Introduction to Predictive Modeling

This module provides a brief overview of predictive modeling problems, illustrating their broad applications. It focuses on the simplest form of predictive models: simple linear regression. The module follows a graphical approach to illustrate the structure of a simple linear regression model, the intuition for Ordinary Least Squares, and related concepts.

Building on Week 1, this module introduces multiple linear regression and its broad applications. It covers fitting multiple linear regression models using Excel's Regression tool and Trend() function, discusses overfitting/underfitting problems, and introduces backward elimination for model selection.

This module covers preparing datasets for predictive modeling using Excel tools. It discusses different types of variables, handling categorical/string/datetime values, high-order and interaction variables, multicollinearity, and missing values. It introduces Excel tools like Pivot Table, IF() function, VLOOKUP function, and relative reference.

This module focuses on time series forecasting, covering the nature of time-series data and forecasting problems. It introduces various models for stationary data and data with trends and seasonality, including moving average, exponential smoothing, Holt's method, and Holt-Winters' method, as well as regression-based and composite forecasting techniques.