Portfolio and data_science_portfolio

These are **competitors** — both are individual portfolio repositories showcasing similar data science project collections with nearly identical purposes (academic, self-learning, and hobby projects), where a viewer would choose one portfolio over the other based on project quality and relevance rather than using them together.

Portfolio
49
Emerging
Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 23/25
Maintenance 0/25
Adoption 9/25
Maturity 8/25
Community 21/25
Stars: 220
Forks: 80
Downloads:
Commits (30d): 0
Language:
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 Portfolio

archd3sai/Portfolio

This Portfolio is a compilation of all the Data Science and Data Analysis projects I have done for academic, self-learning and hobby purposes. This portfolio is updated on the regular basis.

This portfolio showcases a collection of data science and data analysis projects, demonstrating skills in various real-world applications. It includes examples of predicting customer churn, recommending news articles, and forecasting equipment failures. The projects use diverse datasets to solve practical business and engineering problems, making it valuable for recruiters, hiring managers, and collaborators looking for an experienced data scientist or analyst.

customer-analytics predictive-maintenance recommendation-systems financial-risk quality-control

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

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