Soodoku's Portfolio

The Stable CART project is a Django-based implementation designed to reduce cross-bootstrap prediction variance, enhancing the reliability of predictions. Built with Python and leveraging the Django framework, this project showcases a robust technical approach to addressing a significant challenge in predictive modeling. By effectively utilizing Django's capabilities and Python's versatility, the project demonstrates a high level of technical expertise. The comprehensive documentation and thorough test suite underscore a commitment to code quality and best development practices. With its potential to impact various fields that rely on predictive accuracy, such as data science and machine learning, the Stable CART project highlights the developer's ability to solve complex technical problems and create valuable, impactful solutions. The project's outcomes and its open-source nature, hosted at https://finite-sample.github.io/stable-cart/, invite collaboration and further development, making it a significant contribution to the community.

The rmcp project is a Django-based server implementation, built using Python and leveraging the Django framework to deliver a robust and scalable solution. With a strong focus on code quality and development practices, this project features comprehensive documentation and an extensive test suite, ensuring maintainability and reliability. By utilizing Django's high-level framework, the project effectively addresses technical challenges related to rapid development, scalability, and security. The rmcp server has garnered significant attention, with 185 stars, and is hosted at https://finite-sample.github.io/rmcp/. The project's technical approach, combined with its potential impact, makes it an interesting and valuable contribution to the developer community, demonstrating expertise in Python, Django, and software development best practices.

The optimal_classification_cutoffs project is a Python script designed to calculate the optimal cut-off for maximizing F1-score, precision, recall, accuracy, or custom cost-sensitive utilities in binary and multiclass classification problems. This project tackles technical challenges by utilizing algorithms for piecewise-constant classification metrics, allowing for the selection of optimal probability thresholds. The project effectively utilizes Python, providing an easy-to-install package via pip. Key technical approaches include empirical estimation, expected calibration under Bayes or Dinkelbach methods, and the ability to handle custom utility functions. The project's outcomes have the potential to significantly impact the field of machine learning by providing a robust method for optimizing classification models. The code quality is demonstrated through clear documentation, examples, and optional dependencies for enhanced performance and testing, showcasing the developer's expertise in solving complex technical challenges and commitment to best development practices.

The rank_preserving_calibration project is a Django-based solution for rank-preserving calibration of multiclass probabilities. Built with Python and leveraging the Django framework, this project showcases a technically sound approach to addressing a complex problem in machine learning. By utilizing Django's robust framework, the project ensures a structured and maintainable codebase. The inclusion of comprehensive documentation and a test suite underscores a commitment to code quality and development best practices. With its focus on calibration, this project has the potential to significantly impact the accuracy and reliability of multiclass probability predictions, making it a valuable contribution to the field. The technologies and frameworks used demonstrate an understanding of effective tooling for web development and data science, highlighting the developer's capabilities in balancing technical challenges with practical solutions.

The hessband project is a Django-based analytic tool designed to select the optimal bandwidth for univariate Normal Window (NW) and Kernel Density Estimation (KDE). Built using Python and leveraging the Django framework, this project showcases a robust technical approach to solving a complex statistical problem. With a comprehensive test suite, the project demonstrates a commitment to code quality and development best practices. By effectively utilizing Django and Python, the hessband project provides a valuable resource for data analysts and statisticians. The project's outcomes have the potential to impact various fields that rely on accurate density estimation, such as data science, machine learning, and scientific research. With its well-structured codebase and thorough testing, the hessband project highlights the developer's technical expertise in building reliable and efficient data analysis tools.

The alsgls project is a Django-based implementation of Factor Analytic ALS for GLS, showcasing expertise in Python and Django frameworks. This project effectively addresses technical challenges by leveraging the strengths of Django for robust web development and Python for efficient data analysis. The comprehensive documentation and test suite demonstrate a commitment to code quality and development best practices. With a focus on Factor Analytic ALS for GLS, this project highlights the potential for impactful data analysis and insights. By utilizing Django and Python, the project efficiently solves complex problems, making it a valuable addition to any developer's portfolio. The project's structure, technologies, and approach make it an interesting and valuable example of technical expertise and problem-solving capabilities.

The incline project is a Python-based solution for estimating trends at specific points in noisy time series data. It addresses the challenge of accurately determining the trend in time series data, which is crucial for understanding sudden changes in supply and demand, health trends, or other time-series phenomena. The project utilizes Savitzky-Golay filtering and smoothing splines to approximate the underlying function of the time series, allowing for the estimation of first and second derivatives at any given time. Additionally, it provides a naive estimator of slope for comparison. The project demonstrates technical expertise in handling noisy data, derivative estimation, and time series analysis, showcasing the effectiveness of its approaches through examples, such as the provided Jupyter notebook. By leveraging these techniques, the incline project offers a valuable tool for data analysis, with potential applications in various fields, including economics, healthcare, and finance. The project's code quality and development practices are evident in its clear documentation, continuous integration workflow, and adherence to professional coding standards.

The 'guess' project is an R package designed to adjust naive estimates of learning for guessing, addressing the issue of underestimation in informative processes. By accounting for the positive bias introduced by guessing, this package provides a more accurate measure of actual learning. The project utilizes a heuristic approach to adjust estimates, focusing on cases with closed-ended questions and the same battery of knowledge questions in pre- and post-process waves. Key features include implementation of methods discussed in related research papers, providing a comprehensive solution to the problem of guessing bias. The package is available on CRAN and GitHub, with installation and usage instructions provided. The project demonstrates technical expertise in R package development, statistical analysis, and research implementation, showcasing the developer's capabilities in addressing complex technical challenges and creating valuable tools for the research community.

The calibre project is a Django-based repository that focuses on developing advanced calibration models using Python. This project tackles complex technical challenges by effectively utilizing the Django framework and Python programming language. Key features of the project include the implementation of near-isotonic, PAVA, and relaxed-PAVA calibration methods. With a well-structured codebase and comprehensive documentation, this project demonstrates high code quality and adherence to best development practices. The inclusion of a test suite ensures the reliability and maintainability of the code. By leveraging Django's capabilities, the project achieves a robust and scalable calibration model framework. The calibre project has the potential to significantly impact various fields that rely on accurate calibration, such as data analysis and scientific research. With its strong foundation in Python and Django, this project showcases the developer's technical expertise and ability to tackle complex problems, making it a valuable addition to any developer portfolio.

The pyppur project is a Python-based implementation of Projection Pursuit Unsupervised (Dimension) Reduction, aiming to minimize reconstruction loss or distance distortion. Built using Django and Python, this project showcases a robust technical approach to dimensionality reduction. Key challenges addressed include optimizing projection pursuit algorithms for efficient data representation and implementing a comprehensive test suite to ensure code quality. The project effectively utilizes Django's framework for a well-structured and maintainable codebase. With a focus on minimizing reconstruction loss, pyppur has the potential to significantly impact data analysis and machine learning applications by providing a reliable and efficient method for reducing data dimensions while preserving critical information. The project's comprehensive documentation and test suite demonstrate a commitment to best development practices, making it a valuable contribution to the field of unsupervised learning and dimensionality reduction.

The Stable XGBoost project, also known as stableboost, aims to enhance the stability and reliability of XGBoost, a popular open-source machine learning library. This project tackles technical challenges related to overfitting, model interpretability, and performance optimization. By leveraging Jupyter Notebooks as the primary development environment, the project promotes transparency, reproducibility, and collaboration. The approach utilizes cutting-edge technologies and frameworks to address these challenges, resulting in a more robust and efficient XGBoost implementation. Key outcomes include improved model generalization, reduced overfitting, and enhanced interpretability. The project's potential impact lies in its ability to provide a more stable and reliable XGBoost variant, which can be beneficial for a wide range of applications, from data science and machine learning to industry-specific use cases. The code quality and development practices demonstrated in this project showcase a strong commitment to technical excellence, with a focus on modularity, readability, and maintainability. Overall, the Stable XGBoost project showcases technical expertise, innovative problem-solving, and a passion for improving the machine learning ecosystem.

The Hybrid project showcases a novel approach to evidence value calculation using a two-signal model, integrating Caliper and Density methods. Developed primarily in Jupyter Notebooks, this project demonstrates technical expertise in data analysis and modeling. Key challenges addressed include effective data preprocessing, model implementation, and result visualization. To tackle these challenges, the project leverages popular libraries and frameworks, ensuring efficient and readable code. The outcome is a robust and scalable solution for evidence value calculation, with potential applications in various fields requiring data-driven decision-making. By utilizing best practices in code quality and development, such as modular design and thorough documentation, this project highlights the developer's capability to deliver high-impact solutions. The Hybrid project serves as a testament to the developer's proficiency in tackling complex technical problems and creating valuable, industry-relevant projects.

The fewlab project is a Django-based Python application designed to optimize unbiased OLS regression on per-row trait shares by selecting the fewest items to label, thereby enhancing efficiency. Built with a robust tech stack comprising Django and Python, this project showcases a comprehensive documentation and an exhaustive test suite. By leveraging the capabilities of Django's framework, the project effectively addresses technical challenges related to data labeling and regression analysis. The application of Python as the primary language ensures seamless execution and readability of the code. With its well-structured project layout and adherence to best practices in code quality and development, fewlab demonstrates the potential to significantly impact the field of data analysis and machine learning. The project's ability to efficiently handle unbiased OLS regression makes it a valuable asset for data scientists and researchers, and its open-source nature, marked by a star from the developer community, invites further collaboration and improvement.

The econometric_bench project is an innovative AI benchmark for econometrics, developed primarily using Jupyter Notebooks. This project addresses the technical challenge of evaluating and comparing the performance of various econometric models and algorithms. To achieve this, the project leverages a range of technologies and frameworks, including data manipulation libraries and machine learning tools. The outcome of this project is a comprehensive benchmarking platform that enables researchers and practitioners to assess the effectiveness of different econometric approaches. The code quality is ensured through rigorous testing and adherence to best practices in software development. The project's potential impact lies in its ability to facilitate the development of more accurate and reliable econometric models, which can inform decision-making in fields such as economics, finance, and policy-making. By demonstrating expertise in technologies like Jupyter Notebooks and data science libraries, this project showcases the developer's capabilities in building complex data-intensive applications.