About
This 4-hour Python workshop provides a practical introduction to building, training, tuning, and interpreting supervised machine learning (ML) models for predictive analytics. Designed for data scientists, ML engineers, programmers, statisticians, and researchers, it focuses on applying open-source libraries to develop predictive models for classification and regression tasks using real-world data. Participants will prepare datasets and conduct Exploratory Data Analysis (EDA), build reproducible ML pipelines that process, impute, and assist in model fine-tuning, and evaluate model performance using the Scikit-Learn (Sklearn) ecosystem. Through guided exercises, attendees will understand how to balance model complexity and interpretability, address overfitting and underfitting, and apply techniques for feature selection and hyperparameter optimization. The workshop explores and performs hyperparameter fine-tuning and model selection on key algorithms including LASSO regularization, decision trees, random forests, and gradient-boosted ensembles (AdaBoost, XGBoost). It also covers essential concepts including the bias-variance tradeoff, model evaluation strategies, and performance diagnostics. By the end of the session, attendees will have hands-on experience developing, refining, and interpreting models in Python using libraries such as scikit-learn, pandas, numpy, scipy, matplotlib, and seaborn. Participants will receive a Certificate of Completion, the PDF slides, a fully documented Jupyter Notebook, and the workshop dataset to continue practicing independently, equipping them with the tools and confidence to train, tune, and evaluate predictive models in Python.