Month: March 2025

Today, I practiced using the K-means clustering algorithm on the ACLED dataset in the U.S. I discovered that the locations of events are not randomly distributed; instead, they tend to cluster in certain regions. This pattern forms distinct clusters. By applying the K-means algorithm, I was able to divide the data into two clusters.

Additionally, I’ve learned different methods to determine whether data is randomly distributed, such as using the uniform and Poisson distributions. Moving forward, I plan to use this dataset to predict future events based on location and time.

Today, I downloaded the Armed Conflict Location and Event Data (ACLED) of the USA and started off by understanding the dataset’s structure and the meaning of each feature (column). I strived to understand what each column represents to gain a clearer understanding of the dataset. Then, I researched the K-Nearest Neighbors (KNN) and K-Clustering algorithms, examining how these algorithms could be applied to my dataset for analysis.

Today, my partner and I focused on our report. We created a GitHub repository and uploaded our code for the report. Then, we reviewed the report, added references, and included our code.