Feature Selection: To provide precise predictions, machine learning algorithms need pertinent features. The most important factors that have an impact on market demand are determined using feature selection approaches. Product characteristics, pricing details, marketing initiatives, seasonality, and outside variables like economic indicators are a few examples of these elements.
Regression, decision trees, random forests, and neural networks are just a few examples of the machine learning models that may be trained utilising preprocessed data. The models discover patterns and connections between the feature inputs and market demand. The models’ internal parameters are changed during training in order to reduce prediction errors.
Model Evaluation and Validation: The trained models are evaluated and validated using historical data that was not used for training. This helps assess the performance and accuracy of the models in predicting market demand. Various evaluation metrics, such as mean absolute error (MAE) or root mean square error (RMSE), are calculated to measure the predictive performance.
