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https://github.com/md-emon-hasan/machine-learning-with-scikit-learn-from-w3schools
A repository featuring machine learning tutorials using scikit-learn sourced from W3Schools, covering practical implementations and examples in Python.
https://github.com/md-emon-hasan/machine-learning-with-scikit-learn-from-w3schools
artificial-intelligence eda machine-learning machine-learning-algorithms ml statistics w3schools-clone
Last synced: 24 days ago
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A repository featuring machine learning tutorials using scikit-learn sourced from W3Schools, covering practical implementations and examples in Python.
- Host: GitHub
- URL: https://github.com/md-emon-hasan/machine-learning-with-scikit-learn-from-w3schools
- Owner: Md-Emon-Hasan
- License: apache-2.0
- Created: 2023-06-10T12:57:01.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-08T16:44:13.000Z (7 months ago)
- Last Synced: 2024-11-13T20:42:11.726Z (3 months ago)
- Topics: artificial-intelligence, eda, machine-learning, machine-learning-algorithms, ml, statistics, w3schools-clone
- Language: Jupyter Notebook
- Homepage:
- Size: 90.8 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Machine Learning with Scikit-learn from W3Schools
Welcome to the **Machine Learning with Scikit-learn from W3Schools** repository! This project contains practical examples and exercises based on the Scikit-learn machine learning library, sourced from W3Schools tutorials, aimed at helping you learn and implement various machine learning algorithms.
## 📋 Contents
- [Introduction](#introduction)
- [Objective](#objective)
- [Key Features](#key-features)
- [Technology Stack](#technology-stack)
- [Getting Started](#getting-started)
- [Contributing](#contributing)
- [Challenges Faced](#challenges-faced)
- [Lessons Learned](#lessons-learned)
- [Why I Created This Project](#why-i-created-this-project)
- [License](#license)
- [Contact](#contact)
---
## 📖 Introduction
This repository hosts a series of machine learning examples and exercises using the Scikit-learn library, inspired by W3Schools tutorials. It aims to provide practical implementations of various machine learning algorithms and techniques to facilitate learning and experimentation.
---
## 🎯 Objective
The objective of this project is to offer a hands-on approach to learning machine learning with Scikit-learn through practical examples and exercises. It is designed to cater to beginners as well as intermediate learners who wish to deepen their understanding and skills in machine learning.
---
## ✨ Key Features
- **Comprehensive Examples:** Step-by-step implementation of machine learning algorithms.
- **Practical Exercises:** Real-world datasets and exercises to apply learned concepts.
- **Interactive Learning:** Jupyter notebooks for interactive exploration and experimentation.
- **Clear Documentation:** Well-commented code and explanations for easy understanding.
- **Diverse Topics:** Coverage of various machine learning algorithms including classification, regression, clustering, and more.
---
## 🛠️ Technology Stack
- **Python:** The primary programming language used in this project.
- **Scikit-learn:** A machine learning library for Python that provides simple and efficient tools for data mining and data analysis.
- **Jupyter Notebook:** An open-source web application for creating and sharing documents that contain live code, equations, visualizations, and narrative text.
- **Pandas:** A powerful data analysis and manipulation library for Python.
- **NumPy:** A fundamental package for scientific computing with Python.
---
## 🚀 Getting Started
To get a local copy of this project up and running on your machine, follow these simple steps:
### Prerequisites
Ensure you have Python and Jupyter Notebook installed on your local machine. You can download Python from [here](https://www.python.org/downloads/) and Jupyter Notebook from [here](https://jupyter.org/install).
### Installation
1. **Clone the repository:**
```bash
git clone https://github.com/Md-Emon-Hasan/Machine-Learning-with-Scikit-learn-From-W3Schools.git
```
2. **Navigate to the project directory:**
```bash
cd Machine-Learning-with-Scikit-learn-From-W3Schools
```
3. **Install the required packages:**
```bash
pip install -r requirements.txt
```
4. **Launch Jupyter Notebook:**
```bash
jupyter notebook
```
5. **Open any notebook and start exploring:**
- Navigate to the `notebooks` directory and open any `.ipynb` file to start learning.
---
## 🤝 Contributing
Contributions are welcome and encouraged! Here's how you can contribute to this project:
1. **Fork the repository:**
```bash
git clone https://github.com/Md-Emon-Hasan/Machine-Learning-with-Scikit-learn-From-W3Schools.git
```
2. **Create a new branch:**
```bash
git checkout -b feature/new-feature
```
3. **Make your changes:**
- Make updates or add new features to the project.
4. **Commit your changes:**
```bash
git commit -am 'Add a new feature'
```
5. **Push to the branch:**
```bash
git push origin feature/new-feature
```
6. **Submit a pull request:**
- Go to the [repository](https://github.com/Md-Emon-Hasan/Machine-Learning-with-Scikit-learn-From-W3Schools) and click on the "Pull Requests" tab.
- Click the green "New pull request" button.
- Select the branch you made your changes on.
- Click "Create pull request."
---
## 🛠️ Challenges Faced
During the development of this project, several challenges were encountered:
- **Model Selection:** Choosing the right machine learning models for different types of datasets and problems.
- **Performance Tuning:** Optimizing model performance and accuracy through parameter tuning and feature selection.
---
## 📚 Lessons Learned
Through the development process, several key lessons were learned:
- **Practical Application:** Applied theoretical machine learning concepts to real-world datasets and problems.
- **Model Evaluation:** Gained insights into evaluating model performance and choosing appropriate metrics.
- **Workflow Efficiency:** Streamlined the machine learning workflow using Python and Scikit-learn, enhancing productivity.
---
## 🌟 Why I Created This Project
I created this project to provide a practical and structured learning experience for individuals interested in mastering machine learning with Scikit-learn. By leveraging the tutorials and resources from W3Schools, this project aims to offer comprehensive examples and exercises that facilitate learning and skill development.
---
## 📜 License
This project is licensed under the Apache License 2.0. See the [LICENSE](LICENSE) file for more details.
---
## 📬 Contact
- **Email:** [[email protected]](mailto:[email protected])
- **WhatsApp:** [+8801834363533](https://wa.me/8801834363533)
- **GitHub:** [Md-Emon-Hasan](https://github.com/Md-Emon-Hasan)
- **LinkedIn:** [Md Emon Hasan](https://www.linkedin.com/in/md-emon-hasan)
- **Facebook:** [Md Emon Hasan](https://www.facebook.com/mdemon.hasan2001/)
Feel free to reach out for any questions, feedback, or collaboration opportunities!