Top days shooting

The histogram that shows the distribution of incident times in the dataset, with the x-axis showing the hour of the day and the y-axis showing the count. The most common time of day for incidents will be the bar with the highest height.

This visualization shows that most incidents happen between the hours of 6pm and midnight. This is likely because people are more likely to be out and about during these hours.

We can also say that usually during the nights few victims might be  drunkard and the things can go few insane consciously and unconsciously.

K-Means and DBSCAN Clustering

K-Means and DBSCAN are two widely used clustering algorithms in machine learning. These algorithms are used to group similar data points together, but they work in different ways.

K-Means Clustering

K-Means clustering is a centroid-based clustering algorithm. This means that it assigns each data point to a cluster based on its distance to the cluster centroid. The cluster centroid is the average of all the data points in the cluster.

K-Means clustering is efficient algorithm, but has some limitations.

• • The number of clusters (k) must be specified in advance.
  • K-Means clustering is sensitive to outliers. Outliers can skew the cluster centroids, which can lead to inaccurate clustering results.

DBSCAN Clustering

DBSCAN clustering is a density-based clustering algorithm. This means that it groups data points together based on their density. Density is defined as the number of data points within a given radius (epsilon) of a data point. The DBSCAN clustering is a powerful algorithm that can handle outliers and data of varying density. However, it can be computationally expensive for large datasets.