# tkarim45/Beginner-Data-Science-Projects

This repository is a curated collection of hands-on data science projects tailored for beginners. Whether you're just starting your journey in data science or looking to strengthen your skills, these projects provide a practical and interactive way to apply your knowledge.

Repository: https://github.com/tkarim45/Beginner-Data-Science-Projects
Canonical: https://ross.abutalabs.com/products/beginner-data-science-projects
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: artificial-intelligence, data-science, deep-learning, machine-learning, neural-network
Last push: 2026-07-29T06:48:58+00:00

## Health v2 (maintenance only)
Score: 68/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 95, release rhythm 35, longevity 64
- inputs: {"age_days": 906, "days_push": 35, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3086, forks 580 (observed 2026-08-28T04:07:42.111509+00:00)

## What it is
A curated collection of beginner-friendly data science projects organized as standalone Jupyter notebooks with real datasets and explanations. It covers classification, regression, time series, NLP, clustering, recommendation systems, anomaly detection, EDA, and computer vision, ordered by difficulty.

## Use cases
- learn data science through hands-on projects
- find beginner machine learning project ideas with code
- practice data cleaning and exploratory data analysis on real datasets
- get started with pandas and scikit-learn workflows
- explore beginner NLP and computer vision notebooks
- follow a structured learning path from fundamentals to deep learning
- build portfolio projects for a data science career switch

## When to choose
- you are a student, self-learner, or career switcher wanting practical projects
- you want runnable Jupyter notebooks with real datasets and explanations
- you need a difficulty-ordered learning path across ML, NLP, and CV topics

## When to avoid
- you need production-ready or deployable data science code
- you want advanced or research-level machine learning material
- you need a maintained software library rather than educational notebooks

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, data-science, nlp, computer-vision, data-visualization
- domain: data-science, machine-learning, deep-learning, artificial-intelligence, tutorials, education
- platform: python, cross-platform
- tags: jupyter-notebooks, beginner-projects, hands-on-learning, datasets, learning-path, classification, regression, time-series, recommendation-systems, anomaly-detection, eda

## Member repositories
- tkarim45/Beginner-Data-Science-Projects (main) score 68

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:42.111509+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:27:32.402450+00:00, confidence not recorded.
  - readme: https://github.com/tkarim45/Beginner-Data-Science-Projects (fetched 2026-08-28T04:07:42.111509+00:00, sha 9f0e706b19a4)
- Data as of 2026-08-30T08:39:29.467469+00:00.