Deviation detection and impact analysis I learned an important skill in today’s project adventure: spotting odd data points or outliers in a home appraisal dataset. I identified these outliers that may have weakened our results using advanced statistical tools such as boxplots and Z-scores. It’s like finding an oddly shaped puzzle that doesn’t quite fit together. Why is this relevant? These deviations can distort our predictions and reduce the accuracy of our models. Let’s say you’re trying to predict real estate prices when a mansion suddenly appears in a dataset of typical homes. Your predictions may turn out to be wrong because one manor is so different from the others. We can increase the accuracy and usefulness of our forecasts by understanding and correcting these biases. Finding them is important, but so is understanding their meaning. In other words, it’s like determining how much this odd puzzle piece contributes to the big picture. While some outliers may not have much impact, others can completely change how we interpret the data. Therefore, today’s lesson involves not only identifying anomalies in the data, but also making sure that they do not interfere with our analysis and predictions. Finding and addressing anomalies to improve the accuracy and reliability of our project is like being a data detective.