Predictive Analytics
Course Summary
This course provides a comprehensive introduction to predictive analytics, focusing on statistical modeling, machine learning techniques, and real-world business and research applications. Participants will learn how to analyze historical data, build predictive models, and generate actionable insights using Python. The course covers regression analysis, time-series forecasting, classification models, and deep learning approaches applied to finance, healthcare, marketing, and scientific research.
Key Learning Outcomes
- Understand predictive analytics fundamentals and its real-world applications.
- Implement regression, classification, and time-series forecasting models.
- Learn feature engineering, model selection, and hyperparameter tuning.
- Evaluate model performance using RMSE, R², precision-recall, and AUC-ROC metrics.
- Apply predictive analytics in business intelligence, healthcare, customer behavior analysis, and risk assessment.
