rasterpolygonotif4_shp-rplot

The aim of this analysis was to explore whether Atlantic hurricanes can be meaningfully grouped based on selected characteristics describing their intensity, duration, and basic spatial properties, such as the location of origin and first landfall. Rather than attempting to classify hurricanes according to predefined categories, the analysis follows an exploratory approach. The objective was to examine whether recurring patterns emerge directly from historical data and whether these patterns remain stable across different clustering methods. This analysis was prepared as part of the course Unsupervised Machine Learning at the University of Warsaw.

Probability Distributions

This report analyzes the New York City Jobs dataset using Principal Component Analysis (PCA) and K-means clustering. The analysis focuses on numeric features such as the number of positions and salary ranges to identify patterns in job postings. First, the data is cleaned and numeric columns are standardized. PCA is then applied to reduce dimensionality, summarizing the main variation in the dataset into two principal components. The optimal number of clusters is determined using the Elbow and Silhouette methods, followed by K-means clustering on the PCA-reduced data. The resulting clusters are visualized and summarized in a table, providing insights into the distribution of job positions and salary ranges. This workflow allows readers to quickly understand patterns in the NYC Jobs dataset and can serve as a foundation for further analysis, such as investigating salary trends by agency or job type.