AD-702 (D) RGPV B.Tech
Predictive Analytics (PA)
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Syllabus, notes and important questions for AD-702 (D)
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Syllabus units
Topics as the official RGPV syllabus lists them.
Unit 1 Introduction and Understanding Data 8 topics
- Introduction to predictive analytics – Business analytics: types, applications
- Predictive models, descriptive models, decision models
- Applications of predictive analytics
- Analytical techniques used in predictive analytics
- Data types and associated techniques
- Complexities of data
- Data preparation and preprocessing
- Exploratory data analysis
Unit 2 Principles and Techniques 8 topics
- Predictive modeling: propensity models, cluster models, collaborative filtering, applications and limitations
- Statistical analysis: univariate and multivariate statistical analysis
- Model selection: supervised versus unsupervised methods
- Statistical and data mining methodology
- Cross‑validation techniques
- Overfitting and bias‑variance trade‑off
- Balancing the training dataset
- Establishing baseline performance
Unit 3 Regression and Classification Models 9 topics
- Measuring performance in regression models
- Linear regression and its variants
- 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 for classification
Unit 4 Time Series Analysis 7 topics
- Introduction to time series, examples of time series, stationary models and autocorrelation function
- Estimation and elimination of trend and seasonal components
- Stationary process and ARMA models – basic properties and linear processes
- Introduction to ARMA models, properties of sample mean and autocorrelation function
- Forecasting stationary time series using ARMA(p, q) processes
- ACF and PACF analysis
- Modeling and forecasting with ARMA
Unit 5 Nonstationary and Seasonal Time Series Models 6 topics
- ARIMA models, identification techniques, unit roots in time series, forecasting ARIMA models
- Seasonal ARIMA models
- Regression with ARMA errors
- Multivariate time series analysis
- State‑space models
- Deep learning techniques for time series forecasting