ML Arena 4 - K-Means Clustering

This folder is a dedicated repo-style package for Problem 4: K-Means Clustering.

This repository is part of the ML Arena family, a set of 5 dedicated repositories where each repository focuses on one core ML problem.

ML Arena Family Repositories

• ML-Arena-1 - Linear Regression
• ML-Arena-2 - Logistic Regression
• ML-Arena-3 - Decision Tree
• ML-Arena-4 - K-Means Clustering (Current Repo)
• ML-Arena-5 - Neural Network

Dataset

• File: dataset.csv
• Source: UCI Wholesale Customers
• Rows: 440
• Columns: 6 features
• Note: No target variable is provided

Repository Layout

K-Means-Clustering/
|- README.md
|- CONTRIBUTING.md
|- dataset.csv
|- exploration/exploration.ipynb
|- library/training.ipynb
|- scratch/training.ipynb
|- PULL_REQUEST_TEMPLATE.md

Task Tracks

• Exploration: Data understanding, distributions, correlation, and insights.
• Library: Baseline model with standard ML libraries.
• Scratch: First-principles implementation using NumPy.
• Optimization: Improvements on top of library or scratch baseline.

Contribution Model

Each issue is open to multiple contributors.

• Multiple PRs can be submitted for the same issue.
• Different approaches, implementations, and optimizations are welcome.

This means even if someone has already submitted a PR for an issue, you are still encouraged to submit your own solution.

Quick Start

1. Fork this folder as its own repo (K-Means-Clustering).
2. Clone your fork and create a branch.
3. Pick one issue.
4. Work in only one notebook per issue based on track:
   • exploration/exploration.ipynb
   • library/training.ipynb
   • scratch/training.ipynb
5. Run all cells top to bottom.
6. Open a pull request with Closes #<issue-number>.

See CONTRIBUTING.md for detailed rules.