data-science-portfolio and data_science_portfolio

These are competitors—both are individual portfolio repositories showcasing similar data science projects with identical descriptions, serving the same purpose of demonstrating technical skills to potential employers or collaborators.

data-science-portfolio
51
Established
Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 25/25
Maintenance 0/25
Adoption 9/25
Maturity 8/25
Community 21/25
Stars: 1,224
Forks: 449
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: MIT
Stars: 84
Forks: 39
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License:
Stale 6m No Package No Dependents
No License Stale 6m No Package No Dependents

About data-science-portfolio

sajal2692/data-science-portfolio

Portfolio of data science projects completed by me for academic, self learning, and hobby purposes.

This collection of projects demonstrates various data science techniques to solve real-world problems. It takes in diverse datasets, such as housing prices, customer spending, or social survey results, and produces insights, predictions, or classifications. Aspiring data scientists, analysts, or students looking for practical examples to learn from would find this useful.

data-analysis machine-learning-examples natural-language-processing data-visualization predictive-modeling

About data_science_portfolio

melvfnz/data_science_portfolio

Portfolio of data science projects completed by me for academic, self learning, and hobby purposes.

This is a collection of data analysis and machine learning projects that demonstrate various techniques for understanding data and making predictions. It includes examples of analyzing stock and cryptocurrency market trends, predicting house prices, and even recognizing handwritten digits from images. Financial analysts, marketers, and data enthusiasts can explore these examples to see how data can be transformed into actionable insights and forecasts.

financial-analysis market-forecasting predictive-modeling exploratory-data-analysis machine-learning-applications

Scores updated daily from GitHub, PyPI, and npm data. How scores work