Subject Syllabus
AD-702 (D) - Predictive Analytics
AIDS · Semester 7
Course Modules
Learn topic-wise with Hyper AI and revise using mapped PYQs.
Introduction to predictive analytics - Business analytics: types, applications- Models: predictive models - descriptive models - decision models - applications - analytical techniques.Data types and associated techniques - complexities of data - data preparation, pre- processing - exploratory data analysis.
Predictive modeling: Propensity models, cluster models, collaborative filtering, applications and limitations - Statistical analysis: Univariate and Multivariate Statistical analysis.Model Selection - Preparing to model the data: supervised versus unsupervised methods, statistical and data mining methodology, cross-validation, overfitting, bias-variance trade-off, balancing the training dataset, establishing baseline performance.
Measuring Performance in Regression Models - Linear Regression and Its Cousins - Non- Linear Regression Models - Regression Trees and Rule-Based Models Case Study: Compressive Strength of Concrete Mixtures.Measuring Performance in Classification Models - Discriminant Analysis and Other Linear Classification Models - Non-Linear Classification Models - Classification Trees and Rule-Based Models - Model Evaluation Techniques
autocorrelation functionEstimation and elimination of trend and seasonal componentsStationary Process and ARMA Models -- Basic properties and linear processesIntroduction to ARMA models, properties of sample mean and autocorrelation, functionForecasting stationary time seriesARMA(p, q) processesACF and PACF, Modeling and Forecasting with ARMA.