Introduction

Machine learning (ML) holds immense potential across industries—from personalized healthcare to smart financial systems. However, beneath the surface of powerful algorithms lies a critical truth: the success of any ML model is only as strong as the data it's built on. This project explores the complex data-related challenges affecting ML systems' performance, fairness, and scalability.

Description

This project presents a deep dive into 14 core data challenges encountered in ML development, including data availability, quality, labeling, privacy, bias, and drift. It also touches on issues unique to big data environments—such as real-time processing and interoperability. By unpacking these challenges, we aim to understand better their impact on model accuracy, fairness, and long-term utility.

Objective

To identify and explain key data-related obstacles in ML workflows and propose mitigation strategies that support ethical, scalable, and high-performing AI systems.

Process

I thoroughly analyzed each challenge by examining real-world case studies, academic research, and industry best practices. Particular focus was placed on evaluating how each data issue affects model outcomes and deployment efficiency.

Tools and Technologies Used

This project references beginner-friendly and widely accessible tools, such as:

Google Sheets or Excel – for exploring and cleaning small datasets Pandas – for data wrangling and cleaning in Python; Scikit-learn – for building and evaluating basic ML models; 

Value Proposition

Data scientists and ML practitioners can build more robust, interpretable, and ethical machine learning models by understanding and proactively addressing these challenges.

Unique Value

This project doesn’t just catalog problems—it contextualizes them with practical insights, helping teams move from awareness to actionable strategies.

Relevance

As AI continues to influence high-stakes decisions, ensuring the reliability and fairness of ML systems is more crucial than ever. This project is relevant for developers, analysts, and decision-makers aiming to build trusted AI solutions.

References

“Data Quality Issues that Kill Your Machine Learning Models”

“Big Data in 5 Minutes” (Video)