Two components of imprecision in Predictive Models are: Bias Variance More accurate Predictive Analytics model can be obtained in the presence of more data Variance of the model can be explained in a more effective way by adding new features Treat the missing values by replacing the missing value with the mean or median of the data. Some algorithms work really well when data is distributed normally Normalize the whole data into same scale to improve the accuracy of the predictive model Select subset of attributes based on different matrices such as domain knowledge, data visualization etc Check the performance of the model using different algorithms Ensemble methods, bagging, boosting can be used to produce better results

When your predictive analytics model falls short, start by reviewing data quality to ensure inputs are accurate and relevant. Adjust the algorithm by tweaking parameters or exploring alternative modeling techniques. Implement continuous monitoring to track performance and make iterative improvements, keeping the model sharp and aligned with objectives.

??Evaluate data quality to ensure the model has accurate and relevant inputs. ??Adjust the algorithm by tweaking parameters or considering alternative modeling techniques. ??Continuously monitor model performance, looking for trends and discrepancies. ??Regularly retrain the model with fresh data to keep it aligned with current patterns. ??Test with new features or data sources to improve predictive power. ??Leverage cross-validation to validate model stability across various data subsets. ??Document changes to identify what worked and refine future adjustments.

Start by examining the data used in your model for accuracy, completeness, and relevance. Cleanse the data to remove any errors or outliers that could be skewing results, and ensure that your data is up-to-date. Analyse the features included in your model. Remove irrelevant or redundant features that may not contribute to predictive power and consider adding new features that could provide additional insights.