Portfolio and data-analytics-portfolio

These are competitors—both are individual portfolio repositories showcasing similar data science and analytics projects, serving the same purpose of demonstrating technical skills to potential employers or collaborators, making them mutually substitutable rather than complementary or interdependent.

Portfolio
49
Emerging
Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 23/25
Maintenance 0/25
Adoption 10/25
Maturity 8/25
Community 20/25
Stars: 220
Forks: 80
Downloads:
Commits (30d): 0
Language:
License: MIT
Stars: 120
Forks: 27
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-analytics-portfolio

Iqrar99/data-analytics-portfolio

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

This collection offers practical examples of how to analyze various types of data, such as Pokémon statistics, ramen ratings, student exam results, or hotel booking patterns. It takes raw datasets and demonstrates techniques to explore, visualize, and model the data to reveal insights or make predictions. Data analysts, researchers, or students looking for hands-on examples of data analysis and basic machine learning workflows would find this useful.

data-analysis student-performance market-research hospitality-management customer-feedback

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