Summary

FAQs

• What is predictive analysis?

  Predictive analysis uses historical data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes. It helps in forecasting trends, behaviors, and events to inform decision-making.

• How do we get started with a predictive analysis project?

  The process begins with defining the business problem, followed by data collection, cleaning, exploratory analysis, model selection, training, evaluation, and deployment. Collaboration with stakeholders ensures alignment with business objectives.

• What data is required for predictive analysis?

  The quality and relevance of data are crucial. Required data may include historical records, customer interactions, transaction logs, and any other information pertinent to the business problem.

• How do we ensure the model's accuracy?

  Model accuracy is assessed using metrics like accuracy, precision, recall, F1-score, and ROC-AUC. Cross-validation techniques help in evaluating the model's performance on unseen data.