can be found using k-medians and k-medoids.

1) Importing the required Library and Load Iris Dataset
i)Iris Dataset :
In this analysis, I begin to explore the infamous iris dataset. This dataset is famous in the machine learning world to help develop skills in machine learning techniques. This dataset can be approached in a number of different ways and this will depend on your objectives when starting out your analysis. I shall highlight the objectives later in the analysis. Let’s just describe the data so we know what we’re dealing with. The dataset consists of 50 samples of three different species of iris flowers (Iris Setosa, Iris Versicolor and Iris Virginica). Five features are captured in the dataset. These are: · Sepal length (cm) · Sepal width (cm) · Petal length (cm) · Petal width (cm)

Github repo : https://github.com/22VINAYAK98/-The-Sparks-Foundation---Graduate-Rotational-Internship-Program-/blob/main/Task2%20-%20Prediction%20using%20Unsupervised%20ML%20(Sparks%20Foundation%20internship%20programme).ipynb