https://github.com/frauvate/cheatsheet
This project provides ready-to-use templates for machine learning base models. This repo, which contains templates of common models in Jupyter notebook format, aims to help users quickly implement these models and easily adapt them to their own projects.
https://github.com/frauvate/cheatsheet
machine-learning machinelearning matplotlib numpy pandas python scikit-learn
Last synced: 2 months ago
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This project provides ready-to-use templates for machine learning base models. This repo, which contains templates of common models in Jupyter notebook format, aims to help users quickly implement these models and easily adapt them to their own projects.
- Host: GitHub
- URL: https://github.com/frauvate/cheatsheet
- Owner: frauvate
- License: mit
- Created: 2024-07-10T08:49:16.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-04-05T22:41:13.000Z (about 1 year ago)
- Last Synced: 2025-06-02T10:15:14.735Z (about 1 year ago)
- Topics: machine-learning, machinelearning, matplotlib, numpy, pandas, python, scikit-learn
- Language: Jupyter Notebook
- Homepage:
- Size: 115 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# cheatsheet: Machine Learning Model Templates
This repository contains a collection of templates for basic machine learning models.
The aim is to provide easy-to-use and customizable templates that can be quickly adapted for
various machine learning tasks.
## Contents
- **Data Preprocessing Tools**: A Jupyter notebook with essential tools and functions for data
preprocessing. This includes data cleaning, normalization, and feature engineering techniques.
- **Simple Linear Regression**: A Jupyter notebook template for implementing and understanding
simple linear regression models.
- **Multiple Linear Regression**: A Jupyter notebook template for implementing and understanding
multiple linear regression models.
## Usage
1. **Clone the Repository**:
```bash
git clone https://github.com/yourusername/cheatsheet.git
2. **Navigate to the Desired Notebook**:
Open the notebook in your preferred environment (e.g., Jupyter Notebook, Google Colab).
Customize the templates according to your data and requirements.
3. **Run the Code**:
Each notebook contains code cells that can be executed sequentially.
Modify the code as needed for your specific use case.
## Requirements
To run the notebooks, you will need the following Python libraries:
- numpy
- pandas
- scikit-learn
- matplotlib
- seaborn
You can install these libraries using pip:
```bash
pip install numpy pandas scikit-learn matplotlib seaborn
```
## Contrubuting
Contributions are welcome! If you have templates for other models or improvements to existing
ones, feel free to open a pull request or submit an issue.
## License
This project is licensed under the MIT License - see the LICENSE file for details.
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