research-ml-classical

Classical machine learning model training is supported through scikit-learn, handling data preprocessing, supervised learning, and hyperparameter tuning. You can train models like random forest classifiers directly on your dataset to predict outcomes.

SHAP enables model interpretability by calculating feature importance values to explain model behavior. It identifies which features drive predictions, helping you understand the underlying factors influencing your supervised machine learning models.

UMAP is integrated for dimensionality reduction and visualizing high-dimensional data. It transforms complex datasets into lower-dimensional representations, allowing you to visually explore patterns and relationships within your data.

Customer segmentation is achieved through unsupervised learning and clustering algorithms provided by scikit-learn. This groups users with similar characteristics into distinct segments, enabling targeted analysis for applications like product recommendations.

Data visualization is supported using matplotlib and seaborn alongside scikit-learn, SHAP, and UMAP. These dependencies allow you to generate plots and visual representations to analyze data distributions and model results.

Unsupervised learning, like clustering and dimensionality reduction, is used when your dataset lacks labeled outcomes. Supervised learning applies when you have labeled data to train models for classification or regression predictions.