Real-world datasets are messy. Outliers can hide patterns, distort models, and lead to bad decisions. In this article, we’ll walk through practical ways to detect them in Python - using plots, statistics, and machine learning, then apply it all on a real dataset.
Outliers can significantly skew statistical analysis and machine learning model performance. This guide covers statistical and machine learning methods to detect and handle outliers effectively in Python.
Missing values are inevitable in real-world datasets. This guide covers proven methods to handle missing data in pandas without compromising data integrity or analytical accuracy.
NumPy’s reshape() and flatten() are both used for array manipulation, but they serve different purposes and have distinct behaviors. This guide explains when and how to use each method effectively.