Firstly we need to re-check the data accuracy add more data if possible. Further normalize the data if required and remove outliers. Use crossfolding technique to achieve higher accuracy.

In my experience, data quality is the most important step to diagnose and correct the issue. Usually, I get a small part of the data and deeply review it. After that, adjust the model by reviewing the limits, excluding or including a new factor. Retrain with the updated data, but consider a minimum of 1 year of data.

When a machine learning model's predictions veer off track, it's time to recalibrate. Start by revisiting the data pipeline—ensure input data is clean, unbiased, and representative. Validate feature selection; irrelevant features can distort results. Reassess the model's hyperparameters and consider retraining with fresh, balanced datasets. Use cross-validation to identify inconsistencies and avoid overfitting. Engage stakeholders by explaining findings in layman's terms, fostering trust in the process. Iterate continuously, but remain focused on the problem statement. A proactive, transparent approach turns setbacks into opportunities for delivering sharper, more reliable insights.

I begin by evaluating the model's input data for quality and consistency, checking for biases or inaccuracies. Next, I review feature selection and engineering to ensure relevance. I analyze model performance metrics, tune hyperparameters, and, if needed, experiment with alternative algorithms. Continuous testing and validation realign predictions with expected outcomes.