Before the Model: Understanding the Machine Learning Workflow

2026-07-13 · Source: Artificial Intelligence on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Novice, medium

Summary

A comprehensive 10-step machine learning workflow is outlined, demonstrating that model training is merely one component of a larger system. Using a simple student placement dataset, the entire pipeline is illustrated, starting with reading and exploring data to identify patterns and missing values. Subsequent steps involve pre-processing, defining inputs (CGPA, IQ) and outputs (Placement), splitting data into training and test sets, and scaling features to ensure fair learning. A Logistic Regression model is then trained, predictions are made, and performance is evaluated, achieving 1.0 accuracy on the small test split. The process concludes with visualizing the decision boundary, emphasizing the importance of understanding the full journey from problem framing to evaluation.

Key takeaway

For AI students or junior Machine Learning Engineers building your first projects, prioritize understanding the complete workflow before deep-diving into algorithms. Your initial focus should be on data preparation, exploration, and pre-processing, as these foundational steps dictate model success more than algorithm choice. Ensure you rigorously split data and scale features for fair evaluation. This holistic approach will prevent common pitfalls and build a robust understanding of practical ML development.

Key insights

Understanding the end-to-end ML workflow, not just algorithms, is crucial for effective project development.

Principles

• Models should never be tested on training data.
• Real-world datasets rarely arrive perfectly prepared.
• Features must be scaled to prevent dominance by magnitude.

Method

The ML workflow involves data reading, EDA, pre-processing, input/output definition, train/test split, feature scaling, model training, prediction, evaluation, and visualization of results.

In practice

• Use scatter plots for early data pattern recognition.
• Remove unnecessary columns during pre-processing.
• Split data into training and test sets for honest evaluation.

Topics

• Machine Learning Workflow
• Data Pre-processing
• Exploratory Data Analysis
• Feature Scaling
• Model Evaluation
• Logistic Regression

Code references

• PRIYANSHUSETHI/ML-FOUNDATIONS-REBUILDING

Best for: AI Student, Machine Learning Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence on Medium.