Neo4j Graph Data Science vs. scikit-learn
A side-by-side comparison based on G2 review data.
| Metric | Neo4j Graph Data Science | scikit-learn |
|---|---|---|
| Overall rating | 4.5 | 4.8 |
| Review count | 16 | 60 |
| Ease of use | 8.7 | 9.6 |
| Ease of setup | 8.2 | 9.7 |
| Quality of support | 9.0 | 9.4 |
| Meets requirements | 9.1 | 9.6 |
| NPS | 73.333333 | 90 |
| Small Business | 37.5% | 28.3% |
| Mid-Market | 43.8% | 31.7% |
| Enterprise | 18.8% | 40.0% |
| Total page views | 90 | 197 |
| Total LLM citations | 557 | 381 |
Neo4j Graph Data Science
Neo4j Graph Data Science is a data science and machine learning engine that uses the relationships in your data to improve predictions. It plugs into enterprise data ecosystems so you can get more data science projects into production quickly. Using a catalog of over 65 pretuned graph algorithms, data scientists can explore billions of data points in seconds to identify hidden connections and generate compelling visualizations that lead to better stakeholder decision making.
- Ease of Use
- Model Variety
- Problem Solving
- Machine Learning
- Scalability
- Analytics
scikit-learn
Scikit-learn is a software machine learning library for the Python programming language that has a various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.
- Usage Frequency
- Machine Learning
- Ease of Use