The quality of data for prediction or classification is always dependent on the stationary characteristics of the data set. Outliers from the dataset must be discarded which may be root cause for unexpected prediction accuracy. This is possible by following the statistical approaches like Mean, Variance, ACF, PACF, Hypothesis Testing etc. Prediction is always dependent on the historical data set where choosing the time lags plays an important role. It’s always good for a prediction accuracy to fall below 5% which can be measured through different error indexes like MAPE, MASE. The top most priority is to make the dataset normalized using different available functions based on research articles.

Few things to check for Data quality - Garbage in Garbage out, a most quoted phrase by Data analyst, so check for data quality of the sample you have used for your study or reporting. Data range- Too small or too huge a range of data can lead to micro data problem or irrelevant data problem creating outlayers, which skew your results... Use the range of data which best describes current scenario and ask Apply right method or technique to achieve desired output Have a benchmark to validate your results or hypotheses to ensure you are not way off. Understand the business objective and business itself to relate to what data is predicting

When my data analytics reports show unexpected results, it’s a signal to dig deeper. Here’s my approach: Validate the Data: I always start by checking the integrity of the data sources and processing steps. Even a small glitch can skew the insights. Reevaluate Assumptions: Sometimes, the assumptions we start with no longer hold. Revisiting them ensures that the model is still aligned with the current reality. Engage with Stakeholders: I loop in key stakeholders for their insights—they often provide context that data alone can’t capture. This collaborative approach helps bridge the gap between predictions and reality. Look for Hidden Patterns: Unexpected results sometimes highlight trends that were previously overlooked.

Re-examine the underlying data to ensure its accuracy and consistency with real-world conditions. Review the assumptions made during model development to identify any that may not hold in practice. Conduct a deeper analysis of external factors or events that could have influenced the actual outcomes. Perform sensitivity analysis to understand how changes in input data impact predictions.