https://github.com/bredalis/scikitlearn
🤖 Library to create ML models 🤖
https://github.com/bredalis/scikitlearn
data ia learning-python librery ml python
Last synced: 14 days ago
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🤖 Library to create ML models 🤖
- Host: GitHub
- URL: https://github.com/bredalis/scikitlearn
- Owner: Bredalis
- License: gpl-3.0
- Created: 2024-05-27T20:27:35.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2024-11-19T02:45:31.000Z (over 1 year ago)
- Last Synced: 2025-03-04T06:44:51.819Z (over 1 year ago)
- Topics: data, ia, learning-python, librery, ml, python
- Language: Python
- Homepage:
- Size: 72.3 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
Python Scikit-Learn 🤖
📊 Scikit-Learn is an open-source Python library that provides simple and efficient
tools for data analysis and modeling. 🧠 It is primarily used for machine learning
and data mining, offering a wide range of algorithms for classification, regression,
clustering, dimensionality reduction, and data preprocessing. Scikit-Learn is known
for its ease of use and integration with other scientific Python libraries such as
NumPy and SciPy.
---
## Table of Contents 🧾
* Description 📝
* Included Examples 📦
* Installation 🛠️
* Usage 📘
* Technologies 🧪
* License 📜
---
## Description 📝
Machine learning is an essential field in modern data science because it enables computers to learn patterns from data and make intelligent decisions without explicit programming.
This repository contains practical Python examples demonstrating how to use Scikit-Learn for various machine learning tasks, including supervised and unsupervised learning algorithms, model evaluation, and data preprocessing techniques.
The goal of this project is to help learners understand how to implement machine learning workflows and apply them to real-world datasets.
---
## Included Examples 📦
✔️ Linear and logistic regression
✔️ Classification with KNN and SVM
✔️ Neural networks (MLP)
✔️ Clustering with DBSCAN
✔️ Model evaluation and metrics
✔️ Data preprocessing and feature scaling
✔️ Practical beginner-friendly examples
---
## Installation 🖥️
1. Clone the repository:
```bash
git clone https://github.com/Bredalis/ScikitLearn.git
```
2. Navigate into the project directory:
```bash
cd ScikitLearn
```
---
## Usage ⚙️
To run a program from the terminal:
1️⃣ Go to the file location:
```bash
cd path_to_program
```
2️⃣ Run the Python file:
```bash
python program_name.py
```
---
## Technologies 🧪
* Python 🐍
* Scikit-Learn 🤖
* NumPy 🔢
* Pandas 🐼
* Matplotlib 📊
* Machine Learning Algorithms
---
## License 📜
This project is licensed under the 📜
GPLv3 (GNU General Public License V3.0)
---
## 👩💻 Author
[](https://github.com/Bredalis)
😊 I am a programmer passionate about technology, artificial intelligence, and continuous learning.
I love creating projects that help others understand complex concepts in a simple way.
#python #ai #machinelearning #scikitlearn 🚀
---
## ⭐ Check out my GitHub profile
[](https://github.com/bredalis)
---
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