Titanic Survival Prediction Using Machine Learning

Punita Kumari — 2026-05-01

The maritime disaster of the Titanic is undoubtedly one of the most famous and dangerous disasters in history. The passenger liner Titanic, carrying a significant number of passengers, sank after it got hit with an icefield in the year 1912, resulting in the loss of numerous lives on board. Consequently, the vessel became one of the most lethal merchant ships in history during that period. The legislation regulations controlling ship safety have been strengthened as a result of the horrible accident that rocked the world and left everyone feeling deeply sad and terrified. The structure’s architect, Thomas Andrews, was killed in the accident. Following the sinking of the Titanic, it became clear that certain people had a better chance of survival than others. Priority had been given to children and women. The Titanic was a perfect illustration of its era, which was the beginning of the twentieth century, and established a sharp divide in social strata. Exploratory data analytics (EDA) is utilised in the initial stages to discover truths that were previously concealed or unknown in the current data collection. Following the selection of multiple artificial intelligence and machine learning models, it is necessary to reach a conclusion regarding the study of which categories of people have a higher likelihood of survival. Following that, precision-based comparisons of the obtained machine learning models were performed.

DOI: https://doi.org/10.24321/2455.3190.202504

Study of the Existing Road Network Around Meerut Cityâ€™s Rapid Train Stations

Binod Kumar Singh — 2025-09-12

This paper investigates the existing road network and first/lastâ€‘mile access conditions around the Rapid Rail Transit System (RRTS, branded Namo Bharat/RAPIDX) stations within Meerut cityâ€”Meerut South, Shatabdi Nagar, Begumpul, and Modipuram. We analyse multimodal connectivity, functional road hierarchy, intersection performance, pedestrian/cycling infrastructure, parking and intermediate public transport (IPT) operations, and landâ€‘use interfaces in each station area. Using a replicable GIS-based methodology (OpenStreetMap network data, publicly available corridor maps, and open imagery), we assess 500 m, 1 km, and 2 km catchments, identify network gaps, and propose a prioritised set of lowâ€‘cost and capital projects for seamless multimodal integration with the underâ€‘construction Meerut Metro as well as city buses and IPT. The study concludes with a stationâ€‘wise improvement plan, an implementation roadmap, and key performance indicators (KPIs) to monitor progress.

Journal of Advanced Research in Geo Sciences & Remote Sensing

Titanic Survival Prediction Using Machine Learning

Study of the Existing Road Network Around Meerut Cityâ€™s Rapid Train Stations