Overview This repository consolidates various quantitative research projects that explore a wide range of analytical tasks and solutions in both commodity trading and credit risk analysis. The goal of each project is to develop predictive models, perform statistical analysis, and provide actionable insights based on real-world data. Each project focuses on a different business domain and applies quantitative research methodologies to solve industry-specific problems.
Projects Included:
- Natural Gas Pricing Analysis: Analyse natural gas prices based on historical trends and seasonal patterns, and develop a pricing model for storage contracts.
- Credit Risk Analysis for Personal Loans and Mortgages: Predict the probability of default (PD) for personal loans and mortgages, and estimate expected losses.
Technologies Used
- Python: Main programming language for statistical analysis, modeling, and data processing.
- Jupyter Notebooks: For interactive analysis and model development.
- Machine Learning Libraries:
- scikit-learn for model building and evaluation.
- pandas for data manipulation and analysis.
- matplotlib and seaborn for data visualization.
Key Methodologies
- Exploratory Data Analysis (EDA)
- Predictive Modeling
- Quantization Techniques
License This repository is licensed under the MIT License - see the [LICENSE] file for details.
Copyright © 2025 Kayalvizhi Kamatchi Selvaraj. All rights reserved.