VISUALISASI DATA SPASIAL DENGAN R Nama:Samratul Fuadah Legahati Npm:f1F022016

Autores: Valerie Marin - Maria Juliana Zapata

As an actuarial analyst, the report establishes a defensible, data-driven framework to predict claim frequency for a motor portfolio, enabling pricing, underwriting, and portfolio steering decisions to be made with evidence rather than intuition. With assumed Ni∼Poisson(Viλ(Xi)) with exposure Vi​ as an offset and covariates x={weight, distance, age, carage, gender}. The work delivers validated rating relativities for pricing, risk segmentation that underwrites can act on, and an operational High-risk flag with a documented threshold. Together these support tariff setting, portfolio monitoring, fairness checks, and communication with non-technical stakeholders.

Peer Comperison between Pharma Industries

Task for a coursera course This repotz uses the US National Ocianic and Atmospheric Adiminstration (NOAA) storm database (1950- 2011) to identify which severe weather event types are 1. Most harmful to the health of the population 2. have the greatest economic consequences. we load the raw data file (csv.bz2)directly, standardize event lavels, and compute total fatalities, injuries and inflation unadjusted property and crop damages. we find that a smal number of event classes dominate impacts, which tornados leading human harm and floods driving economic losses. Methods and code are below.

Berikut merupakan hasil pemetaan visualisasi data spasial dengan R. Di mana pada pemetaan kali ini menggunakan pemetaan gempa di Provinsi Banten pada tahun 2024.