49

/ 100

GradeF

Real traction, but rough engineering makes it hard for contributors to trust.

Higher than 35% of 5,156 graded repos

This repository helps you learn Python and Machine Learning from scratch.

Jupyter Notebook2,1771mo ago

A low grade is a to-do list, not a judgment of your code

Most gaps here are documentation, tests, and setup, not the code itself. Closing your top 3 gaps alone would lift this repo to C (77).

See your top fixes
Now
F
49
Potential
C
77

Top fixes

Highest-impact changes first, ranked by point weight

20 to address
  1. 1
    Tests18pt

    Add automated tests. They prove the code works and give contributors confidence to make changes.

  2. 2
    CI/CD14pt

    Add a step like `run: npm test`, `run: pytest`, or `run: tox` to your workflow file.

  3. 3
    CI/CD14pt

    Add `pull_request:` to the workflow `on:` triggers.

  4. 4
    CI/CD14pt

    Add a lint step to catch style issues automatically.

Working through the fixes? Let every push regrade itself.

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Scorecard

Every check, grouped by category and sorted worst-first

Documentation

68

License6pt0

No license detected.

Add a LICENSE file. Without one, nobody can legally use, copy, or contribute to your code.

Contributing guide5pt66

Contributing guide is too short for full depth credit (−6 pts). 400+ words earns the full +12 pts.

Add setup instructions, code style notes, and how to run tests.

README12pt85

README is present.

Install and run instructions9pt90

README documents how to install the project.

Engineering

20

Tests18pt0

No tests detected anywhere in the repository.

Add automated tests. They prove the code works and give contributors confidence to make changes.

Linting and formatting5pt0

No linter or formatter config found.

Add a linter config such as .eslintrc.json, .prettierrc, ruff.toml, or .golangci.yml to enforce consistent code style.

Issue and PR templates6pt0

No issue or PR templates found (−100 pts).

Add .github/ISSUE_TEMPLATE/ with bug_report.md and feature_request.md to guide contributors. It dramatically improves issue quality.

CI/CD14pt40

CI is configured (.github/workflows/static.yml).

Reproducibility6pt70

Lockfile present (Machine Learning/requirements.txt). Installs are reproducible.

Project health

91

Housekeeping3pt40

No .gitignore found (−60 pts).

Add a .gitignore to keep build output, node_modules, and secrets out of version control.

Dependency manifest6pt100

Dependency manifest found (Machine Learning/requirements.txt).

Repository metadata5pt100

Repository has a description.

Activity5pt100

Actively maintained (pushed within the last month).

Repository health signals

Activity, community, and responsiveness at scan time

Activity

  • 25 / 90
    Commits (30d / 90d)
  • 919
    Forks
  • 5
    Releaseslatest 5mo ago

Community

  • 57% - Fair
    Community health
  • 1 bus factorlow
    author own >50% of commits
  • 2,177
    Watchers

Responsiveness

  • 42d 7h
    Median issue response
  • 16d
    Median PR merge time
  • 9
    Open issues
Repository files73 root entries
  • .github
    Good: CI is configured (.github/workflows/static.yml).
  • assets
  • Data Analysis
  • Data Scraping from the Web
  • Data_Science
  • Exploratory Data Analysis
  • Google Translate API
  • Img
  • LinkedIn
  • Machine Learning
    Good: Lockfile present (Machine Learning/requirements.txt). Installs are reproducible.
    Good: Dependency manifest found (Machine Learning/requirements.txt).
  • Machine Learning Advanced Topics
  • Machine Learning Interview Prep Questions
  • Numpy
  • Oil Refineries
  • Pandas
  • Python
  • Release Notes
  • .gitpod.Dockerfile
  • .gitpod.yml
  • Building_Your_First_Machine_Learning_Model.ipynb
  • Cheat_sheet_for_Google_Colab.ipynb
  • CODE_OF_CONDUCT.md
    Good: Code of conduct present.
  • Composition_Over_Inheritance.ipynb
  • content.html
  • CONTRIBUTING.md
    Issue: Contributing guide is too short for full depth credit (−6 pts). 400+ words earns the full +12 pts.Fix: Add setup instructions, code style notes, and how to run tests.
    Issue: Contributing guide lacks a setup section (−12 pts).Fix: Show new contributors how to get a local dev environment running.
    Issue: Contributing guide lacks a code style section (−8 pts).Fix: Describe your linting/formatting rules and how to run them.
    Issue: Contributing guide lacks a testing section (−8 pts).Fix: Show contributors how to run the test suite (e.g. npm test, pytest, cargo test).
    Good: Contributing guide describes the PR/review workflow.
    Issue: Contributing guide has no code examples (−5 pts).Fix: Add code blocks showing example commands for setup, running tests, and submitting a PR.
  • data_load.md
  • Demystifying_Feature_Engineering.ipynb
  • Dependency_Inversion_Principle_in_Python.ipynb
  • Hidden_Layers_of_Understanding_CNN.ipynb
  • Hidden_Markov_Models_in_Python.ipynb
  • How_to_Efficiently_Compute_Euclidean_Distance_in_Python_Using_NumPy.ipynb
  • How_to_get_started_coding_in_Python.ipynb
  • How_to_Handle_Missing_Data_in_Pandas_Like_a_Pro.ipynb
  • How_to_Structure_Machine_Learning_Projects_with_Clean_Code_Principles_in_Python.ipynb
  • index.html
  • Interface_Segregation_Principle.ipynb
  • Law_of_Demeter.ipynb
  • Learning_One_Hot_Encoding_in_Python_the_Easy_Way.ipynb
  • Liskov_Substitution_Principle_in_Python.ipynb
  • Manipulating_the_data_with_Pandas_using_Python.ipynb
  • Mastering_the_Bar_Plot_in_Python.ipynb
  • Normalization_vs_Standardization.ipynb
  • Open_Closed_Principle_in_Python.ipynb
  • Optimizing_Python_Code_with_List_Comprehensions.ipynb
  • Pick_up_Line_Generator.ipynb
  • Playing_with_Titanic_Dataset.ipynb
  • Predicting_Loan_Default_Using_Decision_Trees.ipynb
  • Predicting_PewDiePie's_daily_subscribers_using_Machine_Learning_.ipynb
  • Presenting_Python_code_using_RISE.ipynb
  • Range_built_in_function.ipynb
  • Reading_An_Image_In_Python_(Without_Using_Special_Libraries).ipynb
  • README.md
    Good: README is present.
    Good: README is well structured with multiple sections.
    Good: README includes screenshots or visuals. Great for first impressions.
    Issue: README has no code examples (−15 pts).Fix: Show a quick-start snippet so contributors can see what using your project looks like.
    Good: README links to a live demo or deployed app.
    Good: README includes status badges.
    Good: README documents how to install the project.
    Good: README documents how to run the project.
  • releases.html
  • Rendering_Images_inside_a_Pandas_DataFrame.ipynb
  • Rule_Based_System_with_Python.ipynb
  • Single_Responsibility_Principle.ipynb
  • Smart_Resume_Ranker_with_Python.ipynb
  • Speech_Recognition_using_Python.ipynb
  • Splitting_the_dataset_into_three_sets.ipynb
  • String_Concatenation_Exercise_Answers.ipynb
  • String_Concatenation_Exercise_Questions.ipynb
  • Telling_Stories_With_Data.md
  • The_two_Google_Search_Python_Libraries_you_should_never_miss.ipynb
  • Time_Series_Forecasting_with_Pandas.ipynb
  • Top_Python_Libraries_Used_In_Data Science.ipynb
  • Transit_Data_Calgary_2025.ipynb
  • Understanding_Virtual_Environments_in_Python.ipynb
  • University_of_Regina_Professor's_salary.ipynb
  • Unlocking_Time_Series_Forecasting_with_Facebook_Prophet.ipynb
  • update_readme.py
  • Using_the_Pandas_Data_Frame_as_a_Database_.ipynb
  • Using_the_Pandas_DataFrame_in_Day_To_Day_Life.ipynb
  • Wikipedia_API_for_Python.ipynb
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