This comprehensive analysis develops a machine learning framework for insurance fraud detection, a critical challenge in the insurance industry where fraudulent claims cost billions annually. Using the Ideal Insurance dataset containing 100,000 claims records, we implement a systematic approach combining exploratory data analysis, advanced feature engineering, and gradient boosting algorithms.

This report presents a fuzzy seismic fragility analysis of gravity dams considering spatial variability of material parameters. The analysis implements fuzzy logic approaches to determine damage state probabilities under various seismic intensity measures. The methodology is based on the paper “Fuzzy seismic fragility analysis of gravity dams considering spatial variability of material parameters” and implements fuzzy intervals, membership functions, and fragility curve generation.

This document analyzes options market data for the S&P 500 index during the 2006-2008 period, focusing on the Global Financial Crisis, using methods similar to David Bates’ 1991 study.

The objective of this project is to analyze the rental prices of furnished apartments in comparison to unfurnished ones. This analysis performs multiple regression analysis, builds functions to represent rental prices, includes furniture as a dummy variable, and illustrates the results. Some apartments are comparable with each other due to location, size, and year of construction. This analysis will help understand the premium associated with furnished apartments.

This analysis examines the potential impact of the BEFIT reform on the tax liabilities of the BMW Group and its subsidiaries.

A solution for creating balanced syndicate groups based on multiple criteria. The algorithm considers several important balancing factors. Balanced syndicate groups are strategically formed teams that maximize internal diversity while ensuring equitable distribution of characteristics across all groups. This approach creates teams with varied perspectives internally, yet maintains overall parity between different teams' collective attributes and capabilities.

The goal of this analysis is to develop a robust, data-driven approach to predict house prices in King County accurately. By leveraging machine learning techniques, we aim to create a model that can process the complex dataset, identify key price determinants, and provide reliable price estimates. This tool would be valuable for real estate professionals, potential buyers and sellers, and financial institutions in making informed decisions about property transactions and valuations.

What is the true significance of geopolitical risk in explaining movements in oil prices? Does Geopolitical Risk in the Middle East and North Africa have a stronger relationship with oil prices, than geopolitical risk in the non-Middle East and non-North Africa sectors of the world?

SpatioTemporal Autoregression of Renewable Energy Policy data

EV Customer Detection from Electricity Consumption

Analysis of risk factors for coronary heart disease.

The objective is to predict how many patients will visit the walk-in center each day in the next week. The center is open 7 day a week.

Statistical Analysis of Environmental Social Governance Scores by Enterprise State Ownership and Corruption Perceptions Index Rating