Categories: AI/ML Research

Skewness Be Gone: Transformative Tricks for Data Scientists

Data transformations enable data scientists to refine, normalize, and standardize raw data into a format ripe for analysis. These transformations are not merely procedural steps; they are essential in mitigating biases, handling skewed distributions, and enhancing the robustness of statistical models. This post will primarily focus on how to address skewed data. By focusing on […]

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MinMax vs Standard vs Robust Scaler: Which One Wins for Skewed Data?

You've loaded your dataset and the distribution plots look rough.

October 2, 2025

In "AI/ML Research"

Tips for Handling Imbalanced Data in Machine Learning

Introduction Imperfect data is the norm rather than the exception in machine learning. Comparably common is the binary class imbalance when the classes in a trained data remains majority/minority class, or is moderately skewed. Imbalanced data can undermine a machine learning model by producing model selection biases. Therefore in the…

May 25, 2024

In "AI/ML Research"

Selecting the Right Feature Engineering Strategy: A Decision Tree Approach

In machine learning model development, feature engineering plays a crucial role since real-world data often comes with noise, missing values, skewed distributions, and even inconsistent formats.

May 28, 2025

In "AI/ML Research"

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