Predictive modeling is a statistical technique used to forecast outcomes based on historical data. It involves creating a mathematical model that represents the relationship between input variables (also known as predictors or features) and a target variable (the outcome or response). By analyzing patterns and trends in the existing data, predictive modeling enables the estimation of future events or behaviors, such as customer purchases, disease outbreaks, or system failures.
The process typically includes data collection, data preprocessing, model selection, training the model using historical data, and validating its accuracy with test data. Common techniques used in predictive modeling include regression analysis, decision trees, neural networks, and ensemble methods, among others. These models can be applied across various fields such as finance, marketing, healthcare, and environmental science to enhance decision-making and strategic planning. The effectiveness of predictive modeling relies on the quality of the data and the appropriateness of the chosen model for the specific problem at hand.
IIT Roorkee's predictive model uses Cellular Automata and Geographic Information Systems to analyze urban growth. The model examines urban sprawl in cities like Ahmedabad, Chennai, Jaipur, and Surat over two…