Northwest Florida homes are expiring at a 20-30% higher rate in recent years. Data driven insights can be a powerful tool to reduce losses and increase profitability.

Tools Used: SQL, Tableau, MySQL, Excel.

Concepts: Exploratory Data Analysis, Outlier Detection, Quartile Analysis, Sold Ratio Analysis, Data Cleaning.

Personal Contributions: End-to-end project — database creation, data cleaning and preprocessing, outlier analysis, sold ratio calculations by price category and zip code, and Tableau dashboard development.

This study builds upon a growing body of literature that connects natural language processing, financial analytics, and social media data to improve predictive modeling in stock performance forecasting.

Tools: Python, GitHub Pages (Jekyll), Reveal.js.

Concepts: Support Vector Machines, Natural Language Processing, Sentiment Analysis, Stock Market Prediction, Social Media Data Analysis

Personal Contributions: Site architecture and development using GitHub Pages and Jekyll, slideshow design and build, overall presentation design and layout, repository management and version control, authored majority of the written report.

Credit card fraud plagues 63% of U.S. credit card holders and amounts to a whopping $6.2 billion in losses each year, but Deep Learning is a powerful weapon that could turn the tide in the battle against credit card fraud.

Tools: Python, TensorFlow, Scikit-learn, Pandas, Matplotlib, Seaborn.

Concepts: Deep Learning, Neural Networks, Class Imbalance Handling (SMOTE), Feature Scaling, Data Preprocessing, Model Evaluation (Confusion Matrix, ROC Curve), exploratory analysis.

Personal Contributions: Sole Python developer, data preprocessing and feature scaling, class imbalance handling using SMOTE, neural network architecture and training, and model evaluation, partial author of written report.