galafis/Deep-Learning-Neural-Networks
Pipeline de classificacao com Random Forest, EDA e visualizacoes — projeto educacional (scikit-learn, pandas, matplotlib)
What's novel
Pipeline de classificacao com Random Forest, EDA e visualizacoes — projeto educacional (scikit-learn, pandas, matplotlib)
Code Analysis
4 files read · 2 roundsA single-file script that generates synthetic Gaussian data, trains a scikit-learn RandomForestClassifier, prints metrics, and saves a 2x2 matplotlib/seaborn figure as PNG.
Strengths
The code is clean, readable, and the tests cover the happy path well including determinism, auto-loading, and file output verification. The synthetic data generation with controlled correlations is a reasonable pedagogical choice.
Weaknesses
The README title says 'Deep Learning Neural Networks' but there is zero deep learning code — it's a basic Random Forest wrapper. There is no input validation, no error handling for malformed data, and the entire implementation is ~100 lines of standard scikit-learn boilerplate with no novel algorithms or architecture.
Score Breakdown
Signal breakdown
Innovation
Craft
Traction
Scope
Evidence
Commits
11
Contributors
1
Files
8
Active weeks
4
Repository
Language
Python
Stars
1
Forks
0
License
MIT