HashSlap-Summer-of-Code/ml-core

Standardize Dataset Handling Across All ML Modules

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#11 opened on Jun 18, 2025

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Intermediateenhancementgood first issuehacktoberfesthssoc

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Description

Description:
Right now, different subfolders load and process datasets in inconsistent ways. Create a unified, reusable Python module to handle dataset loading and basic preprocessing (e.g., scaling, splitting). This ensures maintainability and reduces repeated code across ML scripts.

Expected Tasks:

  • Create a Python utility (e.g., data_utils.py) with functions like:
    • load_csv(path)
    • train_test_split(X, y, test_size=0.2)
    • standardize(X)
  • Save this file in a utils/ folder.
  • Refactor at least two existing implementations (e.g., KNN and Perceptron) to use this utility.
  • Update documentation in the root README.md and/or affected folders.

Stretch Goal:

  • Add optional support for downloading public datasets (e.g., from UCI or sklearn).

Contributor guide

Research direction
Review dataset handling in KNN and Perceptron modules, then create a utility module with functions like load csv, train test split, and standardize. Save it in utils/ and refactor the existing modules to use it.
Tech stack
python
Domain
machine learningdata
Issue type
Refactor
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
2
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-3 hours
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Fresh
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
Python
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
75