Top 3 Aspects of AI Predictive Analytics

The following are the top three aspects of AI predictive analytics:

1. Data Collection and Preparation: Involves gathering historical data and fixing it for accuracy and consistency. The quality of the data impacts the reliability of any predictive models.
2. Modeling Techniques: Uses statistical and machine learning techniques to create models which can forecast future outcomes. Techniques include regression analysis, decision trees, and neural networks.
3. Evaluation and Deployment: When models are built, they then need to be evaluated for accuracy and effectiveness. This involves testing the models on new data and refining them as necessary. After evaluation, the models are deployed for predictions on real-world based data.