Real traction, but rough engineering makes it hard for contributors to trust.
This repository helps you learn Python and Machine Learning from scratch.
Documentation
68
No license detected.
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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.
README is present.
README documents how to install the project.
Engineering
20
No tests detected anywhere in the repository.
→ Add automated tests. They prove the code works and give contributors confidence to make changes.
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.
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 is configured (.github/workflows/static.yml).
Lockfile present (Machine Learning/requirements.txt). Installs are reproducible.
Project health
91
No .gitignore found (−60 pts).
→ Add a .gitignore to keep build output, node_modules, and secrets out of version control.
Dependency manifest found (Machine Learning/requirements.txt).
Repository has a description.
Actively maintained (pushed within the last month).
Repository health signals
Activity, community, and responsiveness at scan time
Activity
- —Commits (30d / 90d)
- 919Forks
- 5Releaseslatest 3mo ago
Community
- —Community health
- —authors own >50% of commits
- 2,175Watchers
Responsiveness
- 42d 7hMedian issue response
- 16dMedian PR merge time
- 9Open issues
Repository files73 root entries
- .githubGood: 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 LearningGood: 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.mdGood: Code of conduct present.
- Composition_Over_Inheritance.ipynb
- content.html
- CONTRIBUTING.mdIssue: 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.mdGood: 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