Scikit-learn Exploration Project

This repository serves as a personal exploration and collection of examples utilizing the powerful scikit-learn machine learning library in Python. It aims to demonstrate various machine learning algorithms, data preprocessing techniques, model evaluation, and more, as implemented with scikit-learn.

About Scikit-learn

Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

Features

This project may include examples demonstrating:

• Classification: Building models to categorize data (e.g., Logistic Regression, Support Vector Machines, Decision Trees, Random Forests).
• Regression: Creating models to predict continuous values (e.g., Linear Regression, Ridge Regression, SVR).
• Clustering: Grouping similar data points together (e.g., K-Means, DBSCAN, Agglomerative Clustering).
• Dimensionality Reduction: Techniques to reduce the number of features (e.g., PCA, t-SNE).
• Model Selection & Evaluation: Cross-validation, hyperparameter tuning (GridSearchCV, RandomizedSearchCV), and various metrics (accuracy, precision, recall, F1-score, R²).
• Preprocessing: Techniques like data scaling, normalization, and encoding categorical features.

Installation and Setup

To run the code in this repository, you'll need Python installed on your system, along with the necessary machine learning libraries.

Prerequisites

Recommended Setup (using conda or venv)

• Python (3.7+ recommended)
• Git

Recommended Setup (using a Virtual Environment)

It's highly recommended to use a virtual environment to manage dependencies and avoid conflicts with other Python projects.

Option 1: Using Anaconda/Miniconda (Recommended for Data Science)

If you don't have Anaconda or Miniconda installed, you can download it from their official websites:

• Anaconda Distribution
• Miniconda (a smaller version of Anaconda)

Miniconda (a smaller version of Anaconda) Once installed, follow these steps in your terminal:

1. Create a new conda environment:

   conda create -n scikit_env python=3.9

   (You can choose a different Python version if needed, e.g., python=3.8 or python=3.10)

2. Activate the environment:

   conda activate scikit_env

3. Install the required libraries:

   pip install scikit-learn numpy pandas matplotlib jupyter

   This command installs scikit-learn and common dependencies:

   • numpy (for numerical operations)
   • pandas (for data manipulation)
   • matplotlib (for plotting)
   • jupyter (for running .ipynb notebooks)

Option 2: Using venv (Python's built-in virtual environment)

1. Navigate to your desired project directory and create a virtual environment:

   python -m venv venv

2. Activate the environment:

   • On Windows:

     .\venv\Scripts\activate

   • On macOS/Linux:

     source venv/bin/activate

3. Install the required libraries:

   pip install scikit-learn numpy pandas matplotlib jupyter

Cloning the Repository

Once your environment is set up and activated, clone this repository:

git clone https://github.com/Rub4ik/scikit-learn.git
cd scikit-learn

Usage

After setting up your environment and cloning the repository, you can explore the examples.

Running Jupyter Notebooks

If the project contains Jupyter notebooks (.ipynb files), you can run them as follows:

1. Activate your virtual environment (if not already active).
2. Start the Jupyter Notebook server from the repository's root directory:

   jupyter notebook

This will open a new tab in your web browser, displaying the contents of the repository. You can then navigate to and open any .ipynb file to run the code cells.

Running Python Scripts

. If the project contains standalone Python scripts (.py files), you can run them from your terminal:

1. Activate your virtual environment (if not already active).
2. Navigate to the directory containing the script.
3. Execute a script:

   python your_script_name.py

   (Replace your_script_name.py with the actual file name.)

Contributing

Execute a script: python your_script_name.py (Replace your_script_name.py with the actual file name.) If you'd like to contribute to this project (e.g., add more examples, improve existing ones, fix bugs), please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or fix:

   git checkout -b feature/your-feature-name

3. Make your changes and commit them with a descriptive message:

   git commit -m 'Add new feature: Describe your changes'

4. Push your changes to your forked repository:

   git push origin feature/your-feature-name

5. Open a Pull Request to the original repository.

License

This project is licensed under the MIT License.