Till now in my project I have done the analysis of the different variables in the dataset and formally doing the Monte Carlo Approximation method to check the estimation for the people killed in the different age group and later we can define the race also by the analysis.

Moreover in the project, I have gone through the different clustering techniques and tried to apply that in my analysis for the similarities in the data points. A well-liked unsupervised machine learning approach for clustering data points according to their similarity is K-means clustering, which is utilised in data analysis. A dataset is divided into K clusters with each data point belonging to the cluster with the closest mean (centroid), which is the main goal of K-means clustering. It is an iterative, data-driven methodology that can make the data’s underlying structures clear.

Today’s topic professor teaches in the class is about K-means which is based on analysis of the shooting happened in the different states of the U.S. K-Means clustering does have some restrictions, though. It makes the spherical, equal-sized, and equal-dense cluster assumptions, which may not always hold true in real-world data. The performance of the algorithm can also be affected by the initial selection of centroids, and it might converge to less-than-ideal results.

While doing so, I will perform the task in my analysis and later on I will discuss more about this.