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AD-702 (D) Predictive Analytics Notes

5 notes for Predictive Analytics, unit by unit.

Unit 1 · Short notes Introduction and Understanding Data 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
Unit 2 · Short notes Principles and Techniques 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
Unit 3 · Short notes Regression and Classification Models 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
Unit 4 · Short notes Time Series Analysis 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
Unit 5 · Short notes Nonstationary and Seasonal Time Series Models 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

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