ashishpatel26/Amazing-Feature-Engineering

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

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data-analysis data-mining data-science data-scientists data-visualization deep-learning feature-engineering feature-extraction feature-scaling feature-selection features machine-learning

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Star & Fork Trend (17 data points)

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GitHub

stars 779

forks 276

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ashishpatel26/Amazing-Feature-Engineering has +0 stars this period . Velocity data will be available after more historical data is collected.

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Metric	Amazing-Feature-Engineering	SymbolicRegression.jl	Gesture-Controlled-Virtual-Mouse	get-started-with-JAX
Stars	779	779	779	779
Forks	276	125	241	119
Weekly Growth	+0	+0	+0	+0
Language	Jupyter Notebook	Julia	Python	Jupyter Notebook
Sources	1	1	1	1
License	N/A	Apache-2.0	GPL-3.0	MIT

Capability Radar vs SymbolicRegression.jl

Amazing-Feature-Engineering

SymbolicRegression.jl

Maintenance Activity 0

Last code push 284 days ago.

Community Engagement 100

Fork-to-star ratio: 35.4%. Active community forking and contributing.

Issue Burden 70

Issue data not yet available.

Growth Momentum 30

No measurable growth in the current period (first-day cold start expected).

License Clarity 30

No clear license detected — proceed with caution.

Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.