As a novice or seasoned Data Scientist, your work depends on the data, which is rarely perfect. Properly handling the typical issues with data quality and completeness is crucial, and we review how to avoid six of these common scenarios.

Introduction

In data science or machine learning, we use data for descriptive analytics to draw out meaningful conclusions from the data, or we can use data for predictive purposes to build models that can make predictions on unseen data. The reliability of any model depends on the level of expertise of the data scientist. It is one thing to build a machine learning model. It is another thing to ensure the model is optimal and of the highest quality. This article will discuss six common mistakes that can adversely influence the quality or predictive power of a machine learning model with several case studies included.

#2020 sep opinions #advice #data quality #data science #hyperparameter #mistakes #overfitting