Computer Hardware Benchmarking using Statistical Learning Approaches

Overview

Hardware benchmark numbers are easy to collect but harder to interpret when hardware characteristics vary at the same time. This project explored a systematic, reproducible way to compare performance.

The goal was analysis and interpretation rather than a predictive product.

Inputs & Constraints

• Raw benchmark observations
• Data cleaning and feature preparation
• Multiple hardware characteristics and measurements
• Statistical comparisons and model experiments
• Reproducible visualization and reporting

Approach

The project began as an R workflow and later expanded into Python. Pandas and NumPy handle processing, scikit-learn supports statistical-learning experiments, and Matplotlib creates visualizations.

The pipeline loads results, cleans and normalizes data, prepares features, explores relationships, compares configurations, runs analytical experiments, and generates outputs for interpretation and reporting.

Outcome

The result is a reproducible benchmarking pipeline that produces consistent analytical and visual outputs from experimental data.

Moving from a collection of R analyses to a structured Python workflow made it easier to repeat experiments, compare configurations, and generate figures for academic-style reporting.

What I Learned

• Experimental-data processing
• Exploratory analysis and feature preparation
• Statistical learning
• Visualization
• Reproducible analytical workflows
• Using machine learning to understand data rather than only predict

Practical Application

• System benchmarking
• Performance engineering
• Cloud instance comparison
• Compiler optimization experiments
• Database benchmarks
• Infrastructure performance studies

Tech Stack

• R
• Python
• Pandas
• NumPy
• scikit-learn
• Matplotlib