https://github.com/elprofesoriqo/kagglecompetitions

Kaggle competitions projects
https://github.com/elprofesoriqo/kagglecompetitions

artificial-intelligence machine-learning python pytorch scikit-learn

Last synced: 3 months ago
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Kaggle competitions projects

• Host: GitHub
• URL: https://github.com/elprofesoriqo/kagglecompetitions
• Owner: elprofesoriqo
• Created: 2025-01-03T13:03:04.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-01-18T11:55:32.000Z (over 1 year ago)
• Last Synced: 2025-01-18T12:32:05.627Z (over 1 year ago)
• Topics: artificial-intelligence, machine-learning, python, pytorch, scikit-learn
• Language: Jupyter Notebook
• Homepage:
• Size: 27.3 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Kaggle Data Science and Machine Learning Projects Repository

This repository showcases a collection of practical projects, exercises, and challenges aimed at improving skills in data science, machine learning, and algorithm development. The projects include guided learning tasks, competitive coding challenges, and exploratory analyses, with a strong focus on Kaggle datasets and competitions.

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## Repository Structure

Each project is designed as a self-contained unit, covering the entire pipeline from data exploration to model evaluation and submission, accompanied by detailed explanations and reproducible code.

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## Key Highlights

### Kaggle Projects

- Folders with Jupyter notebooks dedicated to popular Kaggle challenges, including tasks like predictive modeling, classification, computer vision, and NLP.

- Each project includes:

  - Data preprocessing and cleaning.

  - Feature engineering methods.

  - Model training, hyperparameter optimization, and evaluation.

  - Submission-ready model outputs.

### Learning Resources

- Exploratory exercises focusing on specific areas such as data preprocessing, EDA, supervised/unsupervised learning, time series, and NLP, using Kaggle datasets as the foundation.

- Notes and solutions for competitive coding challenges.