Modeling / analytics
ML modeling connects supervised learning, unsupervised learning, regression, classification, clustering, forecasting, and anomaly detection to business questions.
What this system does
Frames predictive and descriptive modeling problems around churn, risk, segmentation, forecasting, and anomaly detection.
Uses feature preparation, train/test splitting, baseline models, and evaluation metrics before making recommendations.
Connects model results to monitoring workflows, dashboards, and stakeholder interpretation.
How I use it
The churn analysis script is a working example of a baseline supervised-learning workflow.
Budget forecasting and decision scoring are marked as future modeling directions, not completed production claims.
Statistics and model evaluation concepts support practical interpretation rather than black-box modeling.
Related projects
Evidence or examples
predictive-churn-analysis.py uses pandas, ColumnTransformer, OneHotEncoder, LinearRegression, train_test_split, RMSE, and ROC AUC.
The portfolio states churn baseline as available and budget forecasting/decision scoring as coming soon.
Skills content includes advanced analytics foundation such as regression, classification, clustering, forecasting, and anomaly detection.
preprocess = ColumnTransformer([
('cat', OneHotEncoder(handle_unknown='ignore'), cat_cols),
('num', 'passthrough', numeric_cols),
])rmse = np.sqrt(mean_squared_error(y_test, pred))
auc = roc_auc_score(y_test, pred)Cluster customers, vendors, or product usage patterns to find segments that need different actions.Use historical spend, employee count, and vendor categories to project budget scenarios before planning cycles.