Predictive Analytics & Machine Learning

Service Description

This service focuses on building predictive models that transform raw data into actionable insights. I combine statistical foundations with modern machine learning frameworks such as PyTorch and TensorFlow to develop scalable, transparent, and interpretable models. Depending on the task, I work with supervised and unsupervised learning techniques — from regression, classification, and time-series forecasting to clustering and anomaly detection. My process begins with understanding your analytical goals and data environment. I conduct thorough exploratory data analysis to detect patterns, assess data quality, and identify relevant features. I then design and train models, using robust cross-validation and hyperparameter tuning techniques to optimize performance. I evaluate models through metrics such as AUC, precision, recall, and calibration, focusing not only on predictive accuracy but also on interpretability and business relevance. Beyond development, I also emphasize operationalization — ensuring models are maintainable and can evolve with new data. I have implemented lightweight MLOps workflows in cloud environments (AWS, Google Cloud) and automated model updates through APIs and containerized solutions. Example applications include credit scoring, churn prediction, and demand forecasting — cases where data-driven foresight directly enhances decision-making. I aim to deliver models that are not black boxes but trusted tools that decision-makers understand and can confidently act upon. The combination of econometric intuition and machine learning precision ensures that every model is statistically sound, transparent, and tailored to real-world use.