Overview

Analysis of high-value customer segments in the travel insurance industry, identifying key patterns and behaviors that drive customer value.

Methodology

Used Python and Jupyter Notebook for exploratory data analysis and statistical modeling to uncover insights about customer behavior and value drivers.

Key Findings

Identified characteristics and patterns of high-value customers to inform targeted marketing and retention strategies.

Overview

Comprehensive analysis comparing Airbnb short-term rental market dynamics with traditional long-term rental housing markets.

Analysis

Examined pricing trends, availability patterns, and market impacts to understand the relationship between Airbnb and traditional rental markets.

Insights

Identified key market dynamics and their implications for housing affordability and rental market stability.

Background

Built a predictive model to forecast hotel booking cancellations, helping hotels optimize inventory management and revenue.

Implementation

Developed using R with various machine learning algorithms to identify booking patterns that indicate likelihood of cancellation.

Results

Model provides actionable insights for hotel management to reduce cancellation rates and improve booking strategies.

Overview

Developed a classification model to assist in cancer diagnosis using machine learning techniques on medical data.

Implementation

Built using Python with scikit-learn, implementing various classification algorithms to achieve high accuracy in cancer detection.

Impact

Created a model that can support medical professionals in diagnostic decision-making with robust classification performance.

Background

Built a time series forecasting model that incorporates temperature data to predict pricing patterns.

Methodology

Implemented using SAS with various time series techniques including ARIMA and seasonal decomposition to capture temperature-driven price variations.

Results

Developed accurate forecasting model that accounts for seasonal and temperature-related price fluctuations.

Overview

Developed a fraud detection model for Travelers Case Competition to identify potentially fraudulent insurance claims.

Approach

Utilized machine learning techniques and feature engineering to build a classification model that flags suspicious claims for further investigation.

Impact

Created a robust solution that balances detection accuracy with false positive rates, improving claim review efficiency.