Translate business decisions into predictive models and communicate risk, retention and model explanations to stakeholders. 1. Who can join Suitable for: Analysts, ML learners and business analytics professionals Mandatory prerequisites: Python, Pandas, data visualization and basic supervised learning 2. Duration 44 live hours • Approximately 8 weeks at 6 hours per week; actual timetable to be confirmed. 3.Mode Classroom - Thaiyur / Kelambakkam, OMR Chennai (subject to schedule) Live Online (subject to schedule) 4. Syllabus — five modules
Module 1: Translating Business Questions into Machine Learning Formulations Learning outcome: Frame measurable targets from real business questions. Module 2: Customer Churn Prediction & Lifetime Value (CLV) Modeling Learning outcome: Prepare churn and customer-value features. Module 3: Credit Risk & Fraud Detection using XGBoost & LightGBM Learning outcome: Compare tree-based models for risk or fraud scenarios. Module 4: Feature Engineering Strategies & Handling Imbalanced Datasets Learning outcome: Handle class imbalance and prevent data leakage. Module 5: Model Interpretability with SHAP & LIME for Stakeholder Reporting Learning outcome: Explain individual predictions using feature attribution tools. 5. Practical project
Source capstone title: Build a Customer Retention & Risk Scoring Machine Learning System Student deliverable: A customer churn and retention scoring pipeline with an explainability report and a simple dashboard or scored customer file.
6. What is included Live trainer-led sessions Practical assignments Sample code or templates, as applicable Project feedback Learning assessment Perizona course-completion certificate subject to assessment 7. Upcoming batch Start date: To be announced. Students can register interest; dates are shared only after confirmation.
