Data Science

A curated collection of data science projects, notebooks, datasets, and utilities maintained by sike990. This repository contains exploratory analyses, model experiments, reproducible pipelines, and helper scripts useful for learning and prototyping data-driven solutions.

Table of Contents

• Repository Structure
• Getting Started
  • Prerequisites
  • Installation
• Usage
  • Notebooks
  • Scripts & Modules
• Data
• Contributing
• Code Style & Best Practices
• License
• Contact

Repository Structure

The repository is organized to separate notebooks, scripts, data, and results. A typical layout:

• notebooks/ - Jupyter notebooks for exploration and demonstration
• src/ - Reusable modules and helper functions
• data/ - Raw and processed datasets (or pointers to them)
• notebooks/output/ - Generated figures, reports, and notebook outputs
• models/ - Saved model checkpoints and artifacts
• requirements.txt - Python dependencies
• README.md - This file

Adjust paths and contents as needed for the actual repository.

Getting Started

These instructions will help you run the projects and notebooks locally.

Prerequisites

• Python 3.8+ (3.10 recommended)
• pip or conda
• git

Installation

1. Clone the repo:

   git clone https://github.com/sike990/Data_Science.git cd Data_Science

2. Create a virtual environment and install dependencies (pip example):

   python -m venv .venv source .venv/bin/activate # macOS / Linux ..venv\Scripts\activate # Windows (PowerShell)

   pip install -r requirements.txt

If this repository uses conda, replace above steps with creating a conda environment and running:

conda env create -f environment.yml conda activate

Usage

Notebooks

Open the notebooks/ folder in JupyterLab or VS Code and run cells in the notebooks. Notebooks are intended to be runnable end-to-end when dependencies and data are available.

Start JupyterLab:

jupyter lab

or Jupyter Notebook:

jupyter notebook

Scripts & Modules

Reusable code lives in src/. Example usage:

python src/train.py --config configs/train_config.yaml

(Replace with actual script names and usage examples present in the repository.)

Data

This repository may include sample data in data/. For large or private datasets, the repo contains download scripts or instructions to acquire data externally. Never commit sensitive or private data. If any dataset requires credentials, follow the provider's instructions and keep keys out of the repository.

Contributing

Contributions are welcome. Please open an issue to discuss major changes before submitting a pull request. For small fixes, fork the repository, create a branch, and submit a PR.

Suggested workflow:

1. Fork the repo
2. Create a feature branch: git checkout -b feature-name
3. Commit changes and push: git push origin feature-name
4. Open a pull request describing your changes

Follow repository coding conventions and include tests where appropriate.

-- End of README --