🔄 Implement supervised machine learning models, minimize errors with cost functions, and optimize using gradient descent for effective model training.
A quantitative analysis of stock market predictions using linear regression techniques (OLS, Lasso, Ridge, Elastic Net) with error metric evaluation (RSE, MSE, RMSE).
FF5 S&P 500: 36-mo rolling OLS betas -> next-month forecast; Top-10 equal-weight; monthly rebalance; backtested 2016-2025 vs SPY with costs.
My daily ML practices
Additional linear models including instrumental variable and panel data models that are missing from statsmodels.
Hot City, Heated Calls: Understanding How Urban Features Affect Quality of Life Under Different Heat Conditions Using New York City's 311 and SHAP
The Terminology Service Suite project is a collection of interactive widgets designed to ease the integration of terminology service functions into third-party applications.
Linear, IV and GMM Regressions With Any Number of Fixed Effects
Python package for conducting power analysis for experiments using regression and/or clustered data.
The study examines how investor sentiment—especially during COVID-19—impacts market volatility across 6 HOSE sectors, finding that sentiment positively correlates with return volatility but not with liquidity volatility
This notebook explores regularized linear regression from a quantitative and mathematical perspective — deriving cost functions and gradients for OLS, Ridge, Lasso, and ElasticNet, and testing how λ and γ shape the bias–variance trade-off in practice
Code and visualization utilized for my doctoral dissertation on the effects of Caribbean migration in Houston's residential inequality.
Multi-criteria site selection (WSM/TOPSIS) + store-level sales regression with sensitivity; R + tidyverse; ready-to-use figures.
fescarefine ⛷️🗻🦮 : Refine & Test ML Models # Features Scaling # Normalization # Datasets
classicsml ⛈️🔄📉 : Classsic Supervised ML # Cost Function # Gradient Descent
Portfolio of reproducible data-science projects (forecasting + NLP) on synthetic retail datasets. Notebooks, figures, and READMEs included.
DeepEcon: Your one-stop Python package for econometric algorithms.
📐 Portfolio project to master statistical modeling, inference, and diagnostics using statsmodels. Includes 15+ concept notebooks, synthetic datasets, custom visual utilities, shared tests with scipy, Streamlit dashboards, Docker support, and markdown cheatsheets.
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