Machine Learning Classifier
An educational and research-focused project built around evaluation, clarity, and error analysis.
Key Outputs
Completed
Status
2 weeks
Duration
Evaluation
Focus
Summary
This project was designed to show the full lifecycle of a classification model, from data preparation to evaluation and documentation.
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Highlights
- Structured and repeatable evaluation
- Error analysis to understand behavior
- Documentation suited for teaching
Challenge
In educational projects, a model should not only work; it should also be explainable. The core challenge was structuring the workflow so that results stayed reproducible and easy to analyze.
Approach
I used a transparent workflow for cleaning data, training the model, measuring accuracy, and reviewing errors so every decision could be traced and presented in an academic setting.
Outcome
The result is a presentable machine-learning workflow that demonstrates technical skill while still being easy to explain in interviews or academic presentations.
Tech Stack
Next Steps