https://github.com/janasunrise/ml-guide-and-implementation
This repository contains the predictions, and plots for the datasets included in the scikit learn library by default and also some other datasets from kaggle or other sources.
https://github.com/janasunrise/ml-guide-and-implementation
machine-learning ml python3 scikit scikit-learn scikitlearn-machine-learning sklearn
Last synced: 2 months ago
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This repository contains the predictions, and plots for the datasets included in the scikit learn library by default and also some other datasets from kaggle or other sources.
- Host: GitHub
- URL: https://github.com/janasunrise/ml-guide-and-implementation
- Owner: janaSunrise
- License: mit
- Created: 2020-12-04T05:21:45.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2023-02-19T15:26:37.000Z (over 2 years ago)
- Last Synced: 2025-03-28T09:11:17.997Z (3 months ago)
- Topics: machine-learning, ml, python3, scikit, scikit-learn, scikitlearn-machine-learning, sklearn
- Language: Python
- Homepage:
- Size: 17 MB
- Stars: 3
- Watchers: 1
- Forks: 4
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS
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README
# Sklearn dataset predictions
This repository contains the predictions, and plots
for the datasets included in the scikit learn library
by default and also some other datasets from kaggle or other sources.
## 🛠️ Tech stack used
- `Pandas`: For the data manipulation
- `Matplotlib`: Doing plotting
- `Numpy`: As a dependency for Pandas
- `Scikit-learn`: The most important library for ML
## ❓ How to run this locally
### NOTE:
Before cloning this repo, you need to ensure you have [GIT LFS](https://git-lfs.github.com/)
installed on your local system. Because this repository contains several `*.csv` files,
which are quite large and aren't accepted by github directly. Sorry for this inconvience.
### Steps for running locally:
- Run for Testing
As the virtualenv for separating the dependencies, I've gone with
pipenv for it. It's really modular and easy to use.
Use `pipenv shell` to activate the virtualenv and then execute the python
commands to run the files and display accuracy.
- Run for development and contributing
We also encourage people to support this repository by contributing, and keeping it alive.
But note that we follow certain steps to ensure code is clean, organized and readable using
linting with `flake8`. We also encourage using pre-commit for pushing clean code.
Steps to set up:
- Install dependencies: `pipenv update -d`
- Setup pre commit: `pipenv run precommit`
- After changes, try linting: `pipenv run lint`
## Datasets implemented
### Diabetes:
This dataset consists of 9 columns.
The target value which has to be predicted is `diabetes`
This is a classifier problem, where the value of diabetes in boolean,
but in integer format.
Algorithm used for the problem: `GradientBoostingClassifier`
Accuracy achieved: `0.74`
## 🤝 Contributing
Contributions, issues and feature requests are welcome. After cloning
& setting up project locally, you can just submit a PR to this
repo and it will be deployed once it's accepted. The contributing
file can be found [here](https://github.com/janaSunrise/sklearn-datasets-implementation/blob/main/CONTRIBUTING.md).
⚠️ It’s good to have descriptive commit messages, or PR titles so that other contributors can understand about your commit or the PR Created.
Read [conventional commits](https://www.conventionalcommits.org/en/v1.0.0-beta.3/) before making the commit message.
And, for contributions we have a Branch named `dev`, So if you're interested in contributing,
Please contribute to that branch instead of the `main` branch.
## 😁 Maintainers
We have 2 maintainers for this project as of now:
- [Sunrit Jana](https://github.com/janaSunrise)
- [Rohith MVK](https://github.com/Rohith04MVK)
## 🙌 Show your support
Be sure to leave a ⭐️ if you like the project, and also be sure to contribute, if you're interested!
Made by Sunrit Jana with ❤️