This notebook leverages a dataset containing records of shark attack incidents worldwide to explore and visualize the frequency of these occurrences. By analyzing this data, we gain insights into the geographic distribution of shark attacks. Initially, the study explores the frequency and geographic distribution of shark attacks, highlighting areas where such incidents are more prevalent. This analysis is complemented by a heat map that visually represents the regions most affected by shark attacks.
Building upon this exploratory analysis, the notebook advances to predictive modeling, where machine learning techniques are employed to forecast the fatality of shark attacks. By integrating various features and training models, the study aims to provide a predictive framework that can assist in understanding and potentially mitigating the risks associated with shark attacks.
This is my first machine learning project, so feel free to teach me and please interact with the notebook. Hope you enjoy it! :)