**3. Predictive models**

Predictive modeling is the term used for the process of utilizing data mining and probability to forecast future outcomes. A predictive (forecasting) model uses several independent variables or predictors that are likely to influence the desired dependent variable (forecasting output). Once data have been collected for the relevant predictors, a statistical algorithm is deployed. This algorithm may be a simple linear equation or a sophisticated ML algorithm such as a neural network. Predictive modeling mainly overlaps with the field of ML, and many of the algorithms utilized in forecasting models are found in the context of ML and AI.

Predictive modeling is often associated with weather forecasting, online advertising, and marketing. However, it also has applications in mining engineering.

One of the most frequently overlooked challenges of the forecasting model is obtaining suitable data to apply when creating algorithms. Data collection and preparation is the most challenging step in developing a predictive model, and it is essential to locate the best predictors to feed into the model. A descriptive analysis of the data and data treatment, including missing values and outlier fixing, is a crucial task that consumes most of the time needed in predictive modeling.

Once the data have been collected, the next step is to select an appropriate model. Linear regressions are among the most accessible models for predictive algorithms, but other multifaceted AI models are available. The complexity of the model does not guarantee the performance of the prediction. Model selection should be considered in relation to data availability and quality and the forecasting period.

After modeling, a production estimation should be provided to measure the accuracy of the model.

Some well-known predictive modeling methods widely used in industrial applications are regression, time series algorithms, deep learning, and ML.
