This repository contains a collection of machine learning experiments and applications using various datasets. Each project is organized into its own folder, making it easy to explore, run, and extend. My goal here is to provide clear, reproducible examples of ML workflows, ranging from data preprocessing and exploratory analysis to model training and evaluation.
Variety of ML Algorithms: Includes regression, classification, clustering, and more.
Modular Project Folders: Each dataset/project is self-contained for easier experimentation.
Reproducible Workflows: Notebooks and scripts include step-by-step pipelines from data preprocessing to model evaluation.
Extensible: Designed for adding new datasets or trying new algorithms.
Clone the repository:
git clone https://github.com/ErfanAZP/ML-Playground.git cd ML-Playground
Install dependencies:
Use the requirements.txt file and run the following command line:
pip install -r requirements.txt
Navigate to any project folder and explore the notebooks or scripts to see the workflow.
Modify datasets or algorithms to run your own experiments.
Contributing
Contributions are welcome! Feel free to add your own dataset/project folder or improve existing ones. Please follow the existing folder structure for consistency.