Linear regression inference

Proyek ini menganalisis data listing Airbnb di New York City untuk memahami faktor-faktor yang mempengaruhi tipe kamar. Dengan menggunakan multinomial logistic regression, penelitian ini mengkaji pengaruh harga, lokasi, availability, dan aktivitas review terhadap tipe kamar. Hasil analisis menunjukkan adanya segmentasi pasar yang jelas serta tantangan dalam memodelkan data yang tidak seimbang.

Ta część obecnie jest w budowie...

RED=present WHITE=absent

This project explores the U.S. National Oceanic and Atmospheric Administration's (NOAA) storm database to evaluate the impacts of severe weather events during the year 2025. The primary objective is to identify which types of weather events are most harmful to population health and to analyze their spatial and temporal distributions. To achieve this, raw CSV data linking event details, locations, and fatalities is processed and joined into a comprehensive dataset. The findings highlight the most dangerous weather phenomena in terms of fatalities and map out the states most frequently affected by specific events. Additionally, the analysis breaks down event occurrences by month to identify seasonal trends. Finally, it explores which events cause the most significant property damage to provide a view of their economic consequences. Ultimately, these insights are designed to help municipal managers and government agencies prioritize resources and prepare effectively for future severe weather.

Results of monthly lab testing of 18 streams in Story County, Iowa

Sümeyye Bacak 240201521